redes_sociais/world_fifa_world_women_cup_20230801.ipynb

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    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H-CcXg2mpqLh"
      },
      "outputs": [],
      "source": [
        "!pip install basedosdados -q"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import basedosdados as bd\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "vTT_mioZps5P"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Quantidade de Gols de EUA e Brasil durante todo o período\n",
        "query = '''\n",
        "WITH tabela_gol AS (\n",
        "  SELECT\n",
        "    year,\n",
        "    team_a_name,\n",
        "    team_a_score,\n",
        "    team_b_score,\n",
        "    team_b_name,\n",
        "    CASE\n",
        "      WHEN team_a_name = 'Brazil' THEN team_a_score\n",
        "      WHEN team_b_name = 'Brazil' THEN team_b_score\n",
        "    END AS gol_brazil,\n",
        "    CASE\n",
        "      WHEN team_a_name = 'USA' THEN team_a_score\n",
        "      WHEN team_b_name = 'USA' THEN team_b_score\n",
        "    END AS gol_usa\n",
        "  FROM `basedosdados.world_fifa_women_world_cup.matches`\n",
        " WHERE team_a_name IN ('Brazil', 'USA') OR team_b_name IN ('Brazil', 'USA')\n",
        ")\n",
        "SELECT\n",
        "  year,\n",
        "  SUM(gol_brazil) AS total_gol_brazil,\n",
        "  SUM(gol_usa) AS total_gol_usa\n",
        "FROM tabela_gol\n",
        "GROUP BY year;\n",
        "'''\n",
        "\n",
        "df = bd.read_sql(query, billing_project_id='seu-billing-id')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7sF5_HRBrMPD",
        "outputId": "6b1b428c-779c-419f-bbd6-eac11c252fd7"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: 100%|██████████| 8/8 [00:00<00:00, 30.24rows/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PNbeZPoFvOnp",
        "outputId": "223a6b44-7a38-4aa0-f2dd-566efe57b6c5"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   year  total_gol_brazil  total_gol_usa\n",
            "0  1991                 1             25\n",
            "1  1995                 3             15\n",
            "2  1999                16             18\n",
            "3  2003                 9             15\n",
            "4  2007                17             12\n",
            "5  2011                 9             13\n",
            "6  2015                 4             14\n",
            "7  2019                 7             26\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# E como foi o desempenho do Brasil nas partidas por ano?\n",
        "query = '''\n",
        "WITH team_performance AS (\n",
        "  SELECT\n",
        "    year,\n",
        "    CASE\n",
        "      WHEN team_a_name = 'Brazil' AND team_a_win = 1 THEN 'win'\n",
        "      WHEN team_b_name = 'Brazil' AND team_b_win = 1 THEN 'win'\n",
        "      WHEN draw = 1 THEN 'draw'\n",
        "      ELSE 'loss'\n",
        "    END AS result\n",
        "  FROM `basedosdados.world_fifa_women_world_cup.matches`\n",
        "  WHERE team_a_name = 'Brazil' OR team_b_name = 'Brazil'\n",
        ")\n",
        "SELECT\n",
        "  year,\n",
        "  COUNTIF(result = 'win') AS wins,\n",
        "  COUNTIF(result = 'loss') AS losses,\n",
        "  COUNTIF(result = 'draw') AS draws\n",
        "FROM team_performance\n",
        "GROUP BY year;\n",
        "'''\n",
        "\n",
        "df = bd.read_sql(query, billing_project_id='seu-billing-id')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "izIFx1ATrQX6",
        "outputId": "1521e072-1263-4529-8fe8-92b41e60f1e0"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: 100%|██████████| 8/8 [00:00<00:00, 30.57rows/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9vuXq2uwvaSz",
        "outputId": "1da5f82d-0358-450a-a6a0-9f71eaf562ca"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   year  wins  losses  draws\n",
            "0  1991     1       2      0\n",
            "1  1995     1       2      0\n",
            "2  1999     4       1      1\n",
            "3  2003     2       1      1\n",
            "4  2007     5       1      0\n",
            "5  2011     3       1      0\n",
            "6  2015     3       1      0\n",
            "7  2019     2       2      0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Elaboração do Gráfico apresentado na BD Letter de Julho/2023"
      ],
      "metadata": {
        "id": "_KOuJwIKrSuu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = bd.read_table(dataset_id='world_fifa_women_world_cup',\n",
        "table_id='matches',\n",
        "billing_project_id=\"seu-billing-id\")"
      ],
      "metadata": {
        "id": "gOHeieHxsG-i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_brasil = df[((df['team_a_name'] == 'Brazil')|(df['team_b_name'] == 'Brazil'))]"
      ],
      "metadata": {
        "id": "Wx1kzQ0DqPe8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_eua = df[((df['team_a_name'] == 'USA')|(df['team_b_name'] == 'USA'))]"
      ],
      "metadata": {
        "id": "Kcj9FDD0qHn4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = [1,2,3,4,5,6,7,8,9,10]"
      ],
      "metadata": {
        "id": "MKGi69e2qkcA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = [1,2,3,4,5,6,7,8,9,10]"
      ],
      "metadata": {
        "id": "619RS9IWq25P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_dtb = df_brasil[df_brasil['extra_time']==0]"
      ],
      "metadata": {
        "id": "Jt_lUw4aq61f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_dtb[['team_a_name','score','team_b_name','extra_time','penalty_shootout','score_penalties']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "BoMJvTLCsCAi",
        "outputId": "458b3b0d-7f12-4ad3-a279-bfa51c46d335"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           team_a_name  score     team_b_name  extra_time  penalty_shootout  \\\n",
              "3                Japan  0 - 1          Brazil           0                 0   \n",
              "10              Brazil  0 - 5             USA           0                 0   \n",
              "13              Brazil  0 - 2          Sweden           0                 0   \n",
              "27              Sweden  0 - 1          Brazil           0                 0   \n",
              "33              Brazil  1 - 2           Japan           0                 0   \n",
              "39              Brazil  1 - 6         Germany           0                 0   \n",
