Team_Notebook_EDA_Project.ipynb

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    "colab": {
      "name": "Team Notebook_EDA Project.ipynb",
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      "name": "python3",
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      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Shantanuh10/Airbnb_EDA_Project-/blob/main/Team_Notebook_EDA_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uqki6HBjG7M_"
      },
      "source": [
        "## **Airbnb EDA Project by Harshiv Bhatt, Shantanu Houzwala and Suloy Mandal**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PwsgVKIme2DQ"
      },
      "source": [
        "## **Breakdown of this Notebook:**\n",
        "\n",
        "1.**Importing Libraries**\n",
        "\n",
        "2.**Loading the dataset**\n",
        "\n",
        "3.**Data Cleaning:**\n",
        "\n",
        "*   Dropping duplicates.\n",
        "*   Cleaning individual columns.\n",
        "*   Remove the Null values from the dataset\n",
        "*   Some Transformations\n",
        "\n",
        "4.**Data Analysis and Visualization:** Using plots to find relations between the features\n",
        "\n",
        "* What is the average preferred price by customers according to the location?\n",
        "\n",
        "* Number of active hosts per location (Where most of the hosts focused to own  property?)\n",
        "\n",
        "* Where the customer pays the highest and lowest rent according to location?\n",
        "\n",
        "* Most popular/demanded host of Airbnb in New York\n",
        "\n",
        "* Find the total count of each room type\n",
        "\n",
        "* Room types and their relation with availability in different neighbourhood groups\n",
        "\n",
        "* Which are the top 25 most used words in listing names?\n",
        "\n",
        "* Find top 10 hosts with most listings\n",
        "\n",
        "* Find the top three hosts based on their turnover\n",
        "\n",
        "* Find total no. of nights spend per location\n",
        "\n",
        "* Total no. of nights spends per room types\n",
        "\n",
        "* Top 10 highest listing neighborhood\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R7QZjyJAlyew"
      },
      "source": [
        "# By Harshiv Bhatt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ozMgtM1w8DT"
      },
      "source": [
        "#Answering following Questions:\n",
        "\n",
        "---\n",
        "*   What is the average preferred price by customers according to the location?\n",
        "\n",
        "*  No. of active host per location (Where most of the host focused to own property)\n",
        "\n",
        "*   What is the highest price and lowest price of rent for customer according to location **OR** which host takes highest rent and lowest rent according to location\n",
        "*   Most famous/demanded host of Airbnb in New york"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wXQewBrQwzBm"
      },
      "source": [
        "#import all library that will be used in entire project\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.rcParams['figure.figsize'] = (10, 7)\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oQKlV-n2x5Oa",
        "outputId": "9f21b67d-3d49-4387-8012-6a092547ada5"
      },
      "source": [
        "#Mount google drive for access the dataset of Airbnb\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "elq6zEKCx_vB"
      },
      "source": [
        "#File path of Airbnb dataset in google drive \n",
        "file_path = \"/content/drive/MyDrive/Colab Notebooks/EDA Project part-1/\"\n",
        "df = pd.read_csv(file_path + \"Airbnb NYC 2019.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WacSO411yHOS",
        "outputId": "08d273f1-d4c1-4353-851b-5bfcdfc287be"
      },
      "source": [
        "#Information about our dataset\n",
        "df.info()"
      ],
      "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": "code",
      "metadata": {
        "id": "uSQL54J4qEsF"
      },
      "source": [
        "#Remove latitude, longitude, last_review and reviews_per_month columns from original dataset\n",
        "df.drop(df.columns[[6,7,12,13]], axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bFtCy8RFyLti"
      },
      "source": [
        "#we apply dropna() function to entire dataset and drop null values with dropna() function \n",
        "airbnb_df = df.dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/"
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        "id": "ULGVgCSKyR_b",
        "outputId": "f818b812-cbc0-4e8e-e867-4727fec7d80d"
      },
      "source": [
        "#Information about our new non null dataframe\n",
        "airbnb_df.info()"
      ],
      "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 12 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   room_type                       48858 non-null  object\n",
            " 7   price                           48858 non-null  int64 \n",
            " 8   minimum_nights                  48858 non-null  int64 \n",
            " 9   number_of_reviews               48858 non-null  int64 \n",
            " 10  calculated_host_listings_count  48858 non-null  int64 \n",
            " 11  availability_365                48858 non-null  int64 \n",
            "dtypes: int64(7), object(5)\n",
            "memory usage: 4.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "tSB_fvO7yVdK",
        "outputId": "7dff2d13-552b-4ff8-bada-56313131d1ce"
      },
      "source": [
        "airbnb_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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>room_type</th>\n",
              "      <th>price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2539</td>\n",
              "      <td>Clean &amp; quiet apt home by the park</td>\n",
              "      <td>2787</td>\n",
              "      <td>John</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Kensington</td>\n",
              "      <td>Private room</td>\n",
              "      <td>149</td>\n",
              "      <td>1</td>\n",
              "      <td>9</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>Entire home/apt</td>\n",
              "      <td>225</td>\n",
              "      <td>1</td>\n",
              "      <td>45</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>Private room</td>\n",
              "      <td>150</td>\n",
              "      <td>3</td>\n",
              "      <td>0</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>Entire home/apt</td>\n",
              "      <td>89</td>\n",
              "      <td>1</td>\n",
              "      <td>270</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>Entire home/apt</td>\n",
              "      <td>80</td>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     id  ... availability_365\n",
              "0  2539  ...              365\n",
              "1  2595  ...              355\n",
              "2  3647  ...              365\n",
              "3  3831  ...              194\n",
              "4  5022  ...                0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "SnDS7DRzyYdB",
        "outputId": "638ff99f-09b3-4570-b44d-07ca5385a3e3"
      },
      "source": [
        "#Brief information about numerical column in our dataset\n",
        "airbnb_df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>host_id</th>\n",
              "      <th>price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4.885800e+04</td>\n",
              "      <td>4.885800e+04</td>\n",
