Modelagem_de_Dados_com_python_PIB_Mundial.ipynb

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    "colab": {
      "name": "Modelagem de Dados com python - PIB Mundial.ipynb",
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    "kernelspec": {
      "name": "python3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ademarionobre/DataViz/blob/main/Modelagem_de_Dados_com_python_PIB_Mundial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Modelagem de dados do PIB Mundial para visualização dos dados."
      ],
      "metadata": {
        "id": "7FJaW5Nk6HPh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Será utilizando dados contidos no site do wikipedia."
      ],
      "metadata": {
        "id": "t3xKIJTo6byg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "id": "HCdf5dXZSyUn"
      },
      "outputs": [],
      "source": [
        "# Importando as Bibliotecas/Frameworks\n",
        "\n",
        "# Lib para Dados\n",
        "import pandas as pd\n",
        "\n",
        "#Lib para visualização\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Lib para avisos de erros\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ler os dados da Web - site wikipedia.\n",
        "Tabela_Sites = pd.read_html('https://pt.wikipedia.org/wiki/Lista_de_pa%C3%ADses_por_PIB_nominal')\n",
        "\n",
        "print(f'Foi Localizado: { len(Tabela_Sites) } tabelas ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y_c4IHRWTaqm",
        "outputId": "c3ba2c9c-10cf-4e2f-ccaf-5cfcb62b19ee"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Foi Localizado: 11 tabelas \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Separando as Tabelas de interesse.\n",
        "pib2020 = Tabela_Sites[3]\n",
        "pib2018 = Tabela_Sites[4]\n",
        "pib2014 = Tabela_Sites[5]\n",
        "\n",
        "print( len(pib2020), len(pib2018), len(pib2014) )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H-1UI6GMTbyR",
        "outputId": "ca367934-f52f-4852-ea03-4b5ac22b1ed0"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "196 190 212\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dimensão - comando para verificar a estrutura da tabela em linhas e colunas.\n",
        "pib2020.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OaZebSzDVFFp",
        "outputId": "971fa54e-e2e4-438b-a64d-7bc2714a7b78"
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      "execution_count": 41,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(196, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificar campos nulos nas colunas\n",
        "pib2020.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M2QbM0GaWGhA",
        "outputId": "49dc10a2-5d3c-4cdd-a5f2-4573648f687a"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Posição                  5\n",
              "País                     0\n",
              "PIB (Trilhões de US$)    1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Filtrar campos Nulos para retirar posteriormente.\n",
        "pib2020 = pib2020.loc[ pib2020['Posição'].notnull() ]"
      ],
      "metadata": {
        "id": "gMnJos1TWWCV"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificar primeiros dados\n",
        "pib2020.head()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 206
        },
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              "      <th>Posição</th>\n",
              "      <th>País</th>\n",
              "      <th>PIB (Trilhões de US$)</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Estados Unidos</td>\n",
              "      <td>20580250.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>China</td>\n",
              "      <td>14860775.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>Japão</td>\n",
              "      <td>4910580.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4</td>\n",
              "      <td>Alemanha</td>\n",
              "      <td>3780553.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5</td>\n",
              "      <td>Reino Unido</td>\n",
              "      <td>2638296.0</td>\n",
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            "text/plain": [
              "  Posição            País  PIB (Trilhões de US$)\n",
              "1       1  Estados Unidos             20580250.0\n",
              "3       2           China             14860775.0\n",
              "4       3           Japão              4910580.0\n",
              "5       4        Alemanha              3780553.0\n",
              "6       5     Reino Unido              2638296.0"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificar os ultimos dados\n",
        "pib2020.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "MyTFViZHW4Ax",
        "outputId": "fde5782b-52af-4e1f-8bb7-e470e9302722"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "  <div id=\"df-401b9eae-e021-433c-b953-b4ed1074583f\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Posição</th>\n",
              "      <th>País</th>\n",
              "      <th>PIB (Trilhões de US$)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>186</td>\n",
              "      <td>Ilhas Marshall</td>\n",
              "      <td>214.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>187</td>\n",
              "      <td>Kiribati</td>\n",
              "      <td>189.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>188</td>\n",
              "      <td>Nauru</td>\n",
              "      <td>112.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>189</td>\n",
              "      <td>Tuvalu</td>\n",
              "      <td>42.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>—</td>\n",
              "      <td>Síria</td>\n",
              "      <td>NaN</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            "text/plain": [
              "    Posição            País  PIB (Trilhões de US$)\n",
              "191     186  Ilhas Marshall                  214.0\n",
              "192     187        Kiribati                  189.0\n",
              "193     188           Nauru                  112.0\n",
              "194     189          Tuvalu                   42.0\n",
              "195       —           Síria                    NaN"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Colunas - nome das colunas na estrutura do dataframe.\n",
        "pib2020.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A_s5Mf9SXcVX",
        "outputId": "1253b9b2-488c-4a72-ded6-e522dc175c84"
      },
      "execution_count": 46,
      "outputs": [
        {
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            "text/plain": [
              "Index(['Posição', 'País', 'PIB (Trilhões de US$)'], dtype='object')"
            ]