              "53              Brazil  7 - 1          Mexico           0                 0   \n",
              "64              Brazil  2 - 0           Italy           0                 0   \n",
              "72             Germany  3 - 3          Brazil           0                 0   \n",
              "79              Brazil  4 - 3         Nigeria           0                 0   \n",
              "80                 USA  2 - 0          Brazil           0                 0   \n",
              "82              Brazil  0 - 0          Norway           0                 1   \n",
              "89              Brazil  3 - 0  Korea Republic           0                 0   \n",
              "92              Norway  1 - 4          Brazil           0                 0   \n",
              "101             France  1 - 1          Brazil           0                 0   \n",
              "108             Brazil  1 - 2          Sweden           0                 0   \n",
              "121        New Zealand  0 - 5          Brazil           0                 0   \n",
              "131             Brazil  4 - 0        China PR           0                 0   \n",
              "139             Brazil  1 - 0         Denmark           0                 0   \n",
              "143             Brazil  3 - 2       Australia           0                 0   \n",
              "145                USA  0 - 4          Brazil           0                 0   \n",
              "147            Germany  2 - 0          Brazil           0                 0   \n",
              "155             Brazil  1 - 0       Australia           0                 0   \n",
              "163             Brazil  3 - 0          Norway           0                 0   \n",
              "168  Equatorial Guinea  0 - 3          Brazil           0                 0   \n",
              "175             Brazil  2 - 2             USA           0                 1   \n",
              "191             Brazil  2 - 0  Korea Republic           0                 0   \n",
              "201             Brazil  1 - 0           Spain           0                 0   \n",
              "214         Costa Rica  0 - 1          Brazil           0                 0   \n",
              "218             Brazil  0 - 1       Australia           0                 0   \n",
              "237             Brazil  3 - 0         Jamaica           0                 0   \n",
              "247          Australia  3 - 2          Brazil           0                 0   \n",
              "261              Italy  0 - 1          Brazil           0                 0   \n",
              "\n",
              "    score_penalties  \n",
              "3              None  \n",
              "10             None  \n",
              "13             None  \n",
              "27             None  \n",
              "33             None  \n",
              "39             None  \n",
              "53             None  \n",
              "64             None  \n",
              "72             None  \n",
              "79             None  \n",
              "80             None  \n",
              "82            5 - 4  \n",
              "89             None  \n",
              "92             None  \n",
              "101            None  \n",
              "108            None  \n",
              "121            None  \n",
              "131            None  \n",
              "139            None  \n",
              "143            None  \n",
              "145            None  \n",
              "147            None  \n",
              "155            None  \n",
              "163            None  \n",
              "168            None  \n",
              "175           3 - 5  \n",
              "191            None  \n",
              "201            None  \n",
              "214            None  \n",
              "218            None  \n",
              "237            None  \n",
              "247            None  \n",
              "261            None  "
            ],
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              "\n",
              "\n",
              "  <div id=\"df-fb6d6039-dea8-4a49-ab19-e033445cd6ad\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>team_a_name</th>\n",
              "      <th>score</th>\n",
              "      <th>team_b_name</th>\n",
              "      <th>extra_time</th>\n",
              "      <th>penalty_shootout</th>\n",
              "      <th>score_penalties</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Japan</td>\n",
              "      <td>0 - 1</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>0 - 5</td>\n",
              "      <td>USA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>0 - 2</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Sweden</td>\n",
              "      <td>0 - 1</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>1 - 2</td>\n",
              "      <td>Japan</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>1 - 6</td>\n",
              "      <td>Germany</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>7 - 1</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>2 - 0</td>\n",
              "      <td>Italy</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>Germany</td>\n",
              "      <td>3 - 3</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>4 - 3</td>\n",
              "      <td>Nigeria</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>USA</td>\n",
              "      <td>2 - 0</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>0 - 0</td>\n",
              "      <td>Norway</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5 - 4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>3 - 0</td>\n",
              "      <td>Korea Republic</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>Norway</td>\n",
              "      <td>1 - 4</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>France</td>\n",
              "      <td>1 - 1</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>1 - 2</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>New Zealand</td>\n",
              "      <td>0 - 5</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>4 - 0</td>\n",
              "      <td>China PR</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>1 - 0</td>\n",
              "      <td>Denmark</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>3 - 2</td>\n",