              "      <td>48858.000000</td>\n",
              "      <td>48858.000000</td>\n",
              "      <td>48858.000000</td>\n",
              "      <td>48858.000000</td>\n",
              "      <td>48858.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.902335e+07</td>\n",
              "      <td>6.763169e+07</td>\n",
              "      <td>152.740309</td>\n",
              "      <td>7.012444</td>\n",
              "      <td>23.273098</td>\n",
              "      <td>7.148369</td>\n",
              "      <td>112.801425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.098289e+07</td>\n",
              "      <td>7.862389e+07</td>\n",
              "      <td>240.232386</td>\n",
              "      <td>20.019757</td>\n",
              "      <td>44.549898</td>\n",
              "      <td>32.964600</td>\n",
              "      <td>131.610962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.539000e+03</td>\n",
              "      <td>2.438000e+03</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>9.475980e+06</td>\n",
              "      <td>7.818669e+06</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.969114e+07</td>\n",
              "      <td>3.079133e+07</td>\n",
              "      <td>106.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>45.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.915765e+07</td>\n",
              "      <td>1.074344e+08</td>\n",
              "      <td>175.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>227.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.648724e+07</td>\n",
              "      <td>2.743213e+08</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>1250.000000</td>\n",
              "      <td>629.000000</td>\n",
              "      <td>327.000000</td>\n",
              "      <td>365.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id  ...  availability_365\n",
              "count  4.885800e+04  ...      48858.000000\n",
              "mean   1.902335e+07  ...        112.801425\n",
              "std    1.098289e+07  ...        131.610962\n",
              "min    2.539000e+03  ...          0.000000\n",
              "25%    9.475980e+06  ...          0.000000\n",
              "50%    1.969114e+07  ...         45.000000\n",
              "75%    2.915765e+07  ...        227.000000\n",
              "max    3.648724e+07  ...        365.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ajuSundyfMh",
        "outputId": "bd025ee9-19d1-44b9-e2b3-2c8bee5976c9"
      },
      "source": [
        "#Sum of null values in dataset\n",
        "airbnb_df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                0\n",
              "name                              0\n",
              "host_id                           0\n",
              "host_name                         0\n",
              "neighbourhood_group               0\n",
              "neighbourhood                     0\n",
              "room_type                         0\n",
              "price                             0\n",
              "minimum_nights                    0\n",
              "number_of_reviews                 0\n",
              "calculated_host_listings_count    0\n",
              "availability_365                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UrZxr1OIyj27"
      },
      "source": [
        "#1.What is the average preferred price by customers according to the location?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7cTNV5wSyi-y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "ac0e8321-0d3e-4038-ee8c-2e0cb1ecd0ec"
      },
      "source": [
        "airbnb_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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>room_type</th>\n",
              "      <th>price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2539</td>\n",
              "      <td>Clean &amp; quiet apt home by the park</td>\n",
              "      <td>2787</td>\n",
              "      <td>John</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Kensington</td>\n",
              "      <td>Private room</td>\n",
              "      <td>149</td>\n",
              "      <td>1</td>\n",
              "      <td>9</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>Entire home/apt</td>\n",
              "      <td>225</td>\n",
              "      <td>1</td>\n",
              "      <td>45</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>Private room</td>\n",
              "      <td>150</td>\n",
              "      <td>3</td>\n",
              "      <td>0</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>Entire home/apt</td>\n",
              "      <td>89</td>\n",
              "      <td>1</td>\n",
              "      <td>270</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>Entire home/apt</td>\n",
              "      <td>80</td>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     id  ... availability_365\n",
              "0  2539  ...              365\n",
              "1  2595  ...              355\n",
              "2  3647  ...              365\n",
              "3  3831  ...              194\n",
              "4  5022  ...                0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JA1GY_mYyoM0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "outputId": "df269a25-30d6-44a8-a311-bb05f320ba88"
      },
      "source": [
        "#Average_price of property according to the location\n",
        "avg_preffered_price_df = airbnb_df.groupby(['neighbourhood_group','room_type'], as_index=False)['price'].mean().rename(columns={'neighbourhood_group':'Location','price':'Average Price'})\n",
        "avg_preffered_price_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Location</th>\n",
              "      <th>room_type</th>\n",
              "      <th>Average Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bronx</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>127.645503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bronx</td>\n",
              "      <td>Private room</td>\n",
              "      <td>66.788344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bronx</td>\n",
              "      <td>Shared room</td>\n",
              "      <td>58.610169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>178.362609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Private room</td>\n",
              "      <td>76.510619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Shared room</td>\n",
              "      <td>50.527845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>249.238211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Private room</td>\n",
              "      <td>116.805594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Shared room</td>\n",
              "      <td>88.977083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Queens</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>147.050573</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Queens</td>\n",
              "      <td>Private room</td>\n",
              "      <td>71.776855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Queens</td>\n",
              "      <td>Shared room</td>\n",
              "      <td>69.020202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Staten Island</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>173.846591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Staten Island</td>\n",
              "      <td>Private room</td>\n",
              "      <td>62.292553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Staten Island</td>\n",
