brasileirão_parte_1.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib as plt\n",
        "import matplotlib.colors as colors\n",
        "import matplotlib.pyplot as plt\n",
        "import statsmodels.formula.api as smf"
      ]
    },
    {
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      "source": [
        "df=pd.read_csv('https://raw.githubusercontent.com/mths-andrade/brasileiro/main/brasileiro_tab.csv')\n",
        "df.set_index('Time',inplace=True)\n",
        "df['Aproveitamento'] = df['Aproveitamento'].str.replace(',', '.').astype(float)"
      ],
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        "df.describe().round(1)"
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              "       Posicao  Pontos  Vitorias  Empates  Derrotas  Saldo  Aproveitamento\n",
              "count    218.0   218.0     218.0    218.0     218.0  218.0           218.0\n",
              "mean       7.8    57.5      15.8     10.2      12.1    7.3            50.4\n",
              "std        4.9    10.5       3.8      2.7       3.6   13.7             9.2\n",
              "min        1.0    27.0       5.0      3.0       3.0  -30.0            23.7\n",
              "25%        3.2    50.0      13.0      8.0      10.0   -1.0            43.9\n",
              "50%        7.0    56.0      15.0     10.0      12.0    7.0            49.1\n",
              "75%       12.0    65.0      18.0     12.0      14.0   17.0            57.0\n",
              "max       20.0    90.0      28.0     17.0      22.0   49.0            79.0"
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              "      <th></th>\n",
              "      <th>Posicao</th>\n",
              "      <th>Pontos</th>\n",
              "      <th>Vitorias</th>\n",
              "      <th>Empates</th>\n",
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              "      <th>count</th>\n",
              "      <td>218.0</td>\n",
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              "      <th>mean</th>\n",
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              "      <td>13.7</td>\n",
              "      <td>9.2</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.0</td>\n",
              "      <td>27.0</td>\n",
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              "      <td>23.7</td>\n",
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              "      <th>25%</th>\n",
              "      <td>3.2</td>\n",
              "      <td>50.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>43.9</td>\n",
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              "      <th>50%</th>\n",
              "      <td>7.0</td>\n",
              "      <td>56.0</td>\n",
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              "      <td>7.0</td>\n",
              "      <td>49.1</td>\n",
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              "      <th>75%</th>\n",
              "      <td>12.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>17.0</td>\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Posicao\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.4843595758772,\n        \"min\": 1.0,\n        \"max\": 218.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7.8,\n          7.0,\n          218.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pontos\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 63.74726884905961,\n        \"min\": 10.5,\n        \"max\": 218.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          57.5,\n          56.0,\n          218.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Vitorias\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 72.49200448522384,\n        \"min\": 3.8,\n        \"max\": 218.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15.8,\n          15.0,\n          218.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Empates\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.04460889992502,\n        \"min\": 2.7,\n        \"max\": 218.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10.2,\n          10.0,\n          218.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Derrotas\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 73.44615009846135,\n        \"min\": 3.0,\n        \"max\": 218.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          12.1,\n          12.0,\n          218.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Saldo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 77.02954442475766,\n        \"min\": -30.0,\n        \"max\": 218.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7.3,\n          7.0,\n          218.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Aproveitamento\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 64.79224904040827,\n        \"min\": 9.2,\n        \"max\": 218.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          50.4,\n          49.1,\n          218.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "execution_count": 3
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    {
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      "source": [
        "pos_media=df['Posicao'].mean().round(1)\n",
        "pt_media=df['Pontos'].mean().round(1)\n",
        "vit_media=df['Vitorias'].mean().round(1)\n",
        "emp_media=df['Empates'].mean().round(1)\n",
        "der_media=df['Derrotas'].mean().round(1)\n",
        "sg_media=df['Saldo'].mean().round(1)\n",
        "ap_media=df['Aproveitamento'].mean().round(1)"
      ],
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        "id": "otWiMuCjE5gj"
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      "source": [
        "media_pos=df.groupby('Time')['Posicao'].mean().round(1)\n",
        "df.sort_values(by=\"Posicao\")\n",
        "media_pos=pd.DataFrame(media_pos)\n",
        "media_pos=media_pos.sort_values(by=\"Posicao\")\n",
        "media_pos"
      ],
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            "text/plain": [
              "      Posicao\n",
              "Time         \n",
              "GRE       5.6\n",
              "FLA       6.2\n",
              "SAO       6.2\n",
              "INT       6.8\n",
              "PAL       6.8\n",
              "CAM       6.9\n",
              "COR       7.3\n",
              "CRU       7.6\n",
              "SAN       8.1\n",
              "FLU       9.1\n",
              "CAP       9.5\n",
              "BOT      10.2\n",
              "VAS      11.9"
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              "      <th>GRE</th>\n",
              "      <td>5.6</td>\n",
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              "      <td>6.2</td>\n",
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              "      <th>SAO</th>\n",
              "      <td>6.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>INT</th>\n",
              "      <td>6.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAL</th>\n",
              "      <td>6.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CAM</th>\n",
              "      <td>6.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COR</th>\n",
              "      <td>7.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CRU</th>\n",
              "      <td>7.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SAN</th>\n",
              "      <td>8.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FLU</th>\n",
              "      <td>9.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CAP</th>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BOT</th>\n",
              "      <td>10.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VAS</th>\n",
              "      <td>11.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        document.querySelector('#df-9a77d59e-5eb8-40a6-a6b3-472c5eea67ef button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
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              "\n",
              "  <div id=\"id_ed3b2ead-6356-46b3-8d9b-a070b39c5984\">\n",
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              "        height: 32px;\n",
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              "\n",
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              "\n",
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              "\n",
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              "        google.colab.notebook.generateWithVariable('media_pos');\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "media_pos",
              "summary": "{\n  \"name\": \"media_pos\",\n  \"rows\": 13,\n  \"fields\": [\n    {\n      \"column\": \"Time\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"BOT\",\n          \"FLU\",\n          \"GRE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Posicao\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8314559151753473,\n        \"min\": 5.6,\n        \"max\": 11.9,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          7.6,\n          5.6,\n          10.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "media_pt=df.groupby('Time')['Pontos'].mean().round(1)\n",
        "df.sort_values(by=\"Pontos\")\n",
        "media_pt=pd.DataFrame(media_pt)\n",
        "media_pt=media_pt.sort_values(by=\"Pontos\")\n",
        "media_pt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "her3DVz5Hr6v",
        "outputId": "43db0508-832b-4360-b3cb-5a56dc2b1000"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Pontos\n",
              "Time        \n",
              "VAS     49.8\n",
              "BOT     51.3\n",
              "CAP     53.4\n",
              "FLU     55.2\n",
              "SAN     55.7\n",
              "CRU     57.4\n",
              "COR     58.5\n",
              "INT     59.1\n",
              "CAM     59.3\n",
              "PAL     60.5\n",
              "GRE     61.4\n",
              "SAO     61.4\n",
              "FLA     61.6"
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pontos</th>\n",
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              "      <th>Time</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>VAS</th>\n",
              "      <td>49.8</td>\n",
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              "      <td>51.3</td>\n",
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              "      <td>53.4</td>\n",
              "    </tr>\n",
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              "      <th>FLU</th>\n",
              "      <td>55.2</td>\n",
              "    </tr>\n",
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              "      <td>55.7</td>\n",
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              "      <td>58.5</td>\n",
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              "      <th>INT</th>\n",
              "      <td>59.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CAM</th>\n",
              "      <td>59.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>60.5</td>\n",
              "    </tr>\n",
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              "      <td>61.4</td>\n",
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              "      <td>61.4</td>\n",
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              "    <tr>\n",
              "      <th>FLA</th>\n",
              "      <td>61.6</td>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-47e4123b-1dbf-47b7-a162-862c5daa42dd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-47e4123b-1dbf-47b7-a162-862c5daa42dd')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-47e4123b-1dbf-47b7-a162-862c5daa42dd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_68af0580-0042-43c8-b3d9-bdf8d99355c9\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('media_pt')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_68af0580-0042-43c8-b3d9-bdf8d99355c9 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('media_pt');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "media_pt",
              "summary": "{\n  \"name\": \"media_pt\",\n  \"rows\": 13,\n  \"fields\": [\n    {\n      \"column\": \"Time\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"SAO\",\n          \"PAL\",\n          \"VAS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pontos\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.9313173886442208,\n        \"min\": 49.8,\n        \"max\": 61.6,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          61.4,\n          60.5,\n          49.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "media_vit=df.groupby('Time')['Vitorias'].mean().round(1)\n",
        "df.sort_values(by=\"Vitorias\")\n",
        "media_vit=pd.DataFrame(media_vit)\n",
        "media_vit=media_vit.sort_values(by=\"Vitorias\")\n",
        "media_vit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "3lz6hmYiHvbN",
        "outputId": "8a87d8d6-2367-4dc5-e3f8-3579ab6c54fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Vitorias\n",
              "Time          \n",
              "VAS       12.8\n",
              "BOT       13.7\n",
              "CAP       14.6\n",
              "FLU       15.1\n",
              "SAN       15.1\n",
              "COR       15.7\n",
              "CRU       16.0\n",
              "INT       16.2\n",
              "CAM       16.6\n",
              "SAO       16.8\n",
              "PAL       16.9\n",
              "FLA       17.2\n",
              "GRE       17.3"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-44417093-7205-4c16-b710-d2363a123829\" 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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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "    .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>Vitorias</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>VAS</th>\n",
              "      <td>12.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BOT</th>\n",
              "      <td>13.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CAP</th>\n",
              "      <td>14.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FLU</th>\n",
              "      <td>15.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SAN</th>\n",
              "      <td>15.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COR</th>\n",
              "      <td>15.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CRU</th>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>INT</th>\n",
              "      <td>16.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CAM</th>\n",
              "      <td>16.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SAO</th>\n",
              "      <td>16.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAL</th>\n",
              "      <td>16.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FLA</th>\n",
              "      <td>17.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GRE</th>\n",
              "      <td>17.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44417093-7205-4c16-b710-d2363a123829')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-44417093-7205-4c16-b710-d2363a123829 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-44417093-7205-4c16-b710-d2363a123829');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-962a8d6d-a5f5-4402-8c17-354e23914109\">\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-962a8d6d-a5f5-4402-8c17-354e23914109 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_38ab572d-a86d-44ec-bffc-48f342990290\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('media_vit')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
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              "      (() => {\n",
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              "        document.querySelector('#id_38ab572d-a86d-44ec-bffc-48f342990290 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('media_vit');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "media_vit",
              "summary": "{\n  \"name\": \"media_vit\",\n  \"rows\": 13,\n  \"fields\": [\n    {\n      \"column\": \"Time\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"FLA\",\n          \"SAO\",\n          \"VAS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Vitorias\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3829084920687622,\n        \"min\": 12.8,\n        \"max\": 17.3,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          17.2,\n          16.9,\n          12.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "media_emp=df.groupby('Time')['Empates'].mean().round(1)\n",
        "df.sort_values(by=\"Empates\")\n",
        "media_emp=pd.DataFrame(media_emp)\n",
        "media_emp=media_emp.sort_values(by=\"Empates\")\n",
        "media_emp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "Eg93jzGZI2Nn",
        "outputId": "b91f581f-65b7-465d-e2ac-83f3a1613f4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Empates\n",
              "Time         \n",
              "CAM       9.4\n",
              "CRU       9.4\n",
              "CAP       9.5\n",
              "GRE       9.5\n",
              "PAL       9.8\n",
              "FLU       9.9\n",
              "FLA      10.1\n",
              "BOT      10.2\n",
              "SAN      10.3\n",
              "INT      10.4\n",
              "SAO      10.9\n",
              "VAS      11.3\n",
              "COR      11.4"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Empates</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>CAM</th>\n",
              "      <td>9.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CRU</th>\n",
              "      <td>9.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CAP</th>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GRE</th>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAL</th>\n",
              "      <td>9.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FLU</th>\n",
              "      <td>9.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FLA</th>\n",
              "      <td>10.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BOT</th>\n",
              "      <td>10.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SAN</th>\n",
              "      <td>10.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>INT</th>\n",
              "      <td>10.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SAO</th>\n",
