NYC_Airbnb_DataAnalysis.ipynb

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        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import datetime\n",
        "%matplotlib inline"
      ]
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      "source": [
        "nyc_logs = pd.read_csv(\"/content/AB_NYC_2019.csv\")\n",
        "nyc_logs.head()"
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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>-73.95976</td>\n",
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              "      <td>2019-07-05</td>\n",
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              "             id                                               name   host_id  \\\n",
              "48890  36484665    Charming one bedroom - newly renovated rowhouse   8232441   \n",
              "48891  36485057      Affordable room in Bushwick/East Williamsburg   6570630   \n",
              "48892  36485431            Sunny Studio at Historical Neighborhood  23492952   \n",
              "48893  36485609               43rd St. Time Square-cozy single bed  30985759   \n",
              "48894  36487245  Trendy duplex in the very heart of Hell's Kitchen  68119814   \n",
              "\n",
              "           host_name neighbourhood_group       neighbourhood  latitude  \\\n",
              "48890        Sabrina            Brooklyn  Bedford-Stuyvesant  40.67853   \n",
              "48891        Marisol            Brooklyn            Bushwick  40.70184   \n",
              "48892  Ilgar & Aysel           Manhattan              Harlem  40.81475   \n",
              "48893            Taz           Manhattan      Hell's Kitchen  40.75751   \n",
              "48894     Christophe           Manhattan      Hell's Kitchen  40.76404   \n",
              "\n",
              "       longitude        room_type  price  minimum_nights  number_of_reviews  \\\n",
              "48890  -73.94995     Private room     70               2                  0   \n",
              "48891  -73.93317     Private room     40               4                  0   \n",
              "48892  -73.94867  Entire home/apt    115              10                  0   \n",
              "48893  -73.99112      Shared room     55               1                  0   \n",
              "48894  -73.98933     Private room     90               7                  0   \n",
              "\n",
              "      last_review  reviews_per_month  calculated_host_listings_count  \\\n",
              "48890         NaN                NaN                               2   \n",
              "48891         NaN                NaN                               2   \n",
              "48892         NaN                NaN                               1   \n",
              "48893         NaN                NaN                               6   \n",
              "48894         NaN                NaN                               1   \n",
              "\n",
              "       availability_365  \n",
              "48890                 9  \n",
              "48891                36  \n",
              "48892                27  \n",
              "48893                 2  \n",
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          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N-oUAjbdp9S-",
        "outputId": "90c90de9-2a58-41f0-b439-d52a0bac3891"
      },
      "execution_count": null,
      "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": "markdown",
      "source": [
        "# Insights\n",
        "---\n",
        "There are 15 columns and about 48894 rows in the given dataset.\n",
        "According to the .info()\n",
        "The NaN/Null values in the data are:\n",
        "1. Name\n",
        "2. Host-Name\n",
        "3. Last-Review\n",
        "4. Reviews-per-month\n",
        "\n",
        "Reasons for the missing values:\n",
        "1. The host or the person might have forgetten to enter the values in the dataset.\n",
        "2. Hardware or Software Errors in the collecion may affect the data.\n",
        "\n",
        "---\n",
        "Therefore, we can either remove these rows or we can fill them with the mean values of the column.\n",
        "\n"
      ],
      "metadata": {
        "id": "IHPzgaSHqX6v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Identifying and Replacing the Missing Values**"
      ],
      "metadata": {
        "id": "EBRf-Wt5sq8r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Rows with missing values for Name\")\n",
        "print(nyc_logs['name'].isnull().value_counts())\n",
        "print(\"\\n\")\n",
        "print(\"Rows with missing value for Host_Name\")\n",
        "print(nyc_logs['host_name'].isnull().value_counts())\n",
        "print(\"\\n\")\n",
        "print(\"Rows with missing value for Last_Review\")\n",
        "print(nyc_logs['last_review'].isnull().value_counts())\n",
        "print(\"\\n\")\n",
        "print(\"Rows with missing values with Reviews_Per_Month\")\n",
        "print(nyc_logs['reviews_per_month'].isnull().value_counts())"
      ],
      "metadata": {
        "id": "XLFmKEWpqCD8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d88ff685-86cc-42f6-cbd6-bbd33bb14df1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rows with missing values for Name\n",
            "False    48879\n",
            "True        16\n",
            "Name: name, dtype: int64\n",
            "\n",
            "\n",
            "Rows with missing value for Host_Name\n",
            "False    48874\n",
            "True        21\n",
            "Name: host_name, dtype: int64\n",
            "\n",
            "\n",
            "Rows with missing value for Last_Review\n",
            "False    38843\n",
            "True     10052\n",
            "Name: last_review, dtype: int64\n",
            "\n",
            "\n",
            "Rows with missing values with Reviews_Per_Month\n",
            "False    38843\n",
            "True     10052\n",
            "Name: reviews_per_month, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Replacing Missing Values**"
      ],
      "metadata": {
        "id": "VuebR6Gb1khG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs['reviews_per_month'] = nyc_logs['reviews_per_month'].fillna(0.0)\n",
        "nyc_logs.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "adoPdkC6zCjd",
        "outputId": "59e4983f-1a54-419a-8c5d-850d98588619"
      },
      "execution_count": null,
      "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",
              "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",
              "5   5099         Large Cozy 1 BR Apartment In Midtown East     7322   \n",
              "6   5121                                   BlissArtsSpace!     7356   \n",
              "7   5178                  Large Furnished Room Near B'way      8967   \n",
              "8   5203                Cozy Clean Guest Room - Family Apt     7490   \n",
              "9   5238                Cute & Cozy Lower East Side 1 bdrm     7549   \n",
              "10  5295                  Beautiful 1br on Upper West Side     7702   \n",
              "11  5441                   Central Manhattan/near Broadway     7989   \n",
              "12  5803    Lovely Room 1, Garden, Best Area, Legal rental     9744   \n",
              "13  6021  Wonderful Guest Bedroom in Manhattan for SINGLES    11528   \n",
              "14  6090                     West Village Nest - Superhost    11975   \n",
              "15  6848                  Only 2 stops to Manhattan studio    15991   \n",
              "16  7097                 Perfect for Your Parents + Garden    17571   \n",
              "17  7322                                   Chelsea Perfect    18946   \n",
              "18  7726   Hip Historic Brownstone Apartment with Backyard    20950   \n",
              "19  7750                 Huge 2 BR Upper East  Cental Park    17985   \n",
              "\n",
              "           host_name neighbourhood_group       neighbourhood  latitude  \\\n",
              "0               John            Brooklyn          Kensington  40.64749   \n",
              "1           Jennifer           Manhattan             Midtown  40.75362   \n",
              "2          Elisabeth           Manhattan              Harlem  40.80902   \n",
              "3        LisaRoxanne            Brooklyn        Clinton Hill  40.68514   \n",
              "4              Laura           Manhattan         East Harlem  40.79851   \n",
              "5              Chris           Manhattan         Murray Hill  40.74767   \n",
              "6              Garon            Brooklyn  Bedford-Stuyvesant  40.68688   \n",
              "7           Shunichi           Manhattan      Hell's Kitchen  40.76489   \n",
              "8          MaryEllen           Manhattan     Upper West Side  40.80178   \n",
              "9                Ben           Manhattan           Chinatown  40.71344   \n",
              "10              Lena           Manhattan     Upper West Side  40.80316   \n",
              "11              Kate           Manhattan      Hell's Kitchen  40.76076   \n",
              "12            Laurie            Brooklyn         South Slope  40.66829   \n",
              "13           Claudio           Manhattan     Upper West Side  40.79826   \n",
              "14             Alina           Manhattan        West Village  40.73530   \n",
              "15     Allen & Irina            Brooklyn        Williamsburg  40.70837   \n",
              "16              Jane            Brooklyn         Fort Greene  40.69169   \n",
              "17              Doti           Manhattan             Chelsea  40.74192   \n",
              "18  Adam And Charity            Brooklyn       Crown Heights  40.67592   \n",
              "19              Sing           Manhattan         East Harlem  40.79685   \n",
              "\n",
              "    longitude        room_type  price  minimum_nights  number_of_reviews  \\\n",
              "0   -73.97237     Private room    149               1                  9   \n",
              "1   -73.98377  Entire home/apt    225               1                 45   \n",
              "2   -73.94190     Private room    150               3                  0   \n",
              "3   -73.95976  Entire home/apt     89               1                270   \n",
              "4   -73.94399  Entire home/apt     80              10                  9   \n",
              "5   -73.97500  Entire home/apt    200               3                 74   \n",
              "6   -73.95596     Private room     60              45                 49   \n",
              "7   -73.98493     Private room     79               2                430   \n",
              "8   -73.96723     Private room     79               2                118   \n",
              "9   -73.99037  Entire home/apt    150               1                160   \n",
              "10  -73.96545  Entire home/apt    135               5                 53   \n",
              "11  -73.98867     Private room     85               2                188   \n",
              "12  -73.98779     Private room     89               4                167   \n",
              "13  -73.96113     Private room     85               2                113   \n",
              "14  -74.00525  Entire home/apt    120              90                 27   \n",
              "15  -73.95352  Entire home/apt    140               2                148   \n",
              "16  -73.97185  Entire home/apt    215               2                198   \n",
              "17  -73.99501     Private room    140               1                260   \n",
              "18  -73.94694  Entire home/apt     99               3                 53   \n",
              "19  -73.94872  Entire home/apt    190               7                  0   \n",
              "\n",
              "   last_review  reviews_per_month  calculated_host_listings_count  \\\n",
              "0   2018-10-19               0.21                               6   \n",
              "1   2019-05-21               0.38                               2   \n",
              "2          NaN               0.00                               1   \n",
              "3   2019-07-05               4.64                               1   \n",
              "4   2018-11-19               0.10                               1   \n",
              "5   2019-06-22               0.59                               1   \n",
              "6   2017-10-05               0.40                               1   \n",
              "7   2019-06-24               3.47                               1   \n",
              "8   2017-07-21               0.99                               1   \n",
              "9   2019-06-09               1.33                               4   \n",
              "10  2019-06-22               0.43                               1   \n",
              "11  2019-06-23               1.50                               1   \n",
              "12  2019-06-24               1.34                               3   \n",
              "13  2019-07-05               0.91                               1   \n",
              "14  2018-10-31               0.22                               1   \n",
              "15  2019-06-29               1.20                               1   \n",
              "16  2019-06-28               1.72                               1   \n",
              "17  2019-07-01               2.12                               1   \n",
              "18  2019-06-22               4.44                               1   \n",
              "19         NaN               0.00                               2   \n",
              "\n",
              "    availability_365  \n",
              "0                365  \n",
              "1                355  \n",
              "2                365  \n",
              "3                194  \n",
              "4                  0  \n",
              "5                129  \n",
              "6                  0  \n",
              "7                220  \n",
              "8                  0  \n",
              "9                188  \n",
              "10                 6  \n",
              "11                39  \n",
              "12               314  \n",
              "13               333  \n",
              "14                 0  \n",
              "15                46  \n",
              "16               321  \n",
              "17                12  \n",
              "18                21  \n",
              "19               249  "
            ],
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              "    <div class=\"colab-df-container\">\n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>name</th>\n",
              "      <th>host_id</th>\n",
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              "      <th>neighbourhood_group</th>\n",
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              "    </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",
              "      <td>0.21</td>\n",
              "      <td>6</td>\n",
              "      <td>365</td>\n",
              "    </tr>\n",
              "    <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>2</th>\n",
              "      <td>3647</td>\n",
              "      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
              "      <td>4632</td>\n",
              "      <td>Elisabeth</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Harlem</td>\n",
              "      <td>40.80902</td>\n",
              "      <td>-73.94190</td>\n",
              "      <td>Private room</td>\n",
              "      <td>150</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>365</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",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5121</td>\n",
              "      <td>BlissArtsSpace!</td>\n",
              "      <td>7356</td>\n",
              "      <td>Garon</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>40.68688</td>\n",
              "      <td>-73.95596</td>\n",
              "      <td>Private room</td>\n",
              "      <td>60</td>\n",
              "      <td>45</td>\n",
              "      <td>49</td>\n",
              "      <td>2017-10-05</td>\n",
              "      <td>0.40</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5178</td>\n",
              "      <td>Large Furnished Room Near B'way</td>\n",
              "      <td>8967</td>\n",
              "      <td>Shunichi</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Hell's Kitchen</td>\n",
              "      <td>40.76489</td>\n",
              "      <td>-73.98493</td>\n",
              "      <td>Private room</td>\n",
              "      <td>79</td>\n",
              "      <td>2</td>\n",
              "      <td>430</td>\n",
              "      <td>2019-06-24</td>\n",
              "      <td>3.47</td>\n",
              "      <td>1</td>\n",
              "      <td>220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>5203</td>\n",
              "      <td>Cozy Clean Guest Room - Family Apt</td>\n",
              "      <td>7490</td>\n",
              "      <td>MaryEllen</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Upper West Side</td>\n",
              "      <td>40.80178</td>\n",
              "      <td>-73.96723</td>\n",
              "      <td>Private room</td>\n",
              "      <td>79</td>\n",
              "      <td>2</td>\n",
              "      <td>118</td>\n",
              "      <td>2017-07-21</td>\n",
              "      <td>0.99</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>5238</td>\n",
              "      <td>Cute &amp; Cozy Lower East Side 1 bdrm</td>\n",
              "      <td>7549</td>\n",
              "      <td>Ben</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Chinatown</td>\n",
              "      <td>40.71344</td>\n",
              "      <td>-73.99037</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>150</td>\n",
              "      <td>1</td>\n",
              "      <td>160</td>\n",
              "      <td>2019-06-09</td>\n",
              "      <td>1.33</td>\n",
              "      <td>4</td>\n",
              "      <td>188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>5295</td>\n",
              "      <td>Beautiful 1br on Upper West Side</td>\n",
              "      <td>7702</td>\n",
              "      <td>Lena</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Upper West Side</td>\n",
              "      <td>40.80316</td>\n",
              "      <td>-73.96545</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>135</td>\n",
              "      <td>5</td>\n",
              "      <td>53</td>\n",
              "      <td>2019-06-22</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>5441</td>\n",
              "      <td>Central Manhattan/near Broadway</td>\n",
              "      <td>7989</td>\n",
              "      <td>Kate</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Hell's Kitchen</td>\n",
              "      <td>40.76076</td>\n",
              "      <td>-73.98867</td>\n",
              "      <td>Private room</td>\n",
              "      <td>85</td>\n",
              "      <td>2</td>\n",
              "      <td>188</td>\n",
              "      <td>2019-06-23</td>\n",
              "      <td>1.50</td>\n",
              "      <td>1</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>5803</td>\n",
              "      <td>Lovely Room 1, Garden, Best Area, Legal rental</td>\n",
              "      <td>9744</td>\n",
              "      <td>Laurie</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>South Slope</td>\n",
              "      <td>40.66829</td>\n",
              "      <td>-73.98779</td>\n",
              "      <td>Private room</td>\n",
              "      <td>89</td>\n",
              "      <td>4</td>\n",
              "      <td>167</td>\n",
              "      <td>2019-06-24</td>\n",
              "      <td>1.34</td>\n",
              "      <td>3</td>\n",
              "      <td>314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>6021</td>\n",
              "      <td>Wonderful Guest Bedroom in Manhattan for SINGLES</td>\n",
              "      <td>11528</td>\n",
              "      <td>Claudio</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Upper West Side</td>\n",
              "      <td>40.79826</td>\n",
              "      <td>-73.96113</td>\n",
              "      <td>Private room</td>\n",
              "      <td>85</td>\n",
              "      <td>2</td>\n",
              "      <td>113</td>\n",
              "      <td>2019-07-05</td>\n",
              "      <td>0.91</td>\n",
              "      <td>1</td>\n",
              "      <td>333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>6090</td>\n",
              "      <td>West Village Nest - Superhost</td>\n",
              "      <td>11975</td>\n",
              "      <td>Alina</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>West Village</td>\n",
              "      <td>40.73530</td>\n",
              "      <td>-74.00525</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>120</td>\n",
              "      <td>90</td>\n",
              "      <td>27</td>\n",
              "      <td>2018-10-31</td>\n",
              "      <td>0.22</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>6848</td>\n",
              "      <td>Only 2 stops to Manhattan studio</td>\n",
              "      <td>15991</td>\n",
              "      <td>Allen &amp; Irina</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Williamsburg</td>\n",
              "      <td>40.70837</td>\n",
              "      <td>-73.95352</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>140</td>\n",
              "      <td>2</td>\n",
              "      <td>148</td>\n",
              "      <td>2019-06-29</td>\n",
              "      <td>1.20</td>\n",
              "      <td>1</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>7097</td>\n",
              "      <td>Perfect for Your Parents + Garden</td>\n",
              "      <td>17571</td>\n",
              "      <td>Jane</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Fort Greene</td>\n",
              "      <td>40.69169</td>\n",
              "      <td>-73.97185</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>215</td>\n",
              "      <td>2</td>\n",
              "      <td>198</td>\n",
              "      <td>2019-06-28</td>\n",
              "      <td>1.72</td>\n",
              "      <td>1</td>\n",
              "      <td>321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>7322</td>\n",
              "      <td>Chelsea Perfect</td>\n",
              "      <td>18946</td>\n",
              "      <td>Doti</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Chelsea</td>\n",
              "      <td>40.74192</td>\n",
              "      <td>-73.99501</td>\n",
              "      <td>Private room</td>\n",
              "      <td>140</td>\n",
              "      <td>1</td>\n",
              "      <td>260</td>\n",
              "      <td>2019-07-01</td>\n",
              "      <td>2.12</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>7726</td>\n",
              "      <td>Hip Historic Brownstone Apartment with Backyard</td>\n",
              "      <td>20950</td>\n",
              "      <td>Adam And Charity</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Crown Heights</td>\n",
              "      <td>40.67592</td>\n",
              "      <td>-73.94694</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>99</td>\n",
              "      <td>3</td>\n",
              "      <td>53</td>\n",
              "      <td>2019-06-22</td>\n",
              "      <td>4.44</td>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>7750</td>\n",
              "      <td>Huge 2 BR Upper East  Cental Park</td>\n",
              "      <td>17985</td>\n",
              "      <td>Sing</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>East Harlem</td>\n",
              "      <td>40.79685</td>\n",
              "      <td>-73.94872</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>190</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>249</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(nyc_logs['reviews_per_month'].isnull().value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r2QOtO9z5uJM",
        "outputId": "5f1b0a4e-e85b-4d85-edc9-95c852d89bcb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "False    48895\n",
            "Name: reviews_per_month, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs['last_review'] = nyc_logs['last_review'].fillna('2016-12-31')\n",
        "nyc_logs.head(10)"
      ],
      "metadata": {
        "id": "i2TuM4bw5I3W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 774
        },
        "outputId": "5f2eb846-bb1f-4c50-e677-0f061c15da31"
      },
      "execution_count": null,
      "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",
              "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",
              "5  5099         Large Cozy 1 BR Apartment In Midtown East     7322   \n",
              "6  5121                                   BlissArtsSpace!     7356   \n",
              "7  5178                  Large Furnished Room Near B'way      8967   \n",
              "8  5203                Cozy Clean Guest Room - Family Apt     7490   \n",
              "9  5238                Cute & Cozy Lower East Side 1 bdrm     7549   \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",
              "5        Chris           Manhattan         Murray Hill  40.74767  -73.97500   \n",
              "6        Garon            Brooklyn  Bedford-Stuyvesant  40.68688  -73.95596   \n",
              "7     Shunichi           Manhattan      Hell's Kitchen  40.76489  -73.98493   \n",
              "8    MaryEllen           Manhattan     Upper West Side  40.80178  -73.96723   \n",
              "9          Ben           Manhattan           Chinatown  40.71344  -73.99037   \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  2016-12-31   \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",
              "6     Private room     60              45                 49  2017-10-05   \n",
              "7     Private room     79               2                430  2019-06-24   \n",
              "8     Private room     79               2                118  2017-07-21   \n",
              "9  Entire home/apt    150               1                160  2019-06-09   \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               0.00                               1               365  \n",
              "3               4.64                               1               194  \n",
              "4               0.10                               1                 0  \n",
              "5               0.59                               1               129  \n",
              "6               0.40                               1                 0  \n",
              "7               3.47                               1               220  \n",
              "8               0.99                               1                 0  \n",
              "9               1.33                               4               188  "
            ],
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              "      <th></th>\n",
              "      <th>id</th>\n",
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              "      <th>0</th>\n",
              "      <td>2539</td>\n",
              "      <td>Clean &amp; quiet apt home by the park</td>\n",
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              "      <td>2595</td>\n",
              "      <td>Skylit Midtown Castle</td>\n",
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              "      <td>Jennifer</td>\n",
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              "      <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>2</th>\n",
              "      <td>3647</td>\n",
              "      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
              "      <td>4632</td>\n",
              "      <td>Elisabeth</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Harlem</td>\n",
              "      <td>40.80902</td>\n",
              "      <td>-73.94190</td>\n",
              "      <td>Private room</td>\n",
              "      <td>150</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2016-12-31</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>365</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",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5121</td>\n",
              "      <td>BlissArtsSpace!</td>\n",
              "      <td>7356</td>\n",
              "      <td>Garon</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>40.68688</td>\n",
              "      <td>-73.95596</td>\n",
              "      <td>Private room</td>\n",
              "      <td>60</td>\n",
              "      <td>45</td>\n",
              "      <td>49</td>\n",
              "      <td>2017-10-05</td>\n",
              "      <td>0.40</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5178</td>\n",
              "      <td>Large Furnished Room Near B'way</td>\n",
              "      <td>8967</td>\n",
              "      <td>Shunichi</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Hell's Kitchen</td>\n",
              "      <td>40.76489</td>\n",
              "      <td>-73.98493</td>\n",
              "      <td>Private room</td>\n",
              "      <td>79</td>\n",
              "      <td>2</td>\n",
              "      <td>430</td>\n",
              "      <td>2019-06-24</td>\n",
              "      <td>3.47</td>\n",
              "      <td>1</td>\n",
              "      <td>220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>5203</td>\n",
              "      <td>Cozy Clean Guest Room - Family Apt</td>\n",
              "      <td>7490</td>\n",
              "      <td>MaryEllen</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Upper West Side</td>\n",
              "      <td>40.80178</td>\n",
              "      <td>-73.96723</td>\n",
              "      <td>Private room</td>\n",
              "      <td>79</td>\n",
              "      <td>2</td>\n",
              "      <td>118</td>\n",
              "      <td>2017-07-21</td>\n",
              "      <td>0.99</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>5238</td>\n",
              "      <td>Cute &amp; Cozy Lower East Side 1 bdrm</td>\n",
              "      <td>7549</td>\n",
              "      <td>Ben</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Chinatown</td>\n",
              "      <td>40.71344</td>\n",
              "      <td>-73.99037</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>150</td>\n",
              "      <td>1</td>\n",
              "      <td>160</td>\n",
              "      <td>2019-06-09</td>\n",
              "      <td>1.33</td>\n",
              "      <td>4</td>\n",
              "      <td>188</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-278b9284-fdaf-474b-b1c5-b2f8abba2fde')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-278b9284-fdaf-474b-b1c5-b2f8abba2fde 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-278b9284-fdaf-474b-b1c5-b2f8abba2fde');\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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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking, any NULL values in the data\n",
        "nyc_logs['last_review'].isnull().value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5wq0mJ5p2z9s",
        "outputId": "6b22657f-99b8-443c-f5af-ca8733e97bf7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    48895\n",
              "Name: last_review, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Deleting the Missing Values from the Data**"
      ],
      "metadata": {
        "id": "PywVy6c85Z6E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs['name'].isnull().value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tWPfTRGA5nAD",
        "outputId": "007e5330-8369-43bc-c4f2-a859f8e3e3a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    48879\n",
              "True        16\n",
              "Name: name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We deleted the NULL rows in Column 'Name', as there was no values for those rows, thus we cannot depict the name and thus the whole row is of no use."
      ],
      "metadata": {
        "id": "F26hF_fk51z9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs.dropna(subset=['name'], inplace = True)\n",
        "nyc_logs['name'].isnull().value_counts()\n",
        "# Here we have deleted the NULL values from the column = 'name'."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gaAWY0875UxG",
        "outputId": "5863fbe9-6ea3-4096-e20a-0eb8d116f749"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    48879\n",
              "Name: name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs['host_name'].isnull().value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hzi4UNRK5uWu",
        "outputId": "2906ff43-d941-4716-b917-2db149850271"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    48858\n",
              "True        21\n",
              "Name: host_name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs.dropna(subset=['host_name'], inplace=True)\n",
        "nyc_logs['host_name'].isnull().value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a46VOEsr63Sx",
        "outputId": "6e17d459-57f2-4edc-c072-3269cecdb95d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    48858\n",
              "Name: host_name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AiaE0lfn7FJT",
        "outputId": "c40b09d9-a9e7-49f2-eab0-a0507d85bcbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 48858 entries, 0 to 48894\n",
            "Data columns (total 16 columns):\n",
            " #   Column                          Non-Null Count  Dtype  \n",
            "---  ------                          --------------  -----  \n",
            " 0   id                              48858 non-null  int64  \n",
            " 1   name                            48858 non-null  object \n",
            " 2   host_id                         48858 non-null  int64  \n",
            " 3   host_name                       48858 non-null  object \n",
            " 4   neighbourhood_group             48858 non-null  object \n",
            " 5   neighbourhood                   48858 non-null  object \n",
            " 6   latitude                        48858 non-null  float64\n",
            " 7   longitude                       48858 non-null  float64\n",
            " 8   room_type                       48858 non-null  object \n",
            " 9   price                           48858 non-null  int64  \n",
            " 10  minimum_nights                  48858 non-null  int64  \n",
            " 11  number_of_reviews               48858 non-null  int64  \n",
            " 12  last_review                     48858 non-null  object \n",
            " 13  reviews_per_month               48858 non-null  float64\n",
            " 14  calculated_host_listings_count  48858 non-null  int64  \n",
            " 15  availability_365                48858 non-null  int64  \n",
            "dtypes: float64(3), int64(7), object(6)\n",
            "memory usage: 6.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Correcting the format of Data**"
      ],
      "metadata": {
        "id": "WB8DhjP48ZKn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs['last_review'] = pd.to_datetime(nyc_logs['last_review'])"
      ],
      "metadata": {
        "id": "1L16meG-9CM1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XgXoHgzz8OIv",
        "outputId": "f96f704a-77d0-4613-dc6d-e7a7f159c925"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 48858 entries, 0 to 48894\n",
            "Data columns (total 16 columns):\n",
            " #   Column                          Non-Null Count  Dtype         \n",
            "---  ------                          --------------  -----         \n",
            " 0   id                              48858 non-null  int64         \n",
            " 1   name                            48858 non-null  object        \n",
            " 2   host_id                         48858 non-null  int64         \n",
            " 3   host_name                       48858 non-null  object        \n",
            " 4   neighbourhood_group             48858 non-null  object        \n",
            " 5   neighbourhood                   48858 non-null  object        \n",
            " 6   latitude                        48858 non-null  float64       \n",
            " 7   longitude                       48858 non-null  float64       \n",
            " 8   room_type                       48858 non-null  object        \n",
            " 9   price                           48858 non-null  int64         \n",
            " 10  minimum_nights                  48858 non-null  int64         \n",
            " 11  number_of_reviews               48858 non-null  int64         \n",
            " 12  last_review                     48858 non-null  datetime64[ns]\n",
            " 13  reviews_per_month               48858 non-null  float64       \n",
            " 14  calculated_host_listings_count  48858 non-null  int64         \n",
            " 15  availability_365                48858 non-null  int64         \n",
            "dtypes: datetime64[ns](1), float64(3), int64(7), object(5)\n",
            "memory usage: 6.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs['host_id'] = nyc_logs['host_id'].astype(str)"
      ],
      "metadata": {
        "id": "DeubYHq39HIj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ho8S0Nqc9n5U",
        "outputId": "a068267b-63d4-4177-ffe2-1fce3bbd01d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 48858 entries, 0 to 48894\n",
            "Data columns (total 16 columns):\n",
            " #   Column                          Non-Null Count  Dtype         \n",
            "---  ------                          --------------  -----         \n",
            " 0   id                              48858 non-null  int64         \n",
            " 1   name                            48858 non-null  object        \n",
            " 2   host_id                         48858 non-null  object        \n",
            " 3   host_name                       48858 non-null  object        \n",
            " 4   neighbourhood_group             48858 non-null  object        \n",
            " 5   neighbourhood                   48858 non-null  object        \n",
            " 6   latitude                        48858 non-null  float64       \n",
            " 7   longitude                       48858 non-null  float64       \n",
            " 8   room_type                       48858 non-null  object        \n",
            " 9   price                           48858 non-null  int64         \n",
            " 10  minimum_nights                  48858 non-null  int64         \n",
            " 11  number_of_reviews               48858 non-null  int64         \n",
            " 12  last_review                     48858 non-null  datetime64[ns]\n",
            " 13  reviews_per_month               48858 non-null  float64       \n",
            " 14  calculated_host_listings_count  48858 non-null  int64         \n",
            " 15  availability_365                48858 non-null  int64         \n",
            "dtypes: datetime64[ns](1), float64(3), int64(6), object(6)\n",
            "memory usage: 6.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Creating New Features to add in the Data**"
      ],
      "metadata": {
        "id": "pqBz159-9z5X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating the column Month with the Name of Month\n",
        "nyc_logs['month'] = nyc_logs['last_review'].apply(lambda time : time.month)"
      ],
      "metadata": {
        "id": "V0tU1bF99rG_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KUOl2YWC-Iil",
        "outputId": "eb74b609-547f-49a5-8b84-e3a735d6910a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 48858 entries, 0 to 48894\n",
            "Data columns (total 17 columns):\n",
            " #   Column                          Non-Null Count  Dtype         \n",
            "---  ------                          --------------  -----         \n",
            " 0   id                              48858 non-null  int64         \n",
            " 1   name                            48858 non-null  object        \n",
            " 2   host_id                         48858 non-null  object        \n",
            " 3   host_name                       48858 non-null  object        \n",
            " 4   neighbourhood_group             48858 non-null  object        \n",
            " 5   neighbourhood                   48858 non-null  object        \n",
            " 6   latitude                        48858 non-null  float64       \n",
            " 7   longitude                       48858 non-null  float64       \n",
            " 8   room_type                       48858 non-null  object        \n",
            " 9   price                           48858 non-null  int64         \n",
            " 10  minimum_nights                  48858 non-null  int64         \n",
            " 11  number_of_reviews               48858 non-null  int64         \n",
            " 12  last_review                     48858 non-null  datetime64[ns]\n",
            " 13  reviews_per_month               48858 non-null  float64       \n",
            " 14  calculated_host_listings_count  48858 non-null  int64         \n",
            " 15  availability_365                48858 non-null  int64         \n",
            " 16  month                           48858 non-null  int64         \n",
            "dtypes: datetime64[ns](1), float64(3), int64(7), object(6)\n",
            "memory usage: 6.7+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "month_name = {1:'January', 2:'February', 3:'March', 4:'April', 5:'May', 6:'June', 7:'July', 8:'August', 9:'September', 10:'October', 11:'November', 12:'December'}"
      ],
      "metadata": {
        "id": "1bSL-riD-MdO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs['month'] = nyc_logs['month'].map(month_name)"
      ],
      "metadata": {
        "id": "0fOUFAWV-08G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 774
        },
        "id": "_pkeBj5c-8eq",
        "outputId": "ab6de59c-542c-4a5e-a22d-e87f6673c5cd"
      },
      "execution_count": null,
      "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",
              "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",
              "5  5099         Large Cozy 1 BR Apartment In Midtown East    7322   \n",
              "6  5121                                   BlissArtsSpace!    7356   \n",
              "7  5178                  Large Furnished Room Near B'way     8967   \n",
              "8  5203                Cozy Clean Guest Room - Family Apt    7490   \n",
              "9  5238                Cute & Cozy Lower East Side 1 bdrm    7549   \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",
              "5        Chris           Manhattan         Murray Hill  40.74767  -73.97500   \n",
              "6        Garon            Brooklyn  Bedford-Stuyvesant  40.68688  -73.95596   \n",
              "7     Shunichi           Manhattan      Hell's Kitchen  40.76489  -73.98493   \n",
              "8    MaryEllen           Manhattan     Upper West Side  40.80178  -73.96723   \n",
              "9          Ben           Manhattan           Chinatown  40.71344  -73.99037   \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  2016-12-31   \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",
              "6     Private room     60              45                 49  2017-10-05   \n",
              "7     Private room     79               2                430  2019-06-24   \n",
              "8     Private room     79               2                118  2017-07-21   \n",
              "9  Entire home/apt    150               1                160  2019-06-09   \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               0.00                               1               365   \n",
              "3               4.64                               1               194   \n",
              "4               0.10                               1                 0   \n",
              "5               0.59                               1               129   \n",
              "6               0.40                               1                 0   \n",
              "7               3.47                               1               220   \n",
              "8               0.99                               1                 0   \n",
              "9               1.33                               4               188   \n",
              "\n",
              "      month  \n",
              "0   October  \n",
              "1       May  \n",
              "2  December  \n",
              "3      July  \n",
              "4  November  \n",
              "5      June  \n",
              "6   October  \n",
              "7      June  \n",
              "8      July  \n",
              "9      June  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ef04c7b7-7e95-406a-b80a-ac89040c2601\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "      <th>month</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",
              "      <td>0.21</td>\n",
              "      <td>6</td>\n",
              "      <td>365</td>\n",
              "      <td>October</td>\n",
              "    </tr>\n",
              "    <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",
              "      <td>May</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3647</td>\n",
              "      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
              "      <td>4632</td>\n",
              "      <td>Elisabeth</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Harlem</td>\n",
              "      <td>40.80902</td>\n",
              "      <td>-73.94190</td>\n",
              "      <td>Private room</td>\n",
              "      <td>150</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2016-12-31</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>365</td>\n",
              "      <td>December</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",
              "      <td>July</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",
              "      <td>November</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",
              "      <td>June</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5121</td>\n",
              "      <td>BlissArtsSpace!</td>\n",
              "      <td>7356</td>\n",
              "      <td>Garon</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>40.68688</td>\n",
              "      <td>-73.95596</td>\n",
              "      <td>Private room</td>\n",
              "      <td>60</td>\n",
              "      <td>45</td>\n",
              "      <td>49</td>\n",
              "      <td>2017-10-05</td>\n",
              "      <td>0.40</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>October</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5178</td>\n",
              "      <td>Large Furnished Room Near B'way</td>\n",
              "      <td>8967</td>\n",
              "      <td>Shunichi</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Hell's Kitchen</td>\n",
              "      <td>40.76489</td>\n",
              "      <td>-73.98493</td>\n",
              "      <td>Private room</td>\n",
              "      <td>79</td>\n",
              "      <td>2</td>\n",
              "      <td>430</td>\n",
              "      <td>2019-06-24</td>\n",
              "      <td>3.47</td>\n",
              "      <td>1</td>\n",
              "      <td>220</td>\n",
              "      <td>June</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>5203</td>\n",
              "      <td>Cozy Clean Guest Room - Family Apt</td>\n",
              "      <td>7490</td>\n",
              "      <td>MaryEllen</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Upper West Side</td>\n",
              "      <td>40.80178</td>\n",
              "      <td>-73.96723</td>\n",
              "      <td>Private room</td>\n",
              "      <td>79</td>\n",
              "      <td>2</td>\n",
              "      <td>118</td>\n",
              "      <td>2017-07-21</td>\n",
              "      <td>0.99</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>July</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>5238</td>\n",
              "      <td>Cute &amp; Cozy Lower East Side 1 bdrm</td>\n",
              "      <td>7549</td>\n",
              "      <td>Ben</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Chinatown</td>\n",
              "      <td>40.71344</td>\n",
              "      <td>-73.99037</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>150</td>\n",
              "      <td>1</td>\n",
              "      <td>160</td>\n",
              "      <td>2019-06-09</td>\n",
              "      <td>1.33</td>\n",
              "      <td>4</td>\n",
              "      <td>188</td>\n",
              "      <td>June</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Listing the Coordinated**"
      ],
      "metadata": {
        "id": "rarhgZ0e_Tzd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creaiting a DataFrame for the 'longitude and latitude'\n",
        "location_logs = nyc_logs[['latitude', 'longitude']]\n",
        "\n",
        "# Creating a list of tuples\n",
        "location_logs = location_logs.to_records(index=False)\n",
        "\n",
        "# There is now a column which have the listing named as Longitude and Latitude for the Data \n",
        "nyc_logs['listing_coordinates'] =   list(location_logs)"
      ],
      "metadata": {
        "id": "83Cf0cXt--6Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 774
        },
        "id": "gqRUBzmgAF-E",
        "outputId": "99a6ca5a-db7f-463d-b30e-764710367e90"
      },
      "execution_count": null,
      "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",
              "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",
              "5  5099         Large Cozy 1 BR Apartment In Midtown East    7322   \n",
              "6  5121                                   BlissArtsSpace!    7356   \n",
              "7  5178                  Large Furnished Room Near B'way     8967   \n",
              "8  5203                Cozy Clean Guest Room - Family Apt    7490   \n",