              "      <td>Shared room</td>\n",
              "      <td>57.444444</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Location        room_type  Average Price\n",
              "0           Bronx  Entire home/apt     127.645503\n",
              "1           Bronx     Private room      66.788344\n",
              "2           Bronx      Shared room      58.610169\n",
              "3        Brooklyn  Entire home/apt     178.362609\n",
              "4        Brooklyn     Private room      76.510619\n",
              "5        Brooklyn      Shared room      50.527845\n",
              "6       Manhattan  Entire home/apt     249.238211\n",
              "7       Manhattan     Private room     116.805594\n",
              "8       Manhattan      Shared room      88.977083\n",
              "9          Queens  Entire home/apt     147.050573\n",
              "10         Queens     Private room      71.776855\n",
              "11         Queens      Shared room      69.020202\n",
              "12  Staten Island  Entire home/apt     173.846591\n",
              "13  Staten Island     Private room      62.292553\n",
              "14  Staten Island      Shared room      57.444444"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "R-WdmLNR0HJi",
        "outputId": "e27bdc01-763e-48a2-8a7e-4cf51da06167"
      },
      "source": [
        "#Unstack the group by information for plot the graph\n",
        "avg_preffered_price_df = airbnb_df.groupby(['neighbourhood_group','room_type'])['price'].mean().unstack()\n",
        "avg_preffered_price_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>room_type</th>\n",
              "      <th>Entire home/apt</th>\n",
              "      <th>Private room</th>\n",
              "      <th>Shared room</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>neighbourhood_group</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Bronx</th>\n",
              "      <td>127.645503</td>\n",
              "      <td>66.788344</td>\n",
              "      <td>58.610169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brooklyn</th>\n",
              "      <td>178.362609</td>\n",
              "      <td>76.510619</td>\n",
              "      <td>50.527845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Manhattan</th>\n",
              "      <td>249.238211</td>\n",
              "      <td>116.805594</td>\n",
              "      <td>88.977083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Queens</th>\n",
              "      <td>147.050573</td>\n",
              "      <td>71.776855</td>\n",
              "      <td>69.020202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Staten Island</th>\n",
              "      <td>173.846591</td>\n",
              "      <td>62.292553</td>\n",
              "      <td>57.444444</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "room_type            Entire home/apt  Private room  Shared room\n",
              "neighbourhood_group                                            \n",
              "Bronx                     127.645503     66.788344    58.610169\n",
              "Brooklyn                  178.362609     76.510619    50.527845\n",
              "Manhattan                 249.238211    116.805594    88.977083\n",
              "Queens                    147.050573     71.776855    69.020202\n",
              "Staten Island             173.846591     62.292553    57.444444"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "sGz4Kt7M0aYq",
        "outputId": "434ae8f4-e735-414d-bcf0-b85adc194ca4"
      },
      "source": [
        "#Graph of average price of property according to location\n",
        "avg_preffered_price_df.plot.bar()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f38ef2bc250>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3IUayR_dywOq"
      },
      "source": [
        "#2. No. of active host per location (Where most of the host focused to own property)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "OCkMczsjyyta",
        "outputId": "7b33b353-caf1-4701-8b9c-77151bd1b571"
      },
      "source": [
        "#Apply group by operation on neighbourhood_group for find the number of host according to location\n",
        "no_of_host_per_location = airbnb_df.groupby('neighbourhood_group',as_index=False)['host_id'].count().sort_values(['host_id'], ascending=False).rename(columns={'neighbourhood_group':'Location','host_id':'Host'})\n",
        "no_of_host_per_location"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Location</th>\n",
              "      <th>Host</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Manhattan</td>\n",
              "      <td>21643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>20089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Queens</td>\n",
              "      <td>5664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bronx</td>\n",
              "      <td>1089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Staten Island</td>\n",
              "      <td>373</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Location   Host\n",
              "2      Manhattan  21643\n",
              "1       Brooklyn  20089\n",
              "3         Queens   5664\n",
              "0          Bronx   1089\n",
              "4  Staten Island    373"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AHqkIJMvy64a"
      },
      "source": [
        "#Answer: Manhattan is most focused place for host to do their business\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ajCIX7BL08C6",
        "outputId": "07ae2e21-0939-40b9-f8a4-67dd06811106"
      },
      "source": [
        "#Take a simple format of above question without index for plot the line chart\n",
        "no_of_host_per_location = airbnb_df.groupby('neighbourhood_group')['host_id'].count()\n",
        "no_of_host_per_location"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "neighbourhood_group\n",
              "Bronx             1089\n",
              "Brooklyn         20089\n",
              "Manhattan        21643\n",
              "Queens            5664\n",
              "Staten Island      373\n",
              "Name: host_id, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "JahN3mtU1H3q",
        "outputId": "2152de5d-ec51-490e-ee58-624e789605ce"
      },
      "source": [
        "#Graph\n",
        "plt.legend(labels = ['No. of Host'])\n",
        "plt.plot(no_of_host_per_location)\n",
        "plt.title('Number of host per location')\n",
        "plt.ylabel('Host')\n",
        "plt.xlabel('Location')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Location')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "6v_o1s6MD9xB",
        "outputId": "574c1987-549e-488e-c667-cdb79864332a"
      },
      "source": [
        "airbnb_df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>host_id</th>\n",
              "      <th>price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4.885800e+04</td>\n",
              "      <td>4.885800e+04</td>\n",
              "      <td>48858.000000</td>\n",
              "      <td>48858.000000</td>\n",
              "      <td>48858.000000</td>\n",
              "      <td>48858.000000</td>\n",
              "      <td>48858.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.902335e+07</td>\n",
              "      <td>6.763169e+07</td>\n",
              "      <td>152.740309</td>\n",
              "      <td>7.012444</td>\n",
              "      <td>23.273098</td>\n",
              "      <td>7.148369</td>\n",
