Analisando_os_Dados_do_Airbnb_(Boston).ipynb

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        "<a href=\"https://colab.research.google.com/github/rbolsoni/Portfolio-Data-Science/blob/master/Analisando_os_Dados_do_Airbnb_(Boston).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "<img alt=\"Colaboratory logo\" width=\"100%\" src=\"https://miro.medium.com/max/700/1*6Z03TsdOBAnPYQSjW8rAJQ.png\">\n",
        "\n",
        "[![author](https://img.shields.io/badge/author-rbolsoni-orange.svg)](www.linkedin.com/in/rafabolsoni) \n",
        "[![author](https://img.shields.io/badge/see%20more-projects-brightgreen.svg)](https://github.com/rbolsoni) \n",
        "\n",
        "---\n",
        "\n",
        "# Análise dos Dados do Airbnb - *Boston*\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 e gerando milhões de dados de locais para alugar e de usuarios que alugam, ou seja um prato cheio para os Cientistas de Dados analiserem e tirarem diversos insights sobre o comportamento de quem usa esse serviço.\n",
        " \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=\"30%\" src=\"https://miro.medium.com/max/546/1*qSBA0JTCkT_t57-x_QYxDQ.jpeg\"></center>\n",
        "\n",
        "**Neste *notebook*, iremos analisar os dados referentes à cidade Boston, e ver quais insights podem ser extraídos a partir de dados brutos.**"
      ]
    },
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      "source": [
        "## Obtenção dos Dados\n",
        "\n",
        "Todos os dados usados aqui foram obtidos a partir do site [Inside Airbnb](http://insideairbnb.com/get-the-data.html).\n",
        "\n",
        "Para esta análise exploratória inicial, será baixado apenas o seguinte arquivo:\n",
        "*   *listings.csv* - Summary information and metrics for listings in Boston (good for visualisations).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vhp2wMPgJ6zX",
        "colab_type": "code",
        "colab": {}
      },
      "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": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tUmoAT8DLQ5T",
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      },
      "source": [
        "# importar o arquivo listings.csv para um DataFrame\n",
        "df = pd.read_csv('http://data.insideairbnb.com/united-states/ma/boston/2020-04-14/visualisations/listings.csv')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qb5By2nIMS6m",
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      },
      "source": [
        "## Análise dos Dados\n",
        "Esta etapa tem por objetivo criar uma consciência situacional inicial e permitir um entendimento de como os dados estão estruturados."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RYgvuebKNg8-",
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      "source": [
        "**Dicionário das variáveis**\n",
        "\n",
        "* id - número de id gerado para identificar o imóvel\n",
        "* name - nome da propriedade anunciada\n",
        "* host_id - número de id do proprietário (anfitrião) da propriedade\n",
        "* host_name - Nome do anfitrião\n",
        "* neighbourhood_group - esta coluna não contém nenhum valor válido\n",
        "* neighbourhood - nome do bairro\n",
        "* latitude - coordenada da latitude da propriedade\n",
        "* longitude - coordenada da longitude da propriedade\n",
        "* room_type - informa o tipo de quarto que é oferecido\n",
        "* price - preço para alugar o imóvel\n",
        "* minimum_nights - quantidade mínima de noites para reservar\n",
        "* number_of_reviews - número de reviews que a propriedade possui\n",
        "* last_review - data do último review\n",
        "* reviews_per_month - quantidade de reviews por 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 iniciar qualquer análise, vamos verificar a cara do nosso *dataset*, analisando as 5 primeiras entradas."
