PI V-A.ipynb

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      "source": [
        "import contextily\n",
        "import pandas\n",
        "from geobr import read_state\n",
        "import geopandas as gpd\n",
        "import matplotlib.pyplot as plt"
      ]
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      "source": [
        "df = pandas.read_csv(\"dadosqueimadas.444354.csv\")"
      ],
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        "# Tipos de dados em cada coluna\n",
        "df.dtypes"
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              "DataHora         object\n",
              "Satelite         object\n",
              "Pais             object\n",
              "Estado           object\n",
              "Municipio        object\n",
              "Bioma            object\n",
              "DiaSemChuva     float64\n",
              "Precipitacao    float64\n",
              "RiscoFogo       float64\n",
              "Latitude        float64\n",
              "Longitude       float64\n",
              "FRP             float64\n",
              "dtype: object"
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        "# Sumarizando dados\n",
        "# OBS: FRP - “Fire Radiative Power” ou Potência Radiativa do Fogo, mede a energia radiante liberada por unidade de tempo, associada à taxa de queima da vegetação, em MW - megawatts\n",
        "df.describe()"
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              "         DiaSemChuva   Precipitacao      RiscoFogo       Latitude  \\\n",
              "count  348681.000000  348681.000000  348681.000000  368234.000000   \n",
              "mean       23.235017       0.274973      -6.484296     -11.093733   \n",
              "std       101.298379       2.098987      85.257645      10.485585   \n",
              "min      -999.000000       0.000000    -999.000000     -53.662700   \n",
              "25%         7.000000       0.000000       0.830000     -17.443625   \n",
              "50%        20.000000       0.000000       1.000000     -11.691425   \n",
              "75%        45.000000       0.000000       1.000000      -6.610783   \n",
              "max       120.000000     118.770000       1.000000      11.950570   \n",
              "\n",
              "           Longitude            FRP  \n",
              "count  368234.000000  368234.000000  \n",
              "mean      -58.882798      65.587399  \n",
              "std         8.172628     150.361958  \n",
              "min       -91.524770       0.000000  \n",
              "25%       -63.978245      14.400000  \n",
              "50%       -59.185145      27.800000  \n",
              "75%       -53.595890      59.700000  \n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"DiaSemChuva\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 123312.37513583803,\n        \"min\": -999.0,\n        \"max\": 348681.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          23.235017107327327,\n          20.0,\n          348681.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precipitacao\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 123271.23805302828,\n        \"min\": 0.0,\n        \"max\": 348681.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.2749732850370395,\n          118.77,\n          2.0989869798459666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RiscoFogo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 123324.14759414607,\n        \"min\": -999.0,\n        \"max\": 348681.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          348681.0,\n          -6.484296104462242,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 130194.32374005739,\n        \"min\": -53.6627,\n        \"max\": 368234.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -11.09373259527909,\n          -11.691424999999999,\n          368234.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 130208.25305499647,\n        \"min\": -91.52477,\n        \"max\": 368234.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -58.88279788199896,\n          -59.185145,\n          368234.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FRP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 129796.01450796341,\n        \"min\": 0.0,\n        \"max\": 368234.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          65.58739904517235,\n          27.8,\n          368234.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Primeiras cinco linhas de dados\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "3JaPAHpi-TXK",
        "outputId": "faf6d620-2529-425d-9baa-48829761c6fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              DataHora  Satelite    Pais Estado    Municipio           Bioma  \\\n",
              "0  2024/01/02 17:04:00  AQUA_M-T  Brasil  BAHIA     BELMONTE  Mata Atlântica   \n",
              "1  2024/01/02 17:06:00  AQUA_M-T  Brasil  CEARÁ  BREJO SANTO        Caatinga   \n",
              "2  2024/01/02 17:06:00  AQUA_M-T  Brasil  CEARÁ       CARIÚS        Caatinga   \n",
              "3  2024/01/02 17:06:00  AQUA_M-T  Brasil  CEARÁ       CARIÚS        Caatinga   \n",
              "4  2024/01/02 17:06:00  AQUA_M-T  Brasil  CEARÁ      MOMBAÇA        Caatinga   \n",
              "\n",
              "   DiaSemChuva  Precipitacao  RiscoFogo  Latitude  Longitude   FRP  \n",
              "0       -999.0          6.81       0.11 -15.89572  -38.89957  41.5  \n",
              "1          0.0          0.00       0.07  -7.49188  -38.88316  51.7  \n",
              "2          0.0          0.00       0.25  -6.50564  -39.44122  29.0  \n",
              "3          0.0          0.00       0.23  -6.50409  -39.43422  18.9  \n",
              "4          0.0          0.00       0.00  -5.74166  -39.59352  25.7  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0a17071b-4e91-45a2-9bc2-769814909ac2\" class=\"colab-df-container\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DataHora</th>\n",
              "      <th>Satelite</th>\n",
              "      <th>Pais</th>\n",
              "      <th>Estado</th>\n",
              "      <th>Municipio</th>\n",
              "      <th>Bioma</th>\n",
              "      <th>DiaSemChuva</th>\n",
              "      <th>Precipitacao</th>\n",
              "      <th>RiscoFogo</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>FRP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2024/01/02 17:04:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>BAHIA</td>\n",
              "      <td>BELMONTE</td>\n",
              "      <td>Mata Atlântica</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>6.81</td>\n",
              "      <td>0.11</td>\n",
              "      <td>-15.89572</td>\n",
              "      <td>-38.89957</td>\n",
              "      <td>41.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2024/01/02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>CEARÁ</td>\n",
              "      <td>BREJO SANTO</td>\n",
              "      <td>Caatinga</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.07</td>\n",
              "      <td>-7.49188</td>\n",
              "      <td>-38.88316</td>\n",
              "      <td>51.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2024/01/02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>CEARÁ</td>\n",
              "      <td>CARIÚS</td>\n",
              "      <td>Caatinga</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>-6.50564</td>\n",
              "      <td>-39.44122</td>\n",
              "      <td>29.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2024/01/02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>CEARÁ</td>\n",
              "      <td>CARIÚS</td>\n",
              "      <td>Caatinga</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.23</td>\n",
              "      <td>-6.50409</td>\n",
              "      <td>-39.43422</td>\n",
              "      <td>18.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2024/01/02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>CEARÁ</td>\n",
              "      <td>MOMBAÇA</td>\n",
              "      <td>Caatinga</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-5.74166</td>\n",
              "      <td>-39.59352</td>\n",
              "      <td>25.7</td>\n",
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              "  </tbody>\n",
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              "    .colab-df-convert {\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-0a17071b-4e91-45a2-9bc2-769814909ac2 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-0a17071b-4e91-45a2-9bc2-769814909ac2');\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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              "    </script>\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8da5dc72-601b-4915-b5d9-d5618bf4c8f9')\"\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    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",
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              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