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          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificar index\n",
        "pib2020.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8QSPRgrPXtXF",
        "outputId": "1b4a804e-74ad-42b7-c31e-0f1233417fd8"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([  1,   3,   4,   5,   6,   7,   8,   9,  10,  11,\n",
              "            ...\n",
              "            186, 187, 188, 189, 190, 191, 192, 193, 194, 195],\n",
              "           dtype='int64', length=191)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificar tipo da informação - tipo está objeto necessário alterar.\n",
        "pib2020.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5CpJtH5eX1nW",
        "outputId": "4173c9ab-91c9-41d8-ac2f-3b54bd7c4ea9"
      },
      "execution_count": 48,
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 191 entries, 1 to 195\n",
            "Data columns (total 3 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   Posição                191 non-null    object \n",
            " 1   País                   191 non-null    object \n",
            " 2   PIB (Trilhões de US$)  190 non-null    float64\n",
            "dtypes: float64(1), object(2)\n",
            "memory usage: 6.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificar se há campos duplicados\n",
        "pib2020.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yC5IEY1CYEdK",
        "outputId": "f0e69f54-d8dd-4e52-b7b2-2e2f3d9a6807"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
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        "pib2020.describe()"
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        "outputId": "4cde5f92-7990-4c23-d95b-f10f470b24ce"
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      "execution_count": 50,
      "outputs": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PIB (Trilhões de US$)</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.900000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.387746e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.913031e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.200000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.983500e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.631400e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.360055e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       PIB (Trilhões de US$)\n",
              "count           1.900000e+02\n",
              "mean            4.387746e+05\n",
              "std             1.913031e+06\n",
              "min             4.200000e+01\n",
              "25%             7.983500e+03\n",
              "50%             3.631400e+04\n",
              "75%             2.360055e+05\n",
              "max             2.058025e+07"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Redimensar os valores\n",
        "pib2020['PIB (Trilhões de US$)'] = pib2020['PIB (Trilhões de US$)'] / 1000\n",
        "\n",
        "# Verificando\n",
        "pib2020.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "THpcUhRiZQhX",
        "outputId": "a98e8de0-c1a4-41e9-cec2-5caefe4ed380"
      },
      "execution_count": 51,
      "outputs": [
        {
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          "data": {
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              "\n",
              "  <div id=\"df-3f82cd0d-6c71-4503-b6ff-10415c9f618a\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Posição</th>\n",
              "      <th>País</th>\n",
              "      <th>PIB (Trilhões de US$)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Estados Unidos</td>\n",
              "      <td>20580.250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>China</td>\n",
              "      <td>14860.775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>Japão</td>\n",
              "      <td>4910.580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4</td>\n",
              "      <td>Alemanha</td>\n",
              "      <td>3780.553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5</td>\n",
              "      <td>Reino Unido</td>\n",
              "      <td>2638.296</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  Posição            País  PIB (Trilhões de US$)\n",
              "1       1  Estados Unidos              20580.250\n",
              "3       2           China              14860.775\n",
              "4       3           Japão               4910.580\n",
              "5       4        Alemanha               3780.553\n",
              "6       5     Reino Unido               2638.296"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Estatística Descritiva dos dados - Dados bastantes dispersos olhando o desvios padrão (std).\n",
        "pib2020.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "cj4ZkkmDYOjN",
        "outputId": "84ba8015-59b8-461a-f31d-68da67ffe18b"
      },
      "execution_count": 57,
      "outputs": [
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              "\n",
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              "      <th>PIB (Trilhões de US$)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>189.000000</td>\n",
              "      <td>189.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>95.000000</td>\n",
              "      <td>441.081190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>54.703748</td>\n",
              "      <td>1917.847541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.042000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>48.000000</td>\n",
              "      <td>8.093000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>95.000000</td>\n",
              "      <td>37.746000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>142.000000</td>\n",
              "      <td>239.552000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>189.000000</td>\n",
              "      <td>20580.250000</td>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7c98cd2d-0fda-46a5-8373-8c5872beafb4');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          Posição  PIB (Trilhões de US$)\n",
              "count  189.000000             189.000000\n",
              "mean    95.000000             441.081190\n",
              "std     54.703748            1917.847541\n",
              "min      1.000000               0.042000\n",
              "25%     48.000000               8.093000\n",
              "50%     95.000000              37.746000\n",
              "75%    142.000000             239.552000\n",
              "max    189.000000           20580.250000"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Retirando o traço\n",
        "pib2020 = pib2020.loc[ pib2020['Posição'] != '—' ]\n",
        "\n",