              "9  5238                Cute & Cozy Lower East Side 1 bdrm    7549   \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",
              "5        Chris           Manhattan         Murray Hill  40.74767  -73.97500   \n",
              "6        Garon            Brooklyn  Bedford-Stuyvesant  40.68688  -73.95596   \n",
              "7     Shunichi           Manhattan      Hell's Kitchen  40.76489  -73.98493   \n",
              "8    MaryEllen           Manhattan     Upper West Side  40.80178  -73.96723   \n",
              "9          Ben           Manhattan           Chinatown  40.71344  -73.99037   \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  2016-12-31   \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",
              "6     Private room     60              45                 49  2017-10-05   \n",
              "7     Private room     79               2                430  2019-06-24   \n",
              "8     Private room     79               2                118  2017-07-21   \n",
              "9  Entire home/apt    150               1                160  2019-06-09   \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               0.00                               1               365   \n",
              "3               4.64                               1               194   \n",
              "4               0.10                               1                 0   \n",
              "5               0.59                               1               129   \n",
              "6               0.40                               1                 0   \n",
              "7               3.47                               1               220   \n",
              "8               0.99                               1                 0   \n",
              "9               1.33                               4               188   \n",
              "\n",
              "      month    listing_coordinates  \n",
              "0   October  [40.64749, -73.97237]  \n",
              "1       May  [40.75362, -73.98377]  \n",
              "2  December   [40.80902, -73.9419]  \n",
              "3      July  [40.68514, -73.95976]  \n",
              "4  November  [40.79851, -73.94399]  \n",
              "5      June    [40.74767, -73.975]  \n",
              "6   October  [40.68688, -73.95596]  \n",
              "7      June  [40.76489, -73.98493]  \n",
              "8      July  [40.80178, -73.96723]  \n",
              "9      June  [40.71344, -73.99037]  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-46ceeb7e-fe3a-46d2-9ade-ebe11a9587b6\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<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",
              "      <th>month</th>\n",
              "      <th>listing_coordinates</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",
              "      <td>0.21</td>\n",
              "      <td>6</td>\n",
              "      <td>365</td>\n",
              "      <td>October</td>\n",
              "      <td>[40.64749, -73.97237]</td>\n",
              "    </tr>\n",
              "    <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",
              "      <td>May</td>\n",
              "      <td>[40.75362, -73.98377]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3647</td>\n",
              "      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
              "      <td>4632</td>\n",
              "      <td>Elisabeth</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Harlem</td>\n",
              "      <td>40.80902</td>\n",
              "      <td>-73.94190</td>\n",
              "      <td>Private room</td>\n",
              "      <td>150</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2016-12-31</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>365</td>\n",
              "      <td>December</td>\n",
              "      <td>[40.80902, -73.9419]</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",
              "      <td>July</td>\n",
              "      <td>[40.68514, -73.95976]</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",
              "      <td>November</td>\n",
              "      <td>[40.79851, -73.94399]</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",
              "      <td>June</td>\n",
              "      <td>[40.74767, -73.975]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5121</td>\n",
              "      <td>BlissArtsSpace!</td>\n",
              "      <td>7356</td>\n",
              "      <td>Garon</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>40.68688</td>\n",
              "      <td>-73.95596</td>\n",
              "      <td>Private room</td>\n",
              "      <td>60</td>\n",
              "      <td>45</td>\n",
              "      <td>49</td>\n",
              "      <td>2017-10-05</td>\n",
              "      <td>0.40</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>October</td>\n",
              "      <td>[40.68688, -73.95596]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5178</td>\n",
              "      <td>Large Furnished Room Near B'way</td>\n",
              "      <td>8967</td>\n",
              "      <td>Shunichi</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Hell's Kitchen</td>\n",
              "      <td>40.76489</td>\n",
              "      <td>-73.98493</td>\n",
              "      <td>Private room</td>\n",
              "      <td>79</td>\n",
              "      <td>2</td>\n",
              "      <td>430</td>\n",
              "      <td>2019-06-24</td>\n",
              "      <td>3.47</td>\n",
              "      <td>1</td>\n",
              "      <td>220</td>\n",
              "      <td>June</td>\n",
              "      <td>[40.76489, -73.98493]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>5203</td>\n",
              "      <td>Cozy Clean Guest Room - Family Apt</td>\n",
              "      <td>7490</td>\n",
              "      <td>MaryEllen</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Upper West Side</td>\n",
              "      <td>40.80178</td>\n",
              "      <td>-73.96723</td>\n",
              "      <td>Private room</td>\n",
              "      <td>79</td>\n",
              "      <td>2</td>\n",
              "      <td>118</td>\n",
              "      <td>2017-07-21</td>\n",
              "      <td>0.99</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>July</td>\n",
              "      <td>[40.80178, -73.96723]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>5238</td>\n",
              "      <td>Cute &amp; Cozy Lower East Side 1 bdrm</td>\n",
              "      <td>7549</td>\n",
              "      <td>Ben</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Chinatown</td>\n",
              "      <td>40.71344</td>\n",
              "      <td>-73.99037</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>150</td>\n",
              "      <td>1</td>\n",
              "      <td>160</td>\n",
              "      <td>2019-06-09</td>\n",
              "      <td>1.33</td>\n",
              "      <td>4</td>\n",
              "      <td>188</td>\n",
              "      <td>June</td>\n",
              "      <td>[40.71344, -73.99037]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "1FNkaIcUBc8e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Major Attractions in the NYC**"
      ],
      "metadata": {
        "id": "9dlOpUqcBIf6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs['neighbourhood_group'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OxVRFBdKAQJ7",
        "outputId": "16df2601-bfb9-44df-a596-15ab36ff4ef6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Brooklyn', 'Manhattan', 'Queens', 'Staten Island', 'Bronx'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Major attractions in the NYC Neighbourhood :\n",
        "Brooklyn: Brooklyn Bridge\n",
        "\n",
        "*   Brooklyn : Brooklyn Bridge\n",
        "*   Manhattan : Central Park\n",
        "*   Staten Island : St. George's Ferry Terminal (Staten Island Ferry )\n",
        "* Queens: Citi Field\n",
        "* Bronx: Bronx Zoo"
      ],
      "metadata": {
        "id": "tvKhAos-CheN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Entering the coordinated of the famous attractions\n",
        "brooklyn_bridge = (40.706001,-73.997002)\n",
        "central_park = (40.769361,-73.977655)\n",
        "citi_field = (40.75416365,-73.84082997)\n",
        "staten_island_ferry = (40.643333,-74.074167) \n",
        "bronx_zoo = (40.852905,-73.872971)"
      ],
      "metadata": {
        "id": "6twgb5hzBUwd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Dictionary with place and neighbour hood attractions\n",
        "locations = {'Brooklyn':\"Brooklyn Bridge\", 'Manhattan':\"Central Park\", 'Staten Island':\"St.George's  Ferry Terminal\", 'Queens':\"Citi Field\", 'Bronx':\"Bronx Zoo\"}\n",
        "\n",
        "# Dictionary with neighbourhood and coordinates\n",
        "coordinates = {'Brooklyn':brooklyn_bridge, 'Manhattan':central_park, 'Queens':citi_field, 'Bronx':bronx_zoo, 'Staten Island':staten_island_ferry}\n",
        "\n",
        "# mapping locations witht the coordinates\n",
        "nyc_logs['major_attractions'] = nyc_logs['neighbourhood_group'].map(locations)\n",
        "nyc_logs['major_attractions_coordinates'] = nyc_logs['neighbourhood_group'].map(coordinates)\n"
      ],
      "metadata": {
        "id": "vqfAerOXBl_T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install haversine"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hE_Y72KTUE_X",
        "outputId": "5d1e50a0-b58e-4567-b17b-2eeb06dda0f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting haversine\n",
            "  Downloading haversine-2.8.0-py2.py3-none-any.whl (7.7 kB)\n",
            "Installing collected packages: haversine\n",
            "Successfully installed haversine-2.8.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# here we use haversine, it is a library in python which is used to calculate distance (in various units) between two points on Earth using their latitude and longitude.\n",
        "import haversine\n",
        "from haversine import haversine, Unit"
      ],
      "metadata": {
        "id": "SgGTxwa3CKu4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#haversine can calculate the distance (in various units) between two points on Earth using their latitude and longitude.\n",
        "\n",
        "\n",
        "# We use the lambda function to apply haversine to calculate the distance to central park from each listing in Miles\n",
        "# The default units for haversine are Km but we have set it to Miles here\n",
        "# x.listing_coordinates and x.major_attraction_coordinates represent the coordinates of the listing location and the major attraction\n",
        "# nyc_df['distance_to_major_attractions'] = nyc_df.apply(lambda x: haversine(x.listing_coordinates,x.major_attraction_coordinates,unit=Unit.MILES), axis = 1)\n",
        "\n",
        "nyc_logs['distance_to_majorAttractions'] = nyc_logs.apply(lambda x : haversine(x.listing_coordinates, x.major_attractions_coordinates ,unit=Unit.MILES), axis = 1)\n",
        "\n",
        "# Rounding the values for easynes\n",
        "nyc_logs['distance_to_majorAttractions'] = nyc_logs['distance_to_majorAttractions'].round(2)\n",
        "\n"
      ],
      "metadata": {
        "id": "wqtFrRUMM5XE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Checking the Correlation in the Data**"
      ],
      "metadata": {
        "id": "iZOOlbDYYHCT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(nyc_logs.corr(),cmap=\"GnBu\")\n",
        "\n",
        "# .corr(): is used to find the pairwise correlation of all the columns in the DataFrame."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "oZgfFM8bW9cS",
        "outputId": "1fe00856-0c9c-4076-adcb-22081a363d77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs['host_id'].describe()\n",
        "# there are 37425 unique host in the given data, out of which the host_id = 219517861 have the maximum frequency of the listing i.e 327."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QGkUw1NuYQZV",
        "outputId": "167397e2-b567-4d74-db78-fb528962f32b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count         48858\n",
              "unique        37425\n",
              "top       219517861\n",
              "freq            327\n",
              "Name: host_id, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs.head()"
      ],
      "metadata": {
        "id": "ivxYipWZiN7_",
        "outputId": "d2c99900-973b-4671-dea1-58224f10d6de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        }
      },
      "execution_count": null,
      "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",
              "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  ...  number_of_reviews  last_review  \\\n",
              "0     Private room    149  ...                  9   2018-10-19   \n",
              "1  Entire home/apt    225  ...                 45   2019-05-21   \n",
              "2     Private room    150  ...                  0   2016-12-31   \n",
              "3  Entire home/apt     89  ...                270   2019-07-05   \n",
              "4  Entire home/apt     80  ...                  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              0.00                               1               365   \n",
              "3              4.64                               1               194   \n",
              "4              0.10                               1                 0   \n",
              "\n",
              "      month    listing_coordinates major_attractions  \\\n",
              "0   October  [40.64749, -73.97237]   Brooklyn Bridge   \n",
              "1       May  [40.75362, -73.98377]      Central Park   \n",
              "2  December   [40.80902, -73.9419]      Central Park   \n",
              "3      July  [40.68514, -73.95976]   Brooklyn Bridge   \n",
              "4  November  [40.79851, -73.94399]      Central Park   \n",
              "\n",
              "  major_attractions_coordinates distance_to_majorAttractions  \n",
              "0       (40.706001, -73.997002)                         4.24  \n",
              "1       (40.769361, -73.977655)                         1.13  \n",
              "2       (40.769361, -73.977655)                         3.32  \n",
              "3       (40.706001, -73.997002)                         2.43  \n",
              "4       (40.769361, -73.977655)                         2.68  \n",
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              "      <th>id</th>\n",
              "      <th>name</th>\n",
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              "      <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",
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              "      <td>Kensington</td>\n",
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              "      <td>-73.97237</td>\n",
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              "      <td>149</td>\n",
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              "      <td>4.24</td>\n",
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              "      <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",
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              "      <td>40.75362</td>\n",
              "      <td>-73.98377</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>225</td>\n",
              "      <td>...</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",
              "      <td>May</td>\n",
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              "      <td>Central Park</td>\n",
              "      <td>(40.769361, -73.977655)</td>\n",
              "      <td>1.13</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3647</td>\n",
              "      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
              "      <td>4632</td>\n",
              "      <td>Elisabeth</td>\n",
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              "      <td>-73.94190</td>\n",
              "      <td>Private room</td>\n",
              "      <td>150</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>2016-12-31</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>365</td>\n",
              "      <td>December</td>\n",
              "      <td>[40.80902, -73.9419]</td>\n",
              "      <td>Central Park</td>\n",
              "      <td>(40.769361, -73.977655)</td>\n",
              "      <td>3.32</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>...</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",
              "      <td>July</td>\n",
              "      <td>[40.68514, -73.95976]</td>\n",
              "      <td>Brooklyn Bridge</td>\n",
              "      <td>(40.706001, -73.997002)</td>\n",
              "      <td>2.43</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>...</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",
              "      <td>November</td>\n",
              "      <td>[40.79851, -73.94399]</td>\n",
              "      <td>Central Park</td>\n",
              "      <td>(40.769361, -73.977655)</td>\n",
              "      <td>2.68</td>\n",
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              "<p>5 rows × 21 columns</p>\n",
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    {
      "cell_type": "code",
      "source": [
        "nyc_logs[nyc_logs['host_id'] == '219517861'].head()"
      ],
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        "id": "dI3Szytbbs1R",
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        "colab": {
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      "outputs": [
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          "data": {
            "text/plain": [
              "             id                                           name    host_id  \\\n",
              "38293  30181691  Sonder | 180 Water | Incredible 2BR + Rooftop  219517861   \n",
              "38294  30181945     Sonder | 180 Water | Premier 1BR + Rooftop  219517861   \n",
              "38588  30347708    Sonder | 180 Water | Charming 1BR + Rooftop  219517861   \n",
              "39769  30937590        Sonder | The Nash | Artsy 1BR + Rooftop  219517861   \n",
              "39770  30937591    Sonder | The Nash | Lovely Studio + Rooftop  219517861   \n",
              "\n",
              "          host_name neighbourhood_group       neighbourhood  latitude  \\\n",
              "38293  Sonder (NYC)           Manhattan  Financial District  40.70637   \n",
              "38294  Sonder (NYC)           Manhattan  Financial District  40.70771   \n",
              "38588  Sonder (NYC)           Manhattan  Financial District  40.70743   \n",
              "39769  Sonder (NYC)           Manhattan         Murray Hill  40.74792   \n",
              "39770  Sonder (NYC)           Manhattan         Murray Hill  40.74771   \n",
              "\n",
              "       longitude        room_type  price  ...  number_of_reviews  last_review  \\\n",
              "38293  -74.00645  Entire home/apt    302  ...                  0   2016-12-31   \n",
              "38294  -74.00641  Entire home/apt    229  ...                  1   2019-05-29   \n",
              "38588  -74.00443  Entire home/apt    232  ...                  1   2019-05-21   \n",
              "39769  -73.97614  Entire home/apt    262  ...                  8   2019-06-09   \n",
              "39770  -73.97528  Entire home/apt    255  ...                 14   2019-06-10   \n",
              "\n",
              "      reviews_per_month  calculated_host_listings_count  availability_365  \\\n",
              "38293              0.00                             327               309   \n",
              "38294              0.73                             327               219   \n",
              "38588              0.60                             327               159   \n",
              "39769              1.86                             327                91   \n",
              "39770              2.59                             327                81   \n",
              "\n",
              "          month    listing_coordinates major_attractions  \\\n",
              "38293  December  [40.70637, -74.00645]      Central Park   \n",
              "38294       May  [40.70771, -74.00641]      Central Park   \n",
              "38588       May  [40.70743, -74.00443]      Central Park   \n",
              "39769      June  [40.74792, -73.97614]      Central Park   \n",
              "39770      June  [40.74771, -73.97528]      Central Park   \n",
              "\n",
              "      major_attractions_coordinates distance_to_majorAttractions  \n",
              "38293       (40.769361, -73.977655)                         4.61  \n",
              "38294       (40.769361, -73.977655)                         4.52  \n",
              "38588       (40.769361, -73.977655)                         4.50  \n",
              "39769       (40.769361, -73.977655)                         1.48  \n",
              "39770       (40.769361, -73.977655)                         1.50  \n",
              "\n",
              "[5 rows x 21 columns]"
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              "      <th>38293</th>\n",
              "      <td>30181691</td>\n",
              "      <td>Sonder | 180 Water | Incredible 2BR + Rooftop</td>\n",
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              "      <td>[40.70637, -74.00645]</td>\n",
              "      <td>Central Park</td>\n",
              "      <td>(40.769361, -73.977655)</td>\n",
              "      <td>4.61</td>\n",
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              "      <td>30181945</td>\n",
              "      <td>Sonder | 180 Water | Premier 1BR + Rooftop</td>\n",
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              "      <td>229</td>\n",
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              "      <td>(40.769361, -73.977655)</td>\n",
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              "      <th>38588</th>\n",
              "      <td>30347708</td>\n",
              "      <td>Sonder | 180 Water | Charming 1BR + Rooftop</td>\n",
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              "      <td>40.70743</td>\n",
              "      <td>-74.00443</td>\n",
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              "      <td>232</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0.60</td>\n",
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              "      <td>159</td>\n",
              "      <td>May</td>\n",
              "      <td>[40.70743, -74.00443]</td>\n",
              "      <td>Central Park</td>\n",
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              "      <td>30937590</td>\n",
              "      <td>Sonder | The Nash | Artsy 1BR + Rooftop</td>\n",
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              "      <td>262</td>\n",
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              "      <td>Central Park</td>\n",
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              "      <th>39770</th>\n",
              "      <td>30937591</td>\n",
              "      <td>Sonder | The Nash | Lovely Studio + Rooftop</td>\n",
              "      <td>219517861</td>\n",
              "      <td>Sonder (NYC)</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Murray Hill</td>\n",
              "      <td>40.74771</td>\n",
              "      <td>-73.97528</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>255</td>\n",
              "      <td>...</td>\n",
              "      <td>14</td>\n",
              "      <td>2019-06-10</td>\n",
              "      <td>2.59</td>\n",
              "      <td>327</td>\n",
              "      <td>81</td>\n",
              "      <td>June</td>\n",
              "      <td>[40.74771, -73.97528]</td>\n",
              "      <td>Central Park</td>\n",
              "      <td>(40.769361, -73.977655)</td>\n",
              "      <td>1.50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-292cfe57-455a-4c7c-bec2-af4ca943c538')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      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",
              "    [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",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-292cfe57-455a-4c7c-bec2-af4ca943c538 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-292cfe57-455a-4c7c-bec2-af4ca943c538');\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs['neighbourhood'].describe()\n",
        "# Finding out the unique neighbourhood i.e there are 221 neighbourhoods with Williamsburg having listing of 3917 "
      ],
      "metadata": {
        "id": "GVveg5SviFn2",
        "outputId": "95cef82f-91d9-4ba7-eef9-7447ca8a7ed7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count            48858\n",
              "unique             221\n",
              "top       Williamsburg\n",
              "freq              3917\n",
              "Name: neighbourhood, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs['neighbourhood'].value_counts().head(10)\n",
        "# this represents the max times the neighbourhood freq in the given data."