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              "\n",
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              "    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",
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              "      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-8da5dc72-601b-4915-b5d9-d5618bf4c8f9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
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          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificando dados incongruentes\n",
        "df.head(25).sort_values(by=[\"DiaSemChuva\"], ascending=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-XKWu7jD_ndL",
        "outputId": "aef7a81b-6247-4907-943a-a52a7dd606f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               DataHora  Satelite    Pais              Estado  \\\n",
              "0   2024/01/02 17:04:00  AQUA_M-T  Brasil               BAHIA   \n",
              "20  2024/01/02 17:04:00  AQUA_M-T  Brasil               BAHIA   \n",
              "9   2024/01/02 17:02:00  AQUA_M-T  Brasil              PARANÁ   \n",
              "21  2024/01/02 17:04:00  AQUA_M-T  Brasil    DISTRITO FEDERAL   \n",
              "18  2024/01/02 17:04:00  AQUA_M-T  Brasil  MATO GROSSO DO SUL   \n",
              "17  2024/01/02 17:04:00  AQUA_M-T  Brasil  MATO GROSSO DO SUL   \n",
              "16  2024/01/02 17:04:00  AQUA_M-T  Brasil  MATO GROSSO DO SUL   \n",
              "14  2024/01/02 17:04:00  AQUA_M-T  Brasil           SÃO PAULO   \n",
              "13  2024/01/02 17:04:00  AQUA_M-T  Brasil           SÃO PAULO   \n",
              "10  2024/01/02 17:04:00  AQUA_M-T  Brasil           SÃO PAULO   \n",
              "12  2024/01/02 17:04:00  AQUA_M-T  Brasil           SÃO PAULO   \n",
              "6   2024/01/02 17:06:00  AQUA_M-T  Brasil               CEARÁ   \n",
              "4   2024/01/02 17:06:00  AQUA_M-T  Brasil               CEARÁ   \n",
              "3   2024/01/02 17:06:00  AQUA_M-T  Brasil               CEARÁ   \n",
              "2   2024/01/02 17:06:00  AQUA_M-T  Brasil               CEARÁ   \n",
              "1   2024/01/02 17:06:00  AQUA_M-T  Brasil               CEARÁ   \n",
              "24  2024/01/02 17:04:00  AQUA_M-T  Brasil               GOIÁS   \n",
              "5   2024/01/02 17:06:00  AQUA_M-T  Brasil            MARANHÃO   \n",
              "11  2024/01/02 17:04:00  AQUA_M-T  Brasil           SÃO PAULO   \n",
              "22  2024/01/02 17:04:00  AQUA_M-T  Brasil               GOIÁS   \n",
              "15  2024/01/02 17:04:00  AQUA_M-T  Brasil  MATO GROSSO DO SUL   \n",
              "23  2024/01/02 17:04:00  AQUA_M-T  Brasil               BAHIA   \n",
              "8   2024/01/02 17:02:00  AQUA_M-T  Brasil              PARANÁ   \n",
              "7   2024/01/02 17:06:00  AQUA_M-T  Brasil               CEARÁ   \n",
              "19  2024/01/02 17:04:00  AQUA_M-T  Brasil               BAHIA   \n",
              "\n",
              "                   Municipio           Bioma  DiaSemChuva  Precipitacao  \\\n",
              "0                   BELMONTE  Mata Atlântica       -999.0          6.81   \n",
              "20                  BELMONTE  Mata Atlântica       -999.0          6.82   \n",
              "9                  PARANAGUÁ  Mata Atlântica       -999.0          0.00   \n",
              "21                  BRASÍLIA         Cerrado          0.0         59.41   \n",
              "18                 PARANAÍBA  Mata Atlântica          0.0          3.82   \n",
              "17                 PARANAÍBA  Mata Atlântica          0.0          2.78   \n",
              "16                 PARANAÍBA  Mata Atlântica          0.0          2.67   \n",
              "14              MIGUELÓPOLIS         Cerrado          0.0          1.37   \n",
              "13                 PALESTINA         Cerrado          0.0          0.40   \n",
              "10  SANTA CRUZ DAS PALMEIRAS  Mata Atlântica          0.0          0.10   \n",
              "12              NOVA GRANADA         Cerrado          0.0          0.98   \n",
              "6    SÃO GONÇALO DO AMARANTE        Caatinga          0.0          0.06   \n",
              "4                    MOMBAÇA        Caatinga          0.0          0.00   \n",
              "3                     CARIÚS        Caatinga          0.0          0.00   \n",
              "2                     CARIÚS        Caatinga          0.0          0.00   \n",
              "1                BREJO SANTO        Caatinga          0.0          0.00   \n",
              "24    SÃO MIGUEL DO ARAGUAIA         Cerrado          0.0          1.43   \n",
              "5                     CAXIAS         Cerrado          1.0          7.44   \n",
              "11                  ORLÂNDIA         Cerrado          2.0          1.71   \n",
              "22              SÃO DOMINGOS         Cerrado          2.0         27.26   \n",
              "15                ÁGUA CLARA         Cerrado          3.0          7.21   \n",
              "23                CORRENTINA         Cerrado          4.0         28.16   \n",
              "8                  GUARATUBA  Mata Atlântica          4.0          0.00   \n",
              "7    SÃO GONÇALO DO AMARANTE        Caatinga         10.0          1.50   \n",
              "19                  BELMONTE  Mata Atlântica         10.0          6.69   \n",
              "\n",
              "    RiscoFogo  Latitude  Longitude   FRP  \n",
              "0        0.11 -15.89572  -38.89957  41.5  \n",
              "20       0.11 -15.89858  -38.90451  41.3  \n",
              "9     -999.00 -25.51123  -48.52511  20.6  \n",
              "21    -999.00 -15.81388  -48.07776  13.3  \n",
              "18       0.00 -19.53199  -50.97829  29.1  \n",
              "17       0.01 -19.58245  -51.05017  34.6  \n",
              "16       0.01 -19.58589  -51.04503  32.5  \n",
              "14       0.04 -20.15670  -48.18394  13.5  \n",
              "13       0.30 -20.46482  -49.50798  25.9  \n",
              "10       0.05 -21.93134  -47.27146  16.2  \n",
              "12       0.38 -20.54684  -49.43139  25.6  \n",
              "6        0.03  -3.57072  -38.91899  44.2  \n",
              "4        0.00  -5.74166  -39.59352  25.7  \n",
              "3        0.23  -6.50409  -39.43422  18.9  \n",
              "2        0.25  -6.50564  -39.44122  29.0  \n",
              "1        0.07  -7.49188  -38.88316  51.7  \n",
              "24       0.00 -13.47886  -50.07882  15.5  \n",
              "5        0.00  -4.73447  -42.98536  10.4  \n",
              "11    -999.00 -20.72576  -47.88945  16.1  \n",
              "22       0.00 -13.59812  -46.22672  12.6  \n",
              "15       0.00 -19.99615  -52.96258  28.8  \n",
              "23       0.00 -13.50745  -45.91243   9.3  \n",
              "8     -999.00 -25.89196  -48.58046  16.4  \n",
              "7        0.05  -3.53934  -39.27389  19.7  \n",
              "19       0.08 -15.96596  -39.10589  25.3  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-206df0bb-2b5d-41e6-97bc-770e3a127d93\" 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>DataHora</th>\n",
              "      <th>Satelite</th>\n",
              "      <th>Pais</th>\n",
              "      <th>Estado</th>\n",
              "      <th>Municipio</th>\n",
              "      <th>Bioma</th>\n",
              "      <th>DiaSemChuva</th>\n",
              "      <th>Precipitacao</th>\n",
              "      <th>RiscoFogo</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>FRP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2024/01/02 17:04:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>BAHIA</td>\n",
              "      <td>BELMONTE</td>\n",
              "      <td>Mata Atlântica</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>6.81</td>\n",
              "      <td>0.11</td>\n",
              "      <td>-15.89572</td>\n",
              "      <td>-38.89957</td>\n",
              "      <td>41.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2024/01/02 17:04:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>BAHIA</td>\n",
              "      <td>BELMONTE</td>\n",
              "      <td>Mata Atlântica</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>6.82</td>\n",
              "      <td>0.11</td>\n",
              "      <td>-15.89858</td>\n",
              "      <td>-38.90451</td>\n",
              "      <td>41.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2024/01/02 17:02:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>PARANÁ</td>\n",
              "      <td>PARANAGUÁ</td>\n",
              "      <td>Mata Atlântica</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-999.00</td>\n",
              "      <td>-25.51123</td>\n",
              "      <td>-48.52511</td>\n",
              "      <td>20.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2024/01/02 17:04:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>DISTRITO FEDERAL</td>\n",
              "      <td>BRASÍLIA</td>\n",
              "      <td>Cerrado</td>\n",
              "      <td>0.0</td>\n",
