AB_NYC_2019_Project.ipynb

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      "authorship_tag": "ABX9TyO7AgPZUJ7R1YKmzqEnetsK",
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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/LeonardoCarCampos/IME-USP-Python-Course/blob/main/AB_NYC_2019_Project.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": "code",
      "execution_count": 1,
      "metadata": {
        "id": "14Kuf1_eXbwG"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Kna1dCQXoy8",
        "outputId": "e1b754ae-9d56-4cec-e446-bf0ba6ec98c5"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Carregar o arquivo CSV\n",
        "data = pd.read_csv(\"/content/drive/My Drive/AB_NYC_2019_Project/AB_NYC_2019.csv\")\n",
        "\n",
        "# Visualizar as primeiras linhas do DataFrame\n",
        "data.head()"
      ],
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          "height": 417
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        "id": "9kmL4fnldI1T",
        "outputId": "8a359dd9-1bfc-4289-9628-1d1662d278ef"
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      "execution_count": 5,
      "outputs": [
        {
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          "data": {
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              "     id                                              name  host_id  \\\n",
              "0  2539                Clean & quiet apt home by the park     2787   \n",
              "1  2595                             Skylit Midtown Castle     2845   \n",
              "2  3647               THE VILLAGE OF HARLEM....NEW YORK !     4632   \n",
              "3  3831                   Cozy Entire Floor of Brownstone     4869   \n",
              "4  5022  Entire Apt: Spacious Studio/Loft by central park     7192   \n",
              "\n",
              "     host_name neighbourhood_group neighbourhood  latitude  longitude  \\\n",
              "0         John            Brooklyn    Kensington  40.64749  -73.97237   \n",
              "1     Jennifer           Manhattan       Midtown  40.75362  -73.98377   \n",
              "2    Elisabeth           Manhattan        Harlem  40.80902  -73.94190   \n",
              "3  LisaRoxanne            Brooklyn  Clinton Hill  40.68514  -73.95976   \n",
              "4        Laura           Manhattan   East Harlem  40.79851  -73.94399   \n",
              "\n",
              "         room_type  price  minimum_nights  number_of_reviews last_review  \\\n",
              "0     Private room    149               1                  9  2018-10-19   \n",
              "1  Entire home/apt    225               1                 45  2019-05-21   \n",
              "2     Private room    150               3                  0         NaN   \n",
              "3  Entire home/apt     89               1                270  2019-07-05   \n",
              "4  Entire home/apt     80              10                  9  2018-11-19   \n",
              "\n",
              "   reviews_per_month  calculated_host_listings_count  availability_365  \n",
              "0               0.21                               6               365  \n",
              "1               0.38                               2               355  \n",
              "2                NaN                               1               365  \n",
              "3               4.64                               1               194  \n",
              "4               0.10                               1                 0  "
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              "      <td>2539</td>\n",
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              "      <td>-73.94190</td>\n",
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              "      <td>4869</td>\n",
              "      <td>LisaRoxanne</td>\n",
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              "      <td>-73.95976</td>\n",
              "      <td>Entire home/apt</td>\n",
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              "      <td>2019-07-05</td>\n",
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              "      <th>4</th>\n",
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              "      <td>7192</td>\n",
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              "      <td>East Harlem</td>\n",
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              "      <td>-73.94399</td>\n",
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-73.97175\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"room_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Private room\",\n          \"Entire home/apt\",\n          \"Shared room\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 240,\n        \"min\": 0,\n        \"max\": 10000,\n        \"num_unique_values\": 674,\n        \"samples\": [\n          519,\n          675,\n          488\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"minimum_nights\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 1,\n        \"max\": 1250,\n        \"num_unique_values\": 109,\n        \"samples\": [\n          160,\n          60,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"number_of_reviews\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 44,\n        \"min\": 0,\n        \"max\": 629,\n        \"num_unique_values\": 394,\n        \"samples\": [\n          12,\n          144,\n          314\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"last_review\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1764,\n        \"samples\": [\n          \"2016-07-26\",\n          \"2018-05-21\",\n          \"2019-02-27\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"reviews_per_month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6804419952744725,\n        \"min\": 0.01,\n        \"max\": 58.5,\n        \"num_unique_values\": 937,\n        \"samples\": [\n          1.7,\n          0.28,\n          2.14\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"calculated_host_listings_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32,\n        \"min\": 1,\n        \"max\": 327,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          37,\n          17,\n          121\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"availability_365\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 131,\n        \"min\": 0,\n        \"max\": 365,\n        \"num_unique_values\": 366,\n        \"samples\": [\n          335,\n          309,\n          249\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "crRLo4jYdfRw",
