Análise_dos_Dados_do_Airbnb_Dublin.ipynb

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      "name": "Análise dos Dados do Airbnb - Dublin.ipynb",
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        "<a href=\"https://colab.research.google.com/github/JoaoGustavo29/Portfolio_DataScience/blob/master/An%C3%A1lise_dos_Dados_do_Airbnb_Dublin.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "metadata": {
        "id": "kLI0gJtzDGuQ"
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      "source": [
        "<img alt=\"Colaboratory logo\" width=\"15%\" src=\"https://raw.githubusercontent.com/carlosfab/escola-data-science/master/img/novo_logo_bg_claro.png\">\n",
        "\n",
        "#### **Data Science na Prática 2.0**\n",
        "*by [sigmoidal.ai](https://sigmoidal.ai)*\n",
        "\n",
        "---\n",
        "\n",
        "# Análise dos Dados do Airbnb - *Dublin*\n",
        "\n",
        "<center><img alt=\"Dublin\" width=\"75%\" src=\"https://raw.githubusercontent.com/JoaoGustavo29/Relatorios_imgs/main/trinity-college-405783_1920.jpg\"></center>\n",
        "\n",
        "O [Airbnb](https://www.airbnb.com.br/) já é considerado como sendo a **maior empresa hoteleira da atualidade**. Ah, o detalhe é que ele **não possui nenhum hotel**!\n",
        "\n",
        "Conectando pessoas que querem viajar (e se hospedar) com anfitriões que querem alugar seus imóveis de maneira prática, o Airbnb fornece uma plataforma inovadora para tornar essa hospedagem alternativa.\n",
        "\n",
        "No final de 2018, a Startup fundada 10 anos atrás, já havia **hospedado mais de 300 milhões** de pessoas ao redor de todo o mundo, desafiando as redes hoteleiras tradicionais.\n",
        "\n",
        "Uma das iniciativas do Airbnb é disponibilizar dados do site, para algumas das principais cidades do mundo. Por meio do portal [Inside Airbnb](http://insideairbnb.com/get-the-data.html), é possível baixar uma grande quantidade de dados para desenvolver projetos e soluções de *Data Science*.\n",
        "\n",
        "<center><img alt=\"Analisando Airbnb\" width=\"10%\" src=\"https://www.area360.com.au/wp-content/uploads/2017/09/airbnb-logo.jpg\"></center>\n",
        "\n",
        "**Neste *notebook*, iremos analisar os dados referentes à cidade Dublin, e ver quais insights podem ser extraídos a partir de dados brutos.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BIvDWeaY8mew"
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      "source": [
        "## Dublin\n",
        "\n",
        "Capital da Irlanda e um famoso destino turístico, uma metrópole cuja cultura é vibrante, um passado recheado de história. Lar da \"**Trinity College**\", patrimônio cultural da cidade e a mais antiga universidade da Irlanda, ela abriga uma das bibliotecas mais famosas do mundo, a **Old Library**, detentora de um manuscrito do século nove.\n",
        "\n",
        "Contudo, não se deixe enganar, história é apenas um dos motivos para se visitar a Capital, local onde se encontra a **Guinness Storehouse**, podendo assim, agendar uma visita e conhecer o processo de fabricação da **melhor** cerveja do Planeta.\n",
        "\n",
        "Consequentemente, podemos concordar que fazer uma viagem a Dublin não seria nada mal."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0zxQHhIUDF_2"
      },
      "source": [
        "## Obtenção dos Dados\n",
        "\n",
        "Os dados usados foram obtidos a partir do site [Inside Airbnb](http://insideairbnb.com/get-the-data.html).\n",
        "\n",
        "Por esta ser uma análise exploratória inicial, os dados utilizados nesse projeto, são uma versão resumida do dataset.\n",
        "\n",
        "\n",
        "\n",
        "* `listings.csv` - *Summary information and metrics for listings in Dublin (good for visualisations).*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vhp2wMPgJ6zX"
      },
      "source": [
        "# importar os pacotes necessarios\n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tUmoAT8DLQ5T"
      },
      "source": [
        "# importar o arquivo listings.csv para um DataFrame\n",
        "df = pd.read_csv('http://data.insideairbnb.com/ireland/leinster/dublin/2020-12-17/visualisations/listings.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qb5By2nIMS6m"
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      "source": [
        "## Análise dos Dados\n",
        "\n",
        "Nesta etapa o objetivo é garantir um entendimento de como os dados estão estruturados, de forma que o leitor tenha consciência do que será analisado. Sendo assim, será apresentado um dicionário a respeito de cada uma das variáveis."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RYgvuebKNg8-"
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      "source": [
        "**Dicionário das variáveis**\n",
        "\n",
        "* `id` - número de identificação do imóvel\n",
        "* `name` - Título do anúncio da propriedade\n",
        "* `host_id` - número de identificação do proprietário \n",
        "* `host_name` - nome do anfitrião\n",
