Aula_1.ipynb

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              "                              Rua              Bairro     Cidade  Metragem  \\\n",
              "0           Avenida Itacira, 255    Planalto Paulista  São Paulo      1000   \n",
              "1  Rua Aurelia Perez Alvarez, 42   Jardim dos Estados  São Paulo       496   \n",
              "2                 Rua Alba Valdez     Jardim Reimberg  São Paulo       125   \n",
              "3                             NaN      Jardim Morumbi  São Paulo       310   \n",
              "4        Rua Tobias Barreto, 195                Mooca  São Paulo       100   \n",
              "\n",
              "   Quartos  Banheiros  Vagas         Valor  \n",
              "0        4          8      6  R$ 7.000.000  \n",
              "1        4          4      4  R$ 3.700.000  \n",
              "2        4          3      2    R$ 380.000  \n",
              "3        3          2      4    R$ 685.000  \n",
              "4        3          2      2    R$ 540.000  "
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              "      <td>1000</td>\n",
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              "      <td>6</td>\n",
              "      <td>R$ 7.000.000</td>\n",
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              "      <th>1</th>\n",
              "      <td>Rua Aurelia Perez Alvarez, 42</td>\n",
              "      <td>Jardim dos Estados</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>496</td>\n",
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              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>R$ 3.700.000</td>\n",
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              "      <th>2</th>\n",
              "      <td>Rua Alba Valdez</td>\n",
              "      <td>Jardim Reimberg</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>125</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>R$ 380.000</td>\n",
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              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Jardim Morumbi</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>310</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>R$ 685.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Rua Tobias Barreto, 195</td>\n",
              "      <td>Mooca</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>100</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>R$ 540.000</td>\n",
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      ],
      "source": [
        "import pandas as pd\n",
        "url = \"https://gist.githubusercontent.com/tgcsantos/3bdb29eba6ce391e90df2b72205ba891/raw/22fa920e80c9fa209a9fccc8b52d74cc95d1599b/dados_imoveis.csv\"\n",
        "dados = pd.read_csv(url)\n",
        "dados.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# pega de forma aleatória uma linha do dataframe\n",
        "dados.sample(10)"
      ],
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      "outputs": [
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              "                                 Rua            Bairro     Cidade  Metragem  \\\n",
              "3935                    Rua Angatuba          Pacaembu  São Paulo       687   \n",
              "7456                             NaN           Cambuci  São Paulo        96   \n",
              "9041                             NaN     Jardim Europa  São Paulo       305   \n",
              "5352                             NaN  Jardim Marajoara  São Paulo       375   \n",
              "6833                             NaN        Campo Belo  São Paulo       215   \n",
              "7093                             NaN    Cidade Monções  São Paulo       150   \n",
              "9322  Avenida Nova Cantareira, 5871           Tucuruvi  São Paulo       760   \n",
              "6238                             NaN  Jardim Jabaquara  São Paulo       240   \n",
              "3480                Rua Laura Vicuna          Vila Ida  São Paulo       350   \n",
              "9495                             NaN   Bosque da Saúde  São Paulo       240   \n",
              "\n",
              "      Quartos  Banheiros  Vagas          Valor  \n",
              "3935        5          6      4  R$ 10.000.000  \n",
              "7456        3          4      2     R$ 800.000  \n",
              "9041        3          4      3   R$ 6.000.000  \n",
              "5352        4          4      3   R$ 2.300.000  \n",
              "6833        4          4      2   R$ 1.190.000  \n",
              "7093        3          3      2   R$ 1.780.000  \n",
              "9322        6          7     15   R$ 4.990.000  \n",
              "6238        3          4      6     R$ 795.000  \n",
              "3480        4          3      5   R$ 2.950.000  \n",
              "9495        3          1      4   R$ 1.900.000  "
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              "      <td>R$ 800.000</td>\n",
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              "      <th>9041</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Jardim Europa</td>\n",
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              "      <th>5352</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Jardim Marajoara</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>375</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>R$ 2.300.000</td>\n",
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              "    <tr>\n",
              "      <th>6833</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Campo Belo</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>215</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>R$ 1.190.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7093</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Cidade Monções</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>150</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>R$ 1.780.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9322</th>\n",
              "      <td>Avenida Nova Cantareira, 5871</td>\n",
              "      <td>Tucuruvi</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>760</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>15</td>\n",
              "      <td>R$ 4.990.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6238</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Jardim Jabaquara</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>240</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>R$ 795.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3480</th>\n",
              "      <td>Rua Laura Vicuna</td>\n",
              "      <td>Vila Ida</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>350</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>R$ 2.950.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9495</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Bosque da Saúde</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>240</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>R$ 1.900.000</td>\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados['Bairro']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EfLrdwiH7xFV",
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      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         Planalto Paulista\n",
              "1        Jardim dos Estados\n",
              "2           Jardim Reimberg\n",
              "3            Jardim Morumbi\n",
              "4                     Mooca\n",
              "                ...        \n",
              "10003            Vila Sofia\n",
              "10004       Bosque da Saúde\n",
              "10005         Vila da Saúde\n",
              "10006         Vila Oratório\n",
              "10007           Vila Alpina\n",
              "Name: Bairro, Length: 10008, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Para buscar uma linha expecífica na coluna 'Bairro'\n",
        "dados['Bairro'][6522]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "SktnSHn19610",
        "outputId": "456bb4e6-d866-4543-f5d3-92330fcbc95d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Vila Paulo Silas'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GrplddSQ-O4J",
        "outputId": "bb8ecbd3-52bb-4109-e04d-f943136babd6"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10008 entries, 0 to 10007\n",
            "Data columns (total 8 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   Rua        6574 non-null   object\n",
            " 1   Bairro     9996 non-null   object\n",
            " 2   Cidade     10008 non-null  object\n",
            " 3   Metragem   10008 non-null  int64 \n",
            " 4   Quartos    10008 non-null  int64 \n",
            " 5   Banheiros  10008 non-null  int64 \n",
            " 6   Vagas      10008 non-null  int64 \n",
            " 7   Valor      10008 non-null  object\n",
            "dtypes: int64(4), object(4)\n",
            "memory usage: 625.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fazer a média da metragem dos imóveis e outra forma de selecionar uma coluna\n",
        "dados.Metragem.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eW25YKJB-e_a",
        "outputId": "3b78e3fb-ec62-4261-8d0a-70b38c2f9da1"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "442.30065947242207"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sum(dados['Bairro'] == 'Vila Mariana')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LljBrWbh_By8",
        "outputId": "6accee61-a38e-4ea9-8c7f-4c4b88756d0b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "184"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tem_imoveis_vila_mariana = (dados['Bairro'] == 'Vila Mariana')\n",
        "tem_imoveis_vila_mariana"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gvz5FEjRAUTC",
        "outputId": "a70e2652-8115-4e98-aaa2-f916d7e477f0"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        False\n",
              "1        False\n",
              "2        False\n",
              "3        False\n",
              "4        False\n",
              "         ...  \n",
              "10003    False\n",
              "10004    False\n",
              "10005    False\n",
              "10006    False\n",
              "10007    False\n",
              "Name: Bairro, Length: 10008, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imoveis_vila_mariana = dados[tem_imoveis_vila_mariana]\n",
        "imoveis_vila_mariana"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "uexGWAGDApzs",
        "outputId": "9d1343ff-1176-4099-b57a-f39a50cd8edb"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               Rua        Bairro     Cidade  Metragem  \\\n",
              "100               Rua Pero Correia  Vila Mariana  São Paulo       250   \n",
              "120   Praça Doutor Carvalho Franco  Vila Mariana  São Paulo       250   \n",
              "155     Rua Professor João Marinho  Vila Mariana  São Paulo       170   \n",
              "160                            NaN  Vila Mariana  São Paulo       228   \n",
              "334         Rua Pero Correia, 892   Vila Mariana  São Paulo       350   \n",
              "...                            ...           ...        ...       ...   \n",
              "9645               Rua Sousa Ramos  Vila Mariana  São Paulo       285   \n",
              "9701             Rua Benito Juarez  Vila Mariana  São Paulo       171   \n",
              "9737             Rua Benito Juarez  Vila Mariana  São Paulo       171   \n",
              "9807                           NaN  Vila Mariana  São Paulo       275   \n",
              "9852              Rua França Pinto  Vila Mariana  São Paulo       280   \n",
              "\n",
              "      Quartos  Banheiros  Vagas         Valor  \n",
              "100         3          3      1  R$ 1.350.000  \n",
              "120         3          5      2  R$ 2.450.000  \n",
              "155         3          4      2  R$ 1.490.000  \n",
              "160         4          5      3  R$ 2.200.000  \n",
              "334         6          4     10  R$ 1.700.000  \n",
              "...       ...        ...    ...           ...  \n",
              "9645        3          5      5  R$ 1.650.000  \n",
              "9701        3          2      4  R$ 1.650.000  \n",
              "9737        3          2      4  R$ 1.650.000  \n",
              "9807        3          1      6  R$ 1.500.000  \n",
              "9852        3          3      4  R$ 1.600.000  \n",
              "\n",
              "[184 rows x 8 columns]"
            ],
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              "\n",
              "  <div id=\"df-ad7a6e32-e781-47e7-b5c5-9968c5aeefe5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rua</th>\n",
              "      <th>Bairro</th>\n",
              "      <th>Cidade</th>\n",
              "      <th>Metragem</th>\n",
              "      <th>Quartos</th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>Rua Pero Correia</td>\n",
              "      <td>Vila Mariana</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>250</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>R$ 1.350.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>Praça Doutor Carvalho Franco</td>\n",
              "      <td>Vila Mariana</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>250</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>R$ 2.450.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>Rua Professor João Marinho</td>\n",
              "      <td>Vila Mariana</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>170</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>R$ 1.490.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Vila Mariana</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>228</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>R$ 2.200.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>334</th>\n",
              "      <td>Rua Pero Correia, 892</td>\n",
              "      <td>Vila Mariana</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>350</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>R$ 1.700.000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9645</th>\n",
              "      <td>Rua Sousa Ramos</td>\n",