              "      <td>59.41</td>\n",
              "      <td>-999.00</td>\n",
              "      <td>-15.81388</td>\n",
              "      <td>-48.07776</td>\n",
              "      <td>13.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2024/01/02 17:04:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>MATO GROSSO DO SUL</td>\n",
              "      <td>PARANAÍBA</td>\n",
              "      <td>Mata Atlântica</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.82</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-19.53199</td>\n",
              "      <td>-50.97829</td>\n",
              "      <td>29.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2024/01/02 17:04:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>MATO GROSSO DO SUL</td>\n",
              "      <td>PARANAÍBA</td>\n",
              "      <td>Mata Atlântica</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.78</td>\n",
              "      <td>0.01</td>\n",
              "      <td>-19.58245</td>\n",
              "      <td>-51.05017</td>\n",
              "      <td>34.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2024/01/02 17:04:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>MATO GROSSO DO SUL</td>\n",
              "      <td>PARANAÍBA</td>\n",
              "      <td>Mata Atlântica</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.67</td>\n",
              "      <td>0.01</td>\n",
              "      <td>-19.58589</td>\n",
              "      <td>-51.04503</td>\n",
              "      <td>32.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2024/01/02 17:04:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>SÃO PAULO</td>\n",
              "      <td>MIGUELÓPOLIS</td>\n",
              "      <td>Cerrado</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.37</td>\n",
              "      <td>0.04</td>\n",
              "      <td>-20.15670</td>\n",
              "      <td>-48.18394</td>\n",
              "      <td>13.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2024/01/02 17:04:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>SÃO PAULO</td>\n",
              "      <td>PALESTINA</td>\n",
              "      <td>Cerrado</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.30</td>\n",
              "      <td>-20.46482</td>\n",
              "      <td>-49.50798</td>\n",
              "      <td>25.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2024/01/02 17:04:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>SÃO PAULO</td>\n",
              "      <td>SANTA CRUZ DAS PALMEIRAS</td>\n",
              "      <td>Mata Atlântica</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.05</td>\n",
              "      <td>-21.93134</td>\n",
              "      <td>-47.27146</td>\n",
              "      <td>16.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2024/01/02 17:04:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>SÃO PAULO</td>\n",
              "      <td>NOVA GRANADA</td>\n",
              "      <td>Cerrado</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.38</td>\n",
              "      <td>-20.54684</td>\n",
              "      <td>-49.43139</td>\n",
              "      <td>25.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2024/01/02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>CEARÁ</td>\n",
              "      <td>SÃO GONÇALO DO AMARANTE</td>\n",
              "      <td>Caatinga</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.03</td>\n",
              "      <td>-3.57072</td>\n",
              "      <td>-38.91899</td>\n",
              "      <td>44.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2024/01/02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>CEARÁ</td>\n",
              "      <td>MOMBAÇA</td>\n",
              "      <td>Caatinga</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-5.74166</td>\n",
              "      <td>-39.59352</td>\n",
              "      <td>25.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2024/01/02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>CEARÁ</td>\n",
              "      <td>CARIÚS</td>\n",
              "      <td>Caatinga</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.23</td>\n",
              "      <td>-6.50409</td>\n",
              "      <td>-39.43422</td>\n",
              "      <td>18.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2024/01/02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>CEARÁ</td>\n",
              "      <td>CARIÚS</td>\n",
              "      <td>Caatinga</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>-6.50564</td>\n",
              "      <td>-39.44122</td>\n",
              "      <td>29.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2024/01/02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>CEARÁ</td>\n",
              "      <td>BREJO SANTO</td>\n",
              "      <td>Caatinga</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.07</td>\n",
              "      <td>-7.49188</td>\n",
              "      <td>-38.88316</td>\n",
              "      <td>51.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2024/01/02 17:04:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>GOIÁS</td>\n",
              "      <td>SÃO MIGUEL DO ARAGUAIA</td>\n",
              "      <td>Cerrado</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.43</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-13.47886</td>\n",
              "      <td>-50.07882</td>\n",
              "      <td>15.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2024/01/02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>MARANHÃO</td>\n",
              "      <td>CAXIAS</td>\n",
              "      <td>Cerrado</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.44</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-4.73447</td>\n",
              "      <td>-42.98536</td>\n",
              "      <td>10.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2024/01/02 17:04:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>SÃO PAULO</td>\n",
              "      <td>ORLÂNDIA</td>\n",
              "      <td>Cerrado</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.71</td>\n",
              "      <td>-999.00</td>\n",
              "      <td>-20.72576</td>\n",
              "      <td>-47.88945</td>\n",
              "      <td>16.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2024/01/02 17:04:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>GOIÁS</td>\n",
              "      <td>SÃO DOMINGOS</td>\n",
              "      <td>Cerrado</td>\n",
              "      <td>2.0</td>\n",
              "      <td>27.26</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-13.59812</td>\n",
              "      <td>-46.22672</td>\n",
              "      <td>12.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2024/01/02 17:04:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>MATO GROSSO DO SUL</td>\n",
              "      <td>ÁGUA CLARA</td>\n",
              "      <td>Cerrado</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.21</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-19.99615</td>\n",
              "      <td>-52.96258</td>\n",
              "      <td>28.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2024/01/02 17:04:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>BAHIA</td>\n",
              "      <td>CORRENTINA</td>\n",
              "      <td>Cerrado</td>\n",
              "      <td>4.0</td>\n",
              "      <td>28.16</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-13.50745</td>\n",
              "      <td>-45.91243</td>\n",
              "      <td>9.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2024/01/02 17:02:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>PARANÁ</td>\n",
              "      <td>GUARATUBA</td>\n",
              "      <td>Mata Atlântica</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-999.00</td>\n",
              "      <td>-25.89196</td>\n",
              "      <td>-48.58046</td>\n",
              "      <td>16.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2024/01/02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>CEARÁ</td>\n",
              "      <td>SÃO GONÇALO DO AMARANTE</td>\n",
              "      <td>Caatinga</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.50</td>\n",
              "      <td>0.05</td>\n",
              "      <td>-3.53934</td>\n",
              "      <td>-39.27389</td>\n",
              "      <td>19.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2024/01/02 17:04:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>BAHIA</td>\n",
              "      <td>BELMONTE</td>\n",
              "      <td>Mata Atlântica</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6.69</td>\n",
              "      <td>0.08</td>\n",
              "      <td>-15.96596</td>\n",
              "      <td>-39.10589</td>\n",
              "      <td>25.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Filtrando dados apenas do Brasil\n",
        "df = df[df['Pais'] == 'Brasil']\n",