              "      <td>112.801425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.098289e+07</td>\n",
              "      <td>7.862389e+07</td>\n",
              "      <td>240.232386</td>\n",
              "      <td>20.019757</td>\n",
              "      <td>44.549898</td>\n",
              "      <td>32.964600</td>\n",
              "      <td>131.610962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.539000e+03</td>\n",
              "      <td>2.438000e+03</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>9.475980e+06</td>\n",
              "      <td>7.818669e+06</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.969114e+07</td>\n",
              "      <td>3.079133e+07</td>\n",
              "      <td>106.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>45.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.915765e+07</td>\n",
              "      <td>1.074344e+08</td>\n",
              "      <td>175.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>227.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.648724e+07</td>\n",
              "      <td>2.743213e+08</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>1250.000000</td>\n",
              "      <td>629.000000</td>\n",
              "      <td>327.000000</td>\n",
              "      <td>365.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id  ...  availability_365\n",
              "count  4.885800e+04  ...      48858.000000\n",
              "mean   1.902335e+07  ...        112.801425\n",
              "std    1.098289e+07  ...        131.610962\n",
              "min    2.539000e+03  ...          0.000000\n",
              "25%    9.475980e+06  ...          0.000000\n",
              "50%    1.969114e+07  ...         45.000000\n",
              "75%    2.915765e+07  ...        227.000000\n",
              "max    3.648724e+07  ...        365.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5GoFdwyzDyF6"
      },
      "source": [
        "#Points to be noted:\n",
        "\n",
        "1. Here we can see that there is minimum price 0$.We have to fix it anyway because Airbnb is not provide free stay in any hotel.  \n",
        "Solution: For that we check where the price is 0. We create one funtion in which as per minimum nights the price will be set as per formula [Refer below cells for more detail]\n",
        "\n",
        "2. There are some entries in dataset in which minimum nights = 1 but it's respective price is 0\n",
        " Solution: We replace the price value with the mean value of price which amount is less than 100$. [Refer below cells for more detail]\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XTz80VPvG8IJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 874
        },
        "outputId": "9b964871-d64c-4380-a145-71215f406d53"
      },
      "source": [
        "#identify the observations where minimum nights is 1 and price is minimum\n",
        "airbnb_df[airbnb_df['minimum_nights'] ==1].sort_values(['price', 'minimum_nights'], ascending=[True, True])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>name</th>\n",
              "      <th>host_id</th>\n",
              "      <th>host_name</th>\n",
              "      <th>neighbourhood_group</th>\n",
              "      <th>neighbourhood</th>\n",
              "      <th>room_type</th>\n",
              "      <th>price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>25794</th>\n",
              "      <td>20639628</td>\n",
              "      <td>Spacious comfortable master bedroom with nice ...</td>\n",
              "      <td>86327101</td>\n",
              "      <td>Adeyemi</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>Private room</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>93</td>\n",
              "      <td>6</td>\n",
              "      <td>176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25795</th>\n",
              "      <td>20639792</td>\n",
              "      <td>Contemporary bedroom in brownstone with nice view</td>\n",
              "      <td>86327101</td>\n",
              "      <td>Adeyemi</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>Private room</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>95</td>\n",
              "      <td>6</td>\n",
              "      <td>232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25796</th>\n",
              "      <td>20639914</td>\n",
              "      <td>Cozy yet spacious private brownstone bedroom</td>\n",
              "      <td>86327101</td>\n",
              "      <td>Adeyemi</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>Private room</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>95</td>\n",
              "      <td>6</td>\n",
              "      <td>222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21700</th>\n",
              "      <td>17437106</td>\n",
              "      <td>Couch in Harlem Harvey Refugees only</td>\n",
              "      <td>33511962</td>\n",
              "      <td>Morgan</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Harlem</td>\n",
              "      <td>Shared room</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22835</th>\n",
              "      <td>18490141</td>\n",
              "      <td>IT'S SIMPLY CONVENIENT!</td>\n",
              "      <td>97001292</td>\n",
              "      <td>Maria</td>\n",
              "      <td>Queens</td>\n",
              "      <td>Jamaica</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "      <td>252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44034</th>\n",
              "      <td>33998396</td>\n",
              "      <td>3000 sq ft daylight photo studio</td>\n",
              "      <td>3750764</td>\n",
              "      <td>Kevin</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Chelsea</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>6800</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42523</th>\n",
              "      <td>33007610</td>\n",
              "      <td>70' Luxury MotorYacht on the Hudson</td>\n",
              "      <td>7407743</td>\n",
              "      <td>Jack</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Battery Park City</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>7500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45666</th>\n",
              "      <td>34895693</td>\n",
              "      <td>Gem of east Flatbush</td>\n",
              "      <td>262534951</td>\n",
              "      <td>Sandra</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>East Flatbush</td>\n",
              "      <td>Private room</td>\n",
              "      <td>7500</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29662</th>\n",
              "      <td>22779726</td>\n",
              "      <td>East 72nd Townhouse by (Hidden by Airbnb)</td>\n",
              "      <td>156158778</td>\n",
              "      <td>Sally</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Upper East Side</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>7703</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4377</th>\n",
              "      <td>2953058</td>\n",
              "      <td>Film Location</td>\n",
              "      <td>1177497</td>\n",
              "      <td>Jessica</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Clinton Hill</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>8000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>365</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12700 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id  ... availability_365\n",