              "      <td>Australia</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>USA</td>\n",
              "      <td>0 - 4</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>Germany</td>\n",
              "      <td>2 - 0</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>1 - 0</td>\n",
              "      <td>Australia</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>3 - 0</td>\n",
              "      <td>Norway</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>Equatorial Guinea</td>\n",
              "      <td>0 - 3</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>2 - 2</td>\n",
              "      <td>USA</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3 - 5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>2 - 0</td>\n",
              "      <td>Korea Republic</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>1 - 0</td>\n",
              "      <td>Spain</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>214</th>\n",
              "      <td>Costa Rica</td>\n",
              "      <td>0 - 1</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>218</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>0 - 1</td>\n",
              "      <td>Australia</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>3 - 0</td>\n",
              "      <td>Jamaica</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>247</th>\n",
              "      <td>Australia</td>\n",
              "      <td>3 - 2</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>261</th>\n",
              "      <td>Italy</td>\n",
              "      <td>0 - 1</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fb6d6039-dea8-4a49-ab19-e033445cd6ad')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-514b786f-f365-4c37-85bf-679e392b1fe7 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-fb6d6039-dea8-4a49-ab19-e033445cd6ad 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-fb6d6039-dea8-4a49-ab19-e033445cd6ad');\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"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Para o Brasil\n",
        "def lab(df_dtb):\n",
        "    if ((df_dtb['team_a_name'] == 'Brazil') & (df_dtb['team_a_win'] == 1)):\n",
        "        return \"vitória\"\n",
        "    elif ((df_dtb['team_b_name'] == 'Brazil') & (df_dtb['team_b_win'] == 1)):\n",
        "        return \"vitória\"\n",
        "    elif ((df_dtb['team_a_name'] == 'Brazil') & (df_dtb['team_b_win'] == 1)):\n",
        "        return \"derrota\"\n",
        "    elif ((df_dtb['team_b_name'] == 'Brazil') & (df_dtb['team_a_win'] == 1)):\n",
        "        return \"derrota\"\n",
        "    elif  (df_dtb['draw'] == 1):\n",
        "        return \"empate\"\n",
        "\n",
        "df_dtb[\"jogos\"] = df_dtb.apply(lambda df_dtb:lab(df_dtb),axis = 1)"
      ],
      "metadata": {
        "id": "8v3N7MgNsNog"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_dtb[\"jogos\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XkCkvhI_sa5v",
        "outputId": "3aa61051-e049-43fb-f673-e216ed2b0e30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "vitória    21\n",
              "derrota    10\n",
              "empate      2\n",
              "Name: jogos, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x[0] = ((sum(df_dtb[\"jogos\"] == 'vitória') / df_dtb[\"jogos\"].value_counts().sum()) * 100).round(2)\n",
        "x[1] = ((sum(df_dtb[\"jogos\"] == 'derrota') / df_dtb[\"jogos\"].value_counts().sum()) * 100).round(2)\n",
        "x[2] = ((sum(df_dtb[\"jogos\"] == 'empate') / df_dtb[\"jogos\"].value_counts().sum()) * 100).round(2)"
      ],
      "metadata": {
        "id": "sEY2b5zWs9bY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_dta = df_eua[(df_eua['extra_time']==0)]"
      ],
      "metadata": {
        "id": "JlOMXvfVtB1W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_dta[['team_a_name','score','team_b_name','extra_time','penalty_shootout','score_penalties']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "gqlOgmE6tIKW",
        "outputId": "114ede28-835c-4abb-f070-6013246f77fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    team_a_name   score     team_b_name  extra_time  penalty_shootout  \\\n",
              "4        Sweden   2 - 3             USA           0                 0   \n",
              "10       Brazil   0 - 5             USA           0                 0   \n",
              "12        Japan   0 - 3             USA           0                 0   \n",
              "21          USA   7 - 0  Chinese Taipei           0                 0   \n",
              "23      Germany   2 - 5             USA           0                 0   \n",
              "25       Norway   1 - 2             USA           0                 0   \n",
              "30          USA   3 - 3        China PR           0                 0   \n",
              "36          USA   2 - 0         Denmark           0                 0   \n",
              "42          USA   4 - 1       Australia           0                 0   \n",
              "44        Japan   0 - 4             USA           0                 0   \n",
              "48          USA   0 - 1          Norway           0                 0   \n",
              "50     China PR   0 - 2             USA           0                 0   \n",
              "52          USA   3 - 0         Denmark           0                 0   \n",
              "65          USA   7 - 1         Nigeria           0                 0   \n",
              "75          USA   3 - 0       Korea DPR           0                 0   \n",
              "78          USA   3 - 2         Germany           0                 0   \n",
              "80          USA   2 - 0          Brazil           0                 0   \n",
              "83          USA   0 - 0        China PR           0                 1   \n",
              "88          USA   3 - 1          Sweden           0                 0   \n",
              "98          USA   5 - 0         Nigeria           0                 0   \n",
              "105   Korea DPR   0 - 3             USA           0                 0   \n",
              "109         USA   1 - 0          Norway           0                 0   \n",
              "112         USA   0 - 3         Germany           0                 0   \n",
              "114         USA   3 - 1          Canada           0                 0   \n",
              "117         USA   2 - 2       Korea DPR           0                 0   \n",
