Analisando_os_Dados_do_AirBnb_Roma.ipynb

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        "# Análise dos Dados do AirBnb - *Roma, Itália*\n",
        "\n",
        "## A Cidade Eterna\n",
        "\n",
        "Roma é uma das cidades mais fascinantes do mundo, cheia de belezas arquitetônicas, rica em cultura e repleta de história. \n",
        "\n",
        "Durante seus mais de 2700 anos desde a fundação, ela já foi chamada de \"a capital do mundo\" pelo poeta romano Marco Anneo Lucano, que a usou na sua obra \"Farsália\", logo no século I d.C. Na época, Roma era o centro do Império Romano, a cidade mais importante do mundo antigo, e o poeta usou esse termo para descrever sua grandeza e poder.\n",
        "\n",
        "Na mesma época, Roma já recebia outro título ilustre, o de cidade eterna. O poeta Tibullo certamente enxergava Roma como uma cidade imponente, poderosa e, portanto, imortal. Desde então, a cidade é conhecida assim, como referência não somente de sua rica história, mas também de sua importância cultural e da sua capacidade de se reinventar.\n",
        "\n",
        "> <img src=\"https://images.unsplash.com/photo-1529260830199-42c24126f198?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1176&q=80\" height=\"300px\"></center>\n",
        "\n",
        "### O turismo em Roma\n",
        "\n",
        "A história viva de Roma se torna um convite irrecusável para muitas pessoas ao redor do mundo. Pelas ruas da cidade, nos deparamos com grandes monumentos históricos que resistiram a ação do tempo, pedaços das conquistas de grandes homens do passado, e o berço do cristianismo que cresceu para sustentar todo o ocidente a partir de Roma.\n",
        "\n",
        "Uma das principais atrações da cidade é o Coliseu, um dos monumentos mais emblemáticos da cidade. Construído no século I d.C., o Coliseu era um anfiteatro onde gladiadores lutavam até a morte, e um local de entretenimento para o povo romano. A cidade também abriga o Fórum Romano e o Palatino, que foram o centro da vida pública e política da Roma Antiga.\n",
        "\n",
        "> <img src=\"https://images.unsplash.com/photo-1515542622106-78bda8ba0e5b?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1170&q=80\" height=\"300px\"></center>\n",
        "\n",
        "Outro ponto de destaque da cidade é a Cidade do Vaticano, que abriga a Basílica de São Pedro, um dos maiores templos religiosos do mundo. A cidade também abriga o Museu do Vaticano, que contém algumas das mais importantes obras de arte do mundo, incluindo a Capela Sistina, com os afrescos de Michelangelo.\n",
        "\n",
        "> <img src=\"https://images.unsplash.com/photo-1568132273463-95931747cc5f?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1170&q=80\" height=\"300px\"></center>\n",
        "\n",
        "Por falar em Igreja, Roma não decepciona. Isso porque a cidade tem mais de 900 igrejas espalhadas pelo seu território, nos mais diversos estilos arquitetônincos.\n",
        "\n",
        "Além disso, Roma é uma cidade vibrante e moderna, com uma vida noturna agitada e uma cena cultural vibrante. Os bairros de Trastevere e Monti são repletos de bares, restaurantes e lojas descoladas, enquanto o Centro Histórico é uma verdadeira maravilha arquitetônica, repleta de edifícios históricos e praças encantadoras.\n",
        "\n",
        "### Roma no AirBnb\n",
        "\n",
        "Por ter esse apelo tão admirável, muitos procuram se hospedar em Roma pelo AirBnb. O AirBnb é uma plataforma que conecta pessoas que buscam acomodações com anfitriões que têm quartos para alugar.\n",
        "\n",
        "A plataforma funciona de maneira simples: os anfitriões cadastram suas propriedades na plataforma, com fotos, descrições, preços e regras. Os viajantes, por sua vez, procuram por acomodações em suas cidades ou regiões de destino e podem escolher entre diversas opções de quartos, apartamentos e casas.\n",
        "\n",
        "Uma das principais vantagens do AirBnb é que ele oferece uma alternativa às hospedagens tradicionais, como hotéis e pousadas. Muitas vezes, as hospedagens no AirBnb são mais baratas que as tradicionais, tornando o negócio ainda melhor.\n",
        "\n",
        "Com o AirBnb, os viajantes podem ter uma experiência mais autêntica e personalizada, ficando em locais únicos e interagindo com os moradores locais.\n",
        "\n",
        "### Inside AirBnb\n",
        "\n",
        "O AirBnb possui um portal onde ficam disponibilizados dados do site para algumas das principais cidades do mundo. No [Inside AirBnb](http://insideairbnb.com/get-the-data.html) é possível baixar esses dados, realizar análises e desenvolver projetos e soluções de *data science*.\n",
        "\n",
        "Este projeto foi criado utilizando-se dessa ferramenta para fazer a análise dos dados do AirBnb para Roma. Utilizou-se para isso o arquivo `listings.csv`, que fornece uma versão resumida dos dados, porém completa para o propósito deste projeto, que será:\n",
        "\n",
        "* Analisar a distribuição de preços das hospedagens\n",
        "* Analisar o mínimo de noites necessárias para se hospedar\n",
        "* Verificar os principais bairros procurados por turistas no AirBnb\n",
        "* O preço médio nesses bairros mais procurados\n",
        "* Verificar os tipos de imóveis mais alugados"
      ]
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        "## Obtenção dos Dados\n",
        "\n",
        "O conjunto de dados `listings.csv` foi obtido no Inside AirBnb. A seguir, vamos importar as bibliotecas necessárias para desenvolver o projeto de *data science*.\n",
        "\n",
        "Para este projeto, utilizaremos a biblioteca `Pandas` para análise e manipulação dos dados e as bibliotecas `matplotlib.pyplot`, `seaborn` e `pywaffle` para visualização desses dados em gráficos."
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            "\u001b[?25h"
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      "source": [
        "!pip install pywaffle -q"
      ]
    },
    {
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      "source": [
        "# Importar os pacotes necessarios\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from pywaffle import Waffle\n",
        "\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
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      "source": [
        "# Importar o arquivo listings.csv para um DataFrame\n",
        "df_rome = pd.read_csv('http://data.insideairbnb.com/italy/lazio/rome/2022-12-13/visualisations/listings.csv', delimiter = ',')"
      ]
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      "cell_type": "markdown",
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      "source": [
        "## Análise dos Dados\n",
        "\n",
        "O próprio site do Inside AirBnb fornece um documento que lista as variáveis do arquivo, juntamente com uma descrição do que elas representam.\n",
        "\n",
        "Para acessar esse documento e ter uma visão completa, [clique aqui](https://docs.google.com/spreadsheets/d/1iWCNJcSutYqpULSQHlNyGInUvHg2BoUGoNRIGa6Szc4/edit#gid=1322284596).\n",
        "\n",
        "Abaixo está o dicionário de variáveis com o nome da variável e uma breve descrição."
      ]
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        "id": "RYgvuebKNg8-"
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        "**Dicionário das variáveis**\n",
        "\n",
        "* `id`: Número gerado para identificação do imóvel.\n",
        "* `name`: Descrição do tipo e características do apartamento.\n",
        "* `host_id`: Número gerado para identificar o anfitrião.\n",
        "* `host_name`: Nome do anfitrião.\n",
        "* `neighbourhood`: Vizinhança em que se encontra o imóvel.\n",
        "* `latitude`: Coordenada de latitude do imóvel.\n",
        "* `longitude`: Coordenada de longitude do imóvel.\n",
        "* `room_type`: Tipo de quarto disponível para alugar.\n",
        "* `price`: Preço da estadia no imóvel.\n",
        "* `minimum_nights`: Mínimo de noites necessário para reservar o imóvel.\n",
        "* `number_of_reviews`: Número de reviews dados por pessoas que se hospedaram no imóvel.\n",
        "* `last_review`: Data do último review.\n",
        "* `reviews_per_month`: Número de reviews que um imóvel recebe por mês.\n",
        "* `calculated_host_listing_count`: Quantidade de imóveis do mesmo anfitrião.\n",
        "* `availability_365`: A quantidade de dias que o imóvel tem disponibilidade nos próximo 365 dias.\n",
        "\n",
        "Antes de iniciar qualquer análise, vamos verificar a cara do nosso *dataset*, analisando as 5 primeiras entradas."