      ],
      "metadata": {
        "id": "ObNzRm-PiZsk",
        "outputId": "31ad1623-2195-4bd3-9838-3fd219d04912",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Williamsburg          3917\n",
              "Bedford-Stuyvesant    3713\n",
              "Harlem                2655\n",
              "Bushwick              2462\n",
              "Upper West Side       1969\n",
              "Hell's Kitchen        1954\n",
              "East Village          1852\n",
              "Upper East Side       1797\n",
              "Crown Heights         1563\n",
              "Midtown               1545\n",
              "Name: neighbourhood, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs['neighbourhood'].value_counts().head(10).sum()\n",
        "# tells about the total number of listing that the top 10 neighbourhood accounts for"
      ],
      "metadata": {
        "id": "8nAyEBSFlpzn",
        "outputId": "3afe5883-8c50-49b2-c39a-d9fd492004ae",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23427"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "round((23427/48858)*100, 2)\n",
        "# finding the % for which the top 10 neighbourhood accounts in the total data."
      ],
      "metadata": {
        "id": "vssdRd43l0nL",
        "outputId": "6b1a9ff5-22aa-4613-d486-7c88276f3e81",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "47.95"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The top 10 neighbourhood accounts for **47.95%** of the data in the total data."
      ],
      "metadata": {
        "id": "tmJBA6YmWz8x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs['neighbourhood_group'].describe()\n",
        "# describing the most occuring meighbour_hood group in the data i.e Manhattan having listing of 21643."
      ],
      "metadata": {
        "id": "qoCtM53ql6ig",
        "outputId": "3d3386ed-67fe-42c7-ca31-e7c691af4e44",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count         48858\n",
              "unique            5\n",
              "top       Manhattan\n",
              "freq          21643\n",
              "Name: neighbourhood_group, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs['neighbourhood_group'].value_counts()\n",
        "# reviewing the most occuring neighbourhood_group in the data, it comes out to be Manhattan with about 21643 listings."
      ],
      "metadata": {
        "id": "WEsz9wpdl9Bl",
        "outputId": "69a69e9a-b9d1-476f-aa98-459e0f4ad4e0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Manhattan        21643\n",
              "Brooklyn         20089\n",
              "Queens            5664\n",
              "Bronx             1089\n",
              "Staten Island      373\n",
              "Name: neighbourhood_group, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs['room_type'].describe()\n",
        "# checking the room_type that is preffered most."
      ],
      "metadata": {
        "id": "X_83HNrmmSEn",
        "outputId": "b72a9207-b9b7-4bdd-875d-0784670fefb3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count               48858\n",
              "unique                  3\n",
              "top       Entire home/apt\n",
              "freq                25393\n",
              "Name: room_type, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mostly, Entire home/appartment is rented or taken as the room_type i.e for 25393 times"
      ],
      "metadata": {
        "id": "tSmFpO6KX5d4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs['room_type'].value_counts()"
      ],
      "metadata": {
        "id": "Qwxm3_zlmXmu",
        "outputId": "8d368244-9689-47b0-8854-8b1e292ad1b2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Entire home/apt    25393\n",
              "Private room       22306\n",
              "Shared room         1159\n",
              "Name: room_type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='room_type', data=nyc_logs, palette='pastel')\n",
        "plt.title(\"Number of Rooms of Each Type\", fontsize=20)"
      ],
      "metadata": {
        "id": "8XJ6OJSSmb03",
        "outputId": "bf978f5e-5c9d-45ad-8f88-8690801e1abd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Number of Rooms of Each Type')"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig_dim = (12,4)\n",
        "fig, ax = plt.subplots(figsize=fig_dim)\n",
        "sns.countplot(x='month',data=nyc_logs,order = nyc_logs['month'].value_counts().index,palette='pastel')"
      ],
      "metadata": {
        "id": "cLV0L6BNm4rZ",
        "outputId": "5df873d7-874a-4d47-abf4-44e54cc764b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='month', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizing the data for the maximum times the rooms are booked in which time of the year.\n",
        "*   The majority times the rooms are booked are in June, which indicates that maximum times people rental the rooms in June.\n",
        "*   Meanwhile, the least reviews are left in Feburary, which indicates that people rent the room least in Feburary."
      ],
      "metadata": {
        "id": "4Bb40xGmYKrZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs['availability_365'].mean()\n",
        "# finding the average times the listing is available in the year, i.e 112.8 ~ 113."
      ],
      "metadata": {
        "id": "hsC4vKCNJtKM",
        "outputId": "7cbc62cd-aa2a-4bad-e3c2-6b413e522099",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "112.80142453641164"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Identifying the average availability for each neighbourhood group (rounded to 2 decimal places)\n",
        "ngbh_grp = nyc_logs.groupby('neighbourhood_group')['availability_365'].mean().round(2)\n",
        "\n",
        "#Converting the series nbhd to a dataframe\n",
        "ngbh_grp = ngbh_grp.to_frame()\n",
        "\n",
        "\n",
        "#Renaming columns\n",
        "ngbh_grp.rename(columns={'availability_365' : 'average_availability'}, inplace = True)\n",
        "\n",
        "\n",
        "# Identifying the average price for each neighbourhood group (rounded to 2 decimal places)\n",
        "ngbh_grp['average_price'] = nyc_logs.groupby('neighbourhood_group')['price'].mean().round()\n",
        "\n",
        "\n",
        "# Identifying the average number of reviews per listing for each neighbourhood group (rounded to 2 decimal places)\n",
        "ngbh_grp['average_number_of_reviews_per_listing'] = nyc_logs.groupby('neighbourhood_group')['number_of_reviews'].mean().round()\n",
        "\n",
        "# Identifying the total number of reviews for each neighbourhood group(rounded to 2 decimal places)\n",
        "ngbh_grp['total_number_of_reviews'] = nyc_logs.groupby('neighbourhood_group')['number_of_reviews'].sum().round(2)\n",
        "\n",
        "\n",
        "ngbh_grp.sort_values(by = ['average_availability'])\n",
        "\n"
      ],
      "metadata": {
        "id": "7NcI-wAsJ6nk",
        "outputId": "b2a8a78e-c110-41a8-b256-c28a0cd4722a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     average_availability  average_price  \\\n",
              "neighbourhood_group                                        \n",
              "Brooklyn                           100.24          124.0   \n",
              "Manhattan                          112.01          197.0   \n",
              "Queens                             144.49          100.0   \n",
              "Bronx                              165.70           87.0   \n",
              "Staten Island                      199.68          115.0   \n",
              "\n",
              "                     average_number_of_reviews_per_listing  \\\n",
              "neighbourhood_group                                          \n",
              "Brooklyn                                              24.0   \n",
              "Manhattan                                             21.0   \n",
              "Queens                                                28.0   \n",
              "Bronx                                                 26.0   \n",
              "Staten Island                                         31.0   \n",
              "\n",
              "                     total_number_of_reviews  \n",
              "neighbourhood_group                           \n",
              "Brooklyn                              486174  \n",
              "Manhattan                             454126  \n",
              "Queens                                156902  \n",
              "Bronx                                  28334  \n",
              "Staten Island                          11541  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b588a16e-1e69-4e00-b7cb-b6ee1f6e492b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>average_availability</th>\n",
              "      <th>average_price</th>\n",
              "      <th>average_number_of_reviews_per_listing</th>\n",
              "      <th>total_number_of_reviews</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>neighbourhood_group</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Brooklyn</th>\n",
              "      <td>100.24</td>\n",
              "      <td>124.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>486174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Manhattan</th>\n",
              "      <td>112.01</td>\n",
              "      <td>197.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>454126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Queens</th>\n",
              "      <td>144.49</td>\n",
              "      <td>100.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>156902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bronx</th>\n",
              "      <td>165.70</td>\n",
              "      <td>87.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>28334</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Staten Island</th>\n",
              "      <td>199.68</td>\n",
              "      <td>115.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>11541</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b588a16e-1e69-4e00-b7cb-b6ee1f6e492b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "On average,\n",
        "\n",
        "\n",
        "*   Listing in **Staten Island** have the gratest availiblty and receive the most reviews per listing.\n",
        "\n",
        "  *   However, Staten Island also receives the least number of reviews overall.\n",
        "\n",
        "*    Listing in **Manhattan** have the least avaliblity and receive the least reviews per listing.\n",
        "  * However, Manhattan receives the second hoghest number of reviews overall.\n",
        "\n"
      ],
      "metadata": {
        "id": "pGFr_oMqO6ST"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs['minimum_nights'].mean()"
      ],
      "metadata": {
        "id": "7uMOXGOXOZ2n",
        "outputId": "1d8257a3-16e7-4376-88ba-661e959fb82f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.012444226124688"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Average duration of Stay for all Listing is **7 days**"
      ],
      "metadata": {
        "id": "ZSGfjVuoPpgH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs.groupby('neighbourhood')['minimum_nights'].mean().sort_values()\n"
      ],
      "metadata": {
        "id": "MWzt9-daPoVq",
        "outputId": "9f00a71f-2d02-4eaa-bbc0-49a192a3c031",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "neighbourhood\n",
              "Breezy Point                   1.000000\n",
              "New Dorp                       1.000000\n",
              "Oakwood                        1.200000\n",
              "East Morrisania                1.400000\n",
              "Woodlawn                       1.454545\n",
              "                                ...    \n",
              "Bay Terrace, Staten Island    16.500000\n",
              "Vinegar Hill                  18.352941\n",
              "Olinville                     23.500000\n",
              "North Riverdale               41.400000\n",
              "Spuyten Duyvil                48.250000\n",
              "Name: minimum_nights, Length: 221, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Listing in the **Spuyten Duyvil** neighbourhood offer the longest average duration of stay at approx. **48 days**."
      ],
      "metadata": {
        "id": "3nE04JTlP5bY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs.groupby('neighbourhood_group')['minimum_nights'].mean()\n"
      ],
      "metadata": {
        "id": "JeuezfBoPzNA",
        "outputId": "3dd622d8-ab62-46be-cdf1-b48c55a29c25",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "neighbourhood_group\n",
              "Bronx            4.564738\n",
              "Brooklyn         6.057693\n",
              "Manhattan        8.538188\n",
              "Queens           5.182910\n",
              "Staten Island    4.831099\n",
              "Name: minimum_nights, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Listing in the **Manhattan** neighbourhood offer the longest average duration of stay at approx. **9 days**."
      ],
      "metadata": {
        "id": "KKPZ3_Y-QZHs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs['distance_to_majorAttractions'].mean()\n",
        "\n",
        "#this means that on avg, any listing is 3.001 miles away from the closest majot attraction"
      ],
      "metadata": {
        "id": "aAE75w9RQXo2",
        "outputId": "af2afd14-7a35-4b23-ffba-5da204b6deb3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.0813486020713086"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Identifying the average distance to the closest major attraction for each neighbourhood group (rounded to 2 decimal places)\n",
        "ngbh_grp = nyc_logs.groupby('neighbourhood_group')['distance_to_majorAttractions'].mean().round(2)\n",
        "\n",
        "#Converting the series ngbh to a dataframe\n",
        "ngbh_grp = ngbh_grp.to_frame()\n",
        "\n",
        "# Renaming columns\n",
        "ngbh_grp.rename(columns={'distance_to_majorAttractions': 'average_distance_to_majorAttractions'}, inplace=True)\n",
        "\n",
        "\n",
        "# Identifying the average price for each neighbourhood group (rounded to 2 decimal places)\n",
        "ngbh_grp['average_price'] = nyc_logs.groupby('neighbourhood_group')['price'].mean().round()\n",
        "\n",
        "# Identifying the average number of reviews for each neighbourhood group (rounded to 2 decimal places)\n",
        "ngbh_grp['average_number_of_reviews_per_listing'] = nyc_logs.groupby('neighbourhood_group')['number_of_reviews'].mean().round()\n",
        "\n",
        "\n",
        "# Identifying the total number of reviews per listing for each neighbourhood group(rounded to 2 decimal places)\n",
        "ngbh_grp['total_number_of_reviews'] = nyc_logs.groupby('neighbourhood_group')['number_of_reviews'].sum().round(2)\n",
        "\n",
        "ngbh_grp.sort_values(by=['average_distance_to_majorAttractions'])\n",
        "\n"
      ],
      "metadata": {
        "id": "XKiC7cAcQnaV",
        "outputId": "85881495-9859-48b3-8a1d-f6731d0e6286",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     average_distance_to_majorAttractions  average_price  \\\n",
              "neighbourhood_group                                                        \n",
              "Bronx                                                2.42           87.0   \n",
              "Manhattan                                            2.56          197.0   \n",
              "Staten Island                                        3.12          115.0   \n",
              "Brooklyn                                             3.37          124.0   \n",
              "Queens                                               4.19          100.0   \n",
              "\n",
              "                     average_number_of_reviews_per_listing  \\\n",
              "neighbourhood_group                                          \n",
              "Bronx                                                 26.0   \n",
              "Manhattan                                             21.0   \n",
              "Staten Island                                         31.0   \n",
              "Brooklyn                                              24.0   \n",
              "Queens                                                28.0   \n",
              "\n",
              "                     total_number_of_reviews  \n",
              "neighbourhood_group                           \n",
              "Bronx                                  28334  \n",
              "Manhattan                             454126  \n",
              "Staten Island                          11541  \n",
              "Brooklyn                              486174  \n",
              "Queens                                156902  "
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>average_distance_to_majorAttractions</th>\n",
              "      <th>average_price</th>\n",
              "      <th>average_number_of_reviews_per_listing</th>\n",
              "      <th>total_number_of_reviews</th>\n",
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              "      <th>neighbourhood_group</th>\n",
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              "      <th>Bronx</th>\n",
              "      <td>2.42</td>\n",
              "      <td>87.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>28334</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Manhattan</th>\n",
              "      <td>2.56</td>\n",
              "      <td>197.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>454126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Staten Island</th>\n",
              "      <td>3.12</td>\n",
              "      <td>115.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>11541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brooklyn</th>\n",
              "      <td>3.37</td>\n",
              "      <td>124.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>486174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Queens</th>\n",
              "      <td>4.19</td>\n",
              "      <td>100.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>156902</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "On average,\n",
        "\n",
        "*   Listings in the **Bronx** are the closest a major attraction in the city.\n",
        "  * However, the Bronx also has the second lowest number of listings.\n",
        "*   Listings in **Queens** have the least availability and receive the least reviews per listing.\n",
        "*   **Manhattan** has the greatest number of listings and they are second closest to a major attraction in the city."
      ],
      "metadata": {
        "id": "8aj5O3lhb4Dm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Avg price across all the listing\n",
        "avg_allListing = round(nyc_logs['price'].mean(),2)\n",
        "avg_allListing"
      ],
      "metadata": {
        "id": "lzlH-HFbbD6u",
        "outputId": "06790969-5de5-41ae-8db5-20a95e6cc385",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "152.74"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualizing the price and reviews per month\n",
        "fig_sz = (12, 4) # creating the fig size for the plotting\n",
        "fig, ax = plt.subplots(figsize = fig_sz)\n",
        "sns.scatterplot(y = 'reviews_per_month', x = 'price', data=nyc_logs)\n",
        "plt.title(\"Reviews per month vs Price \", fontsize = 20)\n"
      ],
      "metadata": {
        "id": "m8jBCpp9ZTAd",
        "outputId": "5d5e43ae-8596-4e70-fdb7-777da5b86ee0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Reviews per month vs Price ')"
            ]
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, we can see that the maximum number of reviews are given for the least price (0-1000), and minimum number of reviews are gives to the most expensive one i.e 10000."
