im_dados4_alura_aula04.ipynb

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    {
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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/vsommah/im_dados4_alura/blob/main/im_dados4_alura_aula04.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<p>\n",
        "      <img alt='Miniatura de uma casa à venda' src=\"https://drive.google.com/uc?export=view&id=1UcLkGTtpbyKJXPQHefN26EsATNZGTZCq\" align=\"right\" height=\"200\" width=\"200\"> <br>\n",
        "      <h1 align=\"center\">IMERSÃO DADOS 4 - ALURA</h1> <br>\n",
        "      <h2 align=\"center\">PESQUISA DE IMÓVEIS EM SÃO PAULO</h2> <br>\n",
        "</p>"
      ],
      "metadata": {
        "id": "j3HGhkmgJLfe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **AULA 04** 📓"
      ],
      "metadata": {
        "id": "gjEKZAWXkvK8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Base aula 03"
      ],
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        "id": "W1Bo991Sng23"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Base aula 02"
      ],
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        "id": "4rt61PZCJW3s"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h3> Começando... </h3>"
      ],
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        "id": "zhXSQyy4K7Ga"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 122,
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        "id": "H4VKhV6P1Vc8"
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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)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados.head()"
      ],
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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",
              "      <td>4</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",
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              "      <td>4</td>\n",
              "      <td>R$ 3.700.000</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Rua Alba Valdez</td>\n",
              "      <td>Jardim Reimberg</td>\n",
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              "      <td>2</td>\n",
              "      <td>R$ 380.000</td>\n",
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              "      <th>3</th>\n",
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              "      <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",
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              "    <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",
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        "<h3> Modificando a df </h3>"
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      "source": [
        "dados[['Moeda', 'Valor_anuncio', 'Tipo_anuncio']] = dados['Valor'].str.split(expand = True)\n",
        "dados.head()"
      ],
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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 Moeda Valor_anuncio Tipo_anuncio  \n",
              "0        4          8      6  R$ 7.000.000    R$     7.000.000         None  \n",
              "1        4          4      4  R$ 3.700.000    R$     3.700.000         None  \n",
              "2        4          3      2    R$ 380.000    R$       380.000         None  \n",
              "3        3          2      4    R$ 685.000    R$       685.000         None  \n",
              "4        3          2      2    R$ 540.000    R$       540.000         None  "
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              "      <th>0</th>\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",
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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",
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              "      <td>2</td>\n",
              "      <td>R$ 380.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>380.000</td>\n",
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              "      <th>3</th>\n",
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              "      <td>Jardim Morumbi</td>\n",
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              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>R$ 685.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>685.000</td>\n",
              "      <td>None</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Rua Tobias Barreto, 195</td>\n",
              "      <td>Mooca</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>100</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>R$ 540.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>540.000</td>\n",
              "      <td>None</td>\n",
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados[dados['Tipo_anuncio'].isnull()]['Tipo_anuncio'].unique()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CDaUDD0Z7JnE",
        "outputId": "5f14fb42-8f2c-408f-dbed-48aa69c6fa9b"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([None], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_vendas = dados[dados['Tipo_anuncio'].isnull()]\n",
        "dados_vendas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "9ZSE3D107Jkq",
        "outputId": "8ded2c84-8554-432d-8464-f491d77724e9"
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      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  Rua              Bairro     Cidade  \\\n",
              "0               Avenida Itacira, 255    Planalto Paulista  São Paulo   \n",
              "1      Rua Aurelia Perez Alvarez, 42   Jardim dos Estados  São Paulo   \n",
              "2                     Rua Alba Valdez     Jardim Reimberg  São Paulo   \n",
              "3                                 NaN      Jardim Morumbi  São Paulo   \n",
              "4            Rua Tobias Barreto, 195                Mooca  São Paulo   \n",
              "...                               ...                 ...        ...   \n",
              "10002                   Avenida Odila   Planalto Paulista  São Paulo   \n",
              "10003                      Rua Tapuçu          Vila Sofia  São Paulo   \n",
              "10004                   Rua Guararema     Bosque da Saúde  São Paulo   \n",
              "10005               Rua Estero Belaco       Vila da Saúde  São Paulo   \n",
              "10007      Rua Barão de Tramandaí, 0          Vila Alpina  São Paulo   \n",
              "\n",
              "       Metragem  Quartos  Banheiros  Vagas         Valor Moeda Valor_anuncio  \\\n",
              "0          1000        4          8      6  R$ 7.000.000    R$     7.000.000   \n",
              "1           496        4          4      4  R$ 3.700.000    R$     3.700.000   \n",
              "2           125        4          3      2    R$ 380.000    R$       380.000   \n",
              "3           310        3          2      4    R$ 685.000    R$       685.000   \n",
              "4           100        3          2      2    R$ 540.000    R$       540.000   \n",
              "...         ...      ...        ...    ...           ...   ...           ...   \n",
              "10002       267        3          6      4  R$ 1.500.000    R$     1.500.000   \n",
              "10003        95        2          2      2    R$ 665.000    R$       665.000   \n",
              "10004       605        8          7      5  R$ 2.300.000    R$     2.300.000   \n",
              "10005       205        3          3      6  R$ 1.050.000    R$     1.050.000   \n",
              "10007       122        3          2      2    R$ 270.000    R$       270.000   \n",
              "\n",
              "      Tipo_anuncio  \n",
              "0             None  \n",
              "1             None  \n",
              "2             None  \n",
              "3             None  \n",
              "4             None  \n",
              "...            ...  \n",
              "10002         None  \n",
              "10003         None  \n",
              "10004         None  \n",
              "10005         None  \n",
              "10007         None  \n",
              "\n",
              "[9156 rows x 11 columns]"
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              "      <td>R$ 380.000</td>\n",
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              "      <td>540.000</td>\n",
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              "      <th>10002</th>\n",
              "      <td>Avenida Odila</td>\n",
              "      <td>Planalto Paulista</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>267</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>R$ 1.500.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>1.500.000</td>\n",
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              "      <th>10003</th>\n",
              "      <td>Rua Tapuçu</td>\n",
              "      <td>Vila Sofia</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>95</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>R$ 665.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>665.000</td>\n",
              "      <td>None</td>\n",
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              "    <tr>\n",
              "      <th>10004</th>\n",
              "      <td>Rua Guararema</td>\n",
              "      <td>Bosque da Saúde</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>605</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>R$ 2.300.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>2.300.000</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10005</th>\n",
              "      <td>Rua Estero Belaco</td>\n",
              "      <td>Vila da Saúde</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>205</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>R$ 1.050.000</td>\n",
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              "      <th>10007</th>\n",
              "      <td>Rua Barão de Tramandaí, 0</td>\n",
              "      <td>Vila Alpina</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>122</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>R$ 270.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>270.000</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
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              "<p>9156 rows × 11 columns</p>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h3>Convertendo objetos</h3>"
      ],
      "metadata": {
        "id": "7CvGkDETCRJe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados_vendas.info()"
      ],
      "metadata": {
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        "id": "pc8s-Zrf7Je9",
        "outputId": "2f10039c-a9a3-4052-9e2e-10ed3cb66018"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 9156 entries, 0 to 10007\n",
            "Data columns (total 11 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   Rua            6107 non-null   object\n",
            " 1   Bairro         9146 non-null   object\n",
            " 2   Cidade         9156 non-null   object\n",
            " 3   Metragem       9156 non-null   int64 \n",
            " 4   Quartos        9156 non-null   int64 \n",
            " 5   Banheiros      9156 non-null   int64 \n",
            " 6   Vagas          9156 non-null   int64 \n",
            " 7   Valor          9156 non-null   object\n",
            " 8   Moeda          9156 non-null   object\n",
            " 9   Valor_anuncio  9156 non-null   object\n",
            " 10  Tipo_anuncio   0 non-null      object\n",
            "dtypes: int64(4), object(7)\n",
            "memory usage: 858.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_vendas['Valor_anuncio'].str.replace('.', '')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vVj3pVCa7JcU",
        "outputId": "a1115eb5-682a-4c86-ae09-41dd693495be"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
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        "dados_vendas['Valor_anuncio'].str.replace('.', '').astype(float)"
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      "execution_count": 129,
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        {
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          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
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      "cell_type": "code",
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        "dados_vendas['Valor_anuncio_float'] = dados_vendas['Valor_anuncio'].str.replace('.', '').astype(float)"
      ],
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      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
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        "id": "6MJSR50u7JTv",
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      "execution_count": 131,
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        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 9156 entries, 0 to 10007\n",
            "Data columns (total 12 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   Rua                  6107 non-null   object \n",
            " 1   Bairro               9146 non-null   object \n",
            " 2   Cidade               9156 non-null   object \n",
            " 3   Metragem             9156 non-null   int64  \n",
            " 4   Quartos              9156 non-null   int64  \n",
            " 5   Banheiros            9156 non-null   int64  \n",
            " 6   Vagas                9156 non-null   int64  \n",
            " 7   Valor                9156 non-null   object \n",
            " 8   Moeda                9156 non-null   object \n",
            " 9   Valor_anuncio        9156 non-null   object \n",
            " 10  Tipo_anuncio         0 non-null      object \n",
            " 11  Valor_anuncio_float  9156 non-null   float64\n",
            "dtypes: float64(1), int64(4), object(7)\n",
            "memory usage: 929.9+ KB\n"
          ]
        }
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "<h3>Análise de estatísticas</h3>"
      ],
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        "id": "FXC39kYAMIcF"
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        "id": "PebeWelU7JNs",
        "outputId": "2358eff4-6d3b-42ae-d062-1026882f5afe"
      },
      "execution_count": 132,
      "outputs": [
        {
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              "       Metragem  Quartos  Banheiros   Vagas  Valor_anuncio_float\n",
              "count   9156.00  9156.00    9156.00 9156.00              9156.00\n",
              "mean     434.42     3.45       3.80    3.96           3052202.96\n",
              "std     7856.42     1.05       1.97    2.60           3944208.19\n",
              "min       10.00     1.00       1.00    1.00             13000.00\n",
              "25%      168.00     3.00       2.00    2.00            890000.00\n",
              "50%      276.00     3.00       4.00    4.00           1800000.00\n",
              "75%      450.00     4.00       5.00    5.00           3500000.00\n",
              "max   750000.00    16.00      40.00   50.00          65000000.00"
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              "      <th>Vagas</th>\n",
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              "      <td>9156.00</td>\n",
              "      <td>9156.00</td>\n",
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              "      <th>mean</th>\n",
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              "      <td>3.45</td>\n",
              "      <td>3.80</td>\n",
              "      <td>3.96</td>\n",
              "      <td>3052202.96</td>\n",
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              "      <td>3944208.19</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>2.00</td>\n",
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        "dados_vendas.describe()"
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            "text/plain": [
              "       Metragem  Quartos  Banheiros   Vagas  Valor_anuncio_float\n",
              "count   9156.00  9156.00    9156.00 9156.00              9156.00\n",
              "mean     434.42     3.45       3.80    3.96           3052202.96\n",
              "std     7856.42     1.05       1.97    2.60           3944208.19\n",
              "min       10.00     1.00       1.00    1.00             13000.00\n",
              "25%      168.00     3.00       2.00    2.00            890000.00\n",
              "50%      276.00     3.00       4.00    4.00           1800000.00\n",
              "75%      450.00     4.00       5.00    5.00           3500000.00\n",
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        "pd.set_option('display.precision', 2)\n",
        "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
        "dados_vendas.describe()"
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              "       Metragem  Quartos  Banheiros   Vagas  Valor_anuncio_float\n",
              "count   9156.00  9156.00    9156.00 9156.00              9156.00\n",
              "mean     434.42     3.45       3.80    3.96           3052202.96\n",
              "std     7856.42     1.05       1.97    2.60           3944208.19\n",
              "min       10.00     1.00       1.00    1.00             13000.00\n",
              "25%      168.00     3.00       2.00    2.00            890000.00\n",
              "50%      276.00     3.00       4.00    4.00           1800000.00\n",
              "75%      450.00     4.00       5.00    5.00           3500000.00\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Metragem</th>\n",
              "      <th>Quartos</th>\n",
              "      <th>Banheiros</th>\n",
              "      <th>Vagas</th>\n",
              "      <th>Valor_anuncio_float</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9156.00</td>\n",
              "      <td>9156.00</td>\n",
              "      <td>9156.00</td>\n",
              "      <td>9156.00</td>\n",
              "      <td>9156.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>434.42</td>\n",
              "      <td>3.45</td>\n",
              "      <td>3.80</td>\n",
              "      <td>3.96</td>\n",
              "      <td>3052202.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>7856.42</td>\n",
              "      <td>1.05</td>\n",
              "      <td>1.97</td>\n",
              "      <td>2.60</td>\n",
              "      <td>3944208.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>13000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>168.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>890000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>276.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>1800000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>450.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>3500000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>750000.00</td>\n",
              "      <td>16.00</td>\n",
              "      <td>40.00</td>\n",
              "      <td>50.00</td>\n",
              "      <td>65000000.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5d838438-f573-4b66-a8a2-82b9f3b3df62')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5d838438-f573-4b66-a8a2-82b9f3b3df62 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-5d838438-f573-4b66-a8a2-82b9f3b3df62');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.set_option('display.precision', 2)\n",
        "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
        "dados_vendas.describe(include = 'all')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "qv-TTJA-LdnV",
        "outputId": "ab553f15-2c26-4b00-b7c6-c8fe47e61933"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Rua             Bairro     Cidade  Metragem  Quartos  \\\n",
              "count                6107               9146       9156   9156.00  9156.00   \n",
              "unique               3006                697          1       NaN      NaN   \n",
              "top     Rua Alberto Faria  Alto de Pinheiros  São Paulo       NaN      NaN   \n",
              "freq                   24                369       9156       NaN      NaN   \n",
              "mean                  NaN                NaN        NaN    434.42     3.45   \n",
              "std                   NaN                NaN        NaN   7856.42     1.05   \n",
              "min                   NaN                NaN        NaN     10.00     1.00   \n",
              "25%                   NaN                NaN        NaN    168.00     3.00   \n",
              "50%                   NaN                NaN        NaN    276.00     3.00   \n",
              "75%                   NaN                NaN        NaN    450.00     4.00   \n",
              "max                   NaN                NaN        NaN 750000.00    16.00   \n",
              "\n",
              "        Banheiros   Vagas         Valor Moeda Valor_anuncio Tipo_anuncio  \\\n",
              "count     9156.00 9156.00          9156  9156          9156            0   \n",
              "unique        NaN     NaN           939     1           939            0   \n",
              "top           NaN     NaN  R$ 2.500.000    R$     2.500.000          NaN   \n",
              "freq          NaN     NaN           182  9156           182          NaN   \n",
              "mean         3.80    3.96           NaN   NaN           NaN          NaN   \n",
              "std          1.97    2.60           NaN   NaN           NaN          NaN   \n",
              "min          1.00    1.00           NaN   NaN           NaN          NaN   \n",
              "25%          2.00    2.00           NaN   NaN           NaN          NaN   \n",
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              "75%          5.00    5.00           NaN   NaN           NaN          NaN   \n",
              "max         40.00   50.00           NaN   NaN           NaN          NaN   \n",
              "\n",
              "        Valor_anuncio_float  \n",
              "count               9156.00  \n",
              "unique                  NaN  \n",
              "top                     NaN  \n",
              "freq                    NaN  \n",
              "mean             3052202.96  \n",
              "std              3944208.19  \n",
              "min                13000.00  \n",
              "25%               890000.00  \n",
              "50%              1800000.00  \n",
              "75%              3500000.00  \n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Lidando com os desafios aula 02"
      ],
      "metadata": {
        "id": "pxqpy7DGJPW9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados_vendas.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "cj9jTs23JUL1",
        "outputId": "83ce1339-fb44-4676-c10e-078c8bc1b783"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              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 Moeda Valor_anuncio Tipo_anuncio  \\\n",
              "0        4          8      6  R$ 7.000.000    R$     7.000.000         None   \n",
              "1        4          4      4  R$ 3.700.000    R$     3.700.000         None   \n",
              "2        4          3      2    R$ 380.000    R$       380.000         None   \n",
              "3        3          2      4    R$ 685.000    R$       685.000         None   \n",
              "4        3          2      2    R$ 540.000    R$       540.000         None   \n",
              "\n",
              "   Valor_anuncio_float  \n",
              "0           7000000.00  \n",
              "1           3700000.00  \n",
              "2            380000.00  \n",
              "3            685000.00  \n",
              "4            540000.00  "
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              "      <th>0</th>\n",
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              "      <th>1</th>\n",
              "      <td>Rua Aurelia Perez Alvarez, 42</td>\n",
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              "      <td>3700000.00</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",
              "      <td>R$</td>\n",
              "      <td>380.000</td>\n",
              "      <td>None</td>\n",
              "      <td>380000.00</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Jardim Morumbi</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>310</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>R$ 685.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>685.000</td>\n",
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              "      <td>685000.00</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Rua Tobias Barreto, 195</td>\n",
              "      <td>Mooca</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>100</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>R$ 540.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>540.000</td>\n",
              "      <td>None</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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            ]
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          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# cálculo do valor pelo M2\n",
        "dados_vendas['Valor_anuncio_float']/dados_vendas['Metragem']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "seAYtY2M_8JM",
        "outputId": "60106767-947c-4e8a-baf3-20dda9d66313"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       7000.00\n",
              "1       7459.68\n",
              "2       3040.00\n",
              "3       2209.68\n",
              "4       5400.00\n",
              "          ...  \n",
