PythonDay2.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4pKIcHBKRwkt",
        "outputId": "c340b15c-bd65-4469-fcfb-7918a5b025d2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hello world\n"
          ]
        }
      ],
      "source": [
        "print(\"Hello world\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#A level 1 heading"
      ],
      "metadata": {
        "id": "6b_cTxIcSV9j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## A level 2 heading\n"
      ],
      "metadata": {
        "id": "cM1N11VOSc4o"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### A level 3 heading"
      ],
      "metadata": {
        "id": "G-1ZBgnhSi2F"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* use asterisks to\n",
        "* create bullet points\n",
        "* for you documentations"
      ],
      "metadata": {
        "id": "8_5x6G4pSyqj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. use numbers to\n",
        "2. create numbered list"
      ],
      "metadata": {
        "id": "-6YsxIOMS_O0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* this is the main text\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "EPnQ_of2TEHl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "[The link of the python workshop]"
      ],
      "metadata": {
        "id": "ufMiNGKbTc89"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Variables and assignments"
      ],
      "metadata": {
        "id": "n-UC6gbHVaL6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "12*12"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3qDLQAt5Sx--",
        "outputId": "cd22542d-e4d1-4f2f-8fa1-c1a52414bea3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "144"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "age=12"
      ],
      "metadata": {
        "id": "aC6CgtgQScpg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "name='Dan'"
      ],
      "metadata": {
        "id": "OyZRkeXbSiWc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(age)"
      ],
      "metadata": {
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        },
        "id": "QyKqVYOFSAS6",
        "outputId": "c3acae80-0dc9-43e3-f36e-b51d303e23c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZN9y8bjlV-Ob",
        "outputId": "d6bd085d-cead-4180-95c3-f72b07980bfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dan\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "myval=10\n",
        "print(myval)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JDBEhgXLWAdm",
        "outputId": "9049f6a4-6f3d-4a92-ab04-fd3a50d5be0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(age)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nwuW7I4iWKbT",
        "outputId": "7e56782d-3ea1-47fc-9665-c309b6b1af0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "int"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pi_val=3.14\n",
        "type(pi_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f4zgqM66Z8xk",
        "outputId": "270d1796-aa41-4907-b4bb-5ee6c181caab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "float"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(atom_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "id": "G3SEY8h3aBIu",
        "outputId": "6b25843f-66d9-41fc-b069-e800fe808e58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'atom_name' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-f43dedbefa46>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0matom_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'atom_name' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(my_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "id": "YLUyXH0baEco",
        "outputId": "76c791dc-5ccd-4fbe-ccf3-d299b65c9b73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'my_list' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-0ef8b20c9912>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmy_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'my_list' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "age+age2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "id": "QeyG6ySIaG59",
        "outputId": "4c82b35e-49d6-4f6b-d132-bf6663713990"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'age2' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-2a326c8630cc>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mage\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mage2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'age2' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "5+pi_val"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yr1MGWvlaVnc",
        "outputId": "671a9068-6c08-468f-d9ac-54ac3678f9d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8.14"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "age+name"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "id": "COR7O05QaYhE",
        "outputId": "663cd271-99bb-48eb-a2a8-d17498c93d34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "unsupported operand type(s) for +: 'int' and 'str'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-2827922a3eba>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mage\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "separator=10*\"<><><\"\n",
        "print(separator)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9vMOGbH3aZyu",
        "outputId": "10150de9-b652-419b-dcda-086ffd8ff2b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<><><<><><<><><<><><<><><<><><<><><<><><<><><<><><\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "full_name='Bill'+' '+'Day'\n",
        "print(full_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Aqa66oUan4D",
        "outputId": "66163b0b-b56f-479f-af4f-b46d8d9f86c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bill Day\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6xedl8awa3up",
        "outputId": "a7e4e62d-1aa8-4021-ddb8-1a1e2a24340b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "bool"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "age=12\n",
        "Age=25\n",
        "print(age)\n",
        "print(Age)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r2v8EzVza6SU",
        "outputId": "5f0c0bb9-2376-472b-f7e0-89b4e955c79f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12\n",
            "25\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%whos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0LDdsRJfbhTM",
        "outputId": "196cd7ab-e4f9-4fdd-c6ed-d7a0fb53a991"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Variable     Type     Data/Info\n",
            "-------------------------------\n",
            "Age          int      25\n",
            "age          int      12\n",
            "full_name    str      Bill Day\n",
            "myval        int      10\n",
            "name         str      Dan\n",
            "pi_val       float    3.14\n",
            "separateor   str      <><><<><><<><><<><><<><><<...><><><<><><<><><<><><<><><\n",
            "separator    str      <><><<><><<><><<><><<><><<...><><><<><><<><><<><><<><><\n",
            "seprate      str      <><><<><><<><><<><><<><><<...><><><<><><<><><<><><<><><\n",
            "seprateor    str      <><><<><><<><><<><><<><><<...><><><<><><<><><<><><<><><\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "4+2.5 # int or float ?"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-QlIi5qebnzY",
        "outputId": "8d625f6e-6b90-48e4-d857-6d87888b4aa2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.5"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Buult-in functions and help"
      ],
      "metadata": {
        "id": "LjHeAd3qcH4I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Before')\n",
        "print()\n",
        "print('after')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SGyqwQQmcGfE",
        "outputId": "ba4ac74b-55c3-45b6-cf7b-2032377733ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before\n",
            "\n",
            "after\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max(1,10,100,-10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nhB7DTHYcQyH",
        "outputId": "ada0dd86-1083-481f-82e5-9d611c2d6c87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "min(-10,10,19)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "84d6kdlzcezU",
        "outputId": "b03848b5-39a3-466c-9a52-5613ac4b4c60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-10"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "id": "-qF2Xj5ccm1F",
        "outputId": "b14ccc00-ca73-4b01-d52c-f141f2e8cb5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'my_list' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-30-9af163499740>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmy_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'my_list' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max(my_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "id": "kmAnhrWncs9s",
        "outputId": "610e77d5-2618-429b-810f-846fd108f6cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'my_list' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-31-e062efbbd118>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmy_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'my_list' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max(1,'A',10,'100')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "id": "OelLChX3cuKH",
        "outputId": "68e1866f-c6ac-410e-946d-1f6d306cbb3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "'>' not supported between instances of 'str' and 'int'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-32-9ea901c3cbf8>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'A'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'100'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: '>' not supported between instances of 'str' and 'int'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max('a','z')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "IBVVK2zpc8G6",
        "outputId": "e34faf12-03f1-4df3-d690-44f5bdf14221"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'z'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "round(2.9)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4nU8feNTdID5",
        "outputId": "e479e2e7-6e76-4394-eda7-ade61d892124"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "round(3.14)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c01YTiEJdUp1",
        "outputId": "3833b4b2-0746-4bed-eb87-e4bc2dcab2b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "string_1='Hello world'"
      ],
      "metadata": {
        "id": "cgaDdvIVdVcy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "string_1.swapcase()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "zG2sVO1SdYNw",
        "outputId": "24b5c98f-7313-475b-89f8-15586d682a9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'hELLO WORLD'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "string_1.isupper()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pT6d4BA-djsA",
        "outputId": "6dadc3f9-7bb0-43b7-b2f3-d054532d28aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "string_1.upper()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "l8Awat_Pdmv3",
        "outputId": "b5eb4b3b-836f-4083-a13d-897094b1b69b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'HELLO WORLD'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "string_1.__len__()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TuukuXw-dqfb",
        "outputId": "eb5e5a38-0fed-4c4b-c381-06f39c832370"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Libraries"
      ],
      "metadata": {
        "id": "TkF-X1U0d3wE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import math"
      ],
      "metadata": {
        "id": "uZ0f86Ofd3h4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('print the pi values:',math.pi)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IN2IGIKgdw5k",
        "outputId": "d44c8a1f-661d-453c-f14d-d57c1ad3053a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "print the pi values: 3.141592653589793\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#cos(pi)\n",
        "math.cos(math.pi)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ff6k-ypheonK",
        "outputId": "a626757d-175b-4aa4-97ef-3603436f42e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-1.0"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import libraryName as nickName\n",
        "import math as m"
      ],
      "metadata": {
        "id": "_Nm9PeGaezA_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "m.cos(m.pi)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N8HeT9JcfMQp",
        "outputId": "afa7ad4a-fdf8-4ce8-d691-1ba8e437b4ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-1.0"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%whos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oBns0cAHfO91",
        "outputId": "0085e7fa-cd07-4089-d495-24d165bc6474"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Variable     Type      Data/Info\n",
            "--------------------------------\n",
            "Age          int       25\n",
            "age          int       12\n",
            "full_name    str       Bill Day\n",
            "m            module    <module 'math' (built-in)>\n",
            "math         module    <module 'math' (built-in)>\n",
            "myval        int       10\n",
            "name         str       Dan\n",
            "pi_val       float     3.14\n",
            "separateor   str       <><><<><><<><><<><><<><><<...><><><<><><<><><<><><<><><\n",
            "separator    str       <><><<><><<><><<><><<><><<...><><><<><><<><><<><><<><><\n",
            "seprate      str       <><><<><><<><><<><><<><><<...><><><<><><<><><<><><<><><\n",
            "seprateor    str       <><><<><><<><><<><><<><><<...><><><<><><<><><<><><<><><\n",
            "string_1     str       Hello world\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from math import cos,pi"
      ],
      "metadata": {
        "id": "yCMDiJjrfSzB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cos(pi)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Trl568BFfkPr",
        "outputId": "04e8b757-ac4c-4e4d-aefb-0a387e99bc20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-1.0"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "math.pow(4,0.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FLfXVNH7fl8T",
        "outputId": "915fc55f-24c6-4e45-a4c8-6e6c8624a40a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.0"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# bases = 'ACTTGCTTGAC'\n",
        "from random import randrange"
      ],
      "metadata": {
        "id": "rrBX8jbFgJo7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "randrange(8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XkJa4h5_gZ2n",
        "outputId": "2119e1ef-a2ef-4f2b-d182-2900d0d22b32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bases='ACTTGCTTGAC'\n",
        "len(bases)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "90_EJEmIgde7",
        "outputId": "9b6054a0-7eeb-4591-d94a-93613aa4a371"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "randrange(11)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zk1jKWk6gjdh",
        "outputId": "f077dd41-c3c3-4203-dfdc-44fe52ba017b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(bases[8])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eUltVzHKgm_E",
        "outputId": "108b49c4-4246-4ff7-e5a3-eb9a575b1f3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "G\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(bases[randrange(len(bases))])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6MK46wZRgqFU",
        "outputId": "bf4ae2e8-e98d-4712-9966-9211970faca0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "T\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print commands:\n"