      ]
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              "      id                                           name  host_id host_name  \\\n",
              "0   2737               Elif's room in cozy, clean flat.     3047      Elif   \n",
              "1   3079      Cozy apartment  (2-4)with Colisseum  view     3504     Laura   \n",
              "2  11834               Rome \"Charming Boschetto Studio\"    44552    Serena   \n",
              "3  12398            Casa Donatello - Home far from Home    11756       Gea   \n",
              "4  19965  DI FRONTE AI MUSEI VATICANI 2 CAMERE DA LETTO    75450      Sasa   \n",
              "\n",
              "   neighbourhood_group         neighbourhood  latitude  longitude  \\\n",
              "0                  NaN     VIII Appia Antica  41.87136   12.48215   \n",
              "1                  NaN      I Centro Storico  41.89500   12.49117   \n",
              "2                  NaN      I Centro Storico  41.89650   12.49211   \n",
              "3                  NaN  II Parioli/Nomentano  41.92582   12.46928   \n",
              "4                  NaN      I Centro Storico  41.90730   12.45349   \n",
              "\n",
              "         room_type  price  minimum_nights  number_of_reviews last_review  \\\n",
              "0     Private room     45               7                  7  2015-05-28   \n",
              "1  Entire home/apt     90              20                 21  2022-04-30   \n",
              "2  Entire home/apt    100               2                173  2022-11-15   \n",
              "3  Entire home/apt     81               1                 44  2022-08-10   \n",
              "4  Entire home/apt     96               3                 95  2022-11-01   \n",
              "\n",
              "   reviews_per_month  calculated_host_listings_count  availability_365  \\\n",
              "0               0.07                               6               365   \n",
              "1               0.14                               5               284   \n",
              "2               1.22                               1               349   \n",
              "3               0.30                               1               294   \n",
              "4               0.71                               9                43   \n",
              "\n",
              "   number_of_reviews_ltm license  \n",
              "0                      0     NaN  \n",
              "1                      2     NaN  \n",
              "2                     31     NaN  \n",
              "3                      1     NaN  \n",
              "4                     33     NaN  "
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              "      <td>2737</td>\n",
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              "      <td>3047</td>\n",
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              "      <td>3079</td>\n",
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              "      <td>3504</td>\n",
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              "      <td>2022-04-30</td>\n",
              "      <td>0.14</td>\n",
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              "      <th>2</th>\n",
              "      <td>11834</td>\n",
              "      <td>Rome \"Charming Boschetto Studio\"</td>\n",
              "      <td>44552</td>\n",
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              "      <td>2022-11-15</td>\n",
              "      <td>1.22</td>\n",
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              "      <td>349</td>\n",
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              "      <th>3</th>\n",
              "      <td>12398</td>\n",
              "      <td>Casa Donatello - Home far from Home</td>\n",
              "      <td>11756</td>\n",
              "      <td>Gea</td>\n",
              "      <td>NaN</td>\n",
              "      <td>II Parioli/Nomentano</td>\n",
              "      <td>41.92582</td>\n",
              "      <td>12.46928</td>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b6fe2b5b-dcb2-45dd-baba-d5fa14e464a4');\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": 4
        }
      ],
      "source": [
        "# Mostrar as 5 primeiras entradas\n",
        "df_rome.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x5zsi5Fhmbmd"
      },
      "source": [
        "E também, as 5 últimas entradas, para avaliar se há mudanças significativas em comparação com o início do dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "ipkdHNhz3NPA",
        "outputId": "0694f28d-b452-4e5b-b1a8-af9cd6d790a1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       id                                     name    host_id  \\\n",
              "24378  779552381059690347           Camera matrimoniale Trastevere   19444683   \n",
              "24379  779557620810535438             apartment 2 levels in center  245357731   \n",
              "24380  779638788545551248             Studio Pantheon Campo Marzio   13012838   \n",
              "24381  779688501955130604  Camera doppia in appartamento condiviso   21768240   \n",
              "24382  780297137357906205           Porta Portese Modern Apartment    2405376   \n",
              "\n",
              "      host_name  neighbourhood_group         neighbourhood   latitude  \\\n",
              "24378     Alice                  NaN      I Centro Storico  41.890398   \n",
              "24379   Mikhail                  NaN      I Centro Storico  41.906710   \n",
              "24380     Paola                  NaN      I Centro Storico  41.901176   \n",
              "24381     Carlo                  NaN  XI Arvalia/Portuense  41.872206   \n",
              "24382   Lorenzo                  NaN       XII Monte Verde  41.878543   \n",
              "\n",
              "       longitude        room_type  price  minimum_nights  number_of_reviews  \\\n",
              "24378  12.470885     Private room    100               1                  0   \n",
              "24379  12.501616  Entire home/apt    141               1                  0   \n",
              "24380  12.474972  Entire home/apt     60               3                  0   \n",
              "24381  12.467717     Private room     73               2                  0   \n",
              "24382  12.468261  Entire home/apt    213               3                  0   \n",
              "\n",
              "      last_review  reviews_per_month  calculated_host_listings_count  \\\n",
              "24378         NaN                NaN                               2   \n",
              "24379         NaN                NaN                               7   \n",
              "24380         NaN                NaN                              10   \n",
              "24381         NaN                NaN                               6   \n",
              "24382         NaN                NaN                              46   \n",
              "\n",
              "       availability_365  number_of_reviews_ltm license  \n",
              "24378               320                      0     NaN  \n",
              "24379               359                      0   11864  \n",
              "24380               265                      0     NaN  \n",
              "24381               361                      0     NaN  \n",
              "24382               364                      0     NaN  "
            ],
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              "      <td>apartment 2 levels in center</td>\n",
              "      <td>245357731</td>\n",
              "      <td>Mikhail</td>\n",
              "      <td>NaN</td>\n",
              "      <td>I Centro Storico</td>\n",
              "      <td>41.906710</td>\n",
              "      <td>12.501616</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>141</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7</td>\n",
              "      <td>359</td>\n",
              "      <td>0</td>\n",
              "      <td>11864</td>\n",
              "    </tr>\n",
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              "      <th>24380</th>\n",
              "      <td>779638788545551248</td>\n",
              "      <td>Studio Pantheon Campo Marzio</td>\n",
              "      <td>13012838</td>\n",
              "      <td>Paola</td>\n",
              "      <td>NaN</td>\n",
              "      <td>I Centro Storico</td>\n",
              "      <td>41.901176</td>\n",
              "      <td>12.474972</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10</td>\n",
              "      <td>265</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24381</th>\n",
              "      <td>779688501955130604</td>\n",
              "      <td>Camera doppia in appartamento condiviso</td>\n",
              "      <td>21768240</td>\n",
              "      <td>Carlo</td>\n",
              "      <td>NaN</td>\n",
              "      <td>XI Arvalia/Portuense</td>\n",
              "      <td>41.872206</td>\n",
              "      <td>12.467717</td>\n",
              "      <td>Private room</td>\n",
              "      <td>73</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
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              "      <td>780297137357906205</td>\n",
              "      <td>Porta Portese Modern Apartment</td>\n",
              "      <td>2405376</td>\n",
              "      <td>Lorenzo</td>\n",
              "      <td>NaN</td>\n",
              "      <td>XII Monte Verde</td>\n",
              "      <td>41.878543</td>\n",
              "      <td>12.468261</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>213</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>46</td>\n",
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              "      <td>NaN</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# Visualizar as 5 últimas entradas\n",
        "df_rome.tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VDMbox0rmprJ"
      },
      "source": [
        "Utilizando o `dtypes`, vamos entender os tipos das variáveis envolvidas."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uma2HEn6KpKj",
        "outputId": "2922e53e-8512-4938-be31-aa991b5b8efc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                  int64\n",
              "name                               object\n",
              "host_id                             int64\n",
              "host_name                          object\n",
              "neighbourhood_group               float64\n",
              "neighbourhood                      object\n",
              "latitude                          float64\n",
              "longitude                         float64\n",
              "room_type                          object\n",
              "price                               int64\n",
              "minimum_nights                      int64\n",
              "number_of_reviews                   int64\n",
              "last_review                        object\n",
              "reviews_per_month                 float64\n",
              "calculated_host_listings_count      int64\n",
              "availability_365                    int64\n",
              "number_of_reviews_ltm               int64\n",
              "license                            object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Analisando os tipos dos dados presentes.\n",
        "df_rome.dtypes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6-B_qtEaYEd-"
      },
      "source": [
        "### **1. Quantos atributos (variáveis) e quantas entradas o nosso conjunto de dados possui? Quais os tipos das variáveis?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_SlmxeA4rT-V"
      },
      "source": [
        "A seguir, o objetivo é ver as dimensões do dataset e converter alguns valores que estão fora do tipo que queremos."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TFRK0tniLV-G",
        "outputId": "39905fed-6039-4713-f7a7-969646d1f413"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Volume de dados do dataset \n",
            "Variáveis:  18 \n",
            "Entradas:  24383\n"
          ]