              "      <td>Vila Mariana</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>285</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>R$ 1.650.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9701</th>\n",
              "      <td>Rua Benito Juarez</td>\n",
              "      <td>Vila Mariana</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>171</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>R$ 1.650.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9737</th>\n",
              "      <td>Rua Benito Juarez</td>\n",
              "      <td>Vila Mariana</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>171</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>R$ 1.650.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9807</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Vila Mariana</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>275</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>R$ 1.500.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9852</th>\n",
              "      <td>Rua França Pinto</td>\n",
              "      <td>Vila Mariana</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>280</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>R$ 1.600.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>184 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ad7a6e32-e781-47e7-b5c5-9968c5aeefe5')\"\n",
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            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imoveis_vila_mariana['Metragem'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5deFgl5rA3ss",
        "outputId": "ce804430-9baa-4472-c153-f60699eb5a33"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "233.6195652173913"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# value_counts() devolve a quantidade de vezes que uma variável apareceu\n",
        "dados['Bairro'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZbioyST-BTzV",
        "outputId": "5d0ea4f7-845f-400d-bc31-d52160327237"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Alto de Pinheiros     409\n",
              "Jardim Guedala        403\n",
              "Jardim Paulista       309\n",
              "Brooklin Paulista     247\n",
              "Jardim Europa         240\n",
              "                     ... \n",
              "Jardim das Imbuias      1\n",
              "Vila Represa            1\n",
              "Vila Nancy              1\n",
              "Sítio Represa           1\n",
              "Vila Invernada          1\n",
              "Name: Bairro, Length: 701, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mostrando os 10 bairros que mais aparecem\n",
        "n_imoveis_bairro = dados['Bairro'].value_counts()\n",
        "n_imoveis_bairro.plot.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "jPi9pby-C26E",
        "outputId": "9d809da2-6426-42ca-aea8-63287d00db71"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fba018d8250>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_imoveis_bairro.head(10).plot.bar();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "ongunHADDfG2",
        "outputId": "e5b9a96e-c758-4a64-8500-d1d0d841149d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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IiB5Kco+I6KEk94iIHkpyj4jooST3iIgeSnKPiOihJPeIiB5Kco+I6KEk94iIHkpyj4jooST3iIgeSnKPiOihJPeIiB4aW3KX9BRJl0taKenIcZUTERF3NJbkLmkj4B+BfYBdgIMk7TKOsiIi4o7GVXPfA1hp+wrbvwM+Cew/prIiImIG2V78Pyo9B3iK7ZfU4xcCj7b9iqHHHA4cXg8fCFy+wGK3Bn62wL+xUJMQA0xGHJMQA0xGHJMQA0xGHJMQA0xGHIsRw462l466Y+MF/uH1Zvs44LjF+nuSVthevlh/b1pjmJQ4JiGGSYljEmKYlDgmIYZJiWPcMYyrWeYaYIeh4+3ruYiIaGBcyf17wM6S7itpU+B5wMljKisiImYYS7OM7VskvQL4d2Aj4MO2LxlHWUMWrYlnASYhBpiMOCYhBpiMOCYhBpiMOCYhBpiMOMYaw1g6VCMioluZoRoR0UNJ7hERPZTkHhHRQ52Nc18oSXcBfmP7NkkPAB4EfNn27xvGsDPwNsoSC5sPztu+X6sYhmK514wY/rNx+RPzWnRN0tOAvwZ2pFxjAmx7yw5ieSrwENb8P3lL6zgmQZevRV2S5RLbD2pRHkx3zf1MYHNJ2wFfBV4IfLRxDB8BjgFuAfYCTgT+uWUAkvaT9APgR8A3gCuBL7eMoZqE12JPSd+T9GtJv5N0q6RftYyhei9wCHBP21va3qKjxH4s8FzgzygfMAdQPnBax3GGpNNn/jSOodPXwvatwOWS7tOqTGxP5Q9wbv39Z8Dr6u3zG8dwTv190cxzDWO4ALgncF493gs4voP/j0l4LVYA9wfOowzBPRR4WwevxRnAktbljojjwhm/7wp8s4M4Hjn08zjg3cA7N7TXglIhvRE4jTLv52Tg5HGVN7XNMoAkPQY4GDisntuocQw3S1oC/KCO67+G8qZp6fe2r5e0RNIS22dIem/jGGAyXgtsr5S0kUtN6SOSzgPe0DiM1wGnSPoGcPNQbO9uHMdv6u+bJN0buB7YtnEM2D5nxqlvSzq7cRiT8Fr8n5aFTXNyfzXlov2c7Usk3Y9SY2rpVcCdgVdS2lj3pnwdb+kGSXel1Ao+Luk64L8bxwB3fC32Al7UOIab6ozo8yW9E7iWbpoe3wr8mtK2u2kH5Q98UdJWwP8FzgUMfKh1EJLuMXS4hFKDv1vjMDp/LWx/Q9I2wKPqqbNtXzeu8qZ+ElNNbNj+ddexdGHQsUy5aA6mXDT/bPvnjeM4wPan5zo35hh2BH5KSah/TnktPmB7ZasYahwX2961ZZlzkbQZsLntX3ZQ9o8oyVSUPpkfAW+x/a2GMWxm++bBbcoH728H5xrFcCDlw+XrlNfifwB/aftfx1LetCZ3SQ+ldNrdg/JCrQJe5PEvc4CkL1DerCPZ3m/cMQzF8g7br5/rXIM4zrX9iLnONYhjU8rIKQOXu+wn0FT91vA1219tXfaIWB4LLGPoW7rtEzsLqCOT8P6UdAHwpEFtXdJSyvvk4eMob5qbZf4JeI3tMwAkPQH4IPDYBmX/XYMy5utJwMxEvs+Ic2MhaR9gX2A7Se8bumtLSi2tmTrU7Vjgh5QP/PtKepnt1qOH/gT4C0k3A7+no6GQkj4G7AScD9xaT5tSKWoZx+bAnwKPr+V/EzjW9m8blP0HwHbAnSTtTvm/gPL+vPO4y59hyYxmmOsZY7PhNCf3uwwSO4Dtr9cmirGz/Y0W5cxG0p9QLpidJF04dNcWwLcbhvJflFEq+wHDHWc3UppGWnoXsNegGUbSTsCXaDw01PYWLcubxXJgF3f/9fxEyvvh/fX4+cDHKMMRx+3JwB9Tlh1/F6uT+43AGxuUP+wrkv4d+EQ9fi5wyrgKm+Zmmc9ROkY+Vk+9AHik7Wc2jKGziTuS7gbcvZY/vAH5ja3b22s8m7hOIJN0d2AH2xfO8bTFjuF7th81dCxKp9WjZnnaYpb/INvflzTyq77tc1vEMRTPp4FX2r62Zbkj4rjU9i5znRtzDM+2/ZlW5c0WB2U4KJShmJ8bV1nTXHN/MXA08FlWf9V7ceMYPgIcBbyHMjrkUBqNzqgdY7+U9L+Bn9i+uTZNPUzSibZvaBHHkFMl7Ud5T50DXCfpO7Zb1t5XSDoFOInynjgA+J6kZwHY/uyYy38t8FJKDXEmU0ZTtbQ1cGkddjg8JLNZn1B1rqQ9bX8XQNKjKd/2Wtpe0paUGvsHgUcAR7buF6kfME0+ZKay5l6n8n7N9l4dx3GO7UdKusj2Q4fPNYzhfMrX72WUr3ifBx5ie99WMdQ4zrO9u6SXUGrtR0m60PbDGsbwkVnutu3WH/6dkvRHo863alaUdBHlQ20Tyj7JgyUx7gN8v3HN/QLbD5f0ZODlwP8GPta4Q/VZwDuAe1Gah8baFzOVNXfbt0q6TdLduhjaNWQSJu7c5rI5yrOA99t+f52409rGkrYFDgTe1EH52D60i3IHBt8Q1qbBN4eZ5X2jDg/d2fbXJN2ZthP9ntawrLkM2tr3BU6sc2M02xPG4J3A021f1qKwqUzu1a+BiySdytCkHduvbBjDJExi+r2kgygThp5ez23SOAaAt1B23vqW7e/VSWU/aBmApO0pnXa3t2kCr7J9daMQBq//vSijtgbrp+wFfIfShNiMpJcCh1OGC+9EGTVyLPDEFuXbvmoolrtT9lUezjlX3eFJ43OOpK8C9wXeIGkL4LaG5QP8tFVihyltlgGQNDKJ2j6hdSxdkrQL5Wvm/7P9CUn3BQ60/Y6OQ2uuftD/C2t2sh9s+0mN4/gqcMigI7N+o/mo7Sc3juN8YA/gLNu713O3NyE2jOOvKSNWfsjq+SG23awPon7D3g24wvYNku4JbNey01/S3wN/APwba/aBjOVDf2qTe5cmaRJT1yS9zvY7Jb2fEa9Jy29Sks63vdtc5xrEcZntBw8dL6Es9/rgWZ42jjjOsv3oof6QjSkL7jXrB6lxXA48tKMJZRMzgmktfUJj6wuaumYZSSfZPnCos2YNjd64g0lMz6J8Eg+Wtj2IMv197CbkdQAYfM1sPfphlOslvYDV44gPokwUae20EeOZv9ZBHN+Q9EbKBJ4nUeZFfKGDOC4GtgLGto7KLCZmBFPrPqGpq7lL2tb2tbWj6A6G2/kaxLLC9vK5zo2p7Il5HSZFfS3eDzyGcuF+hzLOu+nGJTWWZwJ/WA/PHOd45lliWEJZMfV/UToU/x34UOtJTZKWU0ZxXUy3QzI71bpPaOqS+7AZIwHuBGxs+8aG5V8GPNX2FfX4vsAprb9+d2lSmqjq8NgTbR/corw54mi6486kk3QJZbmQixjqxGwxJHOSRjC17hOaumaZgREjAban4UiA6s+Br0u6glIz2hF4WYuCJd3I6KTaeh2TiVhnpw6P3VHSpl207c6I43JJ9+niGwOsMb58pNZt7sBNtt8398PG4umz3GfajmBaanu43f2jkl49rsKmtuY+QSMBNqOsQghlYkazJURjTZJOBB5M2eFmeHhs000yJJ0J7A6cPSOOVt9iZt0+rnWTnaR3U5pjTmbNZpmmyzF0TdJplFntw31Ch9oeS4V0amvuwM22fzeYh1BHArRuS7wz8BpgR9svlbSzpAfa/mLDGEbuydi61qjVa3bPjKPlBtk/rD9LKAuodaXpjjszTWB/y+71955D55p2Zkr6q1Hn3Xaz8BdT2tzfw+o+obF1sk5zcp+EkQAfoayj8ph6fA3waaBZcqesejiwOWWSxuWUXd5bGu5E3pyyrss91vLYsbB9dMvy1qbV9P65SNqTkkweTNnAZCPgvxs22QHQ9TIh1fDuZJtTZs82m1AEt3/ottvrYYqbZTofCTAYGTMYR1zPXeAxLb4/z5geAfyp7Zd0FcNQLK3X2TmD0d8emi7YNSlJVdIK4HmUCsdyyizmB9huuqesytZyfwvc2/Y+deLdY2wf3zKOGTFtBvy77Sc0LPMBwDHANrZ3lfQwYD/bfzOO8qa25m77Nsrqbh/sMIzf1VE6htvXD++0zd32uSqr7jU1Y5LIEkoyaf3++ouh25sDz6bxhiHVPzAiqXYQx6RsGP5RyrfcwZpD/wF8CugsuVOWDdm+cZkfBP6SMnII2xdK+hcgyX2YpMcBb6aMUNmY1aNEWrbxHgV8BdhB0scp41f/uGH5SHrN0OESylKm/9Uyhmp4ksgtwJWURcSasX3OjFPfVlnutrkJSaqTsmH41rZPkvQGAJeF7m6d60mLacYIoo2ApZT1kFq6s+2zZ6xXNrbKx9Qmd8qn/p9T2rybvlEGbJ8q6VxKR5EoExJ+1jiM4Y7DWyht8M03JZiEdlVJw238S4BHUjbJbm1SkuoLKYnsFZRrZQfKt5nW/ruu5TL4hrsn0Ho11+EVKm+hLOLV+lvdz+q3+8Hr8BzKe2MsprnN/SzbzZsfZsTwh6PO2z6zdSyTQGUP04ew5q5UzWpHQyN2RLmAfwS8xfa3WsVQ49iRMtV+E0pSvRvwAdft/zY0tcnu/cCulFmqS4HntFy0ayiWe7Hm+7PZqDKVlVKPo6wY+gvK+/PgcY1umrrkPtS2eyClVvJZOho7W2dnDmxOGXd/TuPV7pYCr+OOSbV1J+KxlHbMvYAPAc+hbHF3WMs4YiInMQ2GKj+Q8sF7ueuWjA3L34/SdHhvygfvjsBltluPKkNlr+cltm+U9Grb7x1LOVOY3M+Y5W63TmrDJO0AvNd2s6++KsvLforSmfhyynryq2y/vlUMNY4LbT9s6PddgS/b/h8Nyn6d7XfW2wfY/vTQfX9ru8lGyHMk1ZspY/DfZvuCMccxmMR0RP09PN3dto+847PGS9Ku3HGv4RMbln8BZVz911xWyNwLeEHXlQ9J/2l75FyVBf/taUvuk0ylp+QSt90+bLDV3+1b2mnGRtGN4jjb9h6SvktZLfN6ymtx/wZln+u6Xdrw7VHHY45jtpmhG1OaJd48GDbbIJ7zZpbV8vUYKvMo4AmU5H4KsA9lU5fnNIxhMGz5AmB327d1PWy5xvVj2zuM429PbYdqHaf6bMreobf/Oxq38Q6vYT7YDKD1lOrB19tra5v3f9F48lD1BUlbAf+X8hqYdsNUtZbbo47HZh5tpz/UWtYVHxNJepztb9eDx9JNx+5zgIcD59k+tI57/+c5nrPYbqjfJs8EPi7pOtac2NSVsdWupza5U5YQ/SVltExXY8uH1zC/BfjE4EJq6G8k3Y2ybvX7gS0pnXjNSNqNMttvW9ufkfRFYHO329/Wa7k96rhTto9qWNxhwIfr+0OUTrwuNgn/Ta0p3yJpS0qb91hqq7PYD/gt5do4mNLJ3aQiqNkX+bvT2Mqd1mYZSRfb3rXrOLoiaXNKG/v9KUupHt/B0K7Bmh0voHzIPprSptx0YlkdM/3frL5YbhrcRfmQ6WJP2YlRkzsNP2xnlv8B4I2UiV2vpex/fL4bbF5RJ/QdR1k59iLgMNuXjrvcSTDNyf044P22L+qg7P2B7W3/Yz0+izK8C+D1wx16Y4zhU5QmmW9S2jCvsv2qcZc7Io5LgEfZvqmOZf5K6/b+SVRnLt/H9uUdx9Hp8NQR8SwDtmw1DLIuwfAGSnPMfsBL3Hgv26500f62WB5P2dH8ckkXSrpIUqtxs6+jLF86sBnwKEqn0csbxbCL7RfY/idKm+bYR6Wsxc22bwKwfT3T/Z5aFJKeDpxPmb2MpN0knTz7s8YSx7GULf7+jPIt5gDKEMDWcTxz6NvDlcB/SnpGo+KX2D7V9s210rV0zmf0xDS3ue/TYdmb2v7x0PG3amK7vo5hbeH2ccJ1OnejYu/gfkOJS8BOw4nMG9hWatWbKXMevg5g+3yVXbpae+zQ8NSjJb0L+HIHcRzloW0Gbd9QR9D8W4Oyt9KauzGtceyGOzG1NnXJXdKWtn8FNNtOb4S7Dx/YfsXQYauawcMl/areFmXp41/Rfiem/WccT8TOTB37ve1fzvjA7aL98zf1902S7k0ZnrptB3GM+jbXKvd8gzV3Yxo+br0TE1ByGGuO8Pv5OMqZuuRO2YPwaZQOvJkMtFg47CxJL53ZcSjpZZTdd8bO9kYtypmLJ2Tt8mGtLp5ZXCLp+cBGknYGXknZmKG1L44YnvqhDuJYobI34E8AAAyISURBVLIb0z/W4yMYff0uuhadtvNV88PRlFE7gw/7seWsqe1Q7VJdn+LfKEMwB+PaH0lpe3+G7Z92FduGbG0XT+OVQgc7dL2JNfca+Gvbv20Zx4yYNqPt8NThsu9C2Z3qf9ZTpwJ/Y3sSxpk3I+kHlHXsmywuOHXJfcTQphfbbrqjylAse7N6x6NLbJ/eRRxRtL54JtWMNuY76HM78yST9BXgWYMBCGMvbwqT+wY7tClm1/riGVH+F5h9wa5WG2TfRhmtc/7g1JphuOlEpklZ3K5rknanbFpyFmsudvjKcZQ3jW3uS2yfWm9/WnUDgA1d1+3MKluI/SWrN08ZxNHyAn4D8J0672DsF88Ig87kZwF/wOop9gcBLZvqnkWZMPQwykzuT7jb5YY/Tlnc7mkMLW7XMgBJmwB/AgyW6f4GcGzj1Sn/CTid0uJw27gLm8aa+xWsuZ3a3w0fb2hfOSeonfkC4FhmbJ7iO+6ONM4Yzga+xYyLx/YJrWKocaywvXyucw3iuAtlNNNzgXsCb+qiA3wSFreT9CHK+vqD98ILgVvdcK/hUQu5jdM01tw32KFNa/EXwK4T0M58i+1jOo5hE9uvmfthY3cXSfezfQVAHePeav7DsN9S1l/6FeUb1eazP3xsJmFxu0fNWAHy9FohaenLkg4HvsCa3ywzFBI27KFNa/FDVq+l0qUvSPpT4HM0eOOuRdOLZxZ/Dny9fssUJbG+rFXhtaP/eZSJVF8D/t72itmfNVadL24H3CppJ9s/hNt3RWq9PedB9fdwU3KGQk6iSRid0bqTZpY4fjTidNPmoUmIYSiWzYAH1cPv2262cmntUL2Q0kRlZnTytnpvTMridjWWJ1Kuk+EP3Bf3eYTb1NXcJ8wk1JqbdtKsje0uptdPXAxDdqZsK7c5ZTZxy52HJuXb7QmsubjdLkDzxe2qb7H6/wSg2YJukva2ffrahqiOq58wNfcFmIRac+tOmhHld/LGnbQYZsTT+c5Dk0DSRbYfWm9vTNlTt+kuUEOx3GEHqlHnxlT20baPkvSREXePbWjq1Nbc6yzA11KWVX1pneb9QNtfbBjGJNSau25n/iPKa/D0Efe16uCehBiGTcLOQ5Og88XtJP0BsB1l7aXdWT3mf0vKhu5j57pBS+v+wqmtudf1zM8BXmR715rsv2N7t4YxdFprrjFMTDtzFFq9n+w5wF6URe4us/2gOZ7aK1q9iQqsuZFKs8XtJB0C/DGwnDV3TrsR+Gijb5azjuCy/e5xlDu1NXdgJ9vPlXQQgMtmEa2rBl3XmjtvZ+7qjTtpMcywoi7Y9UFKBeTXwP9rHEPnJmFxuzrH4QRJz7b9mY7C2KL+fiBl34fBkthPZ4wLDU5zcv+dym43BpC0E+33Um06tGnYBLUzbzH3Q8auk4tnbWz/ab15bF0SodnOQ8PqaJXDuOO0/y72Ue3E8Af/qEpAiw9+20fX8s8EHmH7xnr8ZuBL4yp3mpP7UZSdbnaQ9HHgcZSvX810XGueiHbmoTfuPWZ+Y1GjDSq6unhmkrTWzjlJj7B97truH5OPAd8HnkzZDPpgykbmG5JJqHwMbAP8buj4d/XcWExtmzuAyp6de1La8L7bbCnNyak1TwxJ3wb2cdlIBUkPBj7thpuYS7oceNhgTHkda36h7QfO/sxFK/+MenNzShvvBZT35sOAFbYf0yKOoXjOs737YNp/XV/lm7b3bBlHFJLeBBxImegH8AzgU7bfNo7ypq7mPqJ2dG39fR9J92lUO+q81jyB7cx/S5ml+lRK88iJlJpiSycCZ0savng+2qpw23sBSPos5RvERfV4V8rWe60NRqvcUGP4CXCvDuLoXNdNVLU/8ETKNoeD/Y4PtX3e2Mqctpr7pNWOulLHUsNa2pltv6CDmJ5BWdp1C+DZtv+jYdkCtqdsczi4eM4c58UzSyyX2H7IXOcaxPES4DOUa+MjwF2Bv7J9bMs4JoGkT1OaqJ7PUBOV7WaTqobH/Tcpb9qS+0CtHR01s3bUYqLIJNWaazvzU4fambcAvmT7D2d/5qKV/37WnN7+RMrM3Suh+YSuphfPLHF8gjIEcDC2/WDgrrYPWvuzYpwmoYlK0gnAP9j+Xovypq5ZZsgDB4kdwPbFtZ23hUkandG0k2aEmQtSNVvid4RzJT2q1cUzi0Mpa4cPaoVnAs1WzJykyscEmYQmqkcDB0u6ivLhPxjv/7BxFDbNNffOa0dd15prmU07aeaIZVPgAfXwcrfdCAFJ36csUtXk4plUQ012Iw1GF21IJqGJStKOo87bvmos5U1xct+cNXdWORM4xg03IZ6A0RmT1M78BMpCUVdSkuoOwCG2z2wYQ9OLZ0T5J9k+UNJFjNhub0P7kInRJN2LNTt1/3Ms5Uxrcp8Ek1BrnqB25nOA59u+vB4/gLK92yM7iKXJxTOi3G1tXzsBHzKvs/3OEf0hgziaLgc9CWrF69nAMtbcWOctDWPYD3gXcG/gOsqyw5eNq6N9mtvcO9XF0Ka1mJR25k0GiR3A9n/UTqtm1nbxUIa/tXAXSY+z/e0ZcT2O0sbbyqX1d5cbdEyaz1N2pTqH9jPZB/6aMi/na7Vzdy9gbKPaktzXk21LOqXWmlvPPBzWtJNmFitU9qkc7gNpnVyaXjwjvJc1l6IY+FW9b9S8iHHYR9Iv3Hjv2Am3ve2ndBzD721fL2mJpCW2z5D03nEVNvXJXdJdAWz/uoPiJ6HW/OQOyx72J8ARwOAr/zeBDzSOoenFM8I2wyO4BmxfJGlZwzj+A/g7SdsCJ1Gax5r3w0yY70h66Kj/n4ZuqPnqm8DHJV3H6lUzF93UtrlLeiilWeQelNrqKkoH3sUNY5iY0RldtTPPiGFTyvBQ081oma9R+j3eDtyT0jTzKNuPbVT+D2zvvJb7Vtq+f4s4hsrckbKX6vMoy+1+gpLom00umxSSLqVcqz+iNMs0v1ZVliX/bS37BZQ15T8+c02mRStvipP7d4A32T6jHj8B+NtWF3Its9OOsxpD006aWeJ4At2Plml68Ywo/xPA6bY/OOP8S4An2X5uizhGUdmo4sOU0V2dL8XbWpfXqqQbuWPH9mB58t9SJv29yfZpi1ruFCf3C2w/fK5zjWLprNYs6QJgb2a0M9s+rFUMNY7ORst0dfGMiGMbysip37F6MtdyYFPgmbZbdqoOtrbbh1JzfyLwdcr/yedbxjFJJuEb7jBJGwG7Uiohi7rI3jS3uV8h6f9QljWFUlO7omUAEzA6A7pvZx7obLSM7bUu6zp88dTf44zjp8Bj6wfsoKwv2T59nOXOJOlJlL0G9qXMmP4kcLjtsbXvTroJuVbvwPatwAV12Oqimuaa+92Bo4HHU2pt36SsLXNDwxg6rzV33c48FMdHgFtZc7TMRp6QjSEkvcz2P3UdRwuSTgf+BfiM7V90Hc8kmIRrtbVpTu4H2P70XOfGHMMK28vrG2d327e1bhrqup15KI7NKKNlHl9PfRP4wGD2bkSXJuFabW2ak/u5th8x17kxx9BZrXlS2plrLBsBl3gD2wA6psfQtfo2YGs6+obb0tQld0n7UNoSDwQ+NXTXlsAutvdoGMtE1JpHxDW2TppZyvw88Gddd1BFjCLpLqy+Vg8G7ka5Pq7vNLAxmsbk/nBgd0p7+18N3XUjcEaLNsZJqjXPpmU7s8oKmbtTOvBu77izvV+L8iNiTVOX3AckbWz7lq7jmKmLWvMkkPRHo87b/kbrWCIGhipig8rXIOENJjFt2UlgDUxdcl/bcqpM2NrdG9LojJkkbQ1c72l7c0X0yDQm95EzzQZazg4NkLQnpUP555SFuz5G6bBaArzI9lc6DC82cCr7PrycsvTAhcCHJ/Eb/zhMXXJfG0mPBw6yfUTXsWxIJK0A3kjpoDoO2Mf2dyU9iDIbcvdOA4wNmqRPUbbY+yZltu5VbrgpdpemeYbqYL2M5wMHUBYE+my3EW2QNrb9VQBJb7H9XQDb3y9L3kd0ahfXzWwkHU/7PY47M3XJva5ZclD9+RllOKRs79VpYBuu24Zu/2bGff34WhjT7PaVSW3fsiFVOKauWUbSbZSvWIfZXlnPXWH7ft1GtmGSdCurlzu+E3DT4C5gc9tNd2OKGDb0/oQ136O9Hy0zdTV34FmUVe7OkPQVyqJIG87H8YTZEJePjemxIb8/p67mPlBnnO1PaZ7Zm7Jxx+cG7b8RERuyqU3uw+oKkQcAz7X9xK7jiYjoWi+Se0RErGlJ1wFERMTiS3KPiOihJPeIiB5Kco+I6KH/D97G9XNvYs8fAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Deasfios\n",
        "\n",
        "Desafio (Paulo):\n",
        "\n",
        "1 - Realizar a média de metragem por bairro;\n",
        "\n",
        "Desafio (Thiago):\n",
        "\n",
        "1 - Duas formas de selecionar os dados por bairro (consultar os métodos na documentação do Pandas);\n",
        "\n",
        "2 - Explorar alguns gráficos na documentação e aplicar nas demais colunas do DataFrame, tentar colocar alguma conclusão;\n",
        "\n",
        "Desafio (Vivian):\n",
        "\n",
        "1 - Pegar outras estatísticas dos dados, média, mediana, min, máx;\n",
        "\n",
        "2 - Descobrir quais são os bairros que não tem nome de Rua."