              "10002   5617.98\n",
              "10003   7000.00\n",
              "10004   3801.65\n",
              "10005   5121.95\n",
              "10007   2213.11\n",
              "Length: 9156, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# criação da coluna Valor_M2 dentro da df\n",
        "dados_vendas['Valor_M2'] = dados_vendas['Valor_anuncio_float']/dados_vendas['Metragem']\n",
        "dados_vendas.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "id": "Rmn5jNnfAWgU",
        "outputId": "ad5b5efd-907e-40b5-87f2-4517ea24505b"
      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        },
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          "data": {
            "text/plain": [
              "                              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 Moeda Valor_anuncio Tipo_anuncio  \\\n",
              "0        4          8      6  R$ 7.000.000    R$     7.000.000         None   \n",
              "1        4          4      4  R$ 3.700.000    R$     3.700.000         None   \n",
              "2        4          3      2    R$ 380.000    R$       380.000         None   \n",
              "3        3          2      4    R$ 685.000    R$       685.000         None   \n",
              "4        3          2      2    R$ 540.000    R$       540.000         None   \n",
              "\n",
              "   Valor_anuncio_float  Valor_M2  \n",
              "0           7000000.00   7000.00  \n",
              "1           3700000.00   7459.68  \n",
              "2            380000.00   3040.00  \n",
              "3            685000.00   2209.68  \n",
              "4            540000.00   5400.00  "
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              "      <th>0</th>\n",
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              "      <td>Rua Alba Valdez</td>\n",
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              "      <td>R$</td>\n",
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              "      <td>2209.68</td>\n",
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              "      <td>Rua Tobias Barreto, 195</td>\n",
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              "      <td>540.000</td>\n",
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              "\n",
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              "          const element = document.querySelector('#df-bfd3e79f-8f7a-4874-9cd4-5e80620dd8e3');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# agrupando por bairro e vendo a média\n",
        "dados_vendas.groupby('Bairro').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "3wgSk3voA6GS",
        "outputId": "28ed964b-c2da-4411-badd-22aa0810c5b2"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Metragem  Quartos  Banheiros  Vagas  Valor_anuncio_float  \\\n",
              "Bairro                                                                        \n",
              "Aclimação            246.16     3.27       3.04   2.91           1763200.01   \n",
              "Alto da Boa Vista    418.88     4.12       5.12   4.62           2391250.00   \n",
              "Alto da Lapa         299.25     3.15       3.85   4.00           2083168.75   \n",
              "Alto da Mooca        270.05     3.30       2.60   3.65           1184170.00   \n",
              "Alto de Pinheiros    429.85     3.94       4.83   4.95           4782698.96   \n",
              "...                     ...      ...        ...    ...                  ...   \n",
              "Vila Água Funda      152.20     2.60       2.20   2.60            652000.00   \n",
              "Água Branca          173.58     3.08       3.25   2.25           1226500.00   \n",
              "Água Fria            165.42     2.83       3.42   2.50            927083.33   \n",
              "Água Funda           139.00     3.00       2.50   2.00            545000.00   \n",
              "Água Rasa            186.17     2.67       2.00   3.67            716166.67   \n",
              "\n",
              "                   Valor_M2  \n",
              "Bairro                       \n",
              "Aclimação           7656.17  \n",
              "Alto da Boa Vista   5849.57  \n",
              "Alto da Lapa        7606.73  \n",
              "Alto da Mooca       5691.74  \n",
              "Alto de Pinheiros  11269.98  \n",
              "...                     ...  \n",
              "Vila Água Funda     4467.13  \n",
              "Água Branca         7269.11  \n",
              "Água Fria           5841.10  \n",
              "Água Funda          3876.86  \n",
              "Água Rasa           4509.61  \n",
              "\n",
              "[697 rows x 6 columns]"
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              "      <th>Valor_anuncio_float</th>\n",
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              "    <tr>\n",
              "      <th>Bairro</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th>Aclimação</th>\n",
              "      <td>246.16</td>\n",
              "      <td>3.27</td>\n",
              "      <td>3.04</td>\n",
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              "      <th>Alto da Mooca</th>\n",
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              "      <td>3.30</td>\n",
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              "      <td>3.65</td>\n",
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              "    <tr>\n",
              "      <th>Alto de Pinheiros</th>\n",
              "      <td>429.85</td>\n",
              "      <td>3.94</td>\n",
              "      <td>4.83</td>\n",
              "      <td>4.95</td>\n",
              "      <td>4782698.96</td>\n",
              "      <td>11269.98</td>\n",
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              "      <th>Vila Água Funda</th>\n",
              "      <td>152.20</td>\n",
              "      <td>2.60</td>\n",
              "      <td>2.20</td>\n",
              "      <td>2.60</td>\n",
              "      <td>652000.00</td>\n",
              "      <td>4467.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Água Branca</th>\n",
              "      <td>173.58</td>\n",
              "      <td>3.08</td>\n",
              "      <td>3.25</td>\n",
              "      <td>2.25</td>\n",
              "      <td>1226500.00</td>\n",
              "      <td>7269.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Água Fria</th>\n",
              "      <td>165.42</td>\n",
              "      <td>2.83</td>\n",
              "      <td>3.42</td>\n",
              "      <td>2.50</td>\n",
              "      <td>927083.33</td>\n",
              "      <td>5841.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Água Funda</th>\n",
              "      <td>139.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>2.50</td>\n",
              "      <td>2.00</td>\n",
              "      <td>545000.00</td>\n",
              "      <td>3876.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Água Rasa</th>\n",
              "      <td>186.17</td>\n",
              "      <td>2.67</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3.67</td>\n",
              "      <td>716166.67</td>\n",
              "      <td>4509.61</td>\n",
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              "<p>697 rows × 6 columns</p>\n",
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              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6320f692-2bdb-4683-a40f-51b36c3f1da8');\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# criando um novo df (dados_bairro) e agrupando por bairro com a soma\n",
        "dados_bairro = dados_vendas.groupby('Bairro').sum()\n",
        "dados_bairro"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "H954DzA3C0rL",
        "outputId": "03a06a88-0882-496a-b7d2-5da1190814f6"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Metragem  Quartos  Banheiros  Vagas  Valor_anuncio_float  \\\n",
              "Bairro                                                                        \n",
              "Aclimação             19447      258        240    230         139292801.00   \n",
              "Alto da Boa Vista      3351       33         41     37          19130000.00   \n",
              "Alto da Lapa          23940      252        308    320         166653500.00   \n",
              "Alto da Mooca          5401       66         52     73          23683400.00   \n",
              "Alto de Pinheiros    158616     1453       1782   1827        1764815915.00   \n",
              "...                     ...      ...        ...    ...                  ...   \n",
              "Vila Água Funda         761       13         11     13           3260000.00   \n",
              "Água Branca            2083       37         39     27          14718000.00   \n",
              "Água Fria              1985       34         41     30          11125000.00   \n",
              "Água Funda              278        6          5      4           1090000.00   \n",
              "Água Rasa              1117       16         12     22           4297000.00   \n",
              "\n",
              "                    Valor_M2  \n",
              "Bairro                        \n",
              "Aclimação          604837.39  \n",
              "Alto da Boa Vista   46796.55  \n",
              "Alto da Lapa       608538.25  \n",
              "Alto da Mooca      113834.80  \n",
              "Alto de Pinheiros 4158622.61  \n",
              "...                      ...  \n",
              "Vila Água Funda     22335.64  \n",
              "Água Branca         87229.37  \n",
              "Água Fria           70093.25  \n",
              "Água Funda           7753.73  \n",
              "Água Rasa           27057.65  \n",
              "\n",
              "[697 rows x 6 columns]"
            ],
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              "      <th>Vila Água Funda</th>\n",
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              "      <td>3260000.00</td>\n",
              "      <td>22335.64</td>\n",
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              "      <td>37</td>\n",
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              "      <td>27</td>\n",
              "      <td>14718000.00</td>\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# criando a coluna Valor_M2_bairro utilizando a df agrupada por bairro e soma\n",
        "dados_bairro['Valor_M2_bairro'] = dados_bairro['Valor_anuncio_float']/dados_bairro['Metragem']\n",
        "dados_bairro"
      ],
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          "height": 455
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        "id": "Rrj4IG5RC6qp",
        "outputId": "b2d8d0f1-979f-4291-e2fe-19094f6bb7b7"
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      "execution_count": 141,
      "outputs": [
        {
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              "                   Metragem  Quartos  Banheiros  Vagas  Valor_anuncio_float  \\\n",
              "Bairro                                                                        \n",
              "Aclimação             19447      258        240    230         139292801.00   \n",
              "Alto da Boa Vista      3351       33         41     37          19130000.00   \n",
              "Alto da Lapa          23940      252        308    320         166653500.00   \n",
              "Alto da Mooca          5401       66         52     73          23683400.00   \n",
              "Alto de Pinheiros    158616     1453       1782   1827        1764815915.00   \n",
              "...                     ...      ...        ...    ...                  ...   \n",
              "Vila Água Funda         761       13         11     13           3260000.00   \n",
              "Água Branca            2083       37         39     27          14718000.00   \n",
              "Água Fria              1985       34         41     30          11125000.00   \n",
              "Água Funda              278        6          5      4           1090000.00   \n",
              "Água Rasa              1117       16         12     22           4297000.00   \n",
              "\n",
              "                    Valor_M2  Valor_M2_bairro  \n",
              "Bairro                                         \n",
              "Aclimação          604837.39          7162.69  \n",
              "Alto da Boa Vista   46796.55          5708.74  \n",
              "Alto da Lapa       608538.25          6961.30  \n",
              "Alto da Mooca      113834.80          4385.00  \n",
              "Alto de Pinheiros 4158622.61         11126.34  \n",
              "...                      ...              ...  \n",
              "Vila Água Funda     22335.64          4283.84  \n",
              "Água Branca         87229.37          7065.77  \n",
              "Água Fria           70093.25          5604.53  \n",
              "Água Funda           7753.73          3920.86  \n",
              "Água Rasa           27057.65          3846.91  \n",
              "\n",
              "[697 rows x 7 columns]"
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              "      <td>4385.00</td>\n",
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              "      <th>Alto de Pinheiros</th>\n",
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              "      <td>11</td>\n",
              "      <td>13</td>\n",
              "      <td>3260000.00</td>\n",
              "      <td>22335.64</td>\n",
              "      <td>4283.84</td>\n",
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              "    <tr>\n",
              "      <th>Água Branca</th>\n",
              "      <td>2083</td>\n",
              "      <td>37</td>\n",
              "      <td>39</td>\n",
              "      <td>27</td>\n",
              "      <td>14718000.00</td>\n",
              "      <td>87229.37</td>\n",
              "      <td>7065.77</td>\n",
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              "      <th>Água Fria</th>\n",
              "      <td>1985</td>\n",
              "      <td>34</td>\n",
              "      <td>41</td>\n",
              "      <td>30</td>\n",
              "      <td>11125000.00</td>\n",
              "      <td>70093.25</td>\n",
              "      <td>5604.53</td>\n",
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              "    <tr>\n",
              "      <th>Água Funda</th>\n",
              "      <td>278</td>\n",
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              "      <td>3920.86</td>\n",
              "    </tr>\n",
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              "      <th>Água Rasa</th>\n",
              "      <td>1117</td>\n",
              "      <td>16</td>\n",
              "      <td>12</td>\n",
              "      <td>22</td>\n",
              "      <td>4297000.00</td>\n",
              "      <td>27057.65</td>\n",
              "      <td>3846.91</td>\n",
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              "  </tbody>\n",
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              "<p>697 rows × 7 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 141
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# agrupando a df dados_vendas por bairro e média novamente para comparação\n",
        "dados_vendas.groupby('Bairro').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "b9LIBdj_DX3N",
        "outputId": "15ca2935-e6c4-4bbf-8719-553725e5dd72"
      },
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Metragem  Quartos  Banheiros  Vagas  Valor_anuncio_float  \\\n",
              "Bairro                                                                        \n",
              "Aclimação            246.16     3.27       3.04   2.91           1763200.01   \n",
              "Alto da Boa Vista    418.88     4.12       5.12   4.62           2391250.00   \n",
              "Alto da Lapa         299.25     3.15       3.85   4.00           2083168.75   \n",
              "Alto da Mooca        270.05     3.30       2.60   3.65           1184170.00   \n",
              "Alto de Pinheiros    429.85     3.94       4.83   4.95           4782698.96   \n",
              "...                     ...      ...        ...    ...                  ...   \n",
              "Vila Água Funda      152.20     2.60       2.20   2.60            652000.00   \n",
              "Água Branca          173.58     3.08       3.25   2.25           1226500.00   \n",
              "Água Fria            165.42     2.83       3.42   2.50            927083.33   \n",
              "Água Funda           139.00     3.00       2.50   2.00            545000.00   \n",
              "Água Rasa            186.17     2.67       2.00   3.67            716166.67   \n",
              "\n",
              "                   Valor_M2  \n",
              "Bairro                       \n",
              "Aclimação           7656.17  \n",
              "Alto da Boa Vista   5849.57  \n",
              "Alto da Lapa        7606.73  \n",
              "Alto da Mooca       5691.74  \n",
              "Alto de Pinheiros  11269.98  \n",
              "...                     ...  \n",
              "Vila Água Funda     4467.13  \n",
              "Água Branca         7269.11  \n",
              "Água Fria           5841.10  \n",
              "Água Funda          3876.86  \n",
              "Água Rasa           4509.61  \n",
              "\n",
              "[697 rows x 6 columns]"
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              "      <td>2.60</td>\n",
              "      <td>3.65</td>\n",
              "      <td>1184170.00</td>\n",
              "      <td>5691.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alto de Pinheiros</th>\n",
              "      <td>429.85</td>\n",
              "      <td>3.94</td>\n",
              "      <td>4.83</td>\n",
              "      <td>4.95</td>\n",
              "      <td>4782698.96</td>\n",
              "      <td>11269.98</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vila Água Funda</th>\n",
              "      <td>152.20</td>\n",
              "      <td>2.60</td>\n",
              "      <td>2.20</td>\n",
              "      <td>2.60</td>\n",
              "      <td>652000.00</td>\n",
              "      <td>4467.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Água Branca</th>\n",
              "      <td>173.58</td>\n",
              "      <td>3.08</td>\n",
              "      <td>3.25</td>\n",
              "      <td>2.25</td>\n",
              "      <td>1226500.00</td>\n",
              "      <td>7269.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Água Fria</th>\n",
              "      <td>165.42</td>\n",
              "      <td>2.83</td>\n",
              "      <td>3.42</td>\n",
              "      <td>2.50</td>\n",
              "      <td>927083.33</td>\n",
              "      <td>5841.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Água Funda</th>\n",
              "      <td>139.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>2.50</td>\n",
              "      <td>2.00</td>\n",
              "      <td>545000.00</td>\n",
              "      <td>3876.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Água Rasa</th>\n",
              "      <td>186.17</td>\n",
              "      <td>2.67</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3.67</td>\n",
              "      <td>716166.67</td>\n",
              "      <td>4509.61</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>697 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bb0bd475-c195-4d05-8fba-7ea1febd7222')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# vamos fazer agora com a Vila Mariana usando a 'média de médias'\n",
        "dados_vendas.groupby('Bairro').mean().loc['Vila Mariana']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fIBVqWgoEkIR",
        "outputId": "af03a607-5f89-4c13-9d7f-602dfe7b882d"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Metragem                  231.24\n",
              "Quartos                     3.39\n",
              "Banheiros                   3.24\n",
              "Vagas                       2.95\n",
              "Valor_anuncio_float   1771159.76\n",
              "Valor_M2                 7913.15\n",
              "Name: Vila Mariana, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# comparando com o valor obtido usando a soma\n",
        "dados_bairro.loc['Vila Mariana']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WuOkQ9HSEr1Q",
        "outputId": "b641cb05-aeb0-4255-9c62-e00349ed7a71"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Metragem                  39079.00\n",
              "Quartos                     573.00\n",
              "Banheiros                   547.00\n",
              "Vagas                       499.00\n",
              "Valor_anuncio_float   299326000.00\n",
              "Valor_M2                1337323.16\n",
              "Valor_M2_bairro            7659.51\n",
              "Name: Vila Mariana, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_bairro.loc['Vila Mariana']['Valor_M2_bairro']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0On0z59uFV_E",
        "outputId": "5a837f48-4514-4dbc-a1ad-31ab403efe82"
      },
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7659.510222881855"
            ]
          },
          "metadata": {},
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_bairro['Valor_M2_bairro']['Vila Mariana']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-V9b5kbzHJ3O",
        "outputId": "01b12483-2e17-40c1-9f83-6173eac7cc1b"
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7659.510222881855"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Utilizando gráficos"
      ],
      "metadata": {
        "id": "ASpZgwELIX88"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# vamos descobrir quais são os 10 bairros que tem mais anúncios\n",
        "dados_vendas['Bairro'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RXinv5HbHNkn",
        "outputId": "1e7ca931-4ba0-4216-9c6e-7cb6d57b64d6"
      },
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Alto de Pinheiros      369\n",
              "Jardim Guedala         353\n",
              "Jardim Paulista        266\n",
              "Brooklin Paulista      224\n",
              "Jardim Europa          210\n",
              "                      ... \n",
              "Vila Virginia            1\n",
              "Vila Boaçava             1\n",
              "Vila Agueda              1\n",
              "Jardim Monte Alegre      1\n",
              "Jardim Ivana             1\n",
              "Name: Bairro, Length: 697, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "top_bairro = dados_vendas['Bairro'].value_counts()[:10].index"
      ],
      "metadata": {
        "id": "5yfIofh2Ie6t"
      },
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# precisamos do bairro não como índice, mas como coluna\n",
        "dados_bairro"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "tO9LJpK0J3D_",
        "outputId": "d6113832-e5f1-4933-a694-834b43f31932"
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Metragem  Quartos  Banheiros  Vagas  Valor_anuncio_float  \\\n",
              "Bairro                                                                        \n",
              "Aclimação             19447      258        240    230         139292801.00   \n",
              "Alto da Boa Vista      3351       33         41     37          19130000.00   \n",
              "Alto da Lapa          23940      252        308    320         166653500.00   \n",
              "Alto da Mooca          5401       66         52     73          23683400.00   \n",
              "Alto de Pinheiros    158616     1453       1782   1827        1764815915.00   \n",
              "...                     ...      ...        ...    ...                  ...   \n",
              "Vila Água Funda         761       13         11     13           3260000.00   \n",
              "Água Branca            2083       37         39     27          14718000.00   \n",
              "Água Fria              1985       34         41     30          11125000.00   \n",
              "Água Funda              278        6          5      4           1090000.00   \n",
              "Água Rasa              1117       16         12     22           4297000.00   \n",
              "\n",
              "                    Valor_M2  Valor_M2_bairro  \n",
              "Bairro                                         \n",
              "Aclimação          604837.39          7162.69  \n",
              "Alto da Boa Vista   46796.55          5708.74  \n",
              "Alto da Lapa       608538.25          6961.30  \n",
              "Alto da Mooca      113834.80          4385.00  \n",
              "Alto de Pinheiros 4158622.61         11126.34  \n",
              "...                      ...              ...  \n",
              "Vila Água Funda     22335.64          4283.84  \n",
              "Água Branca         87229.37          7065.77  \n",
              "Água Fria           70093.25          5604.53  \n",
              "Água Funda           7753.73          3920.86  \n",
              "Água Rasa           27057.65          3846.91  \n",
              "\n",
              "[697 rows x 7 columns]"
            ],
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              "      <th>Bairro</th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Aclimação</th>\n",