        }
      ],
      "source": [
        "# identificar o volume de dados do DataFrame\n",
        "print('Volume de dados do dataset',\n",
        "      '\\nVariáveis: ', df_rome.shape[1],\n",
        "      '\\nEntradas: ', df_rome.shape[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GgPpsvIPrkTO"
      },
      "source": [
        "Primeiramente, vamos converter a variável `price` de *int64* para *float*. Assim, podemos contar com os valores decimais dos preços.\n",
        "\n",
        "Além disso, vamos converter as variáveis `id` e `host_id` para objetos do tipo *str*. Fazemos isso para que eles não apareçam nas descrições estatísticas e poluam a visualização. Uma vez que esses números são apenas de identificação, não tem problema deixá-los como *strings*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9cnea33fzTwz",
        "outputId": "1a58300c-0595-4ab1-f69b-90d8d6ab8e10"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                 object\n",
              "name                               object\n",
              "host_id                            object\n",
              "host_name                          object\n",
              "neighbourhood_group               float64\n",
              "neighbourhood                      object\n",
              "latitude                          float64\n",
              "longitude                         float64\n",
              "room_type                          object\n",
              "price                             float64\n",
              "minimum_nights                      int64\n",
              "number_of_reviews                   int64\n",
              "last_review                        object\n",
              "reviews_per_month                 float64\n",
              "calculated_host_listings_count      int64\n",
              "availability_365                    int64\n",
              "number_of_reviews_ltm               int64\n",
              "license                            object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Convertendo price de int64 para float\n",
        "df_rome.price = df_rome.price.astype(float)\n",
        "\n",
        "# Convertendo as variáveis id e host_id para o tipo object\n",
        "df_rome[['id', 'host_id']] = df_rome[['id', 'host_id']].astype('str')\n",
        "\n",
        "df_rome.dtypes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yz1urSUGW9iE"
      },
      "source": [
        "###**2. Qual a porcentagem de valores ausentes no *dataset*?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZxPHLLJozr7U"
      },
      "source": [
        "Nessa seção, o objetivo será o de avaliar a proporção de valores ausentes no dataset. Conhecendo quais variáveis possui mais entradas nulas, podemos avaliar para quais devemos fazer a limpeza ou o preenchimento desses valores ausentes.\n",
        "\n",
        "Primeiramente, vamos avaliar qual o número acumulado de valores ausentes para cada variável, utilizando a função `.isnull()`, somando com `.sum()` e classificando os valores do maior para o menor com `.sort_values()`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "plk2FjbCXOqP",
        "outputId": "ecd9f5b4-0cde-47fb-f970-8146852d31e9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "neighbourhood_group               24383\n",
              "license                           20329\n",
              "reviews_per_month                  3480\n",
              "last_review                        3480\n",
              "name                                  3\n",
              "host_name                             1\n",
              "number_of_reviews                     0\n",
              "number_of_reviews_ltm                 0\n",
              "availability_365                      0\n",
              "calculated_host_listings_count        0\n",
              "id                                    0\n",
              "minimum_nights                        0\n",
              "room_type                             0\n",
              "longitude                             0\n",
              "latitude                              0\n",
              "neighbourhood                         0\n",
              "host_id                               0\n",
              "price                                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "(df_rome.isnull().sum()).sort_values(ascending = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wDykVvBOz3_r"
      },
      "source": [
        "Em seguidam vamos calcular a porcentagem que mostra a proporção de valores nulos com relação ao número total de entradas do dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ExhP03p6z669",
        "outputId": "fcf04212-3396-4f92-e4f3-b4a6d33a6552"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "neighbourhood_group               100.000000\n",
              "license                            83.373662\n",
              "reviews_per_month                  14.272239\n",
              "last_review                        14.272239\n",
              "name                                0.012304\n",
              "host_name                           0.004101\n",
              "number_of_reviews                   0.000000\n",
              "number_of_reviews_ltm               0.000000\n",
              "availability_365                    0.000000\n",
              "calculated_host_listings_count      0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "(df_rome.isnull().sum() / df_rome.shape[0]*100).sort_values(ascending = False)[:10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cwFTQCfZs8-N"
      },
      "source": [
        "Note que temos:\n",
        "\n",
        "* 100% de valores ausentes para a variável `neighbourhood_group`\n",
        "* 83% para a variável `license`\n",
        "* 14% para as variáveis `reviews_per_month` e `last_review`\n",
        "\n",
        "As outras variáveis apresentam um proporção muito pequena de valores nulos, e não influenciam na análise proposta."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_TUquXiwtwTN"
      },
      "source": [
        "**Verificando entradas duplicadas**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LK0UVfezuSXX",
        "outputId": "4603ec9a-7703-484b-d25d-c43be7aa5139"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Não existem entradas duplicadas\n"
          ]
        }
      ],
      "source": [
        "if df_rome.duplicated().sum() > 0:\n",
        "  print('Existem {} entradas duplicadas'.format(df_rome.duplicated().sum()))\n",
        "else:\n",
        "  print('Não existem entradas duplicadas')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iHXvvHIxaoNc"
      },
      "source": [
        "### **3. Qual o tipo de distribuição das variáveis?** "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IR8rcXTq0Go8"
      },
      "source": [
        "Vamos analisar agora as distribuições estatísticas das variáveis, visualizando um conjunto de histogramas com os dados referentes a cada variável numérica."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "Iu2E4s2sWKYM",
        "outputId": "4f12faeb-3e18-449f-c1f6-95e2d428b941"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "df_rome.hist(bins = 15, figsize = (15, 10), grid = False, color = 'r', );\n",
        "\n",
        "plt.title('Distribuições estatísticas - Antes do tratamento dos dados')\n",
        "plt.savefig('histograma_antes.jpg')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HN6hL78R0rAJ"
      },
      "source": [
        "Observa-se, a partir desses histogramas, que as variáveis `price` e `minimum_nights` apresentam outliers, uma vez que a distribuição está totalmente concentrada no intervalo mais próximo de 0 e se estende ate valores muito altos. \n",
        "\n",
        "Vamos analisar a descrição estatística e observar como se comportam a média, mediana e os valores máximos e mínimos. Para isso, utilizaremos a função `describe()`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "PlXqCLLg1GH8",
        "outputId": "d4de99d3-dc27-436a-9821-a8d8a21631f3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       neighbourhood_group  latitude  longitude     price  minimum_nights  \\\n",
              "count                  0.0  24383.00   24383.00  24383.00        24383.00   \n",
              "mean                   NaN     41.89      12.48    235.62            3.53   \n",
              "std                    NaN      0.03       0.05   2261.05           18.79   \n",
              "min                    NaN     41.66      12.25      0.00            1.00   \n",
              "25%                    NaN     41.89      12.46     65.00            1.00   \n",
              "50%                    NaN     41.90      12.48     92.00            2.00   \n",
              "75%                    NaN     41.91      12.50    140.00            3.00   \n",
              "max                    NaN     42.12      12.79  96160.00          999.00   \n",
              "\n",
              "       number_of_reviews  reviews_per_month  calculated_host_listings_count  \\\n",
              "count           24383.00           20903.00                        24383.00   \n",
              "mean               52.69               1.50                            8.57   \n",
              "std                83.18               1.54                           25.51   \n",
              "min                 0.00               0.01                            1.00   \n",
              "25%                 3.00               0.33                            1.00   \n",
              "50%                18.00               1.00                            3.00   \n",
              "75%                66.00               2.22                            6.00   \n",
              "max              1270.00              34.83                          221.00   \n",
              "\n",
              "       availability_365  number_of_reviews_ltm  \n",
              "count          24383.00               24383.00  \n",
              "mean             201.70                  14.16  \n",
              "std              129.26                  19.91  \n",
              "min                0.00                   0.00  \n",
              "25%               79.00                   0.00  \n",
              "50%              226.00                   5.00  \n",
              "75%              327.00                  22.00  \n",
              "max              365.00                 765.00  "
            ],
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              "      <th>neighbourhood_group</th>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "df_rome.describe().round(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tI5SitaU1YcB"
      },
      "source": [
        "Note que, para a variável `price`, temos uma média de € 235,6, enquanto a mediana aponta que metade dos valores estão abaixo de € 92,0. A tendência enviesada da média devido aos outliers fica evidente ao avaliarmos o valor máximo de € 96160, muito distoante do restante dos dados.\n",
        "\n",
        "A mesma análise vale para a variável `minimum_nights`. Note que o valor máximo é de 999 noites, que é muito mais que os 365 dias do ano. Este tipo de entrada não faz sentido, pois ninguém alugaria uma hospedagem cujo mínimo de noites seja maior do que 1 ano.\n",
        "\n",
        "Observando essas duas variáveis, vemos a necessidade de tratar os dados ausentes e outliers."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RKzboHZSWK4y"
      },
      "source": [
        "### **4. Tratamento de valores ausentes**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oBvyIsvxNvSL"
      },
      "source": [
        "Vamos retomar a análise feita anteriormente, em que foi observada a proporção de valores ausentes no dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oQVcVrm4Wavi",
        "outputId": "86b57671-87a1-4640-95a6-effc7c0a6627"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "neighbourhood_group               100.000000\n",
              "license                            83.373662\n",
              "reviews_per_month                  14.272239\n",
              "last_review                        14.272239\n",