      ]
    },
    {
      "cell_type": "code",
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        "id": "2iS_dae7YQtO",
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        "colab": {
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          "height": 394
        },
        "outputId": "9a2704e3-cb54-4bdc-ca5e-0226b98ccbac"
      },
      "source": [
        "# mostrar as 5 primeiras entradas\n",
        "df.head()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>3781</td>\n",
              "      <td>HARBORSIDE-Walk to subway</td>\n",
              "      <td>4804</td>\n",
              "      <td>Frank</td>\n",
              "      <td>NaN</td>\n",
              "      <td>East Boston</td>\n",
              "      <td>42.36413</td>\n",
              "      <td>-71.02991</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>125</td>\n",
              "      <td>28</td>\n",
              "      <td>16</td>\n",
              "      <td>2019-12-21</td>\n",
              "      <td>0.28</td>\n",
              "      <td>1</td>\n",
              "      <td>97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5506</td>\n",
              "      <td>**$49 Special **   Private! Minutes to center!</td>\n",
              "      <td>8229</td>\n",
              "      <td>Terry</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Roxbury</td>\n",
              "      <td>42.32981</td>\n",
              "      <td>-71.09559</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>145</td>\n",
              "      <td>3</td>\n",
              "      <td>106</td>\n",
              "      <td>2019-10-31</td>\n",
              "      <td>0.79</td>\n",
              "      <td>6</td>\n",
              "      <td>307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6695</td>\n",
              "      <td>$99 Special!! Home Away! Condo</td>\n",
              "      <td>8229</td>\n",
              "      <td>Terry</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Roxbury</td>\n",
              "      <td>42.32994</td>\n",
              "      <td>-71.09351</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>169</td>\n",
              "      <td>3</td>\n",
              "      <td>115</td>\n",
              "      <td>2019-11-02</td>\n",
              "      <td>0.88</td>\n",
              "      <td>6</td>\n",
              "      <td>266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8789</td>\n",
              "      <td>Curved Glass Studio/1bd facing Park</td>\n",
              "      <td>26988</td>\n",
              "      <td>Anne</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Downtown</td>\n",
              "      <td>42.35919</td>\n",
              "      <td>-71.06265</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>99</td>\n",
              "      <td>91</td>\n",
              "      <td>24</td>\n",
              "      <td>2019-09-30</td>\n",
              "      <td>0.35</td>\n",
              "      <td>8</td>\n",
              "      <td>290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10730</td>\n",
              "      <td>Bright 1bed facing Golden Dome</td>\n",
              "      <td>26988</td>\n",
              "      <td>Anne</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Downtown</td>\n",
              "      <td>42.35840</td>\n",
              "      <td>-71.06185</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>150</td>\n",
              "      <td>91</td>\n",
              "      <td>31</td>\n",
              "      <td>2019-12-01</td>\n",
              "      <td>0.24</td>\n",
              "      <td>8</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      id  ... availability_365\n",
              "0   3781  ...               97\n",
              "1   5506  ...              307\n",
              "2   6695  ...              266\n",
              "3   8789  ...              290\n",
              "4  10730  ...               42\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6-B_qtEaYEd-",
        "colab_type": "text"
      },
      "source": [
        "### **Q1. Quantos atributos (variáveis) e quantas entradas o nosso conjunto de dados possui? Quais os tipos das variáveis?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TFRK0tniLV-G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "84fe03d9-79ec-419e-bd5c-783a039f8824"
      },
      "source": [
        "# identificar o volume de dados do DataFrame\n",
        "print(f'Entradas:\\t {df.shape[0]}')\n",
        "print(f'Variáveis:\\t {df.shape[1]}\\n')\n",
        "\n",
        "# verificar as 5 primeiras entradas do dataset\n",
        "print(f'{df.dtypes}')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Entradas:\t 3845\n",
            "Variáveis:\t 16\n",
            "\n",
            "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\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yz1urSUGW9iE",
        "colab_type": "text"
      },
      "source": [
        "### **Q2. Qual a porcentagem de valores ausentes no *dataset*?**\n",
        "A qualidade de um dataset está diretamente relacionada à quantidade de valores ausentes. É importante entender logo no início se esses valores nulos são significativos comparados ao total de entradas.\n",
        "\n",