        "* `neighbourhood_group` - coluna sem valores válidos\n",
        "* `neighbourhood` - nome do  bairro\n",
        "* `latitude` - coordenada de latitude da propriedade\n",
        "* `longitude` - coordenada de longitude da propriedade\n",
        "* `room_type` - tipo de acomodação oferecida\n",
        "* `price` - valor do aluguel\n",
        "* `minimum_nights` - menor quantidade de noites para alugar\n",
        "* `number_of_reviews` - número de reviews\n",
        "* `last_review` - data do último review\n",
        "* `reviews_per_month` - quantidade de reviews em um mês\n",
        "* `calculated_host_listings_count` - quantidade de imóveis do mesmo anfitrião\n",
        "* `availability_365` - número de dias de disponibilidade dentro de 365 dias\n",
        "\n",
        "Antes de partirmos para as análises, vamos conhecer um pouco do nosso *dataset*, dando uma olhada nas 5 primeiras entradas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2iS_dae7YQtO",
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          "height": 513
        },
        "outputId": "1e18309c-f6cb-4363-9693-d4181b90560c"
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      "source": [
        "# mostrar as 5 primeiras entradas\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>name</th>\n",
              "      <th>host_id</th>\n",
              "      <th>host_name</th>\n",
              "      <th>neighbourhood_group</th>\n",
              "      <th>neighbourhood</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>room_type</th>\n",
              "      <th>price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>last_review</th>\n",
              "      <th>reviews_per_month</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>44077</td>\n",
              "      <td>cosy comfortable Irish cottage twin</td>\n",
              "      <td>193005</td>\n",
              "      <td>Teresa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Dn Laoghaire-Rathdown</td>\n",
              "      <td>53.28995</td>\n",
              "      <td>-6.25813</td>\n",
              "      <td>Private room</td>\n",
              "      <td>50</td>\n",
              "      <td>3</td>\n",
              "      <td>227</td>\n",
              "      <td>2020-03-06</td>\n",
              "      <td>1.91</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>68483</td>\n",
              "      <td>Unique Central Dublin River-view</td>\n",
              "      <td>339817</td>\n",
              "      <td>John</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Dublin City</td>\n",
              "      <td>53.34501</td>\n",
              "      <td>-6.26552</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>92</td>\n",
              "      <td>3</td>\n",
              "      <td>103</td>\n",
              "      <td>2019-08-12</td>\n",
              "      <td>0.87</td>\n",
              "      <td>1</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>85148</td>\n",
              "      <td>Habitación en Dublín Double room</td>\n",
              "      <td>466111</td>\n",
              "      <td>Don</td>\n",
              "      <td>NaN</td>\n",
              "      <td>South Dublin</td>\n",
              "      <td>53.30110</td>\n",
              "      <td>-6.31992</td>\n",
              "      <td>Private room</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2019-05-11</td>\n",
              "      <td>0.05</td>\n",
              "      <td>1</td>\n",
              "      <td>348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>85156</td>\n",
              "      <td>Cosy Comfortable Irish Cottage  1 Double Bed</td>\n",
              "      <td>193005</td>\n",
              "      <td>Teresa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Dn Laoghaire-Rathdown</td>\n",
              "      <td>53.29178</td>\n",
              "      <td>-6.25618</td>\n",
              "      <td>Private room</td>\n",
              "      <td>51</td>\n",
              "      <td>3</td>\n",
              "      <td>182</td>\n",
              "      <td>2020-03-09</td>\n",
              "      <td>1.56</td>\n",
              "      <td>3</td>\n",
              "      <td>114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>121030</td>\n",
              "      <td>Comfy double room in central area 1</td>\n",
              "      <td>608225</td>\n",
              "      <td>Norah</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Dublin City</td>\n",
              "      <td>53.33648</td>\n",
              "      <td>-6.22075</td>\n",
              "      <td>Private room</td>\n",
              "      <td>55</td>\n",
              "      <td>2</td>\n",
              "      <td>110</td>\n",
              "      <td>2019-03-19</td>\n",
              "      <td>0.95</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
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            ],
            "text/plain": [
              "       id  ... availability_365\n",
              "0   44077  ...                0\n",