              "25794  20639628  ...              176\n",
              "25795  20639792  ...              232\n",
              "25796  20639914  ...              222\n",
              "21700  17437106  ...                0\n",
              "22835  18490141  ...              252\n",
              "...         ...  ...              ...\n",
              "44034  33998396  ...              364\n",
              "42523  33007610  ...              364\n",
              "45666  34895693  ...              179\n",
              "29662  22779726  ...              146\n",
              "4377    2953058  ...              365\n",
              "\n",
              "[12700 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rQc2LOUQHGwS",
        "outputId": "e1b19fbd-d841-4348-b73a-f1fc0f2cfe59"
      },
      "source": [
        "#Get the number of values which price is less than 100$\n",
        "airbnb_df.loc[airbnb_df['price'] < 100,'price'].count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21861"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eDAJALfdKi41"
      },
      "source": [
        "#Make a copy of original dataframe to perform additional operation for get the desire result\n",
        "airbnb_price_df = airbnb_df.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4N1ob0xILGMt",
        "outputId": "b795cb65-ef68-4570-905e-3a6a038d4070"
      },
      "source": [
        "#Find the mean value of price which value is less than 100$\n",
        "round(airbnb_df.loc[airbnb_df['price'] < 100,'price'].mean())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "65"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QQNBuLBvKrAL"
      },
      "source": [
        "#Change the value of price where minimum night = 1 and price of their property is less than 50$\n",
        "airbnb_price_df['price'] = np.where(\n",
        "   (airbnb_price_df['price'] <= 50) & (airbnb_price_df['minimum_nights'] == 1) ,round(airbnb_df.loc[airbnb_df['price'] < 100,'price'].mean()) , airbnb_price_df['price']\n",
        "   )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mAgw2cmFKx66",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "413a5cf6-bac0-43eb-c0c4-0e07c9feab75"
      },
      "source": [
        "#still 8 rows have amount = 0$\n",
        "airbnb_price_df[airbnb_price_df['price']==0].count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                8\n",
              "name                              8\n",
              "host_id                           8\n",
              "host_name                         8\n",
              "neighbourhood_group               8\n",
              "neighbourhood                     8\n",
              "room_type                         8\n",
              "price                             8\n",
              "minimum_nights                    8\n",
              "number_of_reviews                 8\n",
              "calculated_host_listings_count    8\n",
              "availability_365                  8\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4u4Qi13aK3rO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "outputId": "6b7323bf-b697-4a27-c082-9c8d2382f177"
      },
      "source": [
        "#Find the observations where price is 0$\n",
        "airbnb_price_df[airbnb_price_df['price']==0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>name</th>\n",
              "      <th>host_id</th>\n",
              "      <th>host_name</th>\n",
              "      <th>neighbourhood_group</th>\n",
              "      <th>neighbourhood</th>\n",
              "      <th>room_type</th>\n",
              "      <th>price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>23161</th>\n",
              "      <td>18750597</td>\n",
              "      <td>Huge Brooklyn Brownstone Living, Close to it all.</td>\n",
              "      <td>8993084</td>\n",
              "      <td>Kimberly</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>Private room</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25433</th>\n",
              "      <td>20333471</td>\n",
              "      <td>★Hostel Style Room | Ideal Traveling Buddies★</td>\n",
              "      <td>131697576</td>\n",
              "      <td>Anisha</td>\n",
              "      <td>Bronx</td>\n",
              "      <td>East Morrisania</td>\n",
              "      <td>Private room</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>55</td>\n",
              "      <td>4</td>\n",
              "      <td>127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25634</th>\n",
              "      <td>20523843</td>\n",
              "      <td>MARTIAL LOFT 3: REDEMPTION (upstairs, 2nd room)</td>\n",
              "      <td>15787004</td>\n",
              "      <td>Martial Loft</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Bushwick</td>\n",
              "      <td>Private room</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>16</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25753</th>\n",
              "      <td>20608117</td>\n",
              "      <td>Sunny, Quiet Room in Greenpoint</td>\n",
              "      <td>1641537</td>\n",
              "      <td>Lauren</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Greenpoint</td>\n",
              "      <td>Private room</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25778</th>\n",
              "      <td>20624541</td>\n",
              "      <td>Modern apartment in the heart of Williamsburg</td>\n",
              "      <td>10132166</td>\n",
              "      <td>Aymeric</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Williamsburg</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26259</th>\n",
              "      <td>20933849</td>\n",
              "      <td>the best you can find</td>\n",
              "      <td>13709292</td>\n",
              "      <td>Qiuchi</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Murray Hill</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26841</th>\n",
              "      <td>21291569</td>\n",
              "      <td>Coliving in Brooklyn! Modern design / Shared room</td>\n",
              "      <td>101970559</td>\n",
              "      <td>Sergii</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Bushwick</td>\n",
              "      <td>Shared room</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26866</th>\n",
              "      <td>21304320</td>\n",
              "      <td>Best Coliving space ever! Shared room.</td>\n",
              "      <td>101970559</td>\n",
              "      <td>Sergii</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Bushwick</td>\n",
              "      <td>Shared room</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>139</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             id  ... availability_365\n",
              "23161  18750597  ...               28\n",
              "25433  20333471  ...              127\n",
              "25634  20523843  ...                0\n",
              "25753  20608117  ...                0\n",
              "25778  20624541  ...               73\n",
              "26259  20933849  ...                0\n",
              "26841  21291569  ...              333\n",
              "26866  21304320  ...              139\n",
              "\n",
              "[8 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NABgRtCaK7B6"
      },
      "source": [
        "#Function that gives a new price according to minimum night\n",
        "def price_calculator(min_night):\n",