        "# Verificando existência de valores nulos\n",
        "df[df.isnull().any(axis=1)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "RZfePd9fAQpY",
        "outputId": "da39989a-9ad6-4d3e-9dde-26bda7f0e7ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   DataHora  Satelite    Pais        Estado        Municipio  \\\n",
              "334883  2024/09/11 17:39:00  AQUA_M-T  Brasil  MINAS GERAIS      CORDISBURGO   \n",
              "334884  2024/09/11 17:39:00  AQUA_M-T  Brasil  MINAS GERAIS          CURVELO   \n",
              "334885  2024/09/11 17:39:00  AQUA_M-T  Brasil  MINAS GERAIS          CURVELO   \n",
              "334886  2024/09/11 17:39:00  AQUA_M-T  Brasil  MINAS GERAIS        INIMUTABA   \n",
              "334887  2024/09/11 17:39:00  AQUA_M-T  Brasil  MINAS GERAIS        INIMUTABA   \n",
              "...                     ...       ...     ...           ...              ...   \n",
              "368208  2024/09/17 18:26:00  AQUA_M-T  Brasil      RONDÔNIA          VILHENA   \n",
              "368209  2024/09/17 18:26:00  AQUA_M-T  Brasil      RONDÔNIA          VILHENA   \n",
              "368210  2024/09/17 18:26:00  AQUA_M-T  Brasil      RONDÔNIA  ESPIGÃO D'OESTE   \n",
              "368211  2024/09/17 18:26:00  AQUA_M-T  Brasil      RONDÔNIA  ESPIGÃO D'OESTE   \n",
              "368212  2024/09/17 18:26:00  AQUA_M-T  Brasil      RONDÔNIA  ESPIGÃO D'OESTE   \n",
              "\n",
              "           Bioma  DiaSemChuva  Precipitacao  RiscoFogo  Latitude  Longitude  \\\n",
              "334883   Cerrado          NaN           NaN        NaN -19.12846  -44.08802   \n",
              "334884   Cerrado          NaN           NaN        NaN -18.76358  -44.18068   \n",
              "334885   Cerrado          NaN           NaN        NaN -18.76149  -44.16800   \n",
              "334886   Cerrado          NaN           NaN        NaN -18.75338  -44.18267   \n",
              "334887   Cerrado          NaN           NaN        NaN -18.75125  -44.16986   \n",
              "...          ...          ...           ...        ...       ...        ...   \n",
              "368208  Amazônia          NaN           NaN        NaN -11.48365  -60.40946   \n",
              "368209  Amazônia          NaN           NaN        NaN -11.47477  -60.41079   \n",
              "368210  Amazônia          NaN           NaN        NaN -11.39107  -60.66603   \n",
              "368211  Amazônia          NaN           NaN        NaN -11.38584  -60.63005   \n",
              "368212  Amazônia          NaN           NaN        NaN -11.35268  -60.58913   \n",
              "\n",
              "          FRP  \n",
              "334883   19.9  \n",
              "334884  230.0  \n",
              "334885   96.7  \n",
              "334886   54.7  \n",
              "334887   11.3  \n",
              "...       ...  \n",
              "368208   13.1  \n",
              "368209    8.1  \n",
              "368210   15.1  \n",
              "368211   12.9  \n",
              "368212    6.3  \n",
              "\n",
              "[13443 rows x 12 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DataHora</th>\n",
              "      <th>Satelite</th>\n",
              "      <th>Pais</th>\n",
              "      <th>Estado</th>\n",
              "      <th>Municipio</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>334883</th>\n",
              "      <td>2024/09/11 17:39:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>MINAS GERAIS</td>\n",
              "      <td>CORDISBURGO</td>\n",
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              "      <td>2024/09/11 17:39:00</td>\n",
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              "      <th>334885</th>\n",
              "      <td>2024/09/11 17:39:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
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              "      <td>-18.76149</td>\n",
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              "      <th>334886</th>\n",
              "      <td>2024/09/11 17:39:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>MINAS GERAIS</td>\n",
              "      <td>INIMUTABA</td>\n",
              "      <td>Cerrado</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-18.75338</td>\n",
              "      <td>-44.18267</td>\n",
              "      <td>54.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>334887</th>\n",
              "      <td>2024/09/11 17:39:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>MINAS GERAIS</td>\n",
              "      <td>INIMUTABA</td>\n",
              "      <td>Cerrado</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-18.75125</td>\n",
              "      <td>-44.16986</td>\n",
              "      <td>11.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>368208</th>\n",
              "      <td>2024/09/17 18:26:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>RONDÔNIA</td>\n",
              "      <td>VILHENA</td>\n",
              "      <td>Amazônia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-11.48365</td>\n",
              "      <td>-60.40946</td>\n",
              "      <td>13.1</td>\n",
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              "    <tr>\n",
              "      <th>368209</th>\n",
              "      <td>2024/09/17 18:26:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>RONDÔNIA</td>\n",
              "      <td>VILHENA</td>\n",
              "      <td>Amazônia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-11.47477</td>\n",
              "      <td>-60.41079</td>\n",
              "      <td>8.1</td>\n",
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              "    <tr>\n",
              "      <th>368210</th>\n",
              "      <td>2024/09/17 18:26:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>RONDÔNIA</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>-11.39107</td>\n",
              "      <td>-60.66603</td>\n",
              "      <td>15.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>368211</th>\n",
              "      <td>2024/09/17 18:26:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>RONDÔNIA</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>-11.38584</td>\n",
              "      <td>-60.63005</td>\n",
              "      <td>12.9</td>\n",
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              "    <tr>\n",
              "      <th>368212</th>\n",
              "      <td>2024/09/17 18:26:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>RONDÔNIA</td>\n",
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              "      <td>-11.35268</td>\n",
              "      <td>-60.58913</td>\n",
              "      <td>6.3</td>\n",
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              "<p>13443 rows × 12 columns</p>\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-32375f95-70ad-4ae1-97d9-39499e4b6107 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-32375f95-70ad-4ae1-97d9-39499e4b6107');\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-aa55722b-bb57-42c7-b42a-5cd36bb1e5bc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-aa55722b-bb57-42c7-b42a-5cd36bb1e5bc')\"\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",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-aa55722b-bb57-42c7-b42a-5cd36bb1e5bc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Limpando valores nulos como \"Nan\"\n",
        "df = df.dropna()\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "-Uz_0ZUPB-kM",
        "outputId": "1587b3a0-4856-413e-e44e-c13f66b13082"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DataHora        0\n",
              "Satelite        0\n",
              "Pais            0\n",
              "Estado          0\n",
              "Municipio       0\n",
              "Bioma           0\n",
              "DiaSemChuva     0\n",
              "Precipitacao    0\n",
              "RiscoFogo       0\n",
              "Latitude        0\n",
              "Longitude       0\n",
              "FRP             0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>DataHora</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Satelite</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pais</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Estado</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Municipio</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bioma</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DiaSemChuva</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precipitacao</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RiscoFogo</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Latitude</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Longitude</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FRP</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Limpando dados incongruentes\n",
        "# Esse código filtra dados com valores negativos e exorbitantes como -999 nas colunas em que os dados eram para ser >= 0.\n",
        "df = df[df['DiaSemChuva'] >= 0]\n",