        "outputId": "a056b8f5-1818-4e2d-87bd-9198e1c2601a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 48895 entries, 0 to 48894\n",
            "Data columns (total 16 columns):\n",
            " #   Column                          Non-Null Count  Dtype  \n",
            "---  ------                          --------------  -----  \n",
            " 0   id                              48895 non-null  int64  \n",
            " 1   name                            48879 non-null  object \n",
            " 2   host_id                         48895 non-null  int64  \n",
            " 3   host_name                       48874 non-null  object \n",
            " 4   neighbourhood_group             48895 non-null  object \n",
            " 5   neighbourhood                   48895 non-null  object \n",
            " 6   latitude                        48895 non-null  float64\n",
            " 7   longitude                       48895 non-null  float64\n",
            " 8   room_type                       48895 non-null  object \n",
            " 9   price                           48895 non-null  int64  \n",
            " 10  minimum_nights                  48895 non-null  int64  \n",
            " 11  number_of_reviews               48895 non-null  int64  \n",
            " 12  last_review                     38843 non-null  object \n",
            " 13  reviews_per_month               38843 non-null  float64\n",
            " 14  calculated_host_listings_count  48895 non-null  int64  \n",
            " 15  availability_365                48895 non-null  int64  \n",
            "dtypes: float64(3), int64(7), object(6)\n",
            "memory usage: 6.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking for missing values\n",
        "for column in data.columns:\n",
        "    if data[column].isnull().sum() != 0:\n",
        "        print(\"=======================================================\")\n",
        "        print(f\"{column} ==> Missing Values : {data[column].isnull().sum()}, dtypes : {data[column].dtypes}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GD1jcWVrdnSi",
        "outputId": "42d5ef8d-bfd0-4b84-91e6-06aec9a9bed4"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=======================================================\n",
            "name ==> Missing Values : 16, dtypes : object\n",
            "=======================================================\n",
            "host_name ==> Missing Values : 21, dtypes : object\n",
            "=======================================================\n",
            "last_review ==> Missing Values : 10052, dtypes : object\n",
            "=======================================================\n",
            "reviews_per_month ==> Missing Values : 10052, dtypes : float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop null records\n",
        "data.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "nC9tPvpjd4zm"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zeElLa9Zd7Gx",
        "outputId": "827712d2-9642-4165-8171-3b8d0b2e8d1b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                0\n",
              "name                              0\n",
              "host_id                           0\n",
              "host_name                         0\n",
              "neighbourhood_group               0\n",
              "neighbourhood                     0\n",
              "latitude                          0\n",
              "longitude                         0\n",
              "room_type                         0\n",
              "price                             0\n",
              "minimum_nights                    0\n",
              "number_of_reviews                 0\n",
              "last_review                       0\n",
              "reviews_per_month                 0\n",
              "calculated_host_listings_count    0\n",
              "availability_365                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "wozE9t5VeXus",
        "outputId": "002b15be-9d59-40ab-f739-ef01eca11b23"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     id                                              name  host_id  \\\n",
              "0  2539                Clean & quiet apt home by the park     2787   \n",
              "1  2595                             Skylit Midtown Castle     2845   \n",
              "3  3831                   Cozy Entire Floor of Brownstone     4869   \n",
              "4  5022  Entire Apt: Spacious Studio/Loft by central park     7192   \n",
              "5  5099         Large Cozy 1 BR Apartment In Midtown East     7322   \n",
              "\n",
              "     host_name neighbourhood_group neighbourhood  latitude  longitude  \\\n",
              "0         John            Brooklyn    Kensington  40.64749  -73.97237   \n",
              "1     Jennifer           Manhattan       Midtown  40.75362  -73.98377   \n",
              "3  LisaRoxanne            Brooklyn  Clinton Hill  40.68514  -73.95976   \n",
              "4        Laura           Manhattan   East Harlem  40.79851  -73.94399   \n",
              "5        Chris           Manhattan   Murray Hill  40.74767  -73.97500   \n",
              "\n",
              "         room_type  price  minimum_nights  number_of_reviews last_review  \\\n",
              "0     Private room    149               1                  9  2018-10-19   \n",
              "1  Entire home/apt    225               1                 45  2019-05-21   \n",
              "3  Entire home/apt     89               1                270  2019-07-05   \n",