              "name                                0.012304\n",
              "host_name                           0.004101\n",
              "number_of_reviews                   0.000000\n",
              "number_of_reviews_ltm               0.000000\n",
              "availability_365                    0.000000\n",
              "calculated_host_listings_count      0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "(df_rome.isnull().sum() / df_rome.shape[0]*100).sort_values(ascending = False)[:10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j78kv3-c3dpI"
      },
      "source": [
        "Para a nossa análise, as colunas de dados ausentes não apresentam nenhuma relevância com o objetivo proposto. Assim, iremos excluir as colunas `neighbourhood_group`, `license` e `last_review`. \n",
        "\n",
        "Depois, iremos excluir as entradas de valores ausentes, já que representam uma proporção menor do dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "wz2Lkdbc4HXT"
      },
      "outputs": [],
      "source": [
        "df_clean = df_rome.copy() # Criando uma cópia de backup do dataset\n",
        "\n",
        "# Excluindo as colunas com grande proporção de dados ausentes\n",
        "df_clean.drop(columns = ['neighbourhood_group', 'license', 'last_review'], inplace = True)\n",
        "\n",
        "# Completando os valores ausentes da coluna reviews_per_month com sua mediana.\n",
        "median_rpm = df_clean.reviews_per_month.median()\n",
        "df_clean = df_clean.fillna({'reviews_per_month': median_rpm})\n",
        "\n",
        "# Excluindo entradas com dados ausentes\n",
        "df_clean.dropna(axis = 0, inplace = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ogRDO_P1OLOW"
      },
      "source": [
        "Note que, em primeiro lugar, foi criado uma cópia do dataset, utilizando a função `.copy()`. Assim, qualquer mudança feita `inplace` é realizada no novo dataset, preservando o original como *backup* e para comparações.\n",
        "\n",
        "Em seguida, foram excluídas as colunas com grandes proporções de valores ausentes. Se, por outro lado, as entradas fossem excluídas primeiro, muitos dados do dataset seriam perdidos, inclusive aqueles das variáveis que nos interessam. \n",
        "\n",
        "Por isso, apenas depois de excluir as colunas é que foram excluídas as entradas com valores ausentes. Nessa etapa, também já havíamos preenchido com a mediana a coluna `reviews_per_month`, que continha 14% de valores ausentes. \n",
        "\n",
        "Dessa forma, na hora de apagar as entradas com valores nulos, tivemos apenas 0,01% dos dados excluídos."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PjLs6WpxP0Qd"
      },
      "source": [
        "#### Dimensões do dataset após a limpeza de valores ausentes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hlyArp9f4thn",
        "outputId": "67b3e851-ca5e-4ef2-ca52-8fbedfdde6e9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Volume do dataset: \n",
            "Antes:  (24383, 18) \n",
            "Depois:  (24379, 15)\n"
          ]
        }
      ],
      "source": [
        "print('Volume do dataset:',\n",
        "      '\\nAntes: ', df_rome.shape,\n",
        "      '\\nDepois: ', df_clean.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fKrF6rHgXROW"
      },
      "source": [
        "Comparando as dimensões antes e depois, observamos que três colunas foram excluídas e apenas 4 entradas foram apagadas.\n",
        "\n",
        "Checando novamente a porcentagem de valores ausentes após a limpeza:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DXf5R_nL5VlT",
        "outputId": "8e0bd32f-7d1c-4edb-f001-37d7ed4873f1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                0.0\n",
              "name                              0.0\n",
              "host_id                           0.0\n",
              "host_name                         0.0\n",
              "neighbourhood                     0.0\n",
              "latitude                          0.0\n",
              "longitude                         0.0\n",
              "room_type                         0.0\n",
              "price                             0.0\n",
              "minimum_nights                    0.0\n",
              "number_of_reviews                 0.0\n",
              "reviews_per_month                 0.0\n",
              "calculated_host_listings_count    0.0\n",
              "availability_365                  0.0\n",
              "number_of_reviews_ltm             0.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "(df_clean.isnull().sum() / df_rome.shape[0]*100).sort_values(ascending = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wJQfJUHW504i"
      },
      "source": [
        "Note que, agora, não há nenhum dado ausente no dataset. O volume diminuiu em quantidade de entradas e de variáveis. Porém, agora temos um conjunto de dados muito mais interessante para a análise. \n",
        "\n",
        "Vamos novamente utilizar a função `.describe()` para obervar as distribuições estatísticas."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "Uzh9kAe_6Dip",
        "outputId": "e54447ed-40d4-4e33-cfdf-63a1d4d0a7ee"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       latitude  longitude     price  minimum_nights  number_of_reviews  \\\n",
              "count  24379.00   24379.00  24379.00        24379.00           24379.00   \n",
              "mean      41.89      12.48    235.61            3.53              52.70   \n",
              "std        0.03       0.05   2261.23           18.79              83.18   \n",
              "min       41.66      12.25      0.00            1.00               0.00   \n",
              "25%       41.89      12.46     65.00            1.00               3.00   \n",
              "50%       41.90      12.48     92.00            2.00              18.00   \n",
              "75%       41.91      12.50    140.00            3.00              66.00   \n",
              "max       42.12      12.79  96160.00          999.00            1270.00   \n",
              "\n",
              "       reviews_per_month  calculated_host_listings_count  availability_365  \\\n",
              "count           24379.00                        24379.00          24379.00   \n",
              "mean                1.43                            8.57            201.70   \n",
              "std                 1.43                           25.51            129.25   \n",
              "min                 0.01                            1.00              0.00   \n",
              "25%                 0.42                            1.00             79.00   \n",
              "50%                 1.00                            3.00            226.00   \n",
              "75%                 1.96                            6.00            327.00   \n",
              "max                34.83                          221.00            365.00   \n",
              "\n",
              "       number_of_reviews_ltm  \n",
              "count               24379.00  \n",
              "mean                   14.16  \n",
              "std                    19.91  \n",
              "min                     0.00  \n",
              "25%                     0.00  \n",
              "50%                     5.00  \n",
              "75%                    22.00  \n",
              "max                   765.00  "
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
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              "      <th>count</th>\n",
              "      <td>24379.00</td>\n",
              "      <td>24379.00</td>\n",
              "      <td>24379.00</td>\n",
              "      <td>24379.00</td>\n",
              "      <td>24379.00</td>\n",
              "      <td>24379.00</td>\n",
              "      <td>24379.00</td>\n",
              "      <td>24379.00</td>\n",
              "      <td>24379.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>41.89</td>\n",
              "      <td>12.48</td>\n",
              "      <td>235.61</td>\n",
              "      <td>3.53</td>\n",
              "      <td>52.70</td>\n",
              "      <td>1.43</td>\n",
              "      <td>8.57</td>\n",
              "      <td>201.70</td>\n",
              "      <td>14.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.03</td>\n",
              "      <td>0.05</td>\n",
              "      <td>2261.23</td>\n",
              "      <td>18.79</td>\n",
              "      <td>83.18</td>\n",
              "      <td>1.43</td>\n",
              "      <td>25.51</td>\n",
              "      <td>129.25</td>\n",
              "      <td>19.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>41.66</td>\n",
              "      <td>12.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>41.89</td>\n",
              "      <td>12.46</td>\n",
              "      <td>65.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>0.42</td>\n",
              "      <td>1.00</td>\n",
              "      <td>79.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>41.90</td>\n",
              "      <td>12.48</td>\n",
              "      <td>92.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>18.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>226.00</td>\n",
              "      <td>5.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>41.91</td>\n",
              "      <td>12.50</td>\n",
              "      <td>140.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>66.00</td>\n",
              "      <td>1.96</td>\n",
              "      <td>6.00</td>\n",
              "      <td>327.00</td>\n",
              "      <td>22.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>42.12</td>\n",
              "      <td>12.79</td>\n",
              "      <td>96160.00</td>\n",
              "      <td>999.00</td>\n",
              "      <td>1270.00</td>\n",
              "      <td>34.83</td>\n",
              "      <td>221.00</td>\n",
              "      <td>365.00</td>\n",
              "      <td>765.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "df_clean.describe().round(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_aU6Obu-6Jjg"
      },
      "source": [
        "Agora temos menos variáveis influenciando o resumo estatístico. Porém, restam ainda os *outliers*, que estão enviesando as variáveis `price` e `minimum_nights`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vy2j_hhs6Ugv"
      },
      "source": [
        "### **5. Tratando os outliers**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GSr3w32HqKN7"
      },
      "source": [
        "\n",
        "Para tratar dos valores *outliers*, iremos usar o método estatístico do IQR Score. O IQR é uma sigla que significa *Inter-Quartile Range*, ou amplitude entre quartis. Trata-se de um valor que representa a diferença entre o terceiro e o primeiro quartis.\n",
        "\n",
        "Ele é calculado da seguinte forma:\n",
        "\n",
        "$$ IQR = Q_{3} - Q_{1} $$\n",
        "\n",
        "Em que $ Q_{3} $ e $ Q_{1} $ representam o terceiro e primeiro quartis, respectivamente.\n",
        "\n",
        "Com o valor do IQR calculado, podemos definir os limites superior e inferior para fazermos o corte no *dataset* e excluir os *outliers*. Esses valores são calculados da seguinte forma:\n",
        "\n",
        "$$ Q_{sup} = Q_{3} + 1.5\\times{IQR}$$\n",
        "$$ Q_{inf} = Q_{1} - 1.5\\times{IQR} $$\n",
        "\n",
        "Em que $Q_{sup}$ representa o limite superior e $Q_{inf}$ o inferior. A partir desse cálculo, podemos definir um ponto de equilíbrio que limpe os *outliers* sem que para isso comprometa a qualidade das informações do conjunto de dados.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jP1BR-Eg6di9"
      },
      "source": [
        "#### Cálculo do IQR - **Variável `price`**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "456nPkqh6bkC",
        "outputId": "1680b64b-9686-4656-fa4e-36849c48169c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "IQR da variável price:  75.0\n"
          ]
        }
      ],
      "source": [
        "q3_price = df_clean.price.quantile(.75)\n",
        "q1_price = df_clean.price.quantile(.25)\n",
        "\n",