        "  '''Get the price based on the minimum night you are given'''\n",
        "  mean_price = round(airbnb_df.loc[airbnb_df['price'] < 100,'price'].mean())\n",
        "  new_price = mean_price * min_night\n",
        "\n",
        "  return new_price"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J6eIbUzVK_Qb"
      },
      "source": [
        "#Replace the value of price according to minimum night where price = 0$ \n",
        "airbnb_price_df['price'] = np.where(\n",
        "   (airbnb_price_df['price'] == 0) ,price_calculator(airbnb_price_df['minimum_nights']) , airbnb_price_df['price'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "MHJhFEmZMFfD",
        "outputId": "eb371268-d01a-4205-e686-c8a17d44d601"
      },
      "source": [
        "#Our new description of dataset \n",
        "airbnb_price_df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>host_id</th>\n",
              "      <th>price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4.885800e+04</td>\n",
              "      <td>4.885800e+04</td>\n",
              "      <td>48858.000000</td>\n",
              "      <td>48858.000000</td>\n",
              "      <td>48858.000000</td>\n",
              "      <td>48858.000000</td>\n",
              "      <td>48858.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.902335e+07</td>\n",
              "      <td>6.763169e+07</td>\n",
              "      <td>153.846801</td>\n",
              "      <td>7.012444</td>\n",
              "      <td>23.273098</td>\n",
              "      <td>7.148369</td>\n",
              "      <td>112.801425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.098289e+07</td>\n",
              "      <td>7.862389e+07</td>\n",
              "      <td>240.078850</td>\n",
              "      <td>20.019757</td>\n",
              "      <td>44.549898</td>\n",
              "      <td>32.964600</td>\n",
              "      <td>131.610962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.539000e+03</td>\n",
              "      <td>2.438000e+03</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>9.475980e+06</td>\n",
              "      <td>7.818669e+06</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.969114e+07</td>\n",
              "      <td>3.079133e+07</td>\n",
              "      <td>106.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>45.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.915765e+07</td>\n",
              "      <td>1.074344e+08</td>\n",
              "      <td>175.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>227.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.648724e+07</td>\n",
              "      <td>2.743213e+08</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>1250.000000</td>\n",
              "      <td>629.000000</td>\n",
              "      <td>327.000000</td>\n",
              "      <td>365.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id  ...  availability_365\n",
              "count  4.885800e+04  ...      48858.000000\n",
              "mean   1.902335e+07  ...        112.801425\n",
              "std    1.098289e+07  ...        131.610962\n",
              "min    2.539000e+03  ...          0.000000\n",
              "25%    9.475980e+06  ...          0.000000\n",
              "50%    1.969114e+07  ...         45.000000\n",
              "75%    2.915765e+07  ...        227.000000\n",
              "max    3.648724e+07  ...        365.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FuUtRkJSL9kz"
      },
      "source": [
        "#3.Where the customer pays the highest and lowest rent according  to location"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "2dIy2uTvMBNR",
        "outputId": "f98d5dd1-1179-4194-ae9e-f87f0e46d2d9"
      },
      "source": [
        "#Get the highest rent according to location\n",
        "max_price_df = airbnb_price_df.groupby('neighbourhood_group',as_index=False)['price'].max().sort_values(['price'],ascending = False).rename(columns = {'price':'Maximum price','neighbourhood_group':'Location'})\n",
        "max_price_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Location</th>\n",
              "      <th>Maximum price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>10000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Manhattan</td>\n",
              "      <td>10000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Queens</td>\n",
              "      <td>10000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Staten Island</td>\n",
              "      <td>5000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bronx</td>\n",
              "      <td>2500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Location  Maximum price\n",
              "1       Brooklyn          10000\n",
              "2      Manhattan          10000\n",
              "3         Queens          10000\n",
              "4  Staten Island           5000\n",
              "0          Bronx           2500"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "1RA3XO7XN_-h",
        "outputId": "fee33ac5-83c4-4687-de7c-5ba75f2ce051"
      },
      "source": [
        "#Get the lowest rent according to location\n",
        "min_price_df = airbnb_price_df.groupby('neighbourhood_group',as_index=False)['price'].min().sort_values(['price'],ascending = True).rename(columns = {'price':'Minimum price','neighbourhood_group':'Location'})\n",
        "min_price_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Location</th>\n",
              "      <th>Minimum price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Manhattan</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Queens</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bronx</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Staten Island</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Location  Minimum price\n",
              "1       Brooklyn             10\n",
              "2      Manhattan             10\n",
              "3         Queens             10\n",
              "0          Bronx             20\n",
              "4  Staten Island             20"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "y3bRBcMROEpR",
        "outputId": "35e17781-53dd-4b49-8e38-a4c05f9b1ba5"
      },
      "source": [
        "#Get the combine dataframe of minimum and maximum price according to location\n",
        "merge_price_df = pd.merge(max_price_df, min_price_df, on='Location')\n",
        "merge_price_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Location</th>\n",
              "      <th>Maximum price</th>\n",
              "      <th>Minimum price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>10000</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Manhattan</td>\n",
              "      <td>10000</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Queens</td>\n",
              "      <td>10000</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Staten Island</td>\n",
              "      <td>5000</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bronx</td>\n",
              "      <td>2500</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Location  Maximum price  Minimum price\n",
              "0       Brooklyn          10000             10\n",
              "1      Manhattan          10000             10\n",
              "2         Queens          10000             10\n",
              "3  Staten Island           5000             20\n",
              "4          Bronx           2500             20"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V0L3P4ppBp_9"
      },
      "source": [