        "* É possível ver que a coluna neighbourhood_group possui 100% dos seus valores faltantes.\n",
        "* As variáveis reviews_per_month e last_review possuem valores nulos em 21% das linhas.\n",
        "* As outras variaveis não possuem valores nulos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "plk2FjbCXOqP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "a34667e3-21b6-40d0-e74c-a8188cab80e7"
      },
      "source": [
        "# ordenar em ordem decrescente as variáveis por seus valores ausentes\n",
        "((df.isnull().sum() / df.shape[0]) * 100).sort_values(ascending=False)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "neighbourhood_group               100.000000\n",
              "reviews_per_month                  21.456437\n",
              "last_review                        21.456437\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",
              "name                                0.000000\n",
              "id                                  0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iHXvvHIxaoNc",
        "colab_type": "text"
      },
      "source": [
        "### **Q3. Qual o tipo de distribuição das variáveis?** \n",
        "\n",
        "Para identificar a distribuição das variáveis, irei plotar o histograma."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q2wWSfeRbmBo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "a0496449-3c0f-45b4-a33a-1e335be17373"
      },
      "source": [
        "# plotar o histograma das variáveis numéricas\n",
        "df.hist(bins=15, figsize=(20,10));"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bA94HXpjiRgj",
        "colab_type": "text"
      },
      "source": [
        "### **Q4. Há outliers presentes?**\n",
        "Pela distribuição do histograma, é possível verificar indícios da presença de outliers. Olhe por exemplo as variáveis `price`, `minimum_nights` e `calculated_host_listings_count`.\n",
        "\n",
        "Os valores não seguem uma distruição, e distorcem toda a representação gráfica. Para confirmar, há duas maneiras rápidas que auxiliam a detecção de outliers. São elas:\n",
        "\n",
        "* Resumo estatístico por meio do método describe()\n",
        "* Plotar boxplots para a variável."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OGAHuMAEie95",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "8d260929-37af-4aec-91d1-d4cb89b0655d"
      },
      "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()"
      ],
      "execution_count": 28,
      "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>3845.000000</td>\n",
              "      <td>3845.000000</td>\n",
              "      <td>3845.000000</td>\n",
              "      <td>3020.000000</td>\n",
              "      <td>3845.000000</td>\n",
              "      <td>3845.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>182.451235</td>\n",
              "      <td>32.017685</td>\n",
              "      <td>39.779974</td>\n",
              "      <td>1.827662</td>\n",
              "      <td>31.626268</td>\n",
              "      <td>185.347724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>407.777077</td>\n",
              "      <td>42.597891</td>\n",
              "      <td>67.862814</td>\n",
              "      <td>1.872423</td>\n",
              "      <td>48.155154</td>\n",
              "      <td>133.262730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</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>79.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>70.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>133.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>1.220000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>176.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>2.700000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>327.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>10000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>615.000000</td>\n",
              "      <td>19.380000</td>\n",
              "      <td>176.000000</td>\n",
              "      <td>365.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              price  ...  availability_365\n",
              "count   3845.000000  ...       3845.000000\n",
              "mean     182.451235  ...        185.347724\n",
              "std      407.777077  ...        133.262730\n",
              "min        0.000000  ...          0.000000\n",
              "25%       79.000000  ...         70.000000\n",
              "50%      133.000000  ...        176.000000\n",
              "75%      200.000000  ...        327.000000\n",
              "max    10000.000000  ...        365.000000\n",
              "\n",
              "[8 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YdnDGG9fjH2a",
        "colab_type": "text"
      },
      "source": [
        "Olhando o resumo estatístico acima, podemos confirmar algumas hipóteses como:\n",
        "\n",
        "* A quantidade de noite minima (`minimum_nights`) está acima de 365 por ano\n",
        "* A variável `price` possui 75% do valor abaixo de 200, porém seu valor máximo é 10000.\n",
        "* Tem valores na variavel `price` como 0\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "00TeJ2QTrAEE",
        "colab_type": "text"