              "1   68483  ...               43\n",
              "2   85148  ...              348\n",
              "3   85156  ...              114\n",
              "4  121030  ...                0\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6-B_qtEaYEd-"
      },
      "source": [
        "### **Q1. Quantos atributos (variáveis) e quantas entradas o nosso conjunto de dados possui? Quais os tipos das variáveis?**\n",
        "\n",
        "Com algumas linhas de código, é possível notar a presença de 16 variáveis e 7894 entradas, sendo algumas delas dos tipos, inteiro, float e objeto."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TFRK0tniLV-G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "f7b6339e-2341-45fb-ad9c-54a73da1b3eb"
      },
      "source": [
        "# identificar o volume de dados do DataFrame\n",
        "print('Entradas:\\t {}'.format(df.shape[0]))\n",
        "print('Variáveis:\\t {}'.format(df.shape[1]))\n",
        "# verificar as 5 primeiras entradas do dataset\n",
        "display(df.dtypes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Entradas:\t 7894\n",
            "Variáveis:\t 16\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yz1urSUGW9iE"
      },
      "source": [
        "### **Q2. Qual a porcentagem de valores ausentes no *dataset*?**\n",
        "\n",
        "Quando buscamos um *dataset* queremos um com qualidade, é possível mensurar isso de acordo com a quantidade de valores ausentes. É necessário entender se os valores nulos são significativos comparados ao total de entradas.\n",
        "\n",
        "* Na coluna `neighbourhood_group` é possível notar que 100% dos valores não foram inseridos\n",
        "* Aproximadamente 20% dos valores das variáveis `reviews_per_month` e `last_review` são nulos\n",
        "* Apenas 0,1% dos valores da variável `name` são nulos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "plk2FjbCXOqP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a5be0f4-ecae-4aa6-ad31-7dc874b58b85"
      },
      "source": [
        "# ordenar em ordem decrescente as variáveis por seus valores ausentes\n",
        "(df.isnull().sum() / df.shape[0]).sort_values(ascending = False) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "neighbourhood_group               1.000000\n",
              "reviews_per_month                 0.193311\n",
              "last_review                       0.193311\n",
              "name                              0.000127\n",
              "availability_365                  0.000000\n",
              "calculated_host_listings_count    0.000000\n",
              "number_of_reviews                 0.000000\n",
              "minimum_nights                    0.000000\n",
              "price                             0.000000\n",
              "room_type                         0.000000\n",
              "longitude                         0.000000\n",
              "latitude                          0.000000\n",
              "neighbourhood                     0.000000\n",
              "host_name                         0.000000\n",
              "host_id                           0.000000\n",
              "id                                0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ctjirss9StXN",
        "outputId": "81bd31d1-6649-4ca3-c1fd-e33e6fbe4cea"
      },
      "source": [
        "# excluindo valores ausentes\n",
        "df.drop(columns=['neighbourhood_group'], inplace=True)\n",
        "df.dropna(axis=0, inplace=True)\n",
        "\n",
        "# verificando o resultado\n",
        "(df.isnull().sum()).sort_values(ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "availability_365                  0\n",
              "calculated_host_listings_count    0\n",
              "reviews_per_month                 0\n",
              "last_review                       0\n",
              "number_of_reviews                 0\n",
              "minimum_nights                    0\n",
              "price                             0\n",
              "room_type                         0\n",
              "longitude                         0\n",
              "latitude                          0\n",
              "neighbourhood                     0\n",
              "host_name                         0\n",
              "host_id                           0\n",
              "name                              0\n",
              "id                                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iHXvvHIxaoNc"
      },
      "source": [
        "### **Q3. Qual o tipo de distribuição das variáveis?** \n",
        "\n",
        "Para termos uma melhor visualização a respeito da distribuição das variáveis, irei plotar um histograma."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q2wWSfeRbmBo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 879
        },
        "outputId": "1ab5e153-9d05-4dc8-b16d-f181e0f967e8"
      },
      "source": [
        "# plotar o histograma das variáveis numéricas\n",