              "125      Sweden   0 - 2             USA           0                 0   \n",
              "134     Nigeria   0 - 1             USA           0                 0   \n",
              "141         USA   3 - 0         England           0                 0   \n",
              "145         USA   0 - 4          Brazil           0                 0   \n",
              "146      Norway   1 - 4             USA           0                 0   \n",
              "153         USA   2 - 0       Korea DPR           0                 0   \n",
              "161         USA   3 - 0        Colombia           0                 0   \n",
              "170      Sweden   2 - 1             USA           0                 0   \n",
              "175      Brazil   2 - 2             USA           0                 1   \n",
              "176      France   1 - 3             USA           0                 0   \n",
              "179       Japan   2 - 2             USA           0                 1   \n",
              "186         USA   3 - 1       Australia           0                 0   \n",
              "198         USA   0 - 0          Sweden           0                 0   \n",
              "210     Nigeria   0 - 1             USA           0                 0   \n",
              "222         USA   2 - 0        Colombia           0                 0   \n",
              "225    China PR   0 - 1             USA           0                 0   \n",
              "228         USA   2 - 0         Germany           0                 0   \n",
              "231         USA   5 - 2           Japan           0                 0   \n",
              "243         USA  13 - 0        Thailand           0                 0   \n",
              "255         USA   3 - 0           Chile           0                 0   \n",
              "266      Sweden   0 - 2             USA           0                 0   \n",
              "272       Spain   1 - 2             USA           0                 0   \n",
              "277      France   1 - 2             USA           0                 0   \n",
              "280     England   1 - 2             USA           0                 0   \n",
              "283         USA   2 - 0     Netherlands           0                 0   \n",
              "\n",
              "    score_penalties  \n",
              "4              None  \n",
              "10             None  \n",
              "12             None  \n",
              "21             None  \n",
              "23             None  \n",
              "25             None  \n",
              "30             None  \n",
              "36             None  \n",
              "42             None  \n",
              "44             None  \n",
              "48             None  \n",
              "50             None  \n",
              "52             None  \n",
              "65             None  \n",
              "75             None  \n",
              "78             None  \n",
              "80             None  \n",
              "83            5 - 4  \n",
              "88             None  \n",
              "98             None  \n",
              "105            None  \n",
              "109            None  \n",
              "112            None  \n",
              "114            None  \n",
              "117            None  \n",
              "125            None  \n",
              "134            None  \n",
              "141            None  \n",
              "145            None  \n",
              "146            None  \n",
              "153            None  \n",
              "161            None  \n",
              "170            None  \n",
              "175           3 - 5  \n",
              "176            None  \n",
              "179           3 - 1  \n",
              "186            None  \n",
              "198            None  \n",
              "210            None  \n",
              "222            None  \n",
              "225            None  \n",
              "228            None  \n",
              "231            None  \n",
              "243            None  \n",
              "255            None  \n",
              "266            None  \n",
              "272            None  \n",
              "277            None  \n",
              "280            None  \n",
              "283            None  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-84fb88a3-d980-47f6-a006-c236612900bb\">\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>team_a_name</th>\n",
              "      <th>score</th>\n",
              "      <th>team_b_name</th>\n",
              "      <th>extra_time</th>\n",
              "      <th>penalty_shootout</th>\n",
              "      <th>score_penalties</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Sweden</td>\n",
              "      <td>2 - 3</td>\n",
              "      <td>USA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>0 - 5</td>\n",
              "      <td>USA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Japan</td>\n",
              "      <td>0 - 3</td>\n",
              "      <td>USA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>USA</td>\n",
              "      <td>7 - 0</td>\n",
              "      <td>Chinese Taipei</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Germany</td>\n",
              "      <td>2 - 5</td>\n",
              "      <td>USA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Norway</td>\n",
              "      <td>1 - 2</td>\n",
              "      <td>USA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>USA</td>\n",
              "      <td>3 - 3</td>\n",
              "      <td>China PR</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>USA</td>\n",
              "      <td>2 - 0</td>\n",
              "      <td>Denmark</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>USA</td>\n",
              "      <td>4 - 1</td>\n",
              "      <td>Australia</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Japan</td>\n",
              "      <td>0 - 4</td>\n",
              "      <td>USA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>USA</td>\n",
              "      <td>0 - 1</td>\n",
              "      <td>Norway</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>China PR</td>\n",
              "      <td>0 - 2</td>\n",
              "      <td>USA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>USA</td>\n",
              "      <td>3 - 0</td>\n",