      },
      "source": [
        "Boxplot para `minimum_nights`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r_XzXi3BnGof",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "outputId": "b4fce71e-9476-4194-a797-6b5a71209a2d"
      },
      "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 30 dias para minimum_nights\n",
        "print(\"minimum_nights: valores acima de 30:\")\n",
        "print(f\"{len(df[df.minimum_nights > 30])} entradas\")\n",
        "print(\"{:.4f}%\".format((len(df[df.minimum_nights > 30]) / df.shape[0])*100))"
      ],
      "execution_count": 31,
      "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 30:\n",
            "1199 entradas\n",
            "31.1834%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQR_63byrJXA",
        "colab_type": "text"
      },
      "source": [
        "Boxplot para `price`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z6dPmeELrTAv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "outputId": "54fbc0a8-b437-4770-a2b8-dbc1ab05009e"
      },
      "source": [
        "# price\n",
        "df.price.plot(kind='box', vert=False, figsize=(15, 3),)\n",
        "plt.show()\n",
        "\n",
        "# ver quantidade de valores acima de 1500 para price\n",
        "print(\"\\nprice: valores acima de 1500\")\n",
        "print(f\"{len(df[df.price > 1500])} entradas\")\n",
        "print(\"{:.4f}%\".format((len(df[df.price > 1500]) / df.shape[0])*100))\n",
        "\n",
        "# segundo grafico com melhor visualização\n",
        "\n",
        "df.price.plot(kind='box', vert=False, xlim=(50,1300), figsize=(15,3));\n",
        "\n",
        "print(\"\\nprice: valores abaixo de 1500\")\n",
        "print(f\"{len(df[df.price < 1300])} entradas\")\n",
        "print(\"{:.4f}%\".format((len(df[df.price < 1300]) / df.shape[0])*100))\n",
        "\n",
        "# boxplot sem valores menores que 50\n",
        "\n",
        "df.price.plot(kind='box', vert=False, xlim=(50,1300), figsize=(15,3));\n",
        "\n"
      ],
      "execution_count": 46,
      "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 1500\n",
            "12 entradas\n",
            "0.3121%\n",
            "\n",
            "price: valores abaixo de 1500\n",
            "3832 entradas\n",
            "99.6619%\n"
          ],
          "name": "stdout"
        },
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ROalpRp0wIpN",
        "colab_type": "text"
      },
      "source": [
        "Histogramas sem outliers\n",
        "\n",
        "Já que identificamos outliers nas variáveis `price` e `minimum_nights`, vamos agora limpar o DataFrame delas e plotar novamente o histograma."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZxYQrxMzwP77",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "dfc1c80c-8b90-491f-f2d4-cbe0e6587da8"
      },
      "source": [
        "# remover os *outliers* em um novo DataFrame\n",
        "df_clean = df.copy()\n",
        "df_clean.drop(df_clean[df_clean.price > 1500].index, axis=0, inplace=True)\n",
        "df_clean.drop(df_clean[df_clean.minimum_nights > 30].index, axis=0, inplace=True)\n",
        "\n",
        "# remover `neighbourhood_group`, pois está vazio\n",
        "df_clean.drop('neighbourhood_group', axis=1, inplace=True)\n",
        "\n",
        "# plotar o histograma para as variáveis numéricas\n",
        "df_clean.hist(bins=15, figsize=(20,10));"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ff-8gUahLk-C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "59fafb22-7a3d-48f8-801c-b19f2e1175fb"
      },
      "source": [
        "# ver a média da coluna `price``\n",
        "df_clean['price'].describe()\n"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    2636.000000\n",
              "mean      169.455615\n",
              "std       141.573582\n",
              "min         0.000000\n",
              "25%        80.000000\n",
              "50%       133.000000\n",
              "75%       200.000000\n",
              "max      1490.000000\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IXn2oIo5qx-w",
        "colab_type": "text"
      },
      "source": [
        "### **Q4. Qual a correlação existente entre as variáveis**\n",
        "\n",
        "Correlação significa que existe uma relação entre duas coisas. No nosso contexto, estamos buscando relação ou semelhança entre duas variáveis.\n",
        "\n",
        "Essa relação pode ser medida, e é função do coeficiente de correlação 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`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x2ayzBajqxyd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "6badc61e-9d62-40ab-cedc-fef641dbba7c"
      },
      "source": [
        "# criar uma matriz de correlação\n",
        "# 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",
        "\n",
        "# mostrar a matriz de correlação\n",
        "display(corr)"
      ],
      "execution_count": 54,
      "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>-0.118030</td>\n",
              "      <td>-0.105786</td>\n",
              "      <td>-0.104285</td>\n",
              "      <td>0.005571</td>\n",
              "      <td>0.057892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>minimum_nights</th>\n",