        "pib2020.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_3PW2sdpdTm6",
        "outputId": "b6cbc2f7-089b-4924-a6cc-8b404eb1325e"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Estados Unidos</td>\n",
              "      <td>20580.250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>China</td>\n",
              "      <td>14860.775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>Japão</td>\n",
              "      <td>4910.580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4</td>\n",
              "      <td>Alemanha</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5</td>\n",
              "      <td>Reino Unido</td>\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Posição            País  PIB (Trilhões de US$)\n",
              "1        1  Estados Unidos              20580.250\n",
              "3        2           China              14860.775\n",
              "4        3           Japão               4910.580\n",
              "5        4        Alemanha               3780.553\n",
              "6        5     Reino Unido               2638.296"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Conversão para númerico\n",
        "pib2020['Posição'] = pd.to_numeric( pib2020['Posição'] )\n",
        "\n",
        "pib2020.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "crYWpaLAYR9H",
        "outputId": "5cb8aaeb-6796-43f6-c73b-0148c8da02a8"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b1f43cb6-b194-4613-bab7-d43b7e155ddd\">\n",
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              "      <td>20580.250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>China</td>\n",
              "      <td>14860.775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>Japão</td>\n",
              "      <td>4910.580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4</td>\n",
              "      <td>Alemanha</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5</td>\n",
              "      <td>Reino Unido</td>\n",
              "      <td>2638.296</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b1f43cb6-b194-4613-bab7-d43b7e155ddd')\"\n",
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              "\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b1f43cb6-b194-4613-bab7-d43b7e155ddd button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b1f43cb6-b194-4613-bab7-d43b7e155ddd');\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",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Posição            País  PIB (Trilhões de US$)\n",
              "1        1  Estados Unidos              20580.250\n",
              "3        2           China              14860.775\n",
              "4        3           Japão               4910.580\n",
              "5        4        Alemanha               3780.553\n",
              "6        5     Reino Unido               2638.296"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Substituir a informação\n",
        "\n",
        "# Acessar o index\n",
        "pib2020.loc[ pib2020['Posição'] == '—' ].index\n",
        "\n",
        "pib2020.iloc[165,0] = 1"
      ],
      "metadata": {
        "id": "dZly7ii9aF1k"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pib2020.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Y5bqQ0maq0H",
        "outputId": "d60f3b1e-a21b-427d-b28c-50bf275261f5"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 189 entries, 1 to 194\n",
            "Data columns (total 3 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   Posição                189 non-null    int64  \n",
            " 1   País                   189 non-null    object \n",
            " 2   PIB (Trilhões de US$)  189 non-null    float64\n",
            "dtypes: float64(1), int64(1), object(1)\n",
            "memory usage: 5.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ordenação\n",
        "pib2020.sort_index( ascending=False )\n",
        "pib2020['Posição'].sort_values( ascending=False )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5xUo9UH4bYht",
        "outputId": "70007894-157f-4162-d0ca-6d9738d9f729"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "194    189\n",
              "193    188\n",
              "192    187\n",
              "191    186\n",
              "190    185\n",
              "      ... \n",
              "5        4\n",
              "4        3\n",
              "3        2\n",
              "171      1\n",
              "1        1\n",
              "Name: Posição, Length: 189, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Resetando o Index\n",
        "pib2020.reset_index( drop=True )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "pL_rdoBYcP-a",
        "outputId": "678e2b57-2043-4d42-b800-4ecf6e0a2c77"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    <tr>\n",
              "      <th>184</th>\n",
              "      <td>185</td>\n",
              "      <td>Palau</td>\n",
              "      <td>0.284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>185</th>\n",
              "      <td>186</td>\n",
              "      <td>Ilhas Marshall</td>\n",
              "      <td>0.214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186</th>\n",
              "      <td>187</td>\n",
              "      <td>Kiribati</td>\n",
              "      <td>0.189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>188</td>\n",
              "      <td>Nauru</td>\n",
              "      <td>0.112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188</th>\n",
              "      <td>189</td>\n",
              "      <td>Tuvalu</td>\n",
              "      <td>0.042</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>189 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-57fa33d2-a979-4ddc-b851-6a885e495d2e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "          document.querySelector('#df-57fa33d2-a979-4ddc-b851-6a885e495d2e button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-57fa33d2-a979-4ddc-b851-6a885e495d2e');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     Posição            País  PIB (Trilhões de US$)\n",
              "0          1  Estados Unidos              20580.250\n",
              "1          2           China              14860.775\n",
              "2          3           Japão               4910.580\n",
              "3          4        Alemanha               3780.553\n",
              "4          5     Reino Unido               2638.296\n",
              "..       ...             ...                    ...\n",
              "184      185           Palau                  0.284\n",
              "185      186  Ilhas Marshall                  0.214\n",
              "186      187        Kiribati                  0.189\n",