              "      <td>19447</td>\n",
              "      <td>258</td>\n",
              "      <td>240</td>\n",
              "      <td>230</td>\n",
              "      <td>139292801.00</td>\n",
              "      <td>604837.39</td>\n",
              "      <td>7162.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alto da Boa Vista</th>\n",
              "      <td>3351</td>\n",
              "      <td>33</td>\n",
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              "      <td>37</td>\n",
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              "      <td>46796.55</td>\n",
              "      <td>5708.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alto da Lapa</th>\n",
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              "      <td>73</td>\n",
              "      <td>23683400.00</td>\n",
              "      <td>113834.80</td>\n",
              "      <td>4385.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alto de Pinheiros</th>\n",
              "      <td>158616</td>\n",
              "      <td>1453</td>\n",
              "      <td>1782</td>\n",
              "      <td>1827</td>\n",
              "      <td>1764815915.00</td>\n",
              "      <td>4158622.61</td>\n",
              "      <td>11126.34</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>Vila Água Funda</th>\n",
              "      <td>761</td>\n",
              "      <td>13</td>\n",
              "      <td>11</td>\n",
              "      <td>13</td>\n",
              "      <td>3260000.00</td>\n",
              "      <td>22335.64</td>\n",
              "      <td>4283.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Água Branca</th>\n",
              "      <td>2083</td>\n",
              "      <td>37</td>\n",
              "      <td>39</td>\n",
              "      <td>27</td>\n",
              "      <td>14718000.00</td>\n",
              "      <td>87229.37</td>\n",
              "      <td>7065.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Água Fria</th>\n",
              "      <td>1985</td>\n",
              "      <td>34</td>\n",
              "      <td>41</td>\n",
              "      <td>30</td>\n",
              "      <td>11125000.00</td>\n",
              "      <td>70093.25</td>\n",
              "      <td>5604.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Água Funda</th>\n",
              "      <td>278</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1090000.00</td>\n",
              "      <td>7753.73</td>\n",
              "      <td>3920.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Água Rasa</th>\n",
              "      <td>1117</td>\n",
              "      <td>16</td>\n",
              "      <td>12</td>\n",
              "      <td>22</td>\n",
              "      <td>4297000.00</td>\n",
              "      <td>27057.65</td>\n",
              "      <td>3846.91</td>\n",
              "    </tr>\n",
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              "<p>697 rows × 7 columns</p>\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e4d86ef0-9e25-45dc-a933-d76076b3ce54 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-e4d86ef0-9e25-45dc-a933-d76076b3ce54');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# atribuindo o parâmetro inplace para que não seja necessário atribuir \n",
        "# as mudanças a uma nova df\n",
        "dados_bairro.reset_index(inplace=True)"
      ],
      "metadata": {
        "id": "eKmaqIjiKCM-"
      },
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dados_bairro.query('Bairro in @top_bairro')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "O5m6Dkn6LQcx",
        "outputId": "ccc9a768-b642-4efc-935c-613b99187dfe"
      },
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Bairro  Metragem  Quartos  Banheiros  Vagas  \\\n",
              "4    Alto de Pinheiros    158616     1453       1782   1827   \n",
              "19   Brooklin Paulista     70002      778        862    832   \n",
              "57       Cidade Jardim    132220      789       1008   1172   \n",
              "155      Jardim Europa    118898      795        993   1056   \n",
              "168     Jardim Guedala    972212     1443       2027   2161   \n",
              "187      Jardim Leonor     97141      697        941    902   \n",
              "227    Jardim Paulista    130283     1028       1231   1171   \n",
              "338           Pacaembu    100285      774        889   1028   \n",
              "396  Planalto Paulista     59250      636        768    713   \n",
              "564      Vila Madalena     66250      739        786    712   \n",
              "\n",
              "     Valor_anuncio_float   Valor_M2  Valor_M2_bairro  \n",
              "4          1764815915.00 4158622.61         11126.34  \n",
              "19          475796789.00 1627341.87          6796.90  \n",
              "57         1164210900.00 1635692.98          8805.10  \n",
              "155        2414172900.00 4192164.45         20304.57  \n",
              "168        1801165587.00 2833882.79          1852.65  \n",
              "187         646326369.00 1115858.80          6653.49  \n",
              "227        2136081000.00 4279262.19         16395.70  \n",
              "338         867956500.00 1753672.28          8654.90  \n",
              "396         374907000.00 1186380.95          6327.54  \n",
              "564         583253030.00 1792770.40          8803.82  "
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Bairro</th>\n",
              "      <th>Metragem</th>\n",
              "      <th>Quartos</th>\n",
              "      <th>Banheiros</th>\n",
              "      <th>Vagas</th>\n",
              "      <th>Valor_anuncio_float</th>\n",
              "      <th>Valor_M2</th>\n",
              "      <th>Valor_M2_bairro</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Alto de Pinheiros</td>\n",
              "      <td>158616</td>\n",
              "      <td>1453</td>\n",
              "      <td>1782</td>\n",
              "      <td>1827</td>\n",
              "      <td>1764815915.00</td>\n",
              "      <td>4158622.61</td>\n",
              "      <td>11126.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Brooklin Paulista</td>\n",
              "      <td>70002</td>\n",
              "      <td>778</td>\n",
              "      <td>862</td>\n",
              "      <td>832</td>\n",
              "      <td>475796789.00</td>\n",
              "      <td>1627341.87</td>\n",
              "      <td>6796.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Cidade Jardim</td>\n",
              "      <td>132220</td>\n",
              "      <td>789</td>\n",
              "      <td>1008</td>\n",
              "      <td>1172</td>\n",
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              "      <td>1635692.98</td>\n",
              "      <td>8805.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>Jardim Europa</td>\n",
              "      <td>118898</td>\n",
              "      <td>795</td>\n",
              "      <td>993</td>\n",
              "      <td>1056</td>\n",
              "      <td>2414172900.00</td>\n",
              "      <td>4192164.45</td>\n",
              "      <td>20304.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>Jardim Guedala</td>\n",
              "      <td>972212</td>\n",
              "      <td>1443</td>\n",
              "      <td>2027</td>\n",
              "      <td>2161</td>\n",
              "      <td>1801165587.00</td>\n",
              "      <td>2833882.79</td>\n",
              "      <td>1852.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>Jardim Leonor</td>\n",
              "      <td>97141</td>\n",
              "      <td>697</td>\n",
              "      <td>941</td>\n",
              "      <td>902</td>\n",
              "      <td>646326369.00</td>\n",
              "      <td>1115858.80</td>\n",
              "      <td>6653.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>227</th>\n",
              "      <td>Jardim Paulista</td>\n",
              "      <td>130283</td>\n",
              "      <td>1028</td>\n",
              "      <td>1231</td>\n",
              "      <td>1171</td>\n",
              "      <td>2136081000.00</td>\n",
              "      <td>4279262.19</td>\n",
              "      <td>16395.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>338</th>\n",
              "      <td>Pacaembu</td>\n",
              "      <td>100285</td>\n",
              "      <td>774</td>\n",
              "      <td>889</td>\n",
              "      <td>1028</td>\n",
              "      <td>867956500.00</td>\n",
              "      <td>1753672.28</td>\n",
              "      <td>8654.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>Planalto Paulista</td>\n",
              "      <td>59250</td>\n",
              "      <td>636</td>\n",
              "      <td>768</td>\n",
              "      <td>713</td>\n",
              "      <td>374907000.00</td>\n",
              "      <td>1186380.95</td>\n",
              "      <td>6327.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>Vila Madalena</td>\n",
              "      <td>66250</td>\n",
              "      <td>739</td>\n",
              "      <td>786</td>\n",
              "      <td>712</td>\n",
              "      <td>583253030.00</td>\n",
              "      <td>1792770.40</td>\n",
              "      <td>8803.82</td>\n",
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              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4b2f21fe-ffea-4e9c-81b3-0ca29a0cc054')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4b2f21fe-ffea-4e9c-81b3-0ca29a0cc054 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-4b2f21fe-ffea-4e9c-81b3-0ca29a0cc054');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# inserimos essa query dentro de ‘data’\n",
        "plt.figure(figsize=(16, 8))\n",
        "ax = sns.barplot(x='Bairro', y='Valor_M2_bairro', data=dados_bairro.query('Bairro in @top_bairro'))\n",
        "ax.set_title('Valor do m2 dos 10 bairros com mais anúncios de SP')\n",
        "ax.tick_params(axis='x', rotation=45)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "AEgHllk7IvKm",
        "outputId": "89cd891c-6a92-4666-95c9-ff9fcfa00bfa"
      },
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plotando um boxplot, podemos ver que temos um outlier dentro do Jardim Europa\n",
        "\n",
        "plt.figure(figsize=(16, 8))\n",
        "ax=sns.boxplot(data=dados_vendas.query('Bairro in @top_bairro'), x='Bairro', y='Valor_anuncio_float')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "ntjwZCbjBs3k",
        "outputId": "2f37673c-ea1b-4ab3-aeff-21841afb7a6f"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plotando o mesmo gráfico, só que analisando a metragem, vemos que temos um\n",
        "# imóvel muito diferente dentro do Jardim Guedala\n",
        "plt.figure(figsize=(16, 8))\n",
        "ax=sns.boxplot(data=dados_vendas.query('Bairro in @top_bairro'), x='Bairro', y='Metragem')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "XyUDaBERDrkd",
        "outputId": "b87846d6-8016-49c7-ca93-085098a47421"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# podemos removê-lo colocando uma metragem máxima permitida na query\n",
        "plt.figure(figsize=(16, 8))\n",
        "ax=sns.boxplot(data=dados_vendas.query('Bairro in @top_bairro & Metragem < 30000'), x='Bairro', y='Metragem')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "HhiKe8J8DtsD",
        "outputId": "8cd5073f-c650-48b8-9586-ef1e4a0f2650"
      },
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_vendas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 693
        },
        "id": "TpoBV7axD3vj",
        "outputId": "00d10f28-e636-4468-da37-3fde2aef87ec"
      },
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  Rua              Bairro     Cidade  \\\n",
              "0               Avenida Itacira, 255    Planalto Paulista  São Paulo   \n",
              "1      Rua Aurelia Perez Alvarez, 42   Jardim dos Estados  São Paulo   \n",
              "2                     Rua Alba Valdez     Jardim Reimberg  São Paulo   \n",
              "3                                 NaN      Jardim Morumbi  São Paulo   \n",
              "4            Rua Tobias Barreto, 195                Mooca  São Paulo   \n",
              "...                               ...                 ...        ...   \n",
              "10002                   Avenida Odila   Planalto Paulista  São Paulo   \n",
              "10003                      Rua Tapuçu          Vila Sofia  São Paulo   \n",
              "10004                   Rua Guararema     Bosque da Saúde  São Paulo   \n",
              "10005               Rua Estero Belaco       Vila da Saúde  São Paulo   \n",
              "10007      Rua Barão de Tramandaí, 0          Vila Alpina  São Paulo   \n",
              "\n",
              "       Metragem  Quartos  Banheiros  Vagas         Valor Moeda Valor_anuncio  \\\n",
              "0          1000        4          8      6  R$ 7.000.000    R$     7.000.000   \n",
              "1           496        4          4      4  R$ 3.700.000    R$     3.700.000   \n",
              "2           125        4          3      2    R$ 380.000    R$       380.000   \n",
              "3           310        3          2      4    R$ 685.000    R$       685.000   \n",
              "4           100        3          2      2    R$ 540.000    R$       540.000   \n",
              "...         ...      ...        ...    ...           ...   ...           ...   \n",
              "10002       267        3          6      4  R$ 1.500.000    R$     1.500.000   \n",
              "10003        95        2          2      2    R$ 665.000    R$       665.000   \n",
              "10004       605        8          7      5  R$ 2.300.000    R$     2.300.000   \n",
              "10005       205        3          3      6  R$ 1.050.000    R$     1.050.000   \n",
              "10007       122        3          2      2    R$ 270.000    R$       270.000   \n",
              "\n",
              "      Tipo_anuncio  Valor_anuncio_float  Valor_M2  \n",
              "0             None           7000000.00   7000.00  \n",
              "1             None           3700000.00   7459.68  \n",
              "2             None            380000.00   3040.00  \n",
              "3             None            685000.00   2209.68  \n",
              "4             None            540000.00   5400.00  \n",
              "...            ...                  ...       ...  \n",
              "10002         None           1500000.00   5617.98  \n",
              "10003         None            665000.00   7000.00  \n",
              "10004         None           2300000.00   3801.65  \n",
              "10005         None           1050000.00   5121.95  \n",
              "10007         None            270000.00   2213.11  \n",
              "\n",
              "[9156 rows x 13 columns]"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>Rua</th>\n",
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              "      <th>Cidade</th>\n",
              "      <th>Metragem</th>\n",
              "      <th>Quartos</th>\n",
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              "      <th>0</th>\n",
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              "      <td>1000</td>\n",
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              "      <td>7.000.000</td>\n",
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              "      <td>7000000.00</td>\n",
              "      <td>7000.00</td>\n",
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              "      <th>1</th>\n",
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              "      <td>3700000.00</td>\n",
              "      <td>7459.68</td>\n",
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              "      <th>2</th>\n",
              "      <td>Rua Alba Valdez</td>\n",
              "      <td>Jardim Reimberg</td>\n",
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              "      <td>R$</td>\n",
              "      <td>380.000</td>\n",
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              "      <td>380000.00</td>\n",
              "      <td>3040.00</td>\n",
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              "      <th>3</th>\n",
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              "      <td>Jardim Morumbi</td>\n",
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              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>R$ 685.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>685.000</td>\n",
              "      <td>None</td>\n",
              "      <td>685000.00</td>\n",
              "      <td>2209.68</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Rua Tobias Barreto, 195</td>\n",
              "      <td>Mooca</td>\n",
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              "      <td>100</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>R$ 540.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>540.000</td>\n",
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              "      <td>540000.00</td>\n",
              "      <td>5400.00</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>10002</th>\n",
              "      <td>Avenida Odila</td>\n",
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              "      <td>6</td>\n",
              "      <td>4</td>\n",
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              "      <td>R$</td>\n",
              "      <td>1.500.000</td>\n",
              "      <td>None</td>\n",
              "      <td>1500000.00</td>\n",
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              "    <tr>\n",
              "      <th>10003</th>\n",
              "      <td>Rua Tapuçu</td>\n",
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              "      <td>95</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>R$ 665.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>665.000</td>\n",
              "      <td>None</td>\n",
              "      <td>665000.00</td>\n",
              "      <td>7000.00</td>\n",
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              "      <th>10004</th>\n",
              "      <td>Rua Guararema</td>\n",
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              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>R$ 2.300.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>2.300.000</td>\n",
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              "      <td>2300000.00</td>\n",
              "      <td>3801.65</td>\n",
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              "      <th>10005</th>\n",
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              "      <td>3</td>\n",
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              "      <td>6</td>\n",
              "      <td>R$ 1.050.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>1.050.000</td>\n",
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              "      <td>1050000.00</td>\n",
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              "    <tr>\n",
              "      <th>10007</th>\n",
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              "      <td>Vila Alpina</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>122</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>R$ 270.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>270.000</td>\n",
              "      <td>None</td>\n",
              "      <td>270000.00</td>\n",
              "      <td>2213.11</td>\n",
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          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Análise com base do IBGE"
      ],
      "metadata": {
        "id": "94v3gMtSLpgy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# vamos cruzar análises com os endereços em dados_vendas com uma base do IBGE\n",
        "ibge_url = 'https://gist.githubusercontent.com/tgcsantos/85f8c7b0a2edbc3e27fcad619b37d886/raw/a4954781e6bca9cb804062a3eea0b3b84679daf4/Basico_SP1.csv'\n",
        "pd.read_csv(ibge_url)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WTETxO-PK5Ix",
        "outputId": "a305ccae-2b9b-41e8-866d-29fe28264424"
      },
      "execution_count": 157,
      "outputs": [
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              "      <th>26;1119</th>\n",
              "      <th>59;3572955</th>\n",
              "      <th>1;1859</th>\n",
              "      <th>13;4567289</th>\n",
              "      <th>5;869</th>\n",
              "      <th>09;2122409</th>\n",
              "      <th>02;1567</th>\n",
              "      <th>41;2735383</th>\n",
              "      <td>24.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>355030801000005;3;\"Regi�o Sudeste\";35;\"S�o Paulo\";3515;\"Metropolitana de S�o Paulo\";35061;\"S�o Paulo\";20;\"RM S�o Paulo\";3550308;\"S�O PAULO\";355030801;\"�GUA RASA\";35503080100;\"�GUA RASA\";3550308000;\"S�O PAULO (todos os setores)\";1;0;240;754;3</th>\n",
              "      <th>14;2</th>\n",
              "      <th>35;1416</th>\n",
              "      <th>9;2240207</th>\n",
              "      <th>58;1581</th>\n",
              "      <th>66;2240109</th>\n",
              "      <th>39;917</th>\n",
              "      <th>71;1570047</th>\n",
              "      <th>75;1356</th>\n",
              "      <th>35;1725877</th>\n",
              "      <td>85.00</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>355030896000243;3;\"Regi�o Sudeste\";35;\"S�o Paulo\";3515;\"Metropolitana de S�o Paulo\";35061;\"S�o Paulo\";20;\"RM S�o Paulo\";3550308;\"S�O PAULO\";355030896;\"LAJEADO\";35503089600;\"LAJEADO\";3550308000;\"S�O PAULO (todos os setores)\";1;1;96;336;3</th>\n",
              "      <th>5;2</th>\n",
              "      <th>61;772</th>\n",
              "      <th>09;168751</th>\n",
              "      <th>1;814</th>\n",
              "      <th>52;143188</th>\n",
              "      <th>21;467</th>\n",
              "      <th>46;191332</th>\n",
              "      <th>48;699</th>\n",
              "      <th>95;123399</th>\n",
              "      <td>8.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>355030896000244;3;\"Regi�o Sudeste\";35;\"S�o Paulo\";3515;\"Metropolitana de S�o Paulo\";35061;\"S�o Paulo\";20;\"RM S�o Paulo\";3550308;\"S�O PAULO\";355030896;\"LAJEADO\";35503089600;\"LAJEADO\";3550308000;\"S�O PAULO (todos os setores)\";1;1;54;209;3</th>\n",
              "      <th>87;2</th>\n",
              "      <th>49;692</th>\n",
              "      <th>5;239941</th>\n",
              "      <th>27;812</th>\n",
              "      <th>93;182516</th>\n",
              "      <th>2;439</th>\n",
              "      <th>33;194034</th>\n",
              "      <th>99;702</th>\n",
              "      <th>1;124952</th>\n",
              "      <td>38.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>355030896000245;3;\"Regi�o Sudeste\";35;\"S�o Paulo\";3515;\"Metropolitana de S�o Paulo\";35061;\"S�o Paulo\";20;\"RM S�o Paulo\";3550308;\"S�O PAULO\";355030896;\"LAJEADO\";35503089600;\"LAJEADO\";3550308000;\"S�O PAULO (todos os setores)\";1;1;87;339;3</th>\n",
              "      <th>9;2</th>\n",
              "      <th>54;630</th>\n",
              "      <th>06;329705</th>\n",
              "      <th>22;843</th>\n",
              "      <th>31;260395</th>\n",
              "      <th>9;347</th>\n",
              "      <th>6;228162</th>\n",
              "      <th>66;767</th>\n",
              "      <th>5;181129</th>\n",
              "      <td>99.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>355030896000246;3;\"Regi�o Sudeste\";35;\"S�o Paulo\";3515;\"Metropolitana de S�o Paulo\";35061;\"S�o Paulo\";20;\"RM S�o Paulo\";3550308;\"S�O PAULO\";355030896;\"LAJEADO\";35503089600;\"LAJEADO\";3550308000;\"S�O PAULO (todos os setores)\";1;0;38;163;4</th>\n",
              "      <th>29;4</th>\n",
              "      <th>21;597</th>\n",
              "      <th>37;349101;908;244391</th>\n",
              "      <th>67;397</th>\n",
              "      <th>76;436942</th>\n",
              "      <th>16;854</th>\n",
              "      <th>2;550319</th>\n",
              "      <th>43</th>\n",
              "      <th>NaN</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>355030896000247;3;\"Regi�o Sudeste\";35;\"S�o Paulo\";3515;\"Metropolitana de S�o Paulo\";35061;\"S�o Paulo\";20;\"RM S�o Paulo\";3550308;\"S�O PAULO\";355030896;\"LAJEADO\";35503089600;\"LAJEADO\";3550308000;\"S�O PAULO (todos os setores)\";1;1;123;464;3</th>\n",
              "      <th>77;3</th>\n",
              "      <th>28;630</th>\n",
              "      <th>13;183904</th>\n",
              "      <th>29;790</th>\n",
              "      <th>88;102860</th>\n",
              "      <th>11;431</th>\n",
              "      <th>82;186963</th>\n",
              "      <th>45;728</th>\n",
              "      <th>7;98771</th>\n",
              "      <td>74.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>18363 rows × 1 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Lidando com os desafios da aula 03"
      ],
      "metadata": {
        "id": "u_tnKk6HnMr9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "8SeqORdFi3oK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **⚠ Desafio 01 - a03**\n",
        "\n",
        "Tentar vincular dados do IBGE com os dados de imóveis."