              "      <td>Denmark</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>USA</td>\n",
              "      <td>7 - 1</td>\n",
              "      <td>Nigeria</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>USA</td>\n",
              "      <td>3 - 0</td>\n",
              "      <td>Korea DPR</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>USA</td>\n",
              "      <td>3 - 2</td>\n",
              "      <td>Germany</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>USA</td>\n",
              "      <td>2 - 0</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>USA</td>\n",
              "      <td>0 - 0</td>\n",
              "      <td>China PR</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5 - 4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>USA</td>\n",
              "      <td>3 - 1</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>USA</td>\n",
              "      <td>5 - 0</td>\n",
              "      <td>Nigeria</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>Korea DPR</td>\n",
              "      <td>0 - 3</td>\n",
              "      <td>USA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>USA</td>\n",
              "      <td>1 - 0</td>\n",
              "      <td>Norway</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>USA</td>\n",
              "      <td>0 - 3</td>\n",
              "      <td>Germany</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>USA</td>\n",
              "      <td>3 - 1</td>\n",
              "      <td>Canada</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>USA</td>\n",
              "      <td>2 - 2</td>\n",
              "      <td>Korea DPR</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>Sweden</td>\n",
              "      <td>0 - 2</td>\n",
              "      <td>USA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>Nigeria</td>\n",
              "      <td>0 - 1</td>\n",
              "      <td>USA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>USA</td>\n",
              "      <td>3 - 0</td>\n",
              "      <td>England</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>USA</td>\n",
              "      <td>0 - 4</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>Norway</td>\n",
              "      <td>1 - 4</td>\n",
              "      <td>USA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>USA</td>\n",
              "      <td>2 - 0</td>\n",
              "      <td>Korea DPR</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>USA</td>\n",
              "      <td>3 - 0</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>Sweden</td>\n",
              "      <td>2 - 1</td>\n",
              "      <td>USA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>2 - 2</td>\n",
              "      <td>USA</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3 - 5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>France</td>\n",
              "      <td>1 - 3</td>\n",
              "      <td>USA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>Japan</td>\n",
              "      <td>2 - 2</td>\n",
              "      <td>USA</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3 - 1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186</th>\n",
              "      <td>USA</td>\n",
              "      <td>3 - 1</td>\n",
              "      <td>Australia</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>USA</td>\n",
              "      <td>0 - 0</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>210</th>\n",
              "      <td>Nigeria</td>\n",
              "      <td>0 - 1</td>\n",
              "      <td>USA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>222</th>\n",
              "      <td>USA</td>\n",
              "      <td>2 - 0</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>225</th>\n",
              "      <td>China PR</td>\n",
              "      <td>0 - 1</td>\n",
              "      <td>USA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>228</th>\n",
              "      <td>USA</td>\n",
              "      <td>2 - 0</td>\n",
              "      <td>Germany</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>231</th>\n",
              "      <td>USA</td>\n",
              "      <td>5 - 2</td>\n",
              "      <td>Japan</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>243</th>\n",
              "      <td>USA</td>\n",
              "      <td>13 - 0</td>\n",
              "      <td>Thailand</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>255</th>\n",
              "      <td>USA</td>\n",
              "      <td>3 - 0</td>\n",
              "      <td>Chile</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>266</th>\n",
              "      <td>Sweden</td>\n",
              "      <td>0 - 2</td>\n",
              "      <td>USA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>272</th>\n",
              "      <td>Spain</td>\n",
              "      <td>1 - 2</td>\n",
              "      <td>USA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>277</th>\n",
              "      <td>France</td>\n",
              "      <td>1 - 2</td>\n",
              "      <td>USA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>280</th>\n",
              "      <td>England</td>\n",
              "      <td>1 - 2</td>\n",
              "      <td>USA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>283</th>\n",
              "      <td>USA</td>\n",
              "      <td>2 - 0</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-84fb88a3-d980-47f6-a006-c236612900bb')\"\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",
              "\n",
              "\n",
              "    <div id=\"df-1bcb007a-2d3f-410b-8405-3ca8fd598f38\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1bcb007a-2d3f-410b-8405-3ca8fd598f38')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-1bcb007a-2d3f-410b-8405-3ca8fd598f38 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-84fb88a3-d980-47f6-a006-c236612900bb 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-84fb88a3-d980-47f6-a006-c236612900bb');\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"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def lab(df_dta):\n",
        "    if ((df_dta['team_a_name'] == 'USA') & (df_dta['team_a_win'] == 1)):\n",
        "        return \"vitória\"\n",
        "    elif ((df_dta['team_b_name'] == 'USA') & (df_dta['team_b_win'] == 1)):\n",