              "      <td>-0.118030</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.192242</td>\n",
              "      <td>-0.289503</td>\n",
              "      <td>0.393557</td>\n",
              "      <td>0.155080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>number_of_reviews</th>\n",
              "      <td>-0.105786</td>\n",
              "      <td>-0.192242</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.676075</td>\n",
              "      <td>-0.208829</td>\n",
              "      <td>-0.009099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reviews_per_month</th>\n",
              "      <td>-0.104285</td>\n",
              "      <td>-0.289503</td>\n",
              "      <td>0.676075</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.265062</td>\n",
              "      <td>-0.065341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <td>0.005571</td>\n",
              "      <td>0.393557</td>\n",
              "      <td>-0.208829</td>\n",
              "      <td>-0.265062</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.068056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>availability_365</th>\n",
              "      <td>0.057892</td>\n",
              "      <td>0.155080</td>\n",
              "      <td>-0.009099</td>\n",
              "      <td>-0.065341</td>\n",
              "      <td>0.068056</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   price  ...  availability_365\n",
              "price                           1.000000  ...          0.057892\n",
              "minimum_nights                 -0.118030  ...          0.155080\n",
              "number_of_reviews              -0.105786  ...         -0.009099\n",
              "reviews_per_month              -0.104285  ...         -0.065341\n",
              "calculated_host_listings_count  0.005571  ...          0.068056\n",
              "availability_365                0.057892  ...          1.000000\n",
              "\n",
              "[6 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "91UujDr6ueqE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "0928f826-30a7-47d5-c172-3ad30eefcdab"
      },
      "source": [
        "# plotar um heatmap a partir das correlações\n",
        "sns.heatmap(corr, cmap='RdBu', fmt='.2f', square=True, linecolor='white', annot=True);"
      ],
      "execution_count": 55,
      "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-",
        "colab_type": "text"
      },
      "source": [
        "### **Q5. Qual o tipo de imóvel mais alugado no Airbnb?**\n",
        "\n",
        "A coluna da variável `room_type` indica o tipo de locação que está anunciada no Airbnb. Se você já alugou no site, sabe que existem opções de apartamentos/casas inteiras, apenas o aluguel de um quarto ou mesmo dividir o quarto com outras pessoas.\n",
        "\n",
        "Vamos contar a quantidade de ocorrências de cada tipo de aluguel, usando o método `value_counts()`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ueLPNWvPHgfB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "6feaaf16-d10a-4c4c-b098-9b42ca32ee77"
      },
      "source": [
        "# mostrar a quantidade de cada tipo de imóvel disponível\n",
        "df_clean.room_type.value_counts()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Entire home/apt    1608\n",
              "Private room        990\n",
              "Hotel room           26\n",
              "Shared room          12\n",
              "Name: room_type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EvwoO-XAHhIm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "855f0dfb-7aba-453f-b876-53c8df842858"
      },
      "source": [
        "# mostrar a porcentagem de cada tipo de imóvel disponível\n",
        "(df_clean.room_type.value_counts() / df_clean.shape[0]) * 100"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Entire home/apt    61.001517\n",
              "Private room       37.556904\n",
              "Hotel room          0.986343\n",
              "Shared room         0.455235\n",
              "Name: room_type, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MFa5NbnLJMcd",
        "colab_type": "text"
      },
      "source": [
        "### **Q6. Qual a localidade mais cara do dataset?**\n",
        "\n",
        "Uma maneira de se verificar uma variável em função da outra é usando `groupby()`. No caso, queremos comparar os bairros `(neighbourhoods)` a partir do preço de locação.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MD_xa143JZpi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "0581e6fe-66a2-445e-8caa-c30f9e09013b"
      },
      "source": [
        "# ver preços por bairros, na média\n",
        "df_clean.groupby(['neighbourhood']).price.mean().sort_values(ascending=False)[:10]"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "neighbourhood\n",
              "South Boston Waterfront    302.761905\n",
              "West End                   271.303030\n",
              "Mission Hill               255.928571\n",
              "Fenway                     254.546053\n",
              "Back Bay                   239.787402\n",
              "Charlestown                210.924528\n",
              "Beacon Hill                198.886364\n",
              "Downtown                   190.820789\n",