              "4  Entire home/apt     80              10                  9  2018-11-19   \n",
              "5  Entire home/apt    200               3                 74  2019-06-22   \n",
              "\n",
              "   reviews_per_month  calculated_host_listings_count  availability_365  \n",
              "0               0.21                               6               365  \n",
              "1               0.38                               2               355  \n",
              "3               4.64                               1               194  \n",
              "4               0.10                               1                 0  \n",
              "5               0.59                               1               129  "
            ],
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              "\n",
              "  <div id=\"df-c66a43bc-60ef-4499-94e9-b1eded6aaf7e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>2539</td>\n",
              "      <td>Clean &amp; quiet apt home by the park</td>\n",
              "      <td>2787</td>\n",
              "      <td>John</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Kensington</td>\n",
              "      <td>40.64749</td>\n",
              "      <td>-73.97237</td>\n",
              "      <td>Private room</td>\n",
              "      <td>149</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>2018-10-19</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2595</td>\n",
              "      <td>Skylit Midtown Castle</td>\n",
              "      <td>2845</td>\n",
              "      <td>Jennifer</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Midtown</td>\n",
              "      <td>40.75362</td>\n",
              "      <td>-73.98377</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>225</td>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>2019-05-21</td>\n",
              "      <td>0.38</td>\n",
              "      <td>2</td>\n",
              "      <td>355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3831</td>\n",
              "      <td>Cozy Entire Floor of Brownstone</td>\n",
              "      <td>4869</td>\n",
              "      <td>LisaRoxanne</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Clinton Hill</td>\n",
              "      <td>40.68514</td>\n",
              "      <td>-73.95976</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>89</td>\n",
              "      <td>1</td>\n",
              "      <td>270</td>\n",
              "      <td>2019-07-05</td>\n",
              "      <td>4.64</td>\n",
              "      <td>1</td>\n",
              "      <td>194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5022</td>\n",
              "      <td>Entire Apt: Spacious Studio/Loft by central park</td>\n",
              "      <td>7192</td>\n",
              "      <td>Laura</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>East Harlem</td>\n",
              "      <td>40.79851</td>\n",
              "      <td>-73.94399</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>80</td>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
              "      <td>2018-11-19</td>\n",
              "      <td>0.10</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5099</td>\n",
              "      <td>Large Cozy 1 BR Apartment In Midtown East</td>\n",
              "      <td>7322</td>\n",
              "      <td>Chris</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Murray Hill</td>\n",
              "      <td>40.74767</td>\n",
              "      <td>-73.97500</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>200</td>\n",
              "      <td>3</td>\n",
              "      <td>74</td>\n",
              "      <td>2019-06-22</td>\n",
              "      <td>0.59</td>\n",
              "      <td>1</td>\n",
              "      <td>129</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c66a43bc-60ef-4499-94e9-b1eded6aaf7e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c66a43bc-60ef-4499-94e9-b1eded6aaf7e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c66a43bc-60ef-4499-94e9-b1eded6aaf7e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "  </button>\n",
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              "  .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-dfa3e2cc-418c-4fda-8bdd-d24c4e1f3fdf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 38821,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10693722,\n        \"min\": 2539,\n        \"max\": 36455809,\n        \"num_unique_values\": 38821,\n        \"samples\": [\n          4053517,\n          5849991,\n          894015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 38253,\n        \"samples\": [\n          \"Simple Suite with Private Bathroom\",\n          \"Stunning Elevator 1 BR Chelsea\",\n          \"Large Shared Studio (your own bed) - Luxury+Views!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"host_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 75897516,\n        \"min\": 2438,\n        \"max\": 273841667,\n        \"num_unique_values\": 30232,\n        \"samples\": [\n          11429272,\n          229450070,\n          200453889\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"host_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9885,\n        \"samples\": [\n          \"Marcin\",\n          \"Posh\",\n          \"Mary Kate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"neighbourhood_group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Manhattan\",\n          \"Bronx\",\n          \"Queens\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"neighbourhood\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 218,\n        \"samples\": [\n          \"East Elmhurst\",\n          \"Breezy Point\",\n          \"Whitestone\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05499074069781032,\n        \"min\": 40.50641,\n        \"max\": 40.91306,\n        \"num_unique_values\": 17436,\n        \"samples\": [\n          40.769,\n          40.68851,\n          40.69274\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04669296389930319,\n        \"min\": -74.24442,\n        \"max\": -73.71299,\n        \"num_unique_values\": 13639,\n        \"samples\": [\n          -73.94369,\n          -73.99923,\n          -73.97488\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"room_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Private room\",\n          \"Entire home/apt\",\n          \"Shared room\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 196,\n        \"min\": 0,\n        \"max\": 10000,\n        \"num_unique_values\": 581,\n        \"samples\": [\n          2995,\n          193,\n          211\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"minimum_nights\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 1,\n        \"max\": 1250,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          370,\n          70,\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"number_of_reviews\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 48,\n        \"min\": 1,\n        \"max\": 629,\n        \"num_unique_values\": 393,\n        \"samples\": [\n          467,\n          439,\n          153\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"last_review\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1764,\n        \"samples\": [\n          \"2018-05-17\",\n          \"2017-06-18\",\n          \"2019-02-27\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"reviews_per_month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6803277675005874,\n        \"min\": 0.01,\n        \"max\": 58.5,\n        \"num_unique_values\": 937,\n        \"samples\": [\n          1.7,\n          0.28,\n          2.14\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"calculated_host_listings_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 1,\n        \"max\": 327,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          37,\n          232,\n          121\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"availability_365\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 129,\n        \"min\": 0,\n        \"max\": 365,\n        \"num_unique_values\": 366,\n        \"samples\": [\n          282,\n          95,\n          347\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "source": [
        "# @title longitude vs price\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "data.plot(kind='scatter', x='longitude', y='price', s=32, alpha=.8)\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ],
      "cell_type": "code",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "2e20SGzFuZkM",
        "outputId": "efc7fa6d-f489-4a8c-ad85-71d04c5b87f4"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uJbIXuPlt_Cb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "source": [
        "# @title neighbourhood_group vs room_type\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "plt.subplots(figsize=(8, 8))\n",
        "df_2dhist = pd.DataFrame({\n",
        "    x_label: grp['room_type'].value_counts()\n",
        "    for x_label, grp in data.groupby('neighbourhood_group')\n",
        "})\n",
        "sns.heatmap(df_2dhist, cmap='viridis')\n",
        "plt.xlabel('neighbourhood_group')\n",
        "_ = plt.ylabel('room_type')"
      ],
      "cell_type": "code",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "Gn48nM3wt1ah",
        "outputId": "2961206c-0aa7-48ff-b22b-37ec6d05c031"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "vBCmclY3tF2A",
        "outputId": "2dde5d1e-73c7-4ccb-cf85-aaafb3892c5a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 id       host_id      latitude     longitude         price  \\\n",
              "count  3.882100e+04  3.882100e+04  38821.000000  38821.000000  38821.000000   \n",
              "mean   1.810081e+07  6.424582e+07     40.728129    -73.951149    142.332526   \n",
              "std    1.069372e+07  7.589752e+07      0.054991      0.046693    196.994756   \n",
              "min    2.539000e+03  2.438000e+03     40.506410    -74.244420      0.000000   \n",
              "25%    8.721444e+06  7.029525e+06     40.688640    -73.982460     69.000000   \n",
              "50%    1.887286e+07  2.837092e+07     40.721710    -73.954810    101.000000   \n",
              "75%    2.756746e+07  1.018905e+08     40.762990    -73.935020    170.000000   \n",
              "max    3.645581e+07  2.738417e+08     40.913060    -73.712990  10000.000000   \n",
              "\n",
              "       minimum_nights  number_of_reviews  reviews_per_month  \\\n",
              "count    38821.000000       38821.000000       38821.000000   \n",
              "mean         5.869220          29.290255           1.373229   \n",
              "std         17.389026          48.182900           1.680328   \n",
              "min          1.000000           1.000000           0.010000   \n",
              "25%          1.000000           3.000000           0.190000   \n",
              "50%          2.000000           9.000000           0.720000   \n",
              "75%          4.000000          33.000000           2.020000   \n",
              "max       1250.000000         629.000000          58.500000   \n",
              "\n",
              "       calculated_host_listings_count  availability_365  \n",
              "count                    38821.000000      38821.000000  \n",
              "mean                         5.166611        114.886299  \n",
              "std                         26.302954        129.529950  \n",
              "min                          1.000000          0.000000  \n",
              "25%                          1.000000          0.000000  \n",
              "50%                          1.000000         55.000000  \n",
              "75%                          2.000000        229.000000  \n",
              "max                        327.000000        365.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1aebd044-4ff0-4e27-8c6c-c0622c83ce41\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>3.882100e+04</td>\n",
              "      <td>3.882100e+04</td>\n",
              "      <td>38821.000000</td>\n",
              "      <td>38821.000000</td>\n",