      ],
      "metadata": {
        "id": "_ZIaGo-6ax7y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs.head()"
      ],
      "metadata": {
        "id": "aFz1SvDZbJI8",
        "outputId": "43aa04df-ea6b-4083-d35b-0935c71d9c11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        }
      },
      "execution_count": null,
      "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",
              "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  ...  number_of_reviews  last_review  \\\n",
              "0     Private room    149  ...                  9   2018-10-19   \n",
              "1  Entire home/apt    225  ...                 45   2019-05-21   \n",
              "2     Private room    150  ...                  0   2016-12-31   \n",
              "3  Entire home/apt     89  ...                270   2019-07-05   \n",
              "4  Entire home/apt     80  ...                  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              0.00                               1               365   \n",
              "3              4.64                               1               194   \n",
              "4              0.10                               1                 0   \n",
              "\n",
              "      month    listing_coordinates major_attractions  \\\n",
              "0   October  [40.64749, -73.97237]   Brooklyn Bridge   \n",
              "1       May  [40.75362, -73.98377]      Central Park   \n",
              "2  December   [40.80902, -73.9419]      Central Park   \n",
              "3      July  [40.68514, -73.95976]   Brooklyn Bridge   \n",
              "4  November  [40.79851, -73.94399]      Central Park   \n",
              "\n",
              "  major_attractions_coordinates distance_to_majorAttractions  \n",
              "0       (40.706001, -73.997002)                         4.24  \n",
              "1       (40.769361, -73.977655)                         1.13  \n",
              "2       (40.769361, -73.977655)                         3.32  \n",
              "3       (40.706001, -73.997002)                         2.43  \n",
              "4       (40.769361, -73.977655)                         2.68  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3cc98c21-fcb3-48f8-913a-ca9ca647cf34\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>...</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",
              "      <th>month</th>\n",
              "      <th>listing_coordinates</th>\n",
              "      <th>major_attractions</th>\n",
              "      <th>major_attractions_coordinates</th>\n",
              "      <th>distance_to_majorAttractions</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>...</td>\n",
              "      <td>9</td>\n",
              "      <td>2018-10-19</td>\n",
              "      <td>0.21</td>\n",
              "      <td>6</td>\n",
              "      <td>365</td>\n",
              "      <td>October</td>\n",
              "      <td>[40.64749, -73.97237]</td>\n",
              "      <td>Brooklyn Bridge</td>\n",
              "      <td>(40.706001, -73.997002)</td>\n",
              "      <td>4.24</td>\n",
              "    </tr>\n",
              "    <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>...</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",
              "      <td>May</td>\n",
              "      <td>[40.75362, -73.98377]</td>\n",
              "      <td>Central Park</td>\n",
              "      <td>(40.769361, -73.977655)</td>\n",
              "      <td>1.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3647</td>\n",
              "      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
              "      <td>4632</td>\n",
              "      <td>Elisabeth</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Harlem</td>\n",
              "      <td>40.80902</td>\n",
              "      <td>-73.94190</td>\n",
              "      <td>Private room</td>\n",
              "      <td>150</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>2016-12-31</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>365</td>\n",
              "      <td>December</td>\n",
              "      <td>[40.80902, -73.9419]</td>\n",
              "      <td>Central Park</td>\n",
              "      <td>(40.769361, -73.977655)</td>\n",
              "      <td>3.32</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>...</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",
              "      <td>July</td>\n",
              "      <td>[40.68514, -73.95976]</td>\n",
              "      <td>Brooklyn Bridge</td>\n",
              "      <td>(40.706001, -73.997002)</td>\n",
              "      <td>2.43</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>...</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",
              "      <td>November</td>\n",
              "      <td>[40.79851, -73.94399]</td>\n",
              "      <td>Central Park</td>\n",
              "      <td>(40.769361, -73.977655)</td>\n",
              "      <td>2.68</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3cc98c21-fcb3-48f8-913a-ca9ca647cf34')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      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",
              "    [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-3cc98c21-fcb3-48f8-913a-ca9ca647cf34 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-3cc98c21-fcb3-48f8-913a-ca9ca647cf34');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize = fig_sz)\n",
        "sns.scatterplot(y='number_of_reviews', x = 'price', data = nyc_logs)\n",
        "plt.title(\"Number of Reviews vs Price\", fontsize = 20)"
      ],
      "metadata": {
        "id": "_7GlOZYeadcx",
        "outputId": "cf01a359-e1c6-4005-8c4e-aa0192dc9801",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Number of Reviews vs Price')"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The plot shows that, there are no reviews given for the least price."
      ],
      "metadata": {
        "id": "TtMowZWdbmkI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding the average price by the neighbourhood\n",
        "ngbh = nyc_logs.groupby('neighbourhood')['price'].mean().round(2)\n",
        "\n",
        "# converting the series into DataFrames\n",
        "ngbh = ngbh.to_frame()\n",
        "\n",
        "# renaming the column in the converted DataFrame\n",
        "ngbh.rename(columns={'price' : 'avg_price'}, inplace = True)\n",
        "\n",
        "# Identifying the avg number of reviews for each neighbourhood\n",
        "ngbh['avg_number_of_reviews'] = nyc_logs.groupby('neighbourhood')['number_of_reviews'].mean().round(2)\n",
        "\n",
        "ngbh.head(10)"
      ],
      "metadata": {
        "id": "zwDMYvIubjxs",
        "outputId": "bd1d0e38-e4e7-486c-af9d-caff8c0f71ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            avg_price  avg_number_of_reviews\n",
              "neighbourhood                                               \n",
              "Allerton                        87.60                  42.93\n",
              "Arden Heights                   67.25                   7.75\n",
              "Arrochar                       115.00                  14.62\n",
              "Arverne                        171.78                  29.26\n",
              "Astoria                        117.19                  21.46\n",
              "Bath Beach                      81.76                  21.35\n",
              "Battery Park City              367.09                   8.41\n",
              "Bay Ridge                      144.43                  18.57\n",
              "Bay Terrace                    142.00                  41.50\n",
              "Bay Terrace, Staten Island     102.50                   1.50"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-caf9b7a3-a5d0-437f-9620-722f1ad31909\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>avg_price</th>\n",
              "      <th>avg_number_of_reviews</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>neighbourhood</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Allerton</th>\n",
              "      <td>87.60</td>\n",
              "      <td>42.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arden Heights</th>\n",
              "      <td>67.25</td>\n",
              "      <td>7.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arrochar</th>\n",
              "      <td>115.00</td>\n",
              "      <td>14.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arverne</th>\n",
              "      <td>171.78</td>\n",
              "      <td>29.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Astoria</th>\n",
              "      <td>117.19</td>\n",
              "      <td>21.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bath Beach</th>\n",
              "      <td>81.76</td>\n",
              "      <td>21.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Battery Park City</th>\n",
              "      <td>367.09</td>\n",
              "      <td>8.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bay Ridge</th>\n",
              "      <td>144.43</td>\n",
              "      <td>18.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bay Terrace</th>\n",
              "      <td>142.00</td>\n",
              "      <td>41.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bay Terrace, Staten Island</th>\n",
              "      <td>102.50</td>\n",
              "      <td>1.50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-caf9b7a3-a5d0-437f-9620-722f1ad31909')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      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",
              "    [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-caf9b7a3-a5d0-437f-9620-722f1ad31909 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-caf9b7a3-a5d0-437f-9620-722f1ad31909');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize = fig_sz)\n",
        "sns.scatterplot(y = 'avg_number_of_reviews', x = 'avg_price', data = ngbh)\n",
        "plt.title(\"Number of reviews vs Aggregated by Neighbourhood\", fontsize=20)"
      ],
      "metadata": {
        "id": "W6HghCdBdQHU",
        "outputId": "0417a9da-b369-4e82-b710-509e5e287255",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Number of reviews vs Aggregated by Neighbourhood')"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ],
            "image/png": 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u/i+CnrgPm1kvM6sgyK3exKEAO3X/u9x9XHjODxHkk54B/DX8YNCcswmmSywDVlvKzBQE7X0kwXPsUym3SV7Tja77JspzuU1S1LWVfIwXNHdt5VmHqwlScd7v7h909y+5+/+5+zUE6UBxxX0NSO6fqc6Znt/Nae546a8LtxA8N5IfBKYQPD/uyvH8ALj7eoKUkiMIni9Rcn0+p8vnGsxFtm2cy2t1Pu9/cd9LMolb71zuZ8brP+U9VmJSQH0YCb9a+wVB3tYXM+y2Jfx9VPoGMxtOSg9LK4j6yvV0gk/zC1LKng1/jy94jQ6d98z0DeELUbIOLxS4Hs8CvcOvzeO4I/x9uZmNBkYBj2WZzvAaQS/LuDAgzdac8PdEgrSOf7l7Xcq2PsD/EAwynNP45gF3P+Du8919OkEwDHBejHrcSZBWcDFBkNyPIBc+6mvV5Dm3uvssd/8MQdv1IQism5MMkB4lCMbTf+4It09OmR4seW2dnn4wC6Yve1fEeZq6zVAinsdNcfedBDPEnGBmfbK8WS51GA5sdvcnI7ZlSrU4SHSPJcR/Daivs0WvXHlmlsdJ16juYZrTaIJAeXHa5kcJUsM+acG0b8cQ5CxvIX/XEaTcfZnotLpcn8/pFoa/T7O0afZCja6LPI0N0xzSnRn+XgAQpoUsAwaZ2YiI/d8T/k59rS6F97/UazMqsG9Q7xzvZ/Lvpt5jJa7mkqz10zZ/SBuUmFLeh0MLviSnjEodlFhB8Ol1KymzHBD0JiWn7VqRdswryDCAhyYGeZB5xPiTYXlTs3ykDsjoG96nDcApEecpo/FUY3eExxkWs12TKRQHgHFp274WHvPxbNugifNEtk3K9mR6xL8IpzpL2941vX4pj+M2gty9G8JjnN/ENfRkWlly1o5biJhhg6A3JH2WhfLwetoQ3vZbKduGhmXJ6Z0+nHbbdwA9I86TnLnmnhhtehRBUPZPghksnLQZGcL93kPatIxh+SNEDCbMcJ4DBM+xyib2S06p9r6U63Rp1DnIPMvH28k8w8YfkreJcz0S9Jo7wXRnvSK29yZlCroc65Cc/SR9IGxyAFWj1xOCHNW6DNddLq8BrT3Lx28y3Pbb4fbV4e/Tsr2mw9tfQcqgxAyPZXL2mPRZPmI9n8n8mv1EWJ4+y8ekJh7PFWQeZJpszzMjzu1knuVjKw1n+fhWyrWcOotJPw4NIE8dqN+i739Zbo96nU1em19LKz+VQ68t3fO4n+8OyzTLRwv+KIf6MOPum83sWoJBYVHb95vZjcB3gAVm9gDB11/vI+jtaM3V/hYDi8Lc7/0Eb3RvJ5i/sz4v0N1rzexCgpkvnjWzOQS9bE4Q3JxG8IabPugpNnffacGiI38C/mFmfyLoYXoH8H6CPLrP5nueLOoxx8y+QTDt0hIzm0XwwtmNIEj9L4LBJZPSbrcnrPNkghzhWoL2zNb3CQatfY4gB3guweDSIwhyMd9NECDUD/5094MWLBBzblg0J2XbSjNbRvC4HqTxIJnLgM+GedjLCIKmtwPnEAyOuyHbirv7W2b2BMGHkQPAy+6+IGLXB4CdZvYswZu+EfR8vpNgcO3fmznVZIIPEb/zQz3xUW4j6A2aQvAhLGFmnyYINh82s/sI7vMoguffYwTTkNUP/HL3ZWb2fwQDN180s3sIAoL3EXx4fjG8fdbc/TfhgM3PA8vCPN9V4fGOJuih/y3BNZBrHW4gGCD6TDhgdBtBUHQ6wSwoF0ZUbQ7BYzA7HPy7l2DQ7iM5vgZ8gSB4uCHsHX6RoOf8fIIPT+fEabfQY8A/w/u0Nrw/pxNcR5lSL24jyHkeRHBNzsvhvJncQTAG4KQM22M/nzP4AsEH1V+Y