      ],
      "metadata": {
        "id": "j9MdGRAegyNW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Reading tabular data into dataframes"
      ],
      "metadata": {
        "id": "_kkJHb6LhopB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "U3JSvCXvhtRr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DubWlOY5hxMg",
        "outputId": "10f494d6-8a4c-491b-9ae5-ac49684be2cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data  sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_-tbQdioi_fG",
        "outputId": "3df34943-e9b0-4f2f-f5e2-18bb65112fc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"/content/data/gapminder_gdp_oceania.csv\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "15J0lopLjDD8",
        "outputId": "c20a269d-bb93-43f5-8a94-7aa76828b649"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/data/gapminder_gdp_oceania.csv'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls\\data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ui4dv89cjfPJ",
        "outputId": "fcfc4363-b4cb-4f54-cf77-57849ca34dce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bin/bash: line 1: lsdata: command not found\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls /content/data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BJhOFcTujg6W",
        "outputId": "d5731945-ed6d-44ae-97eb-be4eed0f8fea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gapminder_all.csv\t  gapminder_gdp_americas.csv  gapminder_gdp_europe.csv\n",
            "gapminder_gdp_africa.csv  gapminder_gdp_asia.csv      gapminder_gdp_oceania.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " import pandas as pd"
      ],
      "metadata": {
        "id": "WZUdGYfsjih-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('/content/data/gapminder_gdp_oceania.csv',index_col='country')"
      ],
      "metadata": {
        "id": "Gk6DYe2XrQ5z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 149
        },
        "id": "AnLsVHbqryhM",
        "outputId": "7a958d32-2f97-4778-bfca-0008b4dc2c54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       country  gdpPercap_1952  gdpPercap_1957  gdpPercap_1962  \\\n",
              "0    Australia     10039.59564     10949.64959     12217.22686   \n",
              "1  New Zealand     10556.57566     12247.39532     13175.67800   \n",
              "\n",
              "   gdpPercap_1967  gdpPercap_1972  gdpPercap_1977  gdpPercap_1982  \\\n",
              "0     14526.12465     16788.62948     18334.19751     19477.00928   \n",
              "1     14463.91893     16046.03728     16233.71770     17632.41040   \n",
              "\n",
              "   gdpPercap_1987  gdpPercap_1992  gdpPercap_1997  gdpPercap_2002  \\\n",
              "0     21888.88903     23424.76683     26997.93657     30687.75473   \n",
              "1     19007.19129     18363.32494     21050.41377     23189.80135   \n",
              "\n",
              "   gdpPercap_2007  \n",
              "0     34435.36744  \n",
              "1     25185.00911  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-94c7963c-6ecd-4f35-8224-130ce27980e6\" 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>country</th>\n",
              "      <th>gdpPercap_1952</th>\n",
              "      <th>gdpPercap_1957</th>\n",
              "      <th>gdpPercap_1962</th>\n",
              "      <th>gdpPercap_1967</th>\n",
              "      <th>gdpPercap_1972</th>\n",
              "      <th>gdpPercap_1977</th>\n",
              "      <th>gdpPercap_1982</th>\n",
              "      <th>gdpPercap_1987</th>\n",
              "      <th>gdpPercap_1992</th>\n",
              "      <th>gdpPercap_1997</th>\n",
              "      <th>gdpPercap_2002</th>\n",
              "      <th>gdpPercap_2007</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Australia</td>\n",
              "      <td>10039.59564</td>\n",
              "      <td>10949.64959</td>\n",
              "      <td>12217.22686</td>\n",
              "      <td>14526.12465</td>\n",
              "      <td>16788.62948</td>\n",
              "      <td>18334.19751</td>\n",
              "      <td>19477.00928</td>\n",
              "      <td>21888.88903</td>\n",
              "      <td>23424.76683</td>\n",
              "      <td>26997.93657</td>\n",
              "      <td>30687.75473</td>\n",
              "      <td>34435.36744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>New Zealand</td>\n",
              "      <td>10556.57566</td>\n",
              "      <td>12247.39532</td>\n",
              "      <td>13175.67800</td>\n",
              "      <td>14463.91893</td>\n",
              "      <td>16046.03728</td>\n",
              "      <td>16233.71770</td>\n",
              "      <td>17632.41040</td>\n",
              "      <td>19007.19129</td>\n",
              "      <td>18363.32494</td>\n",
              "      <td>21050.41377</td>\n",
              "      <td>23189.80135</td>\n",
              "      <td>25185.00911</td>\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    <script>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"New Zealand\",\n          \"Australia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1952\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 365.5600778799576,\n        \"min\": 10039.59564,\n        \"max\": 10556.57566,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          10556.57566,\n          10039.59564\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1957\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 917.6448059388855,\n        \"min\": 10949.64959,\n        \"max\": 12247.39532,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          12247.39532,\n          10949.64959\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1962\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 677.7273005299764,\n        \"min\": 12217.22686,\n        \"max\": 13175.678,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          13175.678,\n          12217.22686\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1967\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43.98608644059159,\n        \"min\": 14463.91893,\n        \"max\": 14526.12465,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          14463.91893,\n          14526.12465\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1972\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 525.0919802762363,\n        \"min\": 16046.03728,\n        \"max\": 16788.62948,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          16046.03728,\n          16788.62948\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1977\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1485.2635173964327,\n        \"min\": 16233.7177,\n        \"max\": 18334.19751,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          16233.7177,\n          18334.19751\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1982\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1304.328376617109,\n        \"min\": 17632.4104,\n        \"max\": 19477.00928,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          17632.4104,\n          19477.00928\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1987\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2037.668013283948,\n        \"min\": 19007.19129,\n        \"max\": 21888.88903,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          19007.19129,\n          21888.88903\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1992\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3578.979883000657,\n        \"min\": 18363.32494,\n        \"max\": 23424.76683,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          18363.32494,\n          23424.76683\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1997\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4205.533703141605,\n        \"min\": 21050.41377,\n        \"max\": 26997.93657,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          21050.41377,\n          26997.93657\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_2002\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5301.853680018594,\n        \"min\": 23189.80135,\n        \"max\": 30687.75473,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          23189.80135,\n          30687.75473\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_2007\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6540.991103548469,\n        \"min\": 25185.00911,\n        \"max\": 34435.36744,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          25185.00911,\n          34435.36744\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pPk9n2c5r5mo",
        "outputId": "1fe34928-46fe-4ce7-c12c-f22402913b3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 2 entries, Australia to New Zealand\n",
            "Data columns (total 12 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   gdpPercap_1952  2 non-null      float64\n",
            " 1   gdpPercap_1957  2 non-null      float64\n",
            " 2   gdpPercap_1962  2 non-null      float64\n",
            " 3   gdpPercap_1967  2 non-null      float64\n",
            " 4   gdpPercap_1972  2 non-null      float64\n",
            " 5   gdpPercap_1977  2 non-null      float64\n",
            " 6   gdpPercap_1982  2 non-null      float64\n",
            " 7   gdpPercap_1987  2 non-null      float64\n",
            " 8   gdpPercap_1992  2 non-null      float64\n",
            " 9   gdpPercap_1997  2 non-null      float64\n",
            " 10  gdpPercap_2002  2 non-null      float64\n",
            " 11  gdpPercap_2007  2 non-null      float64\n",
            "dtypes: float64(12)\n",
            "memory usage: 208.0+ bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "id": "wBZq91klshzM",
        "outputId": "b23af832-d0aa-4837-f2e3-257c557616da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "'Index' object is not callable",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-81-ea942bf769f0>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\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[0m",
            "\u001b[0;31mTypeError\u001b[0m: 'Index' object is not callable"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "MnE0ocXIsswg",
        "outputId": "8c24db23-0221-4408-cf8c-2cac1691b789"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       gdpPercap_1952  gdpPercap_1957  gdpPercap_1962  gdpPercap_1967  \\\n",
              "count        2.000000        2.000000        2.000000        2.000000   \n",
              "mean     10298.085650    11598.522455    12696.452430    14495.021790   \n",
              "std        365.560078      917.644806      677.727301       43.986086   \n",
              "min      10039.595640    10949.649590    12217.226860    14463.918930   \n",
              "25%      10168.840645    11274.086022    12456.839645    14479.470360   \n",
              "50%      10298.085650    11598.522455    12696.452430    14495.021790   \n",
              "75%      10427.330655    11922.958888    12936.065215    14510.573220   \n",
              "max      10556.575660    12247.395320    13175.678000    14526.124650   \n",
              "\n",
              "       gdpPercap_1972  gdpPercap_1977  gdpPercap_1982  gdpPercap_1987  \\\n",
              "count         2.00000        2.000000        2.000000        2.000000   \n",
              "mean      16417.33338    17283.957605    18554.709840    20448.040160   \n",
              "std         525.09198     1485.263517     1304.328377     2037.668013   \n",
              "min       16046.03728    16233.717700    17632.410400    19007.191290   \n",
              "25%       16231.68533    16758.837652    18093.560120    19727.615725   \n",
              "50%       16417.33338    17283.957605    18554.709840    20448.040160   \n",
              "75%       16602.98143    17809.077558    19015.859560    21168.464595   \n",
              "max       16788.62948    18334.197510    19477.009280    21888.889030   \n",
              "\n",
              "       gdpPercap_1992  gdpPercap_1997  gdpPercap_2002  gdpPercap_2007  \n",
              "count        2.000000        2.000000        2.000000        2.000000  \n",
              "mean     20894.045885    24024.175170    26938.778040    29810.188275  \n",
              "std       3578.979883     4205.533703     5301.853680     6540.991104  \n",
              "min      18363.324940    21050.413770    23189.801350    25185.009110  \n",
              "25%      19628.685412    22537.294470    25064.289695    27497.598692  \n",
              "50%      20894.045885    24024.175170    26938.778040    29810.188275  \n",
              "75%      22159.406358    25511.055870    28813.266385    32122.777858  \n",
              "max      23424.766830    26997.936570    30687.754730    34435.367440  "
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gdpPercap_1952</th>\n",
              "      <th>gdpPercap_1957</th>\n",
              "      <th>gdpPercap_1962</th>\n",
              "      <th>gdpPercap_1967</th>\n",
              "      <th>gdpPercap_1972</th>\n",
              "      <th>gdpPercap_1977</th>\n",
              "      <th>gdpPercap_1982</th>\n",
              "      <th>gdpPercap_1987</th>\n",
              "      <th>gdpPercap_1992</th>\n",
              "      <th>gdpPercap_1997</th>\n",
              "      <th>gdpPercap_2002</th>\n",
              "      <th>gdpPercap_2007</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>10298.085650</td>\n",
              "      <td>11598.522455</td>\n",
              "      <td>12696.452430</td>\n",
              "      <td>14495.021790</td>\n",
              "      <td>16417.33338</td>\n",
              "      <td>17283.957605</td>\n",
              "      <td>18554.709840</td>\n",
              "      <td>20448.040160</td>\n",
              "      <td>20894.045885</td>\n",
              "      <td>24024.175170</td>\n",
              "      <td>26938.778040</td>\n",
              "      <td>29810.188275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>365.560078</td>\n",
              "      <td>917.644806</td>\n",
              "      <td>677.727301</td>\n",
              "      <td>43.986086</td>\n",
              "      <td>525.09198</td>\n",
              "      <td>1485.263517</td>\n",
              "      <td>1304.328377</td>\n",
              "      <td>2037.668013</td>\n",
              "      <td>3578.979883</td>\n",
              "      <td>4205.533703</td>\n",
              "      <td>5301.853680</td>\n",
              "      <td>6540.991104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10039.595640</td>\n",
              "      <td>10949.649590</td>\n",
              "      <td>12217.226860</td>\n",
              "      <td>14463.918930</td>\n",
              "      <td>16046.03728</td>\n",
              "      <td>16233.717700</td>\n",
              "      <td>17632.410400</td>\n",
              "      <td>19007.191290</td>\n",
              "      <td>18363.324940</td>\n",
              "      <td>21050.413770</td>\n",
              "      <td>23189.801350</td>\n",
              "      <td>25185.009110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>10168.840645</td>\n",
              "      <td>11274.086022</td>\n",
              "      <td>12456.839645</td>\n",
              "      <td>14479.470360</td>\n",
              "      <td>16231.68533</td>\n",
              "      <td>16758.837652</td>\n",
              "      <td>18093.560120</td>\n",
              "      <td>19727.615725</td>\n",
              "      <td>19628.685412</td>\n",
              "      <td>22537.294470</td>\n",
              "      <td>25064.289695</td>\n",