        "#Create a copy of merge price dataframe\n",
        "merge_price_df_copy = merge_price_df.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7CwdgIpKC_0d"
      },
      "source": [
        "#Create a function for log transformation of maximum price and minimum price\n",
        "def log_values(values):\n",
        "  '''This function takes a price value as a input and give it's log values'''\n",
        "  new_max_price = np.log2(values)\n",
        "\n",
        "  return new_max_price"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_JI0ZlpoBv79"
      },
      "source": [
        "#Apply above function on merge price dataframe\n",
        "merge_price_df_copy['Maximum price'] = merge_price_df_copy.apply(lambda x: log_values(x['Maximum price']), axis=1)\n",
        "merge_price_df_copy['Minimum price'] = merge_price_df_copy.apply(lambda x: log_values(x['Minimum price']), axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "Ajw0DksTJ1SV",
        "outputId": "fc6de950-3743-46e4-d987-2a2c2fe3c378"
      },
      "source": [
        "#Output of maximum price and minimum price after log transformation  \n",
        "merge_price_df_copy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Location</th>\n",
              "      <th>Maximum price</th>\n",
              "      <th>Minimum price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>13.287712</td>\n",
              "      <td>3.321928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Manhattan</td>\n",
              "      <td>13.287712</td>\n",
              "      <td>3.321928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Queens</td>\n",
              "      <td>13.287712</td>\n",
              "      <td>3.321928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Staten Island</td>\n",
              "      <td>12.287712</td>\n",
              "      <td>4.321928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bronx</td>\n",
              "      <td>11.287712</td>\n",
              "      <td>4.321928</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Location  Maximum price  Minimum price\n",
              "0       Brooklyn      13.287712       3.321928\n",
              "1      Manhattan      13.287712       3.321928\n",
              "2         Queens      13.287712       3.321928\n",
              "3  Staten Island      12.287712       4.321928\n",
              "4          Bronx      11.287712       4.321928"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 532
        },
        "id": "AnjAFcHsPAri",
        "outputId": "d69ae45f-1c95-42f0-bdd0-9825091c4fd8"
      },
      "source": [
        "#Bar graph for display maximum price and minimum price according to location\n",
        "merge_price_df_copy.plot(x=\"Location\", y=['Maximum price','Minimum price'], kind=\"bar\")\n",
        "plt.legend(labels = ['Maximum price','Minimum price'])\n",
        "plt.title('Maximum and minimum price according to location')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Maximum and minimum price according to location')"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NmNjXMUeQFrq"
      },
      "source": [
        "#4.Most Popular/demanded host of Airbnb in New york\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "MG-IZZSMP-vM",
        "outputId": "21a83111-c4ac-4ddb-a7ed-eab19429b2fd"
      },
      "source": [
        "#Get the host based on number of reviews\n",
        "host_based_on_review_df = airbnb_price_df.groupby(['host_id','host_name'],as_index=False)['number_of_reviews'].sum().sort_values(['number_of_reviews'],ascending = False)\n",
        "host_based_on_review_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>host_id</th>\n",
              "      <th>host_name</th>\n",
              "      <th>number_of_reviews</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>21294</th>\n",
              "      <td>37312959</td>\n",
              "      <td>Maya</td>\n",
              "      <td>2273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1052</th>\n",
              "      <td>344035</td>\n",
              "      <td>Brooklyn&amp;   Breakfast    -Len-</td>\n",
              "      <td>2205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18619</th>\n",
              "      <td>26432133</td>\n",
              "      <td>Danielle</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20863</th>\n",
              "      <td>35524316</td>\n",
              "      <td>Yasu &amp; Akiko</td>\n",
              "      <td>1971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21911</th>\n",
              "      <td>40176101</td>\n",
              "      <td>Brady</td>\n",
              "      <td>1818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21799</th>\n",
              "      <td>39706334</td>\n",
              "      <td>Erin</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21802</th>\n",
              "      <td>39724060</td>\n",
              "      <td>Jaime</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21806</th>\n",
              "      <td>39731713</td>\n",
              "      <td>Polina</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21812</th>\n",
              "      <td>39765154</td>\n",
              "      <td>Dave</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37424</th>\n",
              "      <td>274321313</td>\n",
              "      <td>Kat</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>37425 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         host_id                       host_name  number_of_reviews\n",
              "21294   37312959                            Maya               2273\n",
              "1052      344035  Brooklyn&   Breakfast    -Len-               2205\n",
              "18619   26432133                        Danielle               2017\n",
              "20863   35524316                    Yasu & Akiko               1971\n",
              "21911   40176101                           Brady               1818\n",
              "...          ...                             ...                ...\n",
              "21799   39706334                            Erin                  0\n",
              "21802   39724060                           Jaime                  0\n",
              "21806   39731713                          Polina                  0\n",
              "21812   39765154                            Dave                  0\n",
              "37424  274321313                             Kat                  0\n",
              "\n",
              "[37425 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lAHe3ervQOoZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "32c23a50-159b-4e52-de6e-03b4cebef61d"
      },
      "source": [
        "#Get the host based on availability in a year\n",
        "host_based_on_availability_df = airbnb_price_df.groupby(['host_id','host_name'],as_index=False)['availability_365'].mean().sort_values(['availability_365'],ascending = True)\n",
        "host_based_on_availability_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>host_id</th>\n",
              "      <th>host_name</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2438</td>\n",
              "      <td>Tasos</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22244</th>\n",
              "      <td>41764756</td>\n",
              "      <td>Luz</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22243</th>\n",
              "      <td>41762539</td>\n",
              "      <td>Maria</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22242</th>\n",