      ],
      "metadata": {
        "id": "gr6m7TOwPM_g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h2><strong>Relação das variáveis das planilhas</strong></h2>\n",
        "\n",
        "Arquivo Básico (planilha Básico_UF.xls ou Básico_UF.csv)\n",
        "\n",
        "<h3><strong>Situação setor:</strong></h3>\n",
        "\n",
        "\n",
        "*   Situação urbana – códigos: 1, 2 e 3\n",
        "\n",
        "1.   Área urbanizada de cidade ou vila\n",
        "2.   Área não-urbanizada de cidade ou vila\n",
        "3.   Área urbana isolada\n",
        "\n",
        "*   Situação rural – códigos: 4, 5, 6, 7 e 8\n",
        "\n",
        "4.   Aglomerado rural de extensão urbana\n",
        "5.   Aglomerado rural isolado – povoado\n",
        "6.   Aglomerado rural isolado – núcleo\n",
        "7.   Aglomerado rural isolado - outros aglomerados\n",
        "8.   Zona rural, exclusive aglomerado rural\n",
        "\n",
        "<h3><strong>Demais variáveis:</strong></h3>\n",
        "\n",
        "*   V001 - Domicílios particulares permanentes ou pessoas responsáveis por domicílios particulares permanentes\n",
        "*   V002 - Moradores em domicílios particulares permanentes ou população residente em domicílios particulares permanentes\n",
        "*   V003 - Média do número de moradores em domicílios particulares permanentes (obtida pela divisão de Var2 por Var1)\n",
        "*   V004 - Domicílios particulares permanentes ou pessoas responsáveis por domicílios particulares permanentes\n",
        "*   V005 - Valor do rendimento nominal médio mensal das pessoas responsáveis por domicílios particulares permanentes (com e sem rendimento)\n",
        "*   V006 - Variância do rendimento nominal mensal das pessoas responsáveis por domicílios particulares permanentes (com e sem rendimento)\n",
        "*   V007 - Valor do rendimento nominal médio mensal das pessoas responsáveis por domicílios particulares permanentes (com rendimento)\n",
        "*   V008 - Variância do rendimento nominal mensal das pessoas responsáveis por domicílios particulares permanentes (com rendimento)\n",
        "*   V009 - Valor do rendimento nominal médio mensal das pessoas de 10 anos ou mais de idade (com e sem rendimento)\n",
        "*   V010 - Variância do rendimento nominal mensal das pessoas de 10 anos ou mais de idade (com e sem rendimento)\n",
        "*   V011 - Valor do rendimento nominal médio mensal das pessoas de 10 anos ou mais de idade (com rendimento)\n",
        "*   V012 - Variância do rendimento nominal mensal das pessoas de 10 anos ou mais de idade (com rendimento)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "MYtWiwskUN91"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# tratando e exibindo dados IBGE\n",
        "import pandas as pd\n",
        "dados_IBGE = pd.read_csv(ibge_url, encoding = 'ISO-8859-1', sep=';', thousands='.', decimal=',')\n",
        "dados_IBGE.dropna(how='all', axis=1, inplace=True)\n",
        "dados_IBGE.head(10)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 821
        },
        "id": "dh6Z3WSKPRTP",
        "outputId": "dc959f43-b43d-499f-b1da-9d11b4fc8f79"
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Cod_setor  Cod_Grandes Regiï¿½es Nome_Grande_Regiao  Cod_UF  \\\n",
              "0  355030801000001                      3   Regiï¿½o Sudeste      35   \n",
              "1  355030801000002                      3   Regiï¿½o Sudeste      35   \n",
              "2  355030801000003                      3   Regiï¿½o Sudeste      35   \n",
              "3  355030801000004                      3   Regiï¿½o Sudeste      35   \n",
              "4  355030801000005                      3   Regiï¿½o Sudeste      35   \n",
              "5  355030801000006                      3   Regiï¿½o Sudeste      35   \n",
              "6  355030801000007                      3   Regiï¿½o Sudeste      35   \n",
              "7  355030801000008                      3   Regiï¿½o Sudeste      35   \n",
              "8  355030801000009                      3   Regiï¿½o Sudeste      35   \n",
              "9  355030801000010                      3   Regiï¿½o Sudeste      35   \n",
              "\n",
              "   Nome_da_UF   Cod_meso                  Nome_da_meso  Cod_micro  \\\n",
              "0  Sï¿½o Paulo      3515  Metropolitana de Sï¿½o Paulo      35061   \n",
              "1  Sï¿½o Paulo      3515  Metropolitana de Sï¿½o Paulo      35061   \n",
              "2  Sï¿½o Paulo      3515  Metropolitana de Sï¿½o Paulo      35061   \n",
              "3  Sï¿½o Paulo      3515  Metropolitana de Sï¿½o Paulo      35061   \n",
              "4  Sï¿½o Paulo      3515  Metropolitana de Sï¿½o Paulo      35061   \n",
              "5  Sï¿½o Paulo      3515  Metropolitana de Sï¿½o Paulo      35061   \n",
              "6  Sï¿½o Paulo      3515  Metropolitana de Sï¿½o Paulo      35061   \n",
              "7  Sï¿½o Paulo      3515  Metropolitana de Sï¿½o Paulo      35061   \n",
              "8  Sï¿½o Paulo      3515  Metropolitana de Sï¿½o Paulo      35061   \n",
              "9  Sï¿½o Paulo      3515  Metropolitana de Sï¿½o Paulo      35061   \n",
              "\n",
              "  Nome_da_micro  Cod_RM  ... V003  V004    V005       V006    V007       V008  \\\n",
              "0   Sï¿½o Paulo      20  ... 3.01  2.32 2091.32 8532451.78 2164.55 8673276.78   \n",
              "1   Sï¿½o Paulo      20  ... 2.98  2.09 1650.09 3983441.31 1717.44 4030519.99   \n",
              "2   Sï¿½o Paulo      20  ... 3.31  3.08 1280.43 5474593.06 2349.53 7553913.02   \n",
              "3   Sï¿½o Paulo      20  ... 3.16  2.26 1119.59 3572955.10 1859.13 4567289.50   \n",
              "4   Sï¿½o Paulo      20  ... 3.14  2.35 1416.90 2240207.58 1581.66 2240109.39   \n",
              "5   Sï¿½o Paulo      20  ... 3.03  1.70 1471.72 3314333.63 2137.02 3391364.34   \n",
              "6   Sï¿½o Paulo      20  ... 2.77  2.47  901.47 1568830.60 1394.19 1740438.09   \n",
              "7   Sï¿½o Paulo      20  ... 3.34  2.50  886.81 3784891.15 1616.29 5734950.37   \n",
              "8   Sï¿½o Paulo      20  ... 2.83  2.19 1487.51 3089961.53 1584.52 3137972.62   \n",
              "9   Sï¿½o Paulo      20  ... 3.10  2.21 1869.73 4795897.90 2111.93 4906204.60   \n",
              "\n",
              "     V009       V010    V011       V012  \n",
              "0 1227.41 4285771.99 1713.75 5152087.86  \n",
              "1 1045.78 2572133.32 1468.08 2991546.94  \n",
              "2  902.97 2735492.52 1786.79 3837674.74  \n",
              "3  869.09 2122409.02 1567.41 2735383.24  \n",
              "4  917.71 1570047.75 1356.35 1725877.85  \n",
              "5  878.94 1948760.23 1699.09 2375087.03  \n",
              "6  717.13 1180429.97 1308.44 1380654.87  \n",
              "7  686.94 2576914.16 1576.47 4518054.88  \n",
              "8 1006.66 2001789.21 1393.67 2232522.23  \n",
              "9 1293.43 2795012.81 1740.50 2983388.39  \n",
              "\n",
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              "      <th></th>\n",
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              "      <td>35061</td>\n",
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              "      <td>1713.75</td>\n",
              "      <td>5152087.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>355030801000002</td>\n",
              "      <td>3</td>\n",
              "      <td>Regiï¿½o Sudeste</td>\n",
              "      <td>35</td>\n",
              "      <td>Sï¿½o Paulo</td>\n",
              "      <td>3515</td>\n",
              "      <td>Metropolitana de Sï¿½o Paulo</td>\n",
              "      <td>35061</td>\n",
              "      <td>Sï¿½o Paulo</td>\n",
              "      <td>20</td>\n",
              "      <td>...</td>\n",
              "      <td>2.98</td>\n",
              "      <td>2.09</td>\n",
              "      <td>1650.09</td>\n",
              "      <td>3983441.31</td>\n",
              "      <td>1717.44</td>\n",
              "      <td>4030519.99</td>\n",
              "      <td>1045.78</td>\n",
              "      <td>2572133.32</td>\n",
              "      <td>1468.08</td>\n",
              "      <td>2991546.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>355030801000003</td>\n",
              "      <td>3</td>\n",
              "      <td>Regiï¿½o Sudeste</td>\n",
              "      <td>35</td>\n",
              "      <td>Sï¿½o Paulo</td>\n",
              "      <td>3515</td>\n",
              "      <td>Metropolitana de Sï¿½o Paulo</td>\n",
              "      <td>35061</td>\n",
              "      <td>Sï¿½o Paulo</td>\n",
              "      <td>20</td>\n",
              "      <td>...</td>\n",
              "      <td>3.31</td>\n",
              "      <td>3.08</td>\n",
              "      <td>1280.43</td>\n",
              "      <td>5474593.06</td>\n",
              "      <td>2349.53</td>\n",
              "      <td>7553913.02</td>\n",
              "      <td>902.97</td>\n",
              "      <td>2735492.52</td>\n",
              "      <td>1786.79</td>\n",
              "      <td>3837674.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>355030801000004</td>\n",
              "      <td>3</td>\n",
              "      <td>Regiï¿½o Sudeste</td>\n",
              "      <td>35</td>\n",
              "      <td>Sï¿½o Paulo</td>\n",
              "      <td>3515</td>\n",
              "      <td>Metropolitana de Sï¿½o Paulo</td>\n",
              "      <td>35061</td>\n",
              "      <td>Sï¿½o Paulo</td>\n",
              "      <td>20</td>\n",
              "      <td>...</td>\n",
              "      <td>3.16</td>\n",
              "      <td>2.26</td>\n",
              "      <td>1119.59</td>\n",
              "      <td>3572955.10</td>\n",
              "      <td>1859.13</td>\n",
              "      <td>4567289.50</td>\n",
              "      <td>869.09</td>\n",
              "      <td>2122409.02</td>\n",
              "      <td>1567.41</td>\n",
              "      <td>2735383.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>355030801000005</td>\n",
              "      <td>3</td>\n",
              "      <td>Regiï¿½o Sudeste</td>\n",
              "      <td>35</td>\n",
              "      <td>Sï¿½o Paulo</td>\n",
              "      <td>3515</td>\n",
              "      <td>Metropolitana de Sï¿½o Paulo</td>\n",
              "      <td>35061</td>\n",
              "      <td>Sï¿½o Paulo</td>\n",
              "      <td>20</td>\n",
              "      <td>...</td>\n",
              "      <td>3.14</td>\n",
              "      <td>2.35</td>\n",
              "      <td>1416.90</td>\n",
              "      <td>2240207.58</td>\n",
              "      <td>1581.66</td>\n",
              "      <td>2240109.39</td>\n",
              "      <td>917.71</td>\n",
              "      <td>1570047.75</td>\n",
              "      <td>1356.35</td>\n",
              "      <td>1725877.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>355030801000006</td>\n",
              "      <td>3</td>\n",
              "      <td>Regiï¿½o Sudeste</td>\n",
              "      <td>35</td>\n",
              "      <td>Sï¿½o Paulo</td>\n",
              "      <td>3515</td>\n",
              "      <td>Metropolitana de Sï¿½o Paulo</td>\n",
              "      <td>35061</td>\n",
              "      <td>Sï¿½o Paulo</td>\n",
              "      <td>20</td>\n",
              "      <td>...</td>\n",
              "      <td>3.03</td>\n",
              "      <td>1.70</td>\n",
              "      <td>1471.72</td>\n",
              "      <td>3314333.63</td>\n",
              "      <td>2137.02</td>\n",
              "      <td>3391364.34</td>\n",
              "      <td>878.94</td>\n",
              "      <td>1948760.23</td>\n",
              "      <td>1699.09</td>\n",
              "      <td>2375087.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>355030801000007</td>\n",
              "      <td>3</td>\n",
              "      <td>Regiï¿½o Sudeste</td>\n",
              "      <td>35</td>\n",
              "      <td>Sï¿½o Paulo</td>\n",
              "      <td>3515</td>\n",
              "      <td>Metropolitana de Sï¿½o Paulo</td>\n",
              "      <td>35061</td>\n",
              "      <td>Sï¿½o Paulo</td>\n",
              "      <td>20</td>\n",
              "      <td>...</td>\n",
              "      <td>2.77</td>\n",
              "      <td>2.47</td>\n",
              "      <td>901.47</td>\n",
              "      <td>1568830.60</td>\n",
              "      <td>1394.19</td>\n",
              "      <td>1740438.09</td>\n",
              "      <td>717.13</td>\n",
              "      <td>1180429.97</td>\n",
              "      <td>1308.44</td>\n",
              "      <td>1380654.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>355030801000008</td>\n",
              "      <td>3</td>\n",
              "      <td>Regiï¿½o Sudeste</td>\n",
              "      <td>35</td>\n",
              "      <td>Sï¿½o Paulo</td>\n",
              "      <td>3515</td>\n",
              "      <td>Metropolitana de Sï¿½o Paulo</td>\n",
              "      <td>35061</td>\n",
              "      <td>Sï¿½o Paulo</td>\n",
              "      <td>20</td>\n",
              "      <td>...</td>\n",
              "      <td>3.34</td>\n",
              "      <td>2.50</td>\n",
              "      <td>886.81</td>\n",
              "      <td>3784891.15</td>\n",
              "      <td>1616.29</td>\n",
              "      <td>5734950.37</td>\n",
              "      <td>686.94</td>\n",
              "      <td>2576914.16</td>\n",
              "      <td>1576.47</td>\n",
              "      <td>4518054.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>355030801000009</td>\n",
              "      <td>3</td>\n",
              "      <td>Regiï¿½o Sudeste</td>\n",
              "      <td>35</td>\n",
              "      <td>Sï¿½o Paulo</td>\n",
              "      <td>3515</td>\n",
              "      <td>Metropolitana de Sï¿½o Paulo</td>\n",
              "      <td>35061</td>\n",
              "      <td>Sï¿½o Paulo</td>\n",
              "      <td>20</td>\n",
              "      <td>...</td>\n",
              "      <td>2.83</td>\n",
              "      <td>2.19</td>\n",
              "      <td>1487.51</td>\n",
              "      <td>3089961.53</td>\n",
              "      <td>1584.52</td>\n",
              "      <td>3137972.62</td>\n",
              "      <td>1006.66</td>\n",
              "      <td>2001789.21</td>\n",
              "      <td>1393.67</td>\n",
              "      <td>2232522.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>355030801000010</td>\n",
              "      <td>3</td>\n",
              "      <td>Regiï¿½o Sudeste</td>\n",
              "      <td>35</td>\n",
              "      <td>Sï¿½o Paulo</td>\n",
              "      <td>3515</td>\n",
              "      <td>Metropolitana de Sï¿½o Paulo</td>\n",
              "      <td>35061</td>\n",
              "      <td>Sï¿½o Paulo</td>\n",
              "      <td>20</td>\n",
              "      <td>...</td>\n",
              "      <td>3.10</td>\n",
              "      <td>2.21</td>\n",
              "      <td>1869.73</td>\n",
              "      <td>4795897.90</td>\n",
              "      <td>2111.93</td>\n",
              "      <td>4906204.60</td>\n",
              "      <td>1293.43</td>\n",
              "      <td>2795012.81</td>\n",
              "      <td>1740.50</td>\n",
              "      <td>2983388.39</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 33 columns</p>\n",
              "</div>\n",
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          },
          "metadata": {},
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_IBGE.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f8tnn54ZYxc-",
        "outputId": "5bfab95c-faff-4d20-fe11-4355d98ca6cc"
      },
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 18363 entries, 0 to 18362\n",
            "Data columns (total 33 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   Cod_setor              18363 non-null  int64  \n",
            " 1   Cod_Grandes Regiï¿½es  18363 non-null  int64  \n",
            " 2   Nome_Grande_Regiao     18363 non-null  object \n",
            " 3   Cod_UF                 18363 non-null  int64  \n",
            " 4   Nome_da_UF             18363 non-null  object \n",
            " 5   Cod_meso               18363 non-null  int64  \n",
            " 6   Nome_da_meso           18363 non-null  object \n",
            " 7   Cod_micro              18363 non-null  int64  \n",
            " 8   Nome_da_micro          18363 non-null  object \n",
            " 9   Cod_RM                 18363 non-null  int64  \n",
            " 10  Nome_da_RM             18363 non-null  object \n",
            " 11  Cod_municipio          18363 non-null  int64  \n",
            " 12  Nome_do_municipio      18363 non-null  object \n",
            " 13  Cod_distrito           18363 non-null  int64  \n",
            " 14  Nome_do_distrito       18363 non-null  object \n",
            " 15  Cod_subdistrito        18363 non-null  int64  \n",
            " 16  Nome_do_subdistrito    18363 non-null  object \n",
            " 17  Cod_bairro             18363 non-null  int64  \n",
            " 18  Nome_do_bairro         18363 non-null  object \n",
            " 19  Situacao_setor         18363 non-null  int64  \n",
            " 20  Tipo_setor             18363 non-null  int64  \n",
            " 21  V001                   18333 non-null  float64\n",
            " 22  V002                   18333 non-null  float64\n",
            " 23  V003                   18333 non-null  float64\n",
            " 24  V004                   18333 non-null  float64\n",
            " 25  V005                   18333 non-null  float64\n",
            " 26  V006                   18333 non-null  float64\n",
            " 27  V007                   18324 non-null  float64\n",
            " 28  V008                   18324 non-null  float64\n",
            " 29  V009                   18363 non-null  float64\n",
            " 30  V010                   18363 non-null  float64\n",
            " 31  V011                   18352 non-null  float64\n",
            " 32  V012                   18352 non-null  float64\n",
            "dtypes: float64(12), int64(12), object(9)\n",
            "memory usage: 4.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_IBGE.groupby('Nome_do_bairro').sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "id": "ebq9n1ZtEhZJ",
        "outputId": "ca9b08ed-3839-4669-9480-b8f9115bec1c"
      },
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          Cod_setor  Cod_Grandes Regiï¿½es  \\\n",
              "Nome_do_bairro                                                               \n",
              "Sï¿½O PAULO (todos os setores)  6519431478714281984                  55089   \n",
              "\n",
              "                                Cod_UF  Cod_meso  Cod_micro  Cod_RM  \\\n",
              "Nome_do_bairro                                                        \n",
              "Sï¿½O PAULO (todos os setores)  642705  64545945  643825143  367260   \n",
              "\n",
              "                                Cod_municipio   Cod_distrito  Cod_subdistrito  \\\n",
              "Nome_do_bairro                                                                  \n",
              "Sï¿½O PAULO (todos os setores)    65194305804  6519431478712  651943147871200   \n",
              "\n",
              "                                    Cod_bairro  ...     V003     V004  \\\n",
              "Nome_do_bairro                                  ...                     \n",
              "Sï¿½O PAULO (todos os setores)  65194305804000  ... 57863.55 43012.89   \n",
              "\n",
              "                                      V005            V006        V007  \\\n",
              "Nome_do_bairro                                                           \n",
              "Sï¿½O PAULO (todos os setores) 39602687.34 567651100381.86 45660620.51   \n",
              "\n",
              "                                          V008        V009            V010  \\\n",
              "Nome_do_bairro                                                               \n",