        "        return \"vitória\"\n",
        "    elif ((df_dta['team_a_name'] == 'USA') & (df_dta['team_b_win'] == 1)):\n",
        "        return \"derrota\"\n",
        "    elif ((df_dta['team_b_name'] == 'USA') & (df_dta['team_a_win'] == 1)):\n",
        "        return \"derrota\"\n",
        "    elif  (df_dta['draw'] == 1):\n",
        "        return \"empate\"\n",
        "\n",
        "df_dta[\"jogos\"] = df_dta.apply(lambda df_dta:lab(df_dta),axis = 1)"
      ],
      "metadata": {
        "id": "uvFVtdgStM_c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_dta[\"jogos\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DoQyEke1tVVa",
        "outputId": "fc04a101-07ac-41e2-ed97-de1b017b5128"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "vitória    42\n",
              "derrota     5\n",
              "empate      3\n",
              "Name: jogos, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y[0] = ((sum(df_dta[\"jogos\"] == 'vitória') / df_dta[\"jogos\"].value_counts().sum()) * 100).round(2)\n",
        "y[1] = ((sum(df_dta[\"jogos\"] == 'derrota') / df_dta[\"jogos\"].value_counts().sum()) * 100).round(2)\n",
        "y[2] = ((sum(df_dta[\"jogos\"] == 'empate') / df_dta[\"jogos\"].value_counts().sum()) * 100).round(2)"
      ],
      "metadata": {
        "id": "pb5izi3ltZRA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_ppb = df_brasil[(df_brasil['extra_time']==1)&(df_brasil['penalty_shootout']==0)]"
      ],
      "metadata": {
        "id": "hLQQQ3XFtfhp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_ppb[['year','stage_name','match_id','team_a_name','score','team_b_name','extra_time','penalty_shootout','score_penalties']]"
      ],
      "metadata": {
        "colab": {
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        "id": "8AvfzgC4KsCr",
        "outputId": "bb6b4231-5fbf-4e89-89d6-2b508a4b4385"
      },
      "execution_count": null,
      "outputs": [
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            "text/plain": [
              "     year   stage_name   match_id team_a_name  score team_b_name  extra_time  \\\n",
              "271  2019  Round of 16  M-2019-40      France  2 - 1      Brazil           1   \n",
              "\n",
              "     penalty_shootout score_penalties  \n",
              "271                 0            None  "
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def lab(df_ppb):\n",
        "    if ((df_ppb['team_a_name'] == 'Brazil') & (df_ppb['team_a_win'] == 1)):\n",
        "        return \"vitória\"\n",
        "    elif ((df_ppb['team_b_name'] == 'Brazil') & (df_ppb['team_b_win'] == 1)):\n",
        "        return \"vitória\"\n",
        "    elif ((df_ppb['team_a_name'] == 'Brazil') & (df_ppb['team_b_win'] == 1)):\n",
        "        return \"derrota\"\n",
        "    elif ((df_ppb['team_b_name'] == 'Brazil') & (df_ppb['team_a_win'] == 1)):\n",
        "        return \"derrota\"\n",
        "\n",
        "\n",
        "df_ppb[\"jogos\"] = df_ppb.apply(lambda df_ppb:lab(df_ppb),axis = 1)"
      ],
      "metadata": {
        "id": "ftp51KudK1zS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_ppb[\"jogos\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QYLbkNhoLLxr",
        "outputId": "d87fe74c-cceb-4e85-a15c-2434bbf53a3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "derrota    1\n",
              "Name: jogos, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x[3] = ((sum(df_ppb[\"jogos\"] == 'vitória') / df_ppb[\"jogos\"].value_counts().sum()) * 100).round(2)\n",
        "x[4] = ((sum(df_ppb[\"jogos\"] == 'derrota') / df_ppb[\"jogos\"].value_counts().sum()) * 100).round(2)"
      ],
      "metadata": {
        "id": "OisyoG0KLTYa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_ppa = df_eua[(df_eua['extra_time']==1)&(df_eua['penalty_shootout']==0)]"
      ],
      "metadata": {
        "id": "6ZdkoGrhLpyp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_ppa[['year','team_a_score','team_b_score','team_a_name','score','team_b_name','extra_time','penalty_shootout','score_penalties','draw']]"
      ],
      "metadata": {
        "colab": {
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          "height": 49
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        "id": "tt29xibNLvXR",
        "outputId": "944f3433-c4fb-4ac8-8518-da47350339b9"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [year, team_a_score, team_b_score, team_a_name, score, team_b_name, extra_time, penalty_shootout, score_penalties, draw]\n",
              "Index: []"
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def lab(df_ppa):\n",
        "    if ((df_ppa['team_a_name'] == 'USA') & (df_ppa['team_a_win'] == 1)):\n",
        "        return \"vitória\"\n",
        "    elif ((df_ppa['team_b_name'] == 'USA') & (df_ppa['team_b_win'] == 1)):\n",
        "        return \"vitória\"\n",
        "    elif ((df_ppa['team_a_name'] == 'USA') & (df_ppa['team_b_win'] == 1)):\n",
        "        return \"derrota\"\n",
        "    elif ((df_ppa['team_b_name'] == 'USA') & (df_ppa['team_a_win'] == 1)):\n",
        "        return \"derrota\"\n",
        "\n",
        "\n",
        "df_ppa[\"jogos\"] = df_ppa.apply(lambda df_ppa:lab(df_ppa),axis = 1)"
      ],
      "metadata": {
        "id": "SvTpd0EKLyOA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_ppa[\"jogos\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cGmbngRWL56N",
        "outputId": "2bb65743-c8f2-42e2-cdf1-c1dd0651c430"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Series([], Name: jogos, dtype: int64)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y[3] = ((sum(df_ppa[\"jogos\"] == 'vitória') / df_ppa[\"jogos\"].value_counts().sum()) * 100).round(2)\n",
        "y[4] = ((sum(df_ppa[\"jogos\"] == 'derrota') / df_ppa[\"jogos\"].value_counts().sum()) * 100).round(2)"
      ],
      "metadata": {
        "id": "2kly6o6YTc2d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Penaltis"
      ],
      "metadata": {
        "id": "6zRrJdCdTmWf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_pb = df_brasil[df_brasil['penalty_shootout']==1]"
      ],
      "metadata": {
        "id": "FFsWhN4RTi6L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_pb[['team_a_name','score','team_b_name','extra_time','score_penalties','team_a_win','team_b_win']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "y3VT9_kpTr0O",