              "187      188           Nauru                  0.112\n",
              "188      189          Tuvalu                  0.042\n",
              "\n",
              "[189 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Excluir Colunas - iremos utilizar a posição visto termos criado uma anterior.\n",
        "pib2020.drop( columns='Posição' ) #,inplace=True )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "LZiYiRM1cl5U",
        "outputId": "ab4f3a6d-01a4-4601-c0de-97accd07d883"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f94d6e88-d598-44b0-b9e8-968734eef3b1\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>País</th>\n",
              "      <th>PIB (Trilhões de US$)</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Estados Unidos</td>\n",
              "      <td>20580.250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>China</td>\n",
              "      <td>14860.775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Japão</td>\n",
              "      <td>4910.580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Alemanha</td>\n",
              "      <td>3780.553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Reino Unido</td>\n",
              "      <td>2638.296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>190</th>\n",
              "      <td>Palau</td>\n",
              "      <td>0.284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>Ilhas Marshall</td>\n",
              "      <td>0.214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>Kiribati</td>\n",
              "      <td>0.189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>Nauru</td>\n",
              "      <td>0.112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>Tuvalu</td>\n",
              "      <td>0.042</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>189 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f94d6e88-d598-44b0-b9e8-968734eef3b1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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              "      </button>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f94d6e88-d598-44b0-b9e8-968734eef3b1 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-f94d6e88-d598-44b0-b9e8-968734eef3b1');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "               País  PIB (Trilhões de US$)\n",
              "1    Estados Unidos              20580.250\n",
              "3             China              14860.775\n",
              "4             Japão               4910.580\n",
              "5          Alemanha               3780.553\n",
              "6       Reino Unido               2638.296\n",
              "..              ...                    ...\n",
              "190           Palau                  0.284\n",
              "191  Ilhas Marshall                  0.214\n",
              "192        Kiribati                  0.189\n",
              "193           Nauru                  0.112\n",
              "194          Tuvalu                  0.042\n",
              "\n",
              "[189 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pib2020.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "K6GZGgbXfAqj",
        "outputId": "5bf9558d-491c-4d95-f5f5-9cf926ef1de8"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-79596304-0f67-4d94-8e3b-493bf4f9f3b0\">\n",
              "    <div 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>Posição</th>\n",
              "      <th>País</th>\n",
              "      <th>PIB (Trilhões de US$)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Estados Unidos</td>\n",
              "      <td>20580.250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>China</td>\n",
              "      <td>14860.775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>Japão</td>\n",
              "      <td>4910.580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4</td>\n",
              "      <td>Alemanha</td>\n",
              "      <td>3780.553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5</td>\n",
              "      <td>Reino Unido</td>\n",
              "      <td>2638.296</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-79596304-0f67-4d94-8e3b-493bf4f9f3b0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-79596304-0f67-4d94-8e3b-493bf4f9f3b0 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-79596304-0f67-4d94-8e3b-493bf4f9f3b0');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Posição            País  PIB (Trilhões de US$)\n",
              "1        1  Estados Unidos              20580.250\n",
              "3        2           China              14860.775\n",
              "4        3           Japão               4910.580\n",
              "5        4        Alemanha               3780.553\n",
              "6        5     Reino Unido               2638.296"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Renomeando Colunas\n",
        "pib2020.rename(\n",
        "    columns={'PIB (Trilhões de US$)': '2020'}, inplace=True\n",
        ")"
      ],
      "metadata": {
        "id": "86n8rZxufDo7"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pib2020.boxplot( column='2020' )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "s0NpbdBhgKK5",
        "outputId": "bd9e15c3-450e-4704-9c7a-e1b139550395"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3cf4f37450>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Criando uma coluna\n",
        "\n",
        "# Calcular média\n",
        "Media = pib2020['2020'].mean()\n",
        "\n",
        "# Função\n",
        "def Classe_Media (Dados):\n",
        "\n",
        "  # condição\n",
        "  if Dados['2020'] >= Media:\n",
        "    return 'Acima'\n",
        "  else:\n",
        "    return 'Abaixo'\n",
        "\n",
        "# Aplicar a função\n",
        "pib2020['Classe'] = pib2020.apply(Classe_Media, axis=1 )\n",
        "\n",
        "pib2020.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "PTmfyY2dgU7O",
        "outputId": "901c697b-42e2-475d-811e-966ac38dcf73"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-6b81963b-cbdd-4350-b707-7dc967bf9b7d\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "        vertical-align: top;\n",
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              "\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>Posição</th>\n",
              "      <th>País</th>\n",
              "      <th>2020</th>\n",
              "      <th>Classe</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Estados Unidos</td>\n",
              "      <td>20580.250</td>\n",
              "      <td>Acima</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>China</td>\n",
              "      <td>14860.775</td>\n",