              "Sï¿½O PAULO (todos os setores) 650438382064.17 25955144.01 358723189043.28   \n",
              "\n",
              "                                      V011            V012  \n",
              "Nome_do_bairro                                              \n",
              "Sï¿½O PAULO (todos os setores) 37801041.97 492016827669.34  \n",
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              "      <th>Cod_setor</th>\n",
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              "      <td>492016827669.34</td>\n",
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      ]
    },
    {
      "cell_type": "code",
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        "dados_vendas"
      ],
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          "height": 693
        },
        "id": "5TwIxJk1BjSj",
        "outputId": "e3ce3155-0ebd-4577-e303-9dd187e2c19f"
      },
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                  Rua              Bairro     Cidade  \\\n",
              "0               Avenida Itacira, 255    Planalto Paulista  São Paulo   \n",
              "1      Rua Aurelia Perez Alvarez, 42   Jardim dos Estados  São Paulo   \n",
              "2                     Rua Alba Valdez     Jardim Reimberg  São Paulo   \n",
              "3                                 NaN      Jardim Morumbi  São Paulo   \n",
              "4            Rua Tobias Barreto, 195                Mooca  São Paulo   \n",
              "...                               ...                 ...        ...   \n",
              "10002                   Avenida Odila   Planalto Paulista  São Paulo   \n",
              "10003                      Rua Tapuçu          Vila Sofia  São Paulo   \n",
              "10004                   Rua Guararema     Bosque da Saúde  São Paulo   \n",
              "10005               Rua Estero Belaco       Vila da Saúde  São Paulo   \n",
              "10007      Rua Barão de Tramandaí, 0          Vila Alpina  São Paulo   \n",
              "\n",
              "       Metragem  Quartos  Banheiros  Vagas         Valor Moeda Valor_anuncio  \\\n",
              "0          1000        4          8      6  R$ 7.000.000    R$     7.000.000   \n",
              "1           496        4          4      4  R$ 3.700.000    R$     3.700.000   \n",
              "2           125        4          3      2    R$ 380.000    R$       380.000   \n",
              "3           310        3          2      4    R$ 685.000    R$       685.000   \n",
              "4           100        3          2      2    R$ 540.000    R$       540.000   \n",
              "...         ...      ...        ...    ...           ...   ...           ...   \n",
              "10002       267        3          6      4  R$ 1.500.000    R$     1.500.000   \n",
              "10003        95        2          2      2    R$ 665.000    R$       665.000   \n",
              "10004       605        8          7      5  R$ 2.300.000    R$     2.300.000   \n",
              "10005       205        3          3      6  R$ 1.050.000    R$     1.050.000   \n",
              "10007       122        3          2      2    R$ 270.000    R$       270.000   \n",
              "\n",
              "      Tipo_anuncio  Valor_anuncio_float  Valor_M2  \n",
              "0             None           7000000.00   7000.00  \n",
              "1             None           3700000.00   7459.68  \n",
              "2             None            380000.00   3040.00  \n",
              "3             None            685000.00   2209.68  \n",
              "4             None            540000.00   5400.00  \n",
              "...            ...                  ...       ...  \n",
              "10002         None           1500000.00   5617.98  \n",
              "10003         None            665000.00   7000.00  \n",
              "10004         None           2300000.00   3801.65  \n",
              "10005         None           1050000.00   5121.95  \n",
              "10007         None            270000.00   2213.11  \n",
              "\n",
              "[9156 rows x 13 columns]"
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              "      <td>Avenida Itacira, 255</td>\n",
              "      <td>Planalto Paulista</td>\n",
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              "      <td>1000</td>\n",
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              "      <td>Rua Aurelia Perez Alvarez, 42</td>\n",
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              "      <td>496</td>\n",
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              "      <td>4</td>\n",
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              "      <td>3700000.00</td>\n",
              "      <td>7459.68</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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              "      <td>Jardim Morumbi</td>\n",
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              "      <td>310</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>R$ 685.000</td>\n",
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              "      <td>685.000</td>\n",
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              "      <td>685000.00</td>\n",
              "      <td>2209.68</td>\n",
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              "      <th>4</th>\n",
              "      <td>Rua Tobias Barreto, 195</td>\n",
              "      <td>Mooca</td>\n",
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              "      <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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              "      <td>540.000</td>\n",
              "      <td>None</td>\n",
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              "      <td>São Paulo</td>\n",
              "      <td>267</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
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              "      <td>R$</td>\n",
              "      <td>1.500.000</td>\n",
              "      <td>None</td>\n",
              "      <td>1500000.00</td>\n",
              "      <td>5617.98</td>\n",
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              "    <tr>\n",
              "      <th>10003</th>\n",
              "      <td>Rua Tapuçu</td>\n",
              "      <td>Vila Sofia</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>95</td>\n",
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              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "      <td>7000.00</td>\n",
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              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>R$ 2.300.000</td>\n",
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              "      <td>2.300.000</td>\n",
              "      <td>None</td>\n",
              "      <td>2300000.00</td>\n",
              "      <td>3801.65</td>\n",
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              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
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              "      <td>5121.95</td>\n",
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              "      <th>10007</th>\n",
              "      <td>Rua Barão de Tramandaí, 0</td>\n",
              "      <td>Vila Alpina</td>\n",
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              "      <td>122</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>R$ 270.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>270.000</td>\n",
              "      <td>None</td>\n",
              "      <td>270000.00</td>\n",
              "      <td>2213.11</td>\n",
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              "<p>9156 rows × 13 columns</p>\n",
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              "          const element = document.querySelector('#df-f11d1855-c472-4bfb-a8ca-326503ec6b55');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# importando uma nova base de dados direto do Drive para conseguirmos vincular\n",
        "# os dados de longitute/latitute com o endereço e com o código setor \n",
        "enderecos = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/imersão_dados_alura/enderecos.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dh9mtJ_MBuQb",
        "outputId": "4724fd31-05df-40cc-9cb3-9e905c25727e"
      },
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2882: DtypeWarning: Columns (3) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "enderecos.head()"
      ],
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        "colab": {
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        "id": "4g7bS7jFpeE6",
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              "  tipo_logr                      logr_nome  \\\n",
              "0       Rua               Armando Silveira   \n",
              "1       Rua       João Antônio Vasconcelos   \n",
              "2       Rua  Prefeito Agenor Barcelos Feio   \n",
              "3       Rua                Sebastião Gomes   \n",
              "4       Rua            Marquês do Alegrete   \n",
              "\n",
              "                                   logr_compl numero            bairro  \\\n",
              "0                        Rua Armando Silveira    NaN            Divisa   \n",
              "1  Rua João Antônio Vasconcelos - até 627/628    627           Amizade   \n",
              "2           Rua Prefeito Agenor Barcelos Feio    NaN          Registro   \n",
              "3             Rua Sebastião Gomes - até 59/60     59          Registro   \n",
              "4                     Rua Marquês do Alegrete    NaN  Parque das Águas   \n",
              "\n",
              "         nome_localidade sigla_uf         cep  latitude  longitude  \n",
              "0  Santana do Livramento       RS 97573200.00    -30.88     -55.55  \n",
              "1              Araçatuba       SP 16074320.00    -21.19     -50.45  \n",
              "2  Santana do Livramento       RS 97571090.00    -30.86     -55.56  \n",
              "3  Santana do Livramento       RS 97571374.00    -30.86     -55.55  \n",
              "4  Santana do Livramento       RS 97571500.00    -30.86     -55.55  "
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              "      <td>Rua João Antônio Vasconcelos - até 627/628</td>\n",
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              "      <th>2</th>\n",
              "      <td>Rua</td>\n",
              "      <td>Prefeito Agenor Barcelos Feio</td>\n",
              "      <td>Rua Prefeito Agenor Barcelos Feio</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Registro</td>\n",
              "      <td>Santana do Livramento</td>\n",
              "      <td>RS</td>\n",
              "      <td>97571090.00</td>\n",
              "      <td>-30.86</td>\n",
              "      <td>-55.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Rua</td>\n",
              "      <td>Sebastião Gomes</td>\n",
              "      <td>Rua Sebastião Gomes - até 59/60</td>\n",
              "      <td>59</td>\n",
              "      <td>Registro</td>\n",
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              "      <td>-30.86</td>\n",
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              "      <th>4</th>\n",
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              "      <td>Marquês do Alegrete</td>\n",
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              "      <td>Parque das Águas</td>\n",
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              "      <td>-30.86</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# separando apenas os endereços de SP dessa df\n",
        "enderecos_sp = enderecos.query('sigla_uf == \"SP\"')\n",
        "enderecos_sp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        },
        "id": "XOfiKIIBqGBP",
        "outputId": "2ecbf1df-ee6d-4867-c6c5-5dc5c6302bce"
      },
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        tipo_logr                  logr_nome  \\\n",
              "1             Rua   João Antônio Vasconcelos   \n",
              "413269        Rua                          A   \n",
              "413270        Rua                          A   \n",
              "413271        Rua                          A   \n",
              "413272        Rua               João Bandini   \n",
              "...           ...                        ...   \n",
              "1113858       Rua            Daniel Buccieri   \n",
              "1113865       Rua             Ottorino Rizzi   \n",
              "1113873       Rua  Adriano Michelutti Cândia   \n",
              "1113882   Rodovia              Mario Tonolli   \n",
              "1113884       Rua                 Tiradentes   \n",
              "\n",
              "                                         logr_compl numero  \\\n",
              "1        Rua João Antônio Vasconcelos - até 627/628    627   \n",
              "413269                                        Rua A    NaN   \n",
              "413270                                        Rua A    NaN   \n",
              "413271                                        Rua A    NaN   \n",
              "413272                             Rua João Bandini    NaN   \n",
              "...                                             ...    ...   \n",
              "1113858                         Rua Daniel Buccieri    NaN   \n",
              "1113865                          Rua Ottorino Rizzi    NaN   \n",
              "1113873               Rua Adriano Michelutti Cândia    NaN   \n",
              "1113882                       Rodovia Mario Tonolli    NaN   \n",
              "1113884  Rua Tiradentes - de 181 a 369 - lado ímpar    181   \n",
              "\n",
              "                         bairro nome_localidade sigla_uf         cep  \\\n",
              "1                       Amizade       Araçatuba       SP 16074320.00   \n",
              "413269            Jardim Glória       Americana       SP 13468220.00   \n",
              "413270         Jardim São Roque       Americana       SP 13469414.00   \n",
              "413271   Iate Clube de Campinas       Americana       SP 13475620.00   \n",
              "413272            Jardim Miriam       Americana       SP 13469072.00   \n",
              "...                         ...             ...      ...         ...   \n",
              "1113858         Jardim Alvorada       Monte Mor       SP 13198524.00   \n",
              "1113865              Royal Park  Ribeirão Preto       SP 14028563.00   \n",
              "1113873     Jardim San Marco II  Ribeirão Preto       SP 14035198.00   \n",
              "1113882              Morro Alto         Itupeva       SP 13297002.00   \n",
              "1113884            Vila Queiroz  Artur Nogueira       SP 13163000.00   \n",
              "\n",
              "         latitude  longitude  \n",
              "1          -21.19     -50.45  \n",
              "413269     -22.76     -47.34  \n",
              "413270     -22.74     -47.35  \n",
              "413271     -22.74     -47.33  \n",
              "413272     -22.74     -47.35  \n",
              "...           ...        ...  \n",
              "1113858    -22.93     -47.27  \n",
              "1113865    -21.24     -47.83  \n",
              "1113873    -21.20     -47.74  \n",
              "1113882    -23.19     -47.08  \n",
              "1113884    -22.58     -47.17  \n",
              "\n",
              "[298124 rows x 10 columns]"
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              "      <td>Rua</td>\n",
              "      <td>João Antônio Vasconcelos</td>\n",
              "      <td>Rua João Antônio Vasconcelos - até 627/628</td>\n",
              "      <td>627</td>\n",
              "      <td>Amizade</td>\n",
              "      <td>Araçatuba</td>\n",
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              "      <th>413269</th>\n",
              "      <td>Rua</td>\n",
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              "      <td>Jardim Glória</td>\n",
              "      <td>Americana</td>\n",
              "      <td>SP</td>\n",
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              "      <td>-22.76</td>\n",
              "      <td>-47.34</td>\n",
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              "      <th>413270</th>\n",
              "      <td>Rua</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>Jardim São Roque</td>\n",
              "      <td>Americana</td>\n",
              "      <td>SP</td>\n",
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              "      <td>-22.74</td>\n",
              "      <td>-47.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413271</th>\n",
              "      <td>Rua</td>\n",
              "      <td>A</td>\n",
              "      <td>Rua A</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Iate Clube de Campinas</td>\n",
              "      <td>Americana</td>\n",
              "      <td>SP</td>\n",
              "      <td>13475620.00</td>\n",
              "      <td>-22.74</td>\n",
              "      <td>-47.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413272</th>\n",
              "      <td>Rua</td>\n",
              "      <td>João Bandini</td>\n",
              "      <td>Rua João Bandini</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Jardim Miriam</td>\n",
              "      <td>Americana</td>\n",
              "      <td>SP</td>\n",
              "      <td>13469072.00</td>\n",
              "      <td>-22.74</td>\n",
              "      <td>-47.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1113858</th>\n",
              "      <td>Rua</td>\n",
              "      <td>Daniel Buccieri</td>\n",
              "      <td>Rua Daniel Buccieri</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Jardim Alvorada</td>\n",
              "      <td>Monte Mor</td>\n",
              "      <td>SP</td>\n",
              "      <td>13198524.00</td>\n",
              "      <td>-22.93</td>\n",
              "      <td>-47.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1113865</th>\n",
              "      <td>Rua</td>\n",
              "      <td>Ottorino Rizzi</td>\n",
              "      <td>Rua Ottorino Rizzi</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Royal Park</td>\n",
              "      <td>Ribeirão Preto</td>\n",
              "      <td>SP</td>\n",
              "      <td>14028563.00</td>\n",
              "      <td>-21.24</td>\n",
              "      <td>-47.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1113873</th>\n",
              "      <td>Rua</td>\n",
              "      <td>Adriano Michelutti Cândia</td>\n",
              "      <td>Rua Adriano Michelutti Cândia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Jardim San Marco II</td>\n",
              "      <td>Ribeirão Preto</td>\n",
              "      <td>SP</td>\n",
              "      <td>14035198.00</td>\n",
              "      <td>-21.20</td>\n",
              "      <td>-47.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1113882</th>\n",
              "      <td>Rodovia</td>\n",
              "      <td>Mario Tonolli</td>\n",
              "      <td>Rodovia Mario Tonolli</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Morro Alto</td>\n",
              "      <td>Itupeva</td>\n",
              "      <td>SP</td>\n",
              "      <td>13297002.00</td>\n",
              "      <td>-23.19</td>\n",
              "      <td>-47.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1113884</th>\n",
              "      <td>Rua</td>\n",
              "      <td>Tiradentes</td>\n",
              "      <td>Rua Tiradentes - de 181 a 369 - lado ímpar</td>\n",
              "      <td>181</td>\n",
              "      <td>Vila Queiroz</td>\n",
              "      <td>Artur Nogueira</td>\n",
              "      <td>SP</td>\n",
              "      <td>13163000.00</td>\n",
              "      <td>-22.58</td>\n",