              "      <td>27497.598692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>10298.085650</td>\n",
              "      <td>11598.522455</td>\n",
              "      <td>12696.452430</td>\n",
              "      <td>14495.021790</td>\n",
              "      <td>16417.33338</td>\n",
              "      <td>17283.957605</td>\n",
              "      <td>18554.709840</td>\n",
              "      <td>20448.040160</td>\n",
              "      <td>20894.045885</td>\n",
              "      <td>24024.175170</td>\n",
              "      <td>26938.778040</td>\n",
              "      <td>29810.188275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>10427.330655</td>\n",
              "      <td>11922.958888</td>\n",
              "      <td>12936.065215</td>\n",
              "      <td>14510.573220</td>\n",
              "      <td>16602.98143</td>\n",
              "      <td>17809.077558</td>\n",
              "      <td>19015.859560</td>\n",
              "      <td>21168.464595</td>\n",
              "      <td>22159.406358</td>\n",
              "      <td>25511.055870</td>\n",
              "      <td>28813.266385</td>\n",
              "      <td>32122.777858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>10556.575660</td>\n",
              "      <td>12247.395320</td>\n",
              "      <td>13175.678000</td>\n",
              "      <td>14526.124650</td>\n",
              "      <td>16788.62948</td>\n",
              "      <td>18334.197510</td>\n",
              "      <td>19477.009280</td>\n",
              "      <td>21888.889030</td>\n",
              "      <td>23424.766830</td>\n",
              "      <td>26997.936570</td>\n",
              "      <td>30687.754730</td>\n",
              "      <td>34435.367440</td>\n",
              "    </tr>\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"gdpPercap_1952\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4685.568995317567,\n        \"min\": 2.0,\n        \"max\": 10556.57566,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2.0,\n          10298.08565,\n          10427.330655\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1957\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5176.564649129396,\n        \"min\": 2.0,\n        \"max\": 12247.39532,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2.0,\n          11598.522455,\n          11922.9588875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1962\",\n      \"properties\": {\n        \"dtype\": 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   \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
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              "           gdpPercap_1952  gdpPercap_1957  gdpPercap_1962  gdpPercap_1967  \\\n",
              "country                                                                     \n",
              "Australia     10039.59564     10949.64959     12217.22686     14526.12465   \n",
              "\n",
              "           gdpPercap_1972  gdpPercap_1977  gdpPercap_1982  gdpPercap_1987  \\\n",
              "country                                                                     \n",
              "Australia     16788.62948     18334.19751     19477.00928     21888.88903   \n",
              "\n",
              "           gdpPercap_1992  gdpPercap_1997  gdpPercap_2002  gdpPercap_2007  \n",
              "country                                                                    \n",
              "Australia     23424.76683     26997.93657     30687.75473     34435.36744  "
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            }
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "help(df.tail)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K81KWlOVtJWd",
        "outputId": "d1470eb6-2a07-4ae8-afb6-258f18b66ea6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Help on method tail in module pandas.core.generic:\n",
            "\n",
            "tail(n: 'int' = 5) -> 'NDFrameT' method of pandas.core.frame.DataFrame instance\n",
            "    Return the last `n` rows.\n",
            "    \n",
            "    This function returns last `n` rows from the object based on\n",
            "    position. It is useful for quickly verifying data, for example,\n",
            "    after sorting or appending rows.\n",
            "    \n",
            "    For negative values of `n`, this function returns all rows except\n",
            "    the first `|n|` rows, equivalent to ``df[|n|:]``.\n",
            "    \n",
            "    If n is larger than the number of rows, this function returns all rows.\n",
            "    \n",
            "    Parameters\n",
            "    ----------\n",
            "    n : int, default 5\n",
            "        Number of rows to select.\n",
            "    \n",
            "    Returns\n",
            "    -------\n",
            "    type of caller\n",
            "        The last `n` rows of the caller object.\n",
            "    \n",
            "    See Also\n",
            "    --------\n",
            "    DataFrame.head : The first `n` rows of the caller object.\n",
            "    \n",
            "    Examples\n",
            "    --------\n",
            "    >>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',\n",
            "    ...                    'monkey', 'parrot', 'shark', 'whale', 'zebra']})\n",
            "    >>> df\n",
            "          animal\n",
            "    0  alligator\n",
            "    1        bee\n",
            "    2     falcon\n",
            "    3       lion\n",
            "    4     monkey\n",
            "    5     parrot\n",
            "    6      shark\n",
            "    7      whale\n",
            "    8      zebra\n",
            "    \n",
            "    Viewing the last 5 lines\n",
            "    \n",
            "    >>> df.tail()\n",
            "       animal\n",
            "    4  monkey\n",
            "    5  parrot\n",
            "    6   shark\n",
            "    7   whale\n",
            "    8   zebra\n",
            "    \n",
            "    Viewing the last `n` lines (three in this case)\n",
            "    \n",
            "    >>> df.tail(3)\n",
            "      animal\n",
            "    6  shark\n",
            "    7  whale\n",
            "    8  zebra\n",
            "    \n",
            "    For negative values of `n`\n",
            "    \n",
            "    >>> df.tail(-3)\n",
            "       animal\n",
            "    3    lion\n",
            "    4  monkey\n",
            "    5  parrot\n",
            "    6   shark\n",
            "    7   whale\n",
            "    8   zebra\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/data/gapminder_gdp_europe.csv',index_col = 'country')"
      ],
      "metadata": {
        "id": "mW9tLNUmtXJW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['gdpPercap_1592'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633
        },
        "id": "XHbB4h3wtqhU",
        "outputId": "63e24b2f-14b1-4934-a939-77533712a13e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "'gdpPercap_1592'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/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   3652\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-> 3653\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   3654\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;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'gdpPercap_1592'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-92-c7576329c28b>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'gdpPercap_1592'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\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[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3759\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   3760\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-> 3761\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   3762\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   3763\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/python3.10/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   3653\u001b[0m             \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[1;32m   3654\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;32m-> 3655\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3656\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3657\u001b[0m             \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'gdpPercap_1592'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.gdpPercap_2007.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1piKMfbtuELb",
        "outputId": "6c36586f-ac61-461e-ba03-a854b58492c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       30.000000\n",
              "mean     25054.481636\n",
              "std      11800.339811\n",
              "min       5937.029526\n",
              "25%      14811.898210\n",
              "50%      28054.065790\n",
              "75%      33817.962533\n",
              "max      49357.190170\n",
              "Name: gdpPercap_2007, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# you can iloc[] to provide index for a subset of data\n",
        "data.iloc[0,0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0iOY3-kduUcZ",
        "outputId": "d7b3a325-4e8f-4e02-a451-ec87f948992b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1601.056136"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "id": "NZV56efHuc7q",
        "outputId": "d286bc1b-c9d8-400d-edd4-d32cc568e883"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         gdpPercap_1952  gdpPercap_1957  gdpPercap_1962  gdpPercap_1967  \\\n",
              "country                                                                   \n",
              "Albania     1601.056136     1942.284244     2312.888958     2760.196931   \n",
              "\n",
              "         gdpPercap_1972  gdpPercap_1977  gdpPercap_1982  gdpPercap_1987  \\\n",
              "country                                                                   \n",
              "Albania     3313.422188      3533.00391     3630.880722     3738.932735   \n",
              "\n",
              "         gdpPercap_1992  gdpPercap_1997  gdpPercap_2002  gdpPercap_2007  \n",
              "country                                                                  \n",
              "Albania     2497.437901     3193.054604     4604.211737     5937.029526  "
            ],
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10022.40131,\n          8412.902397\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1972\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5509.691410957364,\n        \"min\": 2860.16975,\n        \"max\": 27195.11304,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          27195.11304,\n          12269.27378,\n          9674.167626\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1977\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5874.464895969019,\n        \"min\": 3528.481305,\n        \"max\": 26982.29052,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          26982.29052,\n          14255.98475,\n          10922.66404\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1982\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6453.234826614652,\n        \"min\": 3630.880722,\n        \"max\": 28397.71512,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          28397.71512,\n          16537.4835,\n          11348.54585\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1987\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7482.957960039719,\n        \"min\": 3738.932735,\n        \"max\": 31540.9748,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          30281.70459,\n          19207.23482,\n          12037.26758\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1992\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9109.804361057026,\n        \"min\": 2497.437901,\n        \"max\": 33965.66115,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          31871.5303,\n          22013.64486,\n          9498.467723\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1997\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10065.45771619316,\n        \"min\": 3193.054604,\n        \"max\": 41283.16433,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          32135.32301,\n          24675.02446,\n          12126.23065\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_2002\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11197.355516702492,\n        \"min\": 4604.211737,\n        \"max\": 44683.97525,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          34480.95771,\n          27968.09817,\n          13638.77837\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_2007\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11800.339810888066,\n        \"min\": 5937.029526,\n        \"max\": 49357.19017,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          37506.41907,\n          28569.7197,\n          18678.31435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# [row,column]\n",
        "data.iloc[0,:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Od-ZrB6hufqR",
        "outputId": "3cce3c85-b796-4d55-fc11-a17a23f477bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gdpPercap_1952    1601.056136\n",
              "gdpPercap_1957    1942.284244\n",
              "gdpPercap_1962    2312.888958\n",
              "Name: Albania, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.iloc[0,3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MeXR8wXDuv0_",
        "outputId": "a758fbfd-192f-46d0-d791-8980739e3457"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2760.196931"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.iloc[:3,1:4].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "eNxA7ehdu70G",
        "outputId": "8134308d-ae79-4157-f052-57b9130b557d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       gdpPercap_1957  gdpPercap_1962  gdpPercap_1967\n",
              "count        3.000000        3.000000        3.000000\n",
              "mean      6833.280966     8018.272276     9581.280174\n",
              "std       4258.126445     4942.469771     5909.323175\n",
              "min       1942.284244     2312.888958     2760.196931\n",
              "25%       5392.441137     6531.805034     7797.399666\n",
              "50%       8842.598030    10750.721110    12834.602400\n",
              "75%       9278.779327    10870.963935    12991.821795\n",
              "max       9714.960623    10991.206760    13149.041190"
            ],
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              "      <th></th>\n",
              "      <th>gdpPercap_1957</th>\n",
              "      <th>gdpPercap_1962</th>\n",
              "      <th>gdpPercap_1967</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>6833.280966</td>\n",
              "      <td>8018.272276</td>\n",
              "      <td>9581.280174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4258.126445</td>\n",
              "      <td>4942.469771</td>\n",
              "      <td>5909.323175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1942.284244</td>\n",
              "      <td>2312.888958</td>\n",
              "      <td>2760.196931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5392.441137</td>\n",
              "      <td>6531.805034</td>\n",
              "      <td>7797.399666</td>\n",
              "    </tr>\n",
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              "      <td>12834.602400</td>\n",
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              "      <td>10870.963935</td>\n",