              "      <td>Acima</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>Japão</td>\n",
              "      <td>4910.580</td>\n",
              "      <td>Acima</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4</td>\n",
              "      <td>Alemanha</td>\n",
              "      <td>3780.553</td>\n",
              "      <td>Acima</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5</td>\n",
              "      <td>Reino Unido</td>\n",
              "      <td>2638.296</td>\n",
              "      <td>Acima</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6b81963b-cbdd-4350-b707-7dc967bf9b7d')\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-6b81963b-cbdd-4350-b707-7dc967bf9b7d button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-6b81963b-cbdd-4350-b707-7dc967bf9b7d');\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",
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              "  "
            ],
            "text/plain": [
              "   Posição            País       2020 Classe\n",
              "1        1  Estados Unidos  20580.250  Acima\n",
              "3        2           China  14860.775  Acima\n",
              "4        3           Japão   4910.580  Acima\n",
              "5        4        Alemanha   3780.553  Acima\n",
              "6        5     Reino Unido   2638.296  Acima"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Analises dos países que estão acima e abaixo da média.\n",
        "pib2020.groupby(['Classe']).count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "zlbbRUiVgi5k",
        "outputId": "a6f24157-dde9-4628-cffc-37f08ac93b05"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Posição</th>\n",
              "      <th>País</th>\n",
              "      <th>2020</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Classe</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Abaixo</th>\n",
              "      <td>161</td>\n",
              "      <td>161</td>\n",
              "      <td>161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Acima</th>\n",
              "      <td>28</td>\n",
              "      <td>28</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "      \n",
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              "    .colab-df-convert:hover {\n",
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              "      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",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "          document.querySelector('#df-c90de577-cb35-4e88-8f21-bd9e3de7c9a9 button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c90de577-cb35-4e88-8f21-bd9e3de7c9a9');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        Posição  País  2020\n",
              "Classe                     \n",
              "Abaixo      161   161   161\n",
              "Acima        28    28    28"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Analises \n",
        "pib2020.groupby(['Classe', 'Posição']).count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "o0Vu_X0PgjUZ",
        "outputId": "c0b4de3f-e094-48b8-f8e5-7af3a81ff53f"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-a40bc7f6-deaf-4e42-86c8-99f4ed6e17aa\">\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>País</th>\n",
              "      <th>2020</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Classe</th>\n",
              "      <th>Posição</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">Abaixo</th>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">Acima</th>\n",
              "      <th>24</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>189 rows × 2 columns</p>\n",
              "</div>\n",
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              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
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              "\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a40bc7f6-deaf-4e42-86c8-99f4ed6e17aa button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a40bc7f6-deaf-4e42-86c8-99f4ed6e17aa');\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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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                País  2020\n",
              "Classe Posição            \n",
              "Abaixo 1           1     1\n",
              "       29          1     1\n",
              "       30          1     1\n",
              "       31          1     1\n",
              "       32          1     1\n",
              "...              ...   ...\n",
              "Acima  24          1     1\n",
              "       25          1     1\n",
              "       26          1     1\n",
              "       27          1     1\n",
              "       28          1     1\n",
              "\n",
              "[189 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dados do ano 2018\n",
        "Tabela_Sites[3].columns == pib2018.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j4Y6RrzDh74q",
        "outputId": "7eac2393-4c76-40b3-d051-88af5845339d"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True,  True, False])"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pib2018.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HtrDZcAsiPFO",
        "outputId": "81721b31-f147-4bbc-9169-4b596ec1f376"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Posição', 'País', 'PIB (Millhões de US$)'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pib2020.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I5T9B63uinvN",
        "outputId": "bac1bb2b-81a8-4094-d977-33ea8e044c66"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Posição', 'País', '2020', 'Classe'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Novo_pib2020 = pib2020.drop( columns='Classe' )\n",
        "\n",
        "Novo_pib2018 = pib2018.rename( columns={'PIB (Millhões de US$)' : '2020'} )"
      ],
      "metadata": {
        "id": "54hgI_x2iqyn"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Novo_pib2020.columns == Novo_pib2018.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hw0KzN5Qi3TX",
        "outputId": "18c24ac7-d99c-418b-e724-0567c1f30bd3"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True,  True,  True])"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Empilhando os dados (montar uma nova tabela)\n",
        "pd.concat( [Novo_pib2020, Novo_pib2018] )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "RtkFCxzOjJXc",
        "outputId": "a23b5602-a55d-4bca-ef76-795bb703ceac"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f142582e-33ac-49df-b77f-105b8f089cf1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Posição</th>\n",
              "      <th>País</th>\n",