        "df.hist(bins=15, figsize=(20,15), grid=False);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1080 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_yRUqEDTmHb"
      },
      "source": [
        "### **Q4. Há *Outliers* presentes?**\n",
        "\n",
        "Se prestarmos atenção na forma em como o histograma está distribuindo os dados, podemos encontrar indicativos da presença de *outliers*. É possível pegarmos como exemplo as variáveis `price`, `minimum_nights` e `calculated_host_listings_count`.\n",
        "\n",
        "Os valores não seguem uma distribuição, e distorcem a representação gráfica. Para confirmar, existem duas maneiras rápidas que auxiliam a detecção de *outliers*. São elas:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "xXVvmPWzWSHH",
        "outputId": "48f9662d-5838-4ef8-e9e7-016beeb106ef"
      },
      "source": [
        "# ver o resumo estatístico das variáveis numéricas\n",
        "df[['price', 'minimum_nights', 'number_of_reviews', 'reviews_per_month',\n",
        "    'calculated_host_listings_count', 'availability_365']].describe()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6368.000000</td>\n",
              "      <td>6368.000000</td>\n",
              "      <td>6368.000000</td>\n",
              "      <td>6368.000000</td>\n",
              "      <td>6368.000000</td>\n",
              "      <td>6368.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>122.506281</td>\n",
              "      <td>4.728015</td>\n",
              "      <td>40.018373</td>\n",
              "      <td>1.034852</td>\n",
              "      <td>2.711212</td>\n",
              "      <td>68.038474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>359.463149</td>\n",
              "      <td>23.629984</td>\n",
              "      <td>65.424070</td>\n",
              "      <td>1.387199</td>\n",
              "      <td>3.960023</td>\n",
              "      <td>115.409901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>50.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.130000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>79.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>0.460000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>126.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>1.380000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>89.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>10998.000000</td>\n",
              "      <td>1125.000000</td>\n",
              "      <td>688.000000</td>\n",
              "      <td>14.460000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>365.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              price  ...  availability_365\n",
              "count   6368.000000  ...       6368.000000\n",
              "mean     122.506281  ...         68.038474\n",
              "std      359.463149  ...        115.409901\n",
              "min       10.000000  ...          0.000000\n",
              "25%       50.000000  ...          0.000000\n",
              "50%       79.000000  ...          0.000000\n",
              "75%      126.000000  ...         89.000000\n",
              "max    10998.000000  ...        365.000000\n",
              "\n",
              "[8 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NC9GqrAkXc-Z"
      },
      "source": [
        "Olhando o resumo estatístico acima, podemos confirmar algumas hipóteses como:\n",
        "\n",
        "* A variável `price` possui 75% do valor abaixo de 126, porém seu valor máximo é 10998.\n",
        "* O valor máximo para a variável (`minimum_nights`) está acima de 365 dias no ano.\n",
        "\n",
        "#### **Definindo limites para 'minimum_nights'**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x2dgzXfzXBYi",
        "outputId": "c6dd7064-b092-4599-c2c0-b9abb75b6cdf"
      },
      "source": [
        "q1_minimum_nights = df.minimum_nights.quantile(.25)\n",
        "q3_minimum_nights = df.minimum_nights.quantile(.75)\n",
        "IQR_minimum_nights = q3_minimum_nights - q1_minimum_nights\n",
        "print('IQR da variável minimum_nights: ', IQR_minimum_nights)\n",
        "\n",
        "# definindo os limites\n",
        "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 de minimum_nigths: ', sup_minimum_nights)\n",
        "print('Limite inferios de minimum_nigths: ', inf_minimum_nights)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "IQR da variável minimum_nights:  2.0\n",
            "Limite superior de minimum_nigths:  6.0\n",
            "Limite inferios de minimum_nigths:  -2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4FK9Zhg6XCUX"
      },
      "source": [
        "#### **Boxplot para minimum_nights**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "id": "KYLwshzsl0gY",
        "outputId": "00944574-abc3-4cd7-8b4d-d5187cece0ca"
      },
      "source": [
        "# minimum_nights\n",
        "df.minimum_nights.plot(kind='box', vert=False, figsize=(15, 3))\n",
        "plt.show()\n",
        "\n",
        "# ver quantidade de valores acima de 6 dias para minimum_nights\n",
        "print('minimum_nights: valores acima de 6:')\n",