        "IQR_price = q3_price - q1_price\n",
        "\n",
        "print('IQR da variável price: ', IQR_price)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sp6_GguV8T-J"
      },
      "source": [
        "Calculando agora os limites superior e inferior para a variável `price`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pLl9xbg_8YY4",
        "outputId": "d64fce27-1288-419a-af5a-b945577fe2b4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Limite superior da variável price:  252.5 \n",
            "Limite inferior da variável price:  -47.5\n"
          ]
        }
      ],
      "source": [
        "sup_price = q3_price + 1.5*IQR_price\n",
        "inf_price = q1_price - 1.5*IQR_price\n",
        "\n",
        "print('Limite superior da variável price: ', sup_price,\n",
        "      '\\nLimite inferior da variável price: ', inf_price)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bHHIBhZXR6Td"
      },
      "source": [
        "Para a nossa análise, iremos desconsiderar o limite inferior $Q_{inf} = -47,5$, pois o valor negativo não faz sentido nesse contexto.\n",
        "\n",
        "Vamos analisar qual a proporção de dados que estão acima do valor de corte dado pelo IQR Score."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "sEWEaByu9dbR",
        "outputId": "ab42deb1-e3ac-4ecf-ad09-75ac4997dca9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x216 with 1 Axes>"
            ],
            "image/png": 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JkhrO4CdJkiRJDWfwkyRJkqSGM/hJkiRJUsMZ/CRJkiSp4Qx+kiRJktRwBj9JkiRJajiDnyRJkiQ1nMFPkiRJkhrO4HeQTv7rk3tdBUmSJEnqisFPkiRJkhrO4CdJkiRJDWfwkyRJkqSGM/hJkiRJUsMZ/CRJkiSp4Qx+B2HzBVdM+SlJkiRJS5nBT5IkSZIazuAnSZIkSQ03b8EvIn4/Ip44X8tbym563VMnf0ZEz7rBwcHJz319fXOed+vWrWzdupX+/v6u59uwYQM7duyYsj127NjB6tWrO9atU1eff8uWLfT397Nlyxa2bt06pb9eXvu0rXGdhk9n69atDA8PExEMDw+zdetWTjnllCl1O+WUUx4w31zKWExzrVe308/HttbyN933ZT4sVjuarZyDrUc3yz3qqKMecMxVZbbtt1Dtbr7qeSjtd8eOHWzYsGHG36sLVe9eWyrf+5nmO9R9s5Dr2Mv9eqhtfim2x7rlUMd5kZmH3AH987Gc9u60007LpQZIILdcsmXycy+7k046KU899dQZp1mxYsXk58HBwXz5y1+eRx55ZEZEAtnX15eDg4MzLqM17cqVK3NkZCQnJiYyM3NiYiKHh4cTyPPOOy/PP//8yXnWrVuX69atm7KMkZGR7O/vnxw2MTGRJ554Yu7cuTMPHDiQF110UQ4MDORFF12UBw4cyJ07d+aJJ56YExMTD5i2NW58fHza4a061o2Pj+fAwEBu27Yt9+/fn9u2bZusyznnnJN33HFHnnPOOQnkySefPDlfp7KnK2MxzbVe3U4/H9tay99035eBgYEcHx8/pOUu1vdptnIOth7dLHfNmjXZ39+fr3/96/ODH/xgrl27Nvv6+g552zXBbNtvodrdfNXzUI6DExMTOTIykscee2xeeeWVeeWVV+Zxxx035ffqQtW718fppfK9n2m+kZGRPO644yb3zbHHHjunfbOQ69jL/XooZS/V9li3HOoI7M75yGyzTgCbgS8AfwtcB1wKrARuBF4HfBJ4NnAJ8MwyzxnAvwOfBj4BrAH6gT8ErgY+A/zqbGUb/KaGrlbX19eXQA4PD2dETBkfETk0NDTtPBGRg4ODOTY2ljt37pwcVw8/q1evfkC5AwMDuW7duhwYGMjBwcHcvHlzjo2NZWbm2NhYAvniF794sr+/v38yDNbLGRoayp07d+bY2Nhk0GzVpWVsbCy3bds2ufzMnJynfdrWuNZy24fXl9EyNDSU27Zte8A+7evrmzKsFf7q9eq2jMU013p1O/18bGstf9N9X7Zt25ZDQ0OHtNzF+j7NVs7B1qOb5Q4ODk7Zdjt37sx169Yd8rZrgtm230K1u7laiOPg2NhYbt68ecr8O3funPJ7daHq3evj9FL53s8036Hum4Vcx17u10Mpe6m2x7rlUMf5Cn5RLauziNgM3ACclZkfiYi/Aj4PjAN/lpmvL9NdAlwOXFaC4rMy8+qIeAhwN/DLwDGZ+eqIGAI+AvxsZt7QVt55wHkAGzduPO2mm26asX6LrXWL4pZLtnDt86/tcW0OTl9fH9/97ndZsWIFAPv372fVqlUzzhMRrT8ETN4Ccd9999Hf38/999/P3r17Ofzwwyf7Ww4cODBZTqt/eHiYD3/4w5x11lmTdRkcHASgv7+fffv2sWbNGu677z4A7r33XoaHhwGmTNsat2LFCg4cOPCA4cPDw5PLqK/H/v37Wbly5ZRhAPXvwp49exgZGZkc1t/fP23Z05WxmOZar26n7zTdXLa1lr/pvi933303q1atYrbfHTNZrO/TbOUcbD26We79998/Zdvde++9DA0N1f+o+qA12/ZbqHY3X/U8lONgf38/mck999wzOX/9d9x8tP+m/L5a7HL6+/uBqecZre9t65xnocruRi/366GUvVTbY91yqGNEXJOZpx/qcrp9xu+WzPxI+fwO4Kzy+V3TTPsI4LbMvBogM7+Vmd8DngQ8NyI+BXwcOAo4qX3mzPzLzDw9M08fGRnpfk0arP48HFTBDZh8/qE+PiImD1Lt87SeMxkdHWXXrl2T47Zv3z75efXq1Q8od2BggGOOOYaBgQEGBwfZtGkTo6OjAJM/X/nKV0729/f3T/4Sq5czNDTErl27GB0d5Sd+4icmp69PMzo6yvbt2yeX21rG6OjoA6ZtjWstt314fRn1OtTXt7596l74whdO6e9U9nRlLKa51qvb6edjW2v5m+77sn37doaGhg5puYv1fZqtnIOtRzfLHRwcnLLtdu3axTHHHHPI264JZtt+C9Xu5mohjoOjo6Ns2rRpyvy7du1i48aN89b+m/L7arHLGR0dZePGjQ/YN/VznoUqu9fLXsiyl2p7rFsOdZw3s10SpLrV86Za/+OBv6e61fPo2vBLgGcCJwMfmWY57wXOnsvlSG/19Bk/n/Gbmc/4aSH5jN/BL9dn/DrzGT+f8fMZv8Vbr/ngM369xyI/45fA40r/W4DzZwh+K4CvAGeU4WuAAarbN98HDJbhDwdWzVT2Ugx+mblkgt/AwMADgtlc5h0fH8/x8fHJZwa76davXz9tSFi1alXHunXq6vOPjY1lX19fjo2N5fj4+JT+ennt09ZPtDrN0258fDyHhoYSqucOx8fH8+STT55St3rom63sXptrvbqdfj62tZa/6b4v82Gx2tFs5RxsPbpZ7tq1ax9wzFVltu23UO1uvup5KO13YmIi169fP+Pv1YWqd68tle/9TPMd6r5ZyHXs5X491Da/FNtj3VKv43wFv26f8fsHYDdwGtXzfb9Ufp6emXvKdJcAl2fmpRFxBvB/gcOA7wBPpHrO79XA04AA7gCenpl3dSr79NNPz927d89Yv17YfMEVrBm9gH3XXcyNFz+l19WRJEmS1FDz9YzfQJfTfS8zf7Ft2OZ6T2Y+v/b5auDMaZZzYekkSZIkSYtk3v6BuyRJkiRpaZo1+GXmjZm5ZTEqs1y0bu/0Nk9JkiRJy4FX/CRJkiSp4Qx+kiRJktRwBj9JkiRJajiDnyRJkiQ1nMFPkiRJkhrO4HeQPvu8z/a6CpIkSZLUFYOfJEmSJDWcwU+SJEmSGs7gJ0mSJEkNZ/CTJEmSpIYz+EmSJElSwxn8JEmSJKnhDH6SJEmS1HAGP0mSJElqOIOfJEmSJDWcwU+SJEmSGs7gJ0mSJEkNZ/CTJEmSpIYz+EmSJElSwxn8JEmSJKnhDH6SJEmS1HAGP0mSJElqOIOfJEmSJDWcwU+SJEmSGs7gJ0mSJEkNZ/CTJEmSpIYz+EmSJElSwxn8JEmSJKnhDH6SJEmS1HAGP0mSJElqOIOfJEmSJDWcwU+SJEmSGs7gJ0mSJEkNZ/CTJEmSpIYz+EmSJElSwxn8JEmSJKnhDH6SJEmS1HAGP0mSJElqOIOfJEmSJDWcwU+SJEmSGs7gJ0mSJEkNZ/CTJEmSpIYz+EmSJElSwxn8JEmSJKnhDH6SJEmS1HAGP0mSJElqOIOfJEmSJDWcwU+SJEmSGs7gJ0mSJEkNZ/CTJEmSpIYz+EmSJElSwxn8JEmSJKnhDH6SJEmS1HAGP0mSJElqOIOfJEmSJDWcwU+SJEmSGs7gJ0mSJEkNZ/CTJEmSpIYz+EmSJElSwxn8JEmSJKnhDH6SJEmS1HAGP0mSJElqOIOfJEmSJDWcwU+SJEmSGs7gJ0mSJEkNZ/CTJEmSpIYz+EmSJElSwxn8JEmSJKnhDH6SJEmS1HAGP0mSJElqOIOfJEmSJDWcwU+SJEmSGs7gJ0mSJEkNZ/CTJEmSpIaLzOx1HTqKiDuAm3pdj2kcDezpdSX0oGO7U6/Y9tQLtjv1gu1OvTBbu9uUmSOHWsiSDn5LVUTszszTe10PPbjY7tQrtj31gu1OvWC7Uy8sVrvzVk9JkiRJajiDnyRJkiQ1nMHv4PxlryugByXbnXrFtqdesN2pF2x36oVFaXc+4ydJkiRJDecVP0mSJElqOIPfHEXET0XEFyPi+oi4oNf10fISERsi4kMR8fmI+FxE/K8yfG1EXBURXyo/jyzDIyLeWNrbZyLi0bVlPa9M/6WIeF5t+GkR8dkyzxsjIhZ/TbUURUR/RPxHRFxe+k+MiI+XtvKuiFhRhg+V/uvL+M21ZbyyDP9iRJxdG+6xUdOKiCMi4tKI+EJEXBcRj/OYp4UWES8rv2evjYgdETHsMU8LISL+KiK+HhHX1oYt+DGuUxkzyky7LjugH/gy8FBgBfBp4JG9rpfd8umA44BHl89rgP8EHgm8HrigDL8AeF35/GTg/wEBnAl8vAxfC3yl/DyyfD6yjPtEmTbKvD/d6/W2Wxod8OvABHB56X838OzyeTvw4vL514Dt5fOzgXeVz48sx70h4MRyPOz32Gg3Uwf8NfAr5fMK4AiPeXYL2QEnADcAh5X+dwPP95hntxAd8N+ARwPX1oYt+DGuUxkzdV7xm5vHANdn5lcy8wDwTuDcHtdJy0hm3paZnyyf9wHXUf2COpfq5Ijy8+nl87nA32TlY8AREXEccDZwVWZ+MzPvBK4CfqqMe0hmfiyrI8Hf1JalB7GIWA88BXhL6Q/g8cClZZL2dtdqj5cCTyjTnwu8MzPvycwbgOupjoseGzWtiDic6qTorQCZeSAz9+IxTwtvADgsIgaAlcBteMzTAsjMDwPfbBu8GMe4TmV0ZPCbmxOAW2r9t5Zh0pyVW0lOBT4OrMvM28qo24F15XOnNjfT8FunGS79MfCbwP2l/yhgb2Z+r/TX28pk+yrj7yrTz7U9SicCdwBvK7cZvyUiVuExTwsoM78K/BFwM1Xguwu4Bo95WjyLcYzrVEZHBj+pByJiNfBe4KWZ+a36uPIXHV+3q3kTEU8Fvp6Z1/S6LnrQGaC6BerPM/NUYD/VLUmTPOZpvpVnnc6l+sPD8cAq4Kd6Wik9aC3GMa7bMgx+c/NVYEOtf30ZJnUtIgapQt/fZubflcFfK5fzKT+/XoZ3anMzDV8/zXA9uP0IcE5E3Eh1S9LjgT+husVkoExTbyuT7auMPxz4BnNvj9KtwK2Z+fHSfylVEPSYp4X0ROCGzLwjM+8F/o7qOOgxT4tlMY5xncroyOA3N1cDJ5W3Qq2gegD4sh7XSctIeWbgrcB1mfmG2qjLgNYbnJ4HvL82/LnlLVBnAneVy/r/CDwpIo4sf9l8EvCPZdy3IuLMUtZza8vSg1RmvjIz12fmZqrj1s7M/AXgQ8Azy2Tt7a7VHp9Zps8y/NnlDXgnAidRPXTusVHTyszbgVsi4hFl0BOAz+MxTwvrZuDMiFhZ2kWr3XnM02JZjGNcpzI6W4y33TSpo3obz39Svc3pol7Xx255dcBZVJfiPwN8qnRPpnqW4J+BLwH/BKwt0wfwp6W9fRY4vbasX6Z60Px64AW14acD15Z53gREr9fbbul0wI/z/bd6PpTqJOZ64D3AUBk+XPqvL+MfWpv/otK2vkjt7YkeG+06dcCjgN3luPc+qjfWecyzW9AO+D3gC6VtvJ3qzZwe8+wWoq3toHqW9F6quxxeuBjHuE5lzNS1ZpQkSZIkNZS3ekqSJElSwxn8JEmSJKnhDH6SJEmS1HAGP0mSJElqOIOfJEmSJDWcwU+SJEmSGs7gJ0mSJEkNZ/CTJEmSpIb7/9S+124Q1vMxAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O dataset possui 24379 entradas\n",
            "2083 entradas acima de 252,5\n",
            "Representam 8.54% do dataset.\n"
          ]
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize = (15,3))\n",
        "df_clean.price.plot(kind = 'box', vert = False);\n",
        "ax.set_title('Dataset antes da limpeza de outliers - variável price')\n",
        "plt.show()\n",
        "\n",
        "print('O dataset possui {} entradas'.format(df_clean.shape[0]))\n",
        "print('{} entradas acima de 252,5'.format(len(df_clean[df_clean.price > 252.5])))\n",
        "print('Representam {:.2f}% do dataset.'.format(len(df_clean[df_clean.price > 252.5]) / df_clean.shape[0]*100))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jflzk9TC_Cwq"
      },
      "source": [
        "#### Cálculo do IQR - **Variável `minimum_nights`**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zpOavcN0SPtC"
      },
      "source": [
        "Agora farei o mesmo procedimento para a variável `minimum_nights` antes de prosseguir com a limpeza dos outliers."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AYkzQu1S_cQ4",
        "outputId": "574482a4-347e-4664-a646-cbc00d32649d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "IQR da variável minimum_nights:  2.0\n"
          ]
        }
      ],
      "source": [
        "q3_minimum_nights = df_clean.minimum_nights.quantile(.75)\n",
        "q1_minimum_nights = df_clean.minimum_nights.quantile(.25)\n",
        "\n",
        "IQR_minimum_nights = q3_minimum_nights - q1_minimum_nights\n",
        "\n",
        "print('IQR da variável minimum_nights: ', IQR_minimum_nights)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rIOLkW8g_6Ro"
      },
      "source": [
        "**Cálculo dos limites superior e inferior**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iSJsLsEQ_2UB",
        "outputId": "9aa936ef-daec-41fc-b7e1-907304b8cdbd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Limite superior da variável minimum_nights:  6.0 \n",
            "Limite inferior da variável minimum_nights:  -2.0\n"
          ]
        }
      ],
      "source": [
        "sup_minimum_nights = q3_minimum_nights + 1.5*IQR_minimum_nights\n",
        "inf_minimum_nights = q1_minimum_nights - 1.5*IQR_minimum_nights\n",
        "\n",
        "print('Limite superior da variável minimum_nights: ', sup_minimum_nights,\n",
        "      '\\nLimite inferior da variável minimum_nights: ', inf_minimum_nights)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0M_SVdr7AQzZ"
      },
      "source": [
        "**Visualizando o boxplot e proporção do dataset que representa o corte do limite superior**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "L6Gr4IMPAWjn",
        "outputId": "ee355f6f-82d1-474d-b192-35ba357259c8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x216 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O dataset possui 24379 entradas\n",
            "1039 entradas acima de 6.0\n",
            "Representam 4.26% do dataset\n"
          ]
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize = (15,3))\n",
        "df_clean.minimum_nights.plot(kind = 'box', vert = False)\n",
        "ax.set_title('Dataset antes da limpeza de outliers - variável minimum_nights')\n",
        "plt.show()\n",
        "\n",
        "print('O dataset possui {} entradas'.format(df_clean.shape[0]))\n",
        "print('{} entradas acima de 6.0'.format(len(df_clean[df_clean.minimum_nights > 6.0])))\n",
        "print('Representam {:.2f}% do dataset'.format(len(df_clean[df_clean.minimum_nights > 6.0]) / df_clean.shape[0]*100))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wr1EJidQBJ-9"
      },
      "source": [
        "#### Limpando os outliers\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AK7AGSMPSpgR"
      },
      "source": [
        "Prosseguindo agora com a limpeza dos *outliers* para as duas variáveis. Vamos utilizar a função `.drop()`, selecionando todas as entradas que estão acima do limite superior calculado no IQR, excluindo-as do dataset e já implementando as mudanças `inplace`.\n",
        "\n",
        "Apenas para a variável `price`, ainda vamos excluir o valor igual a zero, uma vez que não faz sentido um anfitrião ofertar sua hospedagem de graça."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nI1sfVNWBUm-",
        "outputId": "48a532ec-a572-4eb7-eb3e-00a84fa1674a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape do dataset: \n",
            "Antes:  (24383, 18) \n",
            "Depois:  (21335, 15)\n"
          ]
        }
      ],
      "source": [
        "# Limpando os outliers de price e minimum_nights\n",
        "df_clean.drop(df_clean[df_clean.price > 252.5].index, axis = 0, inplace = True)\n",
        "df_clean.drop(df_clean[df_clean.price == 0.0].index, axis = 0, inplace = True)\n",
        "df_clean.drop(df_clean[df_clean.minimum_nights > 6.0].index, axis = 0, inplace = True)\n",
        "\n",
        "# Comparando o shape antes e depois da limpeza de outliers e de dados ausentes\n",
        "print('Shape do dataset:',\n",
        "      '\\nAntes: ', df_rome.shape,\n",