              "      <td>10.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VAS</th>\n",
              "      <td>11.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COR</th>\n",
              "      <td>11.4</td>\n",
              "    </tr>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "\n",
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              "\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    }\n",
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              "\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "\n",
              "  <div id=\"id_e2382f65-99af-4ca4-bc88-6efa08fc06f6\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      }\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('media_emp')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "        document.querySelector('#id_e2382f65-99af-4ca4-bc88-6efa08fc06f6 button.colab-df-generate');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('media_emp');\n",
              "      }\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "media_emp",
              "summary": "{\n  \"name\": \"media_emp\",\n  \"rows\": 13,\n  \"fields\": [\n    {\n      \"column\": \"Time\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"VAS\",\n          \"INT\",\n          \"CAM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Empates\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6886441528327742,\n        \"min\": 9.4,\n        \"max\": 11.4,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          10.2,\n          9.4,\n          11.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "media_der=df.groupby('Time')['Derrotas'].mean().round(1)\n",
        "df.sort_values(by=\"Derrotas\")\n",
        "media_der=pd.DataFrame(media_der)\n",
        "media_der=media_der.sort_values(by=\"Derrotas\")\n",
        "media_der"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "IDaCObQ6I2LO",
        "outputId": "9749acaf-2911-4be5-daef-99d427975fd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Derrotas\n",
              "Time          \n",
              "SAO       10.2\n",
              "FLA       10.8\n",
              "COR       10.9\n",
              "GRE       11.2\n",
              "PAL       11.3\n",
              "INT       11.4\n",
              "CAM       12.0\n",
              "CRU       12.6\n",
              "SAN       12.6\n",
              "FLU       13.0\n",
              "VAS       13.8\n",
              "CAP       13.9\n",
              "BOT       14.1"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Derrotas</th>\n",
              "    </tr>\n",
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              "      <th>Time</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>SAO</th>\n",
              "      <td>10.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FLA</th>\n",
              "      <td>10.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COR</th>\n",
              "      <td>10.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GRE</th>\n",
              "      <td>11.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAL</th>\n",
              "      <td>11.3</td>\n",
              "    </tr>\n",
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              "      <th>INT</th>\n",
              "      <td>11.4</td>\n",
              "    </tr>\n",
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              "      <th>CAM</th>\n",
              "      <td>12.0</td>\n",
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              "      <th>CRU</th>\n",
              "      <td>12.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SAN</th>\n",
              "      <td>12.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FLU</th>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VAS</th>\n",
              "      <td>13.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CAP</th>\n",
              "      <td>13.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BOT</th>\n",
              "      <td>14.1</td>\n",
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              "  @keyframes spin {\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "\n",
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              "\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('media_der')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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              "        google.colab.notebook.generateWithVariable('media_der');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "media_der",
              "summary": "{\n  \"name\": \"media_der\",\n  \"rows\": 13,\n  \"fields\": [\n    {\n      \"column\": \"Time\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"CAP\",\n          \"FLU\",\n          \"SAO\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Derrotas\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2945645738615883,\n        \"min\": 10.2,\n        \"max\": 14.1,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          13.9,\n          13.8,\n          10.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "media_sg=df.groupby('Time')['Saldo'].mean().round(1)\n",
        "df.sort_values(by=\"Saldo\")\n",
        "media_sg=pd.DataFrame(media_sg)\n",
        "media_sg=media_sg.sort_values(by=\"Saldo\")\n",
        "media_sg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "3APVGX1SI2Iw",
        "outputId": "7c8b4499-e6b7-4e4f-f9e9-64ac609e2e5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Saldo\n",
              "Time       \n",
              "VAS    -6.0\n",
              "BOT     0.2\n",
              "CAP     1.6\n",
              "FLU     4.7\n",
              "SAN     6.7\n",
              "CRU     7.9\n",
              "COR     8.3\n",
              "CAM     8.6\n",
              "INT     9.2\n",
              "PAL    11.5\n",
              "FLA    12.2\n",
              "SAO    12.6\n",
              "GRE    12.8"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c4c73462-2109-492b-a761-b0ddfe770eb3\" 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>Saldo</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>VAS</th>\n",
              "      <td>-6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BOT</th>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CAP</th>\n",
              "      <td>1.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FLU</th>\n",
              "      <td>4.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SAN</th>\n",
              "      <td>6.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CRU</th>\n",
              "      <td>7.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COR</th>\n",
              "      <td>8.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CAM</th>\n",
              "      <td>8.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>INT</th>\n",
              "      <td>9.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAL</th>\n",
              "      <td>11.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FLA</th>\n",
              "      <td>12.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SAO</th>\n",
              "      <td>12.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GRE</th>\n",
              "      <td>12.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c4c73462-2109-492b-a761-b0ddfe770eb3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c4c73462-2109-492b-a761-b0ddfe770eb3 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-c4c73462-2109-492b-a761-b0ddfe770eb3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
              "<div id=\"df-ef0902f8-4e53-48ec-81d8-2050463e230d\">\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ef0902f8-4e53-48ec-81d8-2050463e230d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_fc751d95-57a4-4a4f-8191-949244c6ff2e\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('media_sg')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "      (() => {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('media_sg');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "media_sg",
              "summary": "{\n  \"name\": \"media_sg\",\n  \"rows\": 13,\n  \"fields\": [\n    {\n      \"column\": \"Time\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"SAO\",\n          \"PAL\",\n          \"VAS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Saldo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.56725776072556,\n        \"min\": -6.0,\n        \"max\": 12.8,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          12.6,\n          11.5,\n          -6.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "media_ap=df.groupby('Time')['Aproveitamento'].mean().round(1)\n",
        "df.sort_values(by=\"Aproveitamento\")\n",
        "media_ap=pd.DataFrame(media_ap)\n",
        "media_ap=media_ap.sort_values(by=\"Aproveitamento\")\n",
        "media_ap"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "aRDEbgP9I2Be",
        "outputId": "2a764390-57ae-4f69-9baa-e9fb8f13ea61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Aproveitamento\n",
              "Time                \n",
              "VAS             43.7\n",
              "BOT             45.0\n",
              "CAP             46.9\n",
              "FLU             48.4\n",
              "SAN             48.8\n",
              "CRU             50.4\n",
              "COR             51.3\n",
              "INT             51.8\n",
              "CAM             52.0\n",
              "PAL             53.0\n",
              "GRE             53.9\n",
              "SAO             53.9\n",
              "FLA             54.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-289e183c-89bc-4573-905b-57ce758f75c6\" 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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              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Aproveitamento</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>VAS</th>\n",
              "      <td>43.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BOT</th>\n",
              "      <td>45.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CAP</th>\n",
              "      <td>46.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FLU</th>\n",
              "      <td>48.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SAN</th>\n",
              "      <td>48.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CRU</th>\n",
              "      <td>50.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COR</th>\n",
              "      <td>51.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>INT</th>\n",
              "      <td>51.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CAM</th>\n",
              "      <td>52.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAL</th>\n",
              "      <td>53.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GRE</th>\n",
              "      <td>53.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SAO</th>\n",
              "      <td>53.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FLA</th>\n",
              "      <td>54.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
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              "      width: 32px;\n",
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              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-289e183c-89bc-4573-905b-57ce758f75c6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-641da59a-ec0b-437c-898b-10efc366761c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_4bd6ddcb-d8b8-402e-8cf1-bf5d17f59504\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('media_ap')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_4bd6ddcb-d8b8-402e-8cf1-bf5d17f59504 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('media_ap');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "media_ap",
              "summary": "{\n  \"name\": \"media_ap\",\n  \"rows\": 13,\n  \"fields\": [\n    {\n      \"column\": \"Time\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"SAO\",\n          \"PAL\",\n          \"VAS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Aproveitamento\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.440818328425778,\n        \"min\": 43.7,\n        \"max\": 54.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          53.9,\n          53.0,\n          43.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(5,4))\n",
        "eixo = fig.add_axes([0,0,1,1])\n",
        "mu, mediana, moda = df['Posicao'].mean(), df['Posicao'].median(), df['Posicao'].mode().iloc[0]\n",
        "\n",
        "N, bins, patches = eixo.hist(df['Posicao'], bins=20, edgecolor='black')\n",
        "eixo.set_title('Frequência da posição', fontsize=15, pad=10)\n",
        "eixo.set_xlabel('Posição', fontsize=15)\n",
        "\n",
        "fracs = N / N.max()\n",
        "\n",
        "norm = colors.Normalize(fracs.min(), fracs.max())\n",
        "\n",
        "for thisfrac, thispatch in zip(fracs, patches):\n",
        "    color = plt.cm.Purples(norm(thisfrac))\n",
        "    thispatch.set_facecolor(color)\n",
        "\n",
        "eixo.annotate('$média = {0:.1f}$'.format(mu), xy=(14.5, 15.5), fontsize=15, color='black')\n",
        "eixo.annotate('$mediana = {0:.0f}$'.format(mediana), xy=(14.5, 14), fontsize=15, color='goldenrod')\n",
        "eixo.annotate('$moda={0:.0f}$'.format(moda), xy=(14.5, 12.5), fontsize=15, color='yellowgreen')\n",
        "\n",
        "eixo.axvline(mu, color = 'k', linestyle='--')\n",
        "eixo.axvline(mediana, color='goldenrod', linestyle='--')\n",
        "eixo.axvline(moda, color='yellowgreen', linestyle='--')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "JRseD8isI8PM",
        "outputId": "c3284701-7434-4cfc-af27-efe61364937e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(5,4))\n",
        "eixo = fig.add_axes([0,0,1,1])\n",
        "mu, mediana, moda = df['Pontos'].mean(), df['Pontos'].median(), df['Pontos'].mode().iloc[0]\n",
        "\n",
        "N, bins, patches = eixo.hist(df['Pontos'], bins=20, edgecolor='black')\n",
        "eixo.set_title('Frequência da pontuação', fontsize=15, pad=10)\n",
        "eixo.set_xlabel('Pontuação', fontsize=15)\n",
        "\n",
        "fracs = N / N.max()\n",
        "\n",
        "norm = colors.Normalize(fracs.min(), fracs.max())\n",
        "\n",
        "for thisfrac, thispatch in zip(fracs, patches):\n",
        "    color = plt.cm.Oranges(norm(thisfrac))\n",
        "    thispatch.set_facecolor(color)\n",
        "\n",
        "eixo.annotate('$média = {0:.1f}$'.format(mu), xy=(65, 29), fontsize=15, color='black')\n",
        "eixo.annotate('$mediana = {0:.0f}$'.format(mediana), xy=(65, 26.5), fontsize=15, color='teal')\n",
        "eixo.annotate('$moda={0:.0f}$'.format(moda), xy=(65, 24), fontsize=15, color='lawngreen')\n",
        "\n",
        "eixo.axvline(mu, color = 'k', linestyle='--')\n",
        "eixo.axvline(mediana, color='teal', linestyle='--')\n",
        "eixo.axvline(moda, color='lawngreen', linestyle='--')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "CkUXbyCYI16D",
        "outputId": "fce170c3-1e5e-46c7-d409-7c5007bd7c91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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P4tlnn61wn8wDVF955RVcuXLFsv748eN4++237fbhiy++sFq/dOlSfPPNNxXug9lnn32GvXv3Wp7n5ORg/PjxEBE8+eSTlktRnp6eeOqpp2AymTBu3Dir/ZCcnIwZM2YAQKmfd0WYzxocPnzY5vZu3boBAD766CPk5ORY1m/cuBH/+te/SpQPCAiAj48P9u/fj61bt1rWG41GvPHGGzaDmuDgYDzxxBPIy8vDqFGjSiQIvHjxInbu3Gl5XpnPZ/z48QCACRMm4NKlS5b1qampeO211wDA5pk4qoHUm8hDNRkqmGfE3jTUyZMnWxKCtWvXToYOHSoPP/ywtG3bVrRarfj4+FiVv3z5siXHQVBQkAwbNkz69OkjLi4uMm7cOJtTe0VEnnjiCQEgPj4+cv/990uvXr3E09NThg4dWmrSs+XLl1uSdjVq1Ejuv/9+GT58uHTp0sWSLGrv3r2W8uapvTdPBTUrbcrnokWLxNXVVQBIdHS0DB8+XPr371/upGcGg0GaN29uyZcxZMgQGTBggHh4eEjHjh2lY8eOdqei3sxgMEinTp0sSc+GDBkiAwcOFL1eLwMHDrSZ9CwuLk60Wq0AkJiYGBkxYoQlv8irr75a4emsNyc969u3rzzyyCMSGBhoyX1hTpxnVjzpWUBAgAwdOlTuu+8+cXd3LzPpmS2///67zYRgP/74oyUHysMPPyxjxoyRMWPGWLbn5ORYvqNhYWEyZMgQ6dChg2g0mlL3xcyZMwWAaLVaS9KziIgI0ev1lu/1zd+rzMxMy2fr6ekp/fv3l2HDhsk999wjbm5uVnlYKvP5FBYWSv/+/S3/bx566CEZNGiQJenZoEGDmPSMRIR5RkgljgxGRIr+UA4dOlSCg4PF1dVV/P39pVWrVvLCCy/YTJSVlpYmzz33nAQGBopOp5NmzZrJhx9+KCaTqdRgxGAwyMSJEyU0NFTc3NykYcOGMmPGDCksLLR7QDp69Kg8//zz0rhxY3F3dxdvb29p2rSpDB8+XH744QebSc8qGoyIiCQmJspjjz0mISEh4urqKgEBAdKpUyf56KOPpKCgoMx9euXKFXnuueckIiJCdDqdNGjQQN544w3Jzs4uMy+GLdnZ2TJp0iQJCwsTNzc3iYiIkMmTJ4vBYCh1f23fvl169uwpfn5+4u3tLR07dpQff/yxUrk1zG0YjUb58MMPpVmzZqLT6SQoKEjGjRsnV65csfm6rKwsmT59ukRHR4tOpxNvb2/p3LmzLFy40G47tpQWjIgUJbEzt2Hr/8OZM2dkxIgR4ufnJ3q9Xtq3by9Lliyxuy/+97//Sdu2bcXDw0P8/f3lwQcflMTERLvfK4PBILNnz7YkLgOKsrg++eSTEh8fb1W2Mp9PQUGBzJ4929IvDw8Pad++vcyZM0cKCwtt7jeqeXijPKKbmKcc2rovCN05avrN+CqrR48e6NOnDyZNmqR2V6gG4ZgRIiKyeOCBB/DZZ5+p3Q2qYTi1l4iohktMTMQrr7yCgIAArF27tsSMHKLqxjMjREQ1nIeHB06cOIGlS5fCx8cHn3/+udpdohqGY0aIiIhIVTwzQkRERKpiMEJERESqYjBCREREqmIwQkRERKpiMEJERESqYjBCREREqmIwQkRERKq67TKwmkwmnDt3Dt7e3lAURe3uEBERUSWICK5du4bg4GBoNPbPfdx2wci5c+cQGhqqdjeIiIjIAU6fPo369evbLXPbBSPe3t4Aijpfq1YtlXtDdGfKl2x8YAoGALyuOQc3xbPKdWbn5yP4o48AAOdeeQWebm6ll83ORnBwUfvnzp2Dp2fV2yeiO0tmZiZCQ0Mtx3V7brtgxHxpplatWgxGiCqpUNzRT6YCAHwVf7gopQcO5eVuNGJq374AAH8/P7jZuZmau7s7pk4tat/f3x9udgIXInJu5RlycdvdmyYzMxM+Pj7IyMhgMEJERHSHqsjxnLNpiIiISFW33WUaIqo6k5hwCUkAgLqIgkap+u8OkwiSLl0CAETVrQuNnVOvJpMJSUlF7UdFRZU5kp6IajYGI0ROqBC5+LepBQBgiiYLbqj6ANLcggK0mDsXAJA1aZLdAay5ublo0aKo/aysLA5gJSK7+HOFiIiIVMVghIiIiFTFYISIiIhUxWCEiIiIVMVghIiIiFTFYISIiIhUxam9RE5IA1d0Ul61LDuCq1aLV2NjLct2y7q64tVXX7UsExHZw3TwRER3iNOnT+Prr79GaGgoxowZo3Z3iOyqtnTwc+fORatWrSw3sYuNjcWqVass2/Py8jBu3Dj4+/vDy8sLQ4YMwYULFyr3LoiIyOLq1avo168f5s2bhx49eqjdHSKHqlAwUr9+fbz33nuIj4/H7t270bNnTzz44IM4cOAAAODvf/87Vq5ciSVLliAuLg7nzp3D4MGDq6XjRFQ6k5hwVU7iqpyESUwOqlNwMj0dJ9PTYSrjhKrJZMLJkydx8uRJmEyOab8my83NxcCBA5GTk4O4uDg0aNCgwnWMHDkSiqIgLi7Osm716tVQFAXPPvusI7tbLbp37w5FUUp9rF692lJ206ZNdsuaH2+//Xa1tE8VV6ExI/fff7/V85kzZ2Lu3LnYsWMH6tevj6+//hoLFy5Ez549AQDz5s1DVFQUduzYgXvuucdxvSYiuwqRi49NkQAcmw4+cvZsAOVLBx8ZWdQ+08FXjdFoxPDhw5Gamoq4uDiEhYVVqp74+HgoioK2bdta1u3YsQMA7qi/z0OGDIGXl1eJ9SEhIZblwMBAjBo1yubrjUYjFixYAADo0qVLtbRPFVfpAaxGoxFLlixBdnY2YmNjER8fj4KCAvTu3dtSplmzZggLC8P27dtL/bIbDAYYDAbL88zMzMp2iajGSklJQVpamuV5oSYXaFm0nJCYABeTvtJ116lTp9IHQKq6ffv2oV27dpgzZw7q169fqTquXbuG5ORkNGjQwOra/TPPPIPhw4ffUZ/vhx9+iIiICLtlmjVrhvnz59vctmrVKixYsAChoaHo3r17tbRPFVfhYOSvv/5CbGws8vLy4OXlhWXLliE6OhoJCQlwc3ODr6+vVfl69eohNTW11PpmzZqF6