              "      <td>-47.17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>298124 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-905489e4-6284-4cac-8225-229247bff5b5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      </button>\n",
              "      \n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-905489e4-6284-4cac-8225-229247bff5b5 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-905489e4-6284-4cac-8225-229247bff5b5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# precisamos criar uma nova coluna de dados unindo tipo_logr com logr_nome\n",
        "enderecos_sp['rua'] = enderecos_sp['tipo_logr'] + ' ' + enderecos_sp['logr_nome']\n",
        "enderecos_sp['rua'] = enderecos_sp['rua'].str.lower().str.strip()\n",
        "enderecos_sp.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "XBRkhgKDrgBb",
        "outputId": "33044251-d622-49d7-c97d-87f80d9587e0"
      },
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       tipo_logr                 logr_nome  \\\n",
              "1            Rua  João Antônio Vasconcelos   \n",
              "413269       Rua                         A   \n",
              "413270       Rua                         A   \n",
              "413271       Rua                         A   \n",
              "413272       Rua              João Bandini   \n",
              "\n",
              "                                        logr_compl numero  \\\n",
              "1       Rua João Antônio Vasconcelos - até 627/628    627   \n",
              "413269                                       Rua A    NaN   \n",
              "413270                                       Rua A    NaN   \n",
              "413271                                       Rua A    NaN   \n",
              "413272                            Rua João Bandini    NaN   \n",
              "\n",
              "                        bairro nome_localidade sigla_uf         cep  latitude  \\\n",
              "1                      Amizade       Araçatuba       SP 16074320.00    -21.19   \n",
              "413269           Jardim Glória       Americana       SP 13468220.00    -22.76   \n",
              "413270        Jardim São Roque       Americana       SP 13469414.00    -22.74   \n",
              "413271  Iate Clube de Campinas       Americana       SP 13475620.00    -22.74   \n",
              "413272           Jardim Miriam       Americana       SP 13469072.00    -22.74   \n",
              "\n",
              "        longitude                           rua  \n",
              "1          -50.45  rua joão antônio vasconcelos  \n",
              "413269     -47.34                         rua a  \n",
              "413270     -47.35                         rua a  \n",
              "413271     -47.33                         rua a  \n",
              "413272     -47.35              rua joão bandini  "
            ],
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              "\n",
              "  <div id=\"df-7406d405-3d03-4b78-acaf-4e95b022df7e\">\n",
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              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>sigla_uf</th>\n",
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              "      <th>1</th>\n",
              "      <td>Rua</td>\n",
              "      <td>João Antônio Vasconcelos</td>\n",
              "      <td>Rua João Antônio Vasconcelos - até 627/628</td>\n",
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              "      <td>rua joão antônio vasconcelos</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>Jardim Glória</td>\n",
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              "      <th>413270</th>\n",
              "      <td>Rua</td>\n",
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              "      <th>413271</th>\n",
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              "      <td>Iate Clube de Campinas</td>\n",
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              "      <td>-22.74</td>\n",
              "      <td>-47.33</td>\n",
              "      <td>rua a</td>\n",
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              "    <tr>\n",
              "      <th>413272</th>\n",
              "      <td>Rua</td>\n",
              "      <td>João Bandini</td>\n",
              "      <td>Rua João Bandini</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Jardim Miriam</td>\n",
              "      <td>Americana</td>\n",
              "      <td>SP</td>\n",
              "      <td>13469072.00</td>\n",
              "      <td>-22.74</td>\n",
              "      <td>-47.35</td>\n",
              "      <td>rua joão bandini</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-7406d405-3d03-4b78-acaf-4e95b022df7e');\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": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# para conseguir criar a relação entre df, precisamos remover os nºs dos\n",
        "# endereços da nossa df dados_vendas\n",
        "dados_vendas['Rua'].str.extract(r'(^[\\w ]+)')[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "OCB8Kvc9sPXR",
        "outputId": "8b3d1025-17f8-4eeb-a299-8febf8d36daa"
      },
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           0\n",
              "0            Avenida Itacira\n",
              "1  Rua Aurelia Perez Alvarez\n",
              "2            Rua Alba Valdez\n",
              "3                        NaN\n",
              "4         Rua Tobias Barreto\n",
              "5            Rua Graham Bell\n",
              "6        Rua Francisco Paulo\n",
              "7      Rua Guilherme Valente\n",
              "8        Rua Sagrada Família\n",
              "9                 Rua Tapaji"
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              "  "
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "# criando essa nova coluna e editando as strings\n",
        "dados_vendas['apenas_rua'] = dados_vendas['Rua'].str.extract(r'(^[\\w ]+)')\n",
        "dados_vendas['apenas_rua'] = dados_vendas['apenas_rua'].str.lower().str.strip()\n",
        "dados_vendas.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        },
        "id": "JXnhM8fvtM2I",
        "outputId": "096cc2ea-9ba7-463a-be22-f1ca43bf2eaf"
      },
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
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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 Moeda Valor_anuncio Tipo_anuncio  \\\n",
              "0        4          8      6  R$ 7.000.000    R$     7.000.000         None   \n",
              "1        4          4      4  R$ 3.700.000    R$     3.700.000         None   \n",
              "2        4          3      2    R$ 380.000    R$       380.000         None   \n",
              "3        3          2      4    R$ 685.000    R$       685.000         None   \n",
              "4        3          2      2    R$ 540.000    R$       540.000         None   \n",
              "\n",
              "   Valor_anuncio_float  Valor_M2                 apenas_rua  \n",
              "0           7000000.00   7000.00            avenida itacira  \n",
              "1           3700000.00   7459.68  rua aurelia perez alvarez  \n",
              "2            380000.00   3040.00            rua alba valdez  \n",
              "3            685000.00   2209.68                        NaN  \n",
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              "      <td>3</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "# criando uma nova df com esses dados unificados\n",
        "dados_geo = pd.merge(left=dados_vendas, right=enderecos_sp[['rua', 'cep', 'latitude', 'longitude']], how='left', left_on='apenas_rua', right_on='rua').drop_duplicates(subset=dados_vendas.columns).query('cep > 0')\n",
        "dados_geo"
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      "metadata": {
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          "height": 748
        },
        "id": "P3d1FIJutht-",
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      "execution_count": 168,
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          "data": {
            "text/plain": [
              "                                  Rua              Bairro     Cidade  \\\n",
              "0               Avenida Itacira, 255    Planalto Paulista  São Paulo   \n",
              "5      Rua Aurelia Perez Alvarez, 42   Jardim dos Estados  São Paulo   \n",
              "6                     Rua Alba Valdez     Jardim Reimberg  São Paulo   \n",
              "7                                 NaN      Jardim Morumbi  São Paulo   \n",
              "26           Rua Tobias Barreto, 195                Mooca  São Paulo   \n",
              "...                               ...                 ...        ...   \n",
              "89956                   Avenida Odila   Planalto Paulista  São Paulo   \n",
              "89958                      Rua Tapuçu          Vila Sofia  São Paulo   \n",
              "89959                   Rua Guararema     Bosque da Saúde  São Paulo   \n",
              "89976               Rua Estero Belaco       Vila da Saúde  São Paulo   \n",
              "89977      Rua Barão de Tramandaí, 0          Vila Alpina  São Paulo   \n",
              "\n",
              "       Metragem  Quartos  Banheiros  Vagas         Valor Moeda Valor_anuncio  \\\n",
              "0          1000        4          8      6  R$ 7.000.000    R$     7.000.000   \n",
              "5           496        4          4      4  R$ 3.700.000    R$     3.700.000   \n",
              "6           125        4          3      2    R$ 380.000    R$       380.000   \n",
              "7           310        3          2      4    R$ 685.000    R$       685.000   \n",
              "26          100        3          2      2    R$ 540.000    R$       540.000   \n",
              "...         ...      ...        ...    ...           ...   ...           ...   \n",
              "89956       267        3          6      4  R$ 1.500.000    R$     1.500.000   \n",
              "89958        95        2          2      2    R$ 665.000    R$       665.000   \n",
              "89959       605        8          7      5  R$ 2.300.000    R$     2.300.000   \n",
              "89976       205        3          3      6  R$ 1.050.000    R$     1.050.000   \n",
              "89977       122        3          2      2    R$ 270.000    R$       270.000   \n",
              "\n",
              "      Tipo_anuncio  Valor_anuncio_float  Valor_M2                 apenas_rua  \\\n",
              "0             None           7000000.00   7000.00            avenida itacira   \n",
              "5             None           3700000.00   7459.68  rua aurelia perez alvarez   \n",
              "6             None            380000.00   3040.00            rua alba valdez   \n",
              "7             None            685000.00   2209.68                        NaN   \n",
              "26            None            540000.00   5400.00         rua tobias barreto   \n",
              "...            ...                  ...       ...                        ...   \n",
              "89956         None           1500000.00   5617.98              avenida odila   \n",
              "89958         None            665000.00   7000.00                 rua tapuçu   \n",
              "89959         None           2300000.00   3801.65              rua guararema   \n",
              "89976         None           1050000.00   5121.95          rua estero belaco   \n",
              "89977         None            270000.00   2213.11     rua barão de tramandaí   \n",
              "\n",
              "                             rua         cep  latitude  longitude  \n",
              "0                avenida itacira 11443160.00    -23.98     -46.20  \n",
              "5      rua aurelia perez alvarez  4642020.00    -23.64     -46.69  \n",
              "6                rua alba valdez  4845200.00    -23.75     -46.70  \n",
              "7                            NaN 18125000.00    -23.53     -47.26  \n",
              "26            rua tobias barreto 13474663.00    -22.70     -47.29  \n",
              "...                          ...         ...       ...        ...  \n",
              "89956              avenida odila  4058020.00    -23.61     -46.65  \n",
              "89958                 rua tapuçu  4671185.00    -23.66     -46.69  \n",
              "89959              rua guararema 13030640.00    -22.92     -47.08  \n",
              "89976          rua estero belaco  4145020.00    -23.62     -46.63  \n",
              "89977     rua barão de tramandaí  3207000.00    -23.60     -46.56  \n",
              "\n",
              "[8520 rows x 18 columns]"
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              "      <td>3.700.000</td>\n",
              "      <td>None</td>\n",
              "      <td>3700000.00</td>\n",
              "      <td>7459.68</td>\n",
              "      <td>rua aurelia perez alvarez</td>\n",
              "      <td>rua aurelia perez alvarez</td>\n",
              "      <td>4642020.00</td>\n",
              "      <td>-23.64</td>\n",
              "      <td>-46.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</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",
              "      <td>R$</td>\n",
              "      <td>380.000</td>\n",
              "      <td>None</td>\n",
              "      <td>380000.00</td>\n",
              "      <td>3040.00</td>\n",
              "      <td>rua alba valdez</td>\n",
              "      <td>rua alba valdez</td>\n",
              "      <td>4845200.00</td>\n",
              "      <td>-23.75</td>\n",
              "      <td>-46.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Jardim Morumbi</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>310</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>R$ 685.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>685.000</td>\n",
              "      <td>None</td>\n",
              "      <td>685000.00</td>\n",
              "      <td>2209.68</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18125000.00</td>\n",
              "      <td>-23.53</td>\n",
              "      <td>-47.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Rua Tobias Barreto, 195</td>\n",
              "      <td>Mooca</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>100</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>R$ 540.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>540.000</td>\n",
              "      <td>None</td>\n",
              "      <td>540000.00</td>\n",
              "      <td>5400.00</td>\n",
              "      <td>rua tobias barreto</td>\n",
              "      <td>rua tobias barreto</td>\n",
              "      <td>13474663.00</td>\n",
              "      <td>-22.70</td>\n",
              "      <td>-47.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89956</th>\n",
              "      <td>Avenida Odila</td>\n",
              "      <td>Planalto Paulista</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>267</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>R$ 1.500.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>1.500.000</td>\n",
              "      <td>None</td>\n",
              "      <td>1500000.00</td>\n",
              "      <td>5617.98</td>\n",
              "      <td>avenida odila</td>\n",
              "      <td>avenida odila</td>\n",
              "      <td>4058020.00</td>\n",
              "      <td>-23.61</td>\n",
              "      <td>-46.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89958</th>\n",
              "      <td>Rua Tapuçu</td>\n",
              "      <td>Vila Sofia</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>95</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>R$ 665.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>665.000</td>\n",
              "      <td>None</td>\n",
              "      <td>665000.00</td>\n",
              "      <td>7000.00</td>\n",
              "      <td>rua tapuçu</td>\n",
              "      <td>rua tapuçu</td>\n",
              "      <td>4671185.00</td>\n",
              "      <td>-23.66</td>\n",
              "      <td>-46.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89959</th>\n",
              "      <td>Rua Guararema</td>\n",
              "      <td>Bosque da Saúde</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>605</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>R$ 2.300.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>2.300.000</td>\n",
              "      <td>None</td>\n",
              "      <td>2300000.00</td>\n",
              "      <td>3801.65</td>\n",
              "      <td>rua guararema</td>\n",
              "      <td>rua guararema</td>\n",
              "      <td>13030640.00</td>\n",
              "      <td>-22.92</td>\n",
              "      <td>-47.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89976</th>\n",
              "      <td>Rua Estero Belaco</td>\n",
              "      <td>Vila da Saúde</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>205</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>R$ 1.050.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>1.050.000</td>\n",
              "      <td>None</td>\n",
              "      <td>1050000.00</td>\n",
              "      <td>5121.95</td>\n",
              "      <td>rua estero belaco</td>\n",
              "      <td>rua estero belaco</td>\n",
              "      <td>4145020.00</td>\n",
              "      <td>-23.62</td>\n",
              "      <td>-46.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89977</th>\n",
              "      <td>Rua Barão de Tramandaí, 0</td>\n",
              "      <td>Vila Alpina</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>122</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>R$ 270.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>270.000</td>\n",
              "      <td>None</td>\n",
              "      <td>270000.00</td>\n",
              "      <td>2213.11</td>\n",
              "      <td>rua barão de tramandaí</td>\n",
              "      <td>rua barão de tramandaí</td>\n",
              "      <td>3207000.00</td>\n",
              "      <td>-23.60</td>\n",
              "      <td>-46.56</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8520 rows × 18 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e1a3e78-f857-4b44-9ff2-8a01249641a6')\"\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",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0e1a3e78-f857-4b44-9ff2-8a01249641a6 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-0e1a3e78-f857-4b44-9ff2-8a01249641a6');\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",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# iremos importar essa biblioteca para trabalhar com sistemas de conjuntos de\n",
        "# superfícies com dados de latitude/longitude\n",
        "from shapely.geometry import Point\n",
        "latitude = -23.56\n",
        "longitude = -46.59\n",
        "Point(longitude, latitude)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "id": "LQz7epIgwgML",
        "outputId": "f8aa8178-f74f-4db3-b988-b21feb271e34"
      },
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<shapely.geometry.point.Point at 0x7fa7e1c11a50>"
            ],
            "image/svg+xml": "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-47.59 -24.56 2.0 2.0\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,-47.12)\"><circle cx=\"-46.59\" cy=\"-23.56\" r=\"0.06\" stroke=\"#555555\" stroke-width=\"0.02\" fill=\"#66cc99\" opacity=\"0.6\" /></g></svg>"
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# além de um ponto, podemos ter um polígono\n",
        "from shapely.geometry import Polygon\n",
        "Polygon([[0, 0], [1, 0], [1, 1], [0, 1], [0, 0]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "id": "-_Fu-7oGCIp0",
        "outputId": "1f60ecec-8d59-460a-f8df-5615025a8072"
      },
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<shapely.geometry.polygon.Polygon at 0x7fa7e1c15a10>"
            ],