2VnASwTTKV5AMDf3uaRct3l6Cvi0mZ0annMgwbdOZQTTQKamA15H8Jw5l+DanAV0IZgq9AjgJ+5eP5i2hN7/Pkdw334aXps1BNfxRwna8ZPecGBm3Pv5TzP7OcE31a+kvcduIXM+tjSl2BG9fgrzQ4Ye6nBbJw59anUaz4NqBC/+ywh6XFYRBOBdaHraoCsiznUmufdQdyJYrOBNgjfP5QSDZxpNI5dyvJsIemPqCHL7XiMIvs9L2/cOcuihTrn9OwnevDemtNEtRPcWZ2yDJo4f2TYR+51OsBjFf8J6bCT4CvZ6oLqJ2yQf+583cw09GVFuBIHuHIKekn0cWqDhW6T0UqbcJjn93TYaT12XXHTmuYjbnRq264vhufYQ9Kr8liwWdYk43n+n3PevZtjnc+Fju5xgoZDNBF/Dfp2I6brSblsWXgtOWu9rxL5dCHrC9tGwN+xUgqnGdoQ/fycICG8Kjzs64liXhXWsC6+B3xHkcL8CbM3leiTIA3+UoNd3H8GHxecJnpNR03NlXYeU4z8b3setBL1yZ5Dh9YTgW5dbCHpyDxD92jGMLF8Dwv2HEwTwWwmmSpxHkA4UWYcm2uqacP8zw9suDK/VjeG1OrCZ2z8Q3j5y2r9mbpusa6Me6nD7Bzh0zUdN3Zb185kmXpcIFne5P6Itk9/cnZe2/wpy66G+g2DmkYcIgr/dBMHnBzIcqzK8H6+Ej8mO8L5dkmH/Fnv/y3J7ptfZQQTX+8qwHpsIeqDf2YL380qCjqu9BO8hNxOktWR8bPST+cfChhURkRJmwRLupxKkwOzKYv8eBKk0C939tOb2L4RSqEOpC/OOlxIMEBvoDXtY2zwz+z3BjCMj3f31YtdHpFA0KFFEpESYWRcz6xVRfgXBoMS/pQfTZtY/fVBZOJjp/xH0Wj1QsAqXUB3asAsJUmnuaqvBtJmVmVmjWSbMbCJBOsarCqalvVMPtYhIiQinM1xAkPKxlCB/cwxBms5WguWDF6fd5nMEg8v+TjDYNDkTyTEEqQfvcvc9Ba530evQ1oRjIPoQ5NB3IBj81xKLQbU6M6skSDF4giDF5gDBoknvI0hXmOTRM7qItBsKqEVESoSZ9SaYP/i/COaV7USQu/x34IceLP6RfpsxBIOoTuHQYh1vEuSzTvfoVeVaut5Fr0NbkzIn+asECwY9XuQq5SycevAGgmkxBxPkG28iGED4Y48e/CvSriigFhERERHJQ5ufNq9fv34+bNiwYldDRERERNq5+fPnb3L3RivmtvmAetiwYdTU1BS7GiIiIiLSzpnZyqhyzfIhIiIiIpIHBdQiIiIiInlQQC0iIiIikoeCBtRm9hsz22Bmr6SU/dTMXjOzl8zsgdRFDMzsm2a21MxeN7MPFLJuIiIiIiItodA91HcAk9LKHgdOdPdRwBvANwHM7HjgYwSTwU8CfhHObSkiIiIiUrIKGlC7+1PA5rSyv7n7gfDfZwkmgQc4F7jb3fe6+5sEq4SdUsj6FVsi4SzfuJN5yzaxfONOEgnNCS4iIiLS1hR72rxPAfeEfw8iCLCTVodljZjZFILlWhkyZEgh61cwiYQze9E6vnLvQur2J6isKOP6i0Yz6YQjKSuzYldPRERERLJUtEGJZvZt4ADw+7i3dfdb3b3a3av79280t3absKJ2V30wDVC3P8FX7l3IitpdRa6ZiIiIiMRRlIDazK4AzgY+7ofWPl8DHJWy2+CwrF1av72uPphOqtufYMOOuiLVSERERERy0eoBtZlNAr4OfNjdd6dsehj4mJl1MrOjgRHA861dv9YyoEcllRUNm7+yoowjulcWqUYiIiIikotCT5v3R2AecKyZrTazycBNQHfgcTNbaGa/BHD3RcC9wKvAbOAL7n6wkPUrpmF9u3L9RaPrg+pkDvWwvl2LXDMRERERicMOZVy0TdXV1V5TU1PsauQkkXBW1O5iw446juheybC+XTUgUURERKREmdl8d69OLy/2LB+HtbIyo6p/N6r6dyt2VUREREQkR1p6XEREREQkDwqoRURERETyoIBaRERERCQPCqhFRERERPKggFpEREREJA8KqEVERERE8qCAWkREREQkDwqoRURERETyoIBaRERERCQPCqhFRERERPKggFpEREREJA8KqEVERERE8qCAWkREREQkDwqoRURERETyoIBaRERERCQPCqhFRERERPKggFpEREREJA8KqEVERERE8qCAWkREREQkDwqoRURERETyoIBaRERERCQPCqhFRERERPKggFpEREREJA8FDajN7DdmtsHMXkkp62Nmj5vZkvB377DczGyGmS01s5fMbGwh6yYiIiIi0hIK3UN9BzAprewbwBx3HwHMCf8H+CAwIvyZAtxS4LqJiIiIiOStoAG1uz8FbE4rPhe4M/z7TuC8lPK7PPAs0MvMBhayfiIiIiIi+SpGDvUAd18b/r0OGBD+PQh4K2W/1WFZI2Y2xcxqzKxm48aNhaupiIiIiEgzijoo0d0d8Bxud6u7V7t7df/+/QtQMxERERGR7BQjoF6fTOUIf28Iy9cAR6XsNzgsExEREREpWcUIqB8GLg//vhx4KKX8E+FsH+OAbSmpISIiIiIiJalDIQ9uZn8EzgT6mdlq4LvAj4F7zWwysBK4KNx9FnAWsBTYDXyykHUTEREREWkJBQ2o3f2SDJsmRuzrwBcKWR8RERERkZamlRJFRERERPKggFpEREREJA8KqEVERERE8qCAWkREREQkDwqoRURERETykHVAbWbvNrOu4d//bWbXm9nQwlVNRERERKT0xemhvgXYbWYnA18FlgF3FaRWIiIiIiJtRJyA+kA4V/S5wE3ufjPQvTDVEhERERFpG+Is7LLDzL4J/DdwhpmVARWFqZaIiIiISNsQp4f6YmAvMNnd1wGDgZ8WpFYiIiIiIm1EnB7qDwGPuPsSAHdfhXKoRUREROQwFyegHgL8ysyGAfOBp4Cn3P3FQlRMRERERKQtyDrlw92/6+4TgBOAp4H/BV4oVMVERERERNqCrHuozexq4N1AN2AB8DWCwFpERERE5LAVJ+XjI8AB4C/AP4B57r63ILUSEREREWkj4qR8jAXeCzwPvA942cyeKVTFRERERETagjgpHycC44H/AqqBt1DKh4iIiIgc5uKkfPyYIICeAfzb3fcXpkoiIiIiIm1H1gG1u59tZp2BIQqmRUREREQCWedQm9k5wEJgdvj/aDN7uED1EhERERFpE+IsPX4NcAqwFcDdFwJHt3iNRERERETakDgB9X5335ZW5i1ZGRERERGRtibOoMRFZnYpUG5mI4CpwL8KUy1JSiScFbW7WL+9jgE9KhnWtytlZVbsaomIiIhIKE5A/UXg28Be4I/AX4HvF6JSEkgknNmL1vGVexdStz9BZUUZ1180mkknHKmgWkRERKRExFnYZbe7f9vd3+nu1eHfdbme2My+bGaLzOwVM/ujmVWa2dFm9pyZLTWze8ysY67Hbw9W1O6qD6YB6vYn+Mq9C1lRu6vINRMRERGRpGYDajO7Ifz9iJk9nP6Ty0nNbBBByki1u58IlAMfA6YDP3P34cAWYHIux28v1m+vqw+mk+r2J9iwI+fPMSIiIiLSwrJJ+ZgZ/r6uAOfubGb7gS7AWmACcGm4/U6CmUVuaeHzlrxk3vSe/QeZNnE499asZu22IIiurCjjiO6VRa6hiIiIiCQ1G1C7+/zwz77AX9x9b74ndfc1ZnYdsArYA/wNmA9sdfcD4W6rgUH5nquticqbnjZxBHfNW8mW3fu4/qLRDOvbtdjVFBEREZFQnEGJ5wA/M7OngHuA2SnBbyxm1hs4l2Ae663An4BJMW4/BZgCMGTIkFyqULKi8qZvnLOEOz95Cv27d9IsHyIiIiIlJs6gxE8CwwmC30uAZWZ2W47nfS/wprtvDJcxvx94N9DLzJJB/mBgTYa63BoOjKzu379/jlUoTZnyph2nqn83BdMiIiIiJSbOwi6Ewe9jwN0EKRrn5XjeVcA4M+tiZgZMBF4FngAuDPe5HHgox+O3WQN6VFJZ0fBhUd60iIiISOnKOqA2sw+a2R3AEuAC4DbgyFxO6u7PAX8GXgBeDutxK3AV8BUzW0qQs317Lsdvy4b17cr1F42uD6qTc0+35bzpRMJZvnEn85ZtYvnGnSQSWmBTRERE2g9zzy64MbM/EuROP9YSAxNbSnV1tdfU1BS7Gi0qOcvHhh11HNG9ba+OqMVpREREpL0ws/nuXp1eHieH+hJgATA+PGBnM+veclWUpLIyo6p/N8ZV9WvzedNanEZERETauzgpH58hSNP4VVg0GHiwAHWSdkSL04iIiEh7F2dQ4hcIZuLYDuDuS4AjClEpaT80yFJERETauzgB9V5335f8J5zeTqPLpEntcZCliIiISKo4C7v8w8y+RbBc+PuAzwOPFKZa0l6UlRmTTjiSkVPHt4tBliIiIiLp4gTUVwGfJpjm7rPALIKp80SalBxkWdW/W7GrIiIiItLisgqozawcWOTuI4FfF7ZKIiIiIiJtR1Y51O5+EHjdzIYUuD4iIiIiIm1KnJSP3sAiM3seqJ9E2N0/3OK1ageSi7Os317HgB7KGxYRERFpr+IE1N8pWC3aGa0OKCIiInL4yDqgdvd/NLXdzOa5+2n5V6nty7Q64Mip4ws2ME894iIiIiLFEaeHujlaqSPU1OqAhQio1SMuIiIiUjxxFnZpjhZ5CbX26oCZesRX1O5q5pYiIiIikq+WDKgl1NqrAzbVIy4iIiIihdVsyoeZdXL3vVkcS7kFodZeHTDZI54aVBeyR1xEREREDsmmh3oegJnNbGa/y/KvTvuRXB1wXFU/qvp3K2guc2v3iIuIiIjIIdkMSuxoZpcC7zKzj6RvdPf7w9+vtHTlJDut3SMuIiIiIodkE1B/Dvg40As4J22bA/e3cJ0kB8ke8UJNyyciIiIi0ZoNqN39GeAZM6tx99tboU4iIiIiIm1GnHmoZ5rZVOCM8P9/AL909/0tXy0RERERkbYhTkD9C6Ai/A3BIMRbgE+3dKVERERERNqKOAH1O9395JT/55rZiy1dIRERERGRtiTOwi4HzeztyX/MrAo42PJVEhERERFpO+L0UP8v8ISZLSdYxGUo8MmC1EpEREREpI3IOqB29zlmNgI4Nix6PXUFRTN7n7s/3tIVFBEREREpZXFSPnD3ve7+UviTvhz59DjHMrNeZvZnM3vNzBab2Wlm1sfMHjezJeHv3nGOKSIiIiLS2mIF1M2IuyzfjcBsdx8JnAwsBr4BzHH3EcCc8H8RERERkZLVkgG1Z7ujmfUkmM/6dgB33+fuW4FzgTvD3e4EzmvB+omIiIiItLiWDKjjOBrYCPzWzBaY2W1m1hUY4O5rw33WAQOibmxmU8ysxsxqNm7c2EpVFhERERFpLKuA2szKzOxdzey2IsZ5OwBjgVvcfQywi7T0Dnd3MvR6u/ut7l7t7tX9+/ePcVoRERERkZaVVUDt7gng5mb2+UiM864GVrv7c+H/fyYIsNeb2UCA8PeGGMdsFxIJZ/nGncxbtonlG3eSSGSdSSMiIiIiRRAn5WOOmV1gZnEHHzbi7uuAt8wsOQXfROBV4GHg8rDscuChfM/VliQSzuxF6zhrxtNc8uvnOGvG08xetE5BtYiIiEgJsyCzIosdzXYAXQlWR9xDMKuHu3uPnE5sNhq4DegILCdYJKYMuBcYAqwELnL3zU0dp7q62mtqanKpQslZvnEnZ814mrr9ifqyyooyZk0dT1X/bq1al0TCWVG7i/Xb6xjQo5JhfbtSVpb3ZykRERGRNsvM5rt7dXp5nIVdurdkhdx9IdCoQgS91Yel9dvrGgTTAHX7E2zYUdeqAXWyp/wr9y6kbn+Cyooyrr9oNJNOOFJBtYiIiEiarFM+LPDfZvad8P+jzOyUwlXt8DOgRyWVFQ0fksqKMo7oXtmq9VhRu6s+mIYgqP/KvQtZUburVeshIiIi0hbEyaH+BXAacGn4/06aGago8Qzr25XrLxpdH1Qne4aH9e3aqvVoqqdcRERERBrKOuUDONXdx5rZAgB332JmHQtUr8NSWZkx6YQjGTl1PBt21HFE9+LkLid7ytNzuVu7p1xERESkLYjTQ73fzMoJ54Y2s/5AoumbSFxlZUZV/26Mq+pHVf9uRclZLpWechEREZG2IE4P9QzgAWCAmf0QuBC4uiC1kqIqlZ5yERERkbYgziwfvzez+RyaheM8d19cmGpJsSV7ylt7uj4RERGRtiZODzVAFyCZ9tG55asjIiIiItK2xJk27/+AO4E+QD/gt2amlI82Qkuai4iIiBRGnB7qjwMnu3sdgJn9GFgI/KAA9ZIWpIVaRERERAonziwf/wFS503rBKxp2eocflqj51gLtYiIiIgUTrM91Gb2c4Kc6W3AIjN7PPz/fcDzha1e+5Ztz3Ei4ayo3cX67XUM6BF/xo1SWdJcREREpD3KJuWjJvw9n2DavKQnW7w2h5lMPccjp46vD3Sjgu5rzz+JsUN6MaRPdoG1FmoRERERKZxmA2p3v7M1KnI4yqbnOCro/tYDLzPljCpGHtkjqzzo5EIt6T3hWqhFREREJH9ZD0o0s7OB7wNDw9sZ4O7eo0B1a/ey6TnOFHQnHL5y70KO/eJ43n5E02kbWqhFREREpHDiDEq8Abgc6OvuPdy9u4Lp/GSzxHcy6E5VWVGGexBYr9qc3cDCUljSXERERKQ9ijNt3lvAK+6uCYxbSDY9x1HpGlMnjGDmsyuprCijS8e4a/OIiIiISEuKE419HZhlZv8A9iYL3f36Fq/VYaS5Jb6TQffgKeN4Y8NOVm3ezcxnV7Jl9z6mTRzBgB6dWrnGIiIiIpIqTkD9Q2AnwVzUHQtTncNbpunxysqMEwf1YsPOvazaDBe8YzBlBiMGdGNIHw0sFBERESmmOAH129z9xILVpB2KM390c3NSl5UZE44dQFW/bhpYKCIiIlJC4gxKnGVm7y9YTdqZZIB81oynueTXz3HWjKeZvWhdxpUQs1nNMJkecsqwvgA892Zto9UVW2PlRRERERE5JE5A/T/AbDPbY2bbzWyHmW0vVMXaurjLfTc1J3WqZKD+yTue55mltTy4cA3/XLqJAwcSsYN4EREREclf1ikf7t69kBVpb+Iu953taoYrancxffZiLq4ewoy5S+rTQ6ZfMIqTB/dsduXF9irf5dlFREREchVnYZczosrd/amWq077EXe572xXM1y/vY6zRw2qD6YhCJyvuu8l7vzkKbGC+Ka0ZIBa6GC3ufxzERERkUKKMyjxf1P+rgROAeYDE1q0Ru1EcwFyVJCZzWqGA3pUUl5Go8C5d5eOHEgkmDpxOAmH++avZu22uiaD+ExaMkBtjWA3U3rN4dAzLyIiIsUXJ+XjnNT/zewogtUTc2Zm5UANsMbdzzazo4G7gb4Ewfpl7r4vn3MUS1OLtjQVZDY1JzUEgfo7h/Zp0Ps9sGclnzhtKJPvrGmw+Ms9Nau4atJxDOndheUbd2bdQ9ySAWprBLtx02tEREREWlKcQYnpVgPH5Xn+acDilP+nAz9z9+HAFmBynscvqkzLfccdsJh+zNOq+jL9glH1S5J/tHowN85pmAIyY+4SZnxsDO8/bgB/W7w+1kDFbAdIZqMlj5VJpuXZ4/bMi4iIiOQi64DazH5uZjPCn5uAp4EXcj2xmQ0GPgTcFv5vBOkjfw53uRM4L9fjl7J8g8wOHco4Z9TbmDV1PHdPOZXRR/WKPN6e/QdZtWV37OC9JQPUXI8VZ/q/ZHpN8jyZ8s9FRERECiFODnVNyt8HgD+6+z/zOPcNBMuZJ2cP6QtsdfcD4f+rgUFRNzSzKcAUgCFDhuRRheKIO2AxSuqS5cs37mx0vKF9O9O5opw31u/g0+Or6nOqofl0iGwHSGYjl2PFzbtuKr0mF5oxREREROIw99afo9jMzgbOcvfPm9mZwNeAK4Bnw3SPZI72Y82tzlhdXe01NTVN7VJyWnqgXvrxhvbtzBcnjODqB19pkFM989mV9QMVZzWTw5wMKlsyQM32WMs37uSsGU83+sDRXJ1bgmYMERERkUzMbL67V6eXx5k2793ANcDQ8HYGuLtX5VCfdwMfNrOzCGYM6QHcCPQysw5hL/VgYE0Oxy55yR7V46eNZ/32vezad4ChfXJPT0jvoe1cUc7Ftz7bKKd68ulV3P7M8qx6m1N7wPMV91jFHGSoGUNEREQkrjgpH7cDXyaYfeNgPid1928C3wRI9lC7+8fN7E/AhQQzfVwOPJTPeUrdq2t3NNkTGif1IDVonbdsU2RAetKgHtwzZRz7DiZYUburaKkMzd2vlkiJyZVmDBEREZG44gTU29z9sYLVJHAVcLeZ/QBYQBDEt0vN9YTmk3qQKSDt0rFDfc91sVIZsrlfLZnDHVcxg3kRERFpm7LOoTazHwPlwP3A3mS5u+c800dLaIs51ADzlm3ikl8/16j87imnMq6qX8Y84numjGP3voNN9lhHBa3TLxjF9Y+/zsraPQ2O1xp5yamyzY9uyRzuOJRDLSIiIpnknUMNnBr+Tj2Io5USc9JcT2im1IM5r21gxpyljQK9AwcSLFq7jbXb6hjYszPvPfYIZqXMelG7a2+DYDp5vNZOZcg2paIlc7jjaOkZQ0RERKT9i7NS4nua2m5ml7v7nflX6fDQXFpDpoD7YCJYGfEjYwfz2rrtDOrVmeMGdOfhl//TYFaPH5x3IuedPIhhfbuyonYXO+oOMG3icO6tOTR9XnJqvXnLNrXa9HBtIaWiWMG8iIiItE0tNm2emb3g7mNb5GAxtNWUD2g6rSEq9WDqhBHMfmUtk04cyIy5S+rLf3T+Sdww541G6Rx//txprNq8p8Expk0cwV3zVtKxgzWaWq81UhuUUiEiIiJtVaaUj5YMqBe4+5gWOVgMbTmgbk5qwN25opypdy/g7FGDuP2Z5Y16eCefXsXNTyxtcPuZk0/hM3fVNNr3zk+eQmVFWYOp9ZLb4uRU57oASrHyo0VERETy0RI51M1p/RVi2rBsgtHU1INEwrlq0nG8tm57ZA5yedoi8pUVZezdfzByX8fZvS96W7Y51VGLyXz/3JOoKLdYU/yJiIiItHUtGVCrizFLBw4k+NfyWmpWbibh8MiLa7hq0nFMOuFIgMhAu6zMOH5gdxLuDXKQB/as5KPVgxl5ZPf6HOktu/fx0wtH0bFDeZP5yvnkMqdO+zewZyUXVw9hyswapXGIiIjIYaclA+p/tuCx2q1EwvnLK2u56r6XGuRGT5+9mGMHdOf19ZkXe9m4cy9vbd7Nd84+nu8/+iq9u3TkE6cN5cY5SxoMRhx5ZHcqO5Qz+a5/M3XCiAb51tMvGFU/8DGfuZ5TZ+v4yNjB9eeAlltdMNeUkkIfS0RERCRVnKXHvxJRvA2Y7+4L3f3KlqtW+7Widld9MA0NlwVftTnzYi/D+nblP1vruHHOEnp36ciUM6oYc1Qv/uf3LzTY/+oHX2HW1PGs317Hyto9zHx2JZNPr8IM3GFQr8r6QDI5PdzmXXupKC9j976DWa+gmDpbhxktvrpg3MGLTQXMGggpIiIihVTW/C71qoHPAYPCn88Ck4Bfm9nXC1C3dinTPMzlZdClY4eMgWlqIL52Wx0z5ixlwVtbM+6fDHjXbqvj5ieWctPcpdz+zHL6dO1EIuEs37iT596spcxgw459XHzrs1zy6+c4a8bTzF60jkSi6ZT45LR/lRXBJZT8nZTvVHiZVpJcUbur0b7JgPmsGU9H3oc4xxIRERGJK05APRgY6+5fdfevAu8AjgDOAK4oQN3alGSQOm/ZJpZv3JkxIE0GuqkqK8qoHtqHAT06MbRvZ77wnuFcOSH4Gdq3M0d0r4wMxBMeHcgahjvcdOmYBgHv9ReNZkjvLg2Cz/sXrIkdbCZ7g3t3qeCeKafx3pH9mX7BqEbnymep8KYWgEn35qamA+Y4x8pVto+/iIiItD9xcqiPIGXJcWA/MMDd95jZ3gy3OSzESSmIWtBl+gWjeFdVX4BGc0P/4LwTGdK7C9B4EOEjL65h+gWjGuRjT5s4gi/ds5Atu/dx/UWjmT1tPOu2H5qeLr23NuHR6RrrtwfBZnoKRab7+qETB3LSoJ4Zp8KLm8Oc7QIwiYSzeG30zCdvrN8BBAM3C7mYjFJKREREDm9xAurfA8+Z2UPh/+cAfzCzrsCrLV6zNiRTSsGgKePYve9go9k6Mi1tvXzjTn4e5lNbGIf9fO4Sxg7pHRmIXzXpON5/3ABOGtSTlbW7WPDWVu6atxKAyadX1a+keMqwvvWBXVRvbVSwuf+gc9aMpxsFiJnua3L+6qic6VwCzuZWkkxt+yUbdkTeh5fXbOdL9yzk+otGc9OlY7jyDwtyGoDZnExtku+gTBEREWkb4iw9/n0zewx4d1j0OXdPrqjy8RavWRuSKaVgzmsbmDFnaaMAMtM8zLW79nJx9ZAGs3JMnTCCzbv2UtW/W8ZAvKp/N9ZvD/KqB/as5LJxQ+uPcetTyxucO73n9775q5k2cUSDmUKmXzCK7zz0cmSA2FT6RPr9SfZKr6jdxevrttO7S0fWbqvLKuBs6oNHetvfW7O60WwmUyeMYOazK+vP9ZcvjmdWM8fKVZw2ERERkfYnziwfM4C73f3GAtanTcqUnjDiiO5cOWE4981fnVWPZceyskbTz82Yu4TfTz6Vecs21fd0px4jGbSWmVFZUdbsFHbpPb9bdu9jxIBu/OWL49m4Mwg2a3ftbbCMefI4qYMds0nFiFo6feazK+uD6uYCzuSHhWSqynNv1jZKFxnQo5Itu/fVz2YypE9nVm3eU3+eZN037qxjXFW/ggS42baJiIiItE9xBiXOB642s2Vmdp2ZNVp28XAVNePFtIkjuHbWYm57ejmXjRtK7y4dmx0Et3n3vsiezhW1uyJnr0gGrZ+843n+vWIzPzjvRIb26Zwxn3j5xp1AMF3erKnjuXvKqcyeNp6j+3Zr0HPbt2unjLN2RN3XTKkY6WkQM+Yu4SNjBzc4XnOam8EjWZ8tu/dx8xNL+c/WPdz+zPL6YDrqXC09gDDbNhFp6zT4VkQkWpyUjzuBO82sD3ABMN3Mhrj7iILVro1ITU9IzWVOBnUz5i5hyhlVzQaQXTp2iOzp7NihnIE9K/nEaUNJJJw5r62nql83ystg+uzFDdJEpk0cnjGf+EePLW6wPHj1kD78bfH6RnnK7z9uQMb85TipGFGBvVm8gLO5/OT0+hzZo5Jjj+zRqO5lBvOWbWJgz0peXZt58ZzmZBpcmU2biLRlGnwrIpKZucfrYTCzU4CLgXOBxe5+TiEqlq3q6mqvqalpfsdWMm/ZJi759XONym+6dAxnnTgw4xtPIuG8unYbTy/Z1CCfedrEEXTtWI477N5/sMG2a88/ifXb6+rLgPrAO3W/qRNGMPuVtUw6cWCDPONbL6uuXy48qbKijFnhQjIranflHCAu37izflBj6rHv/OQp9O/eKevjZWrPu6ecyriqfpG3OXAgwaK121i7rY639axkw8699QMSp04czq1PLY+8z82lgyigkMNZpud0Ns8dEZH2wszmu3ujLI04OdQ/Ac4HlgF3A993960tVsN24oju0fm0Iwd0bzKYnr1oHdNnL+ZT7zqaKWdUkXAoM+jasZztdQfYdzDRIBCs25/gWw+8zHUXntzgXGu31XHXvJVcd+HJOPDauh3MfHZlZG51zcrNTQ6myzRrRzaG9O7CD847sdEUgO8Y0psOHbLPNIqbn5xIeINe9/QAOtM0gdkMINRsHnI40+BbEZHM4uRQLwPeBXwXWA6MMrMzClKrNqzMYNrEEY3yqTuUZ+7BTAZqK2v38MunlnMwERzn+CN70LtrR/YdTGQMBCvKrVG+85bd+1i8bgdLN+yozyeOWh4808IwcQfTReVVrtqyu34KwCsnDGfy6VX8fO4SVm3ZHeuYtbv2xlo0Jpt5tnO9z62xQIxIqcq0KJUG34qIxJuHOgHMJVgxcSEwDpgHTGj5arVNiYSz6D/buWveyvq5pN3hrnkrGTOkF8P6NZ6dY/32OsrM6qeUSy4VDvCNDx5LGfCut/dl3rLayJ7aft06NVrcJTmbBsC155/Etx54uX7/9IVhfnT+SXzzgZcbTJmXXEgm2/sclQbRv3tHVtbuqb8vSdn0ZqUfc2jfztx6WXV97ndT6SLNzbP91Osb+O45J/C9RxZFLp7TlHxm84i7sI1Iqcl2bngRkcNR1jnUZvYy8E7gWXcfbWYjgWvd/SOFrGBzSimHevnGnTy0cA2/aiZHNyoInTZxRIOBjEP7duZz/zWc7z2yiN5dOvK/HziG9dv3NsiN/vJ7j8Fwqo7oRv+unZjz+gYOJuD+F1azdlsdlRVlzJ42noTDitqdrN22l+8/+iq9u3Tko9WDOap3F47u14X9B515