        "df = df[df['RiscoFogo'] >= 0]\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Auh0Jq7jCB-i",
        "outputId": "b41bb250-1852-4591-85d4-4553da806209"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         DiaSemChuva   Precipitacao      RiscoFogo       Latitude  \\\n",
              "count  172826.000000  172826.000000  172826.000000  172826.000000   \n",
              "mean       41.467974       0.290123       0.834607     -11.387135   \n",
              "std        38.382449       2.190336       0.301111       6.118793   \n",
              "min         0.000000       0.000000       0.000000     -32.345020   \n",
              "25%        10.000000       0.000000       0.820000     -15.137932   \n",
              "50%        25.000000       0.000000       1.000000      -9.876650   \n",
              "75%        70.000000       0.000000       1.000000      -7.260072   \n",
              "max       120.000000      96.440000       1.000000       5.155660   \n",
              "\n",
              "           Longitude            FRP  \n",
              "count  172826.000000  172826.000000  \n",
              "mean      -54.050898      75.359033  \n",
              "std         6.886233     169.703256  \n",
              "min       -73.659040       0.000000  \n",
              "25%       -58.622972      15.700000  \n",
              "50%       -54.369260      31.200000  \n",
              "75%       -48.961952      69.100000  \n",
              "max       -35.059390    8094.300000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ad9090d0-0993-4205-8fcd-9efea8b6ce96\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DiaSemChuva</th>\n",
              "      <th>Precipitacao</th>\n",
              "      <th>RiscoFogo</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>FRP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>172826.000000</td>\n",
              "      <td>172826.000000</td>\n",
              "      <td>172826.000000</td>\n",
              "      <td>172826.000000</td>\n",
              "      <td>172826.000000</td>\n",
              "      <td>172826.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>41.467974</td>\n",
              "      <td>0.290123</td>\n",
              "      <td>0.834607</td>\n",
              "      <td>-11.387135</td>\n",
              "      <td>-54.050898</td>\n",
              "      <td>75.359033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>38.382449</td>\n",
              "      <td>2.190336</td>\n",
              "      <td>0.301111</td>\n",
              "      <td>6.118793</td>\n",
              "      <td>6.886233</td>\n",
              "      <td>169.703256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-32.345020</td>\n",
              "      <td>-73.659040</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.820000</td>\n",
              "      <td>-15.137932</td>\n",
              "      <td>-58.622972</td>\n",
              "      <td>15.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>25.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-9.876650</td>\n",
              "      <td>-54.369260</td>\n",
              "      <td>31.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>70.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-7.260072</td>\n",
              "      <td>-48.961952</td>\n",
              "      <td>69.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>120.000000</td>\n",
              "      <td>96.440000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.155660</td>\n",
              "      <td>-35.059390</td>\n",
              "      <td>8094.300000</td>\n",
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              "  </tbody>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\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",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .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-ad9090d0-0993-4205-8fcd-9efea8b6ce96 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ad9090d0-0993-4205-8fcd-9efea8b6ce96');\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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              "      --bg-color: #E8F0FE;\n",
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              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "    border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
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              "    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",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"DiaSemChuva\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61087.83263557936,\n        \"min\": 0.0,\n        \"max\": 172826.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          41.467973568791734,\n          25.0,\n          172826.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precipitacao\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61098.23128969837,\n        \"min\": 0.0,\n        \"max\": 172826.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.29012289817504316,\n          96.44,\n          2.1903356408382617\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RiscoFogo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61102.96798221403,\n        \"min\": 0.0,\n        \"max\": 172826.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          172826.0,\n          0.8346074664691655,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61106.48895357568,\n        \"min\": -32.34502,\n        \"max\": 172826.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -11.387135351798921,\n          -9.87665,\n          172826.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61119.2761433609,\n        \"min\": -73.65904,\n        \"max\": 172826.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -54.050897647344726,\n          -54.36926,\n          172826.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FRP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 60741.27556660785,\n        \"min\": 0.0,\n        \"max\": 172826.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          75.35903336303566,\n          31.2,\n          172826.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Abreviando Estados\n",
        "state_abbreviations = {\n",
        "    \"ACRE\": \"AC\",\n",
        "    \"ALAGOAS\": \"AL\",\n",
        "    \"AMAPÁ\": \"AP\",\n",
        "    \"AMAZONAS\": \"AM\",\n",
        "    \"BAHIA\": \"BA\",\n",
        "    \"CEARÁ\": \"CE\",\n",
        "    \"DISTRITO FEDERAL\": \"DF\",\n",
        "    \"ESPÍRITO SANTO\": \"ES\",\n",
        "    \"GOIÁS\": \"GO\",\n",
        "    \"MARANHÃO\": \"MA\",\n",
        "    \"MATO GROSSO\": \"MT\",\n",
        "    \"MATO GROSSO DO SUL\": \"MS\",\n",
        "    \"MINAS GERAIS\": \"MG\",\n",
        "    \"PARÁ\": \"PA\",\n",
        "    \"PARAÍBA\": \"PB\",\n",
        "    \"PARANÁ\": \"PR\",\n",
        "    \"PERNAMBUCO\": \"PE\",\n",
        "    \"PIAUÍ\": \"PI\",\n",
        "    \"RIO DE JANEIRO\": \"RJ\",\n",
        "    \"RIO GRANDE DO NORTE\": \"RN\",\n",
        "    \"RIO GRANDE DO SUL\": \"RS\",\n",
        "    \"RONDÔNIA\": \"RO\",\n",
        "    \"RORAIMA\": \"RR\",\n",
        "    \"SANTA CATARINA\": \"SC\",\n",
        "    \"SÃO PAULO\": \"SP\",\n",
        "    \"SERGIPE\": \"SE\",\n",
        "    \"TOCANTINS\": \"TO\"\n",
        "}\n",
        "df['Estado'] = df['Estado'].replace(state_abbreviations)\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "QJ5rMHw2CcQh",
        "outputId": "fdfa6623-5633-417d-b567-e9386ae0ea65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              DataHora  Satelite    Pais Estado    Municipio     Bioma  \\\n",
              "1  2024/01/02 17:06:00  AQUA_M-T  Brasil     CE  BREJO SANTO  Caatinga   \n",
              "2  2024/01/02 17:06:00  AQUA_M-T  Brasil     CE       CARIÚS  Caatinga   \n",
              "3  2024/01/02 17:06:00  AQUA_M-T  Brasil     CE       CARIÚS  Caatinga   \n",
              "4  2024/01/02 17:06:00  AQUA_M-T  Brasil     CE      MOMBAÇA  Caatinga   \n",
              "5  2024/01/02 17:06:00  AQUA_M-T  Brasil     MA       CAXIAS   Cerrado   \n",
              "\n",
              "   DiaSemChuva  Precipitacao  RiscoFogo  Latitude  Longitude   FRP  \n",
              "1          0.0          0.00       0.07  -7.49188  -38.88316  51.7  \n",
              "2          0.0          0.00       0.25  -6.50564  -39.44122  29.0  \n",
              "3          0.0          0.00       0.23  -6.50409  -39.43422  18.9  \n",
              "4          0.0          0.00       0.00  -5.74166  -39.59352  25.7  \n",
              "5          1.0          7.44       0.00  -4.73447  -42.98536  10.4  "
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DataHora</th>\n",
              "      <th>Satelite</th>\n",
              "      <th>Pais</th>\n",
              "      <th>Estado</th>\n",
              "      <th>Municipio</th>\n",
              "      <th>Bioma</th>\n",
              "      <th>DiaSemChuva</th>\n",
              "      <th>Precipitacao</th>\n",
              "      <th>RiscoFogo</th>\n",