        "print('{} entradas.'.format(len(df[df.minimum_nights > 6])))\n",
        "print('{:.4f}%'.format((len(df[df.minimum_nights > 6]) / df.shape[0])*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "minimum_nights: valores acima de 6:\n",
            "722 entradas.\n",
            "11.3379%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yq9glQpCocZw"
      },
      "source": [
        "Com isso podemos extrair algumas informações:\n",
        "\n",
        "* Cerca de 722 acomodações tem um minimo acima de 6 noites\n",
        "* Essas acomodações representam apenas 11.3% do *dataset*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZoqaXAMko7R1"
      },
      "source": [
        "#### **Definindo limites para 'Price'**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ynLjk2d6UBX1",
        "outputId": "ecaed7cf-4517-4775-a466-21c6add7c997"
      },
      "source": [
        "# identificando os outliers para a variável price\n",
        "q1_price = df.price.quantile(.25)\n",
        "q3_price = df.price.quantile(.75)\n",
        "IQR_price = q3_price - q1_price\n",
        "print('IQR da variável price: ', IQR_price)\n",
        "\n",
        "# definindo os limites \n",
        "sup_price = q3_price + 1.5 * IQR_price\n",
        "inf_price = q1_price - 1.5 * IQR_price\n",
        "\n",
        "print('Limite superior de price: ', sup_price)\n",
        "print('Limite inferior de price: ', inf_price)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "IQR da variável price:  76.0\n",
            "Limite superior de price:  240.0\n",
            "Limite inferior de price:  -64.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uXYEmxenWbT5"
      },
      "source": [
        "#### **Boxplot para price**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "OAjEQss3pdiW",
        "outputId": "4427e021-c8a6-4a30-f51f-8ffe6cf3bf99"
      },
      "source": [
        "# price\n",
        "df.price.plot(kind='box', vert=False, figsize=(15, 3))\n",
        "plt.show()\n",
        "\n",
        "# ver quantidade de valores acima de 240 para price\n",
        "print('\\nprice: valores acima de 240')\n",
        "print('{} entradas'.format(len(df[df.price > 240])))\n",
        "print('{:.4f}%'.format((len(df[df.price > 240]) / df.shape[0])*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "price: valores acima de 240\n",
            "500 entradas\n",
            "7.8518%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Pvhj1OKrSMk"
      },
      "source": [
        "Alguns insights tirados dessa análise :\n",
        "\n",
        "* Existem aproximadamente 500 entradas de acomodações com valores acima de 240\n",
        "* E essas acomodações representam apenas 7,8% do *dataset*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QErkdp9sr8PJ"
      },
      "source": [
        "#### **Limpando o dataset**\n",
        "\n",
        "Após identificarmos *outliers* nas variáveis `price` e `minimum_nights`, vamos limpar o *Dataset*."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XkmqyNJBsoU6",
        "outputId": "b4823a58-3fba-474e-9a77-09a6183ee729"
      },
      "source": [
        "# remover os *outliers* em um novo DataFrame\n",
        "df_clean = df.copy()\n",
        "df_clean.drop(df_clean[df.price > 240].index, axis=0, inplace=True)\n",
        "df_clean.drop(df_clean[df.price == 0.0].index, axis=0, inplace=True)\n",
        "df_clean.drop(df_clean[df.minimum_nights > 6].index, axis=0, inplace=True)\n",
        "\n",
        "print('Shape antes da limpeza: ', df.shape)\n",
        "print('Shape depois da limpeza: ', df_clean.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape antes da limpeza:  (6368, 15)\n",
            "Shape depois da limpeza:  (5202, 15)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KRA__JCTZwDW"
      },
      "source": [
        "#### **Histogramas sem *outliers***\n",
        "\n",
        "Com o Dataset limpo, irei plotar um histograma sem a presença dos outliers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 879
        },
        "id": "MJLXT7WzZz7v",
        "outputId": "cc3dc0f0-1526-4910-96d1-8643e2f0554f"
      },
      "source": [
        "# plotar o histograma para as variáveis numéricas\n",
        "df_clean.hist(bins=15, figsize=(20,15), grid=False);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1080 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "wpyVBOBUaw1E",
        "outputId": "f6349f85-d2b2-477b-fa59-032860e3beae"
      },
      "source": [
        "# verificando a distribuição estatística dos dados limpos\n",
        "df_clean.describe().round(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>host_id</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5202.0</td>\n",
              "      <td>5202.0</td>\n",
              "      <td>5202.0</td>\n",
              "      <td>5202.0</td>\n",
              "      <td>5202.0</td>\n",
              "      <td>5202.0</td>\n",
              "      <td>5202.0</td>\n",
              "      <td>5202.0</td>\n",
              "      <td>5202.0</td>\n",