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              "    </tr>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"gdpPercap_1957\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3561.3458628628005,\n        \"min\": 3.0,\n        \"max\": 9714.960623,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6833.280965666668,\n          8842.59803,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1962\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4159.103798146156,\n        \"min\": 3.0,\n        \"max\": 10991.20676,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8018.272276,\n          10750.72111,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1967\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4971.238762450588,\n        \"min\": 3.0,\n        \"max\": 13149.04119,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          9581.280173666666,\n          12834.6024,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.loc['Albania':'Poland','gdpPercap_1952':'gdpPercap_1977']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 763
        },
        "id": "PKeUce-fvOcU",
        "outputId": "5df10afc-6869-4576-b045-02f300e0b834"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        gdpPercap_1952  gdpPercap_1957  gdpPercap_1962  \\\n",
              "country                                                                  \n",
              "Albania                    1601.056136     1942.284244     2312.888958   \n",
              "Austria                    6137.076492     8842.598030    10750.721110   \n",
              "Belgium                    8343.105127     9714.960623    10991.206760   \n",
              "Bosnia and Herzegovina      973.533195     1353.989176     1709.683679   \n",
              "Bulgaria                   2444.286648     3008.670727     4254.337839   \n",
              "Croatia                    3119.236520     4338.231617     5477.890018   \n",
              "Czech Republic             6876.140250     8256.343918    10136.867130   \n",
              "Denmark                    9692.385245    11099.659350    13583.313510   \n",
              "Finland                    6424.519071     7545.415386     9371.842561   \n",
              "France                     7029.809327     8662.834898    10560.485530   \n",
              "Germany                    7144.114393    10187.826650    12902.462910   \n",
              "Greece                     3530.690067     4916.299889     6017.190733   \n",
              "Hungary                    5263.673816     6040.180011     7550.359877   \n",
              "Iceland                    7267.688428     9244.001412    10350.159060   \n",
              "Ireland                    5210.280328     5599.077872     6631.597314   \n",
              "Italy                      4931.404155     6248.656232     8243.582340   \n",
              "Montenegro                 2647.585601     3682.259903     4649.593785   \n",
              "Netherlands                8941.571858    11276.193440    12790.849560   \n",
              "Norway                    10095.421720    11653.973040    13450.401510   \n",
              "Poland                     4029.329699     4734.253019     5338.752143   \n",
              "\n",
              "                        gdpPercap_1967  gdpPercap_1972  gdpPercap_1977  \n",
              "country                                                                 \n",
              "Albania                    2760.196931     3313.422188     3533.003910  \n",
              "Austria                   12834.602400    16661.625600    19749.422300  \n",
              "Belgium                   13149.041190    16672.143560    19117.974480  \n",
              "Bosnia and Herzegovina     2172.352423     2860.169750     3528.481305  \n",
              "Bulgaria                   5577.002800     6597.494398     7612.240438  \n",
              "Croatia                    6960.297861     9164.090127    11305.385170  \n",
              "Czech Republic            11399.444890    13108.453600    14800.160620  \n",
              "Denmark                   15937.211230    18866.207210    20422.901500  \n",
              "Finland                   10921.636260    14358.875900    15605.422830  \n",
              "France                    12999.917660    16107.191710    18292.635140  \n",
              "Germany                   14745.625610    18016.180270    20512.921230  \n",
              "Greece                     8513.097016    12724.829570    14195.524280  \n",
              "Hungary                    9326.644670    10168.656110    11674.837370  \n",
              "Iceland                   13319.895680    15798.063620    19654.962470  \n",
              "Ireland                    7655.568963     9530.772896    11150.981130  \n",
              "Italy                     10022.401310    12269.273780    14255.984750  \n",
              "Montenegro                 5907.850937     7778.414017     9595.929905  \n",
              "Netherlands               15363.251360    18794.745670    21209.059200  \n",
              "Norway                    16361.876470    18965.055510    23311.349390  \n",
              "Poland                     6557.152776     8006.506993     9508.141454  "
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              "      <th></th>\n",
              "      <th>gdpPercap_1952</th>\n",
              "      <th>gdpPercap_1957</th>\n",
              "      <th>gdpPercap_1962</th>\n",
              "      <th>gdpPercap_1967</th>\n",
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              "      <th>country</th>\n",
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              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>1601.056136</td>\n",
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              "      <td>8842.598030</td>\n",
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              "      <th>Bosnia and Herzegovina</th>\n",
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              "      <td>3528.481305</td>\n",
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              "      <th>Bulgaria</th>\n",
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              "      <td>7612.240438</td>\n",
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              "      <th>Croatia</th>\n",
              "      <td>3119.236520</td>\n",
              "      <td>4338.231617</td>\n",
              "      <td>5477.890018</td>\n",
              "      <td>6960.297861</td>\n",
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              "      <td>10136.867130</td>\n",
              "      <td>11399.444890</td>\n",
              "      <td>13108.453600</td>\n",
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              "      <td>9692.385245</td>\n",
              "      <td>11099.659350</td>\n",
              "      <td>13583.313510</td>\n",
              "      <td>15937.211230</td>\n",
              "      <td>18866.207210</td>\n",
              "      <td>20422.901500</td>\n",
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              "      <th>Finland</th>\n",
              "      <td>6424.519071</td>\n",
              "      <td>7545.415386</td>\n",
              "      <td>9371.842561</td>\n",
              "      <td>10921.636260</td>\n",
              "      <td>14358.875900</td>\n",
              "      <td>15605.422830</td>\n",
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              "      <th>France</th>\n",
              "      <td>7029.809327</td>\n",
              "      <td>8662.834898</td>\n",
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              "      <td>12999.917660</td>\n",
              "      <td>16107.191710</td>\n",
              "      <td>18292.635140</td>\n",
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              "      <td>3530.690067</td>\n",
              "      <td>4916.299889</td>\n",
              "      <td>6017.190733</td>\n",
              "      <td>8513.097016</td>\n",
              "      <td>12724.829570</td>\n",
              "      <td>14195.524280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hungary</th>\n",
              "      <td>5263.673816</td>\n",
              "      <td>6040.180011</td>\n",
              "      <td>7550.359877</td>\n",
              "      <td>9326.644670</td>\n",
              "      <td>10168.656110</td>\n",
              "      <td>11674.837370</td>\n",
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              "    <tr>\n",
              "      <th>Iceland</th>\n",
              "      <td>7267.688428</td>\n",
              "      <td>9244.001412</td>\n",
              "      <td>10350.159060</td>\n",
              "      <td>13319.895680</td>\n",
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              "      <td>5210.280328</td>\n",
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              "      <td>5907.850937</td>\n",
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              "      <td>18794.745670</td>\n",
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              "      <td>11653.973040</td>\n",
              "      <td>13450.401510</td>\n",
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              "      <td>18965.055510</td>\n",
              "      <td>23311.349390</td>\n",
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              "      <td>4734.253019</td>\n",
              "      <td>5338.752143</td>\n",
              "      <td>6557.152776</td>\n",
              "      <td>8006.506993</td>\n",
              "      <td>9508.141454</td>\n",
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"Albania\",\n          \"Netherlands\",\n          \"Italy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1952\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2666.9090325217453,\n        \"min\": 973.5331948,\n        \"max\": 10095.42172,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          1601.056136,\n          8941.571858,\n          4931.404155\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1957\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3175.7773468186765,\n        \"min\": 1353.989176,\n        \"max\": 11653.97304,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          1942.284244,\n          11276.19344,\n          6248.656232\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1962\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3678.3940898532987,\n        \"min\": 1709.683679,\n        \"max\": 13583.31351,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          2312.888958,\n          12790.84956,\n          8243.58234\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1967\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4271.882603285574,\n        \"min\": 2172.352423,\n        \"max\": 16361.87647,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          2760.196931,\n          15363.25136,\n          10022.40131\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1972\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5103.883423229426,\n        \"min\": 2860.16975,\n        \"max\": 18965.05551,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          3313.422188,\n          18794.74567,\n          12269.27378\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1977\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5856.2031645640955,\n        \"min\": 3528.481305,\n        \"max\": 23311.34939,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          3533.00391,\n          21209.0592,\n          14255.98475\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#iloc[start:stop,start:stop]\n",
        "data.iloc[:3,1:4].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "I4I31KGwvWcO",
        "outputId": "956e9d18-e7a7-4dbc-ffec-fdfe6a01bfe4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       gdpPercap_1957  gdpPercap_1962  gdpPercap_1967\n",
              "count        3.000000        3.000000        3.000000\n",
              "mean      6833.280966     8018.272276     9581.280174\n",
              "std       4258.126445     4942.469771     5909.323175\n",
              "min       1942.284244     2312.888958     2760.196931\n",
              "25%       5392.441137     6531.805034     7797.399666\n",
              "50%       8842.598030    10750.721110    12834.602400\n",
              "75%       9278.779327    10870.963935    12991.821795\n",
              "max       9714.960623    10991.206760    13149.041190"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gdpPercap_1957</th>\n",
              "      <th>gdpPercap_1962</th>\n",
              "      <th>gdpPercap_1967</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>6833.280966</td>\n",
              "      <td>8018.272276</td>\n",
              "      <td>9581.280174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4258.126445</td>\n",
              "      <td>4942.469771</td>\n",
              "      <td>5909.323175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1942.284244</td>\n",
              "      <td>2312.888958</td>\n",
              "      <td>2760.196931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5392.441137</td>\n",
              "      <td>6531.805034</td>\n",
              "      <td>7797.399666</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8842.598030</td>\n",
              "      <td>10750.721110</td>\n",
              "      <td>12834.602400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>9278.779327</td>\n",
              "      <td>10870.963935</td>\n",
              "      <td>12991.821795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9714.960623</td>\n",
              "      <td>10991.206760</td>\n",
              "      <td>13149.041190</td>\n",
              "    </tr>\n",
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              "        const element = document.querySelector('#df-d1ebe960-3b6a-49d9-8a0a-f8da9ab4a181');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    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-2c23b940-c21b-4580-b015-3dfea874cf1a 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\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"gdpPercap_1957\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3561.3458628628005,\n        \"min\": 3.0,\n        \"max\": 9714.960623,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6833.280965666668,\n          8842.59803,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1962\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4159.103798146156,\n        \"min\": 3.0,\n        \"max\": 10991.20676,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8018.272276,\n          10750.72111,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1967\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4971.238762450588,\n        \"min\": 3.0,\n        \"max\": 13149.04119,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          9581.280173666666,\n          12834.6024,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.loc['Albania':'Poland','gdpPercap_']"
      ],
      "metadata": {
        "id": "ckAz1g5uwcbw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.loc['Serbia', 'gdpPercap_2007']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WkGaq1HswtZd",
        "outputId": "065d38a6-2b7d-46d1-f92a-af2334551ca2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9786.534714"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.idxmin()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LWE5D-2Iwtkf",
        "outputId": "c52111cc-802c-40d3-8093-f085d112e26f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gdpPercap_1952    Bosnia and Herzegovina\n",
              "gdpPercap_1957    Bosnia and Herzegovina\n",
              "gdpPercap_1962    Bosnia and Herzegovina\n",