              "      <th>2020</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Estados Unidos</td>\n",
              "      <td>20580.250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>China</td>\n",
              "      <td>14860.775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>Japão</td>\n",
              "      <td>4910.580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4</td>\n",
              "      <td>Alemanha</td>\n",
              "      <td>3780.553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5</td>\n",
              "      <td>Reino Unido</td>\n",
              "      <td>2638.296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>185</th>\n",
              "      <td>181</td>\n",
              "      <td>Palau</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186</th>\n",
              "      <td>182</td>\n",
              "      <td>Ilhas Marshall</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>183</td>\n",
              "      <td>Kiribati</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188</th>\n",
              "      <td>184</td>\n",
              "      <td>Nauru</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>189</th>\n",
              "      <td>185</td>\n",
              "      <td>Tuvalu</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>379 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f142582e-33ac-49df-b77f-105b8f089cf1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f142582e-33ac-49df-b77f-105b8f089cf1');\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",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    Posição            País       2020\n",
              "1         1  Estados Unidos  20580.250\n",
              "3         2           China  14860.775\n",
              "4         3           Japão   4910.580\n",
              "5         4        Alemanha   3780.553\n",
              "6         5     Reino Unido   2638.296\n",
              "..      ...             ...        ...\n",
              "185     181           Palau        NaN\n",
              "186     182  Ilhas Marshall        NaN\n",
              "187     183        Kiribati        NaN\n",
              "188     184           Nauru        NaN\n",
              "189     185          Tuvalu        NaN\n",
              "\n",
              "[379 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cruzar os dados das duas tabelas.\n",
        "Novo_pib2020.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "O4T0qLcXjSjw",
        "outputId": "3b406d2f-33dc-41e1-e6bb-d3ac47a30457"
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      "execution_count": 78,
      "outputs": [
        {
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          "data": {
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              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>China</td>\n",
              "      <td>14860.775</td>\n",
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              "    <tr>\n",
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              "      <td>Japão</td>\n",
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              "      <th>5</th>\n",
              "      <td>4</td>\n",
              "      <td>Alemanha</td>\n",
              "      <td>3780.553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5</td>\n",
              "      <td>Reino Unido</td>\n",
              "      <td>2638.296</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-b6a6151a-de9c-4f78-93c7-31bf6ec8509b button.colab-df-convert');\n",
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              "          const element = document.querySelector('#df-b6a6151a-de9c-4f78-93c7-31bf6ec8509b');\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",
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              "  "
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            "text/plain": [
              "   Posição            País       2020\n",
              "1        1  Estados Unidos  20580.250\n",
              "3        2           China  14860.775\n",
              "4        3           Japão   4910.580\n",
              "5        4        Alemanha   3780.553\n",
              "6        5     Reino Unido   2638.296"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Base_Cruzada = pd.merge(Novo_pib2020, Novo_pib2018, how='inner', on=['País'] )"
      ],
      "metadata": {
        "id": "nDpK4vCujuvu"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Base_Cruzada.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2xrUfbUpkJQs",
        "outputId": "e53cd0b0-be39-4428-e8a2-40b76adce5c8"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e4ef7079-1e49-46a1-be64-5a2458beff61\">\n",
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              "      <th>2020_y</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Japão</td>\n",
              "      <td>4910.580</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Alemanha</td>\n",
              "      <td>3780.553</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Reino Unido</td>\n",
              "      <td>2638.296</td>\n",
              "      <td>5</td>\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e4ef7079-1e49-46a1-be64-5a2458beff61 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-e4ef7079-1e49-46a1-be64-5a2458beff61');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Posição_x            País     2020_x Posição_y  2020_y\n",
              "0          1  Estados Unidos  20580.250         1     NaN\n",
              "1          2           China  14860.775         2     NaN\n",
              "2          3           Japão   4910.580         3     NaN\n",
              "3          4        Alemanha   3780.553         4     NaN\n",
              "4          5     Reino Unido   2638.296         5     NaN"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Base_Cruzada.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y4QBBjhLka2p",
        "outputId": "467bd5d2-cb58-4f29-baa8-5ad6abf2a0ab"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 178 entries, 0 to 177\n",
            "Data columns (total 5 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Posição_x  178 non-null    int64  \n",
            " 1   País       178 non-null    object \n",
            " 2   2020_x     178 non-null    float64\n",
            " 3   Posição_y  178 non-null    object \n",
            " 4   2020_y     0 non-null      float64\n",
            "dtypes: float64(2), int64(1), object(2)\n",
            "memory usage: 8.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Base_Cruzada['Posição_y'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O6cTYO6ukgy7",