              "      <td>5202.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>22682123.0</td>\n",
              "      <td>88950441.1</td>\n",
              "      <td>53.3</td>\n",
              "      <td>-6.3</td>\n",
              "      <td>85.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>40.6</td>\n",
              "      <td>1.1</td>\n",
              "      <td>2.5</td>\n",
              "      <td>61.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>11367729.0</td>\n",
              "      <td>86006220.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>47.1</td>\n",
              "      <td>1.1</td>\n",
              "      <td>66.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>3.7</td>\n",
              "      <td>111.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>44077.0</td>\n",
              "      <td>43984.0</td>\n",
              "      <td>53.2</td>\n",
              "      <td>-6.5</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>13847538.2</td>\n",
              "      <td>22189306.0</td>\n",
              "      <td>53.3</td>\n",
              "      <td>-6.3</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>22299855.5</td>\n",
              "      <td>55006639.0</td>\n",
              "      <td>53.3</td>\n",
              "      <td>-6.3</td>\n",
              "      <td>71.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>31988359.8</td>\n",
              "      <td>138695412.2</td>\n",
              "      <td>53.4</td>\n",
              "      <td>-6.2</td>\n",
              "      <td>110.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>77.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>46753941.0</td>\n",
              "      <td>375568846.0</td>\n",
              "      <td>53.6</td>\n",
              "      <td>-6.1</td>\n",
              "      <td>240.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>596.0</td>\n",
              "      <td>14.5</td>\n",
              "      <td>32.0</td>\n",
              "      <td>365.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               id  ...  availability_365\n",
              "count      5202.0  ...            5202.0\n",
              "mean   22682123.0  ...              61.3\n",
              "std    11367729.0  ...             111.3\n",
              "min       44077.0  ...               0.0\n",
              "25%    13847538.2  ...               0.0\n",
              "50%    22299855.5  ...               0.0\n",
              "75%    31988359.8  ...              77.0\n",
              "max    46753941.0  ...             365.0\n",
              "\n",
              "[8 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7x4aw1j5wNmn"
      },
      "source": [
        "### **Q5. Qual a média dos preços de aluguel?**\n",
        "\n",
        "Em uma análise onde temos o valor do aluguel, se torna interessante saber a média desse valor. De modo a tirar *insights* que auxiliam no cálculo do gasto com a acomodação, em uma possível viagem.\n",
        "\n",
        "* Foi possível verificar que o valor *médio* do aluguel das acomodações é **`85,22 euros`**.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ff-8gUahLk-C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f8a2ded-dd9b-459a-a299-d1ddabc0ac87"
      },
      "source": [
        "# ver a média da coluna `price`\n",
        "df_clean['price'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "85.22549019607843"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IXn2oIo5qx-w"
      },
      "source": [
        "### **Q6. Qual a correlação existente entre as variáveis?**\n",
        "\n",
        "Correlação significa que existe uma relação entre duas coisas. No contexto da análise, podemos buscar relação ou semelhança entre duas variáveis.\n",
        "\n",
        "Essas relações podem ser medidas, e é função do coeficiente de relação estabelecer qual a intensidade dela. Para identificar as correlações existentes entre as variáveis de interesse, vou:\n",
        "\n",
        "* Criar uma matriz de correlação\n",
        "* Gerar um *heatmap* a partir dessa matriz, usando a biblioteca `seaborn`\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x2ayzBajqxyd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "f07af0e1-a4f9-4d61-f200-59b509808a84"
      },
      "source": [
        "# criar uma matriz de correlação\n",
        "corr = df_clean[['price', 'minimum_nights', 'number_of_reviews', 'reviews_per_month', 'calculated_host_listings_count', 'availability_365']].corr()\n",
        "# mostrar a matriz de correlação\n",
        "display(corr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>price</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.213105</td>\n",
              "      <td>-0.037996</td>\n",
              "      <td>-0.061857</td>\n",
              "      <td>0.041307</td>\n",
              "      <td>0.033815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>minimum_nights</th>\n",
              "      <td>0.213105</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.084321</td>\n",