dOnV7jjRFQkJSUFzZo2RW5enmWdqwfwxvW4vnOnzijIqXz9end3HDp8GP6BgVXsKVVG27Ztrc5mVMbevXshImjXrp3V+qCgIAQFBVWp7juN+ayI+bIV3R4qnGekadOmSEhIwM6dO/Hcc89h1KhROHjwYKU7MGnSJGRkZFgep0+frnRdRDVRWloacvPy8FCQBmPDtRgbrsWTYTf+az8ZdmN9RR8PBWmQm5dnddaFrJ08eRKKoqBbt25IT0/Ha6+9hsjISOj1erRu3Rq//vqrpezixYvRuXNneHt7IzQ0FBMnTkRBQYHNeg8ePIixY8ciMjISOp0OdevWxciRI3Hq1KlS+7J27Vr06dMHtWrVQu3atfH4448jLS0N8fHxAGAV1Jj7bevsQFxcHF5++WXExMQgICAAHh4eaNasGV577TVcuXKlkntKfdnZ2VixYgUA4PHHH1e5N1Rchc+MuLm5oVGjRgCAmJgY/Pnnn5g9ezaGDRuG/Px8pKenW50duXDhAgLt/KLS6XTQ6XQV7zkRWanrpiDIveiXnrgD5mGjge4KFOEvwOqSkJAAAPD19UXbtm2hKAo6dOiAw4cPIyEhAYMHD0ZiYiLee+89LF26FD169ECPHj2wevVqvP/++/Dz88Mbb7xhVed//vMfjB8/HkajER06dED79u2RmJiIhQsXYsOGDYiPjy8xRmHatGmYPn06dDodevToATc3N6xcuRL79u1Ds2bNAMDqzIi5323atCnxnh577DFkZGSgefPm6N69O/Ly8rB161Z8+OGHWLNmDeLj41XLH/P111/j8uXL0Gg0aNKkCQYNGlTuy0w//fQTsrOz0bZtW0RHR9/y9ql0Vc7AajKZYDAYEBMTA1dXV2zYsMGy7fDhw0hJSUHs9URJRETOJjExEQDw888/Y9SoUThy5AgWL16MPXv24P7770d+fj4GDhyIpKQkHDlyBCtXrsTPP/+MJUuWACg6QBa3YsUKPPfcc4iMjMTevXuxbds2LFmyBElJSXj++edx4cIFzJw50+o1CxYswPTp0xETE4MjR45g1apVWLFiBfbt24fz589b2ip+ZsTc75uDkcLCQsyePRsXL17E9u3b8cMPP+Dnn3/G6dOn0blzZ/z1119Yv3693X1S1swTW4/SxnjcbMaMGZg7dy7mzJmDl156CY0aNcI777xTrteaL9FU5axIVdqn0lXozMikSZPQv39/hIWF4dq1a1i4cCE2bdqENWvWwMfHB2PGjMGECRNQu3Zt1KpVC+PHj0dsbOwdNVKbiKgizGcYhg4dimnTplnWK4qC/v37Y+XKlbhy5Qo2bdpkNT6jf//+AIDz589b1mVlZWHs2LHw8vLC6tWrLTOSAECr1eKDDz7AF198gd9//92yPiMjA+PHj0etWrWwcuVKqzbCwsIwduxYzJw5EyEhIQgICCjR79atW1u9HxcXF5spGTw8PDB06FBs2bKlzMvp/fr1q/AgT/MZ99J07doVTz/9NDp27IigoCCcPn0aS5cuxYwZM/DWW2+hVq1aeOmll0p9/fnz57FhwwZotVqMGDGiQn1zRPtkX4WCkYsXL+KJJ57A+fPn4ePjg1atWmHNmjW49957AQCffPIJNBoNhgwZAoPBgL59++Lzzz+vlo4TkR2FgLJIsSw7gotGg+fbt7cs2y3r4oLnn3/esuzMzAf1qVOnlthmnh04evToEjNhMjIyABTNVjL78ssvcfHiRbz++utWgYiZp6cn/P39kZ6ebln3n//8B+np6Zg8ebLNwajmg/zNg2ATEhLg6uqK5s2b2+z3b7/9hsTERFy+fBn5+flW77V27dolXlPcxIkT7W6vjJtzgjRp0gSTJ09G+/bt0bdvX0ybNg1jx46FXm975tiiRYtgNBrRr18/u0MHqqt9sq9CfyW+/vpru9vd3d0xZ84czJkzp0qdIqKqUQoUKDPs3z+monQuLpgzYED5yup0NeLvQEZGBk6ePImGDRvaPKibL4U88MADJbb99ddfAGA1dmH58uUAgA8++AAffPBBqe22aNHCsvzLL78AQKm/9rOysgBYjxcx97tVq1ZwuylfzKeffoo333zT8jpbmjRpUuq2W61Pnz5o3749du/ejZ07d5Y6XdcRl2iq0j7Z59w/WYiIqpE52Ljrrrtsbk9ISICiKIiJibG5DSg5jsPT09Nyk8HSNG7c2KoenU5nMxgCgN27d9tsByh5iWb27Nl4+eWXLb/6u3TpguDgYLi7u8NoNCIwMBDXrl0rc/Dne++9h0OHDtktc7Onn34anTt3rtBrzBo3bozdu3dbXfIqLikpCXv37oWXlxcGDRpUqTaq0j6VjcEIkRMSCOB3/clVQEHVZ9OICNJyihKW1PHwsJujQUQs04Hr1KnjtPkc7M1Iyc3NRXJyMho2bAhvb+8S280BgTlIyM/PR2ZmJiIiIqzGnthTUFCAa9euwcfHx+Y+zs3NtUxlLWsmjdFoxMyZM6HX67F582bUq1fPqq6NGzciLS0NMTExZV56W716tVXa+fLo3r17pYORq1evAkCpmX6//fZbAMDgwYPh4eFRqTaq0j6VjcEIkTPSA6YtRgCApr0WyK16lTkFBQj48EMAZaeDz8nJsQyWdOZ08DcHFMXt27cPRqOx1IRlNwcEbm5u0Ov1SElJQUZGBnx8fMps39XVFX5+frh69SouXrxoNUAVAN5//32kp6fD39/favqprZk0qampuHTpEqKjo0sEIgUFBZgyZUqp7/VmmzZtKrOMo1y6dAl//PEHAJRI6gYUBcYLFy4EUD25Rcpqn8qnylN7iYhqKluXWsqzLT8/H0lJSQgLC7MaDHrvvffCZDJh7NixyM7OtnqNiGDbtm3YvHmz1XrzJaApU6ZAit3AcP78+ZYpp7YGrwLWl2nq1KkDFxcXHD16FMnJyZb1165dw6hRo7Bz585S309127ZtG5YvXw6j0Wi1/uTJk3jooYeQnZ2NBx54wGa6/D/++AOnTp1CSEiI5b5ppXniiSfQrFkzLFu2zGHtU/nwzAgRUSUUFhbiwIEDCAkJQd26dUts37t3LwDbB+8DBw6goKCgxLb3338fW7ZswQ8//ICNGzfirrvuQu3atZGWloaEhARcuHABv/32m9Vrpk2bhg0bNuDLL7/E1q1b0bx5cxw8eBD79+9Hhw4dsHPnTqt2zP2uX78+/P39Let1Oh0ef/xxzJs3DzExMZb7jMXFxaFx48bo3r07Nm3apEowkpycjCeffBKBgYFo164dfH19cerUKcTHxyMvLw/NmzfHV199ZfO15oGrjz76KDRlzAJLSUnB4cOHLTOdHNE+lQ+DESKiSjh06BAMBoPN8SKA/fEkpSUca9asGfbu3Yt3330Xa9euxYYNG6DT6RAYGIjOnTtjwIABJWZrdOrUCT/++COmTZuGpKQknDt3Dm3btsWyZcuwc+dO7Ny50+rygb1+z5kzB/7+/vj++++xZs0aNGrUCJMnT8aLL76I5s2bQ6PRoFWrVuXdRQ7ToUMHPPfcc9i5cyf+/PNPXL16FZ6enmjTpg2GDh2K5557zuaUWoPBgKVLlwIoyip7q9un8lOk+Hm920BmZiZ8fHyQkZFhdXdJIrJtz549iImJwdhw7Y108HqBafeNMSNKbuUGkJ7PE3x5yoj4+Hg0bdECXrNmASh7zEh2drblNuvOPGaEiEpXkeM5x4wQERGRqhiMEBERkao4ZoTIGRUCynLHp4MfdX32RXnSwY8aNcqyTERkD/9KEDkhpUCB8qbj08HPL2f2Sp1OV+67sBIR8TINERERqYpnRoickEAA80zDXMelg88pKAAAeLi6lpkOPud66niPMlLHExHxzAiRM9IDpt3Goum9Dkp/kFNQAK9Zs+A1a5YlKCm1bE4OvLy84OXlZQlKiIhKw2CEiIiIVMVghIiIiFTFYISIiIhUxWCEiIiIVMVghIiIiFTFYISIiIhUxTwjRM7ICGCNcmPZAbQaDR6OjrYs2y2r1eLhhx+2LBMR2cNghMgJKfkKtBMcGwS4u7hgydCh5Svr7o4lS5Y4tH0icl4MRoiIaoCRP/2EhX/9hU2jRqFbRAQAYPXRo+j/3Xd4JiYGXwwcqG4HbxPd589H3KlTpW5fNXIk+jVqZHPbpexsvL91K1YmJyMlIwN6FxdE+PqiV2Qk/tmnT3V12SkwGCEiqgHiz52DAqBtUJBl3Y4zZwAA99Svr1Kvbl9DoqLg5eZWYn2It7fN8vHnzqHvggW4nJuL5nXr4sGmTZFpMODgpUv4ZMcOBiNlYDBC5IREL0Wp4AFo2muh5Fb93jDZ+fnwmjULAJA1aRI8bfyhtpTNzoaXl1dR2awseHp6Vrl9qrxrBgOSL19GAz8/1NLpLOufiYnB8BYtEObjo2Lvbk8f9umDCF/fcpW9lJ2Nft99h9zCQqwYPhwPNG1qtX3X2bPV0EPnwmCEiMjJ7U1NhQBoV+ysCAAEeXsjqJRf+lR+UzdtQlpODubcd1+JQAQA7g4JUaFXdxYGI0REVXAyPR2Rs2eja3g4VgwfjpmbN2NpUhJSs7LQxN8f7/bsiQFNmgAAFu/fj3/v2oXECxfg6+6OkS1b4p0ePeBqY8bRwUuX8K8dO7Du+HGcu3YNPjod+jVqhHd79UL9WrVs9mXtsWP4cNs27DhzBi4aDQY0aYJP+vZF/LlzAIC2gYEl+t0tPBybRo+2qifu5EksO3QIf6Sk4HRGBrLy8xHm44P7mzTBpC5dUFtvfffFo1euoPFnn6F3gwZYPmwYPty2DYsPHMCp9HQEeXvjmZgYvNaxY4m7N1e0ndtRbkEBFuzbB09XVzzZpo3a3bljMRghIqqChNRUAICvuzva/uc/UAB0CAnB4cuXkZCaisE//IDEZ5/Fe1u2YOnBg+gRGYkeERFYffQo3t+6FX7u7nijc2erOv+zezfGr1oFowg6hISgfXAwElNT8e2+fdh44gTix45FveuXwcymbdqE6XFx0Gm16BEZCTetFisPH8a+CxfQrE4dANZnRsz9blMsQDF7bNkyZOTloXlAALpHRCCvsBBbT5/Gh9u3Y82xY4gfO9YqgEq8XldtvR6tv/gCBqMRsfXrI7RWLWw4cQJvrF+POh4eeKpt2yq1cyt9vWcPLufmQqMoaOLvj0HNmtm8nLX73Dlcy89H57Aw6F1dserIEaw7fhx5hYVo4u+PR5o3RzDPPpWJwQgRURWYD8Q/Hz6Mqd26YUrXrtBqNBARPLh4MVYmJ2PgwoXw9/DAkfHjLZdFVhw6hEHff4+fDh2yCkZWHDqE5379FY39/bFk6FC0qlcPAGA0mfDiqlX4fPduzNqyBf/q18/ymgX79mF6XBxigoKwbNgwhF4/aKZkZKD9l1/irwsXAFgPXk0sJRgpNJkwu18/3Ne4MdxdbhwicgoK0HfBAmxJScH648fRv3HjG3Vdr/+HAwcwuXNnTO/RAy7Xc9HMT0jAkytW4KekJKtgpDLt3KysmS+2zHvwQYwuxxmMGX/8YfX81bVrMaVrV0zp1s1q/cFLlwAAAZ6eGLR4MVYcPmy1ffKGDfj6gQcwomXLCvWzpmEwQkRUBQnXD8RDo6MxrXt3y3pFUdC/USOsTE7GldxcbBo92mp8hvkge/7aNcu6rPx8jP3lF3i5uWH1yJGI9POzbNNqNPjg3nvxRXw81h8/blmfkZeH8atWoZZOh5UjRli1Eebjg7ExMZj5xx8I8fZGQLGBxOZ+t74e7Ji5aDQYHBVV4n16uLpiaHQ0tqSk4HRmpvU+uB7YDImKwsxevay2mafBXszOrnI7N+vXqFG5B5maNapd2+72ruHheLpdO3QMDUWQlxdOZ2Zi6cGDmLF5M97atAm1dDq8dM89lvJX8/IAFAWjWkXBnPvuw9DoaOQUFODfu3bhw+3bMWr5ckTVrWvzLBQVYTBCRFQF5gPx1Jt+MQNApsEAABjdpk2JcR4Z1w9idTw8LOu+jI/HxexsvN6xo1UgYubp5gZ/vR6Xc3Mt6/4TH4/0vDxM7tzZ5mBU88G37U2DVxNSU+Gq0aB5QIDNfv925AgSU1NxOTcX+Uaj1Xu9eSyH+czIWzb2QVpODgDYvFRR0XZuNvGmy1uO8HaPHlbPm/j7Y3KXLmgfHIy+CxZgWlwcxsbEQO/qCgAwiQAoOtMzs3dvPH/XXZbX/rNPH5zKyMCSgwfxz23b8N3gwQ7vr7NgMELkjIwA4hyfDv6+67/my5MO/r777rMsO6uMvDycTE9HQz8/mwd180Ha1gyLvy5eBABE161rWbf80CEAwAfbtuGDbdtKbTfq+hgQAPglORkASr0MkJWfDwBoV+xXubnfrerVg9tNn8+nO3fizY0bLa+zpYm/v2U5PS8PKRkZiPT1tVxSKm7f9X3Q4qb9U9F21NanYUO0Dw7G7nPnsPPsWXS/njiueC4SWwNYn2zTBksOHkTcyZO3pqN3KAYjRE5IyVegfd7x6eB/ffTR8pV1d8evv/7q0PZvR+Zg465Spm4mpKZCARBz01kJ8zbAeoZL4oUL0Gm1GN6ihd12WxY7sCekpkKn1aJ5saCmuN3mmTTFx4uUcolm9o4deHnNmqKzAZ07o0t4OIK9veHu4gKjyYTAjz7CNYPBKoAyv4/Spq/uPX8egPXYlMq0Y8t7W7bgUFqa3TI3e7pdO3QOC6vQa8wa166N3efOWV1aC78+PsfD1RV1beTTMV9GuvkyFVljMEJEVEmWGSk2zgjkFhQg+fJlNKxdG97FEo2ZJd40qDTfaESmwYBIX1/MHzSoXO0XGI24lp8PH52uxLRZcx/MAyrLmkljNJkw848/oHdxwebRo0vM1tl44gTScnIQExRkGZwK3BgI27aU8RB7b2qrsu3Ysvro0QoPYO0eEVHpYMQ8PqR4wj/z55dbUABDYSF0LtaH1SvXL6nZyuZKNzAYISKqJMuB2MaZj30XLsAoUupB+uaAwE2rhd7FBaczM5GVn1+ug5erVgs/d3dczcvDxexsqwGqAPD+1q1Iz8uDv15vNS3V1kya1KwsXMrJQXTduiUChAKjEVN+/73ovd70fm4Oqm62NzUV3m5uaHh9DExl27Hl5vwo1elSdjb+uB74FA/swnx80LpePSReuIC4U6fQp2FDq9eZg6XS9g8VsR92EtEdSfQC45+FMP5ZCNGLQ+rMzs+H57vvwvPdd5Ft5zo/UJQO3tPTE56ensh24tPT5hkptg6cti7DmOUbjUi6dAlhPj5WgzTvbdgQhSYTnvnlF+QUFFi9RkSw8cQJywHRLCY4GAAwZeNGiNz4rOcnJOCdzZuL+nDz4FUbl2nqeHjARaPB0StXkHz5smX9NYMBo5Yvx87rKc1tDYQt7X2mZGTgSm4uWtWrZzlzU9l2boVtp09j+aFDMJpMVutPpqfjoe+/R3ZBAR5o2rTEYOTXO3UCUDT9t/glnITUVHy0fTsA4NmYmGru/Z2NZ0aInJVH2UUq6uYDpN2y12dROKtCkwkHLl5EiLe3zbECe+2cNTlw8SIKTKYSB/D3e/fGlpQULPzrL6w7dgztg4NR+/rsmcTUVJzPysIvI0ZYvWZat27YcPw4vtyzB1tPn0bzgAAcvHQJ+y9eRIeQEOw8e9aqHXO/69eqBf9iM3l0Li54vFUrzEtIQMyXX6J3gwYAirKkNvb3R/eICGw6ebJEXQcvXSp9H9gYL1KZdm6V5MuX8eSKFQj08kK7oCD4urvjVHo64s+fR15hIZrXrYuv7r+/xOsebdkSa48dw/8SExH9+efoGBqK3IICbDt9GgajEX9r1w5Dmze/5e/nTsJghIioEg6lpcFgNJaaO8JehlPzpY2btzWrUwd7n3kG7/7xB9YeO4YNJ07ATatFgKcn7g4JwYDGjdEjMtLqNZ3CwvDjI49gWlwcki5dwrlr19D2evKznWfOYOfZs1aXFez1e85998Ffr8f3Bw5gzdGjaFS7NiZ36YIXO3RA888/h0ZRrGbMlLUPbh4vUtl2bpUOISF4rn177Dx7Fn+ePYureXnwdHVFm8BADI2OxnPt21um9N5s3oMPolNoKP4TH49NJ09CQdHlnGdiYjCKaeLLpEjx83q3gczMTPj4+CAjIwO1Srn/AhHdsGfPHsTExGBsuBZB7kWnwh11197zeYIvTxkRHx+Ppi1a8K69RFRuFTmec8wIERERqYrBCBEREamKwQgRERGpigNYiZyRCcCuYssOoFEUdAsPtyzbLavRoNv1+5RoykhcRUTEYITICSkGBdonHfvfW+/qWu4kU3q9Hps2bXJo+0TkvPiThYiIiFTFYISIiIhUxWCEyAmJXmD8oxDGPxybDr7uP/+Juv/8Z7nSwdetWxd169Z16nTwROQYHDNC5KxqO77KtAqkeE+r4K3diajm4pkRIiIiUhWDESIiIlIVgxEiIiJSFYMRIiIiUlWFgpFZs2bhrrvugre3NwICAjBo0CAcPnzYqkz37t2hKIrV49lnn3Vop4mIiMh5VGg2TVxcHMaNG4e77roLhYWFmDx5Mvr06YODBw9a3SL8b3/7G95++23Lcw8PD8f1mIjKZgKwv9iyA2gUBe2Dgy3LdstqNGjfvr1lmYjIngoFI6tXr7Z6Pn/+fAQEBCA+Ph5du3a1rPfw8EBgYKBjekhEFaYYFGiHOT4d/J9/+1v5yur1+PPPPx3aPlXNEtNI7JOFeEqzCZFKN7W7U6qvjd1xEnGlbn9CswqNlX4l1m81fYxTsgUX8BeycRGFyIMXAhGhdENn5TUEKi2rs9tURVX6a5WRkQEAqF3bOqHBd999hwULFiAwMBD3338/pkyZUurZEYPBAIPBYHmemZlZlS4REZEN5yQeChQEoa3aXSmXaAyBTvEqsd4bITbLb5Z3kY9sBKIV6qEo8LiIA0iUb7FfFmOE5ic0VQZWa5+p8iodjJhMJrz88svo1KkTWrRoYVn/6KOPIjw8HMHBwdi3bx/eeOMNHD58GD/99JPNembNmoXp06dXthtERFQGg1zDZSTDDw3grtRSuzvl0k/zIfyUiHKXf1SzAsGIgavibrV+p+lz/CLjsNz0NF7VnIFWYa7P21GlP5Vx48Zh//792LJli9X6sWPHWpZbtmyJoKAg9OrVC8eOHUPDhg1L1DNp0iRMmDDB8jwzMxOhoaGV7RYRARB3gelnIwBA84AWSp79MR7lkVNQgOg5cwAAB8eNg4era+llc3IQHR1dVPbgQY4bU9l57IVAEKS0U7sr1SZc6WRzfQfN89hm/BhXcAyXcBCBaHWLe0blUalg5IUXXsAvv/yCzZs3o379+nbLdujQAQBw9OhRm8GITqeDTqerTDeIqDQKYDmbXfU4BAAgIjh1/dKsiP373YgITp06Va6yd7qrchIfmyIRga54VLMCcTITB2QpspAKfzTBvZp30VQZAADYZ1qMnfJvpCIR7vBFa2UkeinvQKuUDOxSZDt2yKc4JX8gG5fgjSA0Vx5GD2WazcsXAHBU1mKL6UOcwQ5o4IImygD0Vz7BOYkHgBKXaE5IHJJkGU7JH8jAaeQjCz4IQ1PlfnRVJsFDqYZ7CqhAg6L9q4Wbyj2h0lQoGBERjB8/HsuWLcOmTZsQGRlZ5msSEhIAAEFBQZXqIBHR7ew8EgAA7vDF56a2UKCgvtIBaXIYqUjAItNgjNMk4g95DwdkKSLRA5HogaNYjT/kfbjDD12VN6zqjDO9iw0yBRpoEYFuqI97cAY7sVU+wknZjDGazSUuR2w0TcPvMh0u0CESPaCFGw7LSqTKPtRVmgEAgm86M7LU9BgMyEAAmiNC6Y5CyUMKtmKrfIijsgbPaeJtBkq3Qrx8jVy5DAUa+KMJopRB8FXCKlxPgulbXMZh+KMx/NG4GnpKjlChYGTcuHFYuHAhVqxYAW9vb6SmpgIAfHx8oNfrcezYMSxcuBD33Xcf/P39sW/fPvz9739H165d0aoVT40RkfNJlUQAwCH8jB7KVHRXpkCjaCEi+M70IA5jJRaYBsID/nhZcwTeStEPsyRZgYWmQUiSn9AVN4KRXaa5WC9vIght8ahmGXyVcABAvuTgW1N/nMRm7JYvEau8aHlNgmkBfpfpCEYMHtUsg49SdKk7XVLwhak9LspfAKzPjBilEPdpZqMJ7rMKbPIlB9+Y+uIUtuAY1qMJ+pf63sua+WLLQ8o8tNOMLrNcnMywer5GXkU3ZQp6aKbYfd0W0z9xEQeQL9m4hCRcxAF4IxhDNYugUbQV6ivdOhUKRubOnQugKLFZcfPmzcPo0aPh5uaG9evX41//+heys7MRGhqKIUOG4B//+IfDOkxEdDtJlQQAQHNlKHpqplnWK4qCJkp/HJaVyMUVPKXZZAlEAKDx9YP8NZy3rMuQM1gtr8AD/nhc8xu8lRspEtwUD3TRTMRJ02YckVWIRVEwkicZ+FXGQ4daeEyz0qoNXyUM7ZWxiJOZqIUQeCkBlm1axQXNMbjE+3FTPNBcGYpTsgUZctruZb7GSj/4IaI8u8nCX2lkd3uE0hUxeBphSkd4IwgZOI0DshRxMgMb5S24m2ohVvNSqa8/ImtwHBssz30RjiGabxCixFSon3RrVfgyjT2hoaGIi6tYlExEdCczX6bpoUwtsS0PRakK2iqj4aNYj68zoGj8jQfqWNbtlq9QgFx0VF6xCkTM/NAAAJCDy5Z1f8p/kId0dFUmWwUiZrVRdPC3NaU3TzKRLL8hFYnIxWUYkV/0nq4HWPoyxox01Uy0u70yemnetnpeB03QTZmMEGmP/5n6YqNMQ3sZC1dFb/P1T2rXAwByJR0X8Bc2md7G16Zu6KXMQHfNmw7vLzkG5zgREVVSnmQgHSdRGw1RT2leYnsqii7hNFMeKLHtAooundRVoi3rkuVXAECM8pTN9gqRBwDQw8+y7rD8AgBopYyw+Zp8ZAFAiZk0202fYr28adluSx00KXXbrdZI6YMQtMdZ7MYZ7EQkutstr1d8EYEueFzzG740xWKjTEEj6YP6yl23psNUIQxGiJyRADhabNkBFEVBdN26luUyy16f2ltW2TuZOdgIKeUAlyoJUKAgGCUvEZjPPgQXO2NxAX/BFR7wU2xPDrgoBwAAgUrrYn1IgAt0CEDJYAgAzmE3ACBIudHOdtNs/CYvw//6WYdwpQu8EQxXxR0mMeJ9UyDycQ11EW2zTrPNpveQhkN2y9wsRnka4UrnCr3GrLbSGGdlN67J+XLPEtMqrmipDMM5icdhWclg5DbFYITICSl5CrQPOva/t4erKw48/3z5ynp44MCBAw5t/3ZkDigC0abEtgLJvZ5orCF0ineJ7eZAxhwkFIoBRuTDFaXnZDkoRckjzenQjVIAA67BHT42g74CyUWSrAAABKPozIhJjIiTmXCFHk9rNsNLqWf1muPYiBykIRgxZSYIOyKrKzyANQLdKx2M5MlVAICr4llGSWvmS2HZuFSpdqn6MRghIqokc0ARrJQcj5GKfTDBaHVGoribAxkXRQd3+CAP6ciRKyVyfKTIdiRhGeoiCpHoAaDoV78efsjFVWTJRasBqgDwh7yPPKTDA/6WabFZSEU2LqEuoksEIkYpwAZT0WyV0vpd3BjtpjLLOEq2XMIp/AHgRmBVXuaAqTZK5rqi2wNvp0lEVEnmmTS2Bofa21Yo+UhDEnwQZhV0NFaKZtjcPK31tOzAQtMgaOGKBzVfWZ0FMV8C2iBTrCYZ7DHNxyZ5p0QfPFAHGrjgCo4iTZIt6w1yDT/KKJzBzlL7Xd1SZBsOynKYxGi1/qqcxELTQ8hHNprhgRKDgU/JVhyR1TCJ9S2qjVKAHabPkCDfwhV6tFSGVft7oMrhmREiJyTuAtP319PBD6t6OvikpCTkFhbiiT+Kfpl+06UL9C6l//nIzc3FE088UVT2m2+g15ec+VCnTh2EhVU8idXtwiiFuIgDqIUQeCp1S2w/j70AbJ81uYgDMKKgxAG/tzIDR2UttsknOGpchwClOTLkFM5gJ1zhgaGaRSXSnvfQTMNx0wbsli+RIlsRoDTHRTmIi9iP+uiAM9hpdZbDRdGhjfI49sg8zDXFoCF6Ayg6e+CPxohEd5zApnKdGXG0NEnGMnkSXghEMNrBXfFFupzCOcSjEHkIQHM8qPmqxOsuyxEskyfhgToIRgw8FH/kSBou4C9cw3m4wB0PKfMt+Vfo9sNghMgZKQAaFVuupKxCgQbAY489Bri6Am8WTY3s3LkzUFBQrjo6d7Y9PsDD3R1Jhw/fsQFJGg6hEAab40UA++NJzInSghTrbbWVhnhGsxPr5U0ck/W4IkfgjWDcpTyLTsorqK2UvMwQrnTCcM2P2GiahjQk4ZqcQxDaopdmGc7ITpyRnQi66bLGQGUO9PDHfvkeR7EGtdEIXZXJuEd5EZ+ZmkOBRpV7uNRXOuBuPIczshNn8Sdy5Src4IkgtEFzZSjuVp6zOaU3UumGrpiMkxKHC9iHHEmDFm7wRQSaKw/jHuXFMvObkLoUuc1uHJGZmQkfHx9kZGSgVq074+6SRGras2cPYmJiMDZciyD3oshD9ALT7utnRtproeRWLiL5K8OEn1JNeCtKQVAtVzzXfzIAYO6qd6Ezlh6MGIyC54pODGBuW0CntW7/ZDbwdpIgPj4e7do5783biGqyihzPeWaEiMoU4QmEed0IKBp7KdCbSg9wco2AeU5xY28Feu3NZW+r30BEpDIOYCUiIiJVMRghIiIiVTEYISIiIlVxzAiRMxIAZ4stO4ACQWBeumXZflkg0P3GMhGRPQxGiJyQkqdA28ex/73dTYX4ceen5SurVfBjLMMQIiofXqYhIiIiVTEYISIiIlUxGCFyQqITGL8vhPH7QojOMYNGDBoXjGk7BmPajoFBY/8SkMEoGLPbhDG7TTAYmVOEiOzjmBEiZ6QB0KLYsgOYoOBQrRDLsv2ywKFrN5aJiOzhmREiIiJSFYMRIiIiUhWDESIiIlIVgxEiIiJSFYMRIiIiUhVn0xA5qyuOr9I3P7v8ZV0d3z4ROScGI0ROSMlVoO3i2P/eelMBft3+UfnKahX82pnp4ImofHiZhoiIiFTFYISIiIhUxWCEyAmJTmCcVwjjPMemg3+h9RN4ofUT5UoH/8JeE17Yy3TwRFQ2jhkhckYaAHcXW3YAExTs9Y2wLNsvC+xNv7FMRGQPz4wQERGRqnhmhKiYlJQUpKWlVUvdBoMBOp3O4fUmJSU5vE5SR3V9/+rUqYOwsDCH10vkKAxGiK5LSUlBVNOmyMnLq5b6NQpg4vAJKkV1fv883N2RdPgwAxK6bTEYIbouLS0NOXl5+KiNgobejs2REXfBhE+SUa11052tur5/x64JXknIQ1paGoMRum0xGCG6SUNvBS18HBswHMtSAEi11k3OoTq+I/x+0O2OwQiRs8pxfJXuxvzyl+XweCIqJwYjRE5IyVWgvcvx6eA3bHmvfGW1CjZ0Yzp4Iiof/nYhIiIiVTEYISIiIlUxGCFyQuImMH5uhPFzI8TNQengFS1ebTECr7YYAYOitV/WKHg10YRXE5kOnojKxjEjRM5IC6Cb3Fh2AJOiwXb/xpZliLH0sgC2X7mxTERkD8+MEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqji1l8gJKbkKtM0dnw5+a9zb5SurVbC1B9PBE1H58MwIERERqYrBCBEREamKl2mInJC4CUzvFeU+1UzUQMmv+iUTg6LFO1EPAQCmJC2Dzk4GVoNR8E5SUQbYKVEKdFpesiGi0vHMCJEz0gLoK0UPB6aD/71uNH6vG12UDt5eWQC/Xyp6MB08EZWFwQgRERGpisEIERERqapCwcisWbNw1113wdvbGwEBARg0aBAOHz5sVSYvLw/jxo2Dv78/vLy8MGTIEFy4cMGhnSYiIiLnUaFgJC4uDuPGjcOOHTuwbt06FBQUoE+fPsjOzraU+fvf/46VK1diyZIliIuLw7lz5zB48GCHd5yIiIicQ4Vm06xevdrq+fz58xEQEID4+Hh07doVGRkZ+Prrr7Fw4UL07NkTADBv3jxERUVhx44duOeeexzXcyIiInIKVZram5GRAQCoXbs2ACA+Ph4FBQXo3bu3pUyzZs0QFhaG7du32wxGDAYDDAaD5XlmZmZVukREhJSUFKSlpVVL3XXq1EFYWFi11E1UU1U6GDGZTHj55ZfRqVMntGjRAgCQmpoKNzc3+Pr6WpWtV68eUlNTbdYza9YsTJ8+vbLdICJbcgFNe61l2RHcTQVY/8csy7LdshpgfVfFsnwrpaSkIKpZU+Tk5lVL/R56dyQdOsyAhMiBKh2MjBs3Dvv378eWLVuq1IFJkyZhwoQJlueZmZkIDQ2tUp1ENZ0CxWFByI06i+5PU66yigK9g/KbVFRaWhpycvMwp7M7mvg4NhJKzjBh3JY8pKWlMRghcqBKBSMvvPACfvnlF2zevBn169e3rA8MDER+fj7S09Otzo5cuHABgYGBNuvS6XTQ6XSV6QYRUama+GjQyl+liIiIKqRCPxtEBC+88AKWLVuGjRs3IjIy0mp7TEwMXF1dsWHDBsu6w4cPIyUlBbGxsY7pMRGVSVwFpplGmGYaIa7ikDrzFS1mNH0AM5o+gHzF/kE+3ySYkWTCjCQT8k2OaZ+InFeFzoyMGzcOCxcuxIoVK+Dt7W0ZB+Lj4wO9Xg8fHx+MGTMGEyZMQO3atVGrVi2MHz8esbGxnElDdCu5ADKoKAhQZgAo39UVu4yKBqsC2wAAXjmyCrBzbxqjAKuuDxN7pUnV2yYi51ahYGTu3LkAgO7du1utnzdvHkaPHg0A+OSTT6DRaDBkyBAYDAb07dsXn3/+uUM6S0RERM6nQsGISNmnW93d3TFnzhzMmTOn0p0iIiKimoP3piEiIiJVMRghIiIiVTEYISIiIlUxGCEiIiJVVeneNERqqY57jyQlJTm0PlXlAprOjk8H/8u2Dy3LdstqgF86qZMOnojuPAxG6I5T3fcecQYKFOCqo+sE/ApyyldWUeDn5tj2ich5MRihO4753iP/6eGJpn6OS/e9LiUfM3czwCEiutUYjNAdq6mfFq3rOO4rnHy19IyidxpxFcgbJgCA8r4GSoFS5TrzFS0+a9gHADD+2Fq42cnAmm8SfHa0KC/R+EYK3DRVb5+InBev5hI5IxdARghkhDjsJ4dR0eCnkLvwU8hdMCr2/3QYBfjpbNHDyFvTEFEZGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqmGeEyBnlAZp7tZZlR9CZCrB0x2zLst2yGmDpPYplmYjIHgYjRE5IEQU459g6NQCCDBnlK6soCNI7tn0icl78zUJERESq4pkRIickrgJ58Xo6+E8dkw6+QNHgP5E9AQDPnNgIVzGVXtYk+M/xotSrzzRQ4Mp08ERkB8+MEDkjF0CeEshTjksHX6hosSi0IxaFdkShYv8GhYUCLDpd9ChkOngiKgODESIiIlIVgxEiIiJSFYMRIiIiUhWDESIiIlIVgxEiIiJSFYMRIiIiUhXzjBA5ozxA84Dj08F/++dcy7Ldshrg27uZDp6IyofBCJETUkQBjjm2Tg2ABjmXyldWUdDA07HtE5Hz4m8WIiIiUhXPjBA5IXEVyN+up4P/ynHp4L8J6wIAeCLljzLTwX9zqij16hPhTAdPRPYxGCFyRi6AjCsKBpR5AOwP8SiXQkWL/4voBgB49PQ2u8FIoQD/d7Jo+dEwwLXqzRORE+NlGiIiIlIVgxEiIiJSFYMRIiIiUhWDESIiIlIVgxEiIiJSFYMRIiIiUhWn9hI5IwOgGaa1LDuCm6kQ/93zX8uy3bIa4L8ximWZiMgeBiNETkgxKcB+x9aphSDq2rnylVUURNVybPtE5Lz4m4WIiIhUxTMjRE5IXAXy2PUMrAsUh6WD/yGkAwDgkbM7y0wH/8OZouVH6oPp4InILgYjRM7IBZBXr9+bZrHWYengP294LwBg8LndZaaD//xYUTA0OERhOngisouXaYiIiEhVPDNCRKpJSkq6I+okourFYISIbrnLBoFGAR577DG1u0JEtwEGI0R0y2UVAiYB5j8cjqi67g6te1VyBqZtSHVonURUvRiMEJFqouq6o22wh0PrPHQpz6H1EVH14wBWIiIiUhXPjBA5IwOgGa2xLDuCm6kQnyX8z7Jst6wG+KwN08ETUfkwGCFyQopJAf50bKIxLQTtMk6Vr6yioJ2fQ5snIifG3yxERESkKp4ZIXJC4iKQodfTwS9RoBRW/SxJoaLBiqB2AIAHz++Bi70MrCbBiuv31HswGHBhOngisqPCZ0Y2b96M+++/H8HBwVAUBcuXL7faPnr0aCiKYvXo16+fo/pLROXhCsg/TJB/mOCoXOwFihYfN74PHze+DwWK1n5ZAT4+Ivj4iKBAHNM+ETmvCgcj2dnZaN26NebMmVNqmX79+uH8+fOWx6JFi6rUSSIiInJeFb5M079/f/Tv399uGZ1Oh8DAwEp3ioiIiGqOahkzsmnTJgQEBMDPzw89e/bEjBkz4O/vb7OswWCAwXBj7mFmZmZ1dImIyGF4Tx0ix3J4MNKvXz8MHjwYkZGROHbsGCZPnoz+/ftj+/bt0GpLXmeeNWsWpk+f7uhuEBE53MVcE++pQ1QNHB6MDB8+3LLcsmVLtGrVCg0bNsSmTZvQq1evEuUnTZqECRMmWJ5nZmYiNDTU0d0iIqqyjPyie+p882hjRAU4No39qkNX8Nbq0w6tk+hOUe1Texs0aIA6derg6NGjNoMRnU4HnU5X3d0gInKYqAAPtKvv5dA6D13McWh9RHeSag9Gzpw5g8uXLyMoKKi6myIis3xA85zGsuwIrqZC/POvRZZlu2UV4J8tFcsyEZE9FQ5GsrKycPToUcvzEydOICEhAbVr10bt2rUxffp0DBkyBIGBgTh27Bhef/11NGrUCH379nVox4modIpRATY7NgpwgaDjlSPlK6tR0LGOQ5snIidW4WBk9+7d6NGjh+W5ebzHqFGjMHfuXOzbtw//+9//kJ6ejuDgYPTp0wfvvPMOL8UQERGRTRUORrp37w6R0lMqrlmzpkodIqKqExeBDLyeDv4Xx6WDXxPQEgDQ9+JfZaaDX3OhaLlvPaaDJyL7eG8aImfkCsjMomBBWaMF7A/xKJcCRYt3mz0IAOh56aDdYKRAgHcPFQVDPQMU/qEhIrt4114iIiJSFYMRIiIiUhWDESIiIlIVgxEiIiJSFYMRIiIiUhWDESIiIlIVZ9wROaN8QPN3x6eDf+fAEsuy3bIK8E5zpoMnovJhMELkhBSjAqx1fDr4nmlJ5SurUdAzwKHNE5ET42UaIiIiUhXPjBA5IdEK0Ov6bRs2KEVnSqqoEAo212kGAOiadgguKP22EIUmwea0ouWudZgOnojsYzBC5IzcANMnRenaNe21QG7VqyzQuGBK86EAgPV/zIKLqaD0sgJMOVAUrKzvynTwRGQfL9MQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqOOOOyBkVAMqbGsuyI7iKEZMPrbAs2y2rAJObMR08EZUPgxEiJ6QUKlCWOzgdvJgw4EJi+cpqFAwIcmjzROTEeJmGiIiIVMUzI0ROSLQCdLqern2r49LB76rdCABw95WjZaaD33WlaPnu2kwHT0T2MRghckZugGmu49PBv9ZyBIDypYN/7S+mgyei8uFlGiIiIlIVgxEiIiJSFYMRIiIiUhWDESIiIlIVgxEiIiJSFYMRIiIiUhVn3BE5owJAmeH4dPATjvxmWbZbVgEmNGY6eCIqHwYjRE5IKVSgLHJ8Ovgh53aXr6xGwZD6Dm2eiJwYL9MQERGRqnhmhMgJiUaAmOvp2uMVKKaqnyUxQkGiTxgAoHVGCrR20sEbRZCYXrTc2hfQKrxWQ0SlYzBC5Ix0gGm+Y9PB52tcML7NKABF6eD1dtLB55uA8Qk30sHrtVVvn4icFy/TEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqji1l8gZFQLKhxrLsiO4iBHPH1tnWbZbVgGeb6hYlomI7GEwQuSElAIFyjzHRgGuYsLIM9vLV1ajYGSYQ5snIifGyzRERESkKp4ZIXJCohEg+vqTg3BYOvhk7yAAQJNr58tMB598rWi5iTfTwRORfQxGiJyRDjB9XzSuw5Hp4J9u9zSA8qWDfzqe6eCJqHx4mYaIiIhUxWCEiIiIVMVghIiIiFTFYISIiIhUxWCEiIiIVMVghIiIiFTFqb1EzqgQUOYolmVHcBEjnjoZZ1m2W1YBnoq4sUxEZA+DESInpBQoUD53bHIPVzFhzKm48pXVKBgTySiEiMqnwpdpNm/ejPvvvx/BwcFQFAXLly+32i4ieOuttxAUFAS9Xo/evXvjyJEjjuovEREROZkKByPZ2dlo3bo15syZY3P7Bx98gE8//RRffPEFdu7cCU9PT/Tt2xd5eXlV7iwRlY8oAml4/aGUnra9IkwAjnvUxXGPujCVVVYEx7OLHiZxTPtE5LwqfJmmf//+6N+/v81tIoJ//etf+Mc//oEHH3wQAPDNN9+gXr16WL58OYYPH1613hJR+bgDpp8dmw7eoHHF43c9B6DsdPAGE/D4LqaDJ6LycehsmhMnTiA1NRW9e/e2rPPx8UGHDh2wfbvtW48bDAZkZmZaPYiIiKjmcGgwkpqaCgCoV6+e1fp69epZtt1s1qxZ8PHxsTxCQ0Md2SUiIiK6zameZ2TSpEnIyMiwPE6fPq12l4iIiOgWcmgwEhgYCAC4cOGC1foLFy5Ytt1Mp9OhVq1aVg8iIiKqORwajERGRiIwMBAbNmywrMvMzMTOnTsRGxvryKaIiIjISVR4Nk1WVhaOHj1qeX7ixAkkJCSgdu3aCAsLw8svv4wZM2agcePGiIyMxJQpUxAcHIxBgwY5st9ERETkJCocjOzevRs9evSwPJ8wYQIAYNSoUZg/fz5ef/11ZGdnY+zYsUhPT0fnzp2xevVquLu7O67XRGRfIaD8n+PTwY84vc2ybLesAowIvbFMRGRPhYOR7t27Q+wkMVIUBW+//TbefvvtKnWMiCpPKVCgfOT4dPAvHF9fvrIaBS80YhRCROWj+mwaIiIiqtl4ozwiJySKAEHXn5wHFKn6WQoTgAs6HwBAPUOG3V8yJhFcuH4HiHrugEbhWRIiKh2DESJn5A6Y1jk+HfzD97wEoHzp4B/ewXTwRFQ+vExDREREqmIwQkRERKpiMEJERESqYjBCREREqmIwQkRERKpiMEJERESq4tReImdUCCiLHJsOXismDD77p2XZblkFGBxyY5mIyB4GI0ROSClQoMxwbHIPNzHilaOryldWo+CVJoxCiKh8eJmGiIiIVMUzI0ROSCCA3/UnVwEFVT9LIQDSXT0AAL4FOXZrFBGkX0/Q6utadANNIqLSMBghckZ6wLTFseng8zSuGNjxVQBlp4PPMwEDtzIdPBGVDy/TEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqji1l8gZFQLKcseng++fmmBZtltWAfoH3lgmIrKHwQiRE1IKFChvOj4d/D8O/1y+shoF/4hiFEJE5cPLNERERKQqnhkhckICAfTXn+Q6Lh18nsYVAOBuKigzHXze9Ss57hqmgyci+3hmhMgZ6QHTbiNMu403gpIqytO4oneXSejdZZIlKCm1rAnovVnQe/ONoISIqDQMRoiIiEhVDEaIiIhIVQxGiIiISFUMRoiIiEhVDEaIiIhIVZzaS0RUAyQlJVVLvXXq1EFYWFi11E01B4MRImdkBLBGubHsABoxocelg5Zlu2UB9Kh7Y5nUcylPoNEoeOyxx6qlfg8PPZKSDjEgoSphMELkhJR8BdoJjk0HrxMjZhxcWr6yWgUzWjDR2e0gsxAwmQTfzHoBUQ1CHFp30vGzeGLSv5GWlsZghKqEwQgRUQ0Q1SAE7aIbqN0NIpt4BpWIiIhUxWCEyAmJXmA8UAjjgUKIXhxSZ67GFZ26vYVO3d5Cbhnp4HONgk6/m9DpdxNyjY5pn4icF4MRIiIiUhWDESIiIlIVgxEiIiJSFYMRIiIiUhWDESIiIlIVgxEiIiJSFZOeETkjI4A4x6eDj718xLJstyyA2No3lomI7GEwQuSElHwF2ucdnw7+w/2LyldWq+DD1kwHT0Tlwx8tREREpCoGI0RERKQqBiNETkj0AuOfhTD+6dh08L06T0SvzhPLlQ6+V5wJveKYDp6IysYxI0TOysPxVeZp3cpf1v4YVyIiC54ZISIiIlUxGCEiIiJVMRghIiIiVTEYISIiIlU5PBiZNm0aFEWxejRr1szRzRAREZGTqJbZNM2bN8f69etvNOLCSTtEt5QJwK5iyw6ggaBt+knLsv2yQFvfG8tERPZUS5Tg4uKCwMDA6qiaiMpBMSjQPunY/946UyH+nfhN+cpqFfy7LdPBE1H5VEswcuTIEQQHB8Pd3R2xsbGYNWsWwsLCbJY1GAwwGAyW55mZmdXRJVJBSkoK0tLSHF5vUlKSw+skIiL1ODwY6dChA+bPn4+mTZvi/PnzmD59Orp06YL9+/fD29u7RPlZs2Zh+vTpju4GqSwlJQVRUVHIyclRuytERHSbc3gw0r9/f8tyq1at0KFDB4SHh+OHH37AmDFjSpSfNGkSJkyYYHmemZmJ0NBQR3eLbrG0tDTk5OTg20/eRFSjcIfWver3HZjy8f85tE5nI3qBaa0RAKDpo4WSW/VLJrkaVzzc4UUAwNKdn0JvKii9rFHw8PaicSVLYxXotbxkQ0Slq/aRpb6+vmjSpAmOHj1qc7tOp4NOp6vubpBKohqFo12LJg6t89DRUw6tz2nVdnyV6W6e5S9beqxCRGSl2ge6Z2Vl4dixYwgKCqrupoiIiOgO5PBg5NVXX0VcXBxOnjyJbdu24aGHHoJWq8WIESMc3RQRERE5AYdfpjlz5gxGjBiBy5cvo27duujcuTN27NiBunXrOropIiIicgIOD0YWL17s6CqJiIjIiTE5IhEREamKedqJnJEJwP5iyw6ggaBZ5lnLsv2yQDPvG8tERPYwGCFyQopBgXaY49PBf7336/KV1Sr4uj1zixBR+fBHCxEREamKwQgRERGpipdpiJyQuAtMP19PB/+AFkpe1S+Z5GlcMPKu5wEA3/35OdxNhaWXNQpG7ioaV/Ld3QrcmQ7eqVXXzSsNBkO1ZOiuU6dOqTdvJXUwGCFyRgqAkGLLDiBQkOrua1m2XxZIzbuxTM4pNS0dGo0Gjz32WLXUr9VoYDQ5aAR2MR4eHkhKSmJAchthMEJERJWSnpkNk8mEbz97G1GNIx1a96qNWzHlgy8cXnfSkRN4fPxbSEtLYzByG2EwQkREVRLVOBLtWjZzaJ2Hjpystrrp9sMBrERERKQqBiNERESkKgYjREREpCqOGSFyRgLgaLFlB1AgiMi+aFm2XxaI8LixTERkD4MRIiek5CnQPujY/97upkJ8t/uL8pXVKviuA8MQIiofXqYhIiIiVTEYISIiIlUxGCFyQuIuMK4ohHFFIcTdMYNG8jQuGNn+WYxs/yzyNPYvAeUZBSN3mjBypwl5RuZgJSL7OGaEyBkpABoVW3YAgYKTngGWZftlgZM5N5aJiOxhMHKHSElJQVpamsPr5Q2jiIhIbQxG7gApKSmIiopCTk6Ow+vmDaOIiEhtDEbuAGlpacjJycG3c95DVJMGDqs3Kfk4Hh83kTeMIiIiVTEYuYNENWmAdq2i1e4GERGRQ3E2DREREamKZ0aInJEAOFts2QEUCALz0i3L9ssCge43lomI7GEwQuSElDwF2j6OTwf/485Py1dWq+DHWIYhRFQ+vExDREREqmIwQkRERKpiMELkhEQnMH5fCOP3hRCdYwaNGDQuGNN2DMa0HQNDGengDUbBmN0mjNltgoHp4ImoDBwzQuSMNABaFFt2ABMUHKoVYlm2XxY4dO3GMhGRPTwzQkRERKrimREiIqpxkpKSqqVe3u+rchiMEBFRjZF6MQ0ajQaPPfZYtdTP+31VDoMRIiKqMdIzsmAymfDtFx8hqklDh9adlHwMjz/7Cu/3VQkMRoiIqMaJatIQ7Vq3KLsg3RIMRoic1RXHV+mbn13+sq6Ob5+InBODESInpOQq0HZx7H9vvakAv27/qHxltQp+7cx08ERUPpzaS0RERKpiMEJERESqYjBC5IREJzDOK4RxnmPTwb/Q+gm80PqJcqWDf2GvCS/sZTp4Iiobx4wQOSMNgLuLLTuACQr2+kZYlu2XBfam31gmIrKHZ0aIiIhIVQxGiIiISFUMRoiIiEhVNW7MSEpKCtLS0qqlbt4giYiIqusmfAaDATqdrlrqVvv4VaOCkZSUFERFRSEnJ6da6ucNkoiIaq7UC5eq9SZ8Wo0GRlP1DAlX+/hVo4KRtLQ05OTkYMFXnyKqSWOH1p2UfASP/e1F3iCJbh/VEHO7G/PLX5YXgamGSc/IhMlkqpZjzG/rNmLKjH867fGrRgUjZlFNGqNdm5Zqd4Oo2ii5CrR3OT4d/IYt75WvrFbBhm5MB081U3UcY5KSj1Zb3bcD/nYhIiIiVTEYISIiIlUxGCFyQuImMH5uhPFzI8TNQengFS1ebTECr7YYAYOitV/WKHg10YRXE5kOnojKViPHjBA5PS2AbnJj2QFMigbb/RtbliHG0ssC2H7lxjIRkT08M0JERESqYjBCREREqqq2YGTOnDmIiIiAu7s7OnTogF27dlVXU0RERHQHq5Zg5Pvvv8eECRMwdepU7NmzB61bt0bfvn1x8eLF6miOiIiI7mDVEox8/PHH+Nvf/oYnn3wS0dHR+OKLL+Dh4YH/+7//q47miIiI6A7m8Nk0+fn5iI+Px6RJkyzrNBoNevfuje3bt5cobzAYYDAYLM8zMjIAAJmZmY7uGrKysgAA8Yn7kJWd7dC6Dx89VlR3fLylHYfVffgwAGBP4kFkZTsux3fy0ZMAqrnP+5ORlZPr0LqTjp4CACRcKkR2geOmjSanFwIADqSbkFPo2OyhxzKl2us+l2dCvul63SZB3ev/hS5lm4C8yrV5Kb9oLszhTEG6mIC8PADAvnQTdHbukWEoNtFmX7pAp7X+nE5e/++351wOsvJLn5VTGUmXir5viZeNDv1+AEByRlFf95y9hiyDg/t9sej/tqO/I+bvx56DJ5CVk+ewegEg6cTZorr3JSEr29H/z09US92WehMPOPTvKQAkHbl+HKiGY0xS8pFqq9t8/MrKynLosddcl0g5/h+Kg509e1YAyLZt26zWv/baa3L33XeXKD916lQBwAcffPDBBx98OOHj9OnTZcYOqucZmTRpEiZMmGB5bjKZcOXKFfj7+0NR7ox7W2RmZiI0NBSnT59GrVq11O7ObYf7xz7uH/u4f+zj/rGP+6ds1bWPRATXrl1DcHBwmWUdHozUqVMHWq0WFy5csFp/4cIFBAYGliiv0+mg0+ms1vn6+jq6W7dErVq1+GW3g/vHPu4f+7h/7OP+sY/7p2zVsY98fHzKVc7hA1jd3NwQExODDRs2WNaZTCZs2LABsbGxjm6OiIiI7nDVcplmwoQJGDVqFNq3b4+7774b//rXv5CdnY0nn3yyOpojIiKiO1i1BCPDhg3DpUuX8NZbbyE1NRVt2rTB6tWrUa9evepoTnU6nQ5Tp04tcbmJinD/2Mf9Yx/3j33cP/Zx/5TtdthHikh55twQERERVQ/em4aIiIhUxWCEiIiIVMVghIiIiFTFYISIiIhUxWCknObOnYtWrVpZksLExsZi1apVlu15eXkYN24c/P394eXlhSFDhpRI/FaTvPfee1AUBS+//LJlXU3eR9OmTYOiKFaPZs2aWbbX5H1jdvbsWTz22GPw9/eHXq9Hy5YtsXv3bst2EcFbb72FoKAg6PV69O7dG0eOHFGxx7dWREREie+QoigYN24cAH6HjEYjpkyZgsjISOj1ejRs2BDvvPOO1X1Ravp36Nq1a3j55ZcRHh4OvV6Pjh074s8//7RsV3X/VP1uNDXDzz//LL/++qskJyfL4cOHZfLkyeLq6ir79+8XEZFnn31WQkNDZcOGDbJ792655557pGPHjir3Wh27du2SiIgIadWqlbz00kuW9TV5H02dOlWaN28u58+ftzwuXbpk2V6T942IyJUrVyQ8PFxGjx4tO3fulOPHj8uaNWvk6NGjljLvvfee+Pj4yPLlyyUxMVEeeOABiYyMlNzcXBV7futcvHjR6vuzbt06ASC///67iPA7NHPmTPH395dffvlFTpw4IUuWLBEvLy+ZPXu2pUxN/w498sgjEh0dLXFxcXLkyBGZOnWq1KpVS86cOSMi6u4fBiNV4OfnJ//9738lPT1dXF1dZcmSJZZtSUlJAkC2b9+uYg9vvWvXrknjxo1l3bp10q1bN0swUtP30dSpU6V169Y2t9X0fSMi8sYbb0jnzp1L3W4ymSQwMFD++c9/Wtalp6eLTqeTRYsW3You3nZeeukladiwoZhMJn6HRGTAgAHy1FNPWa0bPHiwjBw5UkT4HcrJyRGtViu//PKL1fp27drJm2++qfr+4WWaSjAajVi8eDGys7MRGxuL+Ph4FBQUoHfv3pYyzZo1Q1hYGLZv365iT2+9cePGYcCAAVb7AgD3EYAjR44gODgYDRo0wMiRI5GSkgKA+wYAfv75Z7Rv3x5Dhw5FQEAA2rZti6+++sqy/cSJE0hNTbXaRz4+PujQoUON2UfF5efnY8GCBXjqqaegKAq/QwA6duyIDRs2IDk5GQCQmJiILVu2oH///gD4HSosLITRaIS7u7vVer1ejy1btqi+f1S/a++d5K+//kJsbCzy8vLg5eWFZcuWITo6GgkJCXBzcytxg7969eohNTVVnc6qYPHixdizZ4/VNUiz1NTUGr2POnTogPnz56Np06Y4f/48pk+fji5dumD//v01ft8AwPHjxzF37lxMmDABkydPxp9//okXX3wRbm5uGDVqlGU/3JzFuSbto+KWL1+O9PR0jB49GgD/fwHAxIkTkZmZiWbNmkGr1cJoNGLmzJkYOXIkANT475C3tzdiY2PxzjvvICoqCvXq1cOiRYuwfft2NGrUSPX9w2CkApo2bYqEhARkZGRg6dKlGDVqFOLi4tTu1m3h9OnTeOmll7Bu3boSkTfB8usMAFq1aoUOHTogPDwcP/zwA/R6vYo9uz2YTCa0b98e7777LgCgbdu22L9/P7744guMGjVK5d7dfr7++mv079+/XLdmryl++OEHfPfdd1i4cCGaN2+OhIQEvPzyywgODuZ36Lpvv/0WTz31FEJCQqDVatGuXTuMGDEC8fHxaneNs2kqws3NDY0aNUJMTAxmzZqF1q1bY/bs2QgMDER+fj7S09Otyl+4cAGBgYHqdPYWi4+Px8WLF9GuXTu4uLjAxcUFcXFx+PTTT+Hi4oJ69erV+H1UnK+vL5o0aYKjR4/y+wMgKCgI0dHRVuuioqIsl7LM++Hm2SE1aR+ZnTp1CuvXr8fTTz9tWcfvEPDaa69h4sSJGD58OFq2bInHH38cf//73zFr1iwA/A4BQMOGDREXF4esrCycPn0au3btQkFBARo0aKD6/mEwUgUmkwkGgwExMTFwdXXFhg0bLNsOHz6MlJQUxMbGqtjDW6dXr17466+/kJCQYHm0b98eI0eOtCzX9H1UXFZWFo4dO4agoCB+fwB06tQJhw8ftlqXnJyM8PBwAEBkZCQCAwOt9lFmZiZ27txZY/aR2bx58xAQEIABAwZY1vE7BOTk5ECjsT6kabVamEwmAPwOFefp6YmgoCBcvXoVa9aswYMPPqj+/qn2IbJOYuLEiRIXFycnTpyQffv2ycSJE0VRFFm7dq2IFE2rCwsLk40bN8ru3bslNjZWYmNjVe61uorPphGp2fvolVdekU2bNsmJEydk69at0rt3b6lTp45cvHhRRGr2vhEpmg7u4uIiM2fOlCNHjsh3330nHh4esmDBAkuZ9957T3x9fWXFihWyb98+efDBB2vUtEwREaPRKGFhYfLGG2+U2FbTv0OjRo2SkJAQy9Ten376SerUqSOvv/66pUxN/w6tXr1aVq1aJcePH5e1a9dK69atpUOHDpKfny8i6u4fBiPl9NRTT0l4eLi4ublJ3bp1pVevXpZAREQkNzdXnn/+efHz8xMPDw956KGH5Pz58yr2WH03ByM1eR8NGzZMgoKCxM3NTUJCQmTYsGFWOTRq8r4xW7lypbRo0UJ0Op00a9ZMvvzyS6vtJpNJpkyZIvXq1ROdTie9evWSw4cPq9RbdaxZs0YA2HzfNf07lJmZKS+99JKEhYWJu7u7NGjQQN58800xGAyWMjX9O/T9999LgwYNxM3NTQIDA2XcuHGSnp5u2a7m/lFEiqWnIyIiIrrFOGaEiIiIVMVghIiIiFTFYISIiIhUxWCEiIiIVMVghIiIiFTFYISIiIhUxWCEiIiIVMVghIiIiFTFYIToDqEoitVDo9HA19cXXbp0wX//+18wf2HFfPbZZ/Dz80N4eDjee+89HD9+HA0bNsSnn36qdteIahxmYCW6QyiKAgCW26EbjUYcO3YMO3bsgIhg+PDhWLRo0S3t07Rp0zB9+nTMmzcPo0ePvqVtV0VeXh58fHwQExMDANi+fTsAoG7dutizZw/q16+vZveIahwXtTtARBUzf/58q+fr1q3Dfffdh8WLF2PkyJEYOHCgOh27gyiKgjVr1uDuu++Gh4cHzp49i5SUFERHR8PHx0ft7hHVOLxMQ3SHu/fee/H4448DAJYvX65uZ+4QOp0O3bt3h4eHBwAgJCQEsbGxDESIVMJghMgJtG3bFgBw+vRpq/XffvstOnfujFq1asHDwwOtWrXCrFmzkJeXV6KO0aNHQ1EUbNq0CZs3b0bPnj3h7e2NWrVqYcCAATh48KBV+YiICEyfPh0A8OSTT1qNZ9m0aROAoss4iqKUOJtTvA7z5SczEcGiRYswfPhwNGnSBJ6envD29sbdd9+Nzz//HCaTqdT9sHr1ajzwwAOoV68edDodQkNDMXDgQPz4449W5bZt24YXX3wRrVq1Qu3ataHX69G0aVO88cYbSE9PL7X+3377Dffeey/8/Pzg7u6Opk2bYuLEiXZfQ0Rl42UaIidw7do1AEW/+M2eeeYZfPnll3B3d0fPnj3h4eGBTZs2YfLkyVi5ciXWr19vOTNQ3MqVKzF79my0b98e9913HxISEvDbb79h586d2L9/PwIDAwEADz/8MNavX4/ExER06tQJjRo1stRhLlMZBoMBjz76KPz9/REdHY127drh8uXL2LZtG8aNG4ddu3bZDG5eeeUVfPzxx9BoNIiNjUVYWBjOnTuHrVu34syZMxgyZIil7OTJk7Fjxw60bt0aPXv2hMFgwJ49e/DBBx/g119/xY4dO+Dl5WVV/6xZszB58mS4uLigW7duqFOnDrZu3Yr3338fy5Ytw+bNm1GvXr1Kv2+iGk2I6I4AQGz9lzWZTBIbGysA5M033xQRkaVLlwoACQ4OluTkZEvZ9PR06dy5swCQV155xaqeUaNGCQDRaDSybNkyy/rCwkIZMmSIAJApU6ZYvWbq1KkCQObNm2ezz2VtDw8PL/GeCgoKZNmyZZKfn2+1/uLFi9K+fXsBIHFxcVbbvv32W8v73bt3r9W2nJwcWbt2rdW61atXy5UrV6zWGQwGefbZZwWATJ8+3Wrbrl27RKPRiJeXl+zYscOyPi8vT4YOHSoAZMiQITbfIxGVjcEI0R3i5mCksLBQkpOTZfTo0QJAdDqdHD16VEREunbtKgDkP//5T4l6EhMTRVEU8fLyktzcXMt6czAycuTIEq/ZvXu3AJBu3bpZra+OYMSedevWCQCZMGGC1fqoqCgBIIsXLy53Xbbk5uaKi4uLtGvXzmr9E088IQBk0qRJJV5z4cIF0ev1otFoJCUlpUrtE9VUvExDdIe5eYwFAHh7e+N///sfGjZsiIKCAuzYsQMAMHLkyBJlW7VqhVatWiExMREJCQm45557rLb36dOnxGuaNGkCADh//rwj3kK5JCQkYO3atTh16hRycnIgIpbLUUeOHLGUO3fuHJKSkuDr64tHHnmk3PWfPXsWK1euxKFDh5CZmWkZi+Lm5mZVPwD88ccfAGzvz4CAAPTp0wcrVqzA1q1bMXz48Aq/V6KajsEI0R3GnGdEo9GgVq1aaNmyJQYPHgw/Pz8AwOXLl5Gfn486derA09PTZh0RERFITEzE2bNnS2yzlWPD29sbQNF4juqWn5+P0aNH282ZYg5KgBuDdhs0aGAzULPl448/xsSJE1FQUFCu8ufOnQNQtN9sMa+3tT+JqGwMRojuMKXNTKkIewdtjebWTbKzNTPm448/xqJFi9CyZUt88MEHaNeuHfz8/ODq6ork5GQ0bdq0Stlmd+zYgVdeeQU+Pj6YPXs2unfvjsDAQMvg3+Dg4AqfASpvEEREtjEYIXIy/v7+cHNzQ1paGrKzs22eHTl58iSAovwa1cnNzQ0AkJWVVWKb0WhEampqifXLli0DACxatAjNmze32nb8+PES5UNDQy3bRKTMwMBc/8yZMy1nmcxyc3Nt9ik4OBgnTpzAqVOnEB0dXWL7rdqfRM6KeUaInIyrq6tlHMjixYtLbN+/fz8SExPh5eWFNm3aVKktc7BRWFhoc3tQUBAAIDk5ucS233//3eZlkqtXrwKwfbnohx9+KLEuODgYUVFRSE9Px5IlS8rss736lyxZYvOsS5cuXQDA5qWjS5cuYc2aNVAUBZ06dSqzfSIqicEIkRMaP348gKKkY8XPJly7dg0vvPACRATPPPMM3N3dq9ROcHAwAODw4cM2t3ft2hUAsGDBAsvZAwA4ceIEXnzxRZuvMQ+W/eKLL6zWL126FN98843N10ycOBEAMGHCBOzbt89qW15eHtatW1ei/q+//toqGDp48CDeeOMNm/WPGzcOGo0Gn376KXbv3m1Zn5+fj/HjxyM3NxeDBw+2nKUhogpSdS4PEZUbSskzUpqxY8cKANHr9TJgwAAZOnSo1K1bVwDIPffcI9nZ2VblzVN7f//991LbDw8Pt1p39uxZcXd3F61WK/369ZOnnnpKxowZI4cOHbKUMU+L9fHxkfvvv1969eolnp6eMnToUJtTe+Pi4kSr1QoAiYmJkREjRljyi7z66qs2pxiLiIwfP14AiFarlc6dO8uIESOke/fu4uvrK61bt7aUS0tLk8DAQAEgkZGR8sgjj0jv3r3F1dW11D6JiMycOVMAiIuLi/Tu3VuGDx8uoaGhAkAaN24sqamp9j8QIioVgxGiO0RFgxERkW+++UY6duwoXl5e4u7uLs2bN5eZM2dKTk5OibKVCUZERNasWSOdOnUSLy8vSx+L12EwGGTixIkSGhoqbm5u0rBhQ5kxY4YUFhaWeuDfvn279OzZU/z8/MTb21s6duwoP/74o5w4caLUYEREZMWKFdK3b1+pXbu2ABA3NzcZOHCg/PTTT1blTp8+LY8++qiEhISIu7u7REVFyXvvvWe3TyIiv/zyi/Tq1Ut8fHzEzc1NGjVqJK+//nqJBGpEVDGKSBWGpRMR3aamT5+Obdu2Yc2aNWp3hYjKwGCEiJzS3r170a5dOxw+fNgyToSIbk+c2ktETqV79+4IDg7Gnj17ABRNISai2xuDESJyKvn5+fjpp5/g7e2NN998E1FRUWp3iYjKwMs0REREpCrmGSEiIiJVMRghIiIiVTEYISIiIlUxGCEiIiJVMRghIiIiVTEYISIiIlUxGCEiIiJVMRghIiIiVf0/mwO9b9uspOoAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(5,4))\n",
        "eixo = fig.add_axes([0,0,1,1])\n",
        "mu, mediana, moda = df['Vitorias'].mean(), df['Vitorias'].median(), df['Vitorias'].mode().iloc[0]\n",
        "\n",
        "N, bins, patches = eixo.hist(df['Vitorias'], bins=20, edgecolor='black')\n",
        "eixo.set_title('Frequência das vitórias', fontsize=15, pad=10)\n",
        "eixo.set_xlabel('Vitórias', fontsize=15)\n",
        "\n",
        "fracs = N / N.max()\n",
        "\n",
        "norm = colors.Normalize(fracs.min(), fracs.max())\n",
        "\n",
        "for thisfrac, thispatch in zip(fracs, patches):\n",
        "    color = plt.cm.Greens(norm(thisfrac))\n",
        "    thispatch.set_facecolor(color)\n",
        "\n",
        "eixo.annotate('$média = {0:.1f}$'.format(mu), xy=(21, 33), fontsize=10, color='black')\n",
        "eixo.annotate('$moda=mediana = {0:.0f}$'.format(mediana), xy=(21, 31), fontsize=10, color='teal')\n",
        "\n",
        "eixo.axvline(mu, color = 'k', linestyle='--')\n",
        "eixo.axvline(mediana, color='teal', linestyle='--')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "i0aiAGQRI12q",
        "outputId": "d3eea840-9e41-409f-f7af-fbb7b18c166b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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w9NxDhw7hgw8+qHE7ldcMfPXVV/K1IABw8uRJPPnkkzWuM2fOHNjb22Pu3Ln49ttvq83X6XRYv349/v7777rsaq1mzZoFOzs7fPrpp/j6668N5gkhsHXrVoNrTGrStm1bABX7V1hYKE8/e/YsHn/8caNrqrxAdc6cObh8+bI8/cSJE3jppZduWsNHH31kMH39+vX48ssvqy2fmZmJFStWoKioyGB6SUkJdu7cCaDiqFFd9ejRA0FBQdi5cyfefvttANcDSl1VHv1IS0urNs/R0RFTpkyBXq9HbGyswft87NgxvPLKKwBQ68+Tsdq2bYsrV65U+5lYvHix/P5UdfjwYXz77bfVOqW7fPkyDhw4AMC495OIt/ZSgwcj+xm52W2ozz33nNwh2F133SXGjBkjRo8eLbp27SqsrKyEq6urwfKXLl0S7dq1kzvzio6OFvfff7+wtrYWsbGxNd7aK4QQDz/8sAAgXF1dxdChQ8XAgQOFo6OjGDNmTK2dnm3cuFHutKt169Zi6NChckdSjo6OAoA4fPiwvHzlrb01dUglRO23jq5Zs0buzKpDhw5i7NixIjIyss6dnul0OhESEiJ3BDdq1CgRFRUlHBwcxD333CPuueeem/afcSOdTid69eold3o2atQo8cADDwh7e3vxwAMP1NjpWWJiorCyshIARFhYmIiJiZH7F6ns3Kzq7auHDx8WAISDg4Po27evGDdunBg2bJjw8vISAES3bt2q3ap7K5V90wAQkiSJ06dP17hcbbf2btiwQQAQLi4uYvTo0WLq1KkGtwVX7fSsadOmYsyYMWLIkCHCzs7ulp2e1aa2W3u/+uoreV/69OkjYmJiRIcOHYRGoxGzZ8+u1pFcQkKC/PM9cOBAMX78eBEVFSWcnZ0FADF06NBbv4FEVTCMUINnzjAiRMUX2ZgxY4Svr6+wsbERHh4eIjQ0VMycObNaL6dCVHQINX36dOHj4yO0Wq0IDg4Wb7/9ttDr9bWGEZ1OJ5599lnh5+cnbG1tRVBQkHjllVfEtWvXbvqFkZ6eLmbMmCHatGkj7OzshLOzs2jXrp0YO3asWLduXY2dnhkbRoQQIiUlRUyYMEE0b95c2NjYiKZNm4pevXqJd955R5SVld3yPb18+bKYPn26CAwMFFqtVrRq1Uo888wzorCw8JadedWksLBQzJ8/X/j7+wtbW1sRGBgonnvuOaHT6Wp9v/bt2yfuvfde0aRJE+Hs7CzuuecesWHDhhq/cPPz88U777wjhgwZIgIDA4WdnZ3w8PAQ3bp1E4sXLzbo0K6u0tLS5J/Nvn371rrczfqdWbx4sejQoYPcq+qN+1lQUCDi4+PlZZydnUXv3r3F6tWra9yWqWFECCF+/PFH0aNHD+Hs7Czc3NxERESE2LVrl9i5c2e1MHL+/HnxyiuviHvvvVe0aNFC2NraCm9vb9GrVy/xxRdfVOuMjuhW+KA8onqQJAkBAQEGp2OIiMg4vGaEiIiIFMUwQkRERIpiGCEiIiJFsdMzonrgJVdERPXHIyNERESkKIYRIiIiUhTDCBERESmKYYSIiIgUxTBCREREimIYISIiIkUxjBAREZGiGlw/I3q9HufOnYOzszMkSVK6HCIiIjKBEAJXr16Fr68vNJqbH/tocGHk3Llz8PPzU7oMIiIiMoMzZ86gRYsWN12mwYURZ2dnABXFu7i4KFwNUeNWWFoK33feAQCcmzMHjra2dV+3sBC+vr4V6547B0dHR4vUSER3pvz8fPj5+cnf6zfT4MJI5akZFxcXhhEiC7MrL8eCQYMAAB5NmsDWyqru69rZYcGCBRXrenjA1oggQ0TqUZdLLiTRwB6ukZ+fD1dXV+Tl5TGMEBER3aGM+T436m6aZcuWITQ0VD5q0bNnT/zvf/+T5/fv3x+SJBkMjz/+uGl7QURERKpg1GmaFi1a4PXXX0ebNm0ghMDKlSsxbNgwHD58GCEhIQCARx99FC+99JK8joODg3krJiKz0QuB1IsXAQDtvbygMeIONr1ej9TU1Ip127e/5dXyRES1MSqMDB061OD1q6++imXLlmH//v1yGHFwcICPj4/5KiQiiykuK0PHZcsAAAXz5xt1AWtxcTE6duxYsW5BAS9gJSKTmfxfmfLycqxduxaFhYXo2bOnPH3VqlXw9PREx44dMX/+fBQVFd20HZ1Oh/z8fIOBiIiI1MPou2n++OMP9OzZEyUlJXByckJCQgI6dOgAABg3bhwCAgLg6+uL33//Hc888wzS0tLw7bff1treokWLEB8fb/oeEBER0R3N6LtpSktLkZmZiby8PKxfvx6fffYZEhMT5UBS1Y4dOzBw4ECkp6cjKCioxvZ0Oh10Op38uvK+ZN5NQ2R5haWlcFq0CIDxp2kKCwvh5ORUsS5P0xDRDYy5m8boIyO2trZo3bo1ACAsLAy//vor3n//fXz88cfVlg0PDweAm4YRrVYLrVZrbBlERETUSNT78ne9Xm9wZKOq5ORkAECzZs3quxkiIiJqpIw6MjJ//nxERkbC398fV69exerVq7Fr1y5s2bIFGRkZWL16NYYMGQIPDw/8/vvvmD17Nvr27YvQ0FBL1U9ERER3OKPCyIULF/Dwww/j/PnzcHV1RWhoKLZs2YL77rsPZ86cwbZt2/Dee++hsLAQfn5+GDVqFJ5//nlL1U5E9WRjZYW5/9wNZ2NEV/AAYGNjg7lz58rjRESmYnfwREREZHYW6w6eiIiIyNwa3FN7iej20QuBzLw8AIC/q6vR3cFnZmZWrOvvz+7gichkDCNEKlZcVoaW778PwLTu4Fu2bFmxLvsZIaJ6YBghqiIzMxM5OTkWadvT0xP+/v4WadvUuouvXZPHk5OTYW9d/U+CJesmIgIYRohkmZmZaNeuHUpKSizSvp2dHdLS0sz+xV6vum1sgH//GwDQu3dvoKys2iKWqpvuPD/88AOuXLmCiRMnKl0KNTIMI0T/yMnJqfhCD2kCOJr5V6PwGkr+vIKcnByzf6nXq26rKst38wTKrxnOt2DddGdZv349XnvtNWzevPmWy77yyiv44YcfsH//fjz22GMoKCjAqlWrbkOVdKfiFWdEN3K0BlxszTuYO9yYq27nKteIOCtUNzV4P/74Iz744APs2LEDPj4+t1w+JSUFXbp0AVDxMNRPPvnEwhUa2r17N4YOHQpfX19IkoSNGzdWW2bhwoWQJMlgCA4OrrXN8vJyvPDCC2jZsiXs7e0RFBSEl19+GQ2sd4w7Fv/SEBHRTUVFRSEqKqrOy6ekpCAuLg4A4O7ubqmyalVYWIjOnTtjypQpGDlyZK3LhYSEYNu2bfJr6xqumar0xhtvYNmyZVi5ciVCQkJw6NAhPPLII3B1dcWTTz5p1vrViEdGiIgauVOnTkGSJGzYsAF9+/aFvb09unfvjszMTPzyyy/o0aMHHBwcMHDgQOTm5srrZWZmYty4cWjSpAmaNGmCmJgYXL582aDtpKQkuc2uXbviwIEDyMjIQJcuXeTtnjp1Sl5+wYIF6NSpExwdHeHt7Y3p06ejrIZrleojMjISr7zyCkaMGHHT5aytreHj4yMPnp6etS67d+9eDBs2DFFRUQgMDMTo0aNx//334+DBg2atXa0YRojUTOiBc39WDEJv1KrW1taYMWMGZsyYcdP/UZLyUlJSAADLli3Da6+9hr179yI7OxsTJkzA66+/jg8++AA7d+5ESkoKli9fDqDiaethYWFo164d9u/fj+3bt+P06dOYPXu23O5ff/2FAQMGoF+/fjhy5Aief/55DB8+HAAQGhqKlJQUuLm5ITAwEAAghIAQAh9//DGOHj2KFStWYMOGDfjss8+q1fzaa6/BycnppkNlPzemOn78OHx9fdGqVSuMHz/+pu3dc8892L59O44dOya/p3v27EFkZGS9aqAK/AtCpGZCD5zYa9KqWq0WS5cuNXNBZAnJyclwd3fH119/DQ8PDwBAv379sGfPHvz5559wcHAAAHTv3h1ZWVkAgBkzZuCpp54yeL7YnDlzMGfOHPl1bGwshg8fjpdffhkAEBQUhLVr1+KPP/6Ag4MDkpOTDR6UKkkSXnrpJfl1QEAAIiIikJaWVq3mxx9/HA899NBN98vX19fYt0IWHh6OFStWoF27djh//jzi4+PRp08fHDlyBM7OztWWf/bZZ5Gfn4/g4GBYWVmhvLwcr776KsaPH29yDXQdwwgRUSOXkpKCESNGyEEEqDgFEx0dLQeRymnDhg3D6dOnsXXrVmzduhUvvPCCQVvt27cHAJw+fRo7duzAb7/9ZjDfxsZGvni16oWsleu8+eabSExMxNmzZ1FWVoaSkhK8/vrr1Wp2d3e36PUmVY9ohIaGIjw8HAEBAVi3bh2mTp1abfl169Zh1apVWL16NUJCQpCcnIxZs2bB19cXkyZNslidasHTNERqZ21XMRhJCIGLFy/i4sWLvKOggUtOTkZ4eLjBtJSUFPTo0UN+XVJSgrS0NHTu3BkpKSnw8vKST6tUHY4ePSq3aW1tjU6dOhm0e/jwYTmAJCcno3PnzgCAixcvonv37rh06RLeffdd7NmzB3v37oVGo5GXqep2nKapys3NDW3btkV6enqN859++mk8++yzGDt2LDp16oSJEydi9uzZWLRokdlqUDMeGSFSM4010OOfDqz2Lgf0126+fBVFRUVo2rQpAHYH35Dl5+fj1KlT6Nq1qzzt5MmTyMvLM5j2xx9/QAiBTp064ZdffsHly5dx9erVGk9ZAIBGo4Fer0dpaal8zdDmzZvx119/oUuXLvJ2K4PJpk2bUF5ejjVr1kD65xlIH3zwAcrKygyOnlSy9GmaGxUUFCAjI6PWDt2KioqqPX/JysoKer1x11pRzRhGiIgasZSUFFhZWaFjx47ytMprSAICAgymBQUFwcnJCeHh4XBzc8OECRMQHx8PR0dHpKWl4YcffsBHH30EAAgLC4ONjQ2efvppzJkzB0eOHMH06dMBAF26dJG3GxISAgDw8PBAfn4+vv/+e3To0AGbNm3CokWL0Lx5c3h5eVWruz6naQoKCgyOcJw8eVLe58rO++bOnYuhQ4ciICAA586dw4IFC2BlZYWYmBgAFUEpISEB27dvBwAMHToUr776Kvz9/RESEoLDhw/j3XffxZQpU0yqkQzxNA0RUSOWkpKCdu3awc7OzmBa1aMildMqT5e4u7tj8+bNyM/PR79+/RAWFoYFCxYYBBpfX1989tln+P777xESEoJ33nkHDz/8MLy9veHj44OUlBQEBwdDq9UCqPgynzp1KiZOnIjevXvj7NmzeOihh2o8KlJfhw4dQteuXeV9jIuLQ9euXfHiiy/Ky/z999+IiYlBu3bt8NBDD8HDwwP79++Xg1FOTg4yMjLk5f/zn/9g9OjRmDFjBtq3b4+5c+fiscceky/epfqRRAM72Zufnw9XV1fk5eXBxcVF6XJIRX777TeEhYUBd3tV9D5qTvmlwMGLSEpKwl133WXWputVt8YauOeRivGaTtPcpO7CwkI4OTkB4GkaIqrOmO9zHhkhIiIiRTGMEBERkaIYRoiIiEhRvJuGSM2EHsg+dn3cCNbW1nJnT+wOnojqg39BiNRM6IHjiSatqtVqsWLFCvPWQ0SqxNM0REREpCgeGSFSO80/fwaM6H0VqOgOvqioCADg4OAg96pJRGQsHhkhUrPKfkbueeR6KKmjoqIi+RkhlaGEiMgUDCNERESkKIYRIiIiUhTDCBERmdUru3ejx2efKV2Gxdy4f49t2oTx336rYEV3Pl7ASkREZpWSnY0uPj5Kl2ExN+7foogIaK2sFKzIeLtPn8Zbe/ci6dw5nC8oQEJ0NIYHBxsss3DXLsQnGt76387DA3/NnGn2ehhGiIjIrFKyshDXs6fSZVjMjfvnbm+vYDWmKSwtRWdvb0zp0gUj162rdbkQLy9se/hh+bW1xjInVBhGiIgauVO5uWj5/vtYP2YM3j9wAL+eO4eOTZtiw0MP4XRuLp7euhW/Z2ejp58fNjz0ENzs7AAARy5cwLytW7EnMxMONjYY36kTFkVEwLbKUYCkc+cwe8sW/HruHII9PfFRVBQyrlyRjxws2LkT3/71F05cuQInW1uMDA7GkshI2JjxSIKp+5eZl4dnt23D/9LTIQGIbNMGH0RGokmVcHGr/avc9smnnkKgm9st9znj8mW0/s9/sCkmBov378e+M2fg5+qKL4cPR3iLFvJ2Lf2+RbZpg8g2bW65nLVGA59/ns5tSQwjRGomBJBz4vq4EaysrDB69Gh5nBqulKwsAMCyQ4fw2sCBcLSxwbC1azHh22/hrNXigyFDUK7XI2r1aiw/fBize/bE4fPn0W/FCjwZHo4lkZH4Oz8f4zZsgJudHV7o1w8A8FdODgasXImnwsOxfNgwJGdlYfjXXwMAQr29IYSAAPDxAw+gubMzjl68iEkbNyLU2xvTu3evVudrv/yC13755ab7cjQ2Fv6urvXev/TLl9Hz888xvVs37J86FQWlpZixeTOe3roVnz34YJ32r3LbbnZ2chC51T6nZGdDAvDuvn14sV8/NHd2xozNm/Hs9u3Y+c/jFYx530x9z+rq+OXL8H3nHdhZW6Onnx8WDRxocls3wzBCpGaiHPhru0mr2tnZ4ZtvvjFzQWQJyVlZcLe3x9ejR8PDwQEA0C8wEHsyM/HnjBlwsLEBAHRv3hxZBQUAgEc3bcLE0FC8cu+9AIDW7u54pEsX/HD8uBxGYjdvxvDgYLz8zzJB7u5Y++ef+CM7W27zpQED5DoC3NwQ0aoV0i5dqrHOx7t1w0MhITfdF19nZ7Ps34wff8SMbt0QX6W+effcg6e3bpVf12X/krOy5GACAJIk3XSfK8PL16NHw8vREQDwYNu2+Dgpqc5tmOM9q4vw5s2xYtgwtPP0xPmrVxGfmIg+y5fjyPTpcNZqTWqzNgwjRESNXEp2NkYEB8tf1EDFKYrokBD5S7Vy2rB27fBXTg6Szp/HVyNHGrRja2UF3bWKnnpP5+Zix8mT+G3aNINlbDQa+RTG6dxcvPl//4fE06dx9upVlJWXo+TaNbweEVFjne729iZdf2Hs/p3OzcXWEyewJzMT7+zbJ88vFwJ+Li513r/KbXepEkZutc8p2dkYFhwsBxEAOJmbi9bu7nVuwxzvWV1UPY0T6u2N8BYtEPDee1j355+YetddZt0WwwgRUSOXnJWF+b17G0xLycrC7B495Ncl164hLScHnb298eeFC7DRaNDWw8NgnaM5Oej0zxdvclYWrDUa+XWlw1lZmNS5My4WFqL7p5/i3pYt8e6gQWju7IxyIdDtk0/Q+YZ1Kpl6ysHY/UvJzoa7vT0O/Otf1dq3/+cJ1Lfav6rbfqBtWwCo0z6nZGdXqzU5Kwt9AwLq3IY53jNTuNnZoa2HB9IvX653WzdiGCFSs8ru4AFg73Kjnk9TWFgIp38ubCsoKIBjlf/pUcORr9PhVG4uujZrJk87eeUK8nQ6dK3yP/w/srMhAHTy9sbV0lKUC4Gy8nJo//lyPnnlChJSU/F9TAwAQCNJ0AuB0vJy+Q6LzceP46+cHHTx8cGmY8dQLgTWjBolP7fog4MHUabX13rbrymnHEzZv19On8ZVnQ6+zs4GR06qutX+Vd125etb7XNeSUlFrTfsf3JWFp4MD69TG+Z4z0xVUFqKjMuXMTE01CztVcUwQkTUiKVkZcFKo0HHpk3laZXXWAT8c9Fl5bSgJk3gZGuL8ObN4WZnh2e3bcMT4eE4lZuLmZs3Y2zHjhjcujUAIMzXFzYaDZ7++WfMueceHLlwAdN//BEA0MXHB7pr15Cv0+H7tDR08PLCpmPHsGjPHjR3djY4RVGVKaccTNq/Fi3gotXi4YQEvNC3LxxtbZF++TJ+Sk/He4MH12n/qm47xMsLAOBhb3/Tff7l9OlqR1tO5+biSkmJ3Oat2jDHewZUBIuqRzhOXrkiv2+VR1Hm/vwzhrZtiwA3N5y7ehULdu2ClUaDmI4djd7erTCMEBE1YinZ2Wjn4QE7a2uDaTf+7zwlOxud/5nmameHjdHRmLVlCz5KSoKvszMevesuPH3PPfLyvs7O+OzBBzF/+3Z8kZyMu5s3x8OhoVienAwfJycMbdcOU7t2xcSEBNjb2GBCp054qEMHnM7LU3z/3O3tsXn8eDyzbRv6rlgBIQTaeHgYnH651f5Vthns6SkfPbrVPtdU6+Eb7sa5Xe/boXPnMGDlSvl13M8/AwAmde6MFcOHAwD+zs9HzIYNuFRcDC8HB/T298f+qVNrDZP1IQlh5P18Fpafnw9XV1fk5eXB5Z8LiYhuh99++w1hYWHA3V6Ai615G88vBQ5eRFJSEu4y84Vf9ar7VqdpblI3T9MQ0c0Y833OZ9MQERGRohhGiIiISFEMI0RERKQoXsBKpGZCAJczr48bwcrKCkOGDJHHiYhMxTBCpGaiHDi6xaRV7ezs8OM/tzoSEdUHT9MQERGRoowKI8uWLUNoaChcXFzg4uKCnj174n//+588v6SkBLGxsfDw8ICTkxNGjRqF7OxssxdNREREjYdRYaRFixZ4/fXXkZSUhEOHDuHee+/FsGHD8OeffwIAZs+ejU2bNuGbb75BYmIizp07h5E3PGiJiBoQjTXQc3LFoDHurG1hYSEcHR3h6OiIwsJCi5RHROpg1F+foUOHGrx+9dVXsWzZMuzfvx8tWrTA559/jtWrV+Pefx63vHz5crRv3x779+9HjyoPLCKiBsSq5mdz1EVRUZEZCyEitTL5mpHy8nKsXbsWhYWF6NmzJ5KSklBWVoaIKo84Dg4Ohr+/P/ZVeUTzjXQ6HfLz8w0GIiIiUg+jw8gff/wBJycnaLVaPP7440hISECHDh2QlZUFW1tbuFV5MBEAeHt7Iysrq9b2Fi1aBFdXV3nw8/MzeieIiIjozmV0GGnXrh2Sk5Nx4MABTJ8+HZMmTcLRo0dNLmD+/PnIy8uThzNnzpjcFhEREd15jO5nxNbWFq0rHyEdFoZff/0V77//PqKjo1FaWorc3FyDoyPZ2dnwueHpiVVptVpotVrjKyciIqJGod79jOj1euh0OoSFhcHGxgbbt2+X56WlpSEzMxM9e/as72aIiIiokTLqyMj8+fMRGRkJf39/XL16FatXr8auXbuwZcsWuLq6YurUqYiLi4O7uztcXFzwxBNPoGfPnryThqjBEkDeuevjRtBoNOjXr588TkRkKqPCyIULF/Dwww/j/PnzcHV1RWhoKLZs2YL77rsPALB48WJoNBqMGjUKOp0OgwYNwocffmiRwonIDPTlwB+mdelub2+PXbt2mbceIlIlo8LI559/ftP5dnZ2WLp0KZYuXVqvooiIiEg9eGyViIiIFMUwQqRmGmsgfELFYEJ38F5eXvDy8mJ38ERUL0bf2ktEjYyNvcmr5uTkmLEQIlIrhhEiogYiMzPTIgHP09MT/v7+Zm+XyFwYRoiIGoDMzEy0C26HkuISs7dtZ2+HtL/SGEiowWIYISJqAHJyclBSXAKrSH9I7nZma1dcLkHJ/yqOuDCMUEPFMEJE1IBI7nbQeDuYrT292VoishzeTUNERESK4pERIlUTwNWL18eNoNFo0K1bN3mciMhUDCNEaqYvB1I2mrSqvb09fv31V/PWQ0SqxP/OEBERkaIYRoiIiEhRDCNEaqaxArqNrRg0VkatWlRUhMDAQAQGBqKoqMhCBRKRGvCaESJVkwA75+vjRhBC4PTp0/I4EZGpeGSEiIiIFMUwQkRERIpiGCEiIiJFMYwQERGRohhGiIiISFG8m4ZI1QRQeOX6uBEkSUKHDh3kcSIiUzGMEKmZvhw4vN6kVR0cHPDnn3+auSAiUiOepiEiIiJFMYwQERGRohhGiNRMYwV0HV0xmNAdfEhICEJCQtgdPBHVC68ZIVI1CXBscn3cCEIIHD16VB4nIjIVj4wQERGRohhGiIiISFEMI0RERKQohhEiIiJSFMMIERERKYp30xCpmgBKrl4fN4IkSQgICJDHiYhMxTBCpGb6cuDQWpNWdXBwwKlTp8xbDxGpEk/TEBERkaIYRoiIiEhRDCNEaqaxAjoPrxiM7A6+uLgY3bt3R/fu3VFcXGyR8ohIHXjNCJGqSYCz1/VxI+j1ehw6dEgeJyIyFY+MEBERkaIYRoiIiEhRDCNERESkKIYRIiIiUhTDCBERESmKd9MQqV2Z6bflenp6mrEQIlIrhhEiNdNfAw58ZdKqjo6OuHjxopkLIiI14mkaIiIiUhTDCBERESnKqDCyaNEidO/eHc7OzmjatCmGDx+OtLQ0g2X69+8PSZIMhscff9ysRRORmWisgE5RFYMJ3cH3798f/fv3Z3fwRFQvRl0zkpiYiNjYWHTv3h3Xrl3Dc889h/vvvx9Hjx6Fo6OjvNyjjz6Kl156SX7t4OBgvoqJyIwkwNX3+rgR9Ho9EhMT5XEiIlMZFUZ++ukng9crVqxA06ZNkZSUhL59+8rTHRwc4OPjY54KiYiIqFGr1zUjeXl5AAB3d3eD6atWrYKnpyc6duyI+fPno6ioqNY2dDod8vPzDQYiIiJSD5Nv7dXr9Zg1axZ69eqFjh07ytPHjRuHgIAA+Pr64vfff8czzzyDtLQ0fPvttzW2s2jRIsTHx5taBhEREd3hTA4jsbGxOHLkCPbs2WMwfdq0afJ4p06d0KxZMwwcOBAZGRkICgqq1s78+fMRFxcnv87Pz4efn5+pZREREdEdxqQwMnPmTPzwww/YvXs3WrRocdNlw8PDAQDp6ek1hhGtVgutVmtKGURERNQIGBVGhBB44oknkJCQgF27dqFly5a3XCc5ORkA0KxZM5MKJCILKy8zeVXeKUdE5mBUGImNjcXq1avx3XffwdnZGVlZWQAAV1dX2NvbIyMjA6tXr8aQIUPg4eGB33//HbNnz0bfvn0RGhpqkR0gonrQXwP2rTBpVUdHRxQWFpq3HiJSJaPCyLJlywBUdGxW1fLlyzF58mTY2tpi27ZteO+991BYWAg/Pz+MGjUKzz//vNkKJiIiosbF6NM0N+Pn5yd3gkRERERUF3w2DZGaSVZAh0EVg2Rcd/AlJSWIiopCVFQUSkpKLFQgEamBybf2ElEjIEmAu//18Zsf/DRQXl6OzZs3y+NERKbikREiIiJSFMMIERERKYphhIiIiBTFMEJERESKYhghIiIiRTGMEBERkaJ4ay+RmumvAXs+NWlVR0fHW3aESERUFzwyQkRERIpiGCEiIiJFMYwQqZlkBQQPrBhM6A5+zJgxGDNmDLuDJ6J6YRghUjNJAjxbVQySZNSq5eXlWL9+PdavX8/u4ImoXhhGiIiISFEMI0RERKQohhEiIiJSFMMIERERKYphhIiIiBTFMEJERESKYnfwRGqmvwbsXX593AgODg4oKCiQx4mITMUwQqR2RoaQSpIkwdHR0czFEJEa8TQNERERKYphhEjNJA3Qpl/FIBn350Cn02Hy5MmYPHkydDqdhQokIjVgGCFSM0kDeLetGIwMI9euXcPKlSuxcuVKXLtm2qkeIiKAYYSIiIgUxjBCREREimIYISIiIkUxjBAREZGiGEaIiIhIUQwjREREpCj2wEqkZvprwP7/Xh83goODAy5cuCCPExGZimGESO2ulZi0miRJ8PLyMnMxRKRGPE1DREREimIYIVIzSQO0uqdiMKE7+NjYWMTGxrI7eCKqF4YRIjWTNIBvSMVgQnfwH374IT788EN2B09E9cIwQkRERIriBaxEt1Fqauod0SYR0e3EMEJ0O+jKAQmYMGGC0pUQETU4DCNEt8M1PSCAkCd7w6GFq1mbvvTbWZxYm2zWNomIbieGEaLbyKGFK1xaeZi1zcK/88zaHhHR7cYLWImIiEhRPDJCpGb6a8Cva66PG8He3h4nT56Ux4mITMUwQqR2ugKTVtNoNAgMDDRvLUSkSjxNQ0RERIpiGCFSM0kDBN5dMRjZA2tpaSmefvppPP300ygtLbVQgUSkBkb99Vm0aBG6d+8OZ2dnNG3aFMOHD0daWprBMiUlJYiNjYWHhwecnJwwatQoZGdnm7VoIjITSQO06FwxGBlGysrK8Pbbb+Ptt99GWVmZhQokIjUw6q9PYmIiYmNjsX//fmzduhVlZWW4//77UVhYKC8ze/ZsbNq0Cd988w0SExNx7tw5jBw50uyFExERUeNg1AWsP/30k8HrFStWoGnTpkhKSkLfvn2Rl5eHzz//HKtXr8a9994LAFi+fDnat2+P/fv3o0ePHuarnIiIiBqFel0zkpdX0dmSu7s7ACApKQllZWWIiIiQlwkODoa/vz/27dtXYxs6nQ75+fkGAxEREamHyWFEr9dj1qxZ6NWrFzp27AgAyMrKgq2tLdzc3AyW9fb2RlZWVo3tLFq0CK6urvLg5+dnaklERER0BzI5jMTGxuLIkSNYu3ZtvQqYP38+8vLy5OHMmTP1ao+IiIjuLCZ1ejZz5kz88MMP2L17N1q0aCFP9/HxQWlpKXJzcw2OjmRnZ8PHx6fGtrRaLbRarSllEBERUSNgVBgRQuCJJ55AQkICdu3ahZYtWxrMDwsLg42NDbZv345Ro0YBANLS0pCZmYmePXuar2oiMg/9NeC39dfHjWBvb48jR47I40REpjIqjMTGxmL16tX47rvv4OzsLF8H4urqCnt7e7i6umLq1KmIi4uDu7s7XFxc8MQTT6Bnz568k4aooSq6YtJqGo0GISEhZi6GiNTIqDCybNkyAED//v0Npi9fvhyTJ08GACxevBgajQajRo2CTqfDoEGD8OGHH5qlWCIiImp8jD5Ncyt2dnZYunQpli5danJRRHSbSBrAr0vF+JlkQOjrvGppaSlee+01AMBzzz0HW1tb89dHRKrAp/YSqZmkAfzDKsb//t2oMFJWVob4+HgAwNNPP80wQkQm44PyiIiISFEMI0RERKQonqahO1JmZiZycnLM2mZqaqpZ2yMiorphGKE7TmZmJoLbB6O4qFjpUoiIyAwYRuiOk5OTg+KiYtwzLwKufk3M1u7ZX0/j9y8Pmq09IiKqG4YRumO5+jWBexsvs7WXd8a0zr+IiKh+GEaI1ExfDiQnXB83gp2dHQ4ePCiPExGZimGESNUEUGDahcBWVlbo3r27meshIjXirb1ERESkKB4ZIVIzSQP4dqwYP3fE6O7g33//fQDAU089xR5YichkDCNEaiZpgJbhFePnjxrdHfy8efMAADNmzGAYISKT8TQNERERKYphhIiIiBTFMEJERESKYhghIiIiRTGMEBERkaIYRoiIiEhRvLWXSM305cAfP1wfN4KdnR127twpjxMRmYphhEjVBJB33qQ1rays0L9/f/OWQ0SqxNM0REREpCgeGSFSM0kCfNpXjGelAkLUedWysjJ88sknAIBp06bBxsbGEhUSkQowjBCpmWQFBPWqGM8+BohrdV61tLQUM2fOBABMnjyZYYSITMbTNERERKQohhEiIiJSFMMIERERKYphhIiIiBTFMEJERESKYhghIiIiRfHWXiI105cDf/50fdwIWq0WP/zwgzxORGQqhhEiVRPAlTMmrWltbY2oqCgz10NEasTTNERERKQoHhkhUjNJArxaV4xfTDe6O/hVq1YBAMaPH88eWInIZAwjRGomWQFt+1eM55w0ujv4Rx55BAAwZswYhhEiMhlP0xAREZGiGEaIiIhIUTxNQ0SKyMzMRE5OjkXa1ul0Frvd2NPTE/7+/hZpm0itGEaI6LbLzMxEcPtgFBcVW6R9SSNB6Ot+Ma4x7B3s8VfqXwwkRGbEMEJEt11OTg6Ki4oxIn4EvAK9zNr28b3HsfPjnRZp++Kpi0hYkICcnByGESIzYhghIsV4BXqhWXAzs7aZcyrHYm0TkWUwjBCpmb4cSN12fdwIWq0W69atk8eJiEzFMEKkagK4dNKkNa2trTFmzBgz10NEasRbe4mIiEhRPDJCpGoS4BFYMXrpFIC634Fy7do1JCQkAABGjBgBa2v+OSEi0/CvB5GaaayA9hEV43uXA/q6dwev0+nw0EMPAQAKCgoYRojIZEafptm9ezeGDh0KX19fSJKEjRs3GsyfPHkyJEkyGAYPHmyueomIiKiRMTqMFBYWonPnzli6dGmtywwePBjnz5+XhzVr1tSrSCIiImq8jD6uGhkZicjIyJsuo9Vq4ePjY3JRREREpB4WuZtm165daNq0Kdq1a4fp06fj0qVLtS6r0+mQn59vMBAREZF6mD2MDB48GF9++SW2b9+ON954A4mJiYiMjER5ec0dKi1atAiurq7y4OfnZ+6SiIiIqAEz++XvY8eOlcc7deqE0NBQBAUFYdeuXRg4cGC15efPn4+4uDj5dX5+PgMJERGRilj8XrxWrVrB09MT6enpNYYRrVbLrqSJlCLKgWO7ro8bwdbWFsuXL5fHiYhMZfEw8vfff+PSpUto1owPrCJqcIQALhw3aVUbGxtMnjzZvPUQkSoZHUYKCgqQnp4uvz558iSSk5Ph7u4Od3d3xMfHY9SoUfDx8UFGRgbmzZuH1q1bY9CgQWYtnIiIiBoHo8PIoUOHMGDAAPl15fUekyZNwrJly/D7779j5cqVyM3Nha+vL+6//368/PLLPBVD1CBJQJMWFaNX/oax3cFv2bIFADBo0CD2wEpEJjP6r0f//v0hRO1/sCr/OBHRHUBjBYT800OyCd3BP/DAAwDYHTwR1Q+f2ktERESKYhghIiIiRTGMEBERkaJ4kpeIbik1NbXatOLiYnk8OTkZ9vb29WqPiNSLYYSIaqcrh6SRMGHChJsu1rt379tUEBE1RgwjRFS7a3oIvcCkRRPg08rbYFZpSRkWT1oCAJi98knY2tnUudk/fzmKHz74n1lLJaI7F8MIkZqJciDj/66P18KnlTf8Ohg+M6q8rBxjnhsFAAjsFAArG6s6bzbrRLbxtRJRo8UwQqRmQgDnj5q0qpWNFfrF9DFzQUSkRrybhoiIiBTFIyNEqiYBrj4Vo3lZMKY7eH25Hum/ZQAAWt8VBI0V/29DRKbhXw8iNdNYAZ0eqBg0db/mAwDKdGVYMmUplkxZijJdmYUKJCI1YBghIiIiRTGMEBERkaIYRoiIiEhRDCNERESkKIYRIiIiUhTDCBERESmK/YwQqZnQAycPXB83gpWNFYbHPSiPExGZimGESM2EHjj7u0mrWttYI+KRe81cEBGpEU/TEBERkaJ4ZIRI1STAyaNitOASjO0O/kzq3wAAv/Yt2B08EZmMfz2I1ExjBXQZUTGY0B38WzHv4q2Yd9kdPBHVC8MIERERKYphhIiIiBTFMEJERESK4gWsRERGSk1NvSPaJLpTMIwQEdVRwaUCSBoJEyZMULoUokaFYYSIqI5KrpZA6AVmvvM4mgf5mrXtw4kpWLd4g1nbJLpTMIwQqZnQA5lJ18eNYGVjhcjpg+RxNWke5IuWHQPN2ubZjHNmbY/oTsIwQqRmQg9k/mbSqtY21oiaEWnmgohIjXg3DRERESmKR0aI1M6hScW/RVeMWk2v1yP7RDYAwLuVNzQa/t+GiEzDMEKkZhpr4K7RFeN7lwP6a3VetaykDK+OeAMA8M6BN6B10FqiQiJSAf5XhoiIiBTFMEJERESKYhghIiIiRTGMEBERkaIYRoiIiEhRDCNERESkKN7aS6RmQg/8nXJ93AhWNlYYOHmAPE5EZCqGESI1E3rg1EGTVrW2scaIOcPMXBARqRFP0xAREZGieGSESO20ThX/6gqMWk2v1+PK+You5Js0a8Lu4InIZAwjRGqmsQa6x1SMm9Ad/ILBLwNgd/BEVD/8rwwREREpyugwsnv3bgwdOhS+vr6QJAkbN240mC+EwIsvvohmzZrB3t4eEREROH78uLnqJSIiokbG6DBSWFiIzp07Y+nSpTXOf/PNN7FkyRJ89NFHOHDgABwdHTFo0CCUlJTUu1giIiJqfIy+ZiQyMhKRkZE1zhNC4L333sPzzz+PYcMqbvn78ssv4e3tjY0bN2Ls2LH1q5aIiIgaHbNeM3Ly5ElkZWUhIiJCnubq6orw8HDs27evxnV0Oh3y8/MNBiIiIlIPs4aRrKwsAIC3t7fBdG9vb3nejRYtWgRXV1d58PPzM2dJRERE1MApfmvv/PnzERcXJ7/Oz89nICG6XYQeOPfn9XEjaKyt0Ce6tzxORGQqs4YRHx8fAEB2djaaNWsmT8/OzkaXLl1qXEer1UKrZf8ERIoQeuDEXpNWtbG1RvTzo81cEBGpkVlP07Rs2RI+Pj7Yvn27PC0/Px8HDhxAz549zbkpIiIiaiSMPjJSUFCA9PR0+fXJkyeRnJwMd3d3+Pv7Y9asWXjllVfQpk0btGzZEi+88AJ8fX0xfPhwc9ZNROZibVfx7zXjbr8XQqDgSiEAwKmJIyRJMndlRKQSRoeRQ4cOYcCAAfLryus9Jk2ahBUrVmDevHkoLCzEtGnTkJubi969e+Onn36CnZ2d+aomIvPQWAM9JlaMG9kdfGlxKeb3ex4Au4MnovoxOoz0798fQoha50uShJdeegkvvfRSvQojIiIideCzaYiIiEhRDCNERESkKIYRIiIiUhTDCBERESmKYYSIiIgUpXh38ESkIKEHso9dHzeCxtoK4Q92l8eJiEzFMEKkZkIPHE80aVUbW2tMfHW8mQsiIjXiaRoiIiJSFI+MEKmd5p8/A0b0vgpUdAdfWlwKALC1t2V38ERkMh4ZIVIzjTVwzyMVg8a4/5uUFpdiTvgzmBP+jBxKiIhMwTBCREREimIYISIiIkUxjBAREZGiGEaIiIhIUQwjREREpCiGESIiIlIU+xkhUjMhgJwT18eNoLHSoOt9neVxIiJTMYwQqZkoB/7abtKqNlobTH33ETMXRERqxP/OEBERkaIYRoiIiEhRDCNEaqaxBno/WjEY2R28rkiHmZ1mYWanWdAV6SxUIBGpAcMIERERKYphhIiIiBTFMEJERESKYhghIiIiRTGMEBERkaIYRoiIiEhR7IGVSM2EAC5nXh83gsZKg5A+HeRxIiJTMYwQqZkoB45uMWlVG60Npn84zcwFEZEa8b8zREREpCiGESIiIlIUwwiRmmmsgZ6TKwYTuoOPu3se4u6ex+7giaheeM0IkdpZ2Zi8amlxqRkLISK14pERIiIiUhTDCBERESmKYYSIiIgUxTBCREREimIYISIiIkXxbhoiVRNA3rnr40aQNBJadwuSx4mITMUwQqRm+nLgjx9NWtXWzhazlj9h5oKISI14moaIiIgUxTBCREREimIYIVIzjTUQPqFiMKE7+Gf7/hvP9v03u4MnonrhNSNEamdjb/KqBVcKzVgIEakVj4wQERGRosweRhYuXAhJkgyG4OBgc2+GiIiIGgmLnKYJCQnBtm3brm/EmmeDiIiIqGYWSQnW1tbw8fGxRNNERETUyFgkjBw/fhy+vr6ws7NDz549sWjRIvj7+9e4rE6ng053/Ur8/Px8S5RECsjMzEROTo7Z201NTTV7m0REpByzh5Hw8HCsWLEC7dq1w/nz5xEfH48+ffrgyJEjcHZ2rrb8okWLEB8fb+4ySGGZmZlo3749ioqKlC6FbkoAVy9eHzeCpJHgH+InjxMRmcrsYSQyMlIeDw0NRXh4OAICArBu3TpMnTq12vLz589HXFyc/Do/Px9+fn7mLotus5ycHBQVFeG9z95G63atzdr2zp934Z2X3zNrm6qlLwdSNpq0qq2dLeatnWPeeohIlSx+Zambmxvatm2L9PT0GudrtVpotVpLl0EKad2uNTp2CTFrm+lpGWZtj4iIlGXxfkYKCgqQkZGBZs2aWXpTREREdAcyexiZO3cuEhMTcerUKezduxcjRoyAlZUVYmJizL0pIqovjRXQbWzFoLEyatXS4lK8OCgeLw6KR2lxqYUKJCI1MPtpmr///hsxMTG4dOkSvLy80Lt3b+zfvx9eXl7m3hQR1ZsE2DlfHzeCEAKXz12Rx4mITGX2MLJ27VpzN0lERESNGJ9NQ0RERIpiGCEiIiJFMYwQERGRovgEOyIiFbDUYxR0Op3F+ory9PSs9VEi1LgwjBCpmgAKr1wfN4IkSfAJ8pHHqWEShWXQaDSYMGGCRdrXaDTQ6/UWadvBwQGpqakMJCrAMEKkZvpy4PB6k1a1tbfF8xufNXNBZHa6cuj1enzw+XsWeDTDTrzx0jsWaTs9LR0zp85CTk4Ow4gKMIwQEalA63atEdq1k1nbPJ6WbrG2SV14ASsREREpikdGiNRMYwV0HlExnpJQcdqmjkqLS/FmzLsAgHlr4mBrb2uJColIBRhGiFRNAhybXB83ghACWRlZ8jgRkal4moaIiIgUxTBCREREimIYISIiIkUxjBAREZGieAGrymVmZiInJ8fs7Vqq62kiUhdL/C1hN/MND8OIimVmZqJ9+/YoKipSuhRSjABKrl4fN4IkSXD3bSKPE5nThawLFuvGnt3MNzwMIyqWk5ODoqIiLPviA7QJbmPWtrdv2YFF8W+YtU2yAH05cGitSava2tvipS0LzFwQUYW8vHzo9Xqz/306/tdxTJ8yk93MNzAMI4Q2wW3QuWuoWds8nnbcrO0RkTpZ4u8TNTy8gJWIiIgUxSMjRGqmsQI6Da0Y/2OTcd3Bl5Tivcn/AQDMWvEEbO3YHTwRmYZhhEjVJMDZ6/q4EYReIPPPM/I4EZGpeJqGiIiIFMUwQkRERIpiGCEiIiJFMYwQERGRohhGiIiISFG8m4ZI7cqKTV7VqYmjGQshIrViGCFSM/014MBXJq2qddDi9d2vmrkgIlIjnqYhIiIiRTGMEBERkaJ4moZIzTRWQMjgivE/fzK6O/gPp38MAJix7DF2B09EJmMYIVI1CXD1vT5uBKEXSD+UIY8TEZmKp2mIiIhIUQwjREREpCiGESIiIlIUwwgREREpimGEiIiIFKW6u2kyMzORk5NjkbZ1Oh20Wq1F2vb09IS/v79F2iaVKy8zeVVbe97OS1QVv2NMo6owkpmZifbt26OoqMgi7Ws0Guj1eou07eDggNTUVAYSMi/9NWDfCpNW1Tpo8e7BN81bD9EdjN8xplNVGMnJyUFRURE+W/EJ2rVva9a2f/7fVry88FWLtJ2Wegz/mjwNOTk5DCNERA0Uv2NMp6owUqld+7bo0rWLWdtM++uYxdomIqI7B79jjKfKMEJE/5CsgPYRFeOp2wBR9+7gy3Rl+Gz2cgDAvxY/AhutjSUqJCIVYBghUjNJAtz9r48b0au7vlyPP385Ko8TEZmKt/YSERGRohhGiIiISFEWCyNLly5FYGAg7OzsEB4ejoMHD1pqU0RERHQHs0gY+frrrxEXF4cFCxbgt99+Q+fOnTFo0CBcuHDBEpsjIiKiO5hFwsi7776LRx99FI888gg6dOiAjz76CA4ODvjiiy8ssTkiIiK6g5n9bprS0lIkJSVh/vz58jSNRoOIiAjs27ev2vI6nQ46nU5+nZeXBwDIz883d2koKCgAACT/loLCgkKztn0s9ZjF2j5+LB0AkJSUJO+DOaSlpQEAfj/8OwoLzVxzWkXNR5KPoKjQvL0Rpv/zflxKv4iyYtO7Mr9RXuaVipH8MsDcd4cUXgMAXM24hPIS89UMAEVnK35nTKpbYw2UlFSMXymp6JG1qn/qzjx6BroincGsUt31/cg4fAK2Rtzam3UyCwBw7q9zKC0uNa7mW7h46qLF2z755ymUFJWYte2zGecAAPrsIoiyut9ifSv6SxV1/pF8BIVm/l3M+Od30ZJtm/vvU8axDADm/3sKXP+beqd+xxQUFJj1u7eyLSHqcJueMLOzZ88KAGLv3r0G059++mlx9913V1t+wYIFAhU3FHLgwIEDBw4cGtlw5syZW2YHxfsZmT9/PuLi4uTXer0ely9fhoeHByRJMuu28vPz4efnhzNnzsDFxcWsbZNx+Fk0HPwsGg5+Fg0DPwfzEELg6tWr8PX1veWyZg8jnp6esLKyQnZ2tsH07Oxs+Pj4VFteq9VWewqhm5ubucsy4OLiwh+wBoKfRcPBz6Lh4GfRMPBzqD9XV9c6LWf2C1htbW0RFhaG7du3y9P0ej22b9+Onj17mntzREREdIezyGmauLg4TJo0Cd26dcPdd9+N9957D4WFhXjkkUcssTkiIiK6g1kkjERHR+PixYt48cUXkZWVhS5duuCnn36Ct7e3JTZXZ1qtFgsWLKh2WohuP34WDQc/i4aDn0XDwM/h9pOEqMs9N0RERESWwWfTEBERkaIYRoiIiEhRDCNERESkKIYRIiIiUpQqwsjChQshSZLBEBwcrHRZqrB7924MHToUvr6+kCQJGzduNJgvhMCLL76IZs2awd7eHhERETh+/LgyxTZit/ocJk+eXO13ZPDgwcoU28gtWrQI3bt3h7OzM5o2bYrhw4fLzzSpVFJSgtjYWHh4eMDJyQmjRo2q1pEk1V9dPov+/ftX+914/PHHFaq48VJFGAGAkJAQnD9/Xh727NmjdEmqUFhYiM6dO2Pp0qU1zn/zzTexZMkSfPTRRzhw4AAcHR0xaNAglJSY9yFkanerzwEABg8ebPA7smbNmttYoXokJiYiNjYW+/fvx9atW1FWVob777/f4GFws2fPxqZNm/DNN98gMTER586dw8iRIxWsunGqy2cBAI8++qjB78abb76pUMWNmFmejtfALViwQHTu3FnpMlQPgEhISJBf6/V64ePjI9566y15Wm5urtBqtWLNmjUKVKgON34OQggxadIkMWzYMEXqUbsLFy4IACIxMVEIUfE7YGNjI7755ht5mdTUVAFA7Nu3T6kyVeHGz0IIIfr16yeeeuop5YpSCdUcGTl+/Dh8fX3RqlUrjB8/HpmZmUqXpHonT55EVlYWIiIi5Gmurq4IDw/Hvn37FKxMnXbt2oWmTZuiXbt2mD59Oi5duqR0SaqQl5cHAHB3dwdQ8Wj7srIyg9+L4OBg+Pv78/fCwm78LCqtWrUKnp6e6NixI+bPn4+ioiIlymvUFH9q7+0QHh6OFStWoF27djh//jzi4+PRp08fHDlyBM7OzkqXp1pZWVkAUK1nXm9vb3ke3R6DBw/GyJEj0bJlS2RkZOC5555DZGQk9u3bBysrK6XLa7T0ej1mzZqFXr16oWPHjgAqfi9sbW2rPTCUvxeWVdNnAQDjxo1DQEAAfH198fvvv+OZZ55BWloavv32WwWrbXxUEUYiIyPl8dDQUISHhyMgIADr1q3D1KlTFayMqGEYO3asPN6pUyeEhoYiKCgIu3btwsCBAxWsrHGLjY3FkSNHeA1bA1DbZzFt2jR5vFOnTmjWrBkGDhyIjIwMBAUF3e4yGy3VnKapys3NDW3btkV6errSpaiaj48PAFS7SyA7O1ueR8po1aoVPD09+TtiQTNnzsQPP/yAnTt3okWLFvJ0Hx8flJaWIjc312B5/l5YTm2fRU3Cw8MBgL8bZqbKMFJQUICMjAw0a9ZM6VJUrWXLlvDx8cH27dvlafn5+Thw4AB69uypYGX0999/49KlS/wdsQAhBGbOnImEhATs2LEDLVu2NJgfFhYGGxsbg9+LtLQ0ZGZm8vfCzG71WdQkOTkZAPi7YWaqOE0zd+5cDB06FAEBATh37hwWLFgAKysrxMTEKF1ao1dQUGDwP4iTJ08iOTkZ7u7u8Pf3x6xZs/DKK6+gTZs2aNmyJV544QX4+vpi+PDhyhXdCN3sc3B3d0d8fDxGjRoFHx8fZGRkYN68eWjdujUGDRqkYNWNU2xsLFavXo3vvvsOzs7O8nUgrq6usLe3h6urK6ZOnYq4uDi4u7vDxcUFTzzxBHr27IkePXooXH3jcqvPIiMjA6tXr8aQIUPg4eGB33//HbNnz0bfvn0RGhqqcPWNjNK389wO0dHRolmzZsLW1lY0b95cREdHi/T0dKXLUoWdO3cKANWGSZMmCSEqbu994YUXhLe3t9BqtWLgwIEiLS1N2aIboZt9DkVFReL+++8XXl5ewsbGRgQEBIhHH31UZGVlKV12o1TT5wBALF++XF6muLhYzJgxQzRp0kQ4ODiIESNGiPPnzytXdCN1q88iMzNT9O3bV7i7uwutVitat24tnn76aZGXl6ds4Y2QJIQQtzP8EBEREVWlymtGiIiIqOFgGCEiIiJFMYwQERGRohhGiIiISFEMI0RERKQohhEiIiJSFMMIEVnct99+i4ULF+KPP/5QuhQiaoAYRojIopKSkjB27FgcOHAAHTp0ULocImqAGEaIGrFx48ZBkiS8/PLLt1z24MGDkCQJ3t7e2LZtGyRJwuTJk+u1/aKiIowfPx6tW7fG2rVrYWVlZVI7gYGBkCSpXrUQUcPFMELUiE2cOBEAsGrVqlsu+9VXXwEAYmJiYG1d82OrFi5cCEmSsGLFijptf9asWbh48SI2bdoEV1fXuhVNRKqjigflEanV/fffD29vb6SlpeHXX39F9+7da1zu2rVr+PrrrwFUBJj27dsjNTW1XgHiypUraN68OX788UcEBQWZ3A4AbN++HWVlZfVqg4gaLh4ZIWrEqj6duvLIR01+/vlnXLhwAe3bt0dYWBgcHBwQHBxcr8ekN2nSBAsWLDDLk2aDgoIQHBxc73aIqGFiGCFq5CZMmAAA+Prrr1FeXl7jMpWncSqX3bVrV7VrRgIDAxEfHw8AeOSRRyBJkjzs2rXLoL3//ve/6N27N1xcXODg4IDQ0FAsWrQIJSUl1bY9efJkuY0tW7ZgwIABcHNzgyRJyM3Nlbdd0zUjP/74I6ZMmYL27dvDxcUFjo6O6Ny5M1577TXodLoa93Xz5s2477770Lx5c2i1Wvj6+qJ3797yvhHR7ccwQtTIhYWFoX379sjOzsbWrVurzS8sLMR3330HSZIwfvz4WtsZPXo0OnfuDADo1asXJk2aJA8+Pj7yco899hgefvhhJCUloU+fPoiKisL58+fx3HPP4d5770VRUVGN7a9evRqRkZEoLCxEZGQkunfvfsuLVqdOnYoNGzbA3d0dkZGR6NOnD86cOYN///vfGDJkSLXwtXTpUkRFRWHnzp1o3bo1Ro0ahY4dO+L06dNYuHDhTbdFRBYkiKjRe+211wQAMX78+GrzvvzySwFA9OvXT562c+dOAUBMmjTJYNkFCxYIAGL58uU1bmf9+vUCgPD19RXHjh2Tp+fm5orevXsLAGLOnDkG60yaNEkAEADE2rVra2w3ICBA1PTnauPGjaKoqMhgWn5+vnjggQcEALFy5UqDef7+/kKSJPHrr78aTNfr9WLnzp01bpuILI9HRohUYPz48ZAkCRs3bkRhYaHBvMprSSpP0dTHkiVLAAALFixAmzZt5Omurq5YunQpJEnCxx9/XOPpmqioKERHRxu1vWHDhsHe3t5gmrOzMxYvXgwA+O677wzmXbx4EW5ubujWrZvBdEmS0L9/f6O2TUTmw7tpiFTA398fffv2RWJiIjZu3CifjsnOzsb27dthZ2eHMWPG1GsbZWVl2L9/PwDUeLonNDQUoaGhSElJQXJycrULWx988EGTtnv8+HFs3rwZ6enpKCwshF6vhxBCnldVWFgY9uzZg6lTpyIuLg4hISEmbZOIzIthhEglJk6ciMTERHz11VdyWFizZg3Ky8sxcuTIevcDcunSJZSWlsLT0xOOjo41LhMYGIiUlBScPXu22jx/f3+jtieEwNy5c7F48WI5fNzo6tWrBq+XLl2K4cOH44svvsAXX3wBb29v9OvXDyNHjsTo0aNN7pSNiOqHp2mIVGL06NGws7PDtm3bcOHCBQDXT9FUdo5maTe7INXOzs6otr7++mu8++67aNGiBdavX4+zZ8+itLQUQgj5TpobQ0poaCiOHj2KhIQEPProo3BxccG6deswduxY9OnTB6WlpcbvFBHVG8MIkUq4urriwQcfxLVr17BmzRr89ddfSEpKgqenJwYPHlzv9j08PGBra4ucnJxq16VUOnXqFACgefPm9d5eQkICAGDZsmUYNWoUfH19YWNjAwA4ceJErevZ2dlh+PDh+OSTT3Ds2DEcOXIEoaGh2LdvHz777LN610VExmMYIVKRyotUV61aJfctEh0dLX+J34qtrS2Aih5bb2RjYyNfB7J27dpq848cOYKUlBQ4OTmhS5cuppRv4MqVKwCAFi1aVJu3bt26OrcTEhKC2NhYuUYiuv0YRohUZPDgwfD09MSvv/6Kjz76CIBxp2h8fX0BAGlpaTXOf+KJJwBUPMOm6tGJq1evYubMmRBC4LHHHjP6lExN2rZtCwD45JNPDE7H/PLLL3jrrbeqLV9UVIQlS5bIHalV0uv1+OmnnwAAfn5+9a6LiEyg4G3FRKSAmTNnyv16tGnTpsZlautn5OzZs8LOzk5YWVmJwYMHiylTpoipU6eKv/76S15m2rRpAoCwt7cXUVFRYsyYMcLLy0sAED169BCFhYUGbVb2M3Kzfj5q6mckLS1NODo6CgCiQ4cOYuzYsaJPnz5CkiQxd+5cAUAEBATIy1+5ckUAEDY2NqJHjx5i7NixYuTIkcLPz08AEIGBgSInJ6dubyIRmRWPjBCpTNUjIcb2LeLr64vvvvsOPXr0wJ49e/DFF1/g888/x/nz5+VlPv74Y3z55Zfo2rUrEhMTsWnTJjRt2hSvvvoqduzYAQcHB7PsR9u2bXHo0CEMHToUOTk5+P7771FQUICPP/64xiMjTk5OWLp0KYYOHYqLFy/i+++/x44dO9CkSRPEx8cjKSkJHh4eZqmNiIwjCVHLPXFEREREtwGPjBAREZGiGEaIiIhIUQwjREREpCiGESIiIlIUwwgREREpimGEiIiIFMUwQkRERIpiGCEiIiJFMYwQERGRohhGiIiISFEMI0RERKQohhEiIiJS1P8DVuajmJn5IzgAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(5,4))\n",