      ],
      "metadata": {
        "id": "dtKaD2eIEQs1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bairro_metragem = dados.groupby(['Bairro'])['Metragem'].agg('mean').round(2)\n",
        "bairro_metragem"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "apiHGCcNN7KE",
        "outputId": "406f73a5-3121-4635-c9c7-843cadd4c9c3"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Bairro\n",
              "Aclimação            253.06\n",
              "Alto da Boa Vista    418.88\n",
              "Alto da Lapa         294.34\n",
              "Alto da Mooca        270.05\n",
              "Alto de Pinheiros    433.44\n",
              "                      ...  \n",
              "Vila Água Funda      152.20\n",
              "Água Branca          191.00\n",
              "Água Fria            165.42\n",
              "Água Funda           139.00\n",
              "Água Rasa            340.25\n",
              "Name: Metragem, Length: 701, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bairro_metragem.head(10).plot.bar();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "orXthpG_O_Vc",
        "outputId": "6c5d0a2a-ba8e-4ebe-8460-699ea27f388c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bairro_metragem.sort_values(ascending=False).round(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QxZPvgpwSIis",
        "outputId": "209a5127-7d95-423a-d3c9-de672e4c34dc"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Bairro\n",
              "Sítio Represa                         37000.00\n",
              "Vila Castelo                           2850.00\n",
              "Jardim Guedala                         2486.64\n",
              "Jardim das Camélias                    1700.00\n",
              "Chácara Flora                          1260.73\n",
              "                                        ...   \n",
              "Cidade Antônio Estevão de Carvalho       57.50\n",
              "Jardim Nova Germania                     57.00\n",
              "Jardim Benfica                           48.00\n",
              "Vila Leonor                              46.00\n",
              "Vila Guaca                               10.00\n",
              "Name: Metragem, Length: 701, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bairro_metragem.sort_values(ascending=False).head(20).plot.bar();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "id": "KgOsriyHTOHL",
        "outputId": "c9d7d058-63bf-4c07-e431-346836bfc7e0"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Provável que só haja um registro em Sítio Represa, pois o valor está muito diferente quando comparado com os outros bairros."
      ],
      "metadata": {
        "id": "I6bh5-FkATiC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Para comprovar a hipótese:\n",
        "sum(dados['Bairro'] == 'Sítio Represa')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jTuFNqHdBPG0",
        "outputId": "7c5a4099-0aa6-4c02-89f2-ce222b2a3c5a"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Selecionando coluna 'Bairro'\n",
        "dados.Bairro"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TOg1n_xUU959",
        "outputId": "0783ad36-6499-456b-8129-f596a065c85f"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         Planalto Paulista\n",
              "1        Jardim dos Estados\n",
              "2           Jardim Reimberg\n",
              "3            Jardim Morumbi\n",
              "4                     Mooca\n",
              "                ...        \n",
              "10003            Vila Sofia\n",
              "10004       Bosque da Saúde\n",
              "10005         Vila da Saúde\n",
              "10006         Vila Oratório\n",
              "10007           Vila Alpina\n",
              "Name: Bairro, Length: 10008, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Desafio Thiago\n",
        "media_quartos_por_bairro = dados.groupby(['Bairro'])['Quartos'].agg('mean').round(2)\n",
        "media_quartos_por_bairro"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UVWD5ZmLW8o0",
        "outputId": "2c263561-abae-4ddd-9df6-a25531c31464"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Bairro\n",
              "Aclimação            3.32\n",
              "Alto da Boa Vista    4.12\n",
              "Alto da Lapa         3.15\n",
              "Alto da Mooca        3.30\n",
              "Alto de Pinheiros    3.94\n",
              "                     ... \n",
              "Vila Água Funda      2.60\n",
              "Água Branca          3.15\n",
              "Água Fria            2.83\n",
              "Água Funda           3.00\n",
              "Água Rasa            3.50\n",
              "Name: Quartos, Length: 701, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "media_quartos_por_bairro.sort_values(ascending=False).head(20).plot(kind='bar', figsize=(20,5));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "LCD-aJJaYqHd",
        "outputId": "e2f611a3-a83f-4fe6-cce1-e95b023c0eab"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Provável que haja poucos registros de imóveis nos bairros Jardim Gonzaga e Vila Picinin, devido ao alto número de quartos."
      ],
      "metadata": {
        "id": "94IxGYAnFl7M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Teste de hipótese\n",
        "sum(dados['Bairro'] == 'Jardim Gonzaga')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dg6Fy8LCE_Du",
        "outputId": "3293a649-ad71-4639-8c1b-fc43ec3683b6"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sum(dados['Bairro'] == 'Vila Picinin')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LwY1YPF4GcRx",
        "outputId": "963c1561-ed24-4e2e-ccad-7d1c8303b9df"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "total_banheiros = dados.groupby('Banheiros').size()\n",
        "total_banheiros"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wi_tfozoGka8",
        "outputId": "941ff8d8-edb1-46aa-c0d6-1ce3921d4d0b"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Banheiros\n",
              "1     1034\n",
              "2     1632\n",
              "3     1952\n",
              "4     1838\n",
              "5     1615\n",
              "6     1037\n",
              "7      488\n",
              "8      284\n",
              "9       62\n",
              "10      36\n",
              "11      15\n",
              "12       8\n",
              "14       4\n",
              "16       1\n",
              "40       2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "total_banheiros.plot(kind='pie',figsize=(8,8), label='Gráfico dos banheiros');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "_wSUc5WXI7Ih",
        "outputId": "6d2b9f77-2d9e-446d-8b71-fca8c6b1eccf"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": 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IClgPAbhBdQ6RO5pMQ7oMiaSd4ZLXl0X/rffXzpJ5Sf6WMgD/VbNipfzsHyD5C1TEClgzAfyz6hwitxw2TdURRJ7Y6NSvvizy9NTDPHJMir91PoBPZiJTMZHiqYAVsErgLkuRRfHiLVoNXboMiX4xo/tzsY9svCv6uSUxGBf7buvxmhUrx6U1WJExVAcoUl+A2/1DiLc4qeuyzlecV5C9e2+Kftl3gEdfMcBDlQN4CsCNaYhVlGTkmWVWwJoB4O9U5xC5qYeky5A4t1ediWtmR54ef4BHj0/TIW+oWbFSWoFeJCmeWWQFLIK71kpG/OKcooThqjOI3MKM3idid627OfrlKyPw+NJ8+G/WrFg5JM3HLAryQzy7PgxgseoQIocRVQSJekuZ5fKtQITNA++LfgG7uWZRhk4xAsC/APhoho5fsGTkmSVWwBoC4Ouqc4jc1ypdhgSAvc6YdbMjT4/azTWTMnyqe2tWrLw6w+coOFI8s+erAIapDiFy32HT7FKdQajDjPB34jeveU/0nxcF4cvWFYina1asLMnSuQqCXLbNAitgzQdwv+ocIj80mYZ0GSpSUdab74z+U/gVnnJllk89CcDnAazI8nnzlow8M8wKWDrcSULSu1Ykpck0ZWeVItTkjNxwWeTpqld4Sq2iCJ+qWbFylqJz5x0pnpn3IADZS08krcWQC0LFhBmxZ+LvWbUs+m8LelCqcqmSAeCH0rovOfJdmkFWwKoG8CXVOUR+aTN0j+oMIjvirB39UGxF53pnxlLVWRLmAPgruDs9iX7IO4zM+gYAWfQuUnJaky5DxaCVh2yZG/le6XpnxnTVWd7mCzUrVsobuAuQ4pkhVsBaAkC6d4iUBTWSResFjBn2L+JLXl4Q+fbcTgyqUp3nHMYD+LjqELlOimfmfFl1AJGfbEiXoUJlM7XdF/v0rkfjH18GUC5PIvxszYqV5apD5DIpnhlgBaxrAGR7qrkoFETe05p2SnUMkV4nuGL7/Mh39T87l81UnSUJIyA9uPslxTMzZJKQGJAWwzipOoNID2Y4K+35L8+LPDXrBAbn01WFT9esWDlUdYhcJcUzzayAdQPczWaFuGiHTUO6DBUAh+nEJ2MPvfJg7OFlDC3fft5WAHhMdYhclW//mPngi6oDiPzXZJph1RnEwHRy2c7FkW/GVjpXzFGdZQAerFmxcozqELlIimcaWQHrFgCXqc4h8l+zaUiXoTzFDH7ZvnTVnMj3px3DsGrVeQbIB7dtn3gbKZ5pktirU0adIi1aDENXnUGkzmF0Phr/6y0fia1YakMvlCY099asWKmqZWDOkuKZPrcDsFSHEIWhw9C9qjOI1PSwb/dV0W/0/NJedrnqLGmmQyZBvoMUzzSwApYGwK86hygcXZomnanyyGandvXsyA8mN/OoQr0/eFvNipX5fO827aR4psfdAOpVhxCFI0LSZSgfMOPMF2J/teGO6OeXxGAUcks7gtz7fAtiZtUZ8lpiy7EGAFNUZxEFhNl5pemIY8jmDTkrxJ59741+2dPIY2pUZ8kSB8DUpiduOKA6SC6QkefA3Q4pnCLdiLTjun5CdQxxbq85E9bOivxgXBEVTsCtF59UHSJXSPEcOPnPJDKixZQuQ7mGGcGvx+5Ye1P08cUReHyq8yhwr/S8dUnxHAArYM0EsEh1DlGYmkyzR3UG8aYIGwdvjD5+7Lv2LYtVZ1GoEsCHVYfIBVI8B+ZB1QFE4Wo2jYjqDMLV6FyybnbkByNf5wmTVWfJAZ+sWbEyl3eEyQopnhfJCliDAdyjOocoXM2mIbP5FGNG5Hvxm1ZfE/2XRUH4ZJNyVx2Ad6sOoZrM5Lt4HwFQqjqEKFytunQZUinGevOd0c+FtvPUJaqz5KCHAPxBdQiVZOR5ERKt+B5QnUMUtpOGLm/OFDnsjNh4WeT7g7fz1DrVWXLUdTUrVhb1JWwpnhfn3ZDlKSLDzpBWqTpDsWFG7Efxa1YtiT55xRmUyd//+RGAv1UdQiUpnhdHJgqJjIsS8mnj5LwXZ+3YB2Of2ftP8XuXqs6SJz5Ss2LloHQflIj+k4iOE9GudB87naR4psgKWDUAblCdQxQBoooQUVB1jGLQxlVb5kWe8q1zZsxQnSWPVMCd+5Fu/wXg2gwcN62keKbu45C/N5Elxwy9Q3WGQsYM+1l78aorIt+ZexoV0k84dWlftsLMqwGcSucxM0GKQAqsgOUFcJ/qHKJ4HDHMTtUZCpXN1P6x2Kde+/vYA0sBKvp1ixdpKoCrVIdQQYpnat4PYJjqEKJ4NJlGr+oMhegkD3plQeQ72kvOnFmqsxSAolzvLsUzNXeqDiCKS5NpxlRnKCTM4N/bc1fNjXxv5nFUyYSs9Li1ZsXKotu8XZokJMkKWBUA3qM6hyguLaZ8i6aLw3Ty4diDh37rLJTZtOk1GMD1AJ5THSSbZOSZvJsAFN27K6FWm6EX8gbLWdPFpTuvjDwZ/a2zcK7qLAXqA+k6EBH9DMAGALVE1EJEOTnPRIpn8m5THUAUn9OaJv1UB2i1ba26LPL0tKMYXq06SwG7MV1rPpn5bmauZmaTmccw83+k47jpJsUzCVbAGoQ8WHckCk9Q06pUZ8hXzOhaEfvYpg/FHltqQ5fr35nlA3Cr6hDZJMUzOTfC/c8hRFbFIV2GLkYP+3ZfFf3X7v+xl89XnaWI3K46QDZJ8UyOXLIVahD5TmvaadUx8slWZ+rqyyJPT2ri6rGqsxSZd2WiXV+uumDxJKJFRFSW+PqDRPQNIhqf+Wi5wQpYZQCuU51DFK+jhnFCdYZ8wIwzX4z91frbov4lUZgyuS/7vHCv0hWFZEae3wMQJKKZAD4F4ACAH2U0VW65AUCJ6hCieB02jS7VGXJdiD2N74l+7cR/2tctVJ2lyBXNfc9kimecmRnAzQC+w8zfBVA0Q3MU2XV8kXuaTDOsOkMue90Zv3Z25Okx+3jsBNVZBK6rWbGyKOaHJFM8zxDRYwD+CsBKItIAmJmNlRusgFUKd/GvEMo0m0ZcdYZcxIzgN2K3rb0h+tXFYXjl6lBuKEORNJNJpnjeCSAC4KPM3AZgDICvZzRV7rgeQKnqEKK4tRiGrjpDromwcei90S8f/ZZ962LVWcQ7vE91gGy4YPFMFMyfAKgkohsBhJm5WO55vld1ACE6DF0mv5zlgFO9/rLI08Nf44lTVGcR53S16gDZkMxs2zsAbIZ77+8OAJuIqFiWbhTFfwKR27o0rZjmGJwXMyJPx29YfXX0Xxf2oqRcdR5xXmNqVqycpDpEpiXTdeOzAOYx83EAIKLhAP4E4FeZDKaaFbDqAFyiOocQYaKi36Q5xvqRD0Q/27OF65aoziKSsgzuyoyClcw9T62vcCacTPL35TsZdYqc4ADDbcBWnUOVI86wTXMi36vYwnX1qrOIpBX8zjXJjDx/T0R/APCzxOM7AbyQuUg5Q4qnyA1E2nFdb6227aJqbM6M+E/t5es+G/9Ywf8gLkAF/29G7hLO8/wiEcGdXTsPQN+stjXMXND7tlkBSwNwAoA05RY54T9a23dfHo5MU50jW+Kstd4be/TEGudSS3UWcdEmNj1xwyHVITKl35EnMzMRvcDMFoBns5RJubtftq0dE7XWvWNQ4WgkywSEcodNo+fycER1jKxo58Fbr4t8dcIpVErhzG/LABRn8UzYTkTzmHlLxtPkiPdt4Kvet8GexkD3qXLsfXUi9a6ZTsMbxlGto5FsbSSyrhi6DDHD/o2zcO0jsQeXAESq84gBWwbgGdUhMiWZQjAfwD1E1AygFwDBHZRemtFkai0GAAIqhvZg3vKdjOU7GQz0dJZhz84J1LNmOg17fTzV2joVRbcloVazYZz//koBsJmOfyL2yNE/OvMK/l5ZESnof8t+73kCwPl2UGHm5owkygENdfWtAEZd6HUMBLtKsee1GjqzZgZV7aqhurhOnixEFEWmNhJd+6tjbQXZTecUD9pxXeSrl7RjyAjVWUTaTWh64oYm1SEy4bwjTyKqYOZuAGeymEe5hrr6yUiicAIAAaWDg7jsyt2MK3czGAidKcEru8ZT95oZVLlzAtXHDJLuMGLATuh6wbWJZAb/yZmz+m9if7fYgSZzCwrTUgBNqkNkQn+XbX8Kd2+2bQAY7uXaPgxgYgZzqXTR7+4JKKkIYfbCPYyFexgMRHp8eHX3eOpcM50qdkykuqhJ0sBapKxHowrVGdLJYTr1d7EHDv7GWVTQl/YElgEIqA6RCectnsx8Y+JzsW3zsyhdByLAOyiMmfP3MubvZTAQ7fXhtYaxdHLtdBq0fRLVRTzuRuNC9CfidvYqCN1c+tr10a8ObeHhc1VnERm3THWATEnmnicBuAfABGb+EhGNAzCKmTdnI2C2NdTVbwUwJxvnYiAW9GLvnjF0Yt10Kt82mWpDXpI+puKctjQdCfmY8/rKxVp7xqqPxB5dGIchE+2KxyVNT9zQqjpEuiUz2/YpAA6A5QC+BPce6K/hNk4oKA119RqArLUAI8Asi2DGnAOMOQcYDMRDHry+bzR1rJtOZZunUG3IV1iX68TFO2boHRNj8XGqc1wMZnR9Jn7fnp/ZV8tl2uIzHUBRFs/5zHwZEb0CAMx8mqhgZ5ROhML9OwkwSqOYPusQY9YhxgOAHTbR0Diajq+bRiWbp1JtbwlVqson1DpiGKfzsXj2srfhxujjZYf4kvmqswglpsHdTKSgJFM8Y0Skw50k1LeripPRVOrMUB3gbAToJTHUX9rE9Zc2MT7+ApyIib0Hqqlt3TTybaylqT2lJC0Ei0STafYuDeVXr4RtzpTVd0f/cX4Upsw6L14F2VYymeL5LQDPARhBRI8DuA3AP2Y0lTo5VTzfjgDNF0Pt9MNcO/0w4/7fg6MGGg+OwrH10zTvxjqa3FVGw1TnFJnRbBox1RmSxYyer8Tv2fnv9g2yhZgozuLJzD8hom1wdxkhALcwc0PGk6mR08Xz7QggbxxT6lswpb7FwUf/CI7pOHBoFI6ur9fMDfU0ubO8cGZpFrsjZn7MsQmz2Xhz9EvGXh63UHUWkRMKsnhecLYtACQu247EWcWWmQ9nMJcSDXX1u+De3C4YMR2HmkagZUO9pm+op4knKyipBhAi99REYxt+e7R1geoc/Wlwxq29NfqFy0LwFlxTBzEgI5ueuOH4hV+WPy448iSivwXweQDtcDfkJbj3Pwuqt21DXb0JYKrqHOlm2pgwpRUTprQ6+NCfgZiG5sMjcHhjnaatr6cJHYPpEtUZRXJO6VrOFiRmhL5p37rtyfhtBdlCUAzYNADFVTwBPAyglplPZjqMYrUA8uO62ACYDsZPasP4SW0O7nkZiGs4cmQ4mjfValg3jSa0V9Fo1RnFuQU1LScnh0XZOHR79J9ir/JkKZzifKYBeFl1iHRKpngeAdCV6SA5IK/ud6aL4WDshHaMndDu4K7VgK2hpWUomjbXEq+bpo0/NpTybmlEoYoDOdc4/aAzasNN0cdn9KJEmnuI/mRt/Xy29NcY/u8TXx4E8DIRrQTwxm68zPyNDGfLtqIsnm+nOxgzvgNjxncwbl9rwya0HhuKg1umkrN2mja2ZTjVqM5YtIh8nZrWOdhxBquOwozof9jXb/xy/IMym1Yko+AmDfU38ux7J3k48eFJfBQqKZ7noDOqx55A9dgTjFvX23AI7a1VOLB1KtlrpmujD4+gQt0gICcdNYyOwdGo0uIZY73lnuhnujdzvRROkayCK55JzbYtBoU40zYbHEJH+2Ds3zqFYmuna5ccGolJcPshiwz42vET267vDWal9/K5HOWhm6+LfLW2G+XS6UqkakjTEzecVh0iXZKZbTsVwKcB1OCtS1WWZy6WEjLr9CJojOHVpzH8ps2MmzbbcICTHYPRuG0yRdZM16oPVGOKFNP0aTaNkIrzMiP+P/ZV6x6L3y+9acXFmgZgneoQ6ZLMhKFfAvg+gB/CXapScBrq6n0AcnImY77RgKEjOzH0+q2M67faYOB0RyUat0+i0Nrp2sjG0ZjKRJrqnPmqyTSz/j1os9Z6b+z/dax2ZkrhFAMxGUVWPOPM/L2MJ1FLGgdkCAFVI7pw+bXbGddut8FA18lB2PfKJAquna4N345f6MsAACAASURBVDsGtY5Guuqc+eKoYWR1FH+cK7ddG3mi5hQqC2pdt1CioLqdJVM8f0tED8Dtb3v2bNtTGUuVfdWqAxQLAiqHncG8a3Ywrtlhg4HuU+XY++pE6l07nYbvHke1jkbJ/L8sSsd1PSsN1pnh/NZZsPrh2INLGJpcKRDpUFB9t5P5IfXhxOf/d9ZzDHf7rkIhxVMRAiqG9mDe8p2M5TsZDPR0lmHPzgnUs2Y6DXt9PNXaOhV884pkdelaxtdT2kwdD8QebvmDc/myTJ9LFJXiGnky84RsBFFMimeOIKC8qhdzl+5iLN3FYCDYVYrXdtVQ95rpNOS1CVQX1wt2P9kLChMNyeTxT3P5jusiT1S3YcjsTJ5HFKXiKp4AQEQz4M6U8vU9x8w/ylQoBaR45igCSgcHcdni3YzFuxkMhM6U4JVd46l7zQyq3DmB6mMGFc1ekQ4w3AZsHUjrfWJm8EvO7FV/HfvUlQ40uQctMqG4iicRfR7AMrjF8wUA1wFYC0CKp8g6AkoqQpi9cA9j4R4GA5EeH17dPZ4610ynih0TqS5qUonqnBlDpHfoetso207bJDeHcfpTsU/sf865clm6jinEORRX8YS7+fVMAK8w871ENBLAjzMbK+tkjWeeIsA7KIyZ8/cy5u9lMBDt9WJnwzg6vXY6lW+fRHURD5WpzplOR0zjVLqKZzeX7Loh+tWqIzxiXjqOJ0Q/iq54hpjZIaI4EVXA3VZmbIZzZZuMPAsEAZ7yCC6d18iY18hgIBb0YteeMXRi3XQq3zaZakNeyusm5s2GcWbemxPfL9oGe9rqv4qtWBCHIROyRDZU1KxY6Wl64oao6iDpkEzx3EpEgwH8O4BtAHoAbMhoquyT4lmgCDDLIpgx5wBjzgEGA3bIg937RtPxddOpbMsUmhr0UV61mmvymAOqnMzo+lz83oYf29dIb1qRbcMAHFMdIh2SmW37QOLL7xPR7wFUMPPOzMbKnoa6eg0Ftv5InB8BemkU02Yd4mmzDrnFNGyioXE0HV83jUq2TKWpPSWkfNeS/hw2jItuSB1k754bo4+XHORLrkhnJiGSNBzFUjwBgIhuBbAY7vrOtQAKpngCKAEgi8CLFAF6SQz1lzZx/aVNjI+/ACdiYu+BampbN418G2tpak8p5VTrxlbDuKjZsDucSWvujH5uXgQe34VfLURGFMx9z2Rm2z4FtyfhzxJP/Q0RvYuZH8xosuwp2jWD4p0I0Hwx1E4/zLXTDzPu/z04aqDx4CgcWz9N826so8ldZaT0SsUJXU9pNjEzep+I373jafumKzOVSYgkFU/xBLAcQD0n9i4jogCA1zOaKruKZo2gSB0B5I1jSn0LptS3OLjvj0BUx4FDo3B0Q51mrp9GkzvLKas/EM5oVJHsa8Ns7n9f9IvUwOMXZTKTEEkqquK5H8A4AM2Jx2MTzxUKKZ4iJR4bk2qPYlLtUQcfeQmI6TjUNAItG+o1fUM9TTxZQRndaCBKyRXrvc6YdbdEvzQ7BG9pJvMIkYKcnk+QivMWTyL6Ldx7nIMANBDR5sTj+QA2ZydeVshlWzEgpo0JU1oxYUqrgw/9GYhraG4egSMb6zRaX08TOgZTWtcRM9HgCCHsZZzz3iUzwt+2b9n6jfgdi9N5XiHSoGCWRfU38vyXrKVQS0aeIq0MB+MntWH8pDYH97wMxDUcaRmG5o11GtbVU037EBoz0HMcM4zjE2LxcW9/PspG0x3Rf4rs4MlSOEUuKpjWj+ctnsy8KptBFJKRp8gow8HYmuMYW3PcwV2rAVtDS8tQNG2uJV43TRt/bCi9owheyBHDOP324tnkjNxwY/Tx6T0oTfqeqBBZlrbtBoloH9zJrADwAjPfSEQTAPwFwBi4hfqpTE1ulX0TZeQpskx3MGZ8B8aM72DcvtaGTWg9NhQHt0wlZ+00bWzLcKq50DGaTDO4JBQGADAj+ox97cYvxj8kTQ9ErkvnyPMk3Dk4Z9++WAVgJIBQ4vkzaTzfW0jxlJGnUExnVI89geqxJxi3rrfhENpbq3Bg61SKr52mjWkeSe/YO7fJNKIAEGet5YOxx7o2OtOlcIp8kM6aE8RZ91CJiOD2KQ8DKEt8vhvAijSe8w0p/UGIqArA2ELqMAQZeYocozFGjj6FkaM3Mm7eaMMhdLQPxv6tUyi2drp2yaGRmHTENHCMh2y+LvLE1C6UD/geqhBZks6Rpw7g2wAeSTwemvi8H8BEuA1whp7j96VFMk0SXgbw3sRrtwE4TkTrmPnvMxUqy2TkKXKaxhhefRrDb9rMuGmzDQc4uXH20vZnauZpV5UdPdrr7dXjOttsOI5tMNhgB7qtRdjr2GxqEdvDMceDGBuIO6ZmQyeHNWgOs8FxhuNAdxxojgOwAx0OdNuG4dgghx2NbI0cBxoD5NjQ2IEGh8hhEJg1Yk1nBwSGBoYGGwQGEbMGJg3M7nMAiFmDwxo5ms5wXwebCQDgQAODwKTBSbT9YtIocR7mxGsAENMbr2VmgMkBACIwwAyAE0dlEDnknsn9NSIw2AEY0CjxMjggZmbqS5V4vYsAm+mNxO7rEy8gdxkCQ0MiAyde6AaBBqbEl24gMIG4728E1LeKHkxuXvT9GdyDU+LPASb3hIlsROz+Md2XcOL1lHj9G6fr+3vAmxHozQfuHwHuwd98nkB9fwD36zdf2fdq6nvc95dIb5yOEr9IiT+ZmzSiUQtwAwaKiG4EsBvAvrOe7usWtwlAJ4AFAHxERH19CtIpmZFnJTN3E9HHAPyImT9PRDLyFEKRE8NmHg4NuvO9ZYe+f/LdI27eX1Y6fcJ+ve3wQb1d66TgFBANB4gNI9Tl8Zw67fX2nvF6e8NebzDi9fXaHk+QyIxTXNf1qG7oMd30RDWPGSYfQiiN9aIsHkRpPIRSO4gyDqLUCaEUIZRQGD6KwKdH4NFj8BoxGKbNumE7mu44mu4wmeyQAUAnBzaY43DYhvu1TX1fO+zAYZscZjhwEo8dOMzkgOEwEo8BB0zMSHxNcJjBTOSAwExIfNYcBxrb0NghEw5pbEOHQxo7pDNrOsehgTWdbdLA5H52SGPW+16nwdE0sKaDWYfNOhxHJ4d1dhwdNnQ47nNvfLahs8MGvfHrjuG+uWAdDhvU93ts6OQ4BmxoxEgcGzocdp9z+o5NOjmsw6a+xxrO+jX3a2hg0ugtv554w8HunwmceI6hwdHcNzYOae6bDtLgkPsmhQnoe/vAQKJ8w6131Fcj2X1N33MAQO5z5H4+q/q6v5aomoDmvPH2BESERuDxdHwbLALwYbgFE3Cb+XwP7vk/gjcHRQx3L+q0N/ZJpngaRFQN4A4An013gByQ9nckQmRK2FvVumv6x8YQaT5HH7F/dfsvlw71XrJ36ag7h1xmT5wGAB3UvX+f3nrsMHeU9Ma89cHg4AnJHFvT4iGPJ3RqiCd4ptp7usfrbYl6fb0xrzcIjyekm0bE1I1oqa7Hy4mcwQCqiBKDEh1vuSDnQLMj8AbD8IXDKAmFUOKEUOIEURYLojSe+OwEEwU6hFIKoQRh+CgMnx6FV4/CY8RhmjEYHhu614HuZVAJg0pAGd7wnNmGg5hb/BEHcwwOxy/whoDd59mht74hQOIxJz763hC4bwQcuG8M3vmGQOv7TAwNzH2Dez3xa+7fOkMHOPEZBhhG4l/DhPsz3gRg0lnDRoVKmtJwEGZ+jIh+B+A9AD4D4M+J2bZtAOJw+xOUArDhjlDTLpni+UUAfwCwjpm3ENFEAI2ZCKNIr+oAQiTDIS22ed5nToA0CwDMkkUjomd+jpORY7XPNT9pzx76rtWTB82+bDgqJg+PV0xeFK+FAyd+VDv92j792Kmj2qmqKOL1oHMvVHccoyQcHjQ6HE52u1PHNs3IaY8n1OX19vZ4vcGQW2x7bY8nRB5PyCwzQt4Kvbtc05xKgKuI0nOlx2FyIvCGIvAFQygJh1ASCaMkkijKsaA7erbdAl3GIZRwCKV9I2ct4hZnMwbTiMP02NC99tnFGSiBTr6315tzvdPOm3ffzE7iDUHsLW8IGA4cjrsfb7whcBJvCPquEjhnvQFw4MChN98MMBhwrxq8cYUAiTcBoMQbAzgg9mhtafwT/Tfe3Fv6eiJ6Bu6Zq/HmP9zxTFyyBfDGxfai1VBXvwjuTjFC5LTtsx5Z3Tl4yltm1YZPf6sRiE/pe1xmVB5dXn3PsVJj0LxzHSOGeE+T1rGnUW/tbde6LrHhTD7rhlbG6Xqsx/SETns9wW6vtzfo9fVGvN5e2+sJsccT0g0z7DWMWImm2RVEThURlKxZZYDPKs6RMErCQZRGQiiNBVEaTYyY428rzgijRAvDq0Xg1WPwGFF4zDgMjw3D40DzOdC8DCoFUAqiXBgJZtvX266a9Wg6DkREPwNwDdxJQUcBfB7AD+BueVaVeK6GmTNyay6ZCUNj4M5o6mssvQbAw8zckolACsjIU+S8w2OWr3974QQA3VN3zI7ueqN49sa7Rv/2yFOjp1bMXT9ryFVTiLS39ME1YZRPcarnTnHc/d+DiHQc0Nsa9+vtzinqqWHijM7ctW2z3A6Z5eFQcjWRyImZZvi0xxPs8nqDPV5fb9jr7Y15vUH2eILkMcOmbkRLdD1epml2JYAhRANfDkEA+RAp9SFSWomugR7unCLseWPkHHZHz1G3ML9xaTseRCmH3MvbfSNnhFGiJ0bOegymJw7TjEP3ONB9DjRfYuRcCqJc3GpxQBu5n4PztsdhuKNOE24f3Yz9HVxw5ElELwL4KdwhMgB8EMA9zHxNpkJlU0Nd/WQU1mVoUWDOlI85sGXOilEgKnv7r7Fzpj3S9e/DcI4lAB7N17l01J27qjwjF1GSo5xO6j28T29tPqQdN89QqBaEnNrLNBmGEenyeEKdHk+w2+vrDXm9vVGvNxj3eoPkMUOGYUY8uh4r0zR7EBFXEaFcdeZMiMIMh+ELReALhVAaDqI0GkJJxJ0MVhbrRZmdmAzmBN0JYRxGCSWKsxaFx4jBY8ZgmDYMrw3d40ArOas4X8yyk39su2pWWmYMAQAR1QB4nplnJB7vBbCMmVsTc3VeZubadJ3vLedOonjuYOZZF3ouXzXU1Y8C0Ko6hxDnEte9Z9Ys+toJ1szzTvoJdz69Fdw793y/fknp5B0LR9xcqZOR1MShPgx22qlr3z79WPsR/eSgEKL1IGR2oo4CpMXDHjN82uMNdns97ujW5+21Pd6g4/GENNMMewx3dFtO5PSNbnNxVJdVMRjRxMg5lBg5R/pGzu6s7VInMWP7jeJcht6f/Gr5Iz9Mx/mJ6GEADwAYD2AFMz9JRF8HcJKZnyCiFQCGMHNaLhO/4/xJFM+XADyDNzfDvhvAvcx8dSYCZVtDXX05MtjCSYiB2HD55zeESkcs6O818fC29fHQqoX9vUaDHlkw4r0bR5dOWUhEF7WzhQ0nckQ70bBPb+1q1U4Pj8GuQ1EWEXZMM9Lp8YQ6PYllQD5vMOr19joebxCmGTZMM+LT9XipeymZq6gA33RcpL+5evmBHwz0IEQ0A+5clRCAYXBn1X4B7j3PX+DNbTTvYOZTAz3fOTMkUTzHw73nuQDuxLL1AB5i5sOZCJRtDXX1BPcvvhhv3osctnfKnauOjl6y9EKvY46HI53figCovNBrqzwj9y8bdVfIo/usgeaLINZ1UG/fs19vi3RQ91iHOKWRbTHRtFivW2xDXV5vb9Dn7Y14vb1xj7sMSDPNiMfQY6WaO7qtAlBJWZzIlUX3XL38wE8HehAiuh3Atcx8X+Lx5wBEmPmfB3rspDMU+2xbAGioq++B2wtRiJxwcsi0na9aD0wDUVKTXyLdP1nDdvuVSR6eZw65ak1txbxZRJS22axnEGrdr7cdPKC3Uxf1TmbCiHQdu/g4cdMTPu31hLo83mCPz9sbcidK9Toeb0gzzbBhGFGfrsfKNc2pAHgIUV50S7vx6uUHVg70IERUD+A3cAd1IQAvAdjKzH870GMnneF8xZOIvo1+ljAx80OZCpVtDXX1bXA78QuhXMRT0bFuweMOSEv6/6QdO7Qz1vPcpamcp0Qf1La8+gNN5ebgK1JPeWEnEs0amvWOkl5E6kBIdgGpuAi6Hu32eEKnvd5gj8fb2+vz9kbdjlIh9nhChmlEPLoRK9O0+CAiHqxoGdDiq5cfWJeOAxHRfXDvefbC7SAUYeZH+v9d6dNf8fxw4stFcNsb/Tzx+HYAu5n545mPlx0NdfUH4DYSFkIph7T4uoVf3RUzy1OekBc+/WQz4IxP9fdNGjRr02VDrxmnkVad6u9NlgMnfkw7vWef3nryqHayKtJPswaRHUR2xON5YxlQ0OvtdZtceILweINkmhHTMKKlmhYv0zS7r6PUQBu7T796+YG0d/whoq8AaGHmp9J97POeM4l7nhsBLGbmeOKxCWANM2fk3aoKDXX1rwJI6V27EJmw49IHV50aMu2C9znPJdrz/MtObN+yi/m9Jnm6rxx1+45h3tGLKQvrA2OI9zZrJxr26cd627WuahvOlGw2axAXg9kw3IlSXm/wHf2SPZ6wYRgR71nLgIYQofRtBxl19fID7elIQ0QjmPk4EY0D8EcAVzBzZzqOnYxk7qdUAagA0DdjqTzxXCHpVh1AiJZLrtx4sYUTAIySRZOjsX2JfT5SE+NoxZ9bf7JkpK/mtStH3lqia+bki82RDBNG2WRn1NzJzigAQAjRE/v1tsYDept9MgvNGsTFIIrHfVXxuK8qGEyuBPT1S/Z4gme83uCZUdWNJ9IY6NdENBRADMCD2SycQHIjz3sB+AH8Be435RIAfmYOZDxdljTU1f8cbuN7IZToKa0+tHneZ4digBN4wp1PvQoOzxzIMQhabP7w69ePK5t2BREp2XWok3qbG/XWw4e042Y3haaCMERFDpFWHX6/v2AmkV1w5MnMzyS6189PPPUPzJzO5r65oFl1AFG8bM0T3Drn0fhACycA6N5Z3XZ444COwXDMjR3PL93dufHQ8uq7u7x6adYbogzmsvHz4pPHz8NkMJjbqWtPoTdrKAIFVTeSmgafKJa/yXAWlQpizarIT1vmPPqKo3sWXfiVF2b45syywxuDwDvuNaWsO3Ziwv8e/jbPGLx47bTBC2cQ0eA0REwZgWgUD64bFR9ch/gbzRpe2ae3drdqp4clmjUMdCKLyLyC6uQ24AbKBUKKp1CicdKtq4Nl1e9o+H6xiLyDSBuyjp1TaSnGAGhX59rF+8+80nHVqLvXV3iG9tvJKBt0aN4aZ8TsGse9AhhBrOuQfnxvo94almYNOa34Rp5FQC7biqw7NXjq60fGLE/7rHWjZIEv1jvgdehvEbZ7h//u6A+Hjy+fvuXyYddVa6TnzIQeL8zKOnv05XX2aABAD8KtjXrrwYN6OzrdZg2yhjs3FF/xJKKZAPq6l6xh5lczF0kJGXmKrIqa5Sd3zPxkFYjS3hVGM6fOBl5oBTjt6zabe16f19K7r/fKke9fNcI3bjFd3M4aGVUOX/Vse0L1bNsdgJ6gMwca9WNHm/QOX6JZg5I9QkVhXba94HquROf6nwAYkfj4MRFlrQVSNtTvaTgNaQ4vsoRBzqZ5n20G6Zdk4vhEpGlGzb5MHBsAbI6Vvdz2P0v/0vbTvXEnujdT50mXYTxo0oJ47ZK7I4sv/2jkqtJro7Nem2iPXOVl41UwoqrzFZGiG3neB2A+M/cCABF9DcAGuM3iC8lhANNVhxCF77Xp96+JeSouej1nMoySRWOiZw5l8hToCLdMe7b5yficYe9ZNbH80nlENOBJSpmmQTPGOEOtMc5QAEAMdrBZ63itUW/tadc6q+PSrCGTmlQHSKdkimffriN9CnUHEimeIuNaR12x+cSwS9M2Qeh8NGPEJMDcDcSmZfI8DDa2nvj90r2dmw5fVf2BjhKjfE4mz5duJvTSyc6oOWc3azigtzXu19viJ6lngjRrSKs9qgOkUzLF8xkAm4joucTjWwD8R+YiKSP3PUVGBUuGH2mo/WAtiLLy5lP3WifsyPZsnApn4qfH/d+R746rq5y/7tKqpXWJzi95pwSeYTPsccNm2OMAAJ3Ue7hRb20+pB03uilUK80aLlq73+/PagegTEumScI3iOhlAIsTT93LzK9kNJUaMuNWZIytGeHNcx/rBdHYbJ3T8M237Mj2KJC9rar2dG1adPDMzlNXVd+1brBnRLqWyygzmMvGzYtPHtfXrOE4de3Zp7e2H9ZPlCeaNeT8peocUVCjTiCJ4klEVwB4nZm3Jx5XENF8Zt6U8XTZJSNPkTHbZn96i6N7k91vMy1IK6kirWITO93zL/zq9Ik6oSF/OPrMojGlU7cvGPHeoRrpKe/0kosIRCN5cN3IN5s1RFu0k6/u0491HpNmDReS8xPLUpXMZdvvAbjsrMc953iuEBTcP67IDQdrblzbM2hsVgtnH903H/HgiypOjZbgvsuebX4ytHDELauqSyYuoiQ39s4XOjTPeGf4zPHOcABvNGvY06i3RTqoa4xDLNscvqn4Rp5wm8e/0T2emZ1C+yZIeA1AHNI4QqRRZ8XEhqbx185VdX7dM+2yePDFkwCU3IO0OV6ypv1XS4d4qvctq74zZmregp2Ul2jWMP+sZg1t+/XWAwfcZg2TmDBKcUSV0lY8iejvAHwMAMP9uX0vM4fTdfxkJbNv30EieoiIzMTHwwAOZjpYttXvaYgAaFCdQxSOmFHa+cqsR8pB5FOVgUg3yRizS9X5+5yKtk59tvnJ+n1dW1cxc4/qPNlQDt+oWfaERe+PXrHovsjVo94XufzA9PjY1eXs2wwuum0Q0/KzlYhGA3gIwFxmngFAB3BXOo6dqmSK58cBLARwFEAL3N1V/jqToRTKztREUfAY4M3zPtvImp61CULnY5YsypVtoLRXTr209PmWp7uD8e7NqsNk21AeNGlBfOqSuyKLLv9oZHnZddHZuybZI1d52Sz0Zg3dSO+ETANASeIKaCmAY2k8dtIuuJ9nMWmoq38YwJOqc4j893r9R1a1j5yX0UYIqQif/lYjEJ+iOsfZplTM2TB7yPJJRFquFHdlYrCDh7WO3fv01t52rXNUHM7UAmrWsNbv96ftnn/i6ufjAEIA/sjM96Tr2Kk47/09InqUmf+ZiL4N99ryWzDzQxlNpkYhLsERWdY+fM629hFzlUwQOh/dU3fMju7KqeLZ2L1tQVPPrq6lo+5cM8QzajFlaf1rLjKhl05yRs2d9GazhpMH9PZ9+/XW+EnqqWFi5VcwBmBHug5ERFUAbgYwAUAngF8S0QeZ+cfpOkey+pscszvxeWs2guSIHXDfKBTtN7EYmJBv6NHXp907AUTJ3BLJGqNkQZ0d3WUDubWUIuZEKv907EdXVpdMfHXRyPcN0smQGaoASuAZOsMeu2CG7dbMLgoeadRbmw5q7UY3haaC1EwAu0hpK54A3gXgEDN3AAARPQv3tmJOFc87ATwPYDAzfzNLeZSq39PQ3VBXfwDAZNVZRP5xSI9unvtYJ9xJDTmFtEEjQWVbwb3KZv72pzV0cOazTU9Grhhx46oxpbULKAO7zeSzSi4dOzc+aexcTEo0a+jeu08/1pYnzRrSWTwPA7gi0Uc5BOBqKBrg9Vc85xDRJQA+SkQ/wttGY8x8KqPJ1HkFUjzFRdg+65FNtlGSU5drz2b45kbjoVWqY5yXA9u7/vhvlg72jDhw1ai7ez2671LVmXKR26yhsnZkvLL2rGYNO/bprV3HtNNDY4jX51CzhjiA19N1MGbeRES/gju5Mw735/UP0nX8VJx3whARPQTgEwAmwp1pe3bxZObCXADcUFf/GICvqM4h8kvTuHevOzjx5pxuR8ccD0c6vxUBUKk6SxL40qqla+sq519KRPmQN2dEEe92mzW0hjuoe7RNziSFcV73+/0zFJ4/Y8478mTmbwH4FhF9j5k/kcVMqsmkIZGS7kHjGg9OeO9s1TkuhMjwkT5yC9vtOTs6PgvtPL3qysbube3Lqz/QUG5WXaE6UL7wwKiotS+5vNZ2t4t1mzW0JZo19ExkQto3Se/HtiyeK6uSaQz/CQAgohEAfGc9X6i9YGWtp0haTC/p2jb7Ux7kwV6WAGCULKyM9Tx34RfmiJDdM3Jlyw9GTiy/dNOcYe8Zp5GWzR/8BcFt1lAzapZdAwA4SWcONuqtLU16h68H4VpQRq9E5O59ggE652VbIhrNzEcTX98E4N8AjARwEsA4AA3MXLBtthrq6pvh/jmF6Nf6+V/cFC4ZmtXG6wMVPv1kM+DkXbN2gzxnloy6bfsw75grKcdmM+crB2y3aaf37NVbT7RoJysjiNWD4E3jKSb5/f6C60gHnL94fgDANQAeALAJ7oymXzDzVUR0FYAPMvN9WU2aRQ119f8J4F7VOURua6i95+XW6oXLVOdIVbTn+Zed2L5lqnNcrJG+8bsWj3y/19DMnFq3WgjcZg0nGhr1Yz1tWtfIOOzaATRrOOL3+wt2EHLOy7bM/FMiagRwA4AoM3cQkZn4tb8QUaF34XkRUjxFPzqGXrqjddSCfLh3+A5GyaLJ0di+vF3P3B5unvFs85Oxy4df9/L4sulXkMLewYXGbdYwcs4kZyQAt1nDQb193369LX6Szox3iFMphgV7yRbof8LQFgBbiOjjRFQOYBMR/TeAU3DX1xSyP0GaJYjzCHsHt7024/7RIMqV5QAp0fSqMSDfDnB4luosF4vhmJs6Vi5r6NzYdFX1B0779NKcn7CVj0rgGTrdHrtg+pvNGloa9damQ9pxvYuCU0AY1s9vfzkrIRW5YG9bIiqDWywJwD0AqgD8hJlPZD6eOg119TsAzFSdQ+QWh7TY2oVfa4ibpXm9BjEWWr/WDm9crDpHukwfvGjt9MGLpifat4ksYDB3UPe+fXpr22G9oyzoNmsoO+slU/x+/35lATOssNpsggAAHSZJREFU3+JJ7jvrPzHzVdmLlBsa6uq/DuDTqnOI3LJ95sOrO6umLlGdY6CYI2cind/VgZzuTJMSn17WcdWouxorPMMWqs5SjGw40aPaqYZ9+rHONq0TK77w2WWqM2VSvzPWmNkG4BTpIuUXVQcQueXI6GUbCqFwAgCRdxBpQwpqTXPY7h3+u6P/sXDD8d9uddhuUZ2n2OjQPOOcYTPfFbt06QcjSw6pzpNpF1znCaAHwGtE9CKA3r4nC3RXlbOtARAB0jptW+SpM2WjDzROvs1SnSOdjJIFvljvStUx0u5w7+65R4ONvYtH3rpqpG/8YsrTe9N57g+qA2RaMvc8P3yu55k5kJFEOaShrv4lAMtV5xBqxXVvz9pFX2t3NFNlm7O0Y2Yn0vlkO8AF23hgmHd0w5JRd8DUPPWqsxQRB8CIMU9ceVJ1kExKpsNQwRfJfrwIKZ5Fb8ucf9jpaGbB3UcjIk0zavY58UMFWzxPRI7WP9f8pD1n6LtXTRw0cx7lSSeoPLet0Asn0M89TyK6mYgePOvxJiI6mPi4LTvxlPuT6gBCrb2Tb18VKh1ZcIWzj1GyaIzqDJnGYH3ryT8sfaHlB6dC8Z5i2p9Yld+rDpAN/U0YehTA/5312AtgHoBlcHdbKQbb4a5rFUXoZFX9zqOjlxZs4QQAzRgxCTB3X/iV+a8n3jnm/458d+6rp/6yvtCX2ilW8Pc7gf6Lp4eZj5z1eC0zn0w0hC87328qJPV7GhzIrNuiFPFUdLx66QMjkOisVch0r1VUhWRP1+aF/3v4W/rpSPta1VkK0Am4LV0LXn/F8y2LjZn5k2c9HJ6ZODnp16oDiOxikL157mePgrRRqrNkg+GbbwGIqs6RTVEnXPXHY/+1eG37c6/YbDerzlNAfjXmiSvjqkNkQ3/FcxMR3f/2J4nobwBszlyknLMSQFB1CJE9r1qfWBvzlOdt67pUkVZSRVplUW7FdzS4b/azzf828miwcRUzx1TnKQA/VR0gW867VCWxf+f/wl3r2PeNNQfuvc9bmLk9KwlzQENd/S8A3K46h8i8o9WLNu2t/UBebTGWDvHIa5vjwRcvV51DpSrPqMarqu+Kmpq3YLdbzLAjAMaPeeLK/tc/FojzjjyZ+TgzLwTwJQBNiY8vMvOCYiqcCb9QHUBkXm/pqKa9U+8uyvWAumfabLj79Rat09G2Kc82P1m/r2vramY+ozpPHvp5sRRO4ALt+QCAmf/MzN9OfPw5G6Fy0As4q7uSKDy25glumfMPMRBVqM6iApFukjFml+ocOUB75dRLS55v+X5Pb7yrmG5PpcPP/n979x4eVXnnAfz7ziWZiCmgFERCAUFhQO7hbsCidddL3fap3e1Wtz5ttzZtWi8dtQrdbru7bdPddsVLNPVS1622znrBy5RqrZGL3IIgCDIDCJgAgXALSchlkpl5948TKyqXnOSc+c055/t5nnkCGN7zfTCZb87lfV/pANnE3di7IZyItwKISecg+7w15Y63M/48T2+uHCyYM1A6Q65oTTUNju2pnL7+8GtrtM547UpbT2wrKi/x1H1z15anUsqvlHpbKWVV6T1l0TiUY9674AsrWs4+f450Dmm+wJAwENghnSOXvNe8Yebi2vtCR9rrluszrWXqbZ466wRcXJ4AbgEQt3C8V2DMYSIXaeh74dbaoZd77gGhU/HnjamTzpBrOjPJvn/Z/7u5y+uf3ZzOpHZK58lRLE83UEoVAbgawKNWjRlOxDsBRK0aj+R1BPsc3Tjx+32hVJ50llwRKJg1BkBaOkcuOtC2a8LzNfcMrT0eX6q19tS82DPYUFResl06RLa5sjwBLIKxvGDG4nF/Z/F4JERDZaqLF+7WPv8Q6Sy5RPkKB0H1cdU+n1bKIJO3+tBLl7667/G9yXTbJuk8OcJzZ52AC8tTKXUNgINa6/VWjx1OxNcC4D0hF9gy7psrOvL7TpXOkYsCoWKeVZ1BY+ehC16ovW/C1mOrV2itG6XzCNIAnpYOIcF15QlgDoBrlVLvw/ifOl8p9aSF43t5izZX2D9o+rpDAybNlc6Rq/z5E6cA8HIhdJfa3LC85KU9DyabOxvWSIcRsqKovGSvdAgJritPrfXdWusirfVwAF8BUKW1vsHCQzwCY9UlcqDWggF742O+diGUUtJZcpVSgZDyD3pHOodTtKePD1yy9+GZ1YeWVGd0Zr90nix7RDqAFNeVp93CifhBePQyhdNlVCC5rnhBM5TqJ50l1wUKZvPfyKTdxzdPX1xz79kH22qXa62tft4iFx0E8Ix0CCmuLk+t9VKt9TU2DH2fDWOSzdZP+UF12p/vyeX3zPIHR4wHfNxtxKSU7ih848Af5i498HQ8lel0+xOojxaVl3j2Kpyry9Mu4UR8AwDuBeggu4ZftaK5cFiJdA4n8QVH7ZbO4FQH22vHPV9zzwW7mzcv1Vq3S+exQRrAQ9IhJLE8e45nnw5x7FMjEu8Pu2qadA6nCRTMGQXjaUrqAQ0dqD685NI/7Xusvj3d4ral61706oNCH2B59txiGFvwUA7rDJx17O1Jt/WBUiHpLE7j8/cvggpxLmMvNXceGfZi7QNTNjesWKm1PiqdxyIPSAeQxvLsoXAingLwoHQOOjUN6OriBdu1zz9UOotT+fMnHZfO4BZbj62a82LtA5nGjsOrpLP00qai8pI3pENIY3n2zsMA2qRD0MltDd+4LBnq7+kNnnsrEJo6EUCrdA63SGZaB7yy77HZqw++9FZGp5165erX0gFyAcuzF8KJ+FFwt5WcdPDTkzfUD5zGhRB6San8QuU7h8v1Way2JV78fM2ic/e37lqmtXbSWsJ7YdFUPaXUaKXUxhNeTUqpW60YOxtYnr3HB4dyTFvonLotY78xDErx69sCgYJZvF9sg7ROnbW8/pl5r+9/akdnpsPKHaDsdF9ReUmnFQNprbdprSdprScBmArjCsdiK8bOBr659FI4Ed8MwPPX/3NFRvk7qosXNED5zpXO4ha+4EWTAeW1lXOy5khy35jFNYsueq/p7WVa6xbpPKfRDONWlR0uA7BTa+2YucUsT2v8TDoAGd6edMuadKBgnHQON1FK+XzB4dukc7iZhvavP/LneUv2PtzQmmp+SzrPKTxaVF5i15rHX4HDdmdheVognIi/Dp59iqsZevnKxr4jeZ/TBoHQHD6xnAXHU8eKXt7zYPHGI1WrtM4cks5zgjYAv7JjYGXsp3stHLbUH8vTOj+SDuBlTWcP3bHzgi9Mls7hVr7AwJFAcKt0Dq/Y1rRu9gu19wePJg/kykpm9xeVl9TZNPaVADZorettGt8WLE+LhBPxVQCWSOfwopQ/1LR+yu0BKHWWdBY38+ePPyydwUs6Mu39Xqt74pIV9c9tTOv0+4JRjgEot3H8f4TDLtkCLE+r/Qhczizrqovv3qp9gRHSOdwuEJoxHgA3ys6yutb3Jj1fc895e1u2L9VaW/Kkq0n/WVRe0mDHwEqpPgA+B+B5O8a3E8vTQuFE/G0Az0nn8JLERV9d1l4wYKZ0Di9QvoL+ytfXbWu0OkJGp0MrDy6+9LW6J97vyLRvyeKh9wO4167BtdYtWutztdaO23yd5Wm9HwPwwl5+4g6dO35j3eDZl0jn8BJ/aDrfMwQ1dNRfuLjm3rHbGquXa62bsnDIfy8qL+EKUyfBbwSLhRPxOIAnpXO4XXtev/rNF990PpTyS2fxEn/e2MkAjkjn8DjfxqNvzI3teailpbNxrY3HeQ/AIzaO72gsT3v8BIDEvQlPyChfqnragnoo30DpLF6jlD+oAkXZvGxIp9Cabh4c21s5463Dr67N6MwBGw7x46LykpQN47oCy9MG4UR8N4DHpHO41aYJZStTwT4TpHN4VbBgDn9oySE7mzfOeKH2voLD7ftWaK2temBxIyxaw9atWJ72+Q8AbtxBXtSeIfNWN/QfM086h5f5AkPCQGCHdA76UGcm2ff1/U+WLK9/ZnM6k9ppwZALispLOHPgNFieNgkn4vtg04ocXnW8z/m7doz68sXSOQjw542xa8I89cKBtt0Tnq+5Z2jN8a1LtdbJHg6zrKi85E+WBnMhlqe9fgbAip8CPS/ty2t5a8qdGkoVSmchIFAwawwAJ22l5RkZZPLWHHr50lf3/bYumW7b1IMh7rY8lAuxPG0UTsTbAXxPOocbrCv+4aaMPzhSOgcZlK9wEFQf7vOZwxo7D494ofa+Ce82rFphYh7l74rKS1bbGswlWJ42CyfirwB4VjqHk20fdd3y1rPOmy2dgz4qECrmakO5T205tqLkpT0PJps7j56pFBsA3J6NUG7A8syOW2DshUcmHe0/ZvPeIZfOks5Bn+TPnzgFgONWhvGi9vTxgUv2PjJr7aE/Vmd0Zt8pPu3uovKSg1kN5mAszywIJ+J1MFYeIhOSwcJDGyeUDYBSQeks9ElKBULKP+gd6RzUfe8f3zJ9cc2ivvVtNcu11ieuhLYa9m107Uosz+y5HwDvEXWThkpXT1u4F8o3WDoLnVqgYHY/6QxkTkp3nr30wNNz3zjwh0Qq07kNQApAKaemmKOsm1NLZxIfE54BYBX4Q8sZbbq4dNmRAeM5n9MB2hsW1QCZYdI5yDwFlZrQf96/XF55u51bjrkS38SzKJyIrwXXijyjuvNmVR859+K50jmoe3zBUbulM1DPaOiaTQ1Lbds1xc1Yntl3FwDelD+FloJBNYnR14+GUko6C3VPoGDOKHAfWyfSAP45Eo21SQdxIpZnloUT8WMAItI5clHaF2xbV/zDdijVVzoLdZ/P378IKtSTyfgk6zeRaGypdAinYnkKCCfiT4JzPz9h/ZTb12f8+aOlc5B5/vxJx6UzkCl7ANwpHcLJWJ5yboLxBUwAdo64dsXxs4u4sbVDBUJTJwLgpsnO8e1INMa5573A8hQSTsQbANwAIHOmz3W7hr6jttZ85orp0jmo55TKL1S+czgVyxkej0RjXPi9l1iegsKJ+HIAv5DOIakj2Ofoxok3fwpK5Utnod4JFMwKSWegM3oXXG/bEixPeT8BsEY6hAQNlakuXrhL+/xF0lmo93zBiyYDar90DjqlFgBfjkRjvLxuAZansHAingLwVQBN0lmybcvYbyzvyO9bLJ2DrKGU8vmCw7dJ56BTKo1EY3HpEG7B8swB4UR8N4Ay6RzZdGDQtLcOfXoyVxBymUBozlDpDHRSj0aisSelQ7gJyzNHdE1feUo6Rza0hQbs3TrmxpFcCMF9fIGBI4HgVukc9BHvAPi+dAi3YXnmlu8C2CUdwk4ZFUhWF9/dBKX6S2che/jzxx+WzkB/1QzjPme7dBC3YXnmkHAi3gTgehi7HLjS+sm3VacDobHSOcg+gdCM8QC4UXZuuCkSjW2XDuFGLM8cE07E18DYPNt1dg+78s3mTw0vkc5B9lK+gv7K13eDdA5CZSQae1o6hFuxPHNQOBF/EMCD0jms1Fg4fNvu4VdPlc5B2eEPTed7i6y3AdwqHcLN+AWeu24B8BfpEFboDBQ0bph8WwhKFUhnoezw542dDOCIdA6PaoJxnzMpHcTNWJ45qmv+55cBOPp+hQZ0dfGCbdoX4GbJHqKUP6gCRVukc3iQBvCNSDS2UzqI27E8c1jX9mWfB9AgnaWn4mO+tiwZOofr1npQsGDOQOkMHrQgEo09Jx3CC1ieOS6ciG+HcQbquCdwDw6YtOHAoOl8QMijfIEhYSCwQzqHhzwQicbKpUN4BcvTAcKJ+OsAbpbOYUZ7fv/9W8Z98zNQyi+dheT488J10hk84nm49Cn9XMXydIhwIv4QgArpHN2RUb7O6mkLDkP5BkhnIVmBgpljAKSlc7jcmwCuj0Rjnt/eMJtYns5yKxzwBO7bE29ZnQqcNV46B8lTvsJBUH24z6d94gCu5QpC2cfydJATnsDN2bVDa4suW9XYb9Rc6RyUOwKhYq42ZI86AH8bicYc+0Chk7E8HabrCdzLAOTcgxjNZw99772RX5wonYNyiz9/4hQAjdI5XKYRwJWRaKxWOohXsTwdKJyIH4BRoO8LR/mrlD+/+a0pET+U6iOdhXKLUoGQ8g96RzqHi3QA+GIkGuO/qSCWp0OFE/E9MAp0n3QWAFhXfPe72hccIZ2DclOgYHY/6QwuoQHcGInG3pAO4nUsTwcLJ+K7YBRovWSOxEVfWdZW8OmZkhkot/mDI8YDvhrpHC5wBxd7zw0sT4cLJ+LbAFwOoXVED58zblPd4EvmSBybnMUXHLVbOoPD/TwSjf1aOgQZWJ4uEE7EtwC4AsCxbB43mdf34DvjSwdDqUA2j0vOFCiYMwrGZUcyb2EkGlsoHYI+xPJ0iXAivgHAlQCOZ+N4GeVLrZ22cD+Uj+uXUrf4/P2LoEKbpHM4jAZwayQa+7l0EPoolqeLdG2kfTWAVruP9c74765MBftwWgqZEsifnJUf7lwiA+BbkWjsXukg9EksT5cJJ+LLAfwdgBa7jrH3/Llrjp4TnmfX+ORe/tCUicjCD3cukAJwQyQae0w6CJ0cy9OFwon4XwDMB3DY6rGPnzV49/YL/36c1eOSNyiVX6h853C5vtNLArguEo39QToInRrL06XCiXg1gDkALHvCMe3La3lr6p1pKFVo1ZjkPYGCWSHpDDmsFcZatS9KB6HTY3m6WNdeoLMBbLRivHVT79yY8eeNsmIs8i5f8KLJgNovnSMHNcFYq/bP0kHozFieLte1lN88AK/3ZpwdI7+0vLXPYM7npF5TSvl8weHbpHPkmKMALo9EYyukg1D3sDw9IJyINwG4CkCP7qEc7Td6y56iz3IFIbJMIDRnqHSGHFIP4NJINLZOOgh1H8vTI8KJeAeA6wHcY+bvdQQLD2+cWHYulMqzJxl5kS8wcCQQzNmt9bJoK4DZkWhss3QQMofl6SHhRFyHE/EfALgD3VjpRUNl1k5bWAvlH2x/OvIaf/54y58Gd5glAGZForFd0kHIPJanB4UT8V8B+CcAnaf7vM0X37SiM69wSnZSkdcEQjPGw9hey4v+G8DnI9FYk3QQ6hmlNZea9Kr4mPAlAJ4BcN7H/1vdeTOrE6NvmAalVPaTkVckGx9bozONXrqf3gHgO5Fo7LfSQah3eObpYeFE/E0AUwGsOfHPWwsG1iZG3zCaxUl284eme+k96CCAz7E43cFLX7h0EuFEvA7GVJaHASDtC7RXF9/VBqX6yiYjL/DnjZ0MG1bCykFrAUyNRGPLpYOQNViehHAi3hFOxL8N4FsbJkfWZPz5o6UzkTco5Q+qQNG70jls9jCAuZFobK90ELIO73nSR1SUVk2FcR90hHQW8oZMal+8ozkals5hgySAMi7u7k4886SPKKucvx7AFAAvSGchb/AFhoSBwA7pHBbbA+ASFqd78cyTTqmitOoHAMoBBKWzkLt1try2LN2x2S3b3P0ewPci0ViDdBCyD8uTTquitGoGgCcA8D4o2UZnmuuTjY8MAOCXztILh2FMQ3lWOgjZj5dt6bTKKuevBTAZwCJ0Y1Uiop5QvsJBUH2cvM/nSwAuZnF6B888qdsqSqvmAXgcfJiIbJBqX78q1bZstnQOkxoB3BKJxp6QDkLZxTNP6rayyvnLAEwA8BvpLOQ+/vyJU2CUkVO8DmC83cWplOqnlHpWKZVQSsWVUrPsPB51D888qUcqSquuAPAYgCLpLOQeyaanVuh0fYl0jjNoBfBDABWRaMz2N1Cl1BMAVmitH1XG7kZnaa2P2X1cOj2WJ/VYRWlVXwD3ArhROgu5Q7pz9zudxxdPkM5xGqsB3BiJxrIytUYZK31tBHCB5pt1TmF5Uq9VlFZdA+A+8F4oWaC9YVENkBkmneNjOgD8K4D/ikRj6WwdVCk1CcYKRVsBTASwHsAtWuuWbGWgk+M9T+q1ssr5MQBjYby5tAnHIYfzBUftls7wMS8CGBeJxsqzWZxdAjAWLXlIaz0ZQAuAu7KcgU6CZ55kqYrSqmEAfg3gS9JZyJky6Ya9HU2PDwEgvavPJgC3RaKxN6QCKKXOA7BGaz286/clAO7SWl8tlYkMPPMkS5VVzq8pq5x/HYDLYVxqIjLF5+9fBBXaJBihHsBNAKZIFicAaK0PANijlPpgkZLLwO+rnMAzT7JNRWlVAMD3APwEALc4o25Lta1+M9W++pIsHzYJYzGQn0WiseYsH/uUuu57PgogD8AuAF/XWnPpP2EsT7JdRWnVQAC/APB1yF+KIwfQOtmcPFbhB3BWlg75HIA7ItFYrt1vpRzF8qSsqSitmgDjLPQLYInSGSQb/2elzhydY/NhNsC4r8lNqskUlidlXUVp1WQYJXqtcBTKYemObes7W/441abh9wNYCOCJSDSWsekY5GIsTxJTUVpVDOCnAK6SzkK5R2udSR5bVA/owRYOuwvArwA8HonG2i0clzyG5UniurY9+ymAv5HOQrml4/jipZnO3ZdaMNRGAL8E8IzAXE1yIZYn5YyK0qpZAP4NxjQXImRSB3d2ND85shdDvAHgl5Fo7FWrMhEBLE/KQRWlVVMB3AzgHwDkC8chYe0N928FOsea+CsZAC8AKI9EY+tsikUex/KknNU1xeUmAN8BcL5wHBLS2bp0eTq5YW43PrUDwP/CWH92u82xyONYnpTzKkqrgjCW+7sZAPcy9BidaWtINj7UB8YiASfTBGOP2UWRaKwue8nIy1ie5ChdT+h+cEn3VG+m5DLJxsfW6EzjzI/98UoYe8r+XyQa4y4jlFUsT3KkitKqQQC+BuAGALm8/yNZIJXcXJ1qfW06jHVnnwDw20g0tk04FnkYy5Mcr6K06mIYJfpVAEOF45D1OrROvZg8dv+TgF4SicZS0oGIWJ7kGhWlVQpACYwivQ5Af9lE1AsawAoATwF4pqxyPhdCp5zC8iRXqiityoOxctH1AK4BEJJNRN2QgnEf82UYhVkrnIfolFie5HoVpVVnw9gH8cqu12dkE9EJjgH4E4zCfIVnmOQULE/ynIrSqnEwSvQqAJcACMom8pztMMryZQAryyrn8x4mOQ7LkzytorSqEMZZ6VUwCrVINpErHQGwCsBSALGyyvlcwIAcj+VJdIKK0qpRAGZ2vWbBmAYTEA3lPDtg3LtcCeBNANvKKufzjYZcheVJdBoVpVUFAIrxYZnOBGDlFllO1wljQ+k30VWYZZXzD8pGIrIfy5PIpIrSqs8AmAFgIoCxXa9RAPySuWyWAvAegK0A3j3h47ayyvkdksGIJLA8iSzQNTVmNIAxAC6EUaYffDxPMJpZRwHsg/FQz4kluZ0lSfQhlieRzbqmygwGMOiE18BT/LrQ4sNrAO0AWgEcAlB3uldZ5fykxccnciWWJ1EOqSitCgHoA2Mf07wTPuad4s86YRRjW9fH1o/9vo0P6xBZj+VJRERkkk86ABERkdOwPImIiExieRIREZnE8iQiIjKJ5UlERGQSy5OIiMgklicREZFJLE8iIiKTWJ5EREQmcZ9CIpOUUu8DaAaQBpDSWhfLJiKibGN5EvXMZ7XWh6VDEJEMXrYlIiIyieVJZJ4G8Gel1Hql1E3SYYgo+3jZlsi8S7TW+5RSAwG8ppRKaK2XS4ciouzhmSeRSVrrfV0fDwJYDGC6bCIiyjaWJ5EJSqk+SqnCD34N4AoAW2RTEVG28bItkTmDACxWSgHG98/vtdavyEYiomxTWmvpDERERI7Cy7ZEREQmsTyJiIhMYnkSERGZxPIkIiIyieVJRERkEsuTiIjIJJYnERGRSSxPIiIik1ieREREJrE8iYiITGJ5EhERmcTyJCIiMonlSUREZBLLk4iIyCSWJxERkUksTyIiIpNYnkRERCaxPImIiExieRIREZnE8iQiIjKJ5UlERGQSy5OIiMgklicREZFJLE8iIiKTWJ5EREQmsTyJiIhMYnkSERGZxPIkIiIyieVJRERkEsuTiIjIJJYnERGRSf8PlJYr/zSNrzsAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "quarenta_banheiros = (dados['Banheiros'] == 40)\n",
        "quarenta_banheiros"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4hlle0XdMjS2",
        "outputId": "d2ed1e83-6e83-4f4e-8d5c-586a22aa2ffd"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        False\n",
              "1        False\n",
              "2        False\n",
              "3        False\n",
              "4        False\n",
              "         ...  \n",
              "10003    False\n",
              "10004    False\n",
              "10005    False\n",
              "10006    False\n",
              "10007    False\n",
              "Name: Banheiros, Length: 10008, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Deve haver algum erro, pois os imóveis possuem 4 quartos e 40 banheiros\n",
        "dados[quarenta_banheiros]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "J75Bjo2UQo_o",
        "outputId": "c6f6eeaf-59a6-4e6f-8200-0d8e7750cf29"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Rua          Bairro     Cidade  Metragem  Quartos  Banheiros  Vagas  \\\n",
              "2062  NaN  Jardim Guedala  São Paulo       488        4         40      4   \n",
              "2159  NaN  Jardim Guedala  São Paulo       493        4         40      4   \n",
              "\n",
              "             Valor  \n",
              "2062  R$ 2.600.000  \n",
              "2159  R$ 2.600.000  "
            ],
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              "      <td>São Paulo</td>\n",
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              "      <td>R$ 2.600.000</td>\n",
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              "      <th>2159</th>\n",
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              "      <td>Jardim Guedala</td>\n",
              "      <td>São Paulo</td>\n",
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              "      <td>4</td>\n",
              "      <td>40</td>\n",
              "      <td>4</td>\n",
              "      <td>R$ 2.600.000</td>\n",
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              "          document.querySelector('#df-2ce5ba42-9653-4a58-8b6d-95c63da7d2e8 button.colab-df-convert');\n",
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mediana_bairros = dados.groupby(['Bairro'])['Metragem'].agg('median')\n",
        "mediana_bairros"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lADPKTEdSjJe",
        "outputId": "abdd4e26-91fc-4243-ce2c-f8a711e9adb4"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Bairro\n",
              "Aclimação            227.5\n",
              "Alto da Boa Vista    378.0\n",
              "Alto da Lapa         255.0\n",
              "Alto da Mooca        182.5\n",
              "Alto de Pinheiros    412.0\n",
              "                     ...  \n",
              "Vila Água Funda      143.0\n",
              "Água Branca          174.0\n",
              "Água Fria            174.5\n",
              "Água Funda           139.0\n",
              "Água Rasa            180.0\n",
              "Name: Metragem, Length: 701, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mediana_bairros.sort_values(ascending=False).tail(20).plot(kind='bar', figsize=(20,5));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "a3KKMdktV6PQ",