              "gdpPercap_1967    Bosnia and Herzegovina\n",
              "gdpPercap_1972    Bosnia and Herzegovina\n",
              "gdpPercap_1977    Bosnia and Herzegovina\n",
              "gdpPercap_1982                   Albania\n",
              "gdpPercap_1987                   Albania\n",
              "gdpPercap_1992                   Albania\n",
              "gdpPercap_1997                   Albania\n",
              "gdpPercap_2002                   Albania\n",
              "gdpPercap_2007                   Albania\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.max(),data.idxmax()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kmKIBe8nxLV8",
        "outputId": "48c72f0c-451a-42f4-f183-4e14d0688788"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(gdpPercap_1952    14734.23275\n",
              " gdpPercap_1957    17909.48973\n",
              " gdpPercap_1962    20431.09270\n",
              " gdpPercap_1967    22966.14432\n",
              " gdpPercap_1972    27195.11304\n",
              " gdpPercap_1977    26982.29052\n",
              " gdpPercap_1982    28397.71512\n",
              " gdpPercap_1987    31540.97480\n",
              " gdpPercap_1992    33965.66115\n",
              " gdpPercap_1997    41283.16433\n",
              " gdpPercap_2002    44683.97525\n",
              " gdpPercap_2007    49357.19017\n",
              " dtype: float64,\n",
              " gdpPercap_1952    Switzerland\n",
              " gdpPercap_1957    Switzerland\n",
              " gdpPercap_1962    Switzerland\n",
              " gdpPercap_1967    Switzerland\n",
              " gdpPercap_1972    Switzerland\n",
              " gdpPercap_1977    Switzerland\n",
              " gdpPercap_1982    Switzerland\n",
              " gdpPercap_1987         Norway\n",
              " gdpPercap_1992         Norway\n",
              " gdpPercap_1997         Norway\n",
              " gdpPercap_2002         Norway\n",
              " gdpPercap_2007         Norway\n",
              " dtype: object)"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data.max())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fHrvux3exPDX",
        "outputId": "55a90d05-5dd1-4399-dd07-15400c071be0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gdpPercap_1952    14734.23275\n",
            "gdpPercap_1957    17909.48973\n",
            "gdpPercap_1962    20431.09270\n",
            "gdpPercap_1967    22966.14432\n",
            "gdpPercap_1972    27195.11304\n",
            "gdpPercap_1977    26982.29052\n",
            "gdpPercap_1982    28397.71512\n",
            "gdpPercap_1987    31540.97480\n",
            "gdpPercap_1992    33965.66115\n",
            "gdpPercap_1997    41283.16433\n",
            "gdpPercap_2002    44683.97525\n",
            "gdpPercap_2007    49357.19017\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
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        "subset=data.loc['Italy':'Poland','gdpPercap_1957':'gdpPercap_1977']\n",
        "subset>10000"
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              "             gdpPercap_1957  gdpPercap_1962  gdpPercap_1967  gdpPercap_1972  \\\n",
              "country                                                                       \n",
              "Italy                 False           False            True            True   \n",
              "Montenegro            False           False           False           False   \n",
              "Netherlands            True            True            True            True   \n",
              "Norway                 True            True            True            True   \n",
              "Poland                False           False           False           False   \n",
              "\n",
              "             gdpPercap_1977  \n",
              "country                      \n",
              "Italy                  True  \n",
              "Montenegro            False  \n",
              "Netherlands            True  \n",
              "Norway                 True  \n",
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              "summary": "{\n  \"name\": \"subset>10000\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Montenegro\",\n          \"Poland\",\n          \"Netherlands\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1957\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1962\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1967\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1972\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1977\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
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            "text/plain": [
              "             gdpPercap_1957  gdpPercap_1962  gdpPercap_1967  gdpPercap_1972  \\\n",
              "country                                                                       \n",
              "Italy                 False           False            True            True   \n",
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              "      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-67b6419f-e9dd-408a-87d3-902417d19cc2 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-67b6419f-e9dd-408a-87d3-902417d19cc2');\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-151983af-ccdf-48da-ae5e-41f4f603b410\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-151983af-ccdf-48da-ae5e-41f4f603b410')\"\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-151983af-ccdf-48da-ae5e-41f4f603b410 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\": \"subset>10000\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Montenegro\",\n          \"Poland\",\n          \"Netherlands\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1957\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1962\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1967\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1972\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1977\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mask=subset>10000"
      ],
      "metadata": {
        "id": "dDp8MvL2xrVy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "subset[mask].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "KwKen_n4x5HF",
        "outputId": "3c3e3d8f-b63a-4bbe-feeb-ffbd3d0b4ab7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       gdpPercap_1957  gdpPercap_1962  gdpPercap_1967  gdpPercap_1972  \\\n",
              "count        2.000000        2.000000        3.000000        3.000000   \n",
              "mean     11465.083240    13120.625535    13915.843047    16676.358320   \n",
              "std        267.130517      466.373656     3408.589070     3817.597015   \n",
              "min      11276.193440    12790.849560    10022.401310    12269.273780   \n",
              "25%      11370.638340    12955.737548    12692.826335    15532.009725   \n",
              "50%      11465.083240    13120.625535    15363.251360    18794.745670   \n",
              "75%      11559.528140    13285.513522    15862.563915    18879.900590   \n",
              "max      11653.973040    13450.401510    16361.876470    18965.055510   \n",
              "\n",
              "       gdpPercap_1977  \n",
              "count        3.000000  \n",
              "mean     19592.131113  \n",
              "std       4739.277320  \n",
              "min      14255.984750  \n",
              "25%      17732.521975  \n",
              "50%      21209.059200  \n",
              "75%      22260.204295  \n",
              "max      23311.349390  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c385c238-8e08-484e-851c-31f8646f0e3e\" 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>gdpPercap_1957</th>\n",
              "      <th>gdpPercap_1962</th>\n",
              "      <th>gdpPercap_1967</th>\n",
              "      <th>gdpPercap_1972</th>\n",
              "      <th>gdpPercap_1977</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>11465.083240</td>\n",
              "      <td>13120.625535</td>\n",
              "      <td>13915.843047</td>\n",
              "      <td>16676.358320</td>\n",
              "      <td>19592.131113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>267.130517</td>\n",
              "      <td>466.373656</td>\n",
              "      <td>3408.589070</td>\n",
              "      <td>3817.597015</td>\n",
              "      <td>4739.277320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>11276.193440</td>\n",
              "      <td>12790.849560</td>\n",
              "      <td>10022.401310</td>\n",
              "      <td>12269.273780</td>\n",
              "      <td>14255.984750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>11370.638340</td>\n",
              "      <td>12955.737548</td>\n",
              "      <td>12692.826335</td>\n",
              "      <td>15532.009725</td>\n",
              "      <td>17732.521975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>11465.083240</td>\n",
              "      <td>13120.625535</td>\n",
              "      <td>15363.251360</td>\n",
              "      <td>18794.745670</td>\n",
              "      <td>21209.059200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>11559.528140</td>\n",
              "      <td>13285.513522</td>\n",
              "      <td>15862.563915</td>\n",
              "      <td>18879.900590</td>\n",
              "      <td>22260.204295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>11653.973040</td>\n",
              "      <td>13450.401510</td>\n",
              "      <td>16361.876470</td>\n",
              "      <td>18965.055510</td>\n",
              "      <td>23311.349390</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-c385c238-8e08-484e-851c-31f8646f0e3e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c385c238-8e08-484e-851c-31f8646f0e3e 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-c385c238-8e08-484e-851c-31f8646f0e3e');\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",
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              "        element.appendChild(docLink);\n",
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              "\n",
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              "            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",
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              "      --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",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
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              "    animation:\n",
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              "\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",
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              "      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",
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              "    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-3bc104f8-26e6-4033-8082-2af8abdad3c1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"subset[mask]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"gdpPercap_1957\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5246.703755438065,\n        \"min\": 2.0,\n        \"max\": 11653.97304,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2.0,\n          11465.08324,\n          11559.52814\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1962\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5969.80683759006,\n        \"min\": 2.0,\n        \"max\": 13450.40151,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2.0,\n          13120.625535,\n          13285.5135225\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1967\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6121.416261466496,\n        \"min\": 3.0,\n        \"max\": 16361.87647,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          13915.843046666667,\n          15363.25136,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1972\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7341.88966652408,\n        \"min\": 3.0,\n        \"max\": 18965.05551,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          16676.35832,\n          18794.74567,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1977\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8605.960289694112,\n        \"min\": 3.0,\n        \"max\": 23311.34939,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          19592.131113333333,\n          21209.0592,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "subset.describe()"
      ],
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        "colab": {
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          "height": 320
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        "id": "S7b4jODqx6e1",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       gdpPercap_1957  gdpPercap_1962  gdpPercap_1967  gdpPercap_1972  \\\n",
              "count        5.000000        5.000000        5.000000        5.000000   \n",
              "mean      7519.067127     8894.635868    10842.506571    13162.799194   \n",
              "std       3718.321805     4093.410673     4855.106424     5517.298708   \n",
              "min       3682.259903     4649.593785     5907.850937     7778.414017   \n",
              "25%       4734.253019     5338.752143     6557.152776     8006.506993   \n",
              "50%       6248.656232     8243.582340    10022.401310    12269.273780   \n",
              "75%      11276.193440    12790.849560    15363.251360    18794.745670   \n",
              "max      11653.973040    13450.401510    16361.876470    18965.055510   \n",
              "\n",
              "       gdpPercap_1977  \n",
              "count        5.000000  \n",
              "mean     15576.092940  \n",
              "std       6439.906289  \n",
              "min       9508.141454  \n",
              "25%       9595.929905  \n",
              "50%      14255.984750  \n",
              "75%      21209.059200  \n",
              "max      23311.349390  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gdpPercap_1957</th>\n",
              "      <th>gdpPercap_1962</th>\n",
              "      <th>gdpPercap_1967</th>\n",
              "      <th>gdpPercap_1972</th>\n",
              "      <th>gdpPercap_1977</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>7519.067127</td>\n",
              "      <td>8894.635868</td>\n",
              "      <td>10842.506571</td>\n",
              "      <td>13162.799194</td>\n",
              "      <td>15576.092940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3718.321805</td>\n",
              "      <td>4093.410673</td>\n",
              "      <td>4855.106424</td>\n",
              "      <td>5517.298708</td>\n",
              "      <td>6439.906289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3682.259903</td>\n",
              "      <td>4649.593785</td>\n",
              "      <td>5907.850937</td>\n",
              "      <td>7778.414017</td>\n",
              "      <td>9508.141454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4734.253019</td>\n",
              "      <td>5338.752143</td>\n",
              "      <td>6557.152776</td>\n",
              "      <td>8006.506993</td>\n",
              "      <td>9595.929905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>6248.656232</td>\n",
              "      <td>8243.582340</td>\n",
              "      <td>10022.401310</td>\n",
              "      <td>12269.273780</td>\n",
              "      <td>14255.984750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>11276.193440</td>\n",
              "      <td>12790.849560</td>\n",