        "      '\\nDepois: ', df_clean.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rA_ACZ7hT7AM",
        "outputId": "24f0fb14-1086-4b48-9f03-5f99dfedabc5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Foram removidos 3048 outliers \n",
            "Isto representa 14.3% do dataset\n"
          ]
        }
      ],
      "source": [
        "pct_outliers = (df_rome.shape[0] - df_clean.shape[0])/df_clean.shape[0] * 100\n",
        "print('Foram removidos {} outliers'.format(df_rome.shape[0]-df_clean.shape[0]),\n",
        "      '\\nIsto representa {:.1f}% do dataset'.format(pct_outliers))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tjmBPEaWCaP6"
      },
      "source": [
        "#### Comparando as mudanças antes e depois das limpezas de valores ausentes e outliers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ECzcpup8XGiE"
      },
      "source": [
        "Agora, vamos comparar como o tratamento dos dados de valores ausentes e *outliers* afetaram na distribuição das variáveis `price` e `minimum_nights`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ML-pt214fNZX"
      },
      "source": [
        "**Variável `price`**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "gy8-52TBP4Ue",
        "outputId": "94f7dea7-346b-46c7-caa0-aaf1b931947e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize = (20,6))\n",
        "fig.suptitle('Comparação entre os datasets - price', fontsize = 15, fontweight = 550)\n",
        "\n",
        "# Dataset original\n",
        "df_rome.price.hist(ax = ax1, color = 'r', grid = False);\n",
        "ax1.set_title('Antes da limpeza', fontdict = {'fontsize': 15})\n",
        "ax1.set_xlabel('Price', fontsize = 15)\n",
        "ax1.set_ylabel('Frequência', fontsize = 15)\n",
        "ax1.tick_params(axis = 'both', which = 'major', direction = 'in', labelsize = 14)\n",
        "ax1.plot(1,2)\n",
        "\n",
        "# Dataset limpo\n",
        "df_clean.price.hist(ax = ax2, color = 'g', grid = False);\n",
        "ax2.set_title('Depois da limpeza', fontdict = {'fontsize': 15})\n",
        "ax2.set_xlabel('Price', fontsize = 15)\n",
        "ax2.set_ylabel('Frequência', fontsize = 15)\n",
        "ax2.tick_params(axis = 'both', which = 'major', direction = 'in', labelsize = 14)\n",
        "ax2.plot(1,2)\n",
        "\n",
        "# Adicionando o texto no gráfico para a informações estatísticas\n",
        "ax1.text(0.9, 0.9, 'Média = € 235.6', ha = 'right', va = 'top', transform = ax1.transAxes, fontsize = 'large', fontweight = 600)\n",
        "ax1.text(0.92, 0.85, 'Valor mínimo = € 0', ha = 'right', va = 'top', transform = ax1.transAxes, fontsize = 'large', fontweight = 600)\n",
        "ax1.text(0.96, 0.80, 'Valor máximo = € 96160', ha = 'right', va = 'top', transform = ax1.transAxes, fontsize = 'large', fontweight = 600)\n",
        "\n",
        "ax2.text(0.9, 0.9, 'Média = € 98.6', ha = 'right', va = 'top', transform = ax2.transAxes, fontsize = 'large', fontweight = 600)\n",
        "ax2.text(0.92, 0.85, 'Valor mínimo = € 9', ha = 'right', va = 'top', transform = ax2.transAxes, fontsize = 'large', fontweight = 600)\n",
        "ax2.text(0.94, 0.80, 'Valor máximo = € 252', ha = 'right', va = 'top', transform = ax2.transAxes, fontsize = 'large', fontweight = 600)\n",
        "\n",
        "\n",
        "\n",
        "plt.savefig('comparacao_datasets_price.jpg')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XqvSLtp2cO02"
      },
      "source": [
        "Note a partir do gráfico, a mudança na média e nos valores máximo e mínimo para cada dataset. Agora, temos um conjunto de dados para a variável `price` muito menos enviesado para os valores mais altos.\n",
        "\n",
        "Também é possível verificar a mudança na média, nos quartis e nos valores máximos e mínimoss analisando o **boxplot**:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "TxFvBXTjCegq",
        "outputId": "fe983c84-47e8-4368-d016-0e782395139f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O dataset possui (21335, 15) entradas\n",
            "0 entradas acima de 252.5\n",
            "Representam 0.00% do dataset\n"
          ]
        }
      ],
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize = (20,4)) # plt.subplots(1, 2, figsize = (15,3)) - os número (1, 2) representam o número de linhas (1) e colunas(2)\n",
        "fig.suptitle('Comparação entre os datasets - price', fontsize = 15, fontweight = 550)\n",
        "\n",
        "# Dataset original\n",
        "df_rome.price.plot(kind = 'box', ax = ax1, color = 'r', vert = False);\n",
        "ax1.set_title('Antes da limpeza', fontdict = {'fontsize': 15})\n",
        "# Arrumar o tamanho das fontes no label e nos ticks\n",
        "ax1.set_xlabel('Price', fontsize = 14)\n",
        "ax1.tick_params(axis = 'both', which = 'major', direction = 'in', labelsize = 14)\n",
        "\n",
        "# Dataset limpo\n",
        "df_clean.price.plot(kind = 'box', ax= ax2, color = 'g', vert = False);\n",
        "ax2.set_title('Depois da limpeza', fontdict = {'fontsize': 15})\n",
        "ax2.set_xlabel('Price', fontsize = 14)\n",
        "ax2.tick_params(axis = 'both', which = 'major', direction = 'in', labelsize = 14)\n",
        "\n",
        "plt.savefig('comparacao_boxplot_price.jpg')\n",
        "plt.show()\n",
        "\n",
        "print('O dataset possui {} entradas'.format(df_clean.shape))\n",
        "print('{} entradas acima de 252.5'.format(len(df_clean[df_clean.price > 252.5])))\n",
        "print('Representam {:.2f}% do dataset'.format(len(df_clean[df_clean.price > 252.5]) / df_clean.shape[0]*100))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NCqHpJw4aD6s"
      },
      "source": [
        "O boxplot mostra claramente a diferença entre os dados antes e depois. No *dataset* original era impossível visualizar a caixa contendo as informações sobre os quartis, a mediana e os valores máximo e mínimo. \n",
        "\n",
        "Com a limpeza, conseguimos distinguir essas informações com facilidade. \n",
        "\n",
        "* Mediana: 89,0\n",
        "* Média: 98,6\n",
        "* Primeiro quartil: 64,0\n",
        "* Terceiro quartil: 123,0\n",
        "* Valor máximo: 252,0\n",
        "* Valor mínimo: 9,0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2S2jh0UWagX2",
        "outputId": "1b27086f-13fa-4830-b123-78ab80c47c24"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    21335.0\n",
              "mean        98.6\n",
              "std         48.1\n",
              "min          9.0\n",
              "25%         64.0\n",
              "50%         89.0\n",
              "75%        123.0\n",
              "max        252.0\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "df_clean.price.describe().round(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ODoyHBD2fnd8"
      },
      "source": [
        "**Variável `minimum_nights`**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "JqRXx_d-R6Wd",
        "outputId": "4999be81-9879-4370-c02b-9c26d1e46a57"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize = (20,6))\n",
        "fig.suptitle('Comparação entre os datasets - minimum_nights', fontsize = 16, fontweight = 550)\n",
        "\n",
        "# Dataset original\n",
        "df_rome.minimum_nights.hist(ax = ax1, color = 'r', grid = False);\n",
        "ax1.set_title('Antes da limpeza', fontdict = {'fontsize': 15})\n",
        "ax1.set_xlabel('Minimum Nights', fontsize = 15)\n",
        "ax1.set_ylabel('Frequência', fontsize = 15)\n",
        "ax1.tick_params(axis = 'both', which = 'major', direction = 'in', labelsize = 14)\n",
        "\n",
        "# Dataset limpo\n",
        "df_clean.minimum_nights.hist(ax = ax2, color = 'g', grid = False);\n",
        "ax2.set_title('Depois da limpeza', fontdict = {'fontsize': 15})\n",
        "ax2.set_xlabel('Minimum Nights', fontsize = 15)\n",
        "ax2.set_ylabel('Frequência', fontsize = 15)\n",
        "ax2.tick_params(axis = 'both', which = 'major', labelsize = 14, direction = 'in')\n",
        "\n",
        "# Adicionando o texto no gráfico para informações estatísticas\n",
        "ax1.text(0.9, 0.9, 'Média = 3,53 noites', ha = 'right', va = 'top', transform = ax1.transAxes, fontsize = 'large', fontweight = 600)\n",
        "ax1.text(0.92, 0.85, 'Valor mínimo = 1 noite', ha = 'right', va = 'top', transform = ax1.transAxes, fontsize = 'large', fontweight = 600)\n",
        "ax1.text(0.94, 0.80, 'Valor máximo = 999 noites', ha = 'right', va = 'top', transform = ax1.transAxes, fontsize = 'large', fontweight = 600)\n",
        "\n",
        "ax2.text(0.9, 0.9, 'Média = 1,97 noite', ha = 'right', va = 'top', transform = ax2.transAxes, fontsize = 'large', fontweight = 600)\n",
        "ax2.text(0.92, 0.85, 'Valor mínimo = 1 noite', ha = 'right', va = 'top', transform = ax2.transAxes, fontsize = 'large', fontweight = 600)\n",
        "ax2.text(0.933, 0.80, 'Valor máximo = 6 noites', ha = 'right', va = 'top', transform = ax2.transAxes, fontsize = 'large', fontweight = 600)\n",
        "\n",
        "plt.savefig('comparacao_histogramas_minimum_nights.jpg')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "TtdWMg_3DLMq",
        "outputId": "a3681d06-7d81-4977-802f-39e0c3f5aedc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O dataset possui (21335, 15) entradas\n",
            "0 entradas acima de 6.0\n",
            "Representam 0.00% do dataset\n"
          ]
        }
      ],
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize = (20,4))\n",
        "fig.suptitle('Comparação entre os datasets - minimum_nights', fontsize = 15, fontweight = 550)\n",
        "\n",
        "# Dataset original\n",
        "df_rome.minimum_nights.plot(kind = 'box', vert = False, color = 'r', ax = ax1)\n",
        "ax1.set_title('Antes da limpeza', fontdict = {'fontsize': 15})\n",
        "ax1.set_xlabel('Minimum Nights', fontsize = 14)\n",
        "ax1.tick_params(axis = 'x', which = 'major', direction = 'in', labelsize = 14)\n",
        "\n",
        "# Dataset limpo\n",
        "df_clean.minimum_nights.plot(kind = 'box', vert = False, color = 'g', ax = ax2)\n",
        "ax2.set_title('Depois da limpeza', fontdict = {'fontsize': 15})\n",
        "ax2.set_xlabel('Minimum Nights', fontsize = 14)\n",
        "ax2.tick_params(axis = 'x', which = 'major', direction = 'in', labelsize = 14)\n",
        "\n",
        "\n",
        "plt.savefig('comparacao_boxplots_minimum_nights.jpg')\n",
        "plt.show()\n",
        "\n",
        "print('O dataset possui {} entradas'.format(df_clean.shape))\n",
        "print('{} entradas acima de 6.0'.format(len(df_clean[df_clean.minimum_nights > 6.0])))\n",
        "print('Representam {:.2f}% do dataset'.format(len(df_clean[df_clean.minimum_nights > 6.0]) / df_clean.shape[0]*100))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_kwgjrNVb_Zd"
      },
      "source": [
        "Assim como aconteceu com a variável `price`, agora conseguimos distinguir claramente os quartis e os valores máximos e mínimos para `minimum_nights`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IXn2oIo5qx-w"
      },
      "source": [
        "### **6. Qual a correlação entre as variáveis?**\n",
        "\n",
        "Para mostrar a correlação das variáveis, utilizaremos o método `.corr()` para as variáveis de interesse, juntamente com a biblioteca de visualização do `seaborn`.\n",
        "\n",
        "1. Verificar a matriz de correlação entre as variáveis;\n",
        "2. Visualizar graficamente as correlações através do heatmap da biblioteca `seaborn`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "x2ayzBajqxyd",
        "outputId": "18d29f28-8478-4a6b-d350-dbf3cf1aeeb8"
      },
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                                   price  minimum_nights  number_of_reviews  \\\n",
              "price                           1.000000        0.040287          -0.073676   \n",
              "minimum_nights                  0.040287        1.000000           0.070147   \n",
              "number_of_reviews              -0.073676        0.070147           1.000000   \n",
              "reviews_per_month              -0.065749       -0.042551           0.537978   \n",
              "calculated_host_listings_count  0.060721       -0.079497          -0.052564   \n",
              "availability_365                0.158431       -0.080813           0.009259   \n",