              "Chinatown                  188.068966\n",
              "Longwood Medical Area      179.250000\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I0CkInaB7L-_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "8481e6c1-70de-4977-ac61-9e7d7e40d129"
      },
      "source": [
        "# ver preços de bairros mais baratos na média\n",
        "df_clean.groupby(['neighbourhood']).price.mean().sort_values(ascending=False)[15:]"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "neighbourhood\n",
              "Roxbury          160.467532\n",
              "Jamaica Plain    153.184332\n",
              "East Boston      129.491803\n",
              "Allston          116.309524\n",
              "Brighton         115.009434\n",
              "Dorchester       110.788889\n",
              "West Roxbury     103.920000\n",
              "Roslindale       102.169811\n",
              "Hyde Park         93.045455\n",
              "Mattapan          69.447368\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "09jPV0jd8Hoq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "4546750c-1487-4bc1-9770-abd743c88fb0"
      },
      "source": [
        "# ver preço x tipo x bairro\n",
        "df_clean.groupby(['neighbourhood', 'room_type']).price.mean().sort_values(ascending=False)[:15]"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "neighbourhood            room_type      \n",
              "South Boston Waterfront  Entire home/apt    321.263158\n",
              "West End                 Entire home/apt    283.918033\n",
              "Mission Hill             Entire home/apt    277.623377\n",
              "Fenway                   Entire home/apt    263.915493\n",
              "Back Bay                 Entire home/apt    256.280374\n",
              "Charlestown              Entire home/apt    250.766667\n",
              "South Boston             Entire home/apt    212.051020\n",
              "Beacon Hill              Entire home/apt    207.654321\n",
              "Jamaica Plain            Entire home/apt    203.009709\n",
              "Bay Village              Entire home/apt    200.269231\n",
              "Dorchester               Entire home/apt    199.026316\n",
              "North End                Entire home/apt    198.854167\n",
              "Downtown                 Private room       193.333333\n",
              "                         Entire home/apt    193.131356\n",
              "Roxbury                  Entire home/apt    191.894737\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bUawYqWI3Ch4",
        "colab_type": "text"
      },
      "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_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "69fb362a-5bbb-4d99-aaec-a4e06cd6c019"
      },
      "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": 65,
      "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": "464E0RKr3hAZ",
        "colab_type": "text"
      },
      "source": [
        "Mapa da cidade de Boston para realizar um comparativo\n",
        "\n",
        "![alt text](https://miro.medium.com/max/700/1*SuemdLku5aE70pWI1F4KCQ.jpeg?itok=w29v-_-_&timestamp=1456773924)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vTneAMRUYvr1",
        "colab_type": "text"
      },
      "source": [
        "### **Q7. Qual é a média do mínimo de noites para aluguel (minimum_nights)?**\n",
        "\n",
        "Podemos tambem descobrir qual a média de noite minima para cada tipo de hospedagem."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_VzNhrEgY82N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "cec9433f-43ae-4277-d894-101222b39068"
      },
      "source": [
        "# ver a média da coluna `minimum_nights``\n",
        "df_clean.groupby(['room_type']).minimum_nights.mean().sort_values(ascending=False)[:10]"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "room_type\n",
              "Entire home/apt    9.557836\n",
              "Private room       7.758586\n",
              "Shared room        1.250000\n",
              "Hotel room         1.000000\n",
              "Name: minimum_nights, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GkvAg623RjoR",
        "colab_type": "text"
      },
      "source": [
        "## Conclusões\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rbZ3Hsy9Ypw8",
        "colab_type": "text"
      },
      "source": [
        "Analise do tecnica do dataset:\n",
        "* Ja se percebe alguns outliers e algumas variaveis\n",
        "* Algumas localidades com poucos imoveis que podem distorcer os dados\n",
        "\n",
        "Por fim, lembra-se que este dataset é uma versão resumida, ideal apenas para uma abordagem inicial. Recomenda-se que seja usado, em uma próxima análise exploratória, o conjunto de dados completos, com 106 atributos disponíveis.\n",
        "\n",
        "Porém, algumas conclusões preliminares podem ser tiradas com essa analise superficial dos dados de hospedagem do AirBnB de Boston.\n",
        "* Apartamentos inteiros tem uma tendencia de exigir maior numero de noites minimas\n",
        "* Os bairros mais caros são: South Boston Waterfront, West End e Mission Hill  \n",
        "* Os bairros mais baratos são: Mattapan, Hyde Park, Roslindale\n",
        "\n"
      ]
    }
  ]
}