              "      <td>38821.000000</td>\n",
              "      <td>38821.000000</td>\n",
              "      <td>38821.000000</td>\n",
              "      <td>38821.000000</td>\n",
              "      <td>38821.000000</td>\n",
              "      <td>38821.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.810081e+07</td>\n",
              "      <td>6.424582e+07</td>\n",
              "      <td>40.728129</td>\n",
              "      <td>-73.951149</td>\n",
              "      <td>142.332526</td>\n",
              "      <td>5.869220</td>\n",
              "      <td>29.290255</td>\n",
              "      <td>1.373229</td>\n",
              "      <td>5.166611</td>\n",
              "      <td>114.886299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.069372e+07</td>\n",
              "      <td>7.589752e+07</td>\n",
              "      <td>0.054991</td>\n",
              "      <td>0.046693</td>\n",
              "      <td>196.994756</td>\n",
              "      <td>17.389026</td>\n",
              "      <td>48.182900</td>\n",
              "      <td>1.680328</td>\n",
              "      <td>26.302954</td>\n",
              "      <td>129.529950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.539000e+03</td>\n",
              "      <td>2.438000e+03</td>\n",
              "      <td>40.506410</td>\n",
              "      <td>-74.244420</td>\n",
              "      <td>0.000000</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>8.721444e+06</td>\n",
              "      <td>7.029525e+06</td>\n",
              "      <td>40.688640</td>\n",
              "      <td>-73.982460</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.190000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.887286e+07</td>\n",
              "      <td>2.837092e+07</td>\n",
              "      <td>40.721710</td>\n",
              "      <td>-73.954810</td>\n",
              "      <td>101.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.720000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>55.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.756746e+07</td>\n",
              "      <td>1.018905e+08</td>\n",
              "      <td>40.762990</td>\n",
              "      <td>-73.935020</td>\n",
              "      <td>170.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>2.020000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>229.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.645581e+07</td>\n",
              "      <td>2.738417e+08</td>\n",
              "      <td>40.913060</td>\n",
              "      <td>-73.712990</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>1250.000000</td>\n",
              "      <td>629.000000</td>\n",
              "      <td>58.500000</td>\n",
              "      <td>327.000000</td>\n",
              "      <td>365.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1aebd044-4ff0-4e27-8c6c-c0622c83ce41')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-1aebd044-4ff0-4e27-8c6c-c0622c83ce41 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1aebd044-4ff0-4e27-8c6c-c0622c83ce41');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-568e735b-d820-40dd-88dc-4f0421c4986b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-568e735b-d820-40dd-88dc-4f0421c4986b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12815203.076474728,\n        \"min\": 2539.0,\n        \"max\": 36455809.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          18100808.215295844,\n          18872858.0,\n          38821.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"host_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 91119533.3769393,\n        \"min\": 2438.0,\n        \"max\": 273841667.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          64245818.636974834,\n          28370925.0,\n          38821.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13712.960711406142,\n        \"min\": 0.05499074069781032,\n        \"max\": 38821.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          40.72812912547333,\n          40.72171,\n          38821.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13747.73252997355,\n        \"min\": -74.24442,\n        \"max\": 38821.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -73.95114881816544,\n          -73.95481,\n          38821.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13632.354729791858,\n        \"min\": 0.0,\n        \"max\": 38821.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          142.33252621004095,\n          101.0,\n          38821.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"minimum_nights\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13667.52673972275,\n        \"min\": 1.0,\n        \"max\": 38821.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          38821.0,\n          5.869220267381056,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"number_of_reviews\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13688.955763760327,\n        \"min\": 1.0,\n        \"max\": 38821.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          29.290255274207258,\n          9.0,\n          38821.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"reviews_per_month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13722.053529411975,\n        \"min\": 0.01,\n        \"max\": 38821.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.3732291800829448,\n          0.72,\n          38821.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"calculated_host_listings_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13707.400835485509,\n        \"min\": 1.0,\n        \"max\": 38821.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          38821.0,\n          5.16661085494964,\n          327.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"availability_365\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13680.718191379134,\n        \"min\": 0.0,\n        \"max\": 38821.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          38821.0,\n          114.88629865279101,\n          229.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    }
  ]
}