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              "      <th>Longitude</th>\n",
              "      <th>FRP</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2024/01/02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>CE</td>\n",
              "      <td>BREJO SANTO</td>\n",
              "      <td>Caatinga</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.07</td>\n",
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              "      <td>-38.88316</td>\n",
              "      <td>51.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2024/01/02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>CE</td>\n",
              "      <td>CARIÚS</td>\n",
              "      <td>Caatinga</td>\n",
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              "      <td>29.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2024/01/02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>CE</td>\n",
              "      <td>CARIÚS</td>\n",
              "      <td>Caatinga</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.23</td>\n",
              "      <td>-6.50409</td>\n",
              "      <td>-39.43422</td>\n",
              "      <td>18.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2024/01/02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>CE</td>\n",
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              "      <td>Caatinga</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-5.74166</td>\n",
              "      <td>-39.59352</td>\n",
              "      <td>25.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2024/01/02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>MA</td>\n",
              "      <td>CAXIAS</td>\n",
              "      <td>Cerrado</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.44</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-4.73447</td>\n",
              "      <td>-42.98536</td>\n",
              "      <td>10.4</td>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    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-a5b91c29-83d8-4763-84ae-98bbf09f66b4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificando se a regra foi aplicada corretamente\n",
        "df[df['Estado'].str.len() > 2] # Como toda abreviação tem 2 letras, esse código chega as quais possuem mais que 2."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "PtTLG_idHmtQ",
        "outputId": "9b6466da-6012-4f35-8f36-99e288eea911"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [DataHora, Satelite, Pais, Estado, Municipio, Bioma, DiaSemChuva, Precipitacao, RiscoFogo, Latitude, Longitude, FRP]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a547684c-be93-4d28-92e2-5c3c0df92419\" 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>DataHora</th>\n",
              "      <th>Satelite</th>\n",
              "      <th>Pais</th>\n",
              "      <th>Estado</th>\n",
              "      <th>Municipio</th>\n",
              "      <th>Bioma</th>\n",
              "      <th>DiaSemChuva</th>\n",
              "      <th>Precipitacao</th>\n",
              "      <th>RiscoFogo</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>FRP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\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-a547684c-be93-4d28-92e2-5c3c0df92419')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a547684c-be93-4d28-92e2-5c3c0df92419 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-a547684c-be93-4d28-92e2-5c3c0df92419');\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>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "Out of range float values are not JSON compliant: nan"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('dadosqueimadas_tratados.csv', index=False)"
      ],
      "metadata": {
        "id": "xRxMJmikUgCB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Bioma'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SxXus2pbUusM",
        "outputId": "ee7220bb-0823-41b6-b59c-5641c08c33da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Caatinga', 'Cerrado', 'Mata Atlântica', 'Amazônia', 'Pantanal',\n",
              "       'Pampa'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.boxplot(data=df[['DiaSemChuva', 'Precipitacao', 'RiscoFogo', 'FRP']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "MYY2CLMMU4cM",
        "outputId": "71b0c3f2-4955-4db4-d8cf-1f9531714a16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop_duplicates(inplace=True)\n",
        "# Removendo duplicatas"
      ],
      "metadata": {
        "id": "rAnzE_70cSDB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['DataHora'] = pandas.to_datetime(df['DataHora']) # Convertendo para datetime"
      ],
      "metadata": {
        "id": "T2qQ639gcyWU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "2PxCOMT-dKIN",
        "outputId": "dc276dfc-3b16-46e4-acab-3b47685642dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DataHora        datetime64[ns]\n",
              "Satelite                object\n",
              "Pais                    object\n",
              "Estado                  object\n",
              "Municipio               object\n",
              "Bioma                   object\n",
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              "RiscoFogo              float64\n",
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      "source": [
        "# Adicionar coluna de mês\n",
        "df['Mes'] = df['DataHora'].dt.month\n",
        "df.head()"
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              "             DataHora  Satelite    Pais Estado    Municipio     Bioma  \\\n",
              "1 2024-01-02 17:06:00  AQUA_M-T  Brasil     CE  BREJO SANTO  Caatinga   \n",
              "2 2024-01-02 17:06:00  AQUA_M-T  Brasil     CE       CARIÚS  Caatinga   \n",
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              "4 2024-01-02 17:06:00  AQUA_M-T  Brasil     CE      MOMBAÇA  Caatinga   \n",
              "5 2024-01-02 17:06:00  AQUA_M-T  Brasil     MA       CAXIAS   Cerrado   \n",
              "\n",
              "   DiaSemChuva  Precipitacao  RiscoFogo  Latitude  Longitude   FRP  Mes  \n",
              "1          0.0          0.00       0.07  -7.49188  -38.88316  51.7    1  \n",
              "2          0.0          0.00       0.25  -6.50564  -39.44122  29.0    1  \n",
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              "4          0.0          0.00       0.00  -5.74166  -39.59352  25.7    1  \n",
              "5          1.0          7.44       0.00  -4.73447  -42.98536  10.4    1  "
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df['Mes'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mrf9UqMDdeSg",
        "outputId": "8e31f82e-229b-41b3-92d2-eb35fe51bddc"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gdf = gpd.GeoDataFrame(\n",
        "    df, geometry=gpd.points_from_xy(df.Longitude, df.Latitude), crs='EPSG:4326')"
      ],
      "metadata": {
        "id": "O975Ves6eWJA"
      },
      "execution_count": null,
      "outputs": []
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        "outputId": "33ae7e82-8c0e-4418-819f-a34a1bee4887"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             DataHora  Satelite    Pais Estado    Municipio     Bioma  \\\n",
              "1 2024-01-02 17:06:00  AQUA_M-T  Brasil     CE  BREJO SANTO  Caatinga   \n",
              "2 2024-01-02 17:06:00  AQUA_M-T  Brasil     CE       CARIÚS  Caatinga   \n",
              "3 2024-01-02 17:06:00  AQUA_M-T  Brasil     CE       CARIÚS  Caatinga   \n",
              "4 2024-01-02 17:06:00  AQUA_M-T  Brasil     CE      MOMBAÇA  Caatinga   \n",
              "5 2024-01-02 17:06:00  AQUA_M-T  Brasil     MA       CAXIAS   Cerrado   \n",
              "\n",
              "   DiaSemChuva  Precipitacao  RiscoFogo  Latitude  Longitude   FRP  Mes  \\\n",
              "1          0.0          0.00       0.07  -7.49188  -38.88316  51.7    1   \n",
              "2          0.0          0.00       0.25  -6.50564  -39.44122  29.0    1   \n",
              "3          0.0          0.00       0.23  -6.50409  -39.43422  18.9    1   \n",
              "4          0.0          0.00       0.00  -5.74166  -39.59352  25.7    1   \n",
              "5          1.0          7.44       0.00  -4.73447  -42.98536  10.4    1   \n",
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DataHora</th>\n",
              "      <th>Satelite</th>\n",
              "      <th>Pais</th>\n",
              "      <th>Estado</th>\n",
              "      <th>Municipio</th>\n",
              "      <th>Bioma</th>\n",
              "      <th>DiaSemChuva</th>\n",
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              "      <th>1</th>\n",
              "      <td>2024-01-02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
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              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
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              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>CE</td>\n",
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              "      <td>0.23</td>\n",