        "outputId": "f9e3b942-d0b9-476c-fa89-614cfe601415"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "maximo_bairros = dados.groupby(['Bairro'])['Metragem'].agg('max')\n",
        "maximo_bairros"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sztSLZeHWQR1",
        "outputId": "15ed9cf7-f06a-4035-ed7b-2864a868b5ab"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Bairro\n",
              "Aclimação            1500\n",
              "Alto da Boa Vista     595\n",
              "Alto da Lapa         1133\n",
              "Alto da Mooca         805\n",
              "Alto de Pinheiros    1530\n",
              "                     ... \n",
              "Vila Água Funda       200\n",
              "Água Branca           400\n",
              "Água Fria             314\n",
              "Água Funda            153\n",
              "Água Rasa             805\n",
              "Name: Metragem, Length: 701, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "maximo_bairros.sort_values(ascending=False).head(5).plot(kind='bar', figsize=(10,5));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "o2ta76_fWunY",
        "outputId": "ba673c19-2aa9-4d5f-bec0-6c4b67212369"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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DmiRJUocMaZIkSR0ypEmSJHXIkCZJktQhQ5okSVKHDGmSJEkdMqRJkiR1yJAmSZLUIUOaJElShwxpkiRJHTKkSZIkdciQJkmS1CFDmiRJUocMaZIkSR0ypEmSJHXIkCZJktQhQ5okSVKHDGmSJEkdMqRJkiR1yJAmSZLUIUOaJElShwxpkiRJHTKkSZIkdciQJkmS1KE5Q1qSPZKcl+QbSb6e5HWt/UFJzk5yVXvfpbUnyQlJ1iW5PMm+Y5+1uu1/VZLVY+37JbmiHXNCksx2DkmSpMVuPlfS7gD+uKoeCxwAHJPkscCxwDlVtQI4p60DHAqsaK+jgRNhFLiA44CnAPsDx42FrhOBV40dt6q1b+ockiRJi9qcIa2qrq+qS9ryLcA3gd2Bw4FT2m6nAEe05cOBU2vkfGDnJLsBhwBnV9XGqroJOBtY1bbtVFXnV1UBp077rJnOIUmStKgt6Jm0JHsCTwIuAHatquvbpu8Bu7bl3YFrxw5b39pma18/QzuznGN6XUcnWZtk7YYNGxbyI0mSJHVp3iEtyY7Ax4E/qqofjW9rV8DqXq7tbmY7R1WdVFUrq2rlsmXLtmQZkiRJW8W8QlqS+zIKaB+qqk+05u+3W5W09xta+3XAHmOHL29ts7Uvn6F9tnNIkiQtavPp3Rng/cA3q+rvxjatAaZ6aK4GTh9rP7L18jwAuLndsjwLODjJLq3DwMHAWW3bj5Ic0M515LTPmukckiRJi9q289jnacDvAVckubS1vQk4HjgtyVHANcBL2rYzgMOAdcCtwCsBqmpjkrcBF7X93lpVG9vya4APADsAZ7YXs5xDkiRpUZszpFXVV4BsYvNBM+xfwDGb+KyTgZNnaF8L7D1D+40znUOSJGmxc8YBSZKkDhnSJEmSOmRIkyRJ6pAhTZIkqUOGNEmSpA4Z0iRJkjpkSJMkSeqQIU2SJKlDhjRJkqQOGdIkSZI6ZEiTJEnqkCFNkiSpQ4Y0SZKkDhnSJEmSOmRIkyRJ6pAhTZIkqUOGNEmSpA4Z0iRJkjpkSJMkSeqQIU2SJKlDhjRJkqQOGdIkSZI6ZEiTJEnqkCFNkiSpQ4Y0SZKkDs0Z0pKcnOSGJF8ba3tQkrOTXNXed2ntSXJCknVJLk+y79gxq9v+VyVZPda+X5Ir2jEnJMls55AkSVoK5nMl7QPAqmltxwLnVNUK4Jy2DnAosKK9jgZOhFHgAo4DngLsDxw3FrpOBF41dtyqOc4hSZK06M0Z0qrqS8DGac2HA6e05VOAI8baT62R84Gdk+wGHAKcXVUbq+om4GxgVdu2U1WdX1UFnDrts2Y6hyRJ0qK3uc+k7VpV17fl7wG7tuXdgWvH9lvf2mZrXz9D+2znkCRJWvR+5Y4D7QpY3Qu1bPY5khydZG2StRs2bNiSpUiSJG0VmxvSvt9uVdLeb2jt1wF7jO23vLXN1r58hvbZznEPVXVSVa2sqpXLli3bzB9JkiSpH5sb0tYAUz00VwOnj7Uf2Xp5HgDc3G5ZngUcnGSX1mHgYOCstu1HSQ5ovTqPnPZZM51DkiRp0dt2rh2SfAR4FvCQJOsZ9dI8HjgtyVHANcBL2u5nAIcB64BbgVcCVNXGJG8DLmr7vbWqpjojvIZRD9IdgDPbi1nOIUmStOjNGdKq6uWb2HTQDPsWcMwmPudk4OQZ2tcCe8/QfuNM55AkSVoKnHFAkiSpQ4Y0SZKkDhnSJEmSOmRIkyRJ6pAhTZIkqUOGNEmSpA4Z0iRJkjpkSJMkSeqQIU2SJKlDhjRJkqQOGdIkSZI6ZEiTJEnqkCFNkiSpQ4Y0SZKkDhnSJEmSOmRIkyRJ6pAhTZIkqUPbDl2ANJQ9j/3M0CVsUd89/rlDl7BF+f1JWuy8kiZJktQhQ5okSVKHDGmSJEkdMqRJkiR1yJAmSZLUIUOaJElShwxpkiRJHTKkSZIkdaj7kJZkVZIrk6xLcuzQ9UiSJG0NXc84kGQb4F3Ac4D1wEVJ1lTVN4atTJL0q1jMM0Y4W4TuLV2HNGB/YF1VXQ2Q5KPA4YAhTZKkASzmgA19hexU1dA1bFKSFwGrqur32/rvAU+pqj+Ytt/RwNFt9dHAlVu10K3rIcAPhi5Cm8XvbrL5/U0uv7vJtti/v4dV1bKZNvR+JW1equok4KSh69gakqytqpVD16GF87ubbH5/k8vvbrIt5e+v944D1wF7jK0vb22SJEmLWu8h7SJgRZK9kmwHvAxYM3BNkiRJW1zXtzur6o4kfwCcBWwDnFxVXx+4rKEtidu6i5Tf3WTz+5tcfneTbcl+f113HJAkSVqqer/dKUmStCQZ0iRJkjpkSJMkSepQ1x0HpEmXZBdgBbD9VFtVfWm4irQQSZ4LPI67f39vHa4izUeS+wL/L/CM1vRF4D1V9fPhqtJCtBEdHtVWr1yq350dBzqWZAXw18BjufsviYcPVpTmLcnvA69jNL7fpcABwL9U1YGDFqZ5SfIe4NeAZwPvA14EXFhVRw1amOaU5H3AfYFTWtPvAXdOzV6jviV5FqPv7rtAGI2Xunop/oFrSOtYkq8AxwHvAJ4HvBK4T1X9xaCFaV6SXAE8GTi/qvZJ8hjgr6rqBQOXpnlIcnlVPWHsfUfgzKr6zaFr0+ySXFZVT5yrTX1KcjHwO1V1ZVt/FPCRqtpv2Mq2Pp9J69sOVXUOozB9TVW9Gehn5lfN5baqug0gyf2q6luM5pbVZPhpe781yb8Ffg7sNmA9mr87kzxiaiXJw4E7B6xHC3PfqYAGUFX/yujK6JLjM2l9uz3JfYCr2qC+1wE7DlyT5m99kp2B/wmcneQm4JqBa9L8fbp9f38LXAIUo9ue6t+fAOcluZrR7bKHMboTocmwtt2y/h9t/RXA2gHrGYy3OzuW5MnAN4GdgbcBDwT+pqrOH7QwLViSZzL6/j5bVT8buh4tTJL7AdtX1c1D16LZJdkGeC3wbu66cn1lVd0+XFVaiPb/2zHA01vTl4F3L8Xv0JAmbSHtdsv6qrq9PQj7BODUqvrhsJVpNkkOrKpzk8z47GBVfWJr16SFSXJhVe0/dB3Sr8rbnR1K8ilGt1ZmVFXP34rlaPN9HFiZ5JGM5p47HfgwcNigVWkuzwTOZdRZZ7oCDGn9+19J/j/gn4CfTDVW1SXDlaS5tM5Ws/3ue8JWLKcLXknrULs1tklV9cWtVYs2X5JLqmrfJH/KqBPBO5N8taqeNHRtml17FvRFVXXa0LVo4ZKcN0NzOfxN35I8bLbtVbXknuk1pElbSJILgL8H/hx4XlV9J8nXqmrvgUvTPCRZW1Urh65DWopaYFtRVZ9PsgOwbVXdMnRdW5u3OzvmYLYT75XAfwTe3gLaXsAHB65J8/f5JH/CPW+ZbRyuJM2Xs0VMriSvAo4GHgQ8gtGA4O8BDhqyriF4Ja1jDmY7+dpfgA8dH/NHkyHJd2ZoLv9I6p+zRUy2JJcC+wMXTD0ekuSKqnr8sJVtfQ5m2zcHs51gSZ7HaDqoz7b1fZKsGbYqzVdV7TXDy4A2GX6jqo4EbqqqtwBP5a55INW/28eHKkqyLbN0KFjMvN3ZNweznWxvZvTX4BcAqurSNvK5JsAMk3R/AXjvUp3oecJMny3iRpwtYpJ8McmbgB2SPAd4DfCpgWsahFfS+vY6RpfsXwvsx2iS4NWDVqSF+PkMg5/+YpBKNC9Jnp9kp7Z6IqP/797dXvu1NvVv+mwR3wU+MmhFWohjgQ3AFcCrgTOq6s+HLWkYPpMmbSFJ3g+cw+gfnBcyCtv3rar/OGhh2qQ2kfNxVfUKJ+leHJwtYvIkeV1V/cNcbUuBIa1DDma7OCT5NUbDbxzcms4C/nJq0nX1KcluVXV9kkuAF1fVt1v7w4GPVdW+w1aoubSpoZ4L7MnYYz1V9XdD1aT5mxpjclrbkhxj0mfS+vRf2/sLgF/nrklmXw58f5CKtCDtl8RnqurZjIKaJkRVXd8W/xQn6Z5UnwJuY3S7zEcMJkSSlwO/A+w1rZPVTsCSHPrGK2kdm2kwTQfYnBxJzgFe4G2WydVulTlJ94RJcvlSnEJo0rUBbPdiND7osWObbgEur6o7BilsQF5J69v9kzy8qq4GaIOh3n/gmjR/PwauSHI2dx8M9bXDlaS5bGpideCRSZxgfTKcmeTgqvrc0IVo/tq0T9ck+S3gp1X1i/ac6GMYXRVdcgxpfftPwBem3W559bAlaQE+gZNxT6KZJlaf4gTrk+F84JNtCKOfM/r3s6pqp9kPUye+BPxmkl2AzwEXAS8FXjFoVQPwdmfn2u2Wx7TVb3m7ZbIk2Y7R91eMbpf9bI5DJP2K2mwRhwNXlL/kJs5Ux4Ekf8hoUPe/SXJpVe0zdG1bm1fSOtZ6B74eeFhVvSrJiiSPrqpPD12b5pbkMOC9wLcZ/SW/V5JXV9WZw1am2SRZDuxZVV9p66/nrkGkP1xV6wYrTvN1LfA1A9rESpKnMrpyNjWV1zYD1jMYQ1rf/hG4mNGUJjCaceCfAUPaZPg74NlTv9STPAL4DGBI69vfAh8aW381cBKjgaXfwhK85TKBrmb0qMiZwC/vPjgEx8R4HfBG4JNV9fU2/M15A9c0CENa3x5RVS9t3ZKpqluTZOiiNG+3TLvqcjWjXkrq2/Sr1bdW1X8DSPLlgWrSwnynvbZrL1iicz9Ooqr6EqPn0qbWr2Y0GPiSY0jr28+S7ED7x6VdifGZtMmxNskZwGmMvsMXAxdN9R60l2C3tp+2ftDY8kO2ZiHaPG1S9V9Ksj2zdwhRR5IsA94API6x/x+r6sDBihqIc3f27Tjgs8AeST7EaIqhNwxbkhZge0aDDz8TeBajueh2YPTL4reHK0tzuKV1+wegqjYCJHkMXgmdGEm2SXJYkg8ymrvzpQOXpPn7EPAtRmOmvYXR93fRkAUNxd6dnUvyYOAARg+en19VPxi4JGlRS7IKOAF4O6PJuWE0ufqbgNfZ8aNvSZ7JaNT6w4ALgacBD6+qWwctTPOW5OKq2m98UOIkF1XVk4eubWvzdmfHkjyjLU799f7YNpjmlzZ1jPrRrsacCOxaVXsneQLw/Kr6y4FL0yyq6rPtlvQbuOs5mK8xmj3ia8NVprkkWQ/8/4z+v/uTqrolyXcMaBPn5+39+iTPBf4P8KAB6xmMV9I61iZan7I9sD9w8VK8Lz+JknyR0fyP752aGDjJ16pq72ErkxanJH8PHMEoVH8YOJ3RWGkPH7QwLUiS3wa+DOwBvJPR3J1vqao1sx64CBnSJkiSPYC/r6oXDl2L5jZ1eT7JV8dC2pIckFHaWloP+GcBL2d0y/OBjMbaOqOqfjxgadKC2XFgsqwH/t3QRWjeftB65E71zn0RcP2wJUmLW42cV1VHM3rw/OWMZh/47qCFaU5J/jbJPaY+TPLqJMcPUdPQvJLWsSTv5K6xfe4D7AN8t6p+d7iqNF9tAMaTgN8AbmI0btMr2iTCkraiJDtU1U+HrkObluRiYOX0mSLaHKyXL8VHRew40Le1Y8t3AB+pqv81VDFamDYA428luT+jkH0r8DLAkDYB2thaR3HPsZr+w2BFabMZ0CbC/WaayquqfrFUB3I3pHWsqk4ZugYtXJKdgGOA3Rk9uPz5tv7HwOXcfcoh9euDjMZqOgR4K6PpoL45aEXS4vbTJCuq6qrxxiQrgCUZsr3d2aEkhwPLq+pdbf0CYFnb/GdV9c+DFac5JTmd0e3Nf2E0Wv2/YTTO3euq6tIha9P8TXX4mBqrKcl9gS9X1QFD16b5SbIjgB0GJkOSQxn15vxLRvNWA6xkNI/nH1XVGUPVNhSvpPXpDYxui025H/Bk4P6MJl03pPXt4VX1eIAk72PUWeChVXXbsGVpgabGavphkr2B7zEK3OpckscDpzIaWytJNgCrHeeub1V1ZpIjGA1d9Iet+WvAC6vqiuEqG44hrU/bVdW1Y+tfqaobgRvb803q29Qvd6rqziTrDWgT6aQkuwD/GVgD7Aj8xbAlaZ7eC7y+qs4DSPIs7urEo461IL166Dp64e3ODiVZV1WP3MS2b1fVI7Z2TZq/JHcCP5laZTRf561tuapqp6Fqk5aCJJdV1RPnapN655W0Pl2Q5FVV9d/HG9v4MRcOVJPmqaq2GboGbRAgxMwAAAj8SURBVL4kr59te1X93daqRZvt6iT/mVHnD4DfBa4esB5psxjS+vSfgP+Z5He4+wTP92M05YmkLecB7f3RjJ4FnZqK5nn4R9Kk+A/AW4BPMBpr8svAKwetSNoM3u7sWJIDGY3RBPD1qjp3yHqkpSTJl4DnVtUtbf0BwGeq6hnDVqa5JHnx9F7wM7WpT45ReBenhepYVZ1bVe9sLwOatHXtCvxsbP1nrU39e+M829SnDwK/zmiMwi8Cy4FbBq1oIN7ulLagJLsyumUGcGFV3TBkPVqQU4ELk3yyrR8BfGC4cjSXNs7WYcDuSU4Y27QTo1lbNBkeWVUvTnJ4VZ2S5MOMblkvOV5Jk7aQJC9h9AzTi4GXMOoQ8qJhq9J8VdXbGT3HdFN7vbKq/nrYqjSH/8NoOr3bGA2GOvVaw+iqjCbD9DEKH8gSHaPQZ9ImQJtm6JdXPatq44DlaJ6SXAY8Z+rqWZJlwOcdBkDaspLct6p+Pvee6lGS3wc+DjyB0QDuOwJ/UVXvGbSwARjSOtaG3HgLo78Kp76oqqqHD1eV5ivJFVMzD7T1+wCXjbdJuvcleRrwZuBhjP7AnRqj0H87NVF8Jq1vfwLsXVU/GLoQbZbPJjkL+Ehbfymw5OaekwbwfkZDGV0M3DlwLZonxyi8J0Na377NaKR6TaCq+tMkLwSe1ppOqqpPznaM+uPjBhPp5qo6c+gitGAPmHuXpcXbnR1L8iRG9+MvAG6faq+q1w5WlLRE+LjB5EpyPLANo8Fsx//tvGSTB0kdMqR1LMmFwFeAK4BfTLVX1SmDFaU5JflKVT09yS3c9csdnLtzoiS5CniqjxtMniTnzdBcVXXgVi9G85bkDVX1N0neyd3/7QSW5gUKb3f27b5VNes9evWnqp7e3r10P9l83GBCVdWzh65Bm+Ub7X3toFV0xJDWtzOTHA18irtfsveZmAmQ5INV9XtztalbbwT+dxIfN5hASZ7LPacVeutwFWkeDk1yk3eL7mJI69vL2/v4dCYF+EzMZHjc+EqSbYH9BqpFC/de4FymPW6g/iV5D/BrwLOB9wEvYjSwtPr2r8B/TbIbcBrwkar66sA1Dcpn0qR7WZI3Am8CduCu22VhNPfjSVXlHIITIMlXq+pJQ9ehhUtyeVU9Yex9R+DMqvrNoWvT3JI8DHhZe+3AaBijj1TVvw5a2AAMaR1KcmBVnZvkBTNtr6pPbO2aND9Jfr2qvteW/9pANrmS/BXwXXzcYOIkuaCqnpLkfOAFwI3A16vqkQOXpgVqoxycDDyhqrYZup6tzdudfXomo9ssz5thWzHqVq4+HZJkf+C1wD8n2Xf6Dg4DMDF83GByfTrJzsDfApcw+t7++7Alab7aoyGHMrqSdhDwBUYzSCw5XkmT7mVJDgF+Hfj3M2x2GABpK0pyP2D7qrp56Fo0uyTPYfTH0WGMniH8KHB6Vf1k0MIGZEjrkFNjSMPxcYPJleTJwLVjjxwcCbwQuAZ4s7eq+5bkXODDwMer6qah6+mBtzv7NDW+1qOBJwNr2vrzsIdS9/xFMfF83GByvRf4LYAkzwCOB/4Q2Ac4iVEvT3XKuwz35JW0jiX5EvDcqrqlrT8A+ExVPWPYyjSbJJcAv1VVG9svio9y1y+Kf1dV/qKQtoAkl1XVE9vyu4ANVfXmtn5pVe0zZH3SQnklrW+7Mhq2YcrPWpv6ts3Y1bKXMhp24+PAx5NcOmBdmgcfN5ho2yTZtqruYPTA+dFj2/x9p4njf7R9OxW4MMkn2/oRwAeGK0fz5C+KyebjBpPrI8AXk/wA+CnwZYAkjwTsOKCJ4+3OTiUJsBxYBkwNwPilpT768iRI8ueMeif9AHgosG9VVftFcUpVPW3QAjUvPm4wmZIcAOwGfG6qV2CSRwE7OvyNJo0hrWNJrqiqxw9dhxbOXxSTL8mVjAbQvL2t3w+4vKoePWxlkpYKb7307ZIkT66qi4YuRAtTVefP0LbkpjSZcD5uIGlQXknrWJJvAY9kNHTDTxjN/1hV9YRBC5MWOR83kNQDQ1rH2iSz91BV12ztWqSlxscNJA3tPkMXoE2rqmtaIPspo0E0p16StrxL2sDEkjQIr6R1LMnzgf8G/FvgBuBhwDer6nGDFiYtAT5uIGlodhzo29uAA4DPV9WTkjwb+N2Ba5KWikOGLkDS0ubtzr79vKpuBO6T5D5VdR6wcuiipKXAxw0kDc0raX37YZIdGY2a/aEkNzC67SJpC9vU4waAjxtI2ip8Jq1jSX4NuI3RszC/C+wEfGhsXkhJW0iSy4ADmfa4QVUdNXBpkpYIQ1qHktzCPW+rpL3fBnwb+POqOmerFiYtIUnWVtXKFtaeVFW/SHJZVT1x6NokLQ3e7uxQVT1gU9uSbAPsDXyovUvaMnzcQNKgvJI2oZK8uqreO3Qd0mLl4waShmZIk6QxPm4gqReGNEmap/HHDarKxw0kbVGOkyZJ81RVd1bVZcA7h65F0uLnlTRJkqQOeSVNkiSpQ4Y0SZKkDhnSJC0pSe5McmmSy5JckuQ35nHM/94atUnSOJ9Jk7SkJPlxVe3Ylg8B3lRVz9yMz9m2qu7Y1Lok/aq8kiZpKdsJuAkgyY5JzmlX165IcvjUTkl+3N6fleTLSdYA35hhffsk/9iO/2qb71OSNovTQklaanZIcimwPbAbo0nUYTRQ7f9TVT9K8hDg/CRr6p63G/YF9q6q7yR51rT1Pwaqqh6f5DHA55I8qqpu2yo/maRFxStpkpaan1bVPlX1GGAVcGqSMJpV4K+SXA58Htgd2HWG4y+squ9sYv3pwP8AqKpvAdcAj9pCP4ekRc4raZKWrKr6l3bVbBlwWHvfr6p+nuS7jK62TTd9knUnXZe0RXglTdKS1W5JbgPcCDwQuKEFtGcDD9uMj/wy8Ir22Y8CHgpceS+VK2mJ8UqapKVm6pk0GN3iXF1Vdyb5EPCpJFcAa4FvbcZnvxs4sX3GHcC/r6rb75WqJS05DsEhSZLUIW93SpIkdciQJkmS1CFDmiRJUocMaZIkSR0ypEmSJHXIkCZJktQhQ5okSVKHDGmSJEkd+r9XkxJeV0x99QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "minimo_bairros = dados.groupby(['Bairro'])['Metragem'].agg('min')\n",
        "minimo_bairros"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vhhcB2S_X971",
        "outputId": "33360e31-7dbe-4ef8-e64f-d3bccb5b6989"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Bairro\n",
              "Aclimação            104\n",
              "Alto da Boa Vista    315\n",
              "Alto da Lapa          46\n",
              "Alto da Mooca         84\n",
              "Alto de Pinheiros    100\n",
              "                    ... \n",
              "Vila Água Funda       90\n",
              "Água Branca           91\n",
              "Água Fria             60\n",
              "Água Funda           125\n",
              "Água Rasa             86\n",
              "Name: Metragem, Length: 701, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "minimo_bairros.sort_values(ascending=False).tail(5).plot(kind='bar', figsize=(10,5));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "eFfEkrYVY10M",
        "outputId": "8481bd19-7da8-42b1-c661-f7f0c8b65614"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# imóveis que não possuem endereço \n",
        "\n",
        "rua_nan = pd.isna(dados['Rua'])\n",
        "rua_nan"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mPhgBT7gY9lb",
        "outputId": "383a0f4a-1b5e-4f5c-bb4f-ea2a778b2bd6"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        False\n",
              "1        False\n",
              "2        False\n",
              "3         True\n",
              "4        False\n",
              "         ...  \n",
              "10003    False\n",
              "10004    False\n",
              "10005    False\n",
              "10006    False\n",
              "10007    False\n",
              "Name: Rua, Length: 10008, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sem_rua = dados[rua_nan]\n",
        "sem_rua"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "11O84DVMb_N3",
        "outputId": "f0051ce5-169c-4387-b4ad-5a7f3a10fc56"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Rua                            Bairro     Cidade  Metragem  Quartos  \\\n",
              "3     NaN                    Jardim Morumbi  São Paulo       310        3   \n",
              "11    NaN                  Jardim Luzitânia  São Paulo       436        4   \n",
              "31    NaN                    Parque da Lapa  São Paulo       220        4   \n",
              "56    NaN  Chácara Santo Antônio (Zona Sul)  São Paulo       190        2   \n",
              "57    NaN                    Jardim Guedala  São Paulo       650        4   \n",
              "...   ...                               ...        ...       ...      ...   \n",
              "9963  NaN                 Jardim Petrópolis  São Paulo       423        5   \n",
              "9989  NaN              Parque Alves de Lima  São Paulo       127        2   \n",
              "9991  NaN                      Vila Formosa  São Paulo       145        3   \n",
              "9995  NaN                     Jardim Leonor  São Paulo      1350        5   \n",
              "9999  NaN                 Jardim Petrópolis  São Paulo       423        5   \n",
              "\n",
              "      Banheiros  Vagas         Valor  \n",
              "3             2      4    R$ 685.000  \n",
              "11            3      3     R$ 15.000  \n",
              "31            4      3    R$ 849.000  \n",
              "56            2      2  R$ 1.100.000  \n",
              "57            6      4  R$ 5.500.000  \n",
              "...         ...    ...           ...  \n",
              "9963          7      6  R$ 2.800.000  \n",
              "9989          1      2    R$ 400.000  \n",
              "9991          2      3    R$ 750.000  \n",
              "9995          9      7  R$ 9.500.000  \n",
              "9999          7      6  R$ 2.800.000  \n",
              "\n",
              "[3434 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-250865f6-e558-4941-a484-9f211da97006\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rua</th>\n",
              "      <th>Bairro</th>\n",
              "      <th>Cidade</th>\n",
              "      <th>Metragem</th>\n",
              "      <th>Quartos</th>\n",
              "      <th>Banheiros</th>\n",
              "      <th>Vagas</th>\n",
              "      <th>Valor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Jardim Morumbi</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>310</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>R$ 685.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Jardim Luzitânia</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>436</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>R$ 15.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Parque da Lapa</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>220</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>R$ 849.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Chácara Santo Antônio (Zona Sul)</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>190</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>R$ 1.100.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Jardim Guedala</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>650</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>R$ 5.500.000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9963</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Jardim Petrópolis</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>423</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>R$ 2.800.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9989</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Parque Alves de Lima</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>127</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>R$ 400.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9991</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Vila Formosa</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>145</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>R$ 750.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Jardim Leonor</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>1350</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>R$ 9.500.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Jardim Petrópolis</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>423</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>R$ 2.800.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3434 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-250865f6-e558-4941-a484-9f211da97006')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-250865f6-e558-4941-a484-9f211da97006 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-250865f6-e558-4941-a484-9f211da97006');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sem_rua['Bairro']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U7QVwEBwooCs",
        "outputId": "002a6630-5b58-46b2-925c-546fa069e98a"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3                         Jardim Morumbi\n",
              "11                      Jardim Luzitânia\n",
              "31                        Parque da Lapa\n",
              "56      Chácara Santo Antônio (Zona Sul)\n",
              "57                        Jardim Guedala\n",
              "                      ...               \n",
              "9963                   Jardim Petrópolis\n",
              "9989                Parque Alves de Lima\n",
              "9991                        Vila Formosa\n",
              "9995                       Jardim Leonor\n",
              "9999                   Jardim Petrópolis\n",
              "Name: Bairro, Length: 3434, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Z5ZefQrbo6Gq"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}