              "      <td>-0.146216</td>\n",
              "      <td>0.008910</td>\n",
              "      <td>-0.065255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>number_of_reviews</th>\n",
              "      <td>-0.037996</td>\n",
              "      <td>-0.084321</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.868201</td>\n",
              "      <td>0.020238</td>\n",
              "      <td>0.173490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reviews_per_month</th>\n",
              "      <td>-0.061857</td>\n",
              "      <td>-0.146216</td>\n",
              "      <td>0.868201</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.052063</td>\n",
              "      <td>0.172285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <td>0.041307</td>\n",
              "      <td>0.008910</td>\n",
              "      <td>0.020238</td>\n",
              "      <td>0.052063</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.175962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>availability_365</th>\n",
              "      <td>0.033815</td>\n",
              "      <td>-0.065255</td>\n",
              "      <td>0.173490</td>\n",
              "      <td>0.172285</td>\n",
              "      <td>0.175962</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   price  ...  availability_365\n",
              "price                           1.000000  ...          0.033815\n",
              "minimum_nights                  0.213105  ...         -0.065255\n",
              "number_of_reviews              -0.037996  ...          0.173490\n",
              "reviews_per_month              -0.061857  ...          0.172285\n",
              "calculated_host_listings_count  0.041307  ...          0.175962\n",
              "availability_365                0.033815  ...          1.000000\n",
              "\n",
              "[6 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "91UujDr6ueqE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "d334bd90-331c-418f-f982-4e4c06922471"
      },
      "source": [
        "# plotar um heatmap a partir das correlações\n",
        "sns.heatmap(corr, cmap='RdBu' , fmt='.2f', square=True, linecolor='white', annot=True);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7b6RMWpuHKN-"
      },
      "source": [
        "### **Q7. Qual o tipo de imóvel mais alugado no Airbnb?**\n",
        "\n",
        "A coluna `room_type`indica qual categoria de imóvel está anunciado na plataforma. No site, existem opções de apartamentos/casas inteiras, aluguel de um quarto privado ou até mesmo dividir o mesmo quarto com outras pessoas.\n",
        "\n",
        "Vamos contar a quantidade de ocorrências de cada tipo de aluguel, usando o método `value_counts()`.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ueLPNWvPHgfB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59496a2d-e55b-4f2a-d6d8-51210253063c"
      },
      "source": [
        "# mostrar a quantidade de cada tipo de imóvel disponível\n",
        "df_clean.room_type.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Private room       2942\n",
              "Entire home/apt    2122\n",
              "Shared room          91\n",
              "Hotel room           47\n",
              "Name: room_type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fOsgNbQm1_Ls"
      },
      "source": [
        "Os tipos de Imóveis que mais aparece são, em ordem:\n",
        "* Quartos privados\n",
        "* Casa/Apt inteiros\n",
        "* Quartos compartilhados\n",
        "* Quartos de Hotel"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EvwoO-XAHhIm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e0a6f3be-9b30-4e39-f815-97db8d25085d"
      },
      "source": [
        "# mostrar a porcentagem de cada tipo de imóvel disponível\n",
        "df_clean.room_type.value_counts() / df.shape[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Private room       0.461997\n",
              "Entire home/apt    0.333229\n",
              "Shared room        0.014290\n",
              "Hotel room         0.007381\n",
              "Name: room_type, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tavOaPjd3bpy"
      },
      "source": [
        "Os imóveis tem uma distribuição bem interessante, veja:\n",
        "* `Quartos privados` e `Casas/apts inteiros` ocupam cerca de 79% dos imóveis anunciados\n",
        "* E apenas 1% são `Quartos compartilhados`\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MFa5NbnLJMcd"
      },
      "source": [
        "### **Q7. Qual a localidade mais cara de Dublin?**\n",
        "\n",
        "É possível verificar uma variável em função de outra, usando `groupby()`. Neste caso, queremos comparar os bairros (*neighbourhoods*) a partir do preço do aluguel.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MD_xa143JZpi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bee505dc-7a76-4e9e-eda2-ab498ca2216b"
      },
      "source": [
        "# ver preços por bairros, na média\n",