        "outputId": "218a3efc-1d3b-4c52-bbad-7d371b2120ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    team_a_name  score team_b_name  extra_time score_penalties  team_a_win  \\\n",
              "82       Brazil  0 - 0      Norway           0           5 - 4           1   \n",
              "175      Brazil  2 - 2         USA           0           3 - 5           0   \n",
              "\n",
              "     team_b_win  \n",
              "82            0  \n",
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              "          const element = document.querySelector('#df-9dfeffb4-3414-4b51-85af-d132b3254c07');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def lab(df_pb):\n",
        "    if ((df_pb['team_a_name'] == 'Brazil') & (df_pb['team_a_win'] == 1)):\n",
        "        return \"vitória\"\n",
        "    elif ((df_pb['team_b_name'] == 'Brazil') & (df_pb['team_b_win'] == 1)):\n",
        "        return \"vitória\"\n",
        "    elif ((df_pb['team_a_name'] == 'Brazil') & (df_pb['team_b_win'] == 1)):\n",
        "        return \"derrota\"\n",
        "    elif ((df_pb['team_b_name'] == 'Brazil') & (df_pb['team_a_win'] == 1)):\n",
        "        return \"derrota\"\n",
        "\n",
        "df_pb[\"jogos\"] = df_pb.apply(lambda df_pb:lab(df_pb),axis = 1)"
      ],
      "metadata": {
        "id": "QdYq7ZxcTtVI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_pb[\"jogos\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GWWxCGU3T2O4",
        "outputId": "44277b97-66ef-4eeb-b40c-206445551a6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "vitória    1\n",
              "derrota    1\n",
              "Name: jogos, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x[5] = ((sum(df_pb[\"jogos\"] == 'vitória') / df_pb[\"jogos\"].value_counts().sum()) * 100).round(2)\n",
        "x[6] = ((sum(df_pb[\"jogos\"] == 'derrota') / df_pb[\"jogos\"].value_counts().sum()) * 100).round(2)"
      ],
      "metadata": {
        "id": "ZhDsjusZT4pX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Penaltis USA"
      ],
      "metadata": {
        "id": "geQPmZrUT8DQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_pa = df_eua[df_eua['penalty_shootout']==1]"
      ],
      "metadata": {
        "id": "tiEE5l54T6oe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_pa[['team_a_name','score','team_b_name','extra_time','score_penalties']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
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        "id": "gXQn9kZCUBSw",
        "outputId": "46f14d27-c56c-411c-b589-4ac4487ce84e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    team_a_name  score team_b_name  extra_time score_penalties\n",
              "83          USA  0 - 0    China PR           0           5 - 4\n",
              "175      Brazil  2 - 2         USA           0           3 - 5\n",
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              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-33495432-6ea9-4b2c-9458-6dc12bfffc8d button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-1c7b460a-17b7-42b7-9ba9-c200211de3b7 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-1c7b460a-17b7-42b7-9ba9-c200211de3b7');\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"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def lab(df_pa):\n",
        "    if ((df_pa['team_a_name'] == 'USA') & (df_pa['team_a_win'] == 1)):\n",
        "        return \"vitória\"\n",
        "    elif ((df_pa['team_b_name'] == 'USA') & (df_pa['team_b_win'] == 1)):\n",
        "        return \"vitória\"\n",
        "    elif ((df_pa['team_a_name'] == 'USA') & (df_pa['team_b_win'] == 1)):\n",
        "        return \"derrota\"\n",
        "    elif ((df_pa['team_b_name'] == 'USA') & (df_pa['team_a_win'] == 1)):\n",
        "        return \"derrota\"\n",
        "\n",
        "df_pa[\"jogos\"] = df_pa.apply(lambda df_pa:lab(df_pa),axis = 1)"
      ],
      "metadata": {
        "id": "2g3cKnwbUDly"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_pa[\"jogos\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qG-dSvq2ULB3",
        "outputId": "68d9343e-62c2-4dff-8485-3f9a141b3775"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "vitória    2\n",
              "derrota    1\n",
              "Name: jogos, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y[5] = ((sum(df_pa[\"jogos\"] == 'vitória') / df_pa[\"jogos\"].value_counts().sum()) * 100).round(2)\n",
        "y[6] = ((sum(df_pa[\"jogos\"] == 'derrota') / df_pa[\"jogos\"].value_counts().sum()) * 100).round(2)"
      ],
      "metadata": {
        "id": "QjfRz6PgUPoI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_3 = df_brasil"
      ],
      "metadata": {
        "id": "FNiaGLsvURZ2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Total Brasil\n",
        "def lab(df_3):\n",
        "    if ((df_3['team_a_name'] == 'Brazil') & (df_3['team_a_win'] == 1)):\n",
        "        return \"vitória\"\n",
        "    elif ((df_3['team_b_name'] == 'Brazil') & (df_3['team_b_win'] == 1)):\n",
        "        return \"vitória\"\n",
        "    elif ((df_3['team_a_name'] == 'Brazil') & (df_3['team_b_win'] == 1)):\n",
        "        return \"derrota\"\n",
        "    elif ((df_3['team_b_name'] == 'Brazil') & (df_3['team_a_win'] == 1)):\n",
        "        return \"derrota\"\n",
        "    elif  (df_3['draw'] == 1):\n",
        "        return \"empate\"\n",
        "\n",
        "df_3[\"jogos\"] = df_3.apply(lambda df_3:lab(df_3),axis = 1)"
      ],
      "metadata": {
        "id": "xBoO5eErUUWm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_3[\"jogos\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "49zPJPFPU6hn",
        "outputId": "e717c185-9ffc-4e5b-c3f8-64f8260aeb79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "vitória    21\n",
              "derrota    11\n",
              "empate      2\n",
              "Name: jogos, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x[7] = ((sum(df_3[\"jogos\"] == 'vitória') / df_3[\"jogos\"].value_counts().sum()) * 100).round(2)\n",
        "x[8] = ((sum(df_3[\"jogos\"] == 'derrota') / df_3[\"jogos\"].value_counts().sum()) * 100).round(2)\n",
        "x[9] = ((sum(df_3[\"jogos\"] == 'empate') / df_3[\"jogos\"].value_counts().sum()) * 100).round(2)"
      ],
      "metadata": {
        "id": "itO6353RU86o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_2 = df_eua"
      ],
      "metadata": {
        "id": "9j6LRJwRVAeP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def lab(df_2):\n",