              "      <td>15363.251360</td>\n",
              "      <td>18794.745670</td>\n",
              "      <td>21209.059200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>11653.973040</td>\n",
              "      <td>13450.401510</td>\n",
              "      <td>16361.876470</td>\n",
              "      <td>18965.055510</td>\n",
              "      <td>23311.349390</td>\n",
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              "        const element = document.querySelector('#df-70414ced-2d09-4625-9932-4f4bbcaa0d2d');\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",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --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-d0838627-251a-4377-922b-75cf22e29499 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\": \"subset\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"gdpPercap_1957\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3967.8604412531154,\n        \"min\": 5.0,\n        \"max\": 11653.97304,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7519.067126800001,\n          6248.656232,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1962\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4561.820961818338,\n        \"min\": 5.0,\n        \"max\": 13450.40151,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8894.635867599998,\n          8243.58234,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1967\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5505.893686029024,\n        \"min\": 5.0,\n        \"max\": 16361.87647,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10842.506570599999,\n          10022.40131,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1972\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6538.310591565476,\n        \"min\": 5.0,\n        \"max\": 18965.05551,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          13162.799193999997,\n          12269.27378,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1977\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7704.471853544077,\n        \"min\": 5.0,\n        \"max\": 23311.34939,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15576.0929398,\n          14255.98475,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "subset.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "GOQlLdQ9yG2s",
        "outputId": "736f1060-9ee4-4521-e832-eb5d11e37b26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       gdpPercap_1957  gdpPercap_1962  gdpPercap_1967  gdpPercap_1972  \\\n",
              "count        5.000000        5.000000        5.000000        5.000000   \n",
              "mean      7519.067127     8894.635868    10842.506571    13162.799194   \n",
              "std       3718.321805     4093.410673     4855.106424     5517.298708   \n",
              "min       3682.259903     4649.593785     5907.850937     7778.414017   \n",
              "25%       4734.253019     5338.752143     6557.152776     8006.506993   \n",
              "50%       6248.656232     8243.582340    10022.401310    12269.273780   \n",
              "75%      11276.193440    12790.849560    15363.251360    18794.745670   \n",
              "max      11653.973040    13450.401510    16361.876470    18965.055510   \n",
              "\n",
              "       gdpPercap_1977  \n",
              "count        5.000000  \n",
              "mean     15576.092940  \n",
              "std       6439.906289  \n",
              "min       9508.141454  \n",
              "25%       9595.929905  \n",
              "50%      14255.984750  \n",
              "75%      21209.059200  \n",
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              "      <th>gdpPercap_1957</th>\n",
              "      <th>gdpPercap_1962</th>\n",
              "      <th>gdpPercap_1967</th>\n",
              "      <th>gdpPercap_1972</th>\n",
              "      <th>gdpPercap_1977</th>\n",
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              "      <td>5.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
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              "      <td>8894.635868</td>\n",
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              "      <td>13162.799194</td>\n",
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              "      <td>5517.298708</td>\n",
              "      <td>6439.906289</td>\n",
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              "      <th>min</th>\n",
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              "      <td>4649.593785</td>\n",
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              "      <td>7778.414017</td>\n",
              "      <td>9508.141454</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
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              "      <td>5338.752143</td>\n",
              "      <td>6557.152776</td>\n",
              "      <td>8006.506993</td>\n",
              "      <td>9595.929905</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>6248.656232</td>\n",
              "      <td>8243.582340</td>\n",
              "      <td>10022.401310</td>\n",
              "      <td>12269.273780</td>\n",
              "      <td>14255.984750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>11276.193440</td>\n",
              "      <td>12790.849560</td>\n",
              "      <td>15363.251360</td>\n",
              "      <td>18794.745670</td>\n",
              "      <td>21209.059200</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
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              "      <td>13450.401510</td>\n",
              "      <td>16361.876470</td>\n",
              "      <td>18965.055510</td>\n",
              "      <td>23311.349390</td>\n",
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              "summary": "{\n  \"name\": \"subset\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"gdpPercap_1957\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3967.8604412531154,\n        \"min\": 5.0,\n        \"max\": 11653.97304,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7519.067126800001,\n          6248.656232,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1962\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4561.820961818338,\n        \"min\": 5.0,\n        \"max\": 13450.40151,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8894.635867599998,\n          8243.58234,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1967\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5505.893686029024,\n        \"min\": 5.0,\n        \"max\": 16361.87647,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10842.506570599999,\n          10022.40131,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1972\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6538.310591565476,\n        \"min\": 5.0,\n        \"max\": 18965.05551,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          13162.799193999997,\n          12269.27378,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdpPercap_1977\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7704.471853544077,\n        \"min\": 5.0,\n        \"max\": 23311.34939,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15576.0929398,\n          14255.98475,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "first=pd.read_csv('/content/data/gapminder_all.csv')"
      ],
      "metadata": {
        "id": "WxX3_cApyZ3K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "first.head(2)"
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "id": "WSFUHqL2zarU",
        "outputId": "c000d40d-09c0-48ef-b1a8-cc5a19302136"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  continent  country  gdpPercap_1952  gdpPercap_1957  gdpPercap_1962  \\\n",
              "0    Africa  Algeria     2449.008185     3013.976023     2550.816880   \n",
              "1    Africa   Angola     3520.610273     3827.940465     4269.276742   \n",
              "\n",
              "   gdpPercap_1967  gdpPercap_1972  gdpPercap_1977  gdpPercap_1982  \\\n",
              "0     3246.991771     4182.663766     4910.416756     5745.160213   \n",
              "1     5522.776375     5473.288005     3008.647355     2756.953672   \n",
              "\n",
              "   gdpPercap_1987  ...    pop_1962    pop_1967    pop_1972    pop_1977  \\\n",
              "0     5681.358539  ...  11000948.0  12760499.0  14760787.0  17152804.0   \n",
              "1     2430.208311  ...   4826015.0   5247469.0   5894858.0   6162675.0   \n",
              "\n",
              "     pop_1982    pop_1987    pop_1992    pop_1997  pop_2002  pop_2007  \n",
              "0  20033753.0  23254956.0  26298373.0  29072015.0  31287142  33333216  \n",
              "1   7016384.0   7874230.0   8735988.0   9875024.0  10866106  12420476  \n",
              "\n",
              "[2 rows x 38 columns]"
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              "      <th>0</th>\n",
              "      <td>Africa</td>\n",
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              "      <td>2449.008185</td>\n",
              "      <td>3013.976023</td>\n",
              "      <td>2550.816880</td>\n",
              "      <td>3246.991771</td>\n",
              "      <td>4182.663766</td>\n",
              "      <td>4910.416756</td>\n",
              "      <td>5745.160213</td>\n",
              "      <td>5681.358539</td>\n",
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              "      <td>29072015.0</td>\n",
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              "      <td>33333216</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Angola</td>\n",
              "      <td>3520.610273</td>\n",
              "      <td>3827.940465</td>\n",
              "      <td>4269.276742</td>\n",
              "      <td>5522.776375</td>\n",
              "      <td>5473.288005</td>\n",
              "      <td>3008.647355</td>\n",
              "      <td>2756.953672</td>\n",
              "      <td>2430.208311</td>\n",
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              "      <td>5247469.0</td>\n",
              "      <td>5894858.0</td>\n",
              "      <td>6162675.0</td>\n",
              "      <td>7016384.0</td>\n",
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              "      <td>8735988.0</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </button>\n",
              "\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-bg-color: #3B4455;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    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",
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              "    20% {\n",
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              "      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-3148f96a-dea6-4e89-b2eb-e1f401fee202 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": "first"
            }
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mask2=first['continent']=='Americas'"
      ],
      "metadata": {
        "id": "dJ8zBAvHzb7T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "second=first[mask2]"
      ],
      "metadata": {
        "id": "MKjLWNH1zdfW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "third=second.drop('Puerto Rico')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "tyTvW78hz0F5",
        "outputId": "777f7782-bcf9-4f18-f195-62e6e94a9762"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "\"['Puerto Rico'] not found in axis\"",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-141-997ce9b94749>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mthird\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Puerto Rico'\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/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   5256\u001b[0m                 \u001b[0mweight\u001b[0m  \u001b[0;36m1.0\u001b[0m     \u001b[0;36m0.8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5257\u001b[0m         \"\"\"\n\u001b[0;32m-> 5258\u001b[0;31m         return super().drop(\n\u001b[0m\u001b[1;32m   5259\u001b[0m             \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5260\u001b[0m             \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\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/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4547\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\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   4548\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4549\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   4550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4551\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\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/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m   4589\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4590\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4591\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   4592\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4593\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   6697\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\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   6698\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6699\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{list(labels[mask])} not found in axis\"\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   6700\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6701\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\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;31mKeyError\u001b[0m: \"['Puerto Rico'] not found in axis\""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "id": "nkdfoDGA0A15",
        "outputId": "9ab2594b-0b26-4fad-bd69-c6c597125b1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        gdpPercap_1952  gdpPercap_1957  gdpPercap_1962  \\\n",
              "country                                                                  \n",
              "Albania                    1601.056136     1942.284244     2312.888958   \n",
              "Austria                    6137.076492     8842.598030    10750.721110   \n",
              "Belgium                    8343.105127     9714.960623    10991.206760   \n",
              "Bosnia and Herzegovina      973.533195     1353.989176     1709.683679   \n",
              "Bulgaria                   2444.286648     3008.670727     4254.337839   \n",
              "\n",
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              "country                                                                  \n",
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              "country                                                                  \n",
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              "country                                                                 \n",
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            }
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fourth=third.drop('continent',axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "id": "HvEwZJsl0KPs",
        "outputId": "cb319960-283b-459f-fef6-f640c2a12ae8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'third' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-137-e48420df79c4>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfourth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mthird\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'continent'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\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[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'third' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fourth.to_csv('./data/Americas.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "id": "RkwRvp4r0dTx",