              "\n",
              "                                reviews_per_month  \\\n",
              "price                                   -0.065749   \n",
              "minimum_nights                          -0.042551   \n",
              "number_of_reviews                        0.537978   \n",
              "reviews_per_month                        1.000000   \n",
              "calculated_host_listings_count           0.004078   \n",
              "availability_365                         0.095140   \n",
              "\n",
              "                                calculated_host_listings_count  \\\n",
              "price                                                 0.060721   \n",
              "minimum_nights                                       -0.079497   \n",
              "number_of_reviews                                    -0.052564   \n",
              "reviews_per_month                                     0.004078   \n",
              "calculated_host_listings_count                        1.000000   \n",
              "availability_365                                     -0.038554   \n",
              "\n",
              "                                availability_365  \n",
              "price                                   0.158431  \n",
              "minimum_nights                         -0.080813  \n",
              "number_of_reviews                       0.009259  \n",
              "reviews_per_month                       0.095140  \n",
              "calculated_host_listings_count         -0.038554  \n",
              "availability_365                        1.000000  "
            ],
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              "      <th></th>\n",
              "      <th>price</th>\n",
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            ]
          },
          "metadata": {}
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      ],
      "source": [
        "# criar uma matriz de correlação\n",
        "corr = df_clean[['price', 'minimum_nights', 'number_of_reviews', 'reviews_per_month',\n",
        "                 'calculated_host_listings_count', 'availability_365']].corr()\n",
        "\n",
        "# mostrar a matriz de correlação\n",
        "display(corr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "91UujDr6ueqE",
        "outputId": "3e34ae93-bdbf-4a3a-9d04-43ebd5428446"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# plotar um heatmap a partir das correlações\n",
        "fig, ax = plt.subplots(figsize = (8,8));\n",
        "\n",
        "sns.heatmap(corr, cmap = 'RdYlGn', square = True, fmt = '.2f', linecolor = 'white', \n",
        "            annot = True, ax = ax, annot_kws = {'fontsize': 14}, \n",
        "            xticklabels = corr.columns.values, yticklabels = corr.columns.values);\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig('heatmap_roma.jpg')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CtXw9ZkJGhNk"
      },
      "source": [
        "Nota-se que as variáveis são pouco relacionadas entre si. Apenas as variáveis `reviews_per_month` e `number_of_reviews` apresentam uma correlação positiva relevante de + 0.54. Faz sentido, uma vez que quanto mais reviews um imóvel recebe por mês, maior será seu número de reviews.\n",
        "\n",
        "O restante das variáveis apresentam baixa correlação, menores que 10%. Apenas as variáveis `availability_365` e `price` apresentam uma correlação de 16%, o que pode indicar que quanto mais disponível um imóvel está, maiores as chances dele ser mais caro."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7x4aw1j5wNmn"
      },
      "source": [
        "### **7. Qual a média dos preços de aluguel?**\n",
        "\n",
        "Agora que os dados foram limpos e excluídos os outliers, podemos analisar novamente a descrição estatística do conjunto."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "ff-8gUahLk-C",
        "outputId": "8c70e359-3809-4b73-97d6-9bc45cb33e83"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       latitude  longitude     price  minimum_nights  number_of_reviews  \\\n",
              "count  21335.00   21335.00  21335.00        21335.00           21335.00   \n",
              "mean      41.89      12.48     98.56            1.97              56.41   \n",
              "std        0.03       0.05     48.11            1.01              86.30   \n",
              "min       41.66      12.25      9.00            1.00               0.00   \n",
              "25%       41.89      12.46     64.00            1.00               4.00   \n",
              "50%       41.90      12.48     89.00            2.00              20.00   \n",
              "75%       41.91      12.50    123.00            3.00              71.00   \n",
              "max       42.12      12.79    252.00            6.00            1270.00   \n",
              "\n",
              "       reviews_per_month  calculated_host_listings_count  availability_365  \\\n",
              "count           21335.00                        21335.00          21335.00   \n",
              "mean                1.51                            8.14            201.87   \n",
              "std                 1.48                           23.93            128.84   \n",
              "min                 0.01                            1.00              0.00   \n",
              "25%                 0.45                            1.00             79.00   \n",
              "50%                 1.00                            2.00            226.00   \n",
              "75%                 2.11                            6.00            327.00   \n",
              "max                34.83                          221.00            365.00   \n",
              "\n",
              "       number_of_reviews_ltm  \n",
              "count               21335.00  \n",
              "mean                   15.32  \n",
              "std                    20.54  \n",
              "min                     0.00  \n",
              "25%                     0.00  \n",
              "50%                     7.00  \n",
              "75%                    24.00  \n",
              "max                   765.00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b915d5ce-6418-4c80-afae-0631967965f8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>reviews_per_month</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "      <th>number_of_reviews_ltm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>21335.00</td>\n",
              "      <td>21335.00</td>\n",
              "      <td>21335.00</td>\n",
              "      <td>21335.00</td>\n",
              "      <td>21335.00</td>\n",
              "      <td>21335.00</td>\n",
              "      <td>21335.00</td>\n",
              "      <td>21335.00</td>\n",
              "      <td>21335.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>41.89</td>\n",
              "      <td>12.48</td>\n",
              "      <td>98.56</td>\n",
              "      <td>1.97</td>\n",
              "      <td>56.41</td>\n",
              "      <td>1.51</td>\n",
              "      <td>8.14</td>\n",
              "      <td>201.87</td>\n",
              "      <td>15.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.03</td>\n",
              "      <td>0.05</td>\n",
              "      <td>48.11</td>\n",
              "      <td>1.01</td>\n",
              "      <td>86.30</td>\n",
              "      <td>1.48</td>\n",
              "      <td>23.93</td>\n",
              "      <td>128.84</td>\n",
              "      <td>20.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>41.66</td>\n",
              "      <td>12.25</td>\n",
              "      <td>9.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>41.89</td>\n",
              "      <td>12.46</td>\n",
              "      <td>64.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>0.45</td>\n",
              "      <td>1.00</td>\n",
              "      <td>79.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>41.90</td>\n",
              "      <td>12.48</td>\n",
              "      <td>89.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>20.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>226.00</td>\n",
              "      <td>7.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>41.91</td>\n",
              "      <td>12.50</td>\n",
              "      <td>123.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>71.00</td>\n",
              "      <td>2.11</td>\n",
              "      <td>6.00</td>\n",
              "      <td>327.00</td>\n",
              "      <td>24.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>42.12</td>\n",
              "      <td>12.79</td>\n",
              "      <td>252.00</td>\n",
              "      <td>6.00</td>\n",
              "      <td>1270.00</td>\n",
              "      <td>34.83</td>\n",
              "      <td>221.00</td>\n",
              "      <td>365.00</td>\n",
              "      <td>765.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "df_clean.describe().round(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rJKjlnkKFJus",
        "outputId": "ebbfd545-f69a-414e-ffe6-e204913ce6ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A média dos preços de aluguel é de € 98.56\n"
          ]
        }
      ],
      "source": [
        "print('A média dos preços de aluguel é de € {:.2f}'.format(df_clean.price.mean()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CXLFRUAxE-5V"
      },
      "source": [
        "Note que a variável `price` está com sua média muito mais próxima da mediana, o que é um bom sinal."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vTneAMRUYvr1"
      },
      "source": [
        "### **8. Qual é a média do mínimo de noites para aluguel (minimum_nights)?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B7rQgSWa47uR"
      },
      "source": [
        "Da mesma maneira, vamos calcular a média da variável `minimum_nights` utilizando `.mean()`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_VzNhrEgY82N",
        "outputId": "587b160d-973a-4012-a466-ecb66f5ef0b6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A média do mínimo de noites para ficar em Roma é 2.0 noites\n"
          ]
        }
      ],
      "source": [
        " # Média de noites mínimas para ficar hospedado em Roma\n",
        "media_mn = df_clean.minimum_nights.mean() \n",
        "\n",
        "print('A média do mínimo de noites para ficar em Roma é {:.1f} noites'.format(media_mn))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ld-b1X885IAH"
      },
      "source": [
        "Vamos avaliar a média de noites mínimas por bairro:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ni2SyfY4gPWI",
        "outputId": "03e5386d-18f1-458c-9295-b74f13f15e6b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Média de noites mínimas por bairro: \n",
            " neighbourhood\n",
            "XII Monte Verde               2.2\n",
            "IX Eur                        2.2\n",
            "XV Cassia/Flaminia            2.1\n",
            "III Monte Sacro               2.1\n",
            "V Prenestino/Centocelle       2.1\n",
            "VIII Appia Antica             2.1\n",
            "XIV Monte Mario               2.1\n",
            "II Parioli/Nomentano          2.1\n",
            "XI Arvalia/Portuense          2.0\n",
            "X Ostia/Acilia                2.0\n",
            "XIII Aurelia                  2.0\n",
            "VII San Giovanni/Cinecittà    1.9\n",
            "I Centro Storico              1.9\n",
            "IV Tiburtina                  1.9\n",
            "VI Roma delle Torri           1.8\n",
            "Name: minimum_nights, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# Média de noites por bairro\n",
        "media_mn_bairro = df_clean.groupby('neighbourhood').minimum_nights.mean()\n",
        "\n",
        "print('Média de noites mínimas por bairro:',\n",
        "      '\\n', media_mn_bairro.sort_values(ascending = False).round(1))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KbbOOJKsgZPR"
      },
      "source": [