        "    if ((df_2['team_a_name'] == 'USA') & (df_2['team_a_win'] == 1)):\n",
        "        return \"vitória\"\n",
        "    elif ((df_2['team_b_name'] == 'USA') & (df_2['team_b_win'] == 1)):\n",
        "        return \"vitória\"\n",
        "    elif ((df_2['team_a_name'] == 'USA') & (df_2['team_b_win'] == 1)):\n",
        "        return \"derrota\"\n",
        "    elif ((df_2['team_b_name'] == 'USA') & (df_2['team_a_win'] == 1)):\n",
        "        return \"derrota\"\n",
        "    elif  (df_2['draw'] == 1):\n",
        "        return \"empate\"\n",
        "\n",
        "df_2[\"jogos\"] = df_2.apply(lambda df_3:lab(df_3),axis = 1)"
      ],
      "metadata": {
        "id": "Xfw-qYl5VDF3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_2[\"jogos\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9zopPxZEVYr_",
        "outputId": "2beafae0-c40d-430d-b172-a8e508253ddc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "vitória    42\n",
              "derrota     5\n",
              "empate      3\n",
              "Name: jogos, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y[7] = ((sum(df_2[\"jogos\"] == 'vitória') / df_2[\"jogos\"].value_counts().sum()) * 100).round(2)\n",
        "y[8] = ((sum(df_2[\"jogos\"] == 'derrota') / df_2[\"jogos\"].value_counts().sum()) * 100).round(2)\n",
        "y[9] = ((sum(df_2[\"jogos\"] == 'empate') / df_2[\"jogos\"].value_counts().sum()) * 100).round(2)"
      ],
      "metadata": {
        "id": "40uxJFPQVbMv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Z = ['Vitória (dois tempos)','Derrota (dois tempos)', 'Empate (dois tempos)','Vitória (prorrogação)','Derrota (prorrogação)','Vitória (pênaltis)', 'Derrota (pênaltis)', 'Vitória', 'Derrota','Empate']"
      ],
      "metadata": {
        "id": "7r-v7voUVep3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_1 = {'index' : Z, 'Brasil': x, 'EUA': y}"
      ],
      "metadata": {
        "id": "WN3xL9uVVh5h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(data_1)"
      ],
      "metadata": {
        "id": "_u9aDyO_VnxB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.set_index('index')"
      ],
      "metadata": {
        "id": "-72aUFnvVq2A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "UbjNw3WzVstH",
        "outputId": "c4ee1454-6a88-4366-d097-aa5f26642d4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       Brasil    EUA\n",
              "index                               \n",
              "Vitória (dois tempos)   63.64  84.00\n",
              "Derrota (dois tempos)   30.30  10.00\n",
              "Empate (dois tempos)     6.06   6.00\n",
              "Vitória (prorrogação)    0.00    NaN\n",
              "Derrota (prorrogação)  100.00    NaN\n",
              "Vitória (pênaltis)      50.00  66.67\n",
              "Derrota (pênaltis)      50.00  33.33\n",
              "Vitória                 61.76  84.00\n",
              "Derrota                 32.35  10.00\n",
              "Empate                   5.88   6.00"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-401fb32f-3d2b-4fb2-8f17-65ea2b2398d0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th>Brasil</th>\n",
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              "      <td>84.00</td>\n",
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              "    <tr>\n",
              "      <th>Derrota (dois tempos)</th>\n",
              "      <td>30.30</td>\n",
              "      <td>10.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Empate (dois tempos)</th>\n",
              "      <td>6.06</td>\n",
              "      <td>6.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>84.00</td>\n",
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              "      <td>10.00</td>\n",
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              "    <tr>\n",
              "      <th>Empate</th>\n",
              "      <td>5.88</td>\n",
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              "        const containerElement = document.querySelector('#' + key);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-401fb32f-3d2b-4fb2-8f17-65ea2b2398d0 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-401fb32f-3d2b-4fb2-8f17-65ea2b2398d0');\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"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "font_color = '#525252'\n",
        "hfont = {'fontname':'Calibri'}\n",
        "facecolor = '#eaeaf2'\n",
        "color_red = '#fd625e'\n",
        "color_blue = '#01b8aa'\n",
        "index = df.index\n",
        "column0 = df['EUA']\n",
        "column1 = df['Brasil']\n",
        "title0 = 'EUA'\n",
        "title1 = 'Brasil'"
      ],
      "metadata": {
        "id": "FN-X56diVuoX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(figsize=(10,5), facecolor=facecolor, ncols=2, sharey=True)\n",
        "fig.tight_layout()\n",
        "\n",
        "axes[0].barh(index, column0, align='center', color=color_red, zorder=10)\n",
        "axes[0].set_title(title0, fontsize=18, pad=15, color=color_red, **hfont)\n",
        "axes[1].barh(index, column1, align='center', color=color_blue, zorder=10)\n",
        "axes[1].set_title(title1, fontsize=18, pad=15, color=color_blue, **hfont)\n",
        "\n",
        "\n",
        "axes[0].invert_xaxis()\n",
        "\n",
        "plt.gca().invert_yaxis()\n",
        "\n",
        "axes[0].set_xticks([10, 20, 30, 40, 50, 60, 70, 80, 90,100])\n",
        "axes[1].set_xticks([10, 20, 30, 40, 50, 60, 70, 80,90,100])\n",
        "\n",
        "for label in (axes[0].get_xticklabels() + axes[0].get_yticklabels()):\n",
        "    label.set(fontsize=13, color=font_color, **hfont)\n",
        "\n",
        "for label in (axes[1].get_xticklabels() + axes[1].get_yticklabels()):\n",
        "    label.set(fontsize=13, color=font_color, **hfont)\n",
        "\n",
        "plt.subplots_adjust(wspace=0, top=0.85, bottom=0.1, left=0.18, right=0.95)\n",
        "axes[0].grid(linestyle='--', alpha=0.5)\n",
        "axes[1].grid(linestyle='--', alpha=0.5)\n",
        "plt.savefig(\"desempenho_p.svg\", dpi = 600)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lE-1bIV5V7PQ",
        "outputId": "7023bfa5-d7e1-42e7-fc20-0a76df86d0af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.font_manager:findfont: Font family 'Calibri' not found.\n",
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          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}