        "eixo = fig.add_axes([0,0,1,1])\n",
        "mu, mediana, moda = df['Empates'].mean(), df['Empates'].median(), df['Empates'].mode().iloc[0]\n",
        "\n",
        "N, bins, patches = eixo.hist(df['Empates'], bins=20, edgecolor='black')\n",
        "eixo.set_title('Frequência dos empates', fontsize=15, pad=10)\n",
        "eixo.set_xlabel('Empates', fontsize=15)\n",
        "\n",
        "fracs = N / N.max()\n",
        "\n",
        "norm = colors.Normalize(fracs.min(), fracs.max())\n",
        "\n",
        "for thisfrac, thispatch in zip(fracs, patches):\n",
        "    color = plt.cm.Wistia(norm(thisfrac))\n",
        "    thispatch.set_facecolor(color)\n",
        "\n",
        "eixo.annotate('$média = {0:.1f}$'.format(mu), xy=(12.7, 30), fontsize=10, color='black')\n",
        "eixo.annotate('$moda=mediana = {0:.0f}$'.format(mediana), xy=(12.7, 28), fontsize=10, color='blue')\n",
        "\n",
        "eixo.axvline(mu, color = 'k', linestyle='--')\n",
        "eixo.axvline(mediana, color='blue', linestyle='--')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "wReKVSuGI1zy",
        "outputId": "277133e0-d3ad-4f2c-8bea-5bf0d7345427"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(5,4))\n",
        "eixo = fig.add_axes([0,0,1,1])\n",
        "mu, mediana, moda = df['Derrotas'].mean(), df['Derrotas'].median(), df['Derrotas'].mode().iloc[0]\n",
        "\n",
        "N, bins, patches = eixo.hist(df['Derrotas'], bins=20, edgecolor='black')\n",
        "eixo.set_title('Frequência das derrotas', fontsize=15, pad=10)\n",
        "eixo.set_xlabel('Derrotas', fontsize=15)\n",
        "\n",
        "fracs = N / N.max()\n",
        "\n",
        "norm = colors.Normalize(fracs.min(), fracs.max())\n",
        "\n",
        "for thisfrac, thispatch in zip(fracs, patches):\n",
        "    color = plt.cm.Reds(norm(thisfrac))\n",
        "    thispatch.set_facecolor(color)\n",
        "\n",
        "eixo.annotate('$média = {0:.1f}$'.format(mu), xy=(15.5, 23), fontsize=10, color='black')\n",
        "eixo.annotate('$moda=mediana = {0:.0f}$'.format(mediana), xy=(15.5, 21), fontsize=10, color='darkblue')\n",
        "eixo.annotate('média≈moda=mediana', xy=(15.5, 19), fontsize=10, color='purple')\n",
        "\n",
        "eixo.axvline(mu, color = 'k', linestyle='--')\n",
        "eixo.axvline(mediana, color='darkblue', linestyle='--')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "AFBAenAnI1wz",
        "outputId": "42f6781e-205f-4907-81d0-0ced11368370"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(5,4))\n",
        "eixo = fig.add_axes([0,0,1,1])\n",
        "mu, mediana, moda = df['Saldo'].mean(), df['Saldo'].median(), df['Saldo'].mode().iloc[0]\n",
        "\n",
        "N, bins, patches = eixo.hist(df['Saldo'], bins=20, edgecolor='black')\n",
        "eixo.set_title('Frequência dos saldos de gol', fontsize=15, pad=10)\n",
        "eixo.set_xlabel('Saldo de gol', fontsize=15)\n",
        "\n",
        "fracs = N / N.max()\n",
        "\n",
        "norm = colors.Normalize(fracs.min(), fracs.max())\n",
        "\n",
        "for thisfrac, thispatch in zip(fracs, patches):\n",
        "    color = plt.cm.Blues(norm(thisfrac))\n",
        "    thispatch.set_facecolor(color)\n",
        "\n",
        "eixo.annotate('$média≈mediana = {0:.0f}$'.format(mediana), xy=(27, 25), fontsize=10, color='black')\n",
        "eixo.annotate('$moda={0:.0f}$'.format(moda), xy=(27, 23), fontsize=10, color='seagreen')\n",
        "\n",
        "eixo.axvline(mu, color = 'k', linestyle='--')\n",
        "eixo.axvline(mediana, color='darkblue', linestyle='--')\n",
        "eixo.axvline(moda, color='seagreen', linestyle='--')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "j6RhcAO6KYTR",
        "outputId": "3f61dfac-7755-4c78-d370-bd27859f7a52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(5,4))\n",
        "eixo = fig.add_axes([0,0,1,1])\n",
        "mu, mediana, moda = df['Aproveitamento'].mean(), df['Aproveitamento'].median(), df['Aproveitamento'].mode().iloc[0]\n",
        "\n",
        "N, bins, patches = eixo.hist(df['Aproveitamento'], bins=20, edgecolor='black')\n",
        "eixo.set_title('Frequência dos aproveitamentos', fontsize=15, pad=10)\n",
        "eixo.set_xlabel('Aproveitamento', fontsize=15)\n",
        "\n",
        "fracs = N / N.max()\n",
        "\n",
        "norm = colors.Normalize(fracs.min(), fracs.max())\n",
        "\n",
        "for thisfrac, thispatch in zip(fracs, patches):\n",
        "    color = plt.cm.plasma_r(norm(thisfrac))\n",
        "    thispatch.set_facecolor(color)\n",
        "\n",
        "eixo.annotate('$média = {0:.1f}$'.format(mu), xy=(65, 29), fontsize=12, color='black')\n",
        "eixo.annotate('$mediana = {0:.0f}$'.format(mediana), xy=(65, 27.3), fontsize=12, color='darkorange')\n",
        "eixo.annotate('$moda={0:.0f}$'.format(moda), xy=(65, 25.6), fontsize=12, color='deeppink')\n",
        "\n",
        "eixo.axvline(mu, color = 'k', linestyle='--')\n",
        "eixo.axvline(mediana, color='darkorange', linestyle='--')\n",
        "eixo.axvline(moda, color='deeppink', linestyle='--')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "trNupW_2WuKO",
        "outputId": "ad79fbf7-0c84-4fe1-9365-0a57bad7375c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "cores = ['white' if valor <= pos_media else 'cornflowerblue' for valor in media_pos['Posicao']]\n",
        "plt.bar(media_pos.index, media_pos['Posicao'], color=cores,  edgecolor='black')\n",
        "plt.axhline(df['Posicao'].mean(), color = 'k', linestyle='--')\n",
        "plt.annotate('média geral', xy=(5, 8), fontsize=12, color='black')\n",
        "plt.xlabel('Times')\n",
        "plt.ylabel('Média da posição')\n",
        "plt.title('Média das posições dos times no Campeonato Brasileiro')\n",
        "plt.xticks(rotation=90)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "j6lYhfvGKYOI",
        "outputId": "180d2202-d502-47d5-d3d3-ed78b0dc6920"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "cores = ['white' if valor <= pt_media else 'darksalmon' for valor in media_pt['Pontos']]\n",
        "plt.bar(media_pt.index, media_pt['Pontos'], color=cores,  edgecolor='black')\n",
        "plt.axhline(df['Pontos'].mean(), color = 'k', linestyle='--')\n",
        "plt.annotate('média geral', xy=(4, 60), fontsize=12, color='black')\n",
        "plt.xlabel('Times')\n",
        "plt.ylabel('Média da pontuação')\n",
        "plt.title('Média das pontuações dos times no Campeonato Brasileiro')\n",
        "plt.xticks(rotation=90)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "PCwGL-ulY8Oq",
        "outputId": "9898ace1-0324-483a-a358-3a07a9ff3324"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "cores = ['white' if valor <= vit_media else 'palegreen' for valor in media_vit['Vitorias']]\n",
        "plt.bar(media_vit.index, media_vit['Vitorias'], color=cores,  edgecolor='black')\n",
        "plt.axhline(df['Vitorias'].mean(), color = 'k', linestyle='--')\n",
        "plt.annotate('média geral', xy=(4, 16.5), fontsize=12, color='black')\n",
        "plt.xlabel('Times')\n",
        "plt.ylabel('Média das vitórias')\n",
        "plt.title('Média das vitórias dos times no Campeonato Brasileiro')\n",
        "plt.xticks(rotation=90)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "DMxNQqHzY8MN",
        "outputId": "f512fd55-2b51-49b0-a4d5-c350e6fcf822"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "cores = ['white' if valor <= emp_media else 'khaki' for valor in media_emp['Empates']]\n",
        "plt.bar(media_emp.index, media_emp['Empates'], color=cores,  edgecolor='black')\n",
        "plt.axhline(df['Empates'].mean(), color = 'k', linestyle='--')\n",
        "plt.annotate('média geral', xy=(5.5, 10.5), fontsize=12, color='black')\n",
        "plt.xlabel('Times')\n",
        "plt.ylabel('Média dos empates')\n",
        "plt.title('Média dos empates dos times no Campeonato Brasileiro')\n",
        "plt.xticks(rotation=90)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "HKOyZ2_IY8J4",
        "outputId": "76b0f619-65ab-4c89-c86a-e9c90a798bc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "cores = ['white' if valor <= der_media else 'lightcoral' for valor in media_der['Derrotas']]\n",
        "plt.bar(media_der.index, media_der['Derrotas'], color=cores,  edgecolor='black')\n",
        "plt.axhline(df['Derrotas'].mean(), color = 'k', linestyle='--')\n",
        "plt.annotate('média geral', xy=(5, 12.5), fontsize=12, color='black')\n",
        "plt.xlabel('Times')\n",
        "plt.ylabel('Média das derrotas')\n",
        "plt.title('Média das derrotas dos times no Campeonato Brasileiro')\n",
        "plt.xticks(rotation=90)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "AXo7OQacY8He",
        "outputId": "83c3ab6b-d53c-43e9-b360-3b33c3b13f5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "cores = ['white' if valor <= sg_media else 'thistle' for valor in media_sg['Saldo']]\n",
        "plt.bar(media_sg.index, media_sg['Saldo'], color=cores,  edgecolor='black')\n",
        "plt.axhline(df['Saldo'].mean(), color = 'k', linestyle='--')\n",
        "plt.annotate('média geral', xy=(3, 8), fontsize=12, color='black')\n",
        "plt.xlabel('Times')\n",
        "plt.ylabel('Média dos salos de gol')\n",
        "plt.title('Média dos saldos de gol dos times no Campeonato Brasileiro')\n",
        "plt.xticks(rotation=90)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "mijSsLQHY8Ez",
        "outputId": "4fb2db3c-ae36-496f-b2b2-7cbfb522d7c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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eOsXV2KtXL9nZ/d9utmPHjsqbN6/27dsnScqbN6+kh0dpIyIiUlR/auZ7dF1DQ0N18+ZN1a5dWxcvXkzyFPT9+/erUKFCatGihaUtd+7c8a5F/OOPP3Tjxg117drV6ih+o0aN5OHhYTl1NFeuXHJ0dNTRo0dTfLq2JF2/fl2BgYFq166d1dH8+vXrq1y5clZ9U/r5Sci1a9d0+vRp+fn5KU+ePJb22rVry9PTM95rY29vH++2Zv369ZPZbNb+/ftTvH5xy3NwcFDXrl0tbfb29urRo4dVv0dfi4IFC1raK1SooHr16lk+U5KUP39+nThxIt5p+umhQ4cOVtth1apVZTab1aFDB0ubvb29KleurIsXL1raUvp+xb3ve/fuTdEZKo8rV66c1UCLrq6uKlOmjFUt+/btU9WqVa365cmTR507d9bly5eTHE06LCzM0j8lHt0m79+/r1u3bsnd3V358+dPcP+T1tc3TtOmTa3OwqhataqqVatm+bzcvn1bP/30k1q2bKmwsDDL+3Dr1i01aNBA58+ft3yOUvs6tW/f3ura/bj5Hq3zSfbHkjHbo7Ozs7755ht98803WrBggSZPniwXFxcNHDhQQUFB8fp36tQp3qBhKX1f8+fPrzNnzuj8+fPJ1pWUbdu2qUaNGsqfP7/V9lOvXj3FxMTo559/TnYZXbp0sXoc95no27evVXu/fv2spgNIHqeIA0g3rq6uqlu3rjZt2qTIyEjFxMSoefPmCfa9cOGCQkNDVbdu3QSn37hxQ5J05coVSVLp0qXjPVeBAgWSrenKlSuaMWOGdu/eHS/sxf2xnNS81apVi9depkyZeP0kycPDw6rdyclJJUuW1OXLlyVJJUuWVN++ffXNN98oICBANWvWlK+vr1566aUk7/2d0vmOHTummTNn6vjx4/GCeGhoaKLPcfnyZZUqVSreqNGJrefj7XHrfuzYMct6jxw5Uh999JHq16+vatWqqVGjRvLz81OhQoUSXc+45ZcqVSretDJlylj94ZrSz09Sz+Pu7h5vWqlSpaye5/LlyypcuLDlR444ZcuWtUxPjcuXL6tQoULxAlpqXuuyZcvq4MGDloGKRo4cqdGjR6tRo0aqVKmSfHx85Ofnp5IlS6aqtpQoVqyY1eO4z9QzzzwTr/3R7S2l71ft2rXVvHlzffnll1q0aJFq166tpk2bqm3btikaeO3xOiSpQIECVrUktl3Hbb9XrlyJ90NLnLjPwb1795KtRXp4+cXcuXO1du1aXbt2zeo64YR+9Err6xsnoW2ndOnS2rp1qyQpODhYZrNZ06dP1/Tp0xOs+caNGypSpEiqX6fHa4+7XOTRcSieZH8sGbM92tvbq169elZtPj4+evHFF/X555/Huz1YQpe0pPR9HTZsmAYPHqzmzZvL09NTDRo00Msvv6wKFSokW+ejLly4oL///jvR7SfulPekPL4ely9flp2dXbz9YKFChZQ/f/5U79uAnIyADSBdtWnTRuPHj1dISIgaNmwY7/Y/cWJjY/X000/r008/TXB6cteCpkRMTIz69u2rO3fu6NVXX5WHh4dcXFx07do1jR49OtHBytLT6NGj1a5dO+3atUuHDh3SlClTNHfuXK1evTrJ2/okN19wcLD69OkjDw8PjR49Ws8884wcHR21b98+LVq0KMPXtU+fPvL19dXOnTt18OBBTZ8+XfPmzdPixYtVsWLFJ15+Rnx+sotWrVqpZs2a2rFjhw4dOqQFCxZo/vz5mjlzpmU8BKM8eoZGStrjpPT9MplMmjFjho4fP649e/bowIEDGjt2rL755ht9++23yR45Tu/bE8WFy7///jtFdz547733tHbtWvXu3VvVq1dXvnz5ZDKZrK6DfVRaX9+UitsP9OvXTy+88EKCfRL64SklEqsxbj2z4v44TtGiRVWmTJkEjwQnNOZGSt/XWrVqaceOHZb99vfff6/Fixdr0qRJ6tixY4rri42NVf369fXqq68mOP3xH6ATktjo4tyOD3hyBGwA6apZs2Z69913dfz4cU2bNi3Rfu7u7vrxxx/13HPPWZ1u97i4oyLnz5+3OiJ38+bNZE8/Pn36tM6fP6+PPvrIajCgR0dwTUqxYsXijYIrKd5phHE1njt3zqrG6OhoXbp0Kd7RkrhRcQcPHqxff/1VXbt21cqVKzVixIgk60lqvt27dys6OlqzZ8+2OpKU1KnScYoXL67Tp0/LbDZb/bGV2HoGBQXFO5ISFBQU7wiWu7u7+vXrp379+un8+fPy8/PTwoULEw1ZcfOn5DVP6ecnqecJDg6ON+3x5y5evLh+/PFHhYWFWR01O3funGV6ahQvXlw//fST7t27ZxUWk3qtH3fu3Dk99dRTVrfZKVy4sLp3767u3bvrxo0bateunebMmZNkwM7IP6xT+35Vr15d1atX14gRIxQQEKCRI0dqy5YtqQoliSlWrFiir2vc9MQ0bNhQ9vb2CggIiDfAWEJ++OEH+fn5WY3IHhUVlexdA9IqoW3n/Pnzls9p3P7J0dEx3n7pcU/yOiUkNfvjxD6bRm+Pj4qJibEMYpac1LyvBQsW1CuvvKJXXnlF9+7dU48ePTRz5sxUfZbd3d0VHh6e7HuWGsWLF1dsbKwuXLhgOQNAeji43d27d5/otQRyGq7BBpCu8uTJo4kTJ2ro0KHxbnf0qJYtWyomJkazZs2KN+3BgweW0wrr1asnR0dHLVu2zOrIwOLFi5OtJe6IyqPzmc1mLVmyJEXr4uPjo+PHj+vkyZOWtps3byogIMCqX1yNS5cutXqu77//XqGhoZaQExYWpgcPHljN6+npKTs7uyRvi5KS+eKO3D1+quKaNWuSXc+GDRvq+vXr2rZtm6UtIiJCq1evtupXuXJlPf3001q1apVVvfv27dM///yjRo0aWeaNioqymtfd3V158uRJcj0LFy4sb29vrVu3zuoP1UOHDsW73jOln5+EFClSRJ6enpYRl+McPXo03i2NGjZsqJiYGMttg+IsWrRIJpNJDRs2TPR5EtKwYUM9ePBAK1eutLTFxMRo2bJlVv3iXov169dbrcvp06d16NAhy2cqJiYm3h/1Tz/9tAoXLpzsrXbiRjpOr7D3qJS+X3fu3Il3ZDfuSLFRtw7y8fHRyZMn9dtvv1nawsPDtXr1ahUvXjze9f6PeuaZZ9SxY0cdPHgwwTsjxMbGauHChbp69aqkhI+oL126VDExMQasSXw7d+60uhb/5MmTOnHihOVz+vTTT6t27dr69ttvdf369XjzP3qq8ZO8TglJzf447rP5+HZs9PYYJygoSEFBQSk+dTul7+utW7esHufJk0fu7u6p/iy3bNlSv/32mw4cOBBv2t27d+N9P6RE3D7k8e/Sb775xmo6gORxBBtAumvXrl2yfWrXrq3OnTtr7ty5CgwMVP369eXo6Kjz589r27Zteuedd9SiRQu5urqqX79+mjt3rvz9/eXj46M///xT+/fvt9xaKDEeHh5yd3fXRx99pGvXrilv3rz64Ycfkgxfj3r11Ve1YcMGvfrqq+rVq5flNl3FihXT33//benn6uoqf39/ffnll3r11Vfl6+uroKAgrVixQlWqVLHc0uenn37S5MmT1aJFC5UuXVoxMTHasGGD7O3tE71WPaXzxb1+gwYNUpcuXXTv3j199913evrpp/Xff/8luZ6dOnXS8uXLNWrUKJ06dUqFChXShg0b4h1pdHR01MiRIzVmzBj16NFDrVu3ttymq3jx4pb7Ap8/f159+vRRixYtVK5cOdnb22vnzp0KCQlR69atk6zlzTfflL+/v7p166ZXXnlFt2/f1rJly1S+fHmro0sp/fwkZsSIERo8eLC6du2q9u3b6+7du1q+fLk8PT2tQrevr6/q1KmjadOm6fLly/Ly8tKhQ4e0a9cu9e7dO9Wn0/r6+uq5557TZ599psuXL6tcuXLavn17giH37bff1oABA9S5c2d16NDBcpuufPnyaciQIZIeXgvs4+Oj5s2bq0KFCnJxcdHhw4f1+++/Wx1dS0jcLbymTZumVq1aydHRUY0bN7Y6Mm6UlL5f69at08qVK9W0aVO5u7vr3r17Wr16tfLmzZvm8PS4gQMHavPmzRowYIB69uypAgUKaP369bp06ZJmzpyZ7OnYo0eP1sWLFzVlyhRt375djRs3Vv78+fXvv/9q27ZtOnfunOVz3qhRI23YsEF58+ZVuXLldPz4cR0+fNhq4Dojubu7q2vXruratauio6O1ZMkSFSxY0OrU4nfffVfdunVT27Zt1alTJ5UsWVIhISE6fvy4rl69qo0bNxryOj0uNfvjuM/mlClT1KBBA9nb26t169aGbI8PHjzQhg0bJD0M+JcuXdKqVasUGxur119/PUXrktL3tXXr1qpdu7YqVaqkggUL6vfff7fcUi81+vfvr927d2vQoEFq166dKlWqpIiICJ0+fVo//PCDdu3alerLYipUqKB27drp22+/1d27d1WrVi39/vvvWrdunZo2barnn38+VcsDcjICNoAsY/LkyapcubJWrVqladOmyd7eXsWLF9dLL72k5557ztJv+PDhcnJy0qpVq3TkyBFVrVpVCxculL+/f5LLd3R01Jw5cyzXKzs7O6tZs2bq3r27Xn755WTrK1y4sJYsWaIpU6Zo3rx5KliwoLp06aLChQvHu+/o0KFD5erqqmXLlumDDz5QgQIF1KlTJ7355puW+zF7eXmpQYMG2rNnj65du6bcuXPLy8tL8+fPV/Xq1ROtIyXzeXh4aMaMGfriiy/00Ucfyc3NTV27dpWrq2uy94bNnTu3Fi1apPfee0/Lli1Trly51LZtWzVs2DDeNX/t27dXrly5NH/+fH366adycXFR06ZN9b///c9yvX3RokXVunVr/fjjj9q4caPs7e3l4eGhL774IskfEqSHR6imT5+uL774Qp999pnc3d31wQcfaNeuXTp69KhV35R+fhLi6+trGdDos88+U+nSpfXBBx9o/fr1OnPmjKWfnZ2dZs+erRkzZmjLli1au3atihcvrrffftsy2m5qxC1v6tSp2rhxo0wmk3x9fTV69Oh4pxzXq1dPX3/9tWbMmKEZM2bIwcFBtWrV0v/+9z/Lqb65cuVS165ddejQIW3fvl1ms1nu7u6WEJWUqlWr6o033tCqVat04MABxcbGateuXekSsKWUvV+1a9fW77//ri1btigkJET58uVT1apV9emnnxo2aJubm5tWrVqlTz75RMuWLVNUVJS8vLw0Z84cy1kYScmdO7fmz5+vtWvXav369Zo1a5YiIyNVuHBh1alTR59++qllJO933nlHdnZ2CggIUFRUlJ577jl98803iV5L+6T8/PxkZ2enxYsX68aNG6patarGjx+vwoULW/qUK1dOa9as0Zdffql169bp9u3bcnV1VcWKFa0C5pO+To9Lzf74xRdfVM+ePbV582Zt3LhRZrNZrVu3NmR7jI6O1ttvv215nDdvXlWpUkWffPJJooOIPS6l72vPnj21e/duHTp0SNHR0SpWrJiGDx+u/v37p+h54uTOnVtLly7V3LlztW3bNq1fv1558+ZV6dKlNXTo0CQHyUzKlClTVKJECa1bt047d+6Um5ub/P39LT/gAUgZkzmhUTUAAIBefvllubq6Wk6TBLKDS5cuqUmTJnr77bdTHd4AAE+Ga7ABADne/fv34123eOTIEf3111+qXbt2JlUFAACyG04RBwDkeNeuXVPfvn310ksvqXDhwjp37pxWrVqlQoUKqUuXLpldHgAAyCYI2ACAHK9AgQKqVKmSvvvuO928eVMuLi7y8fHRyJEjkx08DwAAIA7XYAMAAAAAYACuwQYAAAAAwAAEbAAAAAAADEDABgAAAADAAAxyZqCaNWsqOjpahQoVyuxSAAAAAAAG+O+//+Tk5KRffvkl2b4EbANFRUUpJiYms8sAAAAAABjkwYMHSunY4ARsAxUuXFiStGvXrkyuBAAAAABghCZNmqS4L9dgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABnDI7AIAAAAAAKkXHByskJCQzC7DEG5ubnJ3d8/sMp4YARsAAAAAspng4GB5e3srPDw8s0sxhIuLiwIDA7N9yM7SAfvChQtasGCBTpw4oTNnzsjDw0ObNm2yTA8LC9M333yjffv26fz583JyclLVqlU1YsQIeXl5JbnsI0eOqFevXvHaW7VqpWnTphm+LgAAAABglJCQEIWHh+uTsZ/Io5RHZpfzRM5dOKf/Tf2fQkJCCNjp6cyZM9q3b5+qVaum2NhYmc1mq+lXrlzRt99+q1deeUXDhw9XVFSUFi5cqM6dO2vNmjUqW7Zsss/xwQcfyMPj/z6QTz31lOHrAQAAAADpwaOUhyp5VsrsMvD/ZemA7evrq6ZNm0qSRo8erT/++MNqeokSJbRjxw7lzp3b0vb888/L19dXK1as0Pjx45N9jvLly6tKlSrGFg4AAAAAyHGydMC2s0t6kHMXF5d4bXny5JG7u7uuX7+eXmUBAAAAyCJsZaAvWxnkK6fL0gE7Le7evaszZ86oXr16Keo/cOBA3b59W4UKFVLr1q31xhtvKFeuXOlcJQAAAIAnZUsDfdnKIF85nc0F7E8++UQmk0ldu3ZNsl++fPn06quvqlatWnJ2dtZPP/2khQsX6ty5c5o7d24GVQsAAAAgrWxloC9bGuQrp7OpgL1mzRqtXr1aH374oYoWLZpk34oVK6pixYqWx3Xr1lXhwoU1efJknTx5UlWrVk3vcgEAAAAYgIG+kFUkfZFzNrJv3z5NmDBBgwcPVrt27dK0jJYtW0pSvMHUAAAAAABIjk0E7OPHj+uNN96Qn5+f3njjjcwuBwAAAACQA2X7gH327Fn5+/vr+eef16RJk55oWZs3b5YkbtsFAAAAAEi1LH0NdkREhPbt2ydJunz5ssLCwrRt2zZJUu3atWU2m9W/f385Ozurd+/eVqd2582bV+XKlbPM26xZMw0ePFhDhgyRJI0cOVKlSpVSxYoVLYOcLVq0SE2bNiVgAwAAAABSLUsH7Bs3bsQ75Tvu8ZIlSyRJV69elST16dPHql/t2rW1dOlSSZLZbFZMTIzMZrNlevny5RUQEKCFCxfq/v37Kl68uAYNGqSBAwem1+oAAAAAAGxYlg7YJUqU0N9//51kn+SmJ7Ycf39/+fv7P1F9AAAAQFYQHByskJCQzC7jibm5uXGbKmRrWTpgAwAAAEhacHCwvL29FR4entmlPDEXFxcFBgYSspFtEbABAACAbCwkJETh4eH6ZOwn8ijlkdnlpNm5C+f0v6n/U0hICAEb2RYBGwAAALABHqU8VMmzUmaXAeRo2f42XQAAAAAAZAUEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAzgkNkFAAAAAE8qODhYISEhmV2GIdzc3OTu7p7ZZQBIAwI2AAAAsrXg4GB5e3srPDw8s0sxhIuLiwIDAwnZQDZEwAYAAEC2FhISovDwcH0y9hN5lPLI7HKeyLkL5/S/qf9TSEgIARvIhgjYAAAAsAkepTxUybNSZpcBIAdjkDMAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAyQpQP2hQsXNGHCBL388suqWLGi2rRpk2C/7777Ts2bN1eVKlX00ksvac+ePSla/rVr1zR06FA9++yzql27tt555x2FhYUZuQoAAAAAgBwiSwfsM2fOaN++fSpVqpTKli2bYJ/Nmzdr/PjxatmypebPn6/q1atryJAhOn78eJLLvn//vl599VWdP39en332mSZOnKiDBw/qrbfeSoc1AQAAAADYOofMLiApvr6+atq0qSRp9OjR+uOPP+L1mTFjhlq3bq3hw4dLkp5//nmdPn1aX331lebPn5/osn/44QedOXNGW7ZskYeHhyQpf/786t+/v06ePKmqVasav0IAAAAAAJuVpY9g29klXd7Fixd1/vx5tWzZ0qq9VatW+vHHHxUdHZ3ovPv375eXl5clXEtS/fr1VbBgQe3bt+/JCgcAAAAA5DhZOmAn59y5c5KkMmXKWLWXLVtW9+/f18WLF5Oc99FwLUkmk0llypSxLBcAAAAAgJTK1gH7zp07kh6e2v2ouMdx0xNy9+5d5cuXL157gQIFkpwPAAAAAICEZOuADQAAAABAVpGtA3aBAgUkSaGhoVbtd+/etZqekPz58yd4S647d+4kOR8AAAAAAAnJ1gE77hrqx6+ZPnfunBwdHVWyZMkk5318PrPZrKCgoHjXZgMAAAAAkJxsHbBLliyp0qVLa9u2bVbtW7ZsUd26deXk5JTovA0bNtRff/2l8+fPW9p+/PFH3b59Wz4+PulVMgAAAADARmXp+2BHRERYbpl1+fJlhYWFWcJ07dq15erqqqFDh2rkyJFyd3dXnTp1tGXLFp08eVLLli2zLOfy5ctq1qyZBg8erCFDhkiSmjdvrrlz52ro0KF68803FRERoY8//liNGjXiHtgAAAAAgFTL0gH7xo0beuONN6za4h4vWbJEderUUZs2bRQREaH58+dr3rx5KlOmjL788ks9++yzlnnMZrNiYmJkNpstbY6Ojvr66681ZcoUvfnmm3JwcFCzZs00duzYjFk5AAAAAIBNydIBu0SJEvr777+T7dexY0d17Ngx1cspUqSIZs6c+UQ1AgAAAAAgZfNrsAEAAAAAyCoI2AAAAAAAGICADQAAAACAAQjYAAAAAAAYgIANAAAAAIABCNgAAAAAABiAgA0AAAAAgAEI2AAAAAAAGICADQAAAACAAQjYAAAAAAAYwCGzCwAAAIAxgoODFRISktllGMLNzU3u7u6ZXQYApAoBGwAAwAYEBwfL29tb4eHhmV2KIVxcXBQYGEjIBpCtELABAABsQEhIiMLDw/XJ2E/kUcojs8t5IucunNP/pv5PISEhBGwA2QoBGwAAwIZ4lPJQJc9KmV0GAORIDHIGAAAAAIABCNgAAAAAABiAgA0AAAAAgAEI2AAAAAAAGICADQAAAACAAQjYAAAAAAAYgIANAAAAAIABCNgAAAAAABiAgA0AAAAAgAEI2ACQhW3cuFEfffSR7t+/n9mlAAAAIBkOmV0AACBhv/zyi7p06aKlS5fK0dExRfOYTCa9++67mjhxoiRp0aJF6tu3r4KCglS6dOn0KzaT9enTR3v37tX58+czuxRkAcHBwQoJCcnsMgzh5uYmd3f3zC4DAJBCBGwAyIJu376tTp066cMPP9Qrr7yS2eUA2UZwcLC8vb0VHh6e2aUYwsXFRYGBgYRsAMgmCNgAkAUdP35c48aNU79+/Z5oOT179lSXLl3k7OxsUGVA1hYSEqLw8HB9MvYTeZTyyOxynsi5C+f0v6n/U0hICAEbALIJAjYAZEGNGjVSo0aNnng59vb2sre3f/KCMlh4eLhcXFwyuwxkYx6lPFTJs1JmlwEAyGEY5AwADDJx4kSZTCadPn1aPXr0UIECBVSoUCGNHz9eZrNZFy9e1Msvv6z8+fOraNGi+uyzz+ItIyoqSu+++67KlSsnZ2dnlSxZUm+//baioqLi9RsxYoQKFSqkfPny6aWXXtKlS5fiLW/RokUymUxW1yZv2LBBrVu3VrFixeTs7KyyZcvqvffeU0xMTIrWc+/evapZs6Zy5cqlsmXLau7cuZZ1f9yyZctUo0YN5c6dW66ururSpYsuXrxo1adRo0aqXLmyjh07poYNG8rFxUVjx441pFYAAICMxBFsADBY586d5e3trQ8//FCbN2/WlClT5Orqqrlz58rX11cfffSRli9frpEjR6pWrVpq2LChJCk2NlYvvfSSDh48qIEDB8rb21u///67pk2bptOnT2v9+vWW53j11Ve1bNkydevWTfXq1dPu3bvVunXrFNW3aNEi5c2bV2+++aby5s2r3bt3a8KECbp7964++eSTJOf97bff1KJFCz3zzDOaNGmSYmJiNHnyZBUqVChe3/fff1/jx49Xp06d9Oqrr+q///7TzJkz1bBhQ/32228qWLCgpe+NGzfUsmVLdenSRT169FCRIkWeuFYAAICMRsAGAIPVrl1bc+fOlSQNHDhQpUuX1ltvvaUPPvhAo0aNkiR17dpVxYoV08KFCy0Be8WKFdq5c6f27dunBg0aWJZXuXJlDRo0SIcPH1a9evV04sQJLVu2TIMHD9ZXX30lSXr99dfVvXt3nTx5Mtn6VqxYody5c1seDxo0SIMGDdKsWbM0ZcqUJK/Xfvfdd2Vvb69Dhw6pWLFikqROnTrJ29vbqt+FCxf07rvvasqUKZaj0ZLUvn17Pfvss5o1a5ZV+9WrVzVnzhz5+/sbVisAAEBGI2ADgMFeffVVy7/t7e1Vs2ZNXbp0Sf3797e0FyxYUF5eXjp37pyl7bvvvpO3t7cqVKhgdYshX19fSdKePXtUr149bdmyRZI0bNgwq+cdPny4VqxYkWx9jwbW0NBQRUVF6YUXXtDcuXP1119/qVq1agnOFxMTo507d6pdu3aWcC1J5cqVU8uWLRUQEGBpW7t2rWJjY9WpUyerdSlatKjKly+vPXv2WAVsZ2dn9e3b17BaYTu3quI2VQCA7ISADQAGezwMFChQQLly5ZKbm1u89hs3blgenzlzRoGBgQmebi1J169fl/Tw6LCdnZ3Kli1rNd3LyytF9Z06dUrjxo3T7t27dffuXatpd+7cSXS+69evKyIiQuXKlYs37fG2M2fOyGw2q3z58gku6/H7ehcvXlxOTk6G1ZrT2dKtqrhNFQAgOyFgA4DBEhq1O7GRvM1ms+XfsbGxqlKlij7//PME+5YsWfKJa7t9+7Z8fHyUP39+TZ48WWXLllWuXLn066+/atSoUYqNjX3i55AerovJZNLWrVsTXPe8efNaPX70SHVG12qLbOVWVdymCgCQ3RCwASCLKFu2rE6cOKEmTZokOCJ3nFKlSik2Nlb//POP1VHrv//+O9nn2Lt3r27cuKG1a9darv2WpKCgoGTnLVy4sHLlyqWzZ8/Gm/Z4W9myZWU2m1WmTBl5enomu2yja8VD3KoKAICMxW26ACCL6NSpky5fvqz58+fHmxYREaF79+5Jklq2bClJmjFjhlWfL774ItnniDua/OiR8+joaM2aNStF8zZt2lTr16/XlStXLO1nz57V1q1brfq2b99e9vb2mjRpktVzxT33o6fGp0etAAAAmYEj2ACQRfTs2VOrV6/WoEGDtGfPHtWvX18xMTH666+/tHr1av3www+qWbOmqlevrq5du2rWrFm6c+eO6tWrp127diV4ZPlx9erV01NPPaXevXtr2LBhMplMWrp0abwQnJiJEydq+/btql+/vl577TXFxMToyy+/VOXKlXX8+HFLv7Jly2rKlCkaM2aMzp8/Lz8/P+XLl09BQUFat26dBg4cqJEjR6ZrrQAAABmNgA0AWYSdnZ3Wr1+vadOmacmSJVq3bp1cXFzk4eGhN954w+pU64ULF6pQoUJavny51q9fL19fX23evDnZ67Sffvppbdq0SW+99ZbGjRunp556Sj169FCTJk3UvHnzZGusUaOGtm7dqpEjR2r8+PEqWbKkJk+erMDAQP31119WfUePHi1PT09NmzZNkyZNkvTwOvIXX3xRL730UrLP9aS1AgAAZDQCNgAYZOLEiZo4cWK89kWLFmnRokXx2vfu3RuvzdHRUW+//bbefvvtJJ8rV65cmj59uqZPn27V/vjR3T59+qhPnz5WbfXq1dOPP/4Yb5kpPTLs6+urX3/91arNz89PJUqUiNe3ffv2at++fZLLS+h1SG2tCb2+AAAAGY1rsAEAqRIREWH1+MyZM9qyZYsaNWqUOQUBAABkERzBBgCkioeHh/r06SMPDw9duHBBs2fPlpOTU7JH3TNDcHCwQkJCMruMJ+bm5sZtqgAAyAYI2ACAVGnRooVWrlypq1evytnZWXXr1tXUqVNVvnz5zC7NSnBwsLy9vRUeHp7ZpTwxFxcXBQYGErIBAMjiCNgAgFT55ptvMruEFAkJCVF4eLg+GfuJPEp5ZHY5aXbuwjn9b+r/FBISQsAGACCLI2ADAGyaRykPVfKslNllAACAHCDbB+yePXvq6NGjCU77/PPP1bp16wSn+fr66vLly/HaT548KWdnZ0NrBAAAAADYvmwfsN99912FhYVZtS1evFjbt29X3bp1k5y3efPm6tevn1Wbk5OT4TUCAAAAAGxftg/Y5cqVi9f21ltvqX79+nJ1dU1yXjc3N1WvXj2dKgMAAAAA5CQ2dx/sX3/9VZcuXVLbtm0zuxQAAAAAQA5icwF706ZNcnFxUZMmTZLtGxAQoMqVK+vZZ5/VgAED9Pfff2dAhQAAAAAAW5TtTxF/1IMHD7R161b5+vrKxcUlyb6+vr6qWrWqihUrposXL2rOnDnq1q2b1q9fr5IlS2ZQxQAAAAAAW5GigH3lypU0LbxYsWJpmi+tDh06pJs3b6pNmzbJ9h03bpzl3zVr1lT9+vXVsmVLLViwQBMnTkzHKgEAAAAAtihFAdvX11cmkynVCw8MDEz1PE9i06ZNKliwoBo0aJDqeQsXLqwaNWro1KlT6VAZAAAAAMDWpShgT506NU0BOyNFRkZq586deumll+To6JjZ5QAAAAAAcpgUBez27dundx1PbPfu3QoPD0/z6OHXrl3TsWPH9PLLLz9RHWazWeHh4QlOs7OzU65cuSyPE+snSSaTSblz505T34iICJnN5gztK8nquvfU9I2MjFRsbKwhfXPnzm35MSgqKkoxMTGG9M2VK5fs7B6OCRgdHa0HDx4Y0tfZ2Vn29vap7nv//n3dv38/0b5OTk5ycHBIdd8HDx4oOjo60b6Ojo6WH7BS0zcmJkZRUVGJ9nVwcLDcgz41fWNjYxUZGWlIX3t7ezk7O0t6uB1HREQY0jc12z37iIT7pnUfERkZKZPJpKjoKEVEPnyPcjnnsmz30fejk97uU9HX2cnZst3ff3A/6W05FX3jPr/Sw205qff50X3EgwcP4q271XIdnaz63n+QxD7ikb4xMTGKvp/Edu/gaNmfpKpvbEyC+5Oo6CiZTCarfVhy+4hH+8bGxioqOun9iaODY4r62tvby8nx4fthNpsVGZX0/iTFfe3srd7nR9+vuPWPjIxUeHh4svuIRz/zUdFRcnZyTnC5j7Ozs7PqGxkVmeR2n8s5V5r6RkVHJf0dnuv/9ifR96Ot1v1xj2/3CW3vCS43OloxscZs9+m1j4g1/99rlJq/DSQlus1LD/cn9nap3+4zch/x6Gf+/v37Kfo74vHv9sT2J3FSs91n5D7i8e09TlLb/eOf+6T2J497fLvPCvuIuHVK7LsuM7OG2WxO8QFnkzmpylIo7oP96Juf0V577TX99ddf2r17d7yV7927t65cuaIdO3ZIengq+Z49e+Tj46PChQvr4sWLmjdvnu7cuaM1a9akeZCzJk2aKDg4ONEPha+vr5YuXWp5XK5cuUT/MK9bt66+//57y+MqVaro5s2bCfatVq2atmzZYnlcp04dXbp0KcG+np6e2rNnj+Vx48aNdfr06QT7lihRQkeOHLE8btWqlU6cOJFgX1dXV/3++++Wxx06dNCPP/6YYN/cuXPr7Nmzlsc9e/bU7t27E+wrSZcvX7b8e+DAgdq8eXOifc+cOWPZSIYPH67vvvsu0b4nT57U008/LUkaO3asFi9enGjfn376yfK5eO+99zRnzpxE++7evVteXl6SpM8++0yff/55on03b95suRf77NmzNWXKlET7fvfdd6pXr54kadGiRXrnnXcS7bt48WI1bdpUkvTtt9/qzTffTLTvnDlzLD9MBQQEaNCgQYn2/fzzz9W5c2dJ0s6dO9W7d+9E+77//vvq06ePJOnw4cPq2LFjon3HjRun1157TZJ0/PhxtW7dOtG+b775pt566y1J0t9//y1fX99E+w4aNEjjx4+XJF28eFHPP/98on179+6tqVOnSpJu3LihqlWrJtq3Y8eO+uKLLyQ9/KIrX758on1bt26tefPmWR4XL1480b7sIx5Kz33E5gWbLX9sfzTnI/1w4IdE+66dvVYF8xeUJE3/Zro27NyQaN8VX6xQ0UJFJUlzVszR6s2rE+274KMFKlOijCRp0ZpFWrJ2SaJ9Z02epZiYGL3i/4reeecdffPNN4n2fXQfkdx+aurIqXr+2Yfbw7Z92/TxvI8T7Tth2AQ1qtNIkrT3yF5NnjE50b5vD3xbLXxaSJJ++u0njf10bKJ9h/UeJr8X/SRJx/88rjffT3w/1bdvX8v+Mbl9RNeuXfXZZ59pzdw1cnFxUf9R/RPt26l1Jw3q9nCfd/W/q+o2vFuifV9u+rLe6PuGJOn23dtq/1riBx+av9BcowaNkvTwj9bW/ROvt2Hthpr4xkTLY9/uie/TUrOPqOZdTdPGTbM8bjeone6E3kmwr5eHl2a/N9vyuOsbXXUt5FqCfUsVL6VvPv6/z2Hft/vqwuULCfYt4lZEK6evtDx+bfxr+vtcwndsKZCvgNbNWWd5PHDsQJ29cDbBvqndR+xe/n/TJk6fqP1H9yfaNyvsI8a+NlZDxg/RsWPHtGfPnhT9HfHrr7+qYcOGKlCgQKJ9P3/nc1WvWF2StH77es1YPCPRvllhH5GavyPu3LmjJZ8vUSXPSvrrn780eMLgRPv2at9LfV55uNygS0FZfh+Rmr8j6lSvow/+94Hlcat+rRIN+lltH3Hq9Cn5v+Nv9QPBozL77wgXFxe5u7tr165diS4nTppHEb9y5Ypmzpypffv26datW5Kkp556Sj4+PhoyZEiSb77R7ty5owMHDqh3794J/rIQGxtr9atiiRIldP36dU2dOlWhoaHKly+fnn/+eQ0bNowRxAEAAAAAaZKmI9j//POPunXrptDQUNWrV09ly5aVJJ07d06HDh1S/vz5tWLFCnl4eBhecFbWpEkTmc1mbdq0KcHpnP6ZcF9OEecUcU4RT31f9hHJ9z1+/LgaNGig5TOWy7uct6Sscfpnak8R/+vsX3rF/xX99NNPqlKlSuLLfWQfcfToUT3//PNW62613Gxyinjg2UB1H9ZdP/74o+rUqWNZblL7iN9//13PP/+81sxdI+9y3lnm9M9E+yZxSmfc+h88eFDVq1dPdh/x6Ge+kmelLHf6Z2pOEf/t1G/qNrSbZd0f9/h2/+uvv8bb3hNabnY4RfzshbPqOKijjh07psqVK6fob4Nff/1VNWrU0IqZKxLc5qXscYr4o5/5WrVqpejviOPHj6t+/fpaM3eNKnlWyraniD++vcdJart//HsuO58ifur0Kb3i/4oOHTqU4DYvZW7WaNOmjUwmU/odwf7ss89kZ2endevWWU6FjXP69Gn16dNHn332mb766qu0LD5bM5lMyd6DO05K+6W276N/8GaHvqm5tCA1feMCkNF9nZycEj19JaP6Phpejezr4OBg+eIzsq+9vX2KP8Op6WtnZ5cufVOzHaemr5R+2z37iPh9c+XKJbPZLGcnZ6s/sOM4OTpJKRwTMzV9HR0cLX+QGdrX0THF77ODg0OS6/5439Rsy7ntU/bepaqvnX2CdTo7OctsNlvtw5LbRzza187OLtn1T0tfk8mULn0l6zAYt/65cuVKcJ0fb3v0M//oH8OPLzc5j18TaVTfx2tKipOjU5LrblVDrlzJbu+W5abwezauhszYR9iZ7P5vuan420BSirZ5KX2/759kH/HoZz6l2/3j3xGJ7U8SkpX2Eclt73EenZbc5z6t+57kpNc+QlKKtnkp47NGagb8tku+S3w///yzevbsGS9cSw+v3+vevbuOHj2alkUDAAAAAJAtpSlgP3jwIMlfAnLnzp3k6SwAAAAAANiaNAVsb29vfffddwoNDY03LSwsTN9//70qVqz4xMUBAAAAAJBdpOka7KFDh2rAgAFq2bKl2rdvr9KlS0uSgoKCtG7dOt2+fVsTJkwwsk4AAAAAALK0NAXsunXrat68efr444+t7ssmPTy6/cknnyR5v1kAAAAAAGxNmu+DXa9ePa1fv17//fefrly5IkkqVqyYChUqZFhxAAAAAABkF2kO2HEKFSpEqAYAAAAA5HhpCtjr169Pto+zs7OKFi2qSpUqper+fQAAAAAAZEdpCtijR4+23GzbbDZbTXu03WQyKW/evBo4cKAGDBjwhKUCAAAAAJB1pfkI9ujRo1WwYEF1795d7u7ukqQLFy5o+fLlCg0N1fjx43Xjxg0tXbpUn3/+ufLkyaNu3boZWjwAIGnBwcEKCQnJ7DIM4ebmZvm+AQAAyIrSFLAXL16sp59+WgsWLLBq9/LyUrNmzfTqq6/q+++/1/vvvy9fX19169ZNK1euJGADQAYKDg6Wt7e3wsPDM7sUQ7i4uCgwMJCQDQAAsqw0BeydO3dqxIgRCU4zmUzy9fXV9OnT9f7778vOzk7NmzfXtGnTnqhQAEDqhISEKDw8XMuWLZO3t3dml/NEAgMD1aNHD4WEhBCwAQBAlpWmgB0bG6ugoKBEpwcFBSk2Ntby2MnJSc7Ozml5KgDAE/L29tZzzz2X2WUAAADYPLu0zOTr66uVK1dq2bJlioqKsrRHRUVp6dKlWrVqlRo3bmxp/+233zjiAAAAAACwaWk6gv3OO+/o4sWLmjJlij766CPLfbD/++8/3b9/X1WrVtU777wj6WHozpUrl/r27Wtc1QAAAAAAZDFpCtgFCxbUypUrtWPHDh08eFCXL1+WJDVo0EANGjRQ06ZNZWf38OC4s7OzpkyZYlzFAAAAAABkQWkK2NLDwcxefPFFvfjii0bWAwAAAABAtpSma7ABAAAAAIA1AjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABggCcO2NevX9dff/2l8PBwI+oBAAAAACBbSnPA3rlzp1q0aCEfHx+1a9dOJ06ckCTdvHlTfn5+2rlzp2FFAgAAAACQ1aUpYO/evVtDhw7VU089pddff11ms9kyzdXVVUWKFNGaNWsMKxIAAAAAgKwuTQH7q6++Us2aNbVy5Up179493vTq1asrMDDwiYsDAAAAACC7SFPAPnPmjFq2bJnodDc3N924cSPNRQEAAAAAkN2kKWDnzp1bERERiU6/ePGiChYsmNaaAAAAAADIdtIUsOvUqaP169frwYMH8ab9999/Wr16tRo0aPDExQEAAAAAkF2kKWAPHz5cV69eVYcOHfTtt9/KZDLp4MGDmjZtmtq2bSuz2azXX3/d6FoBAAAAAMiy0hSwPTw8tGLFChUsWFDTp0+X2WzWggULNHfuXHl6emrFihUqUaKE0bUCAAAAAJBlOaR1xvLly2vRokW6c+eOLly4ILPZrJIlS8rV1dXI+gAAAAAAyBbSHLDjFChQQFWrVjWiFgAAAAAAsq0UBez169enaeF+fn5pmg8AAAAAgOwmRQF79OjR8dpMJpMkyWw2J9guEbABAAAAADlHigL2rl27rB6HhoZq1KhRypcvn3r06KEyZcpIks6dO6dly5bp3r17+vDDD42vFgAAAACALCpFAbt48eJWj8eMGSNXV1ctXLjQ6oi1l5eXmjdvrn79+mnx4sX64IMPjK0WAAAAAIAsKk236dq5c6eaNm1qFa4tC7SzU7NmzeId9QYAAAAAwJalKWCbzWYFBQUlOv2ff/6Jd202AAAAAAC2LE0Bu2nTplq5cqW++eYbRUREWNojIiK0cOFCffvtt2rSpIlhRQIAAAAAkNWl6T7Y77zzji5duqSPPvpIn332mQoXLixJun79uh48eKDnnntOY8eONbRQAAAAAACysjQF7Hz58mnZsmXauXOn9u/frytXrkiSGjRoIB8fH/n6+iZ4fTYAAAAAALYqTQE7TtOmTdW0aVOjagEAAAAAINtK0zXYWcnatWvl5eUV779PP/00yfnMZrPmzZunRo0aqWrVqurcubOOHz+eMUUDAAAAAGzOEx3Bzkq+/vpr5cuXz/K4SJEiSfafP3++ZsyYoZEjR8rLy0vLly9Xv379tGHDBpUsWTK9ywUAAAAA2BibCdiVKlWSq6trivpGRUVp7ty56tevn/r06SNJqlGjhlq0aKEFCxZo4sSJ6VcoAAAAAMAmZftTxNPi119/VVhYmFq2bGlpc3JyUrNmzbR///5MrAwAAAAAkF3ZTMBu06aNvL291aRJE82dO1cxMTGJ9j137pwkycPDw6q9bNmyunLliiIjI9O1VgAAAACA7TH0FPGLFy8qOjpaZcuWNXKxSSpUqJCGDh2qatWqyWQyaffu3friiy907do1TZgwIcF57t69KycnJzk7O1u158+fX2azWXfu3FGuXLkyonwAAAAAgI1IU8BesmSJfvvtN02bNs3SNmbMGK1fv16S5O3trfnz5+vpp582pMikvPDCC3rhhRcsjxs0aCBnZ2ctXrxYgwYNUuHChdO9BgAAAAAA0nSK+HfffWcVng8cOKB169apU6dOGjdunC5duqQvv/zSsCJTq2XLloqJiVFgYGCC0/Pnz6/o6GhFRUVZtd+9e1cmk0kFChTIiDIBAAAAADYkTUewr1y5YnUa+NatW1WiRAlNmjRJkhQSEqINGzYYU2E6iLv2OigoSBUqVLC0nzt3TsWKFeP0cAAAAABAqqXpCLbZbLZ6fOjQITVs2NDyuHjx4goJCXmyyp7Ali1bZG9vr4oVKyY4/bnnnlPevHm1detWS9v9+/e1fft2q/UAAAAAACCl0nQEu3Tp0tq5c6e6du2qAwcO6Pr161bB9OrVq8qfP79hRSalf//+qlOnjry8vCRJu3bt0urVq9WrVy8VKlRIktS7d29duXJFO3bskCQ5OzvL399fM2fOlKurqzw9PbVy5Urdvn1b/fv3z5C6AQAAAAC2JU0Bu3///nrrrbdUq1YtRUREqGzZsmrQoIFl+pEjR6xOvU5PZcqU0Zo1a3T16lXFxsaqdOnSGjt2rHr27GnpExsbG++2XQMGDJDZbNbChQt18+ZNeXt7a8GCBSpZsmSG1A0g4wQHB2fqWTVGcnNzk7u7e2aXAQAAgASkKWC3bt1aBQsW1L59+5Q/f35169ZNDg4PF3X79m0VKFBAL7/8sqGFJmbcuHHJ9lm6dGm8NpPJJH9/f/n7+6dHWQCyiODgYHl7eys8PDyzSzGEi4uLAgMDCdkAAABZUJrvg12/fn3Vr18/XnvBggUzdQRxAHhUSEiIwsPDtWzZMnl7e2d2OU8kMDBQPXr0UEhICAEbAAAgC0pzwJak8PBw/fzzz7p8+bKkh4Ob1apVSy4uLoYUBwBG8fb21nPPPZfZZQAAAMCGpTlgL126VF988YXCw8OtRhXPkyePRowYoR49ehhSIAAAAAAA2UGaAvb69ev1/vvvq3r16urVq5flvtLnzp3T0qVL9f777ytv3rzy8/MzslYAAAAAALKsNAXsb775RrVq1dKiRYtkb29vaa9QoYKaN2+uPn366JtvviFgAwAAAAByDLu0zBQUFKQWLVpYhes49vb2atGihYKCgp64OAAAAAAAsos0Bex8+fLp0qVLiU6/dOmS8ubNm+aiAAAAAADIbtIUsH18fLRs2TJt3rw53rQtW7Zo+fLlaty48RMXBwAAAABAdpGma7BHjhyp48ePa+TIkfrwww9VunRpSdL58+cVEhIiDw8PvfXWW0bWCQAAAABAlpamgO3q6qp169Zp1apV2r9/v65cuSJJ8vT01IABA9S5c2c5OzsbWigAAAAAAFlZmu+D7ezsrN69e6t3795G1gMAAAAAQLaUpmuwAQAAAACAtRQdwe7Vq1eqF2wymbR48eJUzwcAAAAAQHaUooBtNptTveC0zAMAAAAAQHaVooC9dOnS9K4DAAAAAIBsjWuwAQAAAAAwQJpHEY8TFhamsLAwxcbGxptWrFixJ108AAAAAADZQpoD9ooVK7Ro0SJdvHgx0T6BgYFpXTwAAAAAANlKmk4RX7lypSZPnix3d3cNHz5cZrNZvXv31sCBA+Xm5qYKFSro/fffN7pWAAAAAACyrDQF7GXLlqlBgwb6+uuv1alTJ0mSj4+PRowYoS1btujevXu6ffu2kXUCAAAAAJClpSlgBwcHq3HjxpIkR0dHSdL9+/clSfny5VOHDh20YsUKg0oEAAAAACDrS1PAzpcvn2JiYiRJefPmVe7cuXX16lXL9Dx58igkJMSYCgEAAAAAyAbSFLDLly+vv/76y/K4WrVqWrlypa5du6Z///1X3377rUqXLm1UjQAAAAAAZHlpCtgvvfSSzpw5o+joaEnS0KFD9c8//6hRo0by9fVVUFCQhg8fbmSdAAAAAABkaWm6Tdcrr7yiV155xfK4Ro0a2rx5s3bt2iUHBwfVr19fZcqUMaxIAAAAAACyujTfB/txJUuWVJ8+fYxaHAAAAAAA2UqaAnZYWJhCQ0P1zDPPWNquXbumVatWKTo6Ws2bN1fVqlUNKxIAAAAAgKwuTQF7woQJunTpklavXi3pYeDu1KmTrl27Jjs7Oy1ZskRff/216tSpY2ixAAAAAABkVWka5OzYsWNq1KiR5fGGDRv033//adWqVTp69Ki8vLw0e/Zso2oEAAAAACDLS1PAvnXrlooUKWJ5vHv3btWoUUPVq1dX3rx55efnZ3UbLwAAAAAAbF2aAnb+/PkVEhIiSYqMjNSxY8dUv359y3R7e3tFRkYaUyEAAAAAANlAmq7BfvbZZ7VixQp5eHjowIEDioqKUpMmTSzTz58/b3WEGwAAAAAAW5emI9gjR46Ug4ODhg4dqtWrV6tPnz4qX768JCkmJkbbtm1TrVq1DC0UAAAAAICsLE1HsEuVKqVt27bpn3/+Ud68eVWiRAnLtIiICI0fP14VKlQwrEgAAAAAALK6NAVsSXJ0dEwwROfNm1dNmzZ9oqIAAAAAAMhu0nSKOAAAAAAAsEbABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAyQplHEw8LCFBoaqmeeecbSdu3aNa1atUrR0dFq3ry5qlataliRAAAAAABkdWkK2BMmTNClS5e0evVqSQ8Dd+fOnXX16lXZ2dlpyZIl+vrrr1WnTh1DiwUAAAAAIKtK0ynix44dU6NGjSyPN2zYoOvXr2vVqlU6evSovLy8NHv2bKNqBAAAAAAgy0vTEexbt26pSJEilse7d+9WjRo1VL16dUmSn5+fvvzyS0MKTM7WrVu1ceNGnTp1Snfv3lWpUqXUs2dPvfLKKzKZTInO5+vrq8uXL8drP3nypJydndOzZAAAAACADUpTwM6fP79CQkIkSZGRkTp27JgGDRpkmW5vb6/IyEhjKkzGokWLVLx4cY0ePVpPPfWUDh8+rPHjx+vq1asaMmRIkvM2b95c/fr1s2pzcnJKz3IBAAAAADYqTQH72Wef1YoVK+Th4aEDBw4oKipKTZo0sUw/f/681RHu9DR79my5urpaHtetW1e3b9/WN998o8GDB8vOLvGz4N3c3CxH3QEAAAAAeBJpugZ75MiRcnBw0NChQ7V69Wr16dNH5cuXlyTFxMRo27ZtqlWrlqGFJubRcB3H29tbYWFhCg8Pz5AaAAAAAABI0xHsUqVKadu2bfrnn3+UN29elShRwjItIiJC48ePV4UKFQwrMrWOHTumIkWKKG/evEn2CwgI0OrVq+Xo6KiaNWtq5MiR8vLyyqAqAQAAAAC2JE0BW5IcHR0TDNF58+ZV06ZNn6ioJ/HLL79oy5YtGjVqVJL9fH19VbVqVRUrVkwXL17UnDlz1K1bN61fv14lS5bMoGoBAAAAALYizQE7JiZGGzdu1N69e3XlyhVJUrFixdS4cWO1bdtW9vb2hhWZUlevXtWIESNUp04d9erVK8m+48aNs/y7Zs2aql+/vlq2bKkFCxZo4sSJ6VwpAAAAAMDWpClgh4aGqn///vr999+VJ08eyxHfw4cPa/v27Vq5cqUWLFiQ7CnaRrp7964GDBigggULaubMmUkObpaQwoULq0aNGjp16lQ6VQgAAAAAsGVpCtjTpk3TqVOnNG7cOHXq1EmOjo6SpPv37+u7777T+++/r2nTpmn8+PGGFpuYyMhI+fv7KzQ0VN9++63y5cuXIc8LAAAAAECcNI0ivmPHDnXt2lXdu3e3hGvp4XXZ3bp1U9euXfXDDz8YVmRSHjx4oOHDh+vcuXP6+uuv03x7sGvXrunYsWOqUqWKwRUCAAAAAHKCNB3Bvn37tsqUKZPo9DJlyujOnTtpLio1Jk2apD179mj06NEKCwvT8ePHLdMqVqwoJycn9e7dW1euXNGOHTskSZs2bdKePXvk4+OjwoUL6+LFi5o3b57s7e3Vt2/fDKkbAAAAAGBb0nybrt27d6t79+4JTt+9e7fc3d2fqLCUOnTokCTpww8/jDdt165dKlGihGJjYxUTE2NpL1GihK5fv66pU6cqNDRU+fLl0/PPP69hw4YxgjgAAAAAIE3SFLC7du2q9957TwMGDFDv3r1VunRpSVJQUJCWLl2qw4cPZ9j117t37062z9KlS60eV69ePV4bAAAAAABPIk0Bu3v37rp586bmzZungwcPWi/QwUGvv/66unXrZkiBAAAAAABkB2m+D/bQoUPVvXt3/fjjj7p8+bIkqXjx4qpbt65cXV0NKxAAAAAAgOwgzQFbklxdXdW6dWujagEAAAAAINtKUcC+cuVKmhZerFixNM0HAAAAAEB2k6KA7evrK5PJlOqFBwYGpnoeAAAAAACyoxQF7KlTp1oF7NjYWC1ZskRXrlxR27ZtLffEPnfunDZt2qTixYurZ8+e6VMxAAAAAABZUIoCdvv27a0ez549W1FRUdq+fbueeuopq2lDhw5V165dFRISYlyVAAAAAABkcXZpmWnVqlXq3LlzvHAtPRz4rFOnTlq5cuUTFwcAAAAAQHaRpoB9+/ZtRUREJDo9IiJCt2/fTmtNAAAAAABkO2kK2NWqVdPixYv1xx9/xJv2+++/a+nSpapateoTFwcAAAAAQHaRpvtgT5gwQT179lTHjh1VrVo1lS5dWpJ0/vx5nThxQgUKFND48eONrBMAAAAAgCwtTQG7XLlyCggI0Lx587R//35t2bJF0sP7Xvfq1UuvvvqqChUqZGihAAAAAABkZWkK2JLk5uamsWPHauzYsUbWAwAAAABAtpTmgA0gewkODraZ2+e5ubnJ3d09s8sAAAAArBCwgRwgODhY3t7eCg8Pz+xSDOHi4qLAwEBCNgAAALIUAjaQA4SEhCg8PFzLli2Tt7d3ZpfzRAIDA9WjRw+FhIQQsAEAAJClELCBHMTb21vPPfdcZpcBAAAA2KQ03QcbAAAAAABYI2ADAAAAAGCAJzpF/OrVq/rzzz8VGhoqs9kcb7qfn9+TLB4AAAAAgGwjTQE7KipKo0aN0vbt2xUbGyuTyWQJ2CaTydKPgA0AAAAAyCnSdIr4559/rh07dmj48OFaunSpzGazPvzwQy1cuFANGzZUhQoVtGHDBqNrBQAAAAAgy0pTwP7hhx/Uvn17DRw4UOXKlZMkFSlSRPXq1dPcuXOVL18+LV++3NBCAQAAAADIytIUsG/cuKGqVatKknLlyiVJioiIsExv3ry5duzYYUB5AAAAAABkD2kK2G5ubrp165YkKXfu3CpQoICCgoIs08PCwhQVFWVMhQAAAAAAZANpGuSsatWq+vXXXy2PGzdurAULFqhQoUKKjY3VokWLVL16daNqBAAAAAAgy0tTwO7Zs6e2bdum6OhoOTk56Y033tBvv/2mt99+W5Lk7u6ud955x9BCAQAAAADIytIUsGvWrKmaNWtaHj/zzDPaunWrTp8+LTs7O3l4eMjB4YlusQ0AAAAAQLZiWAq2s7NThQoVjFocAAAAAADZSooC9s8//yxJqlWrltXj5MT1BwAAAADA1qUoYPfs2VMmk0knTpyQk5OT5XFizGazTCaTAgMDDSsUAAAAAICsLEUBe8mSJZIkJycnq8cAAAAAAOChFAXs2rVrJ/kYAAAAAICczi6zCwAAAAAAwBak6Aj2mDFjUr1gk8mkqVOnpno+AAAAAACyoxQF7CNHjsRri4yM1M2bNyVJBQoUkCTduXNHkuTq6qrcuXMbVSMAAAAAAFleigL27t27rR6fPXtW/fr1k7+/v3r37i1XV1dJ0s2bN7V48WKtX79e8+bNM75aAAAAAACyqDRdg/3ee++pYcOGGjFihCVcSw+PXI8YMUIvvPCC3nvvPcOKBAAAAAAgq0tTwD5x4oQqVqyY6HRvb2+dOHEizUUBAAAAAJDdpClgFyhQQPv37090+v79+5UvX740FwUAAAAAQHaTpoDduXNn7d27V6+99poOHz6sS5cu6dKlSzp06JAGDRqk/fv3q0uXLkbXCgAAAABAlpWiQc4eN3jwYEVHR2vBggXau3ev1TR7e3sNHDhQgwcPNqI+AAAAAACyhTQFbEkaPny4evXqpcOHD+vKlSuSpOLFi6tu3bpWA58BAAAAAJATpDlgSw9HDW/Tpo1RtQAAAAAAkG2l6RpsSYqJidHmzZs1YcIEvf766/r7778lSaGhodq+fbtCQkIMKzI5//zzj/r27avq1aurfv36+vjjjxUdHZ3sfGazWfPmzVOjRo1UtWpVde7cWcePH0//ggEAAAAANifZI9hhYWHKmzevVdvdu3f16quv6uTJk3JxcVFERIR69OghSXJxcdGUKVPk5+enN998M32qfsSdO3fUu3dvlS5dWjNnztS1a9f04YcfKjIyUhMmTEhy3vnz52vGjBkaOXKkvLy8tHz5cvXr108bNmxQyZIl0712ZLzg4OAM/fEnvbi5ucnd3T2zywAAAADwiGQD9uLFi5UvXz716tXL0vbpp5/qzJkzWrBggby9vVWvXj3LNHt7ezVv3lz79u3LkIC9atUq3bt3T19++aUKFiwo6eHR9UmTJsnf319FihRJcL6oqCjNnTtX/fr1U58+fSRJNWrUUIsWLbRgwQJNnDgx3WtHxgoODpa3t7fCw8Mzu5Qn5uLiosDAQEI2AAAAkIUkG7Br166tIUOG6N9//9WoUaMkSbt27VLPnj1Vv3593bp1K948pUuX1rp164yvNgH79+9X3bp1LeFaklq2bKl3331Xhw4dUvv27ROc79dff1VYWJhatmxpaXNyclKzZs20Y8eO9C4bmSAkJETh4eFatmyZvL29M7ucNAsMDFSPHj0UEhJCwAYAAACykGQDdq1atbR27Vq9+eabWrJkiXr16qXQ0FCVKFEi0XkePHigmJgYQwtNzLlz5/TKK69YteXPn1+FChXSuXPnkpxPkjw8PKzay5Ytq8WLFysyMlK5cuUyvmBkOm9vbz333HOZXQYAAAAAG5OiUcSLFy+u5cuX6+zZs5Ikd3d3nTp1KtH+hw4dUtmyZY2pMBl3795V/vz547UXKFBAd+7cSXI+JycnOTs7W7Xnz59fZrNZd+7csdmAndOvQw4MDEyHajLOk9Sf3dddSvs6sO7Z25Osw7kLif/Ymh08Sf2se/aW1nVg3bO3nPy5Z90zft6swhbWIU6Kb9Pl4OCgChUqSJI6dOigTz/9VHXq1NHzzz8vSTKZTIqOjtZXX32lAwcOaPLkyelTMZ5ITr4O2c3NTS4uLpYB+bIzFxcXubm5pbi/La27lLr1Z91z5rpL/7f+/5v6v3SsKmOw7jlz3aW0bfOse/aXkz/3rHvOXHcp9eufVaXpPti9e/fW2bNn9eabb1qOHo8cOVK3b9/WgwcP1LlzZ3Xs2NHQQhOTP39+hYaGxmu/c+eOChQokOR80dHRioqKsjqKfffuXZlMpiTnzc5y8nXI7u7uCgwMzJFH721p3aXUrT/rnjPXXbKt9Wfdc+a6S2zzrHvK2NL6s+45c90l27lLTpoCtslkstyK64cfftCFCxcUGxsrd3d3tWzZUrVq1TK6zkR5eHjEu9Y6NDRU//33X7zrqx+fT5KCgoIsR+alh9dmFytWzGZPD4+TU69Ddnd3t4kNNy1Yd9Y9J8rJ68+6s+45TU5edylnrz/rnjPXPatKU8COU7NmTdWsWdOoWtKkYcOGmjNnjtW12Nu2bZOdnZ3q16+f6HzPPfec8ubNq61bt1oC9v3797V9+3Y1bNgwQ2oHAAAAANiOJwrYWUGXLl20dOlSvf766/L399e1a9f08ccfq0uXLlb3wO7du7euXLliuQWXs7Oz/P39NXPmTLm6usrT01MrV67U7du31b9//8xaHQAAAABANpXigD1o0KBULdhkMmn27NmpLii1ChQooMWLF+u9997T66+/rjx58qhDhw4aMWKEVb/Y2Nh4tw4bMGCAzGazFi5cqJs3b8rb21sLFixQyZIl071uAAAAAIBtSXHA3rt3r5ydneXm5iaz2Zxsf5PJ9ESFpUbZsmW1aNGiJPssXbo0XpvJZJK/v7/8/f3TqTIAAAAAQE6R4oBdpEgRXbt2TU899ZTatGmj1q1bq1ChQulZGwAAAAAA2YZdSjvu27dPS5YsUcWKFTV79mw1atRIffr00Zo1axQWFpaeNQIAAAAAkOWlOGBLUu3atTV58mQdPHhQ06dPV8GCBfXee++pXr16GjJkiLZt26bo6Oj0qhUAAAAAgCwrVQE7jqOjo5o2baovvvhChw4d0uTJkxUSEqIRI0Zo/vz5RtcIAAAAAECWl6aAHSc6OloHDx7Url279Oeff8rZ2VnFixc3qjYAAAAAALKNVN8HOzY2VocOHdLmzZu1c+dORUZGqm7dunrvvffUrFkzubi4pEedAAAAAABkaSkO2L/++qs2bdqkbdu26fbt26pWrZpGjBihli1bytXVNT1rBAAAAAAgy0txwO7WrZty5cqlhg0bqk2bNpZTwf/991/9+++/Cc5TqVIlY6oEAAAAACCLS9Up4pGRkdq+fbt27NiRZD+z2SyTyaTAwMAnKg4AAAAAgOwixQH7gw8+SM86AAAAAADI1lIcsNu1a5eedQAAAAAAkK090W26AAAAAADAQwRsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAADpldwJOIiYnRwoULtXfvXp09e1Zms1leXl564403VLNmzSTnvXTpkpo0aRKvvVq1alq9enV6lQwAAAAAsFHZOmBHRkZq3rx5ateunQYMGCA7OzutXr1avXr10oIFC1S3bt1kl/Hmm2+qTp06lsd58uRJz5IBAAAAADYqWwfsXLlyaefOnSpQoIClrX79+mrTpo0WL16cooBdqlQpVa9ePR2rBAAAAADkBNn6Gmx7e3urcB3X5uXlpevXr2dSVQAAAACAnChbB+yEPHjwQCdOnJCHh0eK+k+cOFHe3t6qW7euxo0bp9u3b6dvgQAAAAAAm5StTxFPyNdff61r166pT58+SfZzcnJS165d1aBBA+XPn18nTpzQnDlz9Mcff+i7776To6NjxhQMAAAAALAJWS5gh4aGpuj07pIlS8rJycmq7dChQ5o5c6YGDx6sypUrJzl/4cKFNXHiRMvj2rVrq3z58vL399eOHTvUqlWrNNUPAAAAAMiZslzA3rZtm8aNG5dsvy1btqhs2bKWx6dOndLQoUPVpk0bDRkyJE3P7ePjIxcXF506dYqADQAAAABIlSwXsDt27KiOHTumap4LFy5owIABevbZZzVlypR0qgwAAAAAgMRl+0HOrl+/rn79+umZZ57RjBkznuja6T179ig8PFxVqlQxsEIAAAAAQE6Q5Y5gp0ZkZKQGDBigW7du6Z133tGZM2cs05ycnFSxYkXL44oVK8rPz09Tp06VJH344YcymUyqXr268ufPr5MnT2ru3LmqXLmymjZtmuHrAgAAAADI3rJ1wA4JCdFff/0lSXrttdesphUvXly7d++2PI6JiVFsbKzlcdmyZbVy5UqtXr1akZGRKlKkiDp06KBhw4bJwSFbvywAAAAAgEyQrZNkiRIl9Pfff6eo7+P90nKtNwAAAAAAicn212ADAAAAAJAVELABAAAAADAAARsAAAAAAAMQsAEAAAAAMAABGwAAAAAAAxCwAQAAAAAwAAEbAAAAAAADELABAAAAADAAARsAAAAAAAMQsAEAAAAAMAABGwAAAAAAAxCwAQAAAAAwAAEbAAAAAAADELABAAAAADAAARsAAAAAAAMQsAEAAAAAMAABGwAAAAAAAxCwAQAAAAAwAAEbAAAAAAADELABAAAAADAAARsAAAAAAAMQsAEAAAAAMAABGwAAAAAAAxCwAQAAAAAwAAEbAAAAAAADELABAAAAADAAARsAAAAAAAMQsAEAAAAAMAABGwAAAAAAAxCwAQAAAAAwAAEbAAAAAAADELABAAAAADAAARsAAAAAAAMQsAEAAAAAMAABGwAAAAAAAxCwAQAAAAAwAAEbAAAAAAADELABAAAAADAAARsAAAAAAAMQsAEAAAAAMAABGwAAAAAAAxCwAQAAAAAwAAEbAAAAAAADELABAAAAADBAtg/Yo0ePlpeXV7z/9u/fn+y8oaGhGjt2rGrXrq1nn31Ww4YN0/Xr1zOgagAAAACArXHI7AKMULJkSX366adWbWXLlk12vuHDh+vs2bOaOHGinJ2d9cUXX2jAgAFas2aNHBxs4qUBAAAAAGQQm0iRuXLlUvXq1VM1z2+//aaDBw9qwYIFatCggSSpTJkyatWqlbZv365WrVqlQ6UAAAAAAFuV7U8RT6v9+/crf/78ql+/vqXNw8ND3t7eKTq9HAAAAACAR9lEwL5w4YJq1KihypUrq3379tq5c2ey85w7d05lypSRyWSyavfw8NC5c+fSq1QAAAAAgI3K9qeIe3t7q0qVKipXrpxCQ0O1cuVKvf7665o+fbpatGiR6Hx3795Vvnz54rUXKFBAf/zxR3qWDAAAAACwQVkuYIeGhqZoJO+SJUvKyclJvXv3tmr39fVVly5dNGPGjCQDNgAAAAAARspyAXvbtm0aN25csv22bNmS4EjhdnZ2evHFF/XJJ58oMjJSuXLlSnD+/Pnz6+rVq/Ha79y5owIFCqS+cAAAAABAjpblAnbHjh3VsWPHdH8eDw8P/fjjjzKbzVbXYQcFBcnT0zPdnx8AAAAAYFtsYpCzR8XGxmrbtm0qX758okevJalhw4a6c+eOfvzxR0tbUFCQ/vzzTzVs2DAjSgUAAAAA2JAsdwQ7NS5fvqzRo0erdevWKlWqlO7cuaOVK1fqjz/+0MyZM636VqxYUX5+fpo6daok6dlnn1WDBg00duxYjRo1Ss7Ozpo2bZq8vLz04osvZsbqAAAAAACysWwdsPPkyaO8efNq9uzZunHjhhwdHVW5cmXNnz9fL7zwglXfmJgYxcbGWrV98cUX+uCDDzRhwgQ9ePBADRo00Lhx4+TgkK1fFgAAAABAJsjWSbJgwYKaPXt2ivr+/fff8dry5cunqVOnWo5qAwAAAACQVjZ3DTYAAAAAAJmBgA0AAAAAgAEI2AAAAAAAGICADQAAAACAAQjYAAAAAAAYgIANAAAAAIABCNgAAAAAABiAgA0AAAAAgAEI2AAAAAAAGICADQAAAACAAQjYAAAAAAAYgIANAAAAAIABCNgAAAAAABiAgA0AAAAAgAEI2AAAAAAAGMAhswtA5ggMDMzsEp5Idq8fAAAAgO0hYOcwbm5ucnFxUY8ePTK7lCfm4uIiNze3zC4DAAAAACQRsHMcd3d3BQYGKiQkJLNLeWJubm5yd3fP7DIAAAAAQBIBO0dyd3cnmAIAAACAwRjkDAAAAAAAAxCwAQAAAAAwAAEbAAAAAAADELABAAAAADAAARsAAAAAAAMQsAEAAAAAMAABGwAAAAAAAxCwAQAAAAAwAAEbAAAAAAADELABAAAAADAAARsAAAAAAAMQsAEAAAAAMAABGwAAAAAAAxCwAQAAAAAwAAEbAAAAAAADELABAAAAADAAARsAAAAAAAM4ZHYBtuT69euKiYlRkyZNMrsUAAAAAIAB/v33X9nb26eoL0ewDeTs7CwHB36zAAAAAABb4eDgIGdn5xT1NZnNZnM61wMAAAAAgM3jCDYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAZwyOwCAAAAACCruX79uq5evSpJKlq0qAoXLpzJFWW+Bw8e6MaNGypSpEhml5JlEbBhmMjISH3//ff6559/VKhQIbVr107PPPNMZpeVLsaMGaPBgwerZMmSmV1Kpjh79qxWrVqlS5cuqXDhwmrRooXq1auX2WUhHf3888+p6l+rVq10qiTjXblyJdFpDg4OKliwoJycnDKwoqzn3Llzmj9/vj744IPMLsVQY8aMSXFfk8mkqVOnpmM1Gevnn39WxYoVlSdPnswuJUvbsWOHRowYoT/++COzS8lQd+/e1bZt2xQQEKClS5dmdjmGW758uRYvXqyLFy9atZcsWVJ9+vRRt27dMqmy9FOtWjUtW7ZMVapUkSSZzWb1799f7777rkqVKmXpd+rUKXXp0kWBgYGZVWqWR8BGqk2bNk27d+9WQECApS0iIkIdOnTQuXPnZDabJUmLFy/W999/b5MhdN26deratatNrltyfvnlF/Xt21cPHjyQq6urbt++re+++04TJkxQ165dM7u8dLV+/fpEpzk4OOjpp59WlSpVlDdv3owrKoP07NlTJpPJsn0nxGQyWf7/559/ZlRp6c7X19eybgmxs7NTlSpVNGzYMJv8oSkmJkZ//PGHrly5ohIlSlj++JKkkydPau7cudqzZ4/y5MljcwF73bp1ypMnj9zd3ZP87EtK8jOSHfXq1UvffvutqlatmtmlZGmxsbGKiYnJ7DIyRHR0tHbt2qWAgAAdOHBA9+/fV8WKFTO7LEPFxMTojTfe0M6dO1W6dGn16dPHcrDo33//1b59+zR58mT9+OOPmj59uuzsbOdq26ioKKv9XGxsrA4fPqywsLBMrCp7ImAj1Q4dOqTGjRtbtS1evFj//POPBg8erP79+ysoKEjDhg3TnDlz9P7772dSpUgPM2fOlIeHh+bMmaNnnnlGYWFhGjNmjL744gubD9ijR49Otk+uXLnUr18/DRs2LAMqyjhJ/bggSf/9958WLFign376yeaOeH355ZeJTouNjdX169e1Y8cODRgwQF9//bXq1q2bgdWlr6tXr8rf31+nT5+W2WyWyWSSj4+PPvvsM02YMEFbtmxRnjx5NHDgQPXt2zezyzVc9erVdeLECcXExKhNmzZq3bq1ihcvntllZYjkflBAzmA2m3X48GEFBARox44dunfvnkwmk/z8/NSnTx95eXlldomGWrFihfbu3aupU6eqXbt28X44GzVqlNavX69x48Zp5cqV6t69eyZViqyMgI1Uu3jxotURDEnavn27ihUrZgkVlStX1quvvqpFixZlQoVIT6dPn9akSZMsv+jmzZtXo0aNUtOmTfXvv//a7GUBUtKnScfExFiC1qxZs/T000/b1BdvhQoVEmy/fv26vv76a3333XdydHTUkCFD1KtXrwyuLn01bdo02T49evTQkCFDNGvWLJsK2F988YUuXryo4cOHy9vbW5cvX9a8efPUoUMHBQUFqUePHho6dKgKFCiQ2aWmi1WrVunKlSvavHmzNm3apGnTpql69epq06aNWrZsKVdX18wuEUgXJ0+eVEBAgLZu3aobN24ob968at68uRo1aqRhw4apffv2NheuJWnt2rXq3bu32rdvn2gfPz8/nTlzRt9//71Nfc/DOARspFpUVJTy589veRweHq6///5bfn5+Vv3Kly+va9euZXB1Geejjz5Svnz5ku1nMpk0e/bsDKgoY9y6dUtFixa1aosL1bdu3bLpgJ3c+12wYEF5enrq3r17WrVqlU1/8V69elXz5s3TmjVrlDt3bg0aNEjdu3e3ydPjU8rPz09vv/12ZpdhqKNHj2rYsGHq06ePpa18+fLq0aOH/P39NWLEiMwrLoMUK1ZMAwYM0IABA3T27Flt2rRJS5Ys0QcffKDnn39ePXv2lI+PT2aXmS6OHDliGeApOS+++GI6V4OM0rx5cwUHB8vZ2VkNGzZU27Zt5ePjIycnJ4WGhmZ2eekqKCgoRWervfDCC1q+fHkGVITsiICNVCtevLgCAwNVp04dSQ//AIuJibE8jhMeHm5zp4o+KiIiwqauvYFx6tatq2XLlmV2Geni8uXLmjt3rtatW6cCBQpo2LBh6tq1q1xcXDK7tEzn7Oxsc9diXrt2Ld4ZS3GPbTVUJqVcuXIaPny4XnvtNU2fPl2LFi1Srly5bPa1+Pzzz1N0qrjJZLK5AY+mTJmSon7BwcHpXEnGu3DhgqSHZyO++OKLeuGFF3LMQI6Ojo6KiIhItl9kZKQcHR0zoKKMtWnTJh07dkzSw0ugTCaTAgICdPToUUufpAb+xEMEbKRay5YtNWfOHLm6uqpQoUL6/PPPlTdv3njXZR87dsxq1EFbM3HixBw7+Evv3r0THNCne/fuVu0mk8myo85JoqKi5OBgW7vX4OBgzZ49WwEBAXJ1ddX//vc/de7cWc7OzpldWpbx008/2dw+LyYmJt4fkXGf7Vy5cmVGSZkmJiZGBw8e1JYtW7Rr1y7Z29urQ4cO6tChQ2aXlm5mzpwpb2/vzC4jU+zevTvFfW3tzK1169Zp48aN2rp1q0aOHKncuXOrSZMmatu2bbwf3GxN5cqVtXbtWjVq1CjJfmvXrrXJ12LJkiXx2hK63NPWBnU0mm39BYgM8eqrr+rEiROWUyFdXFz0/vvvW50+GxUVpbVr16pLly6ZVSbSyZAhQzK7hCxvw4YNqlSpUmaXYaiWLVsqNjZWderUUYcOHeTk5KR9+/Yl2t+WThe9fft2otNiY2P133//adeuXVq8eHGqbuuUXSxcuFBubm6Wx3FHNL/++ut41yCPGzcuQ2vLCD///LM2bdqkbdu2KTo6Wk2aNNGnn36qBg0a2NwPaY8rXLhwjhnU7XGpCdi2xtvbW97e3nr77bd19OhRywBnmzdvVr58+WQymXT+/Hmbuh1jnL59+2rgwIEaO3ashg4dGu/Hk2vXrmnGjBnasWOH5s2bl0lVpo+//vors0uwGSYzw0QijYKDg3Xnzh2VKVMm3nWX9+7dU1BQkEqXLm2T12RWqFBBq1evzrFHsHOq7du3JzotJiZGISEh2rVrl37++WebG006sUHOEmJrp4tWqFAh2V/rHR0d1bdvX5u7JtnX1zfFfU0mk3bt2pWO1WQ8Hx8f3bp1Sw0bNlTr1q3l6+ubY87a4HsuZcLCwrR9+/YkB8WyBffv39e+ffsUEBCgffv2KSoqSsWKFZOfn5+GDh2a2eUZauHChfrss89kMpnk5eWlYsWKSXp4avTp06cVGxurkSNH2uSdE1Iq7q4SSBgBG+nm/PnzCggIsLkdr/TwnsATJ05U2bJlM7uULOnu3bvatm2bAgICtHTp0swuxzDJhcy4+yEPHTpUDRo0yKCqMsbly5dT1d+WjnqtXbs20T8k7O3t5erqqmrVqqVo0ENkLxUqVJCDg4McHR2T/WPS1i6JIWAnLi5sbty4Ufv27VN0dLRN/aiYnHv37mnHjh0KCAjQkSNH9Mcff2R2SYb7888/tXTpUv3yyy+WAXuLFCmiWrVqqWfPnjn20ono6GitW7dOCxcu1A8//JDZ5WRZBGwY6r///tPmzZsVEBCgP//8Uw4ODvr9998zu6x0FR0drVOnTllGWi1atKgqVaqUYwYEiRMdHa1du3YpICBABw4c0P3791WxYkWtXbs2s0szTFIh08HBQQULFpSzs7Oio6N169YtFSlSJAOryzouXryokiVLZnYZwBNL6h7oCbGlS2guX76swoUL2+RATml15MgRy+nSd+/e1dNPP602bdrIz88vVWf52IqoqCjdvHnT5q5BTylb/K67dOmStmzZon///VclS5ZUu3bt9NRTTykqKkpLly7VokWLFBISoho1ajCKehII2HhicadHxY0yGBsbKy8vL73yyitq06aNnnrqqcwuMV1ERUXpiy++0OrVqxUeHm65LtFkMsnFxUVdunTRsGHDbPp0QrPZrMOHD1v+4Lh3755MJpP8/PzUp08fm7xHZkr88MMPGj58eI46onHz5k1t3bpVAQEBOnHiRI5a9zjnzp3T/Pnz9cEHH2R2KYaLjo7Wtm3b9Msvv1j9mFirVi01b948x/2gmBOsX78+Vf0fv1WnrQgMDNTGjRu1ZcsWXb9+Xblz51aDBg20Y8cOLVmyxCavQ04pvuts67vu+PHj6tu3r9Uo6iVKlNDMmTM1fPhwXbhwQc8995yGDh1qU5fApQfbHp0D6ebx06OioqJUsmRJ9ezZU4sXL9Y777xj0186UVFR6t27t44fP6569erJ19fX8gvuv//+qz179mjBggU6duyYFi9ebHMh++TJkwoICNDWrVt148YN5c2bV82bN1ejRo00bNgwtW/fPseG65wkIiJCO3bs0KZNm3T48GE9ePBAFStWtMmBvmJiYvTHH3/oypUrKlGihNXosSdPntTcuXO1Z88e5cmTx+YC9i+//KKRI0fq2rVrMpvNyp8/v6SHl4KsXr1a06ZN0yeffKIaNWpkcqWZw1YviUnJvYAfPW3e1gL27NmztWnTJp07d0729vZ64YUXNGrUKPn6+io6OjrJMTlgW3LKd93MmTNVqFAhffTRR6pYsaIuXbqkSZMmqVu3bnJwcNDMmTPVrFmzzC4zWyBgI9XGjRtndXpUx44d1bZtW1WrVk2hoaEJDudva77++mudPn1aixcvjnf/b0nq0aOHjh49qtdee00LFy7Ua6+9lglVpo/mzZsrODhYzs7Oatiwodq2bSsfHx85OTkpNDQ0s8tDOouJidGBAwcUEBCg3bt3KzIyUm5uboqJidHnn3+uVq1aZXaJhrt69ar8/f11+vRpy8AuPj4++uyzzzRhwgRt2bJFefLk0cCBA21u0Jt//vlHAwYMULFixfTxxx+rcePGloEr7927pz179mjOnDkaMGCAvv/+e3l4eGRyxRkjsUtibMnPP/+c5PS//vpLX331lX766SeVKFEig6rKONOnT5fJZFK9evX0wQcfqHDhwpZp9+/fz8TKkBFy4nfdX3/9pTFjxujZZ5+VJJUtW1aTJk1Sy5Yt9d577xGuU4GAjVT7/vvvLV86kydPtqnBjFJq8+bN8vf3TzBcx6ldu7YGDBigjRs32lTAvnDhgqSH94p88cUX9cILL3B6aA5w7Ngxy62Kbt26pYIFC+qll15S27ZtVb58edWpU0eFChXK7DLTxRdffKGLFy9q+PDh8vb21uXLlzVv3jx16NBBQUFB6tGjh4YOHaoCBQpkdqmGmzVrlkqXLq2VK1fGu+91njx51KZNGzVp0kTdu3fXV199pc8++yyTKk1/Oe2SmMQG7Tt16pRmzZqlPXv2qGTJkvrggw/00ksvZXB16a9Hjx7atm2bDh06pFatWqlZs2Zq27Ytp8bauJz8XXfjxo1415THPba1/Vt6I2Aj1UaPHq2AgAAdOnRIzZo1U82aNdW2bVs1b948xwzZf+nSJT333HPJ9qtRo4Zmz56dARVlnHXr1mnjxo3aunWrRo4cqdy5c6tJkyZq27at1WmzsC3du3eXyWRSnTp11LdvX9WvX99yD2BbP3Ph6NGjGjZsmPr06WNpK1++vHr06CF/f3+buzXXo3766SeNGTMmXrh+VO7cudWvXz+bOzU+DpfEPHTy5El99dVX2r9/v8qWLauPP/5YrVq1kp2dXWaXli7GjRunsWPH6vDhw9q0aZO2b9+u9evX6+mnn1bjxo1lMplyzN88OUlO/q6TFO8zHfc47jVAyvBqIdX69OmjPn36KCgoSJs2bdKmTZs0fvx4TZ48WbVr15bJZFJsbGxml5mucufOrVu3biXb79atW8qdO3cGVJRxvL295e3trbfffltHjx61HM3ZvHmz8uXLJ5PJpPPnz9vkNfiDBg1KUb+QkJB0riTjeXp66vTp0/r5559lb2+vW7duqWnTpjZ5n/vHXbt2Ld6PR3GPfXx8MqOkDHP37l0VLVo02X5FixbV3bt3M6CijMUlMQ+P6M2aNUuHDh2Sl5eXpk2blmN+ULezs1ODBg3UoEEDTZ48Wbt27dKmTZu0fv16mc1mjR49Wn5+fnr55ZdtajRpvuty5nedJI0cOTLBcYNGjBhhdbaiyWTSxo0bM7K0bIWAjVSLuy1BmTJlNHToUA0dOlQnT57Upk2btGXLFpnNZr322mtq3ry5/Pz8kjyNOruqUaOGli1bpmbNmiX6R4bZbNby5ctTdKQ7O4r7hbdOnTp69913tW/fPgUEBGjfvn2aMGGC5syZIz8/P5u6D/q9e/dS1C937tyqWbNmOleTsTZu3KizZ89q48aN2rx5s0aPHq1cuXLJx8fHcjTHVsXExMS7VVHcr/lJHdm1BUWLFlVgYGCyn+c///wzRUE8u8nJl8QcOXJEX331lX7++WdVqlRJX331lZo0aZLZZWWos2fPatWqVbp06ZIKFy6s5s2b66uvvlJoaKi2bdumTZs2adasWfrqq69sajRpvuty5nedn59fgutXuXLlTKgme+M2XUi1ChUqqHr16mrbtq1atmwpV1dXyzSz2ayffvpJGzdu1M6dOxUWFmZTXzpxTpw4oe7du6t27dp64403VK1aNavpJ0+e1PTp03XkyBGtWLFCVatWzaRKM969e/e0Y8cOBQQE6MiRI/rjjz8yuySkg7jr1H744QfdvHlTJpNJTZs2Va9evWzu7IUKFSqoRYsWcnNzs7TF/YDWqlUrq32g9PDUUlvx0UcfacOGDVqxYoVKly6dYJ8LFy6oe/fuevnll/W///0vYwtMZ3G3aNq6dauuXr0a75KYevXqaenSpTb3mZcefu7jfkhN7kwNk8lkdQmFLfjll1/Ut29fPXjwQK6urrp9+7ZiY2M1YcIEde3a1dLv2rVr2rJli80NcIiHctJ3HYxDwEaqLV26VJs3b9bx48fl4OCgevXqqW3btmratKnV6dDR0dHau3evXnzxxUysNv1s2bJF48aNU0REhAoUKKBixYpJkq5cuaI7d+4oV65cev/9921upMnz58/rzTff1BtvvJHoH1379u3T9OnTNWnSJJu6Lrtt27b67LPP5OnpaWkLCAiQj4+P5dZFOU1MTIwOHjyoTZs2adeuXYqIiFCxYsW0a9euzC7NML6+vinuazKZbGrd7969q44dO+rq1avy8/NT48aNrW5JuHfvXq1fv15FixbVd999l+jAWNmd2Wy2uiTm7t27ypcvn0JDQzV58mR17Ngxs0s0XIUKFVLc12Qy2dyP6b1799bt27c1Z84cPfPMMwoLC9OYMWN09OhRHTlyJLPLS1d818WXE77rJOnQoUN6+umnLdu/2WzW+++/b9UnT548Nj32iBEI2Eizy5cvW67BPnPmjHLnzi1fX1+99NJLatCggezt7TO7xHR3/fp1rV69Wr/88ouuXbsmSSpSpIhq1aqljh07Wt3Ww1ZMmDBB586d07Jly5Ls17NnT5UrV07vvvtuBlWW/ipUqKDVq1dbzkiIiYlR5cqV9f3336tSpUqZXF3mi4yM1M6dO7Vp0ybNmTMns8uBQUJCQjRx4sRE/5Bs2rSp3n33Xasj/Lbs/v37VpfEREVFqVixYjZ3SUxOV7duXU2aNMnqIMGlS5fUtGlT7dmzx/JDky3iuy5ptvpdt3nzZr399ttatWqV5eBITEyMKlWqpEKFClkulbpx44Y++OADmzuAZCSuwUaaFS9eXP7+/pb7w27evNny31NPPaUWLVqoTZs2qlGjRmaXmm4KFy6sIUOGWLWdPXtWZ86c0blz5/T000/b3A8Nhw4dirfOCfl/7d1/TNXVH8fxFyA3JrgyKWsqm2ayiiRZbiQETSxQrmVWf4QYhKGjtFVjaquZaa1JzsUCFPlRNBnaFoY/QtPlKJVRS9dmLbM2aiS7QPzaSgL1fv9o99bt3i8/7HPvHZ/P87E5vR8Od++jjvN5nXvO+Tz22GMqKSkJQEXBxRzl3yIiImS322W324NdiqHGsmqjuLjYVIcdSVJ0dLRKSkp08eJFffXVV16Tia7VO1YRHh6uRYsWadGiRR5bYsrLywnYJtLT0+N1roArVPf09Jg6YPvCWPc3s451Bw4c0JIlS3yuPNy1a5d7cuXtt99WQ0MDAXsYBGwYYs6cOZozZ45efPFFffPNN/rwww+1d+9e7du3T999912wyzOc0+lUZWWljh07psuXLysjI0P5+fl69dVXVV9f7243e/Zs1dTUeO3RHM8cDseoAsT06dPdN+IY/7q7u9XR0eG1bPT7779XWVmZfvrpJ0VHRysnJ2dMS6rHg+rqak2cOHHYfaipqamqrKxUVVWVNm/eHLji/OzfkwuPPPKIVxszTy6MNLkSGRmpyZMnq6enR3V1dUGo0H/mzZs3pgOdzpw548dqgMCw8lh37tw5bdq0acR28+fPV0NDQwAqGr8I2DDM5cuX9cUXX+jQoUP67LPP5HQ6PfbvmElVVZV27NihtLQ0RUZGaufOnTp//rw+//xzbdiwQbNmzdIPP/ygXbt2qayszFSHHkVGRo7qEWW9vb2aOHFiACoKPjOfKuqyY8cOffvtt9q/f7/72q+//qoVK1ZoYGBAsbGxunDhgtauXauamhpTHf5i5VUbVp5ckMbW//r6elOdOZGXl2eJn23DycnJ8fl34HpWsktISIi+/vrrQJYWFFb4/2Dlsa6vr09TpkzxuBYWFqbi4mLFxMS4r0VFRam3tzfA1Y0vBGz8Zy0tLTp8+LCOHj2qvr4+TZs2TU899ZTsdrtuv/32YJfnF/X19Xr22WfdywEfeughrV27Vq+88oqys7MlSSkpKZowYYJqa2tNFbDj4uL0ySef6MEHHxy23eHDh035aAdfN1z/vtmSzHfDdebMGT3++OMe195//3398ccfqqioUHJysgYGBvT000+roqLCVDcdVl61YeXJBcna/bf6cvfR/LubGWPd36wy1k2aNEmdnZ1e19PT0z1ed3Z2mvZAS6MQsHFNzp07p0OHDqmxsVEdHR2aPHmyMjMzZbfbTfvc539qa2vzeL53YmKinE6n1+EfcXFxam9vD3R5fpWVlaXnnntOt912mwoKCrz2mF+9elVlZWU6cuSISktLg1Slf1j5hsvhcHhNmJ04cUJ33HGHkpOTJf21Ly07O1tFRUXBKNFvrLxqw8qTCxL9tzIr/7y3ct+tPNbdfffdamxs1OLFi4dt19jYaKnHz14LAjbGLD09Xb/88ov7eaB2u90yp4a7DA4OKiIiwv3a9WebzebRLjw8XFeuXAlobf6WlpamZ555RiUlJdq7d6/uu+8+9yFH7e3tam5uVldXl1atWmW6/UlWvukICQnx+OSiq6tLbW1tysnJ8Wg3derUUYXR8cTKqzasPLkg0X9YE2OdNce6FStWaM2aNSotLVVBQYFCQ0M9vu50OrVz504dO3bMVKen+wMBG2M2c+ZMPf/880pLS/MImbDG/iRJKiws1Pz581VdXa2jR49qcHBQknTdddcpISFBb7zxxrB7FjH+zJw5U6dPn3bP4J84cUIhISFKSkryaNfZ2WmqQ/0ka6/asPLkgkT/Aaux8liXmpqq1atX691339W+ffuUmJjoPi3f4XCoublZHR0dys/P5x5vBARsjBmzVn8Zzf4kMz/WIjU1Vampqbpy5Yr7sIsbbrjBUisZrGTlypXasGGD+vv7FR0drbq6OsXExGjBggUe7U6ePGm6ww2tvGrDypMLEv0HrMbKY50kvfTSS0pISNB7772nI0eOuD9AsdlsSkhI0JYtWwjXoxDiNHMCAPxkrIfZWHm5FcyjoqJCe/bsUX9/v+666y699tprHnvVfvvtNy1dulTr1q3Tk08+GcRK/aOpqUnV1dU6e/as16qN3Nxc0950bN++XZWVlYqOjh52cqGwsDDIlfqH1fsPWI3VxzoXPkC5dgRsAADGwIo3HVadXHCxev8BAKNHwAYAAKNixcmFf7J6/wEAIyNgAwAAAABggNCRmwAAAAAAgJEQsAEAAAAAMAABGwAAAAAAAxCwAQAAAAAwAAEbAACL27hxoxYuXBjsMgAAGPcmBLsAAABgvNjY2FG1++CDD/xcCQAA1sFjugAAMKGGhgav16dOnVJRUZHH9aSkJF1//fVyOp2y2WyBLBEAANMhYAMAYAFbtmxRbW2tzp8/H+xSAAAwLfZgAwBgcf/eg93W1qbY2FhVVVWptrZWaWlpio+PV15entrb2+V0OlVaWqqUlBTNnTtXBQUF6u3t9XrfpqYmZWVl6Z577tG8efO0evVqXbhwwaNNZ2enXn75ZaWkpCguLk7JyckqKChQW1ubv7sNAIDh2IMNAAB8OnjwoIaGhrRy5Ur19vaqsrJSL7zwghITE9XS0qL8/Hz9/PPP2rNnj7Zt26a33nrL/b0ff/yxNm7cqOTkZBUWFurSpUuqq6tTVlaW9u/fr+nTp0uS1q1bpx9//FHZ2dmaNm2auru7derUKbW3t7vbAAAwXhCwAQCATw6HQ59++qkmTZokSbp69arKy8s1MDCgjz76SBMm/HUb0dPTo4MHD+r111+XzWbT77//rjfffFNPPPGEtm7d6n6/Rx99VBkZGSovL9fWrVvV39+vs2fPav369Vq1apW73Zo1awLbUQAADMIScQAA4FNGRoY7XEvS3LlzJUkPP/ywO1y7rg8NDcnhcEiSTp8+rf7+fmVmZqq7u9v9KzQ0VPHx8WppaZEkRUREKDw8XF9++aX6+voC2DMAAPyDT7ABAIBPt956q8drV9j+f9f7+vo0Y8YMtba2SpJycnJ8vm9UVJQkyWazqbCwUNu2bVNSUpLi4+P1wAMPaNmyZbrpppuM7AoAAAFBwAYAAD6FhYX5vB4a6nsBnOvBJK7fi4qKfAblf75vbm6uFi5cqOPHj+vkyZMqLi7W7t27VVNTozvvvPO/dgEAgIAiYAMAAEPNmDFDkjRlyhQtWLBgxPYxMTHKy8tTXl6eWltbtWzZMlVXV2v79u3+LhUAAEOxBxsAABjq/vvvV1RUlMrLyzU0NOT19e7ubknSpUuX9Oeff3p8LSYmRpGRkRocHAxIrQAAGIlPsAEAgKGioqK0efNmrV+/XsuXL9eSJUt044036uLFi2pqalJCQoI2bdqk1tZW5ebmKiMjQ7Nnz1ZYWJiOHz+urq4uZWZmBrsbAACMGQEbAAAYbunSpbr55pu1e/duVVVVaXBwUFOnTtW9996r5cuXS5JuueUWZWZmqrm5WQcOHFBYWJhmzZqld955R+np6UHuAQAAYxfidJ1EAgAAAAAArhl7sAEAAAAAMAABGwAAAAAAAxCwAQAAAAAwAAEbAAAAAAADELABAAAAADAAARsAAAAAAAMQsAEAAAAAMAABGwAAAAAAAxCwAQAAAAAwAAEbAAAAAAADELABAAAAADAAARsAAAAAAAMQsAEAAAAAMMD/AKiiUx1l8IjlAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "cores = ['white' if valor <= ap_media else 'peachpuff' for valor in media_ap['Aproveitamento']]\n",
        "plt.bar(media_ap.index, media_ap['Aproveitamento'], color=cores,  edgecolor='black')\n",
        "plt.axhline(df['Aproveitamento'].mean(), color = 'k', linestyle='--')\n",
        "plt.annotate('média geral', xy=(4, 52), fontsize=12, color='black')\n",
        "plt.xlabel('Times')\n",
        "plt.ylabel('Média dos aproveitamentos')\n",
        "plt.title('Média dos aproveitamentos dos times no Campeonato Brasileiro')\n",
        "plt.xticks(rotation=90)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "JwuNhYdJY8CE",
        "outputId": "63f3908c-7ed9-4ed4-e8e8-80eb220fa28c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplots(figsize=(20, 5))\n",
        "\n",
        "ax1=plt.subplot(1,2,1)\n",
        "mu, mediana, moda = df['Pontos'].mean(), df['Pontos'].median(), df['Pontos'].mode().iloc[0]\n",
        "\n",
        "N, bins, patches = ax1.hist(df['Pontos'], bins=20, edgecolor='black')\n",
        "ax1.set_title('Frequência da pontuação', fontsize=15, pad=10)\n",
        "ax1.set_xlabel('Pontuação', fontsize=15)\n",
        "\n",
        "fracs = N / N.max()\n",
        "\n",
        "norm = colors.Normalize(fracs.min(), fracs.max())\n",
        "\n",
        "for thisfrac, thispatch in zip(fracs, patches):\n",
        "    color = plt.cm.Oranges(norm(thisfrac))\n",
        "    thispatch.set_facecolor(color)\n",
        "\n",
        "ax1.annotate('$média = {0:.1f}$'.format(mu), xy=(65, 29), fontsize=15, color='black')\n",
        "ax1.annotate('$mediana = {0:.0f}$'.format(mediana), xy=(65, 26.5), fontsize=15, color='teal')\n",
        "ax1.annotate('$moda={0:.0f}$'.format(moda), xy=(65, 24), fontsize=15, color='lawngreen')\n",
        "\n",
        "ax1.axvline(mu, color = 'k', linestyle='--')\n",
        "ax1.axvline(mediana, color='teal', linestyle='--')\n",
        "ax1.axvline(moda, color='lawngreen', linestyle='--')\n",
        "\n",
        "ax2=plt.subplot(1,2,2)\n",
        "mu, mediana, moda = df['Aproveitamento'].mean(), df['Aproveitamento'].median(), df['Aproveitamento'].mode().iloc[0]\n",
        "\n",
        "N, bins, patches = ax2.hist(df['Aproveitamento'], bins=20, edgecolor='black')\n",
        "ax2.set_title('Frequência dos aproveitamentos', fontsize=15, pad=10)\n",
        "ax2.set_xlabel('Aproveitamento', fontsize=15)\n",
        "\n",
        "fracs = N / N.max()\n",
        "\n",
        "norm = colors.Normalize(fracs.min(), fracs.max())\n",
        "\n",
        "for thisfrac, thispatch in zip(fracs, patches):\n",
        "    color = plt.cm.plasma_r(norm(thisfrac))\n",
        "    thispatch.set_facecolor(color)\n",
        "\n",
        "ax2.annotate('$média = {0:.1f}$'.format(mu), xy=(65, 29), fontsize=12, color='black')\n",
        "ax2.annotate('$mediana = {0:.0f}$'.format(mediana), xy=(65, 27.3), fontsize=12, color='darkorange')\n",
        "ax2.annotate('$moda={0:.0f}$'.format(moda), xy=(65, 25.6), fontsize=12, color='deeppink')\n",
        "\n",
        "ax2.axvline(mu, color = 'k', linestyle='--')\n",
        "ax2.axvline(mediana, color='darkorange', linestyle='--')\n",
        "ax2.axvline(moda, color='deeppink', linestyle='--')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "eu8nAlvOthfq",
        "outputId": "2140dd03-8fd2-4317-ae4a-8a32ad9819b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-28-0b86121d3de8>:3: MatplotlibDeprecationWarning: Auto-removal of overlapping axes is deprecated since 3.6 and will be removed two minor releases later; explicitly call ax.remove() as needed.\n",
            "  ax1=plt.subplot(1,2,1)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}