        "outputId": "8d8a4100-365c-4b68-81b4-1db907f9bdd8"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1      1\n",
              "—      1\n",
              "119    1\n",
              "120    1\n",
              "121    1\n",
              "      ..\n",
              "64     1\n",
              "66     1\n",
              "65     1\n",
              "67     1\n",
              "185    1\n",
              "Name: Posição_y, Length: 178, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# retirando a strig traço -\n",
        "Base_Cruzada = Base_Cruzada.loc[ Base_Cruzada['Posição_y'] != '—' ]"
      ],
      "metadata": {
        "id": "Z2fBkaTMkorX"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Base_Cruzada['Posição_y'] = pd.to_numeric( Base_Cruzada['Posição_y'] )"
      ],
      "metadata": {
        "id": "pxEihNd4k058"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Base_Cruzada['Subiu ou Caiu'] = Base_Cruzada['Posição_y'] - Base_Cruzada['Posição_x']"
      ],
      "metadata": {
        "id": "WfgNs-exlCrT"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Base_Cruzada.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "lsw_1A6blOyM",
        "outputId": "2796584e-2885-4983-aed8-56d34968d4d4"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-40746939-033b-40c5-9d86-0f324c60eb69\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "      <th></th>\n",
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              "      <td>20580.250</td>\n",
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              "      <td>14860.775</td>\n",
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              "      <td>Alemanha</td>\n",
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              "      <td>Reino Unido</td>\n",
              "      <td>2638.296</td>\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      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",
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              "          document.querySelector('#df-40746939-033b-40c5-9d86-0f324c60eb69 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-40746939-033b-40c5-9d86-0f324c60eb69');\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",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Posição_x            País     2020_x  Posição_y  2020_y  Subiu ou Caiu\n",
              "0          1  Estados Unidos  20580.250          1     NaN              0\n",
              "1          2           China  14860.775          2     NaN              0\n",
              "2          3           Japão   4910.580          3     NaN              0\n",
              "3          4        Alemanha   3780.553          4     NaN              0\n",
              "4          5     Reino Unido   2638.296          5     NaN              0"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Base_Cruzada.groupby(['Subiu ou Caiu']).count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "EgDjFXXqlPjg",
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      },
      "execution_count": 88,
      "outputs": [
        {
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          "data": {
            "text/html": [
              "\n",
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              "      <th>-6</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <th>-5</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>-4</th>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>-3</th>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
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              "      <th>-2</th>\n",
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              "      <th>-1</th>\n",
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              "      <th>0</th>\n",
              "      <td>30</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ab95dc8f-16f0-4497-a327-f140cb2918c4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ab95dc8f-16f0-4497-a327-f140cb2918c4 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-ab95dc8f-16f0-4497-a327-f140cb2918c4');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "               Posição_x  País  2020_x  Posição_y  2020_y\n",
              "Subiu ou Caiu                                            \n",
              "-14                    1     1       1          1       0\n",
              "-8                     1     1       1          1       0\n",
              "-6                     1     1       1          1       0\n",
              "-5                     5     5       5          5       0\n",
              "-4                    32    32      32         32       0\n",
              "-3                    10    10      10         10       0\n",
              "-2                    28    28      28         28       0\n",
              "-1                    59    59      59         59       0\n",
              " 0                    30    30      30         30       0\n",
              " 1                     5     5       5          5       0\n",
              " 2                     3     3       3          3       0\n",
              " 12                    1     1       1          1       0\n",
              " 160                   1     1       1          1       0"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Filtra país Brasil\n",
        "Base_Cruzada.loc[ Base_Cruzada['País'] == 'Brasil']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "5TXn0irXlXe-",
        "outputId": "4e8f67db-79a3-4c18-c270-c1314c33bdc2"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "  <div id=\"df-457439b6-0da3-4f63-b478-27f77928375c\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>Posição_x</th>\n",
              "      <th>País</th>\n",
              "      <th>2020_x</th>\n",
              "      <th>Posição_y</th>\n",
              "      <th>2020_y</th>\n",
              "      <th>Subiu ou Caiu</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>1363.767</td>\n",
              "      <td>9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-457439b6-0da3-4f63-b478-27f77928375c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-457439b6-0da3-4f63-b478-27f77928375c 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-457439b6-0da3-4f63-b478-27f77928375c');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    Posição_x    País    2020_x  Posição_y  2020_y  Subiu ou Caiu\n",
              "11         12  Brasil  1363.767          9     NaN             -3"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# importando Libs\n",
        "\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "dnCcWxWlln_z"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Base_Cruzada.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8bHYcBESlyu1",