        "> As médias de noites mínimas por bairro se distribuem muito bem em torno da média geral, que é igual a 2. Como se pode ver, a diferença entre a maior média, no bairro IX Eur e a menor, no bairro VI Roma delle Torri, é de 0,4 dias."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7b6RMWpuHKN-"
      },
      "source": [
        "### **9. Qual o tipo de imóvel mais alugado no Airbnb?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nhaTCLM3sPtK"
      },
      "source": [
        "A seguir, vamos analisar o tipo de acomodação mais comum disponível aos turistas quando vão à Roma pelo AirBnb. Para isso, vamos:\n",
        "\n",
        "1. Utilizar o método `.value_counts()` para contar os tipos de entradas na variável `room_type`\n",
        "2. Calcular a porcentagem de cada tipo de acomodação, ordenando do maior para o menor."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ueLPNWvPHgfB",
        "outputId": "23bddccd-890d-422a-ac81-57cb530528d8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Entire home/apt    14258\n",
              "Private room        6318\n",
              "Hotel room           636\n",
              "Shared room          123\n",
              "Name: room_type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "# mostrar a quantidade de cada tipo de imóvel disponível\n",
        "df_clean.room_type.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EvwoO-XAHhIm",
        "outputId": "be8fe1a6-cabc-4811-e872-3a53c6accbdb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Entire home/apt    66.8\n",
              "Private room       29.6\n",
              "Hotel room          3.0\n",
              "Shared room         0.6\n",
              "Name: room_type, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "# mostrar a porcentagem de cada tipo de imóvel disponível\n",
        "((df_clean.room_type.value_counts()) / df_clean.shape[0]*100).round(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KwGvMUL6sqb5"
      },
      "source": [
        "Nota-se que aproximadamente 67% das acomodações são apartamentos ou casas com o espaço inteiro à disposição, enquanto 29,6% são quartos privados e apenas 1% representa o tradicional quarto de hotel.\n",
        "\n",
        "> Podemos inferir que oferecer a opção de ter uma casa inteira à disposição chama mais atenção dos turistas, seja pela comodidade, seja pelo contato com a comunidade e cultura local.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SFdmjbjNdXLF"
      },
      "source": [
        "**Gráfico de waffle para visualizar os tipos de acomodações mais populares**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "VEAhoIyPzpl5",
        "outputId": "fb464088-1326-4f28-c7bb-17601391ceed"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Waffle size 1008x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "data = dict(df_clean.room_type.value_counts())\n",
        "\n",
        "fig = plt.figure(FigureClass=Waffle, \n",
        "                 figsize=(14, 8), \n",
        "                 rows=7, columns=17, \n",
        "                 colors=('#E61919', '#eee8aa', '#228B22', '#B5E48C'), \n",
        "                 values=data,\n",
        "                 legend={'loc': 'upper left', 'bbox_to_anchor': (1, 1)})\n",
        "\n",
        "plt.text(0, 1.15, 'Tipos de acomodações disponíveis em Roma pelo AirBnb', \n",
        "         va='top', fontsize=20, weight='bold')\n",
        "\n",
        "# Para evitar de o gráfico ser cortado quando for salvo na figura\n",
        "fig.set_tight_layout(False)\n",
        "fig.subplots_adjust(left=0.03, bottom=0.03, right=0.97, top=0.95, wspace=0.2, hspace=0.2)\n",
        "plt.savefig('tipos_de_acomodacoes_roma.jpg', bbox_inches = 'tight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MFa5NbnLJMcd"
      },
      "source": [
        "### **10. Qual a localidade mais cara do dataset?**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wDvaj31ouveH"
      },
      "source": [
        "Vamos, agora, destacar as médias de preço para os bairros, ordenando os 10 mais caros na cidade de Roma."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MD_xa143JZpi",
        "outputId": "3eec4d92-a026-43b6-f9a1-7463b2ae35aa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "neighbourhood\n",
              "I Centro Storico              112.2\n",
              "XIII Aurelia                   94.4\n",
              "XV Cassia/Flaminia             91.0\n",
              "II Parioli/Nomentano           88.7\n",
              "XII Monte Verde                86.8\n",
              "XIV Monte Mario                81.1\n",
              "VIII Appia Antica              80.8\n",
              "XI Arvalia/Portuense           79.5\n",
              "VII San Giovanni/Cinecittà     79.1\n",
              "IX Eur                         78.6\n",
              "III Monte Sacro                76.5\n",
              "X Ostia/Acilia                 75.5\n",
              "IV Tiburtina                   66.7\n",
              "VI Roma delle Torri            63.0\n",
              "V Prenestino/Centocelle        61.4\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "# ver preços por bairros, na média\n",
        "df_clean.groupby('neighbourhood').price.mean().sort_values(ascending = False).round(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MciDULFZtXwE"
      },
      "source": [
        "É interessante se atentar para o fato de que, muitas vezes, alguns bairros possuem menos hospedagens, o que acaba levantando a média de preço daquele bairro. Da mesma forma, bairros que possuem muitas hospedagens, acabam tendo a média mais normalizada, apesar de serem mais caros e fazerem mais sentido do ponto de vista do turismo da cidade.\n",
        "\n",
        "Neste caso, faz sentido que o bairro com a maior média de preço seja o I Centro Storico. Para confirmar, vamos avaliar quantas hospedagens estão registradas nele."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nekQ0RLGvnOi",
        "outputId": "b52df439-9fe3-461b-cb78-10abcd53d67a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O bairro I Centro Storico possui 11875 imóveis\n"
          ]
        }
      ],
      "source": [
        "print('O bairro I Centro Storico possui {} imóveis'.format(df_clean[df_clean.neighbourhood == 'I Centro Storico'].shape[0]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xXKDVlr_wZoh"
      },
      "source": [
        "> De fato, o I Centro Storico representa o bairro mais caro de Roma, com valor médio de hospedagem de € 112.16 para 11875 imóveis registrados."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YfbmSCftw5yO"
      },
      "source": [
        "#### Distribuição geográfica dos imóveis em Roma - Classificados por preço"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xJIIdfAExbdc"
      },
      "source": [
        "Para finalizar a análise, vamos criar uma distribuição de dispersão dos imóveis através do território de Roma, classificando-os segundo o preço. \n",
        "\n",
        "* Para isso, vamos utilizar a função `.plot()` com o tipo de gráfico `scatter`, que significa dispersão.\n",
        "\n",
        "* Para os eixos, vamos selecionar a a coluna `longitude` para ser o eixo abcisso, e a coluna `latitude` para ser o eixo ordenado.\n",
        "\n",
        "O resultado está exibido a seguir:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 630
        },
        "id": "UxVJqJ_3W59I",
        "outputId": "549a40b4-5056-4c97-e90c-7c13c08888e7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# plotar os imóveis pela latitude-longitude\n",
        "fig, ax = plt.subplots(figsize = (12, 10))\n",
        "\n",
        "df_clean.plot(kind = 'scatter', x = 'longitude', y = 'latitude', alpha = 0.5, c = df_clean['price'], s = 8,\n",
        "              cmap = plt.get_cmap('brg_r'), ax = ax)\n",
        "\n",
        "ax.set_title('Distribuição geográfica dos imóveis do AirBnb em Roma - Classificados por preço', fontsize = 15)\n",
        "ax.set_xlabel('Longitude', fontsize = 15)\n",
        "ax.set_ylabel('Latitude', fontsize = 15)\n",
        "\n",
        "ax.tick_params(axis = 'both', which = 'major', direction = 'in', labelsize = 14)\n",
        "\n",
        "plt.savefig('distribuicao_imoveis_roma.jpg')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "guN84kT_yLlT"
      },
      "source": [
        "Note que o miolo da cidade representa o bairro I Centro Storico, com os preços mais caros da cidade para acomodações no AirBnb. À medida que afastamos para o perímetro da cidade, os preços abaixam drasticamente."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wxd5Ljcgc9QR"
      },
      "source": [
        "> <img src=\"https://raw.githubusercontent.com/gabrielrflopes/Rome_Airbnb_Data_Analysis/main/roma_airbnb.png\" height=\"500px\"></center>\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5xQ0k8LTd4vm"
      },
      "source": [
        "### Alguns pontos turísticos nos 2 bairros mais caros de Roma\n",
        "\n",
        "#### I Centro Storico\n",
        "\n",
        "O Centro Storico de Roma é a área central histórica da cidade, que abriga muitas atrações turísticas populares. Entre os diversos pontos turísticos que se encontram nessa região, destacam-se:\n",
        "\n",
        "1. Coliseu - O famoso anfiteatro de Roma\n",
        "2. Panteão - Um antigo templo romano que agora é uma igreja, com uma cúpula impressionante e uma arquitetura única.\n",
        "\n",
        "> <img src=\"https://images.unsplash.com/photo-1663875570674-8a2fd321c8e2?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1178&q=80\" height=\"300px\"></center>\n",
        "\n",
        "3. Fontana di Trevi - Uma das fontes mais famosas do mundo, construída nos estilos barroco tardio e neoclássico.\n",
        "4. Escadaria espanhola -  Uma grande escadaria que leva à Igreja de Trinità dei Monti, com vista para a Praça de Espanha e o bairro de Trevi.\n",
        "\n",
        "> <img src=\"https://images.unsplash.com/photo-1603199766715-f01000479bde?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1170&q=80\" height=\"300px\"></center>\n",
        "\n",
        "#### XIII Aurelia\n",
        "\n",
        "O bairro XIII Aurelia de Roma é um bairro residencial, localizado na parte oeste da cidade. Neste bairro está localizada a Cidade do Vaticano, com a Basílica de São Pedro. \n",
        "\n",
        "Também nele se encontra o imponente Castelo de Sant'Angelo, uma antiga fortaleza romana que agora abriga um museu e oferece vistas panorâmicas da cidade.\n",
        "\n",
        "> <img src=\"https://images.unsplash.com/photo-1585391444413-ec7e04ea7e0b?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1170&q=80\" height=\"300px\"></center>\n"
      ]
    },
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      "source": [
        "## Conclusões\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "Este projeto de data science utilizando os dados do AirBnb em Roma trouxe diversos insights interessantes a respeito do turismo na cidade eterna. Entre eles, destacam-se:\n",
        "\n",
        "* A média de preços para se hospedar em Roma é de € 98,56, o que corresponde a R$ 536,16 em fev/23.\n",
        "* A média de noites mínimas para ficar hospedado é de 2 noites.\n",
        "* O tipo de imóvel mais alugado na cidade são apartamentos e casas completas. As pessoas buscam espaços inteiros, seguidos por quartos privados e, apenas por último, estão os tradicionais quartos de hotel.\n",
        "* A média de preços por bairro segue a tendência da proximidade e volume de pontos turísticos localizados em cada bairro. Ou seja, no I Centro Storico, onde se localizam o Coliseu, o Panteão e a Fontana di Trevi, a média de preços é a maior da cidade.\n",
        "* A maior parte dos imóveis distribuídos no centro de Roma apresentam um preço acima da média. À medida que se distancia para o perímetro da cidade, os preços abaixam drasticamente.\n",
        "\n",
        "Com essas informações, fica mais claro como é o comportamento das pessoas ao procurarem hospedagem em uma das mais belas cidades do mundo, utilizando uma plataforma que facilita muito que pessoas conheçam e vislumbre a beleza da história, arquitetura, cultura e religião daquela terra."
      ]
    }
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