        "outputId": "42aec81a-5cbd-4ec2-916f-d9f6d40124e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'fourth' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-138-03d88ecf2dbb>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfourth\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./data/Americas.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'fourth' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Plottting"
      ],
      "metadata": {
        "id": "FZVa0-Vm1HVk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "m3X8JB8s0mO2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "time=[0,1,2,3,4,5]\n",
        "positions=[0,10,15,25,50,100]\n",
        "pos2=[0,17,20,27,60,130]"
      ],
      "metadata": {
        "id": "kO8QWSE51LX5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.Figure(figsize=(12,8))\n",
        "plt.plot(time,positions,'red',marker='^',linestyle='--',markersize=12,label='Car1')\n",
        "plt.plot(time,pos2,'blue',marker='D',linestyle='-.',markersize=10,label='Car2')\n",
        "plt.xlabel('Time(min)',fontsize=15)\n",
        "plt.ylabel('Position',fontsize=15)\n",
        "plt.xticks(fontsize=12)\n",
        "plt.yticks(fontsize=12)\n",
        "plt.legend(fontsize=15,loc=0,frameon=False,ncols=2)\n",
        "plt.savefig('./data/fig1.png',format='PNG',dpi=1000)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "mBDxat_m1aZq",
        "outputId": "b11823df-9465-4575-efa7-9bcf58fbe63f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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OckVERPyazQZr18Lkyb7bagRePObo3Xff5fTp02l3kn399dccOHAAgMGDBxMQEEDnzp05deoUzz77bJa5iurUqUPr1q0BM+Zo7NixDBo0iN69e9O5c2fWrFnDnDlzGDduHGXKlHHvhxMREfFTYWHw1FOerqJgbNbF62x4iZo1a7Jv375s9+3duxeAWrVq5fj+fv36MWvWrEzbpk+fzqRJk9LWVnviiSd48sknsdlsea7LkVV9RURECot//zXjjBz4SnUrR76/vTYceSuFIxERkcwSE6FuXYiIgAULIJe2C49x5Pvba7vVRERExDds3gynTplWI3+4AVzhSERERAqkdWuIjobdu8EfpgZUOBIREZECK1/ePPyBT9/KLyIiIp6TlARr1ni6CudTOBIREZF8mTED2rWDPn08XYlzKRyJiIiIw5KSYMIE8/yaazxbi7MpHImIiIjDPvoIDhwwd6c99JCnq3EuhSMRERFxSFISjB9vno8YASEhnq3H2RSORERExCEzZ/pvqxEoHImIiIgDMrYaPf+8/7UagcKRiIiIOGDmTNi/HypVgocf9nQ1rqFwJCIiInly/rz/txqBwpGIiIjkUWFoNQKFIxEREcmDjK1Gw4dDaKhn63ElhSMRERG5pFmz4N9/ISICBg70dDWupXAkIiIil7R9u/nz+ef9u9UIoIinCxARERHv98Yb8OCDULeupytxPYUjERERyZPGjT1dgXuoW01ERERytGYN7N3r6SrcS+FIREREsnXhAvTtC/Xrw7Jlnq7GfdStJiIiItk6ccIEo4QEaNvW09W4j8KRiIiIZCsiApYuhWPHoFgxT1fjPupWExERkVyVL+/pCtxL4UhEREQyuXABxoyBw4c9XYlnKByJiIhIJrNnw0svQevWkJLi6WrcT+FIRERE0ly4AOPGmeeDBkFgoGfr8QSFIxEREUkzZw78848ZZ/TYY56uxjMUjkRERAQwrUYvv2yeP/ccFC/u2Xo8ReFIREREAJg7V61GoHAkIiIiQHJyeqvRs88W3lYjUDgSERERTKvRnj1Qrhw8/rinq/EshSMREZFCLjkZxo41zwt7qxEoHImIiBR6ajXKTOFIRESkEMs41mjYMChRwrP1eAOFIxERkULs009h924oW9ZM+uhRK1ZAw4bmTw9SOBIRESnESpeG+vXNWCOPthpZFrzwAmzfbv60LI+VUsRjVxYRERGPu/VW6NrVdK951LJlsHGjeb5xo3ndubNHSvHalqP4+HhGjx7NzTffTJkyZbDZbMyaNSvbY7dv387NN99MiRIlKFOmDPfffz/Hjh3LclxqaiqvvfYatWrVIiQkhCZNmvDZZ5+5+JOIiIh4t8BACA72YAGWBZGR6Qu5BQaa1x5qPfLacHT8+HGioqLYvn07TZs2zfG4AwcO0K5dO3bv3s348eMZNmwY3377LZ06deL8+fOZjn3xxRcZPnw4nTp14p133qF69erce++9zJs3z9UfR0RExKt8+SW89x4kJnq6EtJbjVJSzOuUlPTWI0+wvFRiYqIVExNjWZZlbdy40QKsmTNnZjnuscces0JDQ619+/albVu+fLkFWFOnTk3bduDAAato0aLWoEGD0ralpqZa1113nVW1alUrOTk5T3XFxsZagBUbG5vPTyYiIuJZycmWVb++ZYFlTZ7s4WJSUy3rqqssKzDQFGR/BAaa7ampTrmMI9/fXttyFBwcTERExCWPW7RoEbfccgvVq1dP23bjjTdSv3595s+fn7btyy+/5MKFCzyeYQIHm83GY489xoEDB1i/fr1zP4CIiIiXSk2FJ5+Epk3hoYc8XMzFrUZ2Hmw98tpwlBcHDx7k6NGjtGzZMsu+q6++ms2bN6e93rx5M8WLF6dBgwZZjrPvz05SUhJxcXGZHiIiIr6saFEz2ePmzVCypAcLuXis0cU8NPbIp8NRTEwMAJUqVcqyr1KlSpw8eZKkpKS0YytWrIjNZstyHMChQ4eyvcaECRMIDw9Pe1SrVs2ZH0FERMRjLvpKdL+cWo3sPNR65NPhKCEhATBdcBcLCQnJdExCQkKejrvYiBEjiI2NTXvs37/fKbWLiIi4W0oKdO8On3ziBbfuX6rVyM4DrUc+HY5CQ0MB0lqHMkr8b/i9/ZjQ0NA8HXex4OBgwsLCMj1ERER80eefw9dfw9ChcO6ch4u5VKuRnQdaj3w6HNm7xOzdaxnFxMRQpkyZtNaiSpUqcfjwYayLkqf9vZUrV3ZxtSIiIp6TkgJjx5rnzzwDHv23fl5bjezc3Hrk0+GoSpUqlC9fnk2bNmXZ98svv9CsWbO0182aNePcuXNs374903EbNmxI2y8iIuKv5s+HHTvMciGDB3u4mLy2Gtm5ufXIp8MRQK9evfjmm28yjQX6/vvv2blzJ717907bdtttt1G0aFHef//9tG2WZfHBBx9QpUoV2rRp49a6RURE3CVjq9HTT/tYq5GdG1uPvHpttXfffZfTp0+n3Un29ddfc+DAAQAGDx5MeHg4L7zwAgsWLKBDhw48+eSTxMfHM3HiRBo3bkz//v3TzlW1alWGDh3KxIkTuXDhAldddRX/+9//WLNmDXPnziXQ0f9IIiIiPmLBArOeq1e1GjkqY+uRi9dcs1kXD8LxIjVr1mTfvn3Z7tu7dy81a9YE4M8//+Tpp5/mp59+IigoiG7dujFp0iQqVqyY6T2pqam8+uqrTJ06lZiYGOrVq8eIESO477778lxTXFwc4eHhxMbGanC2iIh4vZQUaNzYhKOoKNP44jGWBa1awa+/mpkoHRUQAC1awIYNDs9D4Mj3t1eHI2+kcCQiIr7k88/h7ruhVCmIjobwcA8Wk5QENWrAkSP5P0dEhPkgDq6U68j3t1d3q4mIiEj+paaa1iKAp57ycDACE2jmzzfdYomJpqg+fRw7R4UKDgcjRykciYiI+KmFC+Gvv0yr0ZAhnq4GM/Pkc8+ZYNShA7z+uukq8zLeV5GIiIgU2MWtRqVKebQcY/x4M14oPBw+/tgrgxEoHImIiPilRYvgzz9NDvGKVqNffklPa++/D168Vqm61URERPxQu3YwbBiUL+8FrUZnz5qxRSkpZnT4vfd6uKDcKRyJiIj4oYoVYeJET1fxnyJFoGdPmDfPtBp5OXWriYiI+BGvnKAnOBhefdX085Uu7elqLknhSERExI8sWgSdOsH69Z6uBIiLM3eo2ZUo4blaHKBwJCIi4kcmTIAVK2DpUg8XYlnwwAPQti3s3u3hYhyjMUciIiJ+ZNEiM33Qk096uJBZs+CLL6BoUYiP93AxjlE4EhER8SM1a8K773q4iH/+SZ8/YOxYaNbMo+U4St1qIiIifiAhwdMV/CclBfr2Na1F111n5hPwMQpHIiIiPi41FVq3hl69YP9+Dxfz2muwdi2ULAmffAKBgR4uyHHqVhMREfFx//sf/P676c0qXtyDhfz2G4waZZ6//bbp4/NBCkciIiI+LDUVxowxz598EsqU8WAxxYtD06ZQowb06+fBQgpG4UhERMSHffkl/PGH6cV66ikPF3PZZWaCpYQEsNk8XEz+acyRiIiIj8rYajRkiAdbjc6eTX9etCiEhXmoEOdQOBIREfFRX31lxhp5tNXo5Elo2BBefBHOn/dQEc6lcCQiIuKDLCu91WjwYChb1kNFPPYY/PuvmX0y41IhPkzhSERExAd99RVs2WKWK3v6aQ8V8emnMH8+FCkCc+ZAsWIeKsS5FI5ERER8jFe0Gv37LwwaZJ6PGgUtW3qgCNdQOBIREfExX38Nmzd7sNUoNdXcqh8bC9dcAyNGeKAI11E4EhER8SGWBS+9ZJ4PHgzlynmgiDfegFWrzLxGs2ebbjU/onAkIiLiQ9auNa1GxYt7cKxRmTKmgDfegLp1PVSE6/hX1BMREfFzbdvC6tWwe7eHWo0A+veHm26CypU9VIBrKRyJiIj4mOuuMw+3O38egoLM8ypVPFCAe6hbTURExAdYFpw44cECVq40y4OsWuXBItxD4UhERMQHfPstVK+efgu/W50+be5Oi442cxv5OYUjERERH/DFF3DunFnT1e2eeAL274c6dWDyZA8U4F4acyQiIuIDPvwQevWCq65y84U//xzmzoWAAHPbfokSbi7A/RSOREREfIDNBl27uvmiBw+atdPALCzburWbC/AMdauJiIh4sZ07IS7OAxdOTTW37J86ZZYGiYz0QBGeoXAkIiLipSwL+vSBmjVhxQo3XzwhAcLCIDTULCpbtKibC/AcdauJiIh4qcWLYeNGk0+aNHHzxYsXhwULYNcuqF/fzRf3LLUciYiIeCHLSr9tf9AgqFDBTRdOSTEXBzPQqZAFI1A4EhER8UpLlsAvv5hWo2HD3HjhyEi46y44edKNF/UuCkciIiJeJmOr0eOPQ8WKbrrwTz/Bq6+a7rTVq910Ue+jcCQiIuJlli6FDRtMq9Gzz7rponFx0LevuUutXz/o0cNNF/Y+Ph+Odu3axd13303VqlUpVqwYl19+OVFRUZw7dy7TcevWraNt27YUK1aMiIgIhgwZQnx8vIeqFhERyZ5lwUsvmeePPebGVqOhQ2HvXnNr3Ntvu+mi3smn71bbv38/V199NeHh4TzxxBOUKVOG9evXM3r0aH799Ve+/PJLALZs2cINN9xAgwYNmDx5MgcOHOD1119n165dLF682MOfQkREJN2yZR5oNfq//4OZM80A7E8+MbfwF2I+HY5mz57N6dOn+emnn7jiiisAGDhwIKmpqXzyySecOnWK0qVL88ILL1C6dGlWrVpF2H//wWvWrMnDDz/MsmXLuOmmmzz5MURERIDMrUaPPgoREW64aEwMDBxonj/3HFx3nRsu6t18ulst7r8pQyte1OZYqVIlAgICCAoKIi4ujuXLl9OnT5+0YATQt29fSpQowfz5891as4iISE6WL4eff4aQEJNT3OLQIdNM1bQpREW56aLezafDUfv27QEYMGAAW7ZsYf/+/Xz++edMmTKFIUOGULx4cbZu3UpycjItW7bM9N6goCCaNWvG5s2bc71GUlIScXFxmR4iIiLOdvFYI7e0GgG0aAFbt5qutaAgN13Uu/l0OLr55psZO3Ysy5cvp3nz5lSvXp27776bwYMH88YbbwAQExMDmNaki1WqVIlDhw7leo0JEyYQHh6e9qhWrZrzP4iIiBR6hw5BdLQbW43sEz0ClCoFtWu74aK+wafDEZixQ+3atWPatGksWrSIBx98kPHjx/Puu+8CkJCQAEBwcHCW94aEhKTtz8mIESOIjY1Ne+zfv9/5H0JERAq9KlXgn3/MbfwubzW6cAE6dYKPPsockgTw8QHZ8+bNY+DAgezcuZOqVasCcPvtt5Oamsrw4cO55557CA0NBUz32MUSExPT9uckODg422AlIiLibCEh0K6dGy40bhx8/z38+ivceiuUL++Gi/oOp4Sjc+fOsWnTJmJiYrINIXZ9+/Z1xuXSvP/++zRv3jwtGNl1796dWbNmsXnz5rTuNHv3WkYxMTFUrlzZqTWJiIg4wrLMUiE33QSBgW644IYN8PLL5vkHHygYZaPA4WjUqFG88cYbWSZdzMiyLGw2m9PD0ZEjRyhdunSW7RcuXAAgOTmZRo0aUaRIETZt2sSdd96Zdsz58+fZsmVLpm0iIiLu9v330LUrNG8OGze6OCDFx0OfPmZx2XvvNWuoSRYFCkevvfYaL7/8MoGBgXTr1o369etTsmRJZ9V2SfXr12fZsmXs3LmT+hlWDf7ss88ICAigSZMmhIeHc+ONNzJnzhwiIyPT6ps9ezbx8fH07t3bbfWKiIhc7PBhKF3adKe5vOVo2DDYvRuqVoX33nPxxXyXzbLyPxKrXr16HDp0iDVr1nDllVc6s648Wb16NR07dqRs2bI88cQTlC1blm+++YbFixfz0EMPMX36dAB+++032rRpQ8OGDRk4cCAHDhxg0qRJtGvXjqVLlzp0zbi4OMLDw4mNjc00b5KIiEh+xcVBcjKUKePCi3zzjRlfBKa5qmNHF17M+zjy/V2gu9X279/P9ddf75FgBNCuXTvWrVtHixYteP/99xk6dCh79uxh3LhxTJkyJe24K6+8khUrVhAaGspTTz3FtGnTGDBgAAsXLvRI3SIiIhmFhbk4GAH89ZdZHuTppwtdMHJUgVqOatasyVVXXcWCBQucWZNXU8uRiIg4w88/w4kTZryRzebGizZrZm6LK2Tc1nJ09913s2rVKs6ePVuQ04iIiBQqlmUWlb3lFnjtNTde+JprCmUwclSBwtFLL71EgwYN6N69O7t373ZWTSIiIn5t5Ur46SezWkefPi680J49cMMN8PffLryI/ynQ3Wpdu3YlNTWVVatW0aBBA2rUqEHVqlUJCMiauWw2G99//31BLiciIuLzMq6hNnCgmRnbJZKT4f77Yf16GDoUFi920YX8T4HGHGUXgnK8kM1GSkpKfi/lNTTmSERECmLlSjMeOijILBfisnA0bhyMHAklS5qFZWvUcNGFfIMj398Fajnau3dvQd4uIiJS6NhbjR5+2IXBaNOm9Au9+26hD0aOKlA4qqEftoiISJ6tWgWrV5tWo+efd9FFzp0z3WnJyXDHHea5OKRAA7JFREQk7+yNOQ89ZCapdonhw2HHDqhUyayd5rZ5AvyHUxaePXLkCB999BFr1qzh4MGDAFSpUoV27drRv39/Klas6IzLiIiI+KxVq+DHH13capSYCL/8Yp7PnAlly7roQv6tQAOyARYtWsSDDz5IfHw8F5/KZrNRsmRJZsyYQa9evQpUqLfQgGwREcmPDh1MQHr8cRcva3bhAixfbmaXlDSOfH8XKBxt2rSJNm3akJqaSo8ePbj//vupWbMmNpuN6OhoZs+ezRdffEFgYCBr166lZcuW+b2U11A4EhERR/34I7RvD0WLmqmHqlXzdEWFj9vuVpswYQIpKSksXLiQnj17ZtrXpEkTunfvzhdffEGvXr145ZVXtJaZiIgUSmPGmD8feshFwWjOHNi2DaKiTL+dFEiBwtFPP/1EmzZtsgSjjHr27Mm1117LmjVrCnIpERERn5SUBLVqwYYNMGKECy6wbx8MGgRxceZCjzzigosULgW6Wy02Npbq1atf8rjq1asTGxtbkEuJiIj4pOBgmDEDDhxwQatRSgr07WuCUZs2MGCAky9QOBUoHEVERLB58+ZLHrdlyxYiIiIKcikRERGfVrq0C046ebKZOKlECZg9G4o45Sb0Qq9A4ahz5878/fffvPDCC9kuDWJZFiNHjmTHjh3cfPPNBbmUiIiIz3n5ZfjzTxed/Pff4cUXzfM334TatV10ocKnQHerHThwgObNm3Py5EmqV6/OnXfeSc2aNQHYt28fCxYsIDo6mrJly/Lbb79R1WUzXrmP7lYTEZG8WLMG2rUz3WoHDzp5yqHERLjqKjMIu0cP+L//02SPl+C2u9WqVq3KDz/8wH333ce2bduYOHEitv/+49gzV+PGjZk7d65fBCMREZG8qlABevWC8uVdMBfj779DdLS5yLRpCkZOVuBJIO1WrVrFmjVrOHToEACVK1fmuuuuo3379s44vddQy5GIiDgiNRUCXLFY1z//mCap665zwcn9j9smgSyMFI5ERER8jyPf31p4VkRExInWroWBA02vl9MNGwY//OCCE0tGDo05Wr16NQBXX301ISEhaa/zql27dg4dLyIi4mteeglWrDDPp01z4onnzYNJk+Ctt0yXmtYgcRmHutUCAgKw2Wxs376d+vXrp73Oq+xu9/c16lYTEZGcrF0Lbdua6YZ27YL/buAuuP37oUkTOH0aRo1KX49E8sxld6v17dsXm81GeHh4ptciIiKSnlkeeMCJwSg1Ffr3N8Hoqqtg5EgnnVhyogHZDlLLkYiIZGfdOrj2WtNqtHOnWebMKd58E556CooVg82boX59J524cNGAbBERETeztxr16+fEYLRtGzz/vHk+aZKCkZsUKBzVrl2b4cOHX/K4ESNGUKdOnYJcSkRExGutXw/LlplWoxdecOKJ582DpCTo2hUeecSJJ5bcFGiG7OjoaI4dO3bJ444fP060S+5pFBER8Tx7q1Hfvk5e4mzsWGjYEDp21CzYbuSW5XvPnj1L0aJF3XEpERERt/r5Z1i6FAID09eBdRqbDe6918knlUtx6Zij1NRUtm/fzsqVK6levborLyUiIuIRGccaOaXVKC4OBg2C48edcDLJD4fDUWBgYNoD4OOPP860LeOjaNGiNGrUiCNHjnDPPfc4vXgRERFP2rABlixxcqvRkCHw/vtw222gG8o9wuFutWrVqqXNbfTvv/9SrFgxypUrl+2xQUFBVK5cme7duzNkyJCCVSoiIuJlnD7WaNEi+Phjs1Ltq69qnJGHOByOMg6sDggIoHfv3nz00UfOrElERMQnPPoonDjhpFajmJj0O9KGDzdTbYtHFGhA9sqVK4mIiHBWLSIiIj6le3fzKDDLggcfNEmreXOzQJt4TIEGZF9//fVcdtllzqpFRETEK40da3q6xo510QXef98MXgoJgTlzICjIRReSvHCo5Wj16tUAXH311YSEhKS9zqt27do5dLyIiIinjR1r1nqF9D9374YGDeCJJ6BEiQJeIDkZ3nnHPH/1VTOvkXiUQ2urBQQEYLPZ2L59O/Xr1097nVcpKSn5KtKbaG01EZHCI2MwulhgIOzYAXXrOuFCp0/D9OnwzDOmiUqczpHvb4dajvr27YvNZiM8PDzTaxEREX+TWzACM17aKcEIoFQpePZZJ51MCsqhliNRy5GISGFwqWBkFxUFkZH5vMj69WZh2Yce0i37buDI97dftN399ttvdO/enTJlylCsWDEaNWrE22+/nemYdevW0bZtW4oVK0ZERARDhgwhPj7eQxWLiIi3ymswAnNcvgZpx8fD/ffDwIHw1lv5OIG4ksvWVtu+fTt//vkn1apVo1WrVq66DMuWLePWW2+lefPmREZGUqJECfbs2cOBAwfSjtmyZQs33HADDRo0YPLkyRw4cIDXX3+dXbt2sXjxYpfVJiIivsWRYGRnP96hFqSnn4Y9e6B6dejf37ELiutZBTBv3jyrQ4cO1s8//5xp+7Bhw6yAgIC0R48ePazk5OSCXCpbsbGxVsWKFa2ePXtaKSkpOR7XpUsXq1KlSlZsbGzatunTp1uAtXTpUoevCWQ6l4iI+L6oKMsyEw7l7xEVlccLffmleYPNZlkrV7ryI0kGjnx/F6hbbc6cOWzZsoXmzZunbVu3bh2TJk2iZMmS3H333dSsWZOvvvqKuXPnFizFZePTTz/lyJEjjBs3joCAAM6ePUtqamqmY+Li4li+fDl9+vTJ1MfYt29fSpQowfz5851el4iI+Jb8tBhdLE9dbEePmjFGYO5Ma9++YBcVlyhQONq2bRtNmjQhKMNkVbNnz8ZmszF//nzmzp3Lxo0bKVGiBB9++GGBi73YihUrCAsL4+DBg1x22WWUKFGCsLAwHnvsMRITEwHYunUrycnJtGzZMtN7g4KCaNasGZs3b871GklJScTFxWV6iIiI/3BGMLLLNSBZlglGx45B48bw8svOuag4XYHC0dGjR6lSpUqmbStXrqRChQrcdNNNAJQpU4Z27dqxe/fuglwqW7t27SI5OZnbbruNzp07s2jRIh588EE++OAD+v/XhxsTEwNApUqVsry/UqVKHDp0KNdrTJgwgfDw8LRHtWrVnP45RETEc0aPdtP5fv0Vvv3WzH49Zw4EBzv3wuI0BQpHoaGhmVpSYmJi2LlzJ9dff32m40qVKsWpU6cKcqlsxcfHc+7cOfr27cvbb7/N7bffzttvv80jjzzCvHnz2LVrFwkJCQAEZ/NLGBISkrY/JyNGjCA2NjbtsX//fqd/DhER8ZwxY9x0vpYtYfVqmDIFmjRx7kXFqQoUjmrXrs2aNWs4ffo0AHPnzsVms6W1GtkdPnyYChUqFORS2QoNDQXgnnvuybT93nvvBWD9+vVpxyQlJWV5f2JiYtr+nAQHBxMWFpbpISIi/iMy0sxX5AyXnPfo2mvNArPi1QoUjh544AHi4uJo0aIFvXr1YuTIkZQoUYLbbrst7ZgLFy6wadMm6tevX+BiL1a5cmUAKlasmGm7PYidOnUqrTvN3r2WUUxMTNo5RESk8HJGQMoxGM2aZdYZEZ9RoHD08MMP06tXL/bu3csXX3xB0aJFmTp1KmXLlk075ptvviE2NpaOHTsWuNiLtWjRAoCDBw9m2m4fR1S+fHkaNWpEkSJF2LRpU6Zjzp8/z5YtW2jWrJnT6xIREd8TGWnmZMyPHIPRxo1mEHbz5rBrV4HqE/cpUDgqWrQoCxYs4J9//mHDhg0cOnSIu+++O9MxtWrV4osvvuBBFzQj3nnnnQDMmDEj0/YPP/yQIkWK0L59e8LDw7nxxhuZM2cOZ86cSTtm9uzZxMfH07t3b6fXJSIivufECZg3z/H35RiMzp2DPn0gJQW6d3fiQmziaj6/ttqAAQP46KOPuPPOO7n++utZtWoVCxYsYMSIEYwfPx4wy4u0adOGhg0bMnDgQA4cOMCkSZNo164dS5cudeh6WltNRMR/TZsGCxbANdfk7U77XMcYDRoE778PlSvD1q1QpoxTaxXHOPL97bRwZO+msndxValShWbNmmWaA8kVLly4wPjx45k5cyaHDh2iRo0aDBo0iKFDh2Y67qeffmL48OH89ttvlCxZkjvvvJMJEyZQsmRJh66ncCQi4l8uXICiRdNfp6ZCQMCl5z/KNRgtXgxdu5rny5ZBp05Oq1fyx63hKDExkVGjRjF16tQsC7mWKFGCRx99lDFjxhASElKQy3gNhSMREf/x0UfwxhuwciWUK5d1f04BKddgdPy4meTx8GEYMkQLy3oJt4WjpKQkbrjhBtavXw9AkyZNqFmzJjabjejoaH7//XcAWrduzffff5/tXEO+RuFIRMQ/xMdDw4awfz9MmADPP5/9cRcHpEverv/iizB+PDRoYCZ+vMSUMeIejnx/FynIhd544w3WrVtH27Ztee+992jcuHGm/du2beOJJ55gzZo1vPnmmwwfPrwglxMREXGaEiVMj9fnn0NuX0/2IDR6tJngMddgBOagoCC49VYFIx9VoJajpk2bcvjwYXbv3p3j2J34+Hjq1KlDxYoV+eOPP/JdqLdQy5GIiO+yLPjzT2jUyNOViLs58v1doFv5d+/eTfv27XMd1FyiRAnat2/Pnj17CnIpERGRAklNhccegxYtYPlyJ588JcXcmZbNagziewoUjooUKcK5c+cuedy5c+coUqRAPXgiIiL5duEC9O0LU6ea5wcOOPkCr79ubt2/8UbTPCU+rUDhqHHjxvzwww/8888/OR6zd+9efvjhB5pokT0REfGAxETo3RvmzoUiReCzz6B/fydeYPPm9IFIDz4INpsTTy6eUKBw9Mgjj5CQkED79u2ZMWNGphXuExISmDlzJu3btycxMZFHH320wMWKiIg44uxZMy76yy8hOBi++ALuusuJF0hIMLNgX7gAPXvCAw848eTiKQWe5+iRRx5h+vTp2P5LyuX+myji+PHjAFiWxSOPPMKUKVMKWKp30IBsERHfcPo0dOsG69ZB8eLw9dfQoYOTLzJ0qJnHqGJFMwt2+fJOvoA4i9sGZANMnTqVBQsW0LZtW4oWLcqxY8c4duwYRYsW5brrrmPBggV+E4xERMQ3HDtmgtC6dVCqFKxY4YJgtGJF+gSPH32kYORHnLq2WnJyMidOnACgbNmyfjkIWy1HIiLe7cABs1rHjh1QoYKZy6hp0wKedMUKM9v122+bQdepqWY+gO3b4dFHQY0AXs/lM2R/9913/O9//2P//v0EBwfTtGlT+vfvT82aNfNbs89QOBIR8V579pjsEh0NVauaTHPZZQU8qWVBq1awcSNcdRVs2GAGXe/da2aGnDLF9NuJV3NpOLrvvvuYN28eYMYTAdhsNoKDg5k3bx7du3fPZ9m+QeFIRMQ7HToELVtCTAzUqQPffw81ajjhxEuXws03p79esgQ6d3bCicWdXBaOZsyYwcMPP0yRIkW4//77ad68OWfOnOGbb75h/fr1hIWFsW/fPsLDwwv8IbyVwpGIiHeyLBg4EH7+2XSlVarkpJO2agW//WYmegwMNMlrxw7dsu9jXBaO2rVrx7p161i6dCk33HBDpn39+/fnk08+4cMPP6S/UyeQ8C4KRyIi3islBc6cMYOwneLiViO7xx4zM2KLz3DZ3Wpbt27lmmuuyRKMAF544QUsy2Lr1q2OVSsiIpJPy5bBffeZaYbANOw4LRhZlpncMTAw676fftJM2H7MoXAUFxdHnTp1st1n3x4XF1fwqkRERC7h1Ckz8/Wnn8I777jgAsuWmUHYKSlZ923davaLX3IoHFmWRWB2CRoICDCnSk1NLXhVIiIil1C6NMyZA/fcA0884eST59ZqBGZ7ZKRaj/xUgSeBFBERcafTp9Of33qraTkKCnLyRXJrNQKzfeNGtR75KYcGZAcEBKQtE+LwhWw2kpOT8/Veb6IB2SIinvPqq2YexjVroHZtF13k4jvUchIYCFdemT7vkXg1ly4fYllWvh7qbhMRkfyyLHjxRXj+eTOf0f/+56ILpabC7Nm5txrZqfXIbzkUjlJTUwv0EBERcVRqKjz5JIwfb16/+io8/bQLLnT4MHTpAg89lPNYo4tp7JFf0pgjERHxWsnJMGCAuRvNZjNTCz33nAsu9O230KSJaQW6cOHSrUZ2aj3ySwpHIiLilc6fN3eizZplGmg++cTMvehUiYlmQdlbboFjxyA0FAIc/GpU65HfUTgSERGvc+4c9OgBCxeaO9EWLIA+fZx8kW3bzEKy9kmSevSAhATTj+cItR75HYUjERHxKnFxZujP4sWmIefrr6FnTxdcaPJkE5AqVIDvvoODBx1vNbILCFDrkR8p4ukCRERE7E6cMMFo40YICzNDgdq2ddHF3njDdIm9/LJZc6R/f8dbjexSU2H/ftMXGBzs1DLF/RSORETEKxw5AjfeaBpzypY1vVRXXunECyxZAosWwbRpZnR3eDhMn56+f+NGM+4ovypUUDDyEwpHIiLiFYoXN61FlSvD8uXQsKGTTpyUZCZIevNN87pdO7j//qzHVatmHlLoKRyJiIhXKFHCdKOdOgW1ajnppH/9BffeC7//bl4/8QTccYeTTi7+SgOyRUTEY37/3YyLtitVyknByLLggw+gRQtzkXLlzMjud94xo7xFcqGWIxER8YgjR6B9e7OQbPny2fd05duQIfDuu+b5TTeZyZIqVXLiBcSfqeVIREQ8omJFswxImzZw661OPvm990KxYqZZavFiBSNxiM2yNCmDIxxZ1VdERLJKTU2fTsiynHT3+/nz8Ouv0Lp1+rbjx013mgiOfX+r5UhERNzm88+hQweIjzevbTYnBKO//zahqGNHMwDbTsFI8knhSERE3GLGDLNW2urVZgHZArMs+PBDMxnSb7+ZbrRDh5xwYinsFI5ERMTl3nwTHnrI5JlHHoFhwwp4wpMnzS35Dz9sFmLr2BH++MPMIilSQApHIiLiMpYFUVHw1FPm9bBhMGVK/pcwA2DlSmjSBP7v/6BIEXjtNTNrZJUqTqlZxO/C0bhx47DZbDRq1CjLvnXr1tG2bVuKFStGREQEQ4YMId7e8S0iIk5lWfDcczB6tHk9dqzJMTZbAU+8Zo1ZJLZePfj5Z3j22QKmLZHM/GqeowMHDjB+/HiKFy+eZd+WLVu44YYbaNCgAZMnT+bAgQO8/vrr7Nq1i8WLF3ugWhER/5WSAo8/bpYxA9Ot9uSTBTihZaWnqhdeMKO4Bw0y02qLOJlfhaNhw4ZxzTXXkJKSwvHjxzPte+GFFyhdujSrVq1Ku4WvZs2aPPzwwyxbtoybbrrJEyWLiPidCxegXz/47DOTZz78EB58MJ8nsyz4+GNzkhUrICTEdKUNH+7UmkUy8pt2yNWrV7Nw4ULetC8smEFcXBzLly+nT58+meY26Nu3LyVKlGD+/PlurFRExH8lJppx0p99ZjLMZ58VIBidOgV33w39+8PatTB9ulNrFcmJX7QcpaSkMHjwYB566CEaN26cZf/WrVtJTk6mZcuWmbYHBQXRrFkzNm/e7K5SRUT8Vnw89OgB339vGngWLoRu3fJ5sjVr4L77YP9+k7Kiokw/nYgb+EU4+uCDD9i3bx8rVqzIdn9MTAwAlbKZPr5SpUqsWbMmx3MnJSWRlJSU9jouLq6A1YqI+KcTJ2D7djMM6OuvzbppDrtwwQSh8ePNVNp16sCnn8LVVzu7XJEc+Xy32okTJxg1ahSRkZGUL18+22MSEhIACM5mGtaQkJC0/dmZMGEC4eHhaY9q1ao5p3ARET9To4YZFrRiRT6DEZjF1l5+2QSjBx6AzZsVjMTtfD4cjRw5kjJlyjB48OAcjwkNDQXI1AJkl5iYmLY/OyNGjCA2NjbtsX///oIXLSLiJw4cgGXL0l83aACtWhXghM88Y1LWvHkwcyaULFngGkUc5dPdart27WLatGm8+eabHMowZXxiYiIXLlwgOjqasLCwtO40e/daRjExMVSuXDnHawQHB2fb4iQiUtgdPAht28Lhw7BkST5bi2Jj4dtv4d57zeuaNWHXLiha1ImVijjGp1uODh48SGpqKkOGDKFWrVppjw0bNrBz505q1apFVFQUjRo1okiRImzatCnT+8+fP8+WLVto1qyZZz6AiIgPq1jRLGtWrRrUrp2PE6xbB82amYHXS5akb1cwEg/z6ZajRo0a8cUXX2TZPnLkSM6cOcNbb71FnTp1CA8P58Ybb2TOnDlERkZS8r9m2tmzZxMfH0/v3r3dXbqIiM+z36ofGwsVKjjwxuRkM+A6KsrMFlmrFpQq5aoyRRxmsyzL8nQRzta+fXuOHz/Otm3b0rb99ttvtGnThoYNGzJw4EAOHDjApEmTaNeuHUuXLs3zuePi4ggPDyc2NjbTnEkiIoXBmjWwaBG88UY+lwHZt8+0FK1da1736QPvvQf6+1RczJHvb5/uVnPElVdeyYoVKwgNDeWpp55i2rRpDBgwgIULF3q6NBERn7B0KXTuDG+9ZRaPddiiRdC0qQlGJUvCnDkwe7aCkXgdn+5Wy8mqVauy3d62bVvW2v+1IiIiefZ//2cmq75wAbp2NZNWO+z8edMH17o1zJ1rutNEvJBfhiMREXGeTz4xYSg1FXr3Ng0+QUF5fPO5c1CsmHl+zz1mwdju3c2AJREvVWi61URExHHvvWcWkU1NNQHps8/yGIxSUmDcOLjsMjh6NH377bcrGInXUzgSEZFsvfIKPPGEeT5kCHz4IQQG5uGN+/dDx44wcqSZJXL2bJfWKeJsCkciIpKJZcELL8CIEeb1yJHw5psQkJdvjAULoEkTWL3aLLL28cdmSRARH6K2TRERSZOaalqJ3nvPvH71VXjuuTy8MT4ennwSPvrIvL7qKrNgbN26LqtVxFXUciQiIoCZm7F/fxOMbDZzu36eghHA2LEmGNlsptlp7VoFI/FZajkSEREAtm41670GBsKsWWZ+xjwbORI2bICXXsrnImsi3kPhSEREAGjeHObPN11rPXte4uCDB2HaNBOGbDYzqWMOc8yJ+BqFIxGRQiwuztxpb+8Bu+22PLzpiy/goYfg5EmzqNqgQS6tUcTdNOZIRKSQOnHC3HHfvj3s3ZuHN5w9C488YuYqOnkSrrwSbrzR1WWKuJ3CkYhIIWVZkJAASUlmVY9cbd4MLVqYrjSbzYzUXr/eTPIo4mfUrSYiUkiVKwcrVsDp09CgQS4HzpoFAweahdUqVzbridxwg5uqFHE/tRyJiBQif/9tso1dpUqXCEZgDkhNhR494I8/FIzE76nlSESkkNiyBW66CY4fNzeX5XpH2r//QvXq5nmrVrBxIzRrZrrURPycWo5ERAqB9euhQwc4dsxknLZtczgwIcHcfXbZZbBtW/r25s0VjKTQUDgSEfFhY8eaNc/Gjs35mO+/h06dzNiia6+FlSuhfPlsDvzjD2jZEt5/HxITzYAkkUJI4UhExEeNHQujRpm7zkaNyj4gff01dOtm7sLv1AmWLoXw8IsOsix4+224+mr46y+IiDAHDh3qjo8h4nU05khExAfZg1FG9teRkebPefPg/vvNmmk9epjXwcEXnejIEbOg2uLF5vUtt5g10rJtWhIpHBSORER8THbByM6+PSLCzNdoWWaNtJkzoUh2f+PPnm2CUUgIvP46PP64xhZJoadwJCLiQ3ILRnYZ9z/6KLz3nhmXlK2nnoLdu2HwYLjiCqfVKeLLNOZIRMRH5CUYZdS2rRlbnSkY/fkn3HOPGXANEBgIH3ygYCSSgcKRiIgPcDQYAfz0E7z88n8vLMs0IbVsaQYfjRnj9BpF/IW61UREvFx+gpHdqFHA2bNE/nk3fPON2dili+5EE8mFWo5ERLxYQYKR3ahXizP2m2YQFARvvQXffgsVKzqlPhF/pJYjEREv5YxgZDeKsfDQICKHRDjnhCJ+zGZZluXpInxJXFwc4eHhxMbGEhYW5ulyRMSPBQSYoULOYrOZ9WNFCiNHvr/VciQi4gXOnzdLmf36K2zaZP602ZwbjjQGWyRvFI5ERDxk3Tr45BMThP74wwSki4WGmrVgCyoqKn3mbBHJnQZki4i4weefmwkZf/89fduuXTB1qmkpOn8eSpeGG2+E55+HBQvgn3/MmmhRUQW7toKRiGPUciQi4iQXLpg5Fn/9FbZvh4kT01fimDPH3EnfsCE0bWq2XXcdDB8OLVqY6Ydq1sx+5Q57sMnP4GwFIxHHKRyJiOTDhQtmAfuMY4R+/x2SktKPeeopqFLFPL/rLhOMWrVK31+7NrzySt6uFznSAgtGjc77umcKRiL5o3AkInIJKSnpLUIZg5B9BY6MwsNNS1CLFplbgfr0yceF4+Ph++/hu+9g8WIi/+//YNomRh189JJvjaryAZEjHwG0iKyIoxSOREQySE42LUIpKdC8udl29Gh6V1hGYWHpQcjeNVa7di6LvF6KZZn+uP/CEGvWmCYqu3feIfLgJ8BBM29RDqKIJPLgy7CsFnTunM9iRAovhSMRcYmxY2H0aHP7uLd27SQnmyxSqRKUK2e2TZ0KTzwBXbuaiaTB7G/QwEwqnTEI1alTgCCUnbVrzUCkjOrUMcXcfLMZdBQYSGSKWTAtu4AURSSRvGwWlI2MhJtuyn4gk4jkSOFIRJwu48zO9j89HZCSk2HHjsxdY1u2mNvkP/wQBgwwx7VoASVLQrFimd//559OyhiWBX//nd461KyZGbkNZkBShQqmyaprV7MGWr16Zt/Spabo/0SSNSClBSMwTV8bN8KyZWo9EnGQwpGIOFV2S164OyClpGQfhM6dy3psiRJw+nT666uvNq8vbhEqUDA6exZWrkwPRNHR6fuio9PDUdGicPAgFLnor2bLMj+8wEDz4f5jD0KjGcMYRqcHIzu1Honki5YPcZCWDxHJ2aXWAnPF3VOpqZmDzIMPmjmFcgpCzZubLjF711i9ek7uGsvOZZfBzp3pr4OC4Prr01uH6tfPPbwsXWq61fJryRK1Hkmhp+VDRMTt8rJIqjNbkE6ehO7dTQtRTIxpdLE7dw6KF88+CAUGFvza2Tp3zrQOLV4M69fDhg3pLUAdO5pZHrt0MYGoQwdTYF7YW40CAvK3MFpAgFqPRBzk0y1HGzdu5OOPP2blypVER0dTtmxZrrnmGl5++WXq16+f6djt27fz1FNP8dNPPxEUFES3bt2YPHky5cuXd+iaajkSycrR1ePz0oKUkmIaW379Nb17rF49+Ogjs9+yzIzSsbGwebMZugPmPSkppjHGZUHIbteu9K6yVasyT3L0009w7bXmeUIChITkL5wkJUGNGnDkSP7rjIgw3XfBwfk/h4iPKzQtR6+++ipr166ld+/eNGnShMOHD/Puu+9y5ZVX8vPPP9OoUSMADhw4QLt27QgPD2f8+PHEx8fz+uuvs3XrVn755ReCgoI8/ElEfJejwQiytiClpqYHIfsYoc2bzTQ/GR06lP7cZoP586FaNROE7C76d5HrTJoEw4Zl3la9enrrkD2tgVkgLb+Cg83A6mPH8n+OChUUjEQc4NMtR+vWraNly5aZws2uXbto3Lgxd9xxB3PmzAHg8ccfZ9asWezYsYPq1asDsGLFCjp16sTUqVMZOHBgnq+pliORdPkJRhm1aWN6njZvhjNnsu4vVsxkDHvXWIsWcMUV+b9evuzebVqGFi+Gxx+HW24x2zdsMLfdX3ddeiBq0EBdVyJeypHvb58ORzlp0aIFAL/+d9trxYoVuf7665k/f36m4y677DKqVavGihUr8nxuhSPf5wvz7/iCggaji4WGZg5CLVvC5Ze7oWvsYomJ8OOP6d1lu3al73voIZg+3TxPSTHjjEqWdHOBIpIfhaZbLTuWZXHkyBGu+O+flwcPHuTo0aO0bNkyy7FXX3013333Xa7nS0pKIinDOIK4uDjnFixu5Y3z7/iiMWPgpZecd75Bg+DNN7Pewe52x46Z8T0JCenbihSBtm1Ny5C91QhMalMwEvFLnv6ryOnmzp3LwYMHiYqKAiAmJgaASpUqZTm2UqVKnDx5kqSkJIJz6I+fMGECY8aMcV3B4jbeMP+Otzt40IzbrVLFrBAPZixQZKRZQuPIEfPniRPOve7778O77zr3nLlKTITVq03LUEoKvP222V6+vAlHZ86k32Z/ww1mnRARKTT8Khzt2LGDQYMG0bp1a/r16wdAwn//Aswu/ISEhKQdk1M4GjFiBE8//XTa67i4OKpVq+bs0sXFcusC8teAlJhoGkKOHs398eOPZv4fMN2NM2aYn9fIkWbb+fNm4LMrueXfH9HR6V1lP/yQPhFS8eJmEkb73wE//mhCksYOiRRafhOODh8+TLdu3QgPD2fhwoUE/jdQIfS/u0Qydo3ZJf63pHZoLneSBAcH5xicxDe4e/4dd9i61SyO2rAhNG5stm3bBo88kh568toDfPRoejiqUcMs5ZVx6Yzq1eGtt8wNTxkfU6Y4p2vNFRNDZtG3L8yenXlb5cqmZahLl8xBqEIFFxcjIt7OL8JRbGwsXbp04fTp06xZs4bKlSun7bN3p9m71zKKiYmhTJkyCj9+zJFBw+4OSJZlblW/VMuO/bFrV3rvzjvvmHHBY8akhyObDdaty3yNokWzhprsHnaRkVk/f1gYDBmStf7Ro838ggUZlO30YLRvX/qdZTNmpK8m27ChGSN07bXpgahJE7UOiUi2fD4cJSYmcuutt7Jz505WrFhBw4YNM+2vUqUK5cuXZ9OmTVne+8svv9As41wk4lecMf+Oo86fN4HE/p37449m3p42baB1a7Ptt9+gZ08TeP5rvMyTo0fTw1Hjxmb1iSpV0vfXrAmLFmUOPeHhrv3+t/+c8hOQnBKMkpLMZIuLF5sus+3b0/ctWwb33mueP/IIPPoolCpVwAuKSGHg0+EoJSWFu+66i/Xr1/Pll1/S2v7tc5FevXrx8ccfs3///rTxQt9//z07d+7kqaeecmfJ4iYFuc08Y0BKTYVTp/LeunP6tHmEh5tzfPYZTJ1qup/sv56hofDvv+nXK148b607GYe6DR5sHhkVLw63356/z1wQ+QlITglGy5eblHn2bPq2wEDzg+7Sxaxwb1e6dAEvJiKFiU+Ho2eeeYavvvqKW2+9lZMnT6ZN+mjXp08fAF544QUWLFhAhw4dePLJJ4mPj2fixIk0btyY/v37e6J0cSFnzL8zahTs3w8zZ0JysmPvPXo0PRy1bm1ufMo4cWHt2vDzz1Cxohn3m9cltryZIwHJ4WB0/jysXWtahpo3T28NuuIKE4wqVkzvKuvUSUFIRArMpyeBbN++PT/++GOO+zN+tD///JOnn34609pqkyZNomLFig5dU5NAejdnT0xoV7p0egtO+fK5t/CULu2GVd691KV+/nkORgcOpHeVrViRvo5Ip06mu8zuzz/NrNSF9QcuInlW6GfIdiWFI+8WEGAGOjuLzWbGBWn5vbwbG2UxanTWgU5RYywiR11iAFRqKlxzjVlLLKMKFeDmm6F7d+jVy4nVikhh4cj3t/65JX7F2fPljBmjYOSoyFbLiCJz81AUkUS2Wpb5wIMHzR1lGeYRIyDANL3ZbKZPMirKBKWYGPj4YwUjEXELnx5zJHIx+0zOzpht2S3z7/gby4LISCIDf4MUGM0YxjCayMAJ8GIzCAmBJUtMd9kff6S/75ln0m+9e+cdKFvWPEREPEDdag5St5r32rIFxo0zt7MX9LdawSifli413V95YbPB1VebgdSPPmoGVouIuEihXnhWCqfly+Gmm9Jf3367+a6dMsXxcykYOSglxQyg3rnTzCdks+WcTgMD4c47oVs38x+sfHn31ioikgcKR+Kzjh9PnwC5fXuz7MXVV8OLL6bfOl+pkgfm3/FHqalmboMSJdK7u1avNi0+//xjJmPMi5QU6NcPOnd2Xa0iIgWkcCQ+599/zffr3r1mSY2iRc3jjz8yrwkGLp5/xx+dOWMGQO/alf7YvRv27DEB6K230tcSCQ1Nn5G6SBHzuNSU34GB5od8001aukNEvJbCkfic8uXNd/LJk/DLL2a5LMgajOzyEpAKTTBKTTV3idlDz65d0LGjGfcDZjXbG27I/r1Fi5rpv+2uuMLMOVS3rvkP0q3bpa+fkmLC17Jlaj0SEa+lcCReLTUVvv4aPv8c5swxd3qHhsLcuVC/fuYlNXKTW0Dyu2CUmmpaeUJDzet9+2DoUBOE9uzJ2rpz4UJ6OKpb1/xg69Uzz+vVS39evbppHbIrVsxMymhZcNddplUoJeXS9an1SES8nMKReKWUFHPX2bhx6Xd833473HGHeZ5T40ZusgtIPhuMLAsOHcrc9WV/vmcPPPYYTJpkjg0Nhf/9L/29RYpArVrpoefGG9P3lS0Lf//tWC3LlmWdtDE3aj0SES+ncCReJTkZ5s0zoWjHDrOtZEl44gmzCn1BRUYCe/Yw+uNajOm3l8jIOgU/qavYA5A9+FSuDF27mn2HD0PVqjm/d8+e9Ofly8N775lF3erVgxo1MrcAFbTGyMi8txrZqfVIRLyY5jlykOY5co3z52H2bJgwIf17vVQp0xs0ZIgT1xK1LLNa+8aNcNVVsGGD93w5JybCSy+ltwTt3g3nzqXv79kT/u//zHPLgjJlzO16F3d/2QNQ0aKur9mReY2ys2SJWo9ExC00z5H4jMREs/L9K6+Yu9DAfN8/8ww8/jg4PX9m7AJyV9eOZZmWnou7v3bvhqZNzbIYYNYpefttSEhIf29gINSsaUJPq1bp2202M5dBYKBra8+NvdUoIMCMc3JUQIBaj0TEKykciUecOwfTpsHEiabnCCAiAp591swjWLy4Cy56cReQM7t2LAuOHDGhJzkZOnRI316hggkylxIQYCZpKlEivRWoZs2cF3fzZDAC09z377/5C0aQPnfS+fMQHOzc2kRECkDhSDzi9Gl4/nlzU1W1ajB8OAwYYJbecpmLBw4XZGDwnDnm9vWMrUDx8WbflVfCr7+a5zabCUcnT5qurovvArvsssznffHF/H8+dwsONj+/Y8fyf44KFRSMRMTraMyRgzTmKH9On4Zvv4X77kvfNm6c+W7s1y/nxhGnsY81+u23zAOHAwNNmLGPPbIs82V/8SSIwcHwySfp77v88qx3dQUEmNvdr7zS3Gpnd+iQuQtMIUBExGM05ki8ypkzZmmPkydNQ0nLlma7WxtJcrrdPGPr0dSp8P33EBeX9bhSpUxwsne/3XWX6SqztwLVrWtuj88uAFWu7NSPIiIirqVwJC5x5oy5BR/Mn926mUabjDdfuY1lmT68nAYO28celS1rgpHNZlqAMnaB1a2bORyNGePezyAiIm6jbjUHqVstdwcPwmuvwYwZpkGmQQOzPS7OjDMOCHBzQVOnwuTJZsX4S/ngA2jXzswHpC4wERG/4sj3t7u/qsRPRUebBdpr1zZ3o589a5b8sAsLc0MwOnbMDJTOeCv8rl15C0aBgSbRXX65gpGISCGnbjUpkF27zMSNs2ebO9jBNL5ERuZviQ+HpKTApk2weLF5bNxour7KlUufmLBu3byfS0taiIgICkeST3/9Ze42mzcvfRhPp04wcqQJRy6/+PjxZnbmi+cPatYsPaVZFnz0kRZEFRERhygciUO2bDGhaNEikz3ADLYeORKuucYFF0xNNXMGBQWZ2aTBXHjuXPM8LMyksq5dTWtRxjvDtCCqiIjkg8KR5MnZs3DPPfD11+nbevY0oejKK518sRMnTEBZvNisvXXsmLl1ft48s79hQxg71jRRtW6d/RpiWhBVRETySeFI8qRYMTNPkc1mcsqLL0KjRk68gGWZrrJvvzUTMma85b5kyczridhsJpXlxtFWIzu1HomIFHq6W02ysCz44Qe45RbTiAMmj7z7rlkx47PPnBCMTp0yEy7a2Wzwv//B+vUmGDVqBM89BytXmnFFM2Y49gHsC6Lmh31BVM1yISJSKKnlSLL1zDNmfNFbb0FUlNnWrFkBTpiaak5ov7Ns/XoTQk6cMOOG7BeNi4MuXcyCa/mlBVFFRKQAFI6E1FQzluiGG8xEjTYbjB5tGnYefriAJ//pJ9Pqs2QJHD6ced/ll5sQY2+GuvvuAl7sP1oQVURECkDhqBBLSTF3nY0bB3/8ARMnwrBhZl+PHubhEMuC3383rT5ly5ptv/8Os2aZ58WLmwRmv7OsRg3nfJDsVKtWsNYnEREptBSOCqHkZHPj17hxsGOH2VaiRD6H2MTGwvLl6d1lMTFmGY5HHjH7b7kF/vnHdJVdd51aY0RExOspHBUi58+bmawnTIA9e8y2UqXgySdhyBAoUyaPJzp1CqZNg+++g3Xr0iddBHNbW8aJGWvUgEmTnPURREREXE7hqBBITISZM+GVV8wQHzArbDz9NAwalD4eOkdxcaZF6LLLzGubzdzLb58/6PLLTcuQvXUoJMRln0VERMTVFI782LlzpoFn4kQ4dMhsq1gRnn3WLBKbceqgTCwLtm0z3WTffQdr10LLluYOMzDNTcOGQfXqJhDVquWOjyMiIuIWCkd+yrLM5NF//GFeV60Kw4fDgAEQGprDmxYvhi++MH8eOJB5X2wsJCWljxl65RWX1S4iIuJJmgTSi4wda6b+GTs2f++PjU2f2sdmgz59oGZNmDoVdu+GJ57IEIwsy4zGzjgKe8YMmD7dBKPQUHNX2TvvmDf/9ZcGU4uISKFgsyxNA+yIuLg4wsPDiY2NJeySg3XybuxYGDUq/XVUlJmkOa8mToSXXzb55o47zLakJBO20pYei483kxfZ7yz791/4+2+oX9/s/+ILWLXKdJVdf30uTUwiIiK+xZHvb3WreYGLgxGkv85rQDpzxoybXrgwPRwFB2Nmep4/34Sh1avhwoX0N4WEmLFF9nDUs6d5iIiIFGIKRx6WXTCyyykgHTwIr70G3bubORUBhg41y3v0uDEeTiRlnoTRPrMjQO3aprusa1fTOlSsmDM/joiIiM9TOPKg3IKRXcaAFB1txkHPnGnmLPrtN+jYwcK282/KLF7M7d99B/esNmuUjR9v3tihgwlCN91kusvq1TMDkkRERCRbhSocJSUlMWrUKGbPns2pU6do0qQJL7/8Mp06dXJ7LXkJRnajRpnhQFu3ps+3eN0VJ4gs/SnUngz7ojO/YevW9OfFi8O33zqlZhERkcKgUN2t9sADDzB58mTuu+8+3nrrLQIDA+natSs//fSTW+twJBjZbd5sgtGNN8KPK1NZfeRyOn09BNu+aAgKgk6dYPJkcwfaV1+5pG4REZHCoNDcrfbLL7/QqlUrJk6cyLD/xuAkJibSqFEjKlSowLp16/J0noLerZafYJRR2l1szz1n7j7r0sV0nZUokf+TioiI+DndrZaNhQsXEhgYyMCBA9O2hYSEMGDAAF544QX2799PNRev4l7QYAQZxiC99lrBCxIREZEsCk042rx5M/Xr18+SFq+++moAtmzZkm04SkpKIikpKe11XFxcvq7vjGBk5+ht/iIiIpJ3hWbMUUxMDJUqVcqy3b7tkH3xsYtMmDCB8PDwtEd+W5dGj87X29x2PhERETEKTThKSEggOJvlL0L+W0E+ISEh2/eNGDGC2NjYtMf+/fvzdf0xY/L1NredT0RERIxC060WGhqaqXvMLjExMW1/doKDg7MNVY6yd4E5o2vN0aVFREREJO8KTctRpUqViImJybLdvq1y5couryEy0gSbglAwEhERca1CE46aNWvGzp07swyo3rBhQ9p+d4iMhKj7d+XrvVH371IwEhERcbFCE47uuOMOUlJSmDZtWtq2pKQkZs6cSatWrVx+G38ayyJyx31E2RwbUR1lG03kjvugcExLJSIi4jGFZsxRq1at6N27NyNGjODo0aPUrVuXjz/+mOjoaGbMmOG+QpYtg40biWQjkMooxl7yLVFEEmm9DBv/e3/nzi4vU0REpLAqNC1HAJ988glDhw5l9uzZDBkyhAsXLvDNN9/Qrl079xRgWaZfLcD82CN5mShy7yeLIpJIXjYvAgLM+9V6JCIi4jKFZvkQZynQ8iFJSVCjBhw5kmnzWEZm24KUKRjZRURAdDQ44Q46ERGRwkLLh3ir4GDYuBGOHcu0ORJg+iFGfZB+x1zUo4eIfLgn0DPzOSpUUDASERFxIYUjd6tWzTwuEjkFqGxmvh4zBiIjK5sNIiIi4lbqVnNQgbrVRERExCMc+f4uVAOyRURERC5F4UhEREQkA4UjERERkQwUjkREREQy0N1qDrKPX794jTYRERHxXvbv7bzch6Zw5KAzZ84AuG8tNhEREXGaM2fOEB4enusxupXfQampqRw6dIiSJUtis9mceu64uDiqVavG/v37NU2AC+nn7B76ObuHfs7uoZ+ze7jy52xZFmfOnKFy5coEBOQ+qkgtRw4KCAigatWqLr1GWFiY/udzA/2c3UM/Z/fQz9k99HN2D1f9nC/VYmSnAdkiIiIiGSgciYiIiGSgcORFgoODGT16NMFaWNal9HN2D/2c3UM/Z/fQz9k9vOXnrAHZIiIiIhmo5UhEREQkA4UjERERkQwUjkREREQyUDgSERERyUDhyMOSkpIYPnw4lStXJjQ0lFatWrF8+XJPl+V34uPjGT16NDfffDNlypTBZrMxa9YsT5fldzZu3MgTTzzBFVdcQfHixalevTp33nknO3fu9HRpfuXPP/+kd+/e1K5dm2LFilGuXDnatWvH119/7enS/N64ceOw2Ww0atTI06X4jVWrVmGz2bJ9/Pzzzx6pSTNke9gDDzzAwoULGTp0KPXq1WPWrFl07dqVlStX0rZtW0+X5zeOHz9OVFQU1atXp2nTpqxatcrTJfmlV199lbVr19K7d2+aNGnC4cOHeffdd7nyyiv5+eef9YXiJPv27ePMmTP069ePypUrc+7cORYtWkT37t2ZOnUqAwcO9HSJfunAgQOMHz+e4sWLe7oUvzRkyBCuuuqqTNvq1q3rkVp0K78H/fLLL7Rq1YqJEycybNgwABITE2nUqBEVKlRg3bp1Hq7QfyQlJXHq1CkiIiLYtGkTV111FTNnzuSBBx7wdGl+Zd26dbRs2ZKgoKC0bbt27aJx48bccccdzJkzx4PV+beUlBRatGhBYmIiO3bs8HQ5funuu+/m2LFjpKSkcPz4cbZt2+bpkvzCqlWr6NChAwsWLOCOO+7wdDmAutU8auHChQQGBmb6V15ISAgDBgxg/fr17N+/34PV+Zfg4GAiIiI8XYbfa9OmTaZgBFCvXj2uuOIKtm/f7qGqCofAwECqVavG6dOnPV2KX1q9ejULFy7kzTff9HQpfu3MmTMkJyd7ugyFI0/avHkz9evXz7K43tVXXw3Ali1bPFCViHNZlsWRI0coV66cp0vxO2fPnuX48ePs2bOHN954g8WLF3PDDTd4uiy/k5KSwuDBg3nooYdo3Lixp8vxW/379ycsLIyQkBA6dOjApk2bPFaLxhx5UExMDJUqVcqy3b7t0KFD7i5JxOnmzp3LwYMHiYqK8nQpfueZZ55h6tSpAAQEBHD77bfz7rvvergq//PBBx+wb98+VqxY4elS/FJQUBC9evWia9eulCtXjr/++ovXX3+d6667jnXr1tG8eXO316Rw5EEJCQnZrh8TEhKStl/El+3YsYNBgwbRunVr+vXr5+ly/M7QoUO54447OHToEPPnzyclJYXz5897uiy/cuLECUaNGkVkZCTly5f3dDl+qU2bNrRp0ybtdffu3bnjjjto0qQJI0aMYMmSJW6vSd1qHhQaGkpSUlKW7YmJiWn7RXzV4cOH6datG+Hh4Wnj68S5Lr/8cm688Ub69u3LN998Q3x8PLfeeiu6z8Z5Ro4cSZkyZRg8eLCnSylU6taty2233cbKlStJSUlx+/UVjjyoUqVKxMTEZNlu31a5cmV3lyTiFLGxsXTp0oXTp0+zZMkS/S67yR133MHGjRs1r5ST7Nq1i2nTpjFkyBAOHTpEdHQ00dHRJCYmcuHCBaKjozl58qSny/Rb1apV4/z585w9e9bt11Y48qBmzZqxc+dO4uLiMm3fsGFD2n4RX5OYmMitt97Kzp07+eabb2jYsKGnSyo07F3xsbGxHq7EPxw8eJDU1FSGDBlCrVq10h4bNmxg586d1KpVS2PpXOiff/4hJCSEEiVKuP3aGnPkQXfccQevv/4606ZNS5vnKCkpiZkzZ9KqVSuqVavm4QpFHJOSksJdd93F+vXr+fLLL2ndurWnS/JLR48epUKFCpm2XbhwgU8++YTQ0FAFUidp1KgRX3zxRZbtI0eO5MyZM7z11lvUqVPHA5X5l2PHjmUZz/X777/z1Vdf0aVLFwIC3N+Oo3DkQa1ataJ3796MGDGCo0ePUrduXT7++GOio6OZMWOGp8vzO++++y6nT59Ouwvw66+/5sCBAwAMHjyY8PBwT5bnF5555hm++uorbr31Vk6ePJll0sc+ffp4qDL/8sgjjxAXF0e7du2oUqUKhw8fZu7cuezYsYNJkyZ55F/a/qhcuXL06NEjy3b7XEfZ7RPH3XXXXYSGhtKmTRsqVKjAX3/9xbRp0yhWrBivvPKKR2rSDNkelpiYSGRkJHPmzOHUqVM0adKEsWPH0rlzZ0+X5ndq1qzJvn37st23d+9eatas6d6C/FD79u358ccfc9yvv26cY968ecyYMYOtW7dy4sQJSpYsSYsWLRg8eDDdu3f3dHl+r3379poh24nefvtt5s6dy+7du4mLi6N8+fLccMMNjB49WsuHiIiIiHgDDcgWERERyUDhSERERCQDhSMRERGRDBSORERERDJQOBIRERHJQOFIREREJAOFIxEREZEMFI5EREREMlA4EhEREclA4UhE8s1mszn0sC/R0r59e2w2G9HR0R6tPzerV6/GZrPx3nvvufQ6s2bNwmaz8dJLLxX4XG+++SY2m41ffvml4IWJFGJaeFZE8q1fv35Ztv3000/s2bOHpk2b0qxZs0z7ypUr56bKCsayLIYNG0bVqlV56KGHPF1Onj3yyCO88sorDBs2jNWrV3u6HBGfpXAkIvk2a9asLNseeOAB9uzZQ48ePXJsDfnkk084d+4cVapUcW2B+fS///2PjRs3MnnyZIKDg116rZ49e3LNNdc4JTiGhoYydOhQRowYweLFi+nSpYsTKhQpfNStJiJuV716dS6//HKKFi3q6VKy9f777xMYGMi9997r8muFh4dz+eWXO61V7b777sNmszFlyhSnnE+kMFI4EhG3y2nMkX1cUnJyMmPHjqVu3bqEhobSoEEDZs6cmXbcDz/8QIcOHQgLC6N06dL07duXEydOZHut5ORkpkyZQuvWrQkLCyM0NJRmzZrx5ptvkpycnOX4vXv38v3339OxY0cqVqyYa+2ff/45V111FcWKFaNKlSo899xznD9/HoA9e/Zwzz33UKFCBYoVK0aHDh34448/spwvpzFHDzzwADabjVWrVrF69Wo6duxIyZIlCQsLo1u3bvz111/Zft5q1arRtm1bvvvuOw4dOpTtMSKSO4UjEfE6d955J5MmTaJx48a0a9eOvXv38uCDDzJz5kwWLlxI586dSU5OpnPnzhQvXpzZs2fTo0cPLMvKdJ6EhARuuukmHn/8cXbu3Mk111xDp06diImJ4amnnqJXr16kpqZmes93332HZVm0b98+1xrfeust+vTpQ6lSpbj55ps5f/48EydO5OGHH2bXrl1cc801bNmyhY4dO1K3bl1WrVpFhw4dOHLkiEM/i6+//pqOHTty7tw5unbtSqVKlfjuu+9o164dhw8fzvY97du3JyUlhSVLljh0LRH5jyUi4kT9+vWzAGv06NE5HnP99ddbgLV3795M2wELsBo1amQdPXo0bfsPP/xgAValSpWssmXLWt98803avtjYWOuKK66wAOuHH37IdL7HH3/cAqy77rrLOn36dNr2uLg4q2vXrhZgTZkyJdN77rrrLguwli1blmvtJUqUsDZu3Ji2PSYmxqpYsaJls9msBg0aWM8//7yVmppqWZZlpaamWvfff78FWKNGjcp0vpkzZ2b787L/HAMCAqwvvvgibXtycrLVq1cvC7AiIyOzrfHrr7+2AKtv377Z7heR3KnlSES8zptvvkn58uXTXnfo0IHmzZsTExNDly5d6NatW9q+sLAwBg4cCMCPP/6Ytv3o0aNMnz6datWqMXPmTMLDw9P2lSxZkhkzZhAUFJRlbI696+uyyy7LtcahQ4fSsmXLtNcRERHce++9WJZFUlISUVFR2Gw2wHQXDhs2LEuNeXHPPffQo0ePtNeBgYGMGDECIMc70i6//HIAtmzZ4tC1RMRQOBIRr1K0aNFsu7Rq164NwE033ZTjvpiYmLRtq1at4sKFC9x8882EhoZmeU9ERAT16tVj69atJCQkpG0/evQoAKVLl861ztzqaN++fZbB5tnVmBfZXad+/fq5nqtMmTIAHDt2zKFriYihcCQiXiUiIoLAwMAs20uUKAGQ7e3/9n1JSUlp2+yDvadPn57jpJR//vknlmVx8uTJtPfFxsZmOmdOcqsjrzXmRdWqVbNsK1myZK7nCgsLA+D06dMOXUtEDM1zJCJeJSAg93+zXWq/nX2gdbNmzWjatGmux2acyyg8PJwTJ04QHx+fFkIcrSOvNeZFfs5lD3ilSpVyWh0ihYnCkYj4JXuLS9u2bXnnnXfy/L4KFSpw4sQJTp48mWs48manTp0CyDRuS0TyTt1qIuKXOnToQGBgIN988w0XLlzI8/vsrUx///23q0pzue3btwNkWb5FRPJG4UhE/FKVKlV48MEHiY6O5p577sl2fqHdu3ezaNGiTNuuu+46ADZu3OiWOl3BvvDs9ddf7+FKRHyTwpGI+K233nqLTp06sWjRIurUqUPbtm259957ue2226hXrx716tVj9uzZmd7TpUuXtJmpfdWqVasIDAzk5ptv9nQpIj5JY45ExG+FhoayePFi5s6dy8cff8yWLVv45ZdfKF++PDVq1OD+++/n7rvvzvSeWrVqceONN/LDDz9w+PBhIiIiPFR9/vz777+sXbuWW265hcqVK3u6HBGfZLOsi+bbFxEp5L788kt69OjB66+/zjPPPOPpchwyYcIEXnjhBb777ju6dOni6XJEfJLCkYhINlq1asXBgwfZs2dPplv9vVlCQgK1a9emXr16Oc6eLSKXpjFHIiLZmDhxIgcPHmT69OmeLiXPpk6dyuHDh3n99dc9XYqIT1PLkYiIiEgGajkSERERyUDhSERERCQDhSMRERGRDBSORERERDJQOBIRERHJQOFIREREJAOFIxEREZEMFI5EREREMlA4EhEREcng/wFpfMhgRUSn0AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WMV_KJqf1eeX"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}