        "outputId": "5c92aee5-16b1-4e87-ac7f-802cdb8acd42"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Posição_x</th>\n",
              "      <th>País</th>\n",
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              "      <th>0</th>\n",
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              "      <td>2</td>\n",
              "      <td>China</td>\n",
              "      <td>14860.775</td>\n",
              "      <td>2</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Alemanha</td>\n",
              "      <td>3780.553</td>\n",
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              "      <td>Reino Unido</td>\n",
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              "      </button>\n",
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              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-3265be04-62b0-4104-9640-79c9c3c6499f 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-3265be04-62b0-4104-9640-79c9c3c6499f');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Posição_x            País     2020_x  Posição_y  2020_y  Subiu ou Caiu\n",
              "0          1  Estados Unidos  20580.250          1     NaN              0\n",
              "1          2           China  14860.775          2     NaN              0\n",
              "2          3           Japão   4910.580          3     NaN              0\n",
              "3          4        Alemanha   3780.553          4     NaN              0\n",
              "4          5     Reino Unido   2638.296          5     NaN              0"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Analise_Ranking = Base_Cruzada[['País', '2020_x', 'Subiu ou Caiu']]\n",
        "\n",
        "Analise_Ranking.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "PsYH7r1yl3H_",
        "outputId": "2e2a0a5f-0719-4d97-ab7c-406b0835484a"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-9d6521ac-7a1a-4cc4-af16-a946e96bdebb\">\n",
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              "      <th></th>\n",
              "      <th>País</th>\n",
              "      <th>2020_x</th>\n",
              "      <th>Subiu ou Caiu</th>\n",
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              "  <tbody>\n",
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              "      <td>20580.250</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>China</td>\n",
              "      <td>14860.775</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
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              "      <th>3</th>\n",
              "      <td>Alemanha</td>\n",
              "      <td>3780.553</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Reino Unido</td>\n",
              "      <td>2638.296</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9d6521ac-7a1a-4cc4-af16-a946e96bdebb')\"\n",
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              "        \n",
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              "      </button>\n",
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              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9d6521ac-7a1a-4cc4-af16-a946e96bdebb 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-9d6521ac-7a1a-4cc4-af16-a946e96bdebb');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "             País     2020_x  Subiu ou Caiu\n",
              "0  Estados Unidos  20580.250              0\n",
              "1           China  14860.775              0\n",
              "2           Japão   4910.580              0\n",
              "3        Alemanha   3780.553              0\n",
              "4     Reino Unido   2638.296              0"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot( Analise_Ranking['2020_x'] );"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "G3dsTP6AmAgq",
        "outputId": "2958f010-0219-4463-fcb5-1916cb74effd"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot( Analise_Ranking['Subiu ou Caiu'], color='red' );"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "IIcUtRnFmSRG",
        "outputId": "d4471774-a499-44d4-87ab-c6005fdf45ec"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot( Analise_Ranking['2020_x'] );"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "XjRUQz-FmhiC",
        "outputId": "98b58111-0287-4490-979f-20becc84e03c"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.barh( Analise_Ranking['País'][0:10], Analise_Ranking['2020_x'][0:10] );"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "id": "VtumT_sgmsuv",
        "outputId": "25640c52-ebdc-4912-e641-c48b760ea7bc"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Criando o Relatorio\n",
        "\n",
        "# Tamanho\n",
        "plt.figure( figsize=(18, 10) )\n",
        "\n",
        "# estilo\n",
        "plt.style.use('seaborn-dark')\n",
        "\n",
        "# Dividor o relatorio\n",
        "plt.subplot(2, 2, 1)\n",
        "plt.title('PIB 2020')\n",
        "plt.plot( Analise_Ranking['2020_x'] )\n",
        "plt.ylabel('PIB em Trilhões')\n",
        "plt.xlabel('Posição do Pais')\n",
        "\n",
        "plt.subplot(2, 2, 2)\n",
        "plt.title('Análise de Crescimento')\n",
        "plt.plot( Analise_Ranking['Subiu ou Caiu'], color='red' )\n",
        "plt.ylabel('PIB em Trilhões')\n",
        "plt.xlabel('Posição do Pais 2020 x 2018')\n",
        "\n",
        "plt.subplot(2, 2, 3)\n",
        "sns.boxplot( Analise_Ranking['2020_x'] )\n",
        "plt.ylabel('PIB em Trilhões')\n",
        "plt.xlabel('Distribuição')\n",
        "\n",
        "plt.subplot(2, 2, 4)\n",
        "plt.barh( Analise_Ranking['País'][0:10], Analise_Ranking['2020_x'][0:10], color='#69d' )\n",
        "plt.ylabel('PIB em Trilhões')\n",
        "plt.xlabel(' ')\n",
        "\n",
        "plt.suptitle('Estudo Modelagem de Dados 2020 - PIBs Mundiais', fontsize=20, color='darkblue')\n",
        "\n",
        "plt.annotate(\n",
        "    'Análise efetuada por Ademáio Nobre no evento de Modelagem de dados orientado por Odemir Depieri Jr \\n',\n",
        "    xy=(1, -0.20), xycoords='axes fraction', ha='right', fontsize=12\n",
        ");\n",
        "\n",
        "plt.savefig('Visualização PIB Mundial')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "id": "mgbAdCeFnPvZ",
        "outputId": "50107363-8f15-471f-823d-ad2710a04ec9"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x720 with 4 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evento realizado pela Data Viking para extração de dados da web para tratamento e visualização de dados."
      ],
      "metadata": {
        "id": "YEIe4-YcHaIN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "1AFextRfHj95"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}