            "image/svg+xml": "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-0.04 -0.04 1.08 1.08\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,1.0)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.0216\" opacity=\"0.6\" d=\"M 0.0,0.0 L 1.0,0.0 L 1.0,1.0 L 0.0,1.0 L 0.0,0.0 z\" /></g></svg>"
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# verificando se um ponto está dentro do nosso polígono\n",
        "Polygon([[0, 0], [1, 0], [1, 1], [0, 1], [0, 0]]).contains(Point(0.1, 0.9))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6lroAvgFCnCr",
        "outputId": "69f0d706-4a55-4964-a5c7-5c647e8afa46"
      },
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# essa biblioteca não está instalada no Colab\n",
        "import geopandas"
      ],
      "metadata": {
        "id": "C6Qa2V8UCxnE"
      },
      "execution_count": 172,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# vamos simular uma linha de comando dentro do nosso caderno\n",
        "!pip install geopandas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CnlznusVEm4U",
        "outputId": "9d5673a5-a693-4485-9e74-c0c3033fa4dc"
      },
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: geopandas in /usr/local/lib/python3.7/dist-packages (0.10.2)\n",
            "Requirement already satisfied: pyproj>=2.2.0 in /usr/local/lib/python3.7/dist-packages (from geopandas) (3.2.1)\n",
            "Requirement already satisfied: fiona>=1.8 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.8.21)\n",
            "Requirement already satisfied: shapely>=1.6 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.8.2)\n",
            "Requirement already satisfied: pandas>=0.25.0 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.3.5)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (57.4.0)\n",
            "Requirement already satisfied: click>=4.0 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (7.1.2)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (2022.5.18.1)\n",
            "Requirement already satisfied: six>=1.7 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (1.15.0)\n",
            "Requirement already satisfied: attrs>=17 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (21.4.0)\n",
            "Requirement already satisfied: munch in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (2.5.0)\n",
            "Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (0.7.2)\n",
            "Requirement already satisfied: click-plugins>=1.0 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (1.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (2022.1)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (1.21.6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import geopandas as gpd"
      ],
      "metadata": {
        "id": "F80VHRMrE26X"
      },
      "execution_count": 174,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# vamos ler essa nova base de dados\n",
        "# o que nos importa é esse CD_GEOCODI que é justamente o código censitário que \n",
        "# temos na outra base de dados do IBGE\n",
        "# além disso, temos a coluna geometry que tem justamente o polígono\n",
        "setor_censo = gpd.read_file('/content/drive/MyDrive/Colab Notebooks/imersão_dados_alura/35SEE250GC_SIR.shp')\n",
        "setor_censo.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "J7qapdncE47c",
        "outputId": "1149306e-fe18-4dfb-90ac-86bfad5f5646"
      },
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID       CD_GEOCODI    TIPO   CD_GEOCODS NM_SUBDIST CD_GEOCODD  \\\n",
              "0 98237.00  354100005000009  URBANO  35410000500       None  354100005   \n",
              "1 98232.00  354100005000004  URBANO  35410000500       None  354100005   \n",
              "2 98230.00  354100005000002  URBANO  35410000500       None  354100005   \n",
              "3 98229.00  354100005000001  URBANO  35410000500       None  354100005   \n",
              "4 98231.00  354100005000003  URBANO  35410000500       None  354100005   \n",
              "\n",
              "     NM_DISTRIT CD_GEOCODM    NM_MUNICIP NM_MICRO                     NM_MESO  \\\n",
              "0  PRAIA GRANDE    3541000  PRAIA GRANDE   SANTOS  METROPOLITANA DE SÃO PAULO   \n",
              "1  PRAIA GRANDE    3541000  PRAIA GRANDE   SANTOS  METROPOLITANA DE SÃO PAULO   \n",
              "2  PRAIA GRANDE    3541000  PRAIA GRANDE   SANTOS  METROPOLITANA DE SÃO PAULO   \n",
              "3  PRAIA GRANDE    3541000  PRAIA GRANDE   SANTOS  METROPOLITANA DE SÃO PAULO   \n",
              "4  PRAIA GRANDE    3541000  PRAIA GRANDE   SANTOS  METROPOLITANA DE SÃO PAULO   \n",
              "\n",
              "     CD_GEOCODB  NM_BAIRRO  ID1  \\\n",
              "0  354100005001  Boqueirão    1   \n",
              "1  354100005001  Boqueirão    2   \n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "# iremos fazer uma divisão com base no NM_MUNICIP para ter só dadados de SP\n",
        "# após isso, verificamos se o exemplos de long/lati que usamos anteriormente\n",
        "# estão contidos em algum polígono da df setor_censo_sp\n",
        "setor_censo_sp = setor_censo[setor_censo.NM_MUNICIP == 'SÃO PAULO']\n",
        "setor_censo_sp[setor_censo_sp.contains(Point(longitude, latitude))]"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 214
        },
        "id": "qiQlwjbRFdCM",
        "outputId": "6f599b4d-60cc-4604-80ab-6d4fb070e44a"
      },
      "execution_count": 176,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "             ID       CD_GEOCODI    TIPO   CD_GEOCODS NM_SUBDIST CD_GEOCODD  \\\n",
              "54041 119535.00  355030853000029  URBANO  35503085300       None  355030853   \n",
              "\n",
              "      NM_DISTRIT CD_GEOCODM NM_MUNICIP   NM_MICRO                     NM_MESO  \\\n",
              "54041      MOOCA    3550308  SÃO PAULO  SÃO PAULO  METROPOLITANA DE SÃO PAULO   \n",
              "\n",
              "      CD_GEOCODB NM_BAIRRO    ID1  \\\n",
              "54041       None      None  54042   \n",
              "\n",
              "                                                geometry  \n",
              "54041  POLYGON ((-46.59147 -23.56009, -46.59102 -23.5...  "
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th>ID</th>\n",
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              "      <th>NM_DISTRIT</th>\n",
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              "      <th>NM_MICRO</th>\n",
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              "      <th>54041</th>\n",
              "      <td>119535.00</td>\n",
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              "      <td>SÃO PAULO</td>\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-07494439-9837-43aa-9581-63c2532c9b7a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fazendo o teste com uma das bases da Alura\n",
        "setor_censo_sp[setor_censo_sp.contains(Point(-46.63, -23.58))]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        },
        "id": "bbZqYy3_HmIc",
        "outputId": "71ea3e52-a5ed-4dff-f751-664f039f4d4a"
      },
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             ID       CD_GEOCODI    TIPO   CD_GEOCODS NM_SUBDIST CD_GEOCODD  \\\n",
              "60986 126568.00  355030890000080  URBANO  35503089000       None  355030890   \n",
              "\n",
              "         NM_DISTRIT CD_GEOCODM NM_MUNICIP   NM_MICRO  \\\n",
              "60986  VILA MARIANA    3550308  SÃO PAULO  SÃO PAULO   \n",
              "\n",
              "                          NM_MESO CD_GEOCODB NM_BAIRRO    ID1  \\\n",
              "60986  METROPOLITANA DE SÃO PAULO       None      None  60987   \n",
              "\n",
              "                                                geometry  \n",
              "60986  POLYGON ((-46.62618 -23.58169, -46.62863 -23.5...  "
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              "      <th>NM_MICRO</th>\n",
              "      <th>NM_MESO</th>\n",
              "      <th>CD_GEOCODB</th>\n",
              "      <th>NM_BAIRRO</th>\n",
              "      <th>ID1</th>\n",
              "      <th>geometry</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>60986</th>\n",
              "      <td>126568.00</td>\n",
              "      <td>355030890000080</td>\n",
              "      <td>URBANO</td>\n",
              "      <td>35503089000</td>\n",
              "      <td>None</td>\n",
              "      <td>355030890</td>\n",
              "      <td>VILA MARIANA</td>\n",
              "      <td>3550308</td>\n",
              "      <td>SÃO PAULO</td>\n",
              "      <td>SÃO PAULO</td>\n",
              "      <td>METROPOLITANA DE SÃO PAULO</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>60987</td>\n",
              "      <td>POLYGON ((-46.62618 -23.58169, -46.62863 -23.5...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# vamos agora inserir o trecho de código que vai vincular esses dados e \n",
        "# retornar o código censitário dessa região; criaremos um coluna chamada \n",
        "# 'Point' e para cada long/lati, estamos criando aquele ponto na base de dados\n",
        "# AVISO: imprimindo apenas 10 linhas para teste\n",
        "\n",
        "dados_geo['Point'] = \"\"\n",
        "for i in dados_geo.index:\n",
        "  dados_geo['Point'][i] = Point(dados_geo['longitude'][i], dados_geo['latitude'][i])\n",
        "\n",
        "dados_geo['setor_censo'] = dados_geo['Point'][:10].map(\n",
        "    lambda x: setor_censo_sp.loc[setor_censo_sp.contains(x), 'CD_GEOCODI'].values).str[0]\n",
        "dados_geo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 960
        },
        "id": "h0xCnD-6HuNR",
        "outputId": "06ec7305-652a-4916-ad40-9d748e6e4d2f"
      },
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  Rua              Bairro     Cidade  \\\n",
              "0               Avenida Itacira, 255    Planalto Paulista  São Paulo   \n",
              "5      Rua Aurelia Perez Alvarez, 42   Jardim dos Estados  São Paulo   \n",
              "6                     Rua Alba Valdez     Jardim Reimberg  São Paulo   \n",
              "7                                 NaN      Jardim Morumbi  São Paulo   \n",
              "26           Rua Tobias Barreto, 195                Mooca  São Paulo   \n",
              "...                               ...                 ...        ...   \n",
              "89956                   Avenida Odila   Planalto Paulista  São Paulo   \n",
              "89958                      Rua Tapuçu          Vila Sofia  São Paulo   \n",
              "89959                   Rua Guararema     Bosque da Saúde  São Paulo   \n",
              "89976               Rua Estero Belaco       Vila da Saúde  São Paulo   \n",
              "89977      Rua Barão de Tramandaí, 0          Vila Alpina  São Paulo   \n",
              "\n",
              "       Metragem  Quartos  Banheiros  Vagas         Valor Moeda Valor_anuncio  \\\n",
              "0          1000        4          8      6  R$ 7.000.000    R$     7.000.000   \n",
              "5           496        4          4      4  R$ 3.700.000    R$     3.700.000   \n",
              "6           125        4          3      2    R$ 380.000    R$       380.000   \n",
              "7           310        3          2      4    R$ 685.000    R$       685.000   \n",
              "26          100        3          2      2    R$ 540.000    R$       540.000   \n",
              "...         ...      ...        ...    ...           ...   ...           ...   \n",
              "89956       267        3          6      4  R$ 1.500.000    R$     1.500.000   \n",
              "89958        95        2          2      2    R$ 665.000    R$       665.000   \n",
              "89959       605        8          7      5  R$ 2.300.000    R$     2.300.000   \n",
              "89976       205        3          3      6  R$ 1.050.000    R$     1.050.000   \n",
              "89977       122        3          2      2    R$ 270.000    R$       270.000   \n",
              "\n",
              "      Tipo_anuncio  Valor_anuncio_float  Valor_M2                 apenas_rua  \\\n",
              "0             None           7000000.00   7000.00            avenida itacira   \n",
              "5             None           3700000.00   7459.68  rua aurelia perez alvarez   \n",
              "6             None            380000.00   3040.00            rua alba valdez   \n",
              "7             None            685000.00   2209.68                        NaN   \n",
              "26            None            540000.00   5400.00         rua tobias barreto   \n",
              "...            ...                  ...       ...                        ...   \n",
              "89956         None           1500000.00   5617.98              avenida odila   \n",
              "89958         None            665000.00   7000.00                 rua tapuçu   \n",
              "89959         None           2300000.00   3801.65              rua guararema   \n",
              "89976         None           1050000.00   5121.95          rua estero belaco   \n",
              "89977         None            270000.00   2213.11     rua barão de tramandaí   \n",
              "\n",
              "                             rua         cep  latitude  longitude  \\\n",
              "0                avenida itacira 11443160.00    -23.98     -46.20   \n",
              "5      rua aurelia perez alvarez  4642020.00    -23.64     -46.69   \n",
              "6                rua alba valdez  4845200.00    -23.75     -46.70   \n",
              "7                            NaN 18125000.00    -23.53     -47.26   \n",
              "26            rua tobias barreto 13474663.00    -22.70     -47.29   \n",
              "...                          ...         ...       ...        ...   \n",
              "89956              avenida odila  4058020.00    -23.61     -46.65   \n",
              "89958                 rua tapuçu  4671185.00    -23.66     -46.69   \n",
              "89959              rua guararema 13030640.00    -22.92     -47.08   \n",
              "89976          rua estero belaco  4145020.00    -23.62     -46.63   \n",
              "89977     rua barão de tramandaí  3207000.00    -23.60     -46.56   \n",
              "\n",
              "                                 Point      setor_censo  \n",
              "0       POINT (-46.2013172 -23.975993)              NaN  \n",
              "5      POINT (-46.6883114 -23.6409612)  355030871000023  \n",
              "6        POINT (-46.6958895 -23.75299)  355030830000050  \n",
              "7      POINT (-47.2550228 -23.5310431)              NaN  \n",
              "26     POINT (-47.2944609 -22.6989009)              NaN  \n",
              "...                                ...              ...  \n",
              "89956  POINT (-46.6472577 -23.6090928)              NaN  \n",
              "89958   POINT (-46.6915206 -23.661018)              NaN  \n",
              "89959  POINT (-47.0769595 -22.9227492)              NaN  \n",
              "89976   POINT (-46.632983 -23.6161237)              NaN  \n",
              "89977    POINT (-46.5624146 -23.60033)              NaN  \n",
              "\n",
              "[8520 rows x 20 columns]"
            ],
            "text/html": [
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              "  <div id=\"df-8717a26a-c0fa-4e31-a805-78c1511ef83d\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "      <th>Moeda</th>\n",
              "      <th>Valor_anuncio</th>\n",
              "      <th>Tipo_anuncio</th>\n",
              "      <th>Valor_anuncio_float</th>\n",
              "      <th>Valor_M2</th>\n",
              "      <th>apenas_rua</th>\n",
              "      <th>rua</th>\n",
              "      <th>cep</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>Point</th>\n",
              "      <th>setor_censo</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Avenida Itacira, 255</td>\n",
              "      <td>Planalto Paulista</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>1000</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>R$ 7.000.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>7.000.000</td>\n",
              "      <td>None</td>\n",
              "      <td>7000000.00</td>\n",
              "      <td>7000.00</td>\n",
              "      <td>avenida itacira</td>\n",
              "      <td>avenida itacira</td>\n",
              "      <td>11443160.00</td>\n",
              "      <td>-23.98</td>\n",
              "      <td>-46.20</td>\n",
              "      <td>POINT (-46.2013172 -23.975993)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Rua Aurelia Perez Alvarez, 42</td>\n",
              "      <td>Jardim dos Estados</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>496</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>R$ 3.700.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>3.700.000</td>\n",
              "      <td>None</td>\n",
              "      <td>3700000.00</td>\n",
              "      <td>7459.68</td>\n",
              "      <td>rua aurelia perez alvarez</td>\n",
              "      <td>rua aurelia perez alvarez</td>\n",
              "      <td>4642020.00</td>\n",
              "      <td>-23.64</td>\n",
              "      <td>-46.69</td>\n",
              "      <td>POINT (-46.6883114 -23.6409612)</td>\n",
              "      <td>355030871000023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Rua Alba Valdez</td>\n",
              "      <td>Jardim Reimberg</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>125</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>R$ 380.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>380.000</td>\n",
              "      <td>None</td>\n",
              "      <td>380000.00</td>\n",
              "      <td>3040.00</td>\n",
              "      <td>rua alba valdez</td>\n",
              "      <td>rua alba valdez</td>\n",
              "      <td>4845200.00</td>\n",
              "      <td>-23.75</td>\n",
              "      <td>-46.70</td>\n",
              "      <td>POINT (-46.6958895 -23.75299)</td>\n",
              "      <td>355030830000050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Jardim Morumbi</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>310</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>R$ 685.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>685.000</td>\n",
              "      <td>None</td>\n",
              "      <td>685000.00</td>\n",
              "      <td>2209.68</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18125000.00</td>\n",
              "      <td>-23.53</td>\n",
              "      <td>-47.26</td>\n",
              "      <td>POINT (-47.2550228 -23.5310431)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Rua Tobias Barreto, 195</td>\n",
              "      <td>Mooca</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>100</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>R$ 540.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>540.000</td>\n",
              "      <td>None</td>\n",
              "      <td>540000.00</td>\n",
              "      <td>5400.00</td>\n",
              "      <td>rua tobias barreto</td>\n",
              "      <td>rua tobias barreto</td>\n",
              "      <td>13474663.00</td>\n",
              "      <td>-22.70</td>\n",
              "      <td>-47.29</td>\n",
              "      <td>POINT (-47.2944609 -22.6989009)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89956</th>\n",
              "      <td>Avenida Odila</td>\n",
              "      <td>Planalto Paulista</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>267</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>R$ 1.500.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>1.500.000</td>\n",
              "      <td>None</td>\n",
              "      <td>1500000.00</td>\n",
              "      <td>5617.98</td>\n",
              "      <td>avenida odila</td>\n",
              "      <td>avenida odila</td>\n",
              "      <td>4058020.00</td>\n",
              "      <td>-23.61</td>\n",
              "      <td>-46.65</td>\n",
              "      <td>POINT (-46.6472577 -23.6090928)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89958</th>\n",
              "      <td>Rua Tapuçu</td>\n",