              "      <td>-6.50409</td>\n",
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              "      <th>4</th>\n",
              "      <td>2024-01-02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>CE</td>\n",
              "      <td>MOMBAÇA</td>\n",
              "      <td>Caatinga</td>\n",
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              "      <th>5</th>\n",
              "      <td>2024-01-02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>MA</td>\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "gdf"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gdf = gdf.to_crs(crs='EPSG:4326')\n",
        "states = read_state(year=2020)\n",
        "gdf_filtered = gdf[gdf['RiscoFogo'] == 1]\n",
        "fig, ax = plt.subplots(figsize=(10, 10))\n",
        "states.plot(ax=ax, color='lightgray', edgecolor='black')\n",
        "gdf_filtered.plot(ax=ax, markersize=1, color='red', alpha=0.5)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 747
        },
        "id": "depP9-zTk0GT",
        "outputId": "2f5d842e-d5df-4b8a-cafa-e4972f30910d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gdf.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "O1Fjf3QCs63i",
        "outputId": "dab2effb-fb08-4bfc-ed25-835044678af8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             DataHora  Satelite    Pais Estado    Municipio     Bioma  \\\n",
              "1 2024-01-02 17:06:00  AQUA_M-T  Brasil     CE  BREJO SANTO  Caatinga   \n",
              "2 2024-01-02 17:06:00  AQUA_M-T  Brasil     CE       CARIÚS  Caatinga   \n",
              "3 2024-01-02 17:06:00  AQUA_M-T  Brasil     CE       CARIÚS  Caatinga   \n",
              "4 2024-01-02 17:06:00  AQUA_M-T  Brasil     CE      MOMBAÇA  Caatinga   \n",
              "5 2024-01-02 17:06:00  AQUA_M-T  Brasil     MA       CAXIAS   Cerrado   \n",
              "\n",
              "   DiaSemChuva  Precipitacao  RiscoFogo  Latitude  Longitude   FRP  Mes  \\\n",
              "1          0.0          0.00       0.07  -7.49188  -38.88316  51.7    1   \n",
              "2          0.0          0.00       0.25  -6.50564  -39.44122  29.0    1   \n",
              "3          0.0          0.00       0.23  -6.50409  -39.43422  18.9    1   \n",
              "4          0.0          0.00       0.00  -5.74166  -39.59352  25.7    1   \n",
              "5          1.0          7.44       0.00  -4.73447  -42.98536  10.4    1   \n",
              "\n",
              "                     geometry  \n",
              "1  POINT (-38.88316 -7.49188)  \n",
              "2  POINT (-39.44122 -6.50564)  \n",
              "3  POINT (-39.43422 -6.50409)  \n",
              "4  POINT (-39.59352 -5.74166)  \n",
              "5  POINT (-42.98536 -4.73447)  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f7fb7541-88ae-4b6a-b166-d12a29ca3d9e\" 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>DataHora</th>\n",
              "      <th>Satelite</th>\n",
              "      <th>Pais</th>\n",
              "      <th>Estado</th>\n",
              "      <th>Municipio</th>\n",
              "      <th>Bioma</th>\n",
              "      <th>DiaSemChuva</th>\n",
              "      <th>Precipitacao</th>\n",
              "      <th>RiscoFogo</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>FRP</th>\n",
              "      <th>Mes</th>\n",
              "      <th>geometry</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2024-01-02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>CE</td>\n",
              "      <td>BREJO SANTO</td>\n",
              "      <td>Caatinga</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.07</td>\n",
              "      <td>-7.49188</td>\n",
              "      <td>-38.88316</td>\n",
              "      <td>51.7</td>\n",
              "      <td>1</td>\n",
              "      <td>POINT (-38.88316 -7.49188)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2024-01-02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>CE</td>\n",
              "      <td>CARIÚS</td>\n",
              "      <td>Caatinga</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>-6.50564</td>\n",
              "      <td>-39.44122</td>\n",
              "      <td>29.0</td>\n",
              "      <td>1</td>\n",
              "      <td>POINT (-39.44122 -6.50564)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2024-01-02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>CE</td>\n",
              "      <td>CARIÚS</td>\n",
              "      <td>Caatinga</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.23</td>\n",
              "      <td>-6.50409</td>\n",
              "      <td>-39.43422</td>\n",
              "      <td>18.9</td>\n",
              "      <td>1</td>\n",
              "      <td>POINT (-39.43422 -6.50409)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2024-01-02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>CE</td>\n",
              "      <td>MOMBAÇA</td>\n",
              "      <td>Caatinga</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-5.74166</td>\n",
              "      <td>-39.59352</td>\n",
              "      <td>25.7</td>\n",
              "      <td>1</td>\n",
              "      <td>POINT (-39.59352 -5.74166)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2024-01-02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>MA</td>\n",
              "      <td>CAXIAS</td>\n",
              "      <td>Cerrado</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.44</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-4.73447</td>\n",
              "      <td>-42.98536</td>\n",
              "      <td>10.4</td>\n",
              "      <td>1</td>\n",
              "      <td>POINT (-42.98536 -4.73447)</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f7fb7541-88ae-4b6a-b166-d12a29ca3d9e');\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",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-167ca290-c938-4c87-b636-8993bd96b2b5')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\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",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-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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              "      border-color: transparent;\n",
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              "    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-167ca290-c938-4c87-b636-8993bd96b2b5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "gdf"
            }
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gdf.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "3jY62X-4s-mu",
        "outputId": "7654670b-b278-4968-aa8e-b940ea29628f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            DataHora    DiaSemChuva   Precipitacao  \\\n",
              "count                         172826  172826.000000  172826.000000   \n",
              "mean   2024-07-27 04:18:56.286669824      41.467974       0.290123   \n",
              "min              2024-01-01 16:24:00       0.000000       0.000000   \n",
              "25%              2024-07-19 17:17:00      10.000000       0.000000   \n",
              "50%              2024-08-19 18:32:00      25.000000       0.000000   \n",
              "75%              2024-09-02 18:09:00      70.000000       0.000000   \n",
              "max              2024-09-15 18:49:00     120.000000      96.440000   \n",
              "std                              NaN      38.382449       2.190336   \n",
              "\n",
              "           RiscoFogo       Latitude      Longitude            FRP  \\\n",
              "count  172826.000000  172826.000000  172826.000000  172826.000000   \n",
              "mean        0.834607     -11.387135     -54.050898      75.359033   \n",
              "min         0.000000     -32.345020     -73.659040       0.000000   \n",
              "25%         0.820000     -15.137932     -58.622972      15.700000   \n",
              "50%         1.000000      -9.876650     -54.369260      31.200000   \n",
              "75%         1.000000      -7.260072     -48.961952      69.100000   \n",
              "max         1.000000       5.155660     -35.059390    8094.300000   \n",
              "std         0.301111       6.118793       6.886233     169.703256   \n",
              "\n",
              "                 Mes  \n",
              "count  172826.000000  \n",
              "mean        7.356063  \n",
              "min         1.000000  \n",
              "25%         7.000000  \n",
              "50%         8.000000  \n",
              "75%         9.000000  \n",
              "max         9.000000  \n",
              "std         1.937439  "
            ],
            "text/html": [
              "\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "        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>DataHora</th>\n",
              "      <th>DiaSemChuva</th>\n",
              "      <th>Precipitacao</th>\n",
              "      <th>RiscoFogo</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>FRP</th>\n",