        "df_clean.groupby(['neighbourhood']).price.mean().sort_values(ascending=False)[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "neighbourhood\n",
              "Dublin City              88.379266\n",
              "Dn Laoghaire-Rathdown    85.990177\n",
              "Fingal                   70.808118\n",
              "South Dublin             63.195556\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7PO9s7_Wt8xo"
      },
      "source": [
        "Acima podemos notar que os valores médios de **Dublin City** e **Dn Laoghaire-Rathdown** são bem próximos, diferente de **Fingal** e **South Dublin** que apresentam valores mais baixos. \n",
        "\n",
        "Não sou um conhecedor da cidade de Dublin então não posso falar com propriedade o motivo dessa classificação das médias, mas é possível verificarmos quais bairros possuem mais imóveis anunciados, e esclarecermos a dúvida se isso se deve a quantidade de imóveis presentes em cada um dos bairros. Veja abaixo:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lh2J8pYWwlY7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20f9efab-7ee7-4809-e9b5-7b9ecf1aba5f"
      },
      "source": [
        "# definindo variáveis para ver a quantidade de imóveis de cada região\n",
        "\n",
        "dct = df_clean[df_clean.neighbourhood == 'Dublin City'].shape[0]\n",
        "\n",
        "dlr = df_clean[df_clean.neighbourhood == 'Dn Laoghaire-Rathdown'].shape[0]\n",
        "\n",
        "fng = df_clean[df_clean.neighbourhood == 'Fingal'].shape[0]\n",
        "\n",
        "sd = df_clean[df_clean.neighbourhood == 'South Dublin'].shape[0]\n",
        "\n",
        "# Imprimindo o número de imóveis em cada região\n",
        "print('O número de imóveis em Dublin City é :\\t {}'.format(dct))\n",
        "\n",
        "print('O número de imóveis em Dn Laoghaire-Rathdown é :\\t {}'.format(dlr))\n",
        "\n",
        "print('O número de imóveis em Fingal é :\\t {}'.format(fng))\n",
        "\n",
        "print('O número de imóveis em South Dublin é :\\t {}'.format(sd))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "O número de imóveis em Dublin City é :\t 3926\n",
            "O número de imóveis em Dn Laoghaire-Rathdown é :\t 509\n",
            "O número de imóveis em Fingal é :\t 542\n",
            "O número de imóveis em South Dublin é :\t 225\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iEmhbA2ZxLGh"
      },
      "source": [
        "Com isso conseguimos tirar algumas conclusões:\n",
        "\n",
        "* Dublin City é onde há mais imóveis anunciados e o mais caro\n",
        "* Porém, é possível se hospedar com valores parecidos tanto em Dublin City quanto em Dn Laoghaire-Rathdown"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AKXj4-Ev1VmR"
      },
      "source": [
        "Como são fornecidas as Latitudes e Longitudes para os imóveis, é possível plotar cada ponto. Para isso, considera-se `x=longitude` e `y=latitude`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UxVJqJ_3W59I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "outputId": "180edb66-86de-4e38-a923-c3108a3c184b"
      },
      "source": [
        "# plotar os imóveis pela latitude-longitude\n",
        "df_clean.plot(kind='scatter', x='longitude', y='latitude', alpha=0.4, c=df_clean['price'], s=8,\n",
        "        cmap=plt.get_cmap('jet'), figsize=(12,8));"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vTneAMRUYvr1"
      },
      "source": [
        "### **Q8. Qual é a média do mínimo de noites para aluguel (minimum_nights)?**\n",
        "\n",
        "Podemos ver que a média do mínimo de noites é 2 noites, isso nos mostra que os proprietários tendem a disponibilizar os imóveis por no mínimo 2 noites (fins de semana)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_VzNhrEgY82N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "84c4d741-b7fe-45a5-f12e-0f2f1022d824"
      },
      "source": [
        "# ver a média da coluna `minimum_nights`\n",
        "\n",
        "df_clean['minimum_nights'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.0280661284121493"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GkvAg623RjoR"
      },
      "source": [
        "## Conclusões\n",
        "\n",
        "Neste projeto, conseguimos tirar algumas informações sobre o **Airbnb** na cidade de Dublin.\n",
        "\n",
        "Se considerarmos que uma pessoa trabalha 20 horas semanais na Irlanda, ela chega ao final do mês com 800 euros, ganhando um salário minímo, isso mostra que a hospedagem em dublin é relativamente cara. Pois, a média de preço é de `85,22` euros, sendo cerca de 10% do salário mínimo.\n",
        "\n",
        "Algumas outras informações:\n",
        "\n",
        "* Cerca de 46% dos anúncios são apenas de `private Rooms`\n",
        "* A média de preços mais cara é a de `Dublin City` e logo atrás temos `Dn Laoghaire-Rathdown`\n",
        "* O local com a menor quantidade de imóveis anunciados é `South Dublin`\n",
        "* A média de `minimum_nights` é cerca de 2 noites, geralmente para que os hóspedes aluguem ao menos por um fim de semana (sábado e domingo)."
      ]
    }
  ]
}