              "      <td>Vila Sofia</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>95</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>R$ 665.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>665.000</td>\n",
              "      <td>None</td>\n",
              "      <td>665000.00</td>\n",
              "      <td>7000.00</td>\n",
              "      <td>rua tapuçu</td>\n",
              "      <td>rua tapuçu</td>\n",
              "      <td>4671185.00</td>\n",
              "      <td>-23.66</td>\n",
              "      <td>-46.69</td>\n",
              "      <td>POINT (-46.6915206 -23.661018)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89959</th>\n",
              "      <td>Rua Guararema</td>\n",
              "      <td>Bosque da Saúde</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>605</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>R$ 2.300.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>2.300.000</td>\n",
              "      <td>None</td>\n",
              "      <td>2300000.00</td>\n",
              "      <td>3801.65</td>\n",
              "      <td>rua guararema</td>\n",
              "      <td>rua guararema</td>\n",
              "      <td>13030640.00</td>\n",
              "      <td>-22.92</td>\n",
              "      <td>-47.08</td>\n",
              "      <td>POINT (-47.0769595 -22.9227492)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89976</th>\n",
              "      <td>Rua Estero Belaco</td>\n",
              "      <td>Vila da Saúde</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>205</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>R$ 1.050.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>1.050.000</td>\n",
              "      <td>None</td>\n",
              "      <td>1050000.00</td>\n",
              "      <td>5121.95</td>\n",
              "      <td>rua estero belaco</td>\n",
              "      <td>rua estero belaco</td>\n",
              "      <td>4145020.00</td>\n",
              "      <td>-23.62</td>\n",
              "      <td>-46.63</td>\n",
              "      <td>POINT (-46.632983 -23.6161237)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89977</th>\n",
              "      <td>Rua Barão de Tramandaí, 0</td>\n",
              "      <td>Vila Alpina</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>122</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>R$ 270.000</td>\n",
              "      <td>R$</td>\n",
              "      <td>270.000</td>\n",
              "      <td>None</td>\n",
              "      <td>270000.00</td>\n",
              "      <td>2213.11</td>\n",
              "      <td>rua barão de tramandaí</td>\n",
              "      <td>rua barão de tramandaí</td>\n",
              "      <td>3207000.00</td>\n",
              "      <td>-23.60</td>\n",
              "      <td>-46.56</td>\n",
              "      <td>POINT (-46.5624146 -23.60033)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8520 rows × 20 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8717a26a-c0fa-4e31-a805-78c1511ef83d')\"\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# AVISO: para evitar a necessidade de toda vez criar essa base de dados, vamos\n",
        "# importá-la de um arquivo dados_geo.csv\n",
        "\n",
        "dados_geo = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/imersão_dados_alura/dados_geo.csv')\n",
        "dados_geo.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "AIEkb-h3bUvd",
        "outputId": "b64e43fb-f046-45e5-806a-760f6de29a7e"
      },
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              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        Rua Tobias Barreto, 195                Mooca  São Paulo       100   \n",
              "4                 Rua Graham Bell         Santo Amaro  São Paulo       440   \n",
              "\n",
              "   Quartos  Banheiros  Vagas         Valor  Valor_anuncio  Tipo_anuncio  \\\n",
              "0        4          8      6  R$ 7.000.000     7000000.00           NaN   \n",
              "1        4          4      4  R$ 3.700.000     3700000.00           NaN   \n",
              "2        4          3      2    R$ 380.000      380000.00           NaN   \n",
              "3        3          2      2    R$ 540.000      540000.00           NaN   \n",
              "4        4          4      6  R$ 1.980.000     1980000.00           NaN   \n",
              "\n",
              "   Valor_mm  Valor_m2                        rua        cep  latitude  \\\n",
              "0      7.00   7000.00            Avenida Itacira 4061000.00    -23.62   \n",
              "1      3.70   7459.68  Rua Aurelia Perez Alvarez 4642020.00    -23.64   \n",
              "2      0.38   3040.00            Rua Alba Valdez 4845200.00    -23.75   \n",
              "3      0.54   5400.00         Rua Tobias Barreto 3176000.00    -23.55   \n",
              "4      1.98   4500.00            Rua Graham Bell 4737030.00    -23.59   \n",
              "\n",
              "   longitude                            Point        setor_censo  \n",
              "0     -46.65  POINT (-46.6481622 -23.6157934) 355030877000067.00  \n",
              "1     -46.69  POINT (-46.6883114 -23.6409612) 355030871000023.00  \n",
              "2     -46.70    POINT (-46.6958895 -23.75299) 355030830000050.00  \n",
              "3     -46.59  POINT (-46.5881729 -23.5499597) 355030808000050.00  \n",
              "4     -46.76  POINT (-46.7600773 -23.5937964) 355030865000169.00  "
            ],
            "text/html": [
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              "      <th>Rua</th>\n",
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              "      <th>Metragem</th>\n",
              "      <th>Quartos</th>\n",
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              "      <th>Valor</th>\n",
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              "      <th>Tipo_anuncio</th>\n",
              "      <th>Valor_mm</th>\n",
              "      <th>Valor_m2</th>\n",
              "      <th>rua</th>\n",
              "      <th>cep</th>\n",
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              "      <th>longitude</th>\n",
              "      <th>Point</th>\n",
              "      <th>setor_censo</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>Avenida Itacira, 255</td>\n",
              "      <td>Planalto Paulista</td>\n",
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              "      <td>1000</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>R$ 7.000.000</td>\n",
              "      <td>7000000.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.00</td>\n",
              "      <td>7000.00</td>\n",
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              "      <td>355030877000067.00</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",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
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              "      <td>R$ 3.700.000</td>\n",
              "      <td>3700000.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.70</td>\n",
              "      <td>7459.68</td>\n",
              "      <td>Rua Aurelia Perez Alvarez</td>\n",
              "      <td>4642020.00</td>\n",
              "      <td>-23.64</td>\n",
              "      <td>-46.69</td>\n",
              "      <td>POINT (-46.6883114 -23.6409612)</td>\n",
              "      <td>355030871000023.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <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",
              "      <td>380000.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.38</td>\n",
              "      <td>3040.00</td>\n",
              "      <td>Rua Alba Valdez</td>\n",
              "      <td>4845200.00</td>\n",
              "      <td>-23.75</td>\n",
              "      <td>-46.70</td>\n",
              "      <td>POINT (-46.6958895 -23.75299)</td>\n",
              "      <td>355030830000050.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</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",
              "      <td>540000.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.54</td>\n",
              "      <td>5400.00</td>\n",
              "      <td>Rua Tobias Barreto</td>\n",
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              "      <td>-23.55</td>\n",
              "      <td>-46.59</td>\n",
              "      <td>POINT (-46.5881729 -23.5499597)</td>\n",
              "      <td>355030808000050.00</td>\n",
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              "      <th>4</th>\n",
              "      <td>Rua Graham Bell</td>\n",
              "      <td>Santo Amaro</td>\n",
              "      <td>São Paulo</td>\n",
              "      <td>440</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>R$ 1.980.000</td>\n",
              "      <td>1980000.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.98</td>\n",
              "      <td>4500.00</td>\n",
              "      <td>Rua Graham Bell</td>\n",
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              "      <td>-23.59</td>\n",
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              "      <td>POINT (-46.7600773 -23.5937964)</td>\n",
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              "              style=\"display:none;\">\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-155ce40d-7b92-4f26-8f63-5683e14b9455 button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-155ce40d-7b92-4f26-8f63-5683e14b9455');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# vamos agora fazer um novo merge entre dados_geo e dados_IBGE\n",
        "\n",
        "dados_vendas_censo = pd.merge(left=dados_geo, right=dados_IBGE, how='left', left_on='setor_censo', right_on='Cod_setor')\n",
        "dados_vendas_censo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 765
        },
        "id": "ERO6R3bScxtI",
        "outputId": "1bea0919-86fa-408a-cb74-83214abf7a78"
      },
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 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           Rua Tobias Barreto, 195                Mooca  São Paulo       100   \n",
              "4                    Rua Graham Bell         Santo Amaro  São Paulo       440   \n",
              "...                              ...                 ...        ...       ...   \n",
              "5600                   Avenida Odila   Planalto Paulista  São Paulo       267   \n",
              "5601                      Rua Tapuçu          Vila Sofia  São Paulo        95   \n",
              "5602                   Rua Guararema     Bosque da Saúde  São Paulo       605   \n",
              "5603               Rua Estero Belaco       Vila da Saúde  São Paulo       205   \n",
              "5604      Rua Barão de Tramandaí, 0          Vila Alpina  São Paulo       122   \n",
              "\n",
              "      Quartos  Banheiros  Vagas         Valor  Valor_anuncio  Tipo_anuncio  \\\n",
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              "2           4          3      2    R$ 380.000      380000.00           NaN   \n",
              "3           3          2      2    R$ 540.000      540000.00           NaN   \n",
              "4           4          4      6  R$ 1.980.000     1980000.00           NaN   \n",
              "...       ...        ...    ...           ...            ...           ...   \n",
              "5600        3          6      4  R$ 1.500.000     1500000.00           NaN   \n",
              "5601        2          2      2    R$ 665.000      665000.00           NaN   \n",
              "5602        8          7      5  R$ 2.300.000     2300000.00           NaN   \n",
              "5603        3          3      6  R$ 1.050.000     1050000.00           NaN   \n",
              "5604        3          2      2    R$ 270.000      270000.00           NaN   \n",
              "\n",
              "      ...  V003  V004    V005        V006    V007        V008    V009  \\\n",
              "0     ...  2.66  2.04 3158.96 22210644.93 5570.65 25770816.97 2096.19   \n",
              "1     ...  3.13  1.96 7600.44 36778930.17 7890.53 35886906.64 4149.00   \n",
              "2     ...  3.47  3.06  805.13   769188.78 1011.75   757433.42  518.77   \n",
              "3     ...  3.00  1.99 2338.05  7826370.15 2447.89  7925839.76 1543.29   \n",
              "4     ...  3.51  2.31 1026.36   272168.67 1143.96   167733.64  735.67   \n",
              "...   ...   ...   ...     ...         ...     ...         ...     ...   \n",
              "5600  ...  2.81  1.60 4249.33 24499871.44 5180.14 25047413.33 2618.76   \n",
              "5601  ...  2.99  1.81 2425.09  6878872.25 2543.80  6913867.79 1561.58   \n",
              "5602  ...   NaN   NaN     NaN         NaN     NaN         NaN     NaN   \n",
              "5603  ...  2.75  1.50 5650.20 20344622.14 5750.84 20127679.27 3459.68   \n",
              "5604  ...  2.97  1.75 1244.10  1353001.23 1364.89  1319336.96  895.93   \n",
              "\n",
              "            V010    V011        V012  \n",
              "0    13029851.56 3930.35 17241576.74  \n",
              "1    26760682.90 6063.92 27502674.90  \n",
              "2      426672.78  858.51   414378.88  \n",
              "3     4646566.16 1909.04  5050712.69  \n",
              "4      367088.08 1086.94   159622.46  \n",
              "...          ...     ...         ...  \n",
              "5600 17705032.31 3977.93 21495997.85  \n",
              "5601  4261814.01 2062.09  4596759.57  \n",
              "5602         NaN     NaN         NaN  \n",
              "5603 16021825.60 4362.79 16264378.33  \n",
              "5604   988708.13 1226.86   947807.31  \n",
              "\n",
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              "      <td>3.47</td>\n",
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              "      <td>R$ 540.000</td>\n",
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              "      <th>5601</th>\n",
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              "      <td>1050000.00</td>\n",
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              "      <td>20127679.27</td>\n",
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              "      <th>5604</th>\n",
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              "      <td>1319336.96</td>\n",
              "      <td>895.93</td>\n",
              "      <td>988708.13</td>\n",
              "      <td>1226.86</td>\n",
              "      <td>947807.31</td>\n",
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    {
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      "execution_count": 185,
      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 5605 entries, 0 to 5604\n",
            "Data columns (total 51 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   Rua                    5605 non-null   object \n",
            " 1   Bairro                 5597 non-null   object \n",
            " 2   Cidade                 5605 non-null   object \n",
            " 3   Metragem               5605 non-null   int64  \n",
            " 4   Quartos                5605 non-null   int64  \n",
            " 5   Banheiros              5605 non-null   int64  \n",
            " 6   Vagas                  5605 non-null   int64  \n",
            " 7   Valor                  5605 non-null   object \n",
            " 8   Valor_anuncio          5605 non-null   float64\n",
            " 9   Tipo_anuncio           0 non-null      float64\n",
            " 10  Valor_mm               5605 non-null   float64\n",
            " 11  Valor_m2               5605 non-null   float64\n",
            " 12  rua                    5605 non-null   object \n",
            " 13  cep                    5605 non-null   float64\n",
            " 14  latitude               5605 non-null   float64\n",
            " 15  longitude              5605 non-null   float64\n",
            " 16  Point                  5605 non-null   object \n",
            " 17  setor_censo            5320 non-null   float64\n",
            " 18  Cod_setor              5316 non-null   float64\n",
            " 19  Cod_Grandes Regiï¿½es  5316 non-null   float64\n",
            " 20  Nome_Grande_Regiao     5316 non-null   object \n",
            " 21  Cod_UF                 5316 non-null   float64\n",
            " 22  Nome_da_UF             5316 non-null   object \n",
            " 23  Cod_meso               5316 non-null   float64\n",
            " 24  Nome_da_meso           5316 non-null   object \n",
            " 25  Cod_micro              5316 non-null   float64\n",
            " 26  Nome_da_micro          5316 non-null   object \n",
            " 27  Cod_RM                 5316 non-null   float64\n",
            " 28  Nome_da_RM             5316 non-null   object \n",
            " 29  Cod_municipio          5316 non-null   float64\n",
            " 30  Nome_do_municipio      5316 non-null   object \n",
            " 31  Cod_distrito           5316 non-null   float64\n",
            " 32  Nome_do_distrito       5316 non-null   object \n",
            " 33  Cod_subdistrito        5316 non-null   float64\n",
            " 34  Nome_do_subdistrito    5316 non-null   object \n",
            " 35  Cod_bairro             5316 non-null   float64\n",
            " 36  Nome_do_bairro         5316 non-null   object \n",
            " 37  Situacao_setor         5316 non-null   float64\n",
            " 38  Tipo_setor             5316 non-null   float64\n",
            " 39  V001                   5316 non-null   float64\n",
            " 40  V002                   5316 non-null   float64\n",
            " 41  V003                   5316 non-null   float64\n",
            " 42  V004                   5316 non-null   float64\n",
            " 43  V005                   5316 non-null   float64\n",
            " 44  V006                   5316 non-null   float64\n",
            " 45  V007                   5316 non-null   float64\n",
            " 46  V008                   5316 non-null   float64\n",
            " 47  V009                   5316 non-null   float64\n",
            " 48  V010                   5316 non-null   float64\n",
            " 49  V011                   5316 non-null   float64\n",
            " 50  V012                   5316 non-null   float64\n",
            "dtypes: float64(32), int64(4), object(15)\n",
            "memory usage: 2.2+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "sns.scatterplot(data=dados_vendas_censo, x='V005', y='Valor_m2')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "-if_0rMbfo9Y",
        "outputId": "a6867095-0b0a-4348-d8b7-0350c21484b2"
      },
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa7e5fb1690>"
            ]
          },
          "metadata": {},
          "execution_count": 187
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **⚠ Desafio 01 - a4**\n",
        "\n",
        "Realizar uma análise dos dados do IBGE por mapa, [analisando a distribuição de renda](https://medium.com/creditas-tech/dados-georreferenciados-explora%C3%A7%C3%A3o-e-visualiza%C3%A7%C3%A3o-com-python-edd51e7c53da).\n"
      ],
      "metadata": {
        "id": "8XY0RUdqh3jt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "TJMq7MLWi0oH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **⚠ Desafio 02 - a4**\n",
        "\n",
        "Repassar a aula para entender melhor o que foi realizado.\n"
      ],
      "metadata": {
        "id": "wY4a089_iEFu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "gtOaI62oiHmY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **⚠ Desafio 03 - a4**\n",
        "\n",
        "Aprofundar a análise entre dados de vendas e renda.\n"
      ],
      "metadata": {
        "id": "59EyFn66iIed"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "x5hdm45eiKl3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **⚠ Desafio 04 - a4**\n",
        "\n",
        "Realizar a análise exploratória e encontrar variáveis relevantes para solução do problema."
      ],
      "metadata": {
        "id": "4WMrt-vPiLSQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "072fniVbiGSW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}