              "      <th>Mes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>172826</td>\n",
              "      <td>172826.000000</td>\n",
              "      <td>172826.000000</td>\n",
              "      <td>172826.000000</td>\n",
              "      <td>172826.000000</td>\n",
              "      <td>172826.000000</td>\n",
              "      <td>172826.000000</td>\n",
              "      <td>172826.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2024-07-27 04:18:56.286669824</td>\n",
              "      <td>41.467974</td>\n",
              "      <td>0.290123</td>\n",
              "      <td>0.834607</td>\n",
              "      <td>-11.387135</td>\n",
              "      <td>-54.050898</td>\n",
              "      <td>75.359033</td>\n",
              "      <td>7.356063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2024-01-01 16:24:00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-32.345020</td>\n",
              "      <td>-73.659040</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2024-07-19 17:17:00</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.820000</td>\n",
              "      <td>-15.137932</td>\n",
              "      <td>-58.622972</td>\n",
              "      <td>15.700000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2024-08-19 18:32:00</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-9.876650</td>\n",
              "      <td>-54.369260</td>\n",
              "      <td>31.200000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2024-09-02 18:09:00</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-7.260072</td>\n",
              "      <td>-48.961952</td>\n",
              "      <td>69.100000</td>\n",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2024-09-15 18:49:00</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>96.440000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.155660</td>\n",
              "      <td>-35.059390</td>\n",
              "      <td>8094.300000</td>\n",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>38.382449</td>\n",
              "      <td>2.190336</td>\n",
              "      <td>0.301111</td>\n",
              "      <td>6.118793</td>\n",
              "      <td>6.886233</td>\n",
              "      <td>169.703256</td>\n",
              "      <td>1.937439</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>\n",
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              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"gdf\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"DataHora\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000172826\",\n        \"max\": \"2024-09-15 18:49:00\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"172826\",\n          \"2024-07-27 04:18:56.286669824\",\n          \"2024-09-02 18:09:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiaSemChuva\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61087.83263557936,\n        \"min\": 0.0,\n        \"max\": 172826.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          41.467973568791734,\n          70.0,\n          172826.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precipitacao\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61098.23128969837,\n        \"min\": 0.0,\n        \"max\": 172826.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.29012289817504316,\n          2.1903356408382617,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RiscoFogo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61102.96798221403,\n        \"min\": 0.0,\n        \"max\": 172826.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          172826.0,\n          0.8346074664691655,\n          0.30111080355302355\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61106.48895357568,\n        \"min\": -32.34502,\n        \"max\": 172826.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -11.387135351798921,\n          -7.2600725,\n          172826.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61119.27614336089,\n        \"min\": -73.65904,\n        \"max\": 172826.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -54.050897647344726,\n          -48.961952499999995,\n          172826.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FRP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 60741.27556660786,\n        \"min\": 0.0,\n        \"max\": 172826.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          75.35903336303566,\n          69.1,\n          172826.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61101.03170787882,\n        \"min\": 1.0,\n        \"max\": 172826.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          172826.0,\n          7.356063323805446,\n          9.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gdf.to_file('queimadas.geojson', driver='GeoJSON')\n",
        "gdf.to_csv('queimadasgdp.csv')\n",
        "df.to_csv('queimadas.csv')"
      ],
      "metadata": {
        "id": "8cjJhB8OtmMo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "TOv0C86cuRin"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "cldDTSr0tz8T",
        "outputId": "b303345b-efa0-4830-fe73-d88fba1ac7a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             DataHora  Satelite    Pais Estado    Municipio     Bioma  \\\n",
              "1 2024-01-02 17:06:00  AQUA_M-T  Brasil     CE  BREJO SANTO  Caatinga   \n",
              "2 2024-01-02 17:06:00  AQUA_M-T  Brasil     CE       CARIÚS  Caatinga   \n",
              "3 2024-01-02 17:06:00  AQUA_M-T  Brasil     CE       CARIÚS  Caatinga   \n",
              "4 2024-01-02 17:06:00  AQUA_M-T  Brasil     CE      MOMBAÇA  Caatinga   \n",
              "5 2024-01-02 17:06:00  AQUA_M-T  Brasil     MA       CAXIAS   Cerrado   \n",
              "\n",
              "   DiaSemChuva  Precipitacao  RiscoFogo  Latitude  Longitude   FRP  Mes  \n",
              "1          0.0          0.00       0.07  -7.49188  -38.88316  51.7    1  \n",
              "2          0.0          0.00       0.25  -6.50564  -39.44122  29.0    1  \n",
              "3          0.0          0.00       0.23  -6.50409  -39.43422  18.9    1  \n",
              "4          0.0          0.00       0.00  -5.74166  -39.59352  25.7    1  \n",
              "5          1.0          7.44       0.00  -4.73447  -42.98536  10.4    1  "
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DataHora</th>\n",
              "      <th>Satelite</th>\n",
              "      <th>Pais</th>\n",
              "      <th>Estado</th>\n",
              "      <th>Municipio</th>\n",
              "      <th>Bioma</th>\n",
              "      <th>DiaSemChuva</th>\n",
              "      <th>Precipitacao</th>\n",
              "      <th>RiscoFogo</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>FRP</th>\n",
              "      <th>Mes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2024-01-02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>CE</td>\n",
              "      <td>BREJO SANTO</td>\n",
              "      <td>Caatinga</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.07</td>\n",
              "      <td>-7.49188</td>\n",
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              "      <td>51.7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2024-01-02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>CE</td>\n",
              "      <td>CARIÚS</td>\n",
              "      <td>Caatinga</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2024-01-02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>CE</td>\n",
              "      <td>CARIÚS</td>\n",
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              "      <td>18.9</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2024-01-02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>CE</td>\n",
              "      <td>MOMBAÇA</td>\n",
              "      <td>Caatinga</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-5.74166</td>\n",
              "      <td>-39.59352</td>\n",
              "      <td>25.7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2024-01-02 17:06:00</td>\n",
              "      <td>AQUA_M-T</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>MA</td>\n",
              "      <td>CAXIAS</td>\n",
              "      <td>Cerrado</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.44</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-4.73447</td>\n",
              "      <td>-42.98536</td>\n",
              "      <td>10.4</td>\n",
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              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d9d3bee6-2626-496c-a3ee-62ef49b494e5 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-d9d3bee6-2626-496c-a3ee-62ef49b494e5');\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-0776e1b9-6f76-4856-a6ee-e2ae1b9f8536\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0776e1b9-6f76-4856-a6ee-e2ae1b9f8536')\"\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-0776e1b9-6f76-4856-a6ee-e2ae1b9f8536 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    }
  ]
}