y2s5mIBHX1rDVZOOazIvODU47NKxnItvfbbR/b1nyrjI8mzyLfPJ1Xzxra1cfOu8jDnlUycO56GFazh71KD6DzyPvrSG315xSsFyqJV7Le1F8rmvwbcicrjKO4caqHP3OjPDzDq5+2tmdmwL1rHNy7TIyLXnn9SgFycqF/fGOcFMIMmFYK4+63i+ePeC+n16dq5g994D/Oqyd7B1134qOhi/eWY5p1b158o/LGD2tPGMjJjdYkifQ29433vkVaZNHEGPzhV8/9FXGwTzf6pZXR/Mp+cFJxLOm5t2sXLzLnpWVrBm6x6+HvaIT504PDINYve+gzn3ZuWTq7nv4MEG7b9l9z66VJQz81OncNCd3fsOMmPO0px6znOdzePNTdG518d+cTxH9+uqnmtpMzItSiUicriLE1CvNrNewIPA42a2BVhZiEq1VemLjJgFudBjh/RqECRlChjHHNWLmy4dw3+27Ka83Pj0+CogGJh4y5NL+fipw5i/cgsJh3KDy04bxu1Pv0nd/gTrttfx/uMGcM+UcazdVsfAnp05YWCP+vMO69uVqyYdx2vrtjeYFSQZzE8+vao+yKzbn2BlGOQN7FnJov/s4Kt/yjzILyoNYkCPSk49um+D4HNI7y6RwWN6OsTAnrmnVvTt2ol7alY1SLn5zb/erO+BXr5xZ16LsOQSUKzcvCvy8V67bTevr899Cj8REREpDXHmoT4//PMaM3sC6AnMLkit2qjUHMObn1jaoJc4VaZc3KP7deXAQSfhzotvbeW++avZsnsf3zn7eD500ttYt72uPphN9ix/8KSBvLFhJ0f2qIycUzp1ufPjB3ZnR93+jPNDp9ZlwVtbmTFnabOzZNw3v3GPfOqc1cngM1Paw/uPG8DfFq9n+uzFnD1qEOVl8M6hffjVZWP57MwXYvduJz84ZOoZL0YeaNdM84uXl/OVe+fnNWuIcrNFRESKL/Y81C1yUrOjgLuAAYADt7r7jeGiMfcAw4AVwEXuvqWpYxU7hzo9oBnSuwurtuxuMiUgKri86dIx7DvgDcq+/N5jSLizZ/9B3jG0N5+dOb9RUHbdhSdT2bGM/l07cfGvo3OZd+87yIAewZLi/3hjE7c93TjHe9rEEezce5DyMjhuYA9ueWIpL63ZzpUThnPT3EPpEVdOGN7o9kP7duanF5xMAs94nzPlRd8zZRxT717QYHGa5ODIkwf3ZN32+LmazeV5tnYe6IpNO3nslXWN5hcfeWR3PnlH42u3qTm2UxUzN1uBvIiIHI5aIoe6JR0AvuruL5hZd2C+mT0OXAHMcfcfm9k3gG8AVxWpjs1qKqBpqocxKhfXHT7086cb9Fb+7O9v1OdV/+SCkyJ7ljt2KGPfAWfO6xsit895bUN9Xvb0C0bxxrqtfOfs4xvkUF97/kl06VjOl+45dD+mThjBxp37gIYpHffNX820iSMaBIcfe+cQjujRqcEsJukypbms3VbH2aMGNZoj+6r7XmLW1PFZBZZR7dtUWkZr54EO6dOVEQO6NZhffMSAbgzp0zWv9JNizYutQZYiIiINxZmHusW4+1p3fyH8ewewGBhEsPrineFudwLnFaN+2coU0Kyo3dXsbZNB3biqflT178aGHdEBZyL8AmHjzr2Rc8D27lLBa+u2M+KI7gzt27nR9oOJQ8e6/vHX+cAJg7j1qWVMPr2KqROHc/OlY9m3/0B9MJ3cd8bcJXxk7GDum7+ar7zvmPpzb9m9jyN7VjJt4giunDCcKWdU1QeHTUkueJNev7f1rKS8LPNiK+1BWZkx4dgBnDd6EKcP78t5owcx4dgBHN0vSD/Jdo7tdPnOix01f3g28rnuRURE2qNi9VDXM7NhwBjgOWCAu68NN60jSAkpWS25climvOpjB3RnYM9KfvfsKr783mP42d/fqO8V/P/OPZGv/flFVtbuobKijO+efQK/fGpp/f+p81EDnD1qEN9+MJhzOjkAsbKijO+dcwK9u3TkI2MH1+dS3zd/NWZBAF3VvyuPXnk6b23ZTZeOHTiyZycOHISNO7NPmSgvo1HP9rSJI+heWcE7h/bJq6e22LJJf8jUK57LrCFJ+c6LnWsvs1bMExERaaioAbWZdQPuA77k7tstZWScu7uZRXaZmdkUYArAkCFDWqOqkVpyoY8hvbs0Giw3dcIIrvvba1w2bigzn13JH54PlhQH6NO1I9984CVW1u4BgoDme48u4icXnszSDTs4Y0R/vvqnhfVT4QEZe4J7d+vYYK7mZLB7yrA+fGTMoPogb/iA7g1u+/YjDk2rt3zjziYDyuSS6OkL3ow+qhenDuvTaIGZtrJgRL7pD/mkn+QzwDKfdJF8rvvWEvUhB1Det4iIFETRAmozqyAIpn/v7veHxevNbKC7rzWzgcCGqNu6+63ArRAMSmyVCkfINaBpasaLez4zrn6BlpnPBgu9zJgbzFE9uFcXtu7ey6jBvfj7axvqg+mkuv0Jlm7YwcgjezB6cK9Gs11k6gnuWVkROZXeX77YfHCVbUDZpWMHtuze12D+58qKMrp2LOfvr2/ghjlvMG3iCI7q3YXyMuOYI1qmp7Op3uOWGFhXrDxmyH1ebMivl7nUV8zLdE127GBc+YcFyvsWEZEWV5SA2oKu6NuBxe5+fcqmh4HLgR+Hvx8qQvWylmtAkykImz1tPLW79zFjTsNFR+r2JxjUszM3P7mEs0cNot+2uozzP58xoj+jB/eiQ4eyRnWL6gWffsEoDiY8MrjauLOuvhc67n1JDygTnuC7Z5/A9x49tOT3d88+gYOJYGaT3l064g5f+/OLLRbwNBXsA3kPrEsG5MVMf4jTw91whcvoqfyy6WXOJ5BvDZmuySlnVBXlg4+IiLR/xeqhfjdwGfCymS0My75FEEjfa2aTCRaNuag41cteNgFNek9oVO9g7y4deWHVVlbW7ooMdFZt2cPK2j2Ul8HAnp257ellkQHqjx97lU+d/vb6wDC9blGB0IoM58wmuMq2p7PMjF8+tbRByscvn1rK1R86nrr9CT4ydnCjmT5SA55cepObCvbLDF5bt71+8Zz75q+OFWAlg/XX120v+fQHaPzhYmjfzvzgvBO5+sFXcuplLuUV8zJdk+ljLpX3LSIiLaUoAbW7PwNkioYmtmZdchEnuIvqJf31ZdWNgrCPVg/mWw+8TO8uHRstlJIcXFhZUcZpVX05bkB3pk48lu889HKjAPXsUYOaDAyjAqHmvsJv6v5mm0+7e99BVtbuabTkd4/KCioryjDLPNPHsL5dc+pNzhRYbd61l1Wb9zRYJCfZxtkGWMlgPerxKqX0h6T0Dxcra/fw87lLuGfKOPbsP1j/4QpoNh++1GW6JtPvRil+8BERkbap6LN8tDXZ5Aynf7U+ffbitPzk1/n+uSfynYcO9Q6+vX+3+nmZU5cuHzmgOz+ctZgtu/fx5fcewzfuf4mrJh1Hr84VkQFqMjCN0/OWSDhlRoN5kjt2OHRfmrq/UcH4TZeOwR3mLdtUH5RlCnIG9OjE9ReNbrKnN9c85UznrCgv41vhAMjk8ZJ56tkGWMlgPf3xGj+8H+8c1qdVgtA4H+yiPlysrN3Dnv0H6+f6bi/zS2f6gNixg9VfD6X6wUdERNomBdQxNRfcRQUlyd7P5Iwbp1b156YnljToXV67dU/9m/3abXX1S5dfd+HJXPCOwZQZVIaLuHzl3oXcM+W0yGDRPV7PWyLh/Gt5bYN5qJPH+ssXx2NGk/c3PZ/2yB6VvLp2R/0iNakDLqOCnMG9ugAwoHsnjurTlasfbDzTx3Nv1uaUp5wpsNq972Dk8Y4Z0D3rACs1WE99vD4yZlCrBdNxgt9svkko5gDLlpQpxxtgVonmfYuISNumgDqm5nKGo4KSGXOD4DnZm1xeRqPe5YE9K7n2/JPqe06TU9f9cNbi+kC8sqKs/jjuCaZfMIqr7nupQeB+T80qpl8wiiG9u2R1f1bU7qJm5ebI+/TG+h307FzRbDCbmkayfOPOyKBs1tTxkYMk/7Z4fYO83lsvq6ai3Br0uOY6TVumwCpTzvhxR/bIOsAq9kwXcYPfbOrbnuaXzpTjXap53yIi0rYpoI6pueAuU1AytE9nrpwwnHKDU45uPH3dlt37GDukF7Omjmf99joS7nztTy81mEe6bn8CMxjatzMrN+/h+sdfZ/LpVZSXwZghvVmzeRdnjxrE9Y+/TkV5WdaLdGSaMWRF7S6OH9gjVjDbXFCWGtCkB98ra/cwZWYNs9KCwtRgsHeXjny0ejDHHNEd9zBdpYn7GCdn/Oh+2QfDxZ7pIm7wm01928L80iIiIqVIAXVMzfX0ZQpKVm/dw01zg7SAkwb3jDzGkD5d6/OSX16zjS279zU4d3Jg1ffPPYkpM2uo299wxcPUXvA4i3Q8u2wj3z3nBL73yKEZQ6ZNHEGXinJ+PveNRj3hTfXExgnKsp4hJAwGj582nhdWbW3Qi59Ljm8uwXCmfOVi9XjmEvw2V998Bqemaon5vUVERNoScy/auigtorq62mtqalr1nMmAISoYi8ptnTZxBHfNW9kgdWP2tPEknEY5nm9u2sXitdv5z9bdONZgqfFrzz+JMUf14tW12/nBXxY3Wir8gncM5qa5h9JI7p5yav2As0wOHEjw4Itr2LxzLzv3HSThQU73/S+sZsvufUw+vYr3HNuPPl07ZRV8ZsrtPX5gd9ZuaxhgLd+4k7NmPN0oKEzvoU6Ku3+2mgsAS3GwXqHqlOnazvZ8pdhWIiIiLcXM5rt7daNyBdQtLzUoMYwv3dNwCfCBPSu54eLRJNwbLIucHoh8c9JIduw9wL6DCSaOPIKTBvViRe0unlmykV37DjZaKryyQxnfe3QxkH2gmQxSPz2+qkEwnjR14nDOGz0oVsCaev/7d6vkzdqdkSvURd3npoKvecs2ccmvn2tUns0Hh6bq2lwdChXI56upD3YtLds2KNW2kvZP34yISGvIFFAr5aMA0gfppaZuDOxZySdOG8rlv32+QQB37IDujQaZ/Wj2a/VpHO96e1/Kyoz12+vo372Sa9P2vXHOEm64aDRArAFyqWkXUSkE1UP7xB5ol37/k8F0sq6p6ShNpV6kv0EO7NnyOb7ZDO4r1cF6rZlykm0blGpbSfumb0ZEpNjKil2B9i6Zl1pZETT1R6sH1/csw6EAbuXm6CWszYKg8cgelSzfuJOdew9wIMNS4Qmc315RzZ2fPIXjB3bPqn7JXNz75q9m6oQR9fVMLkv+rqq+eb0hNRVgwaGgcFxVv/pp+ODQG+RZM57mkl8/x1kznubVtTu46dIxDeqY78wazdUPDrVRqsNtsF62baC2kmLI9MF4Re2uItdMRA4X6qHOQzZfMaYPgMs0B3LXTh0yru5206VjeHXtjvo3jGkTh0fu27drp0Y938310KQORJv57EqmnFHFMQO6c9yRPTi6X/5fmeY6c0SmN8i/fHF8i84lnE398p0iL/06GdK7C6u27G5TX01n2wbFnk5QDk/6ZkREik051DnK9SvG9BzTgT0r+Wj1YMYc1YsOZWVc/dDLrKzdUz8IceyQXhxMUL9QSvI2nzhtaKMc6q4dy/m/h1+tP1e2uauFzMXNtZ3i5EvnkzsZZ7BdLm0UdfwfnHciP5+7pP5xzuer6dbMG822DVozt1sElLsvIq1HgxJbWK4v4KkBVu8uHRsFxtMvGMWgXpX06dqpPhCJCi4H9qzk6g8dx+J1O+pn5Uif5QPgt1dUU1lRXtBgK5tZMuIGWNm0byLh9bOiLNmwg3trgplJ4gaohQwAM92P1CkOc33jV95ocWkQXOnQc0FEWosGJbawuF8xpr75HntEN/78udPYsns/n7mrpkFaw1X3vdQouOrSsXE6yJbd+zjo1AfQyfSQVJUVZSx4aysz5iwt2BtMNm9kuQyey2ZO5ExLvMddLruQg/syXSdmDf/P5avp9rJUeFukAK60FHuhJRERDUrMUZzBV+kD7D500zM8vWQTC1ZtbXZAHMC+gwcbDRicOmEE67burv//+otGM2pwzwb7TJs4gj/VrK4/biEG6RRqMFDyDXLW1PHcPeXU+qXLk2+QmZZ4/8jYwZFtWCyZrpPUL4ZyHbSXzYBKKQwNgis9mQY4i4i0BvVQ5yjO4KuoN98b5yzhpxeenNWAvb5dO3FPzSomn16FWbDwyj01q5jxsTHcPeXUBgvDJAfsRc1/XYhBOoUcDNRUz3FTPb+lNKtE1HWSzKGG/GYq0VLhxaNBcCIikkoBdY7ifMWY6c13zdbdTJ0wghlzlzQZlA/r25WrJh3XKHg/aVCv+lXsUnM5TxnWlxW1uyKXLm/pYKtYQV2m85YZJTWrRNR1MqR3F8YO6Z33V9OaUaN4SunDjHK5RUSKT4MSW0FTA9Puf2F1/SwfQ/t2jT1zQqZczvcfN4C/LV5f8BzPYuWSRp03OSvKkD6HT0ChGTWKo1RyqEulHiIihwvN8lFEUW960yaO4K55K3OalSJVU7NhDOvbtVWCrWIFdQompZhK4frTdHEiIq1Ls3wUUfrX/v27VVJeBmOG9Mr7jbi5XM7WWJq6NZfAjtLGPxNKG1Xs6x6Uyy0iUioUULeSqDffYf0avuHlkgvZGrmccerVWvmc+qpbpLRyuUVEDmeaNq9EpE+td9aMp5m9aB2JRNPdr8mBaanT5bXkwLQ49cr1PuRC05aJFP75LyIi2VEOdYnIJxeyGCv9RdWrNfM54yxNLtKelUIut4jI4UI51CUuUy7k5l1767dnSqEoxkp/UTmarZnPWYpfdWv6MimGUsjlFhE53CmgLhFRAeLQvp1Zs7WO/779+bzyhPMJ9OIErq0Z5JbaHMzK6RYpDn2QFZFSoJSPEhEVkN16WTVTZtbklUKRb6AX5/atHVSW0lfdmr5MpPXpg6yItLY2Mw+1mU0CbgTKgdvc/cdN7V9qAXU+vSXpAeL67XV55wm3RKAXJ3AtpSC3NSmnW6T16YOstCX6NiV/pdCGbSKH2szKgZuB9wGrgX+b2cPu/mpxa5adfHtLonIh802haIm85jg5modrPmcp5nSLtHeah1vaCn2bkr9Sb8NSmzbvFGCpuy93933A3cC5Ra5T1lp6KreWmBIrGeilUqDX8jR9mUjr0+ubtBWa6jV/pd6GJdVDDQwC3kr5fzVwavpOZjYFmAIwZMiQ1qlZFlq6tyR9hcVcUihKbfBee9USj5WIxKPXN2kr9G1K/kq9DUstoM6Ku98K3ApBDnWRq1OvEF/755tCoUCv9Ryu6S4ixaLXN2krlBaYv1Jvw1JL+VgDHJXy/+CwrE0o1a/9k4HeuKp+VPXvpjcbEWk39PombUGpxgdtSam3YUnN8mFmHYA3gIkEgfS/gUvdfVGm25TqLB/qLREREZEkxQf5K4U2bBOzfLj7ATO7EvgrwbR5v2kqmC5F+tpfRERE0ik+yF8pt2FJBdQA7j4LmFXseoiIiIiIZKPUcqhFRERERNoUBdQiIiIiInlQQC0iIiIikgcF1CIiIiIieSipafNyYWYbgZXFrkeJ6wdsKnYl2ji1Yf7UhvlTG+ZPbZg/tWF+1H75K2YbDnX3/umFbT6gluaZWU3UnImSPbVh/tSG+VMb5k9tmD+1YX7UfvkrxTZUyoeIiIiISB4UUIuIiIiI5EEB9eHh1mJXoB1QG+ZPbZg/tWH+1Ib5UxvmR+2Xv5JrQ+VQi4iIiIjkQT3UIiIiIiJ5UEAtIiIiIpIHBdTtgJn9xsw2mNkrKWV9zOxxM1sS/u4dlpuZzTCzpWb2kpmNLV7NS4OZHWVmT5jZq2a2yMymheVqwyyZWaWZPW9mL4Zt+L2w/Ggzey5sq3vMrGNY3in8f2m4fVhR70AJMbNyM1tgZo+G/6sNYzCzFWb2spktNLOasEzP5RjMrJeZ/dnMXjOzxWZ2mtowe2Z2bHj9JX+2m9mX1IbxmNmXw/eTV8zsj+H7TMm+Hiqgbh/uACallX0DmOPuI4A54f8AHwRGhD9TgFtaqY6l7ADwVXc/HhgHfMHMjkdtGMdeYIK7nwyMBiaZ2ThgOvAzdx8ObAEmh/tPBraE5T8L95PANGBxyv9qw/je4+6jU+ap1XM5nhuB2e4+EjiZ4HpUG2bJ3V8Pr7/RwDuA3cADqA2zZmaDgKlAtbufCJQDH6OUXw/dXT/t4AcYBryS8v/rwMDw74HA6+HfvwIuidpPP/Vt8hDwPrVhzu3XBXgBOJVgJasOYflpwF/Dv/8KnBb+3SHcz4pd92L/AIMJ3mgnAI8CpjaM3YYrgH5pZXouZ99+PYE3068ltWHO7fl+4J9qw9jtNgh4C+gTvr49CnyglF8P1UPdfg1w97Xh3+uAAeHfyYs0aXVYJkD4NdEY4DnUhrGEqQoLgQ3A48AyYKu7Hwh3SW2n+jYMt28D+rZqhUvTDcDXgUT4f1/UhnE58Dczm29mU8IyPZezdzSwEfhtmHp0m5l1RW2Yq48Bfwz/Vhtmyd3XANcBq4C1BK9v8ynh10MF1IcBDz6yaX7EZphZN+A+4Evuvj11m9qwee5+0IOvOAcDpwAji1ujtsXMzgY2uPv8YteljTvd3ccSfI3+BTM7I3WjnsvN6gCMBW5x9zHALg6lJgBqw2yF+b0fBv6Uvk1t2LQwv/xcgg94bwO60ji1taQooG6/1pvZQIDw94awfA1wVMp+g8Oyw5qZVRAE07939/vDYrVhDtx9K/AEwddxvcysQ7gptZ3q2zDc3hOobd2alpx3Ax82sxXA3QRpHzeiNowl7NnC3TcQ5K2egp7LcawGVrv7c+H/fyYIsNWG8X0QeMHd14f/qw2z917gTXff6O77gfsJXiNL9vVQAXX79TBwefj35QR5wcnyT4SjiscB21K+gjosmZkBtwOL3f36lE1qwyyZWX8z6xX+3ZkgB30xQWB9Ybhbehsm2/ZCYG7YY3PYcvdvuvtgdx9G8DXxXHf/OGrDrJlZVzPrnvybIH/1FfRczpq7rwPeMrNjw6KJwKuoDXNxCYfSPUBtGMcqYJyZdQnfo5PXYem+HhY78Vw/+f8QPGHXAvsJehcmE+QOzQGWAH8H+oT7GnAzQX7rywQjaIt+H4rcfqcTfPX2ErAw/DlLbRirDUcBC8I2fAX4v7C8CngeWErwtWensLwy/H9puL2q2PehlH6AM4FH1Yax260KeDH8WQR8OyzXczleO44GasLn84NAb7Vh7DbsStBD2jOlTG0Yrw2/B7wWvqfMBDqV8uuhlh4XEREREcmDUj5ERERERPKggFpEREREJA8KqEVERERE8qCAWkREREQkDwqoRURERETyoIBaRERERCQPCqhFRCSSmVWb2Yxi10NEpNRpHmoREWnEzDq4+4Fi10NEpC1QD7WISIkyswfNbL6ZLTKzKWb2OTP7acr2K8zspvDv75jZ62b2jJn90cy+1sRxnzSzG81soZm9YmanhOXXmNlMM/snMNPMzjSzR8Nt3czst2b2spm9ZGYXhOXvN7N5ZvaCmf3JzLoVtFFEREqQAmoRkdL1KXd/B1ANTAUeAM5P2X4xcLeZvRO4ADgZ+GC4f3O6uPto4PPAb1LKjwfe6+6XpO3/HWCbu5/k7qOAuWbWD7g63H8swXLVX4l5H0VE2rwOxa6AiIhkNNXMkgH0UcDRwHIzGwcsAUYC/wSmAQ+5ex1QZ2aPZHHsPwK4+1Nm1sPMeoXlD7v7noj93wt8LPmPu28xs7MJAvB/mhlAR2BezPsoItLmKaAWESlBZnYmQRB7mrvvNrMngUrgbuAi4DXgAXf3MJiNK30ATfL/XXGqCTwe0ZstInJYUcqHiEhp6glsCYPpkcC4sPwB4FzgEoLgGoJe6nPMrDLMYT47i+NfDGBmpxOkcmxrZv/HgS8k/zGz3sCzwLvNbHhY1tXMjsnq3omItCMKqEVEStNsoIOZLQZ+TBC84u5bgMXAUHd/Piz7N/Aw8BLwGPAy0FyAXGdmC4BfApOzqM8PgN7hIMYXgfe4+0bgCuCPZvYSQbrHyFj3UkSkHdC0eSIi7YCZdXP3nWbWBXgKmOLuL2TY90nga+5e05p1FBFpr5RDLSLSPtxqZscT5FnfmSmYFhGRlqceahGRdsrMbgbenVZ8o7v/thj1ERFprxRQi4iIiIjkQYMSRURERETyoIBaRERERCQPCqhFRERERPKggFpEREREJA//P7k1hWO12drBAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ngbh[ngbh['avg_price'] > avg_allListing].count()\n",
        "# there are 55 neighbourhood with avg price listing greater than the avg for all price."
      ],
      "metadata": {
        "id": "LE4RLJfRezcw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c931549d-db2a-4e7d-d181-b7e02b801848"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "avg_price                55\n",
              "avg_number_of_reviews    55\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ngbh[ngbh['avg_price'] < avg_allListing].count()\n",
        "# there are 166 neighbourhood with avg listing price lesser than the avg for all price."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XJe792Xm_Lk_",
        "outputId": "eab5a3f6-ffbe-4949-fc8d-f95f845e0c18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "avg_price                166\n",
              "avg_number_of_reviews    166\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Price in each Neighbourhood**"
      ],
      "metadata": {
        "id": "LfnN1FG6ESUw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs.groupby('neighbourhood_group')['price'].std()\n",
        "\n",
        "#it gives the standard deviation of the price grouped with the neighbourhood_group.\n",
        "# the standard deviation price of each neighbourhood_group."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7F95x3PvD_dR",
        "outputId": "4d8455a9-7b17-4779-d258-14ae66983e42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "neighbourhood_group\n",
              "Bronx            106.798933\n",
              "Brooklyn         186.936694\n",
              "Manhattan        291.489822\n",
              "Queens           167.128794\n",
              "Staten Island    277.620403\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Largest price deviation occur in the neighbourhood_group for **Manhattan**."
      ],
      "metadata": {
        "id": "d7Gl0KWpE9bQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=fig_sz)\n",
        "\n",
        "sns.scatterplot(y = 'neighbourhood_group', x = 'price', data=nyc_logs)\n",
        "plt.title(\"Listing price by Neighbourhood_group\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "pc8Oxd8wEpOb",
        "outputId": "364bddbe-c662-4910-c260-8bfc1cc2e1da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Listing price by Neighbourhood_group')"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Average Price, Total Number of Reviews and Number of Listings by Neighbourhood Group**\n"
      ],
      "metadata": {
        "id": "6kbK1M75HYRb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nyc_logs.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "Q08Wh-D9tLME",
        "outputId": "614791fc-e7b9-43fb-8f24-c8f484690916"
      },
      "execution_count": null,
      "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",
              "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  ...  number_of_reviews  last_review  \\\n",
              "0     Private room    149  ...                  9   2018-10-19   \n",
              "1  Entire home/apt    225  ...                 45   2019-05-21   \n",
              "2     Private room    150  ...                  0   2016-12-31   \n",
              "3  Entire home/apt     89  ...                270   2019-07-05   \n",
              "4  Entire home/apt     80  ...                  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              0.00                               1               365   \n",
              "3              4.64                               1               194   \n",
              "4              0.10                               1                 0   \n",
              "\n",
              "      month    listing_coordinates major_attractions  \\\n",
              "0   October  [40.64749, -73.97237]   Brooklyn Bridge   \n",
              "1       May  [40.75362, -73.98377]      Central Park   \n",
              "2  December   [40.80902, -73.9419]      Central Park   \n",
              "3      July  [40.68514, -73.95976]   Brooklyn Bridge   \n",
              "4  November  [40.79851, -73.94399]      Central Park   \n",
              "\n",
              "  major_attractions_coordinates distance_to_majorAttractions  \n",
              "0       (40.706001, -73.997002)                         4.24  \n",
              "1       (40.769361, -73.977655)                         1.13  \n",
              "2       (40.769361, -73.977655)                         3.32  \n",
              "3       (40.706001, -73.997002)                         2.43  \n",
              "4       (40.769361, -73.977655)                         2.68  \n",
              "\n",
              "[5 rows x 21 columns]"
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              "      <th>id</th>\n",
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              "      <th>host_name</th>\n",
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              "      <th>neighbourhood</th>\n",
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              "      <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",
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              "      <td>Kensington</td>\n",
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              "      <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",
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              "      <td>-73.98377</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>225</td>\n",
              "      <td>...</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",
              "      <td>May</td>\n",
              "      <td>[40.75362, -73.98377]</td>\n",
              "      <td>Central Park</td>\n",
              "      <td>(40.769361, -73.977655)</td>\n",
              "      <td>1.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3647</td>\n",
              "      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
              "      <td>4632</td>\n",
              "      <td>Elisabeth</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Harlem</td>\n",
              "      <td>40.80902</td>\n",
              "      <td>-73.94190</td>\n",
              "      <td>Private room</td>\n",
              "      <td>150</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>2016-12-31</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>365</td>\n",
              "      <td>December</td>\n",
              "      <td>[40.80902, -73.9419]</td>\n",
              "      <td>Central Park</td>\n",
              "      <td>(40.769361, -73.977655)</td>\n",
              "      <td>3.32</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>...</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",
              "      <td>July</td>\n",
              "      <td>[40.68514, -73.95976]</td>\n",
              "      <td>Brooklyn Bridge</td>\n",
              "      <td>(40.706001, -73.997002)</td>\n",
              "      <td>2.43</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>...</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",
              "      <td>November</td>\n",
              "      <td>[40.79851, -73.94399]</td>\n",
              "      <td>Central Park</td>\n",
              "      <td>(40.769361, -73.977655)</td>\n",
              "      <td>2.68</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Identifying the average listing price for each neighbourhood (rounded to 2 decimal places)\n",
        "nbhd_group = nyc_logs.groupby('neighbourhood_group')['price'].mean().round(2)\n",
        "\n",
        "#Converting the series nbhd to a dataframe\n",
        "nbhd_group = nbhd_group.to_frame()\n",
        "\n",
        "#Renaming columns\n",
        "nbhd_group.rename(columns={'price': 'average_price'}, inplace=True)\n",
        "\n",
        "# Identifying the average number of reviews for each neighbourhood group(rounded to 2 decimal places)\n",
        "nbhd_group['total_number_of_reviews'] = nyc_logs.groupby('neighbourhood_group')['number_of_reviews'].sum().round(2)\n",
        "\n",
        "nbhd_group['number_of_listings'] = nyc_logs['neighbourhood_group'].value_counts()\n",
        "\n",
        "#Ratio of reviews as compared to total number of listings for each neighbourhood group\n",
        "nbhd_group['ratio'] = (nbhd_group['total_number_of_reviews']/nbhd_group['number_of_listings']).round(2)\n",
        "\n",
        "\n",
        "nbhd_group.head()"
      ],
      "metadata": {
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          "height": 237
        },
        "id": "OCeIV20WFZ-m",
        "outputId": "4806662a-1a45-4105-ad4c-90cc69c23682"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     average_price  total_number_of_reviews  \\\n",
              "neighbourhood_group                                           \n",
              "Bronx                        87.47                    28334   \n",
              "Brooklyn                    124.41                   486174   \n",
              "Manhattan                   196.90                   454126   \n",
              "Queens                       99.54                   156902   \n",
              "Staten Island               114.81                    11541   \n",
              "\n",
              "                     number_of_listings  ratio  \n",
              "neighbourhood_group                             \n",
              "Bronx                              1089  26.02  \n",
              "Brooklyn                          20089  24.20  \n",
              "Manhattan                         21643  20.98  \n",
              "Queens                             5664  27.70  \n",
              "Staten Island                       373  30.94  "
            ],
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              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We notice something interesting in the data here:\n",
        "\n",
        "*   Staten island has the largest number of reviews as compared to the actual number of listings, which indicates that reviews were left more frequently for stays in listings that were within the Staten island neighbourhood group.\n",
        "\n",
        "*   Manhattan has the second largest number of listings but has the least number of reviews compared to the actual number of listings, which indicates that reviews are left less frequently for stays in the Manhattan neighbourhood group. The possible reasons for this are as follows:\n",
        "\n",
        "   *   The average listing price is also the highest of all neighbourhood groups.\n",
        "   *   Manhattan's average listing price is also above the average for all listings."
      ],
      "metadata": {
        "id": "Fnr8AB5C3wjP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "room_typ = nyc_logs.groupby('room_type')['price'].mean().round(2)\n",
        "room_typ = room_typ.to_frame()\n",
        "room_typ.rename(columns={'price' : 'avg_Price'}, inplace=True)\n",
        "room_typ['total_numOf-Reviews'] = nyc_logs.groupby('room_type')['price'].sum().round(2)"
      ],
      "metadata": {
        "id": "Ocjh__8y0iAu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "room_typ"
      ],
      "metadata": {
        "id": "mTVSr8TQ5Cvg",
        "outputId": "db4d35e1-3edf-4f69-d506-b3b84acdc2a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 avg_Price  total_numOf-Reviews\n",
              "room_type                                      \n",
              "Entire home/apt     211.81              5378415\n",
              "Private room         89.79              2002953\n",
              "Shared room          70.08                81218"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-79b902b3-f489-409a-8991-2aa9c1568677\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>avg_Price</th>\n",
              "      <th>total_numOf-Reviews</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>room_type</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Entire home/apt</th>\n",
              "      <td>211.81</td>\n",
              "      <td>5378415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Private room</th>\n",
              "      <td>89.79</td>\n",
              "      <td>2002953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Shared room</th>\n",
              "      <td>70.08</td>\n",
              "      <td>81218</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-79b902b3-f489-409a-8991-2aa9c1568677 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-79b902b3-f489-409a-8991-2aa9c1568677');\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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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Number of Reviews vs Price for Each Room"
      ],
      "metadata": {
        "id": "YYKwsIRK5GNN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "graph = sns.FacetGrid(nyc_logs, col='room_type')\n",
        "graph.map(sns.scatterplot, \"price\", \"number_of_reviews\")\n",
        "graph.fig.subplots_adjust(top=0.8)\n",
        "graph.fig.suptitle('Number of Reviews vs. Price for Each Room Type', fontsize = 2)"
      ],
      "metadata": {
        "id": "JbuI1qpG5EFs",
        "outputId": "b633fed7-2573-4660-95e1-afecd0ad07ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        }
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0.98, 'Number of Reviews vs. Price for Each Room Type')"
            ]
          },
          "metadata": {},
          "execution_count": 121
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x216 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are no more reviews fr=or less expensive listing readings of the room types."
      ],
      "metadata": {
        "id": "bTNbK46IGpPG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Summary of Data**\n",
        "## **Availiblity**\n",
        "*   There are 48858 Listing in the Given Data.\n",
        "*   The majority of the Data/Listing  are Entire Home/Apartment or Private Rooms.\n",
        "*   On average, any given listing is availabe in 113 days in a Year.\n",
        "   *   In **Staten Island** have the greatest availiblity and receive the most reviews per lisitng, however Staten Island also receives the least number of reviews in the listings.\n",
        "   *   In **Manhattan** have the least availiblity and also receives the least reviews per listing"
      ],
      "metadata": {
        "id": "XkzZyZNUHCYO"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SGyojsvFGP7g"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}