              "      <td>41760536</td>\n",
              "      <td>Austin</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22241</th>\n",
              "      <td>41757762</td>\n",
              "      <td>Sara</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1787</th>\n",
              "      <td>746552</td>\n",
              "      <td>Tom</td>\n",
              "      <td>365.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27208</th>\n",
              "      <td>76862848</td>\n",
              "      <td>Alexis</td>\n",
              "      <td>365.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22042</th>\n",
              "      <td>40733012</td>\n",
              "      <td>Victor</td>\n",
              "      <td>365.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1829</th>\n",
              "      <td>772862</td>\n",
              "      <td>Jesse</td>\n",
              "      <td>365.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30118</th>\n",
              "      <td>121429669</td>\n",
              "      <td>Elizanda</td>\n",
              "      <td>365.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>37425 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         host_id host_name  availability_365\n",
              "0           2438     Tasos               0.0\n",
              "22244   41764756       Luz               0.0\n",
              "22243   41762539     Maria               0.0\n",
              "22242   41760536    Austin               0.0\n",
              "22241   41757762      Sara               0.0\n",
              "...          ...       ...               ...\n",
              "1787      746552       Tom             365.0\n",
              "27208   76862848    Alexis             365.0\n",
              "22042   40733012    Victor             365.0\n",
              "1829      772862     Jesse             365.0\n",
              "30118  121429669  Elizanda             365.0\n",
              "\n",
              "[37425 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3kujiEUBQUAz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "ffb60ade-0655-49ed-b943-78fc18fcb7ee"
      },
      "source": [
        "#Merge two dataframe based on number of reviews and availability in a year\n",
        "popular_host_df = pd.merge(host_based_on_review_df,host_based_on_availability_df,on = ['host_id','host_name']).sort_values(['availability_365','number_of_reviews'],ascending = [True,False]).rename(columns = {'number_of_reviews':'Number of reviews','availability_365':'Availability in a year'})\n",
        "\n",
        "popular_host_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>host_id</th>\n",
              "      <th>host_name</th>\n",
              "      <th>Number of reviews</th>\n",
              "      <th>Availability in a year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>22959695</td>\n",
              "      <td>Gurpreet  Singh</td>\n",
              "      <td>1157</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>99392252</td>\n",
              "      <td>Michael</td>\n",
              "      <td>732</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>121391142</td>\n",
              "      <td>Deloris</td>\n",
              "      <td>693</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>792159</td>\n",
              "      <td>Wanda</td>\n",
              "      <td>480</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>37818581</td>\n",
              "      <td>Sofia</td>\n",
              "      <td>479</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37133</th>\n",
              "      <td>37360127</td>\n",
              "      <td>Mel</td>\n",
              "      <td>0</td>\n",
              "      <td>365.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37137</th>\n",
              "      <td>37424221</td>\n",
              "      <td>Trevor</td>\n",
              "      <td>0</td>\n",
              "      <td>365.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37225</th>\n",
              "      <td>35741633</td>\n",
              "      <td>Chen</td>\n",
              "      <td>0</td>\n",
              "      <td>365.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37248</th>\n",
              "      <td>40834217</td>\n",
              "      <td>Jay</td>\n",
              "      <td>0</td>\n",
              "      <td>365.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37263</th>\n",
              "      <td>40733012</td>\n",
              "      <td>Victor</td>\n",
              "      <td>0</td>\n",
              "      <td>365.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>37425 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         host_id        host_name  Number of reviews  Availability in a year\n",
              "12      22959695  Gurpreet  Singh               1157                     0.0\n",
              "41      99392252          Michael                732                     0.0\n",
              "47     121391142          Deloris                693                     0.0\n",
              "122       792159            Wanda                480                     0.0\n",
              "125     37818581            Sofia                479                     0.0\n",
              "...          ...              ...                ...                     ...\n",
              "37133   37360127              Mel                  0                   365.0\n",
              "37137   37424221           Trevor                  0                   365.0\n",
              "37225   35741633             Chen                  0                   365.0\n",
              "37248   40834217              Jay                  0                   365.0\n",
              "37263   40733012           Victor                  0                   365.0\n",
              "\n",
              "[37425 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "bgRq-kSlQXQp",
        "outputId": "cbeeac4a-3f18-4f37-b8aa-556cdfcfefc9"
      },
      "source": [
        "#Most popular top 5 host in new york\n",
        "popular_host_df.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>host_id</th>\n",
              "      <th>host_name</th>\n",
              "      <th>Number of reviews</th>\n",
              "      <th>Availability in a year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>22959695</td>\n",
              "      <td>Gurpreet  Singh</td>\n",
              "      <td>1157</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>99392252</td>\n",
              "      <td>Michael</td>\n",
              "      <td>732</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>121391142</td>\n",
              "      <td>Deloris</td>\n",
              "      <td>693</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>792159</td>\n",
              "      <td>Wanda</td>\n",
              "      <td>480</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>37818581</td>\n",
              "      <td>Sofia</td>\n",
              "      <td>479</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       host_id        host_name  Number of reviews  Availability in a year\n",
              "12    22959695  Gurpreet  Singh               1157                     0.0\n",
              "41    99392252          Michael                732                     0.0\n",
              "47   121391142          Deloris                693                     0.0\n",
              "122     792159            Wanda                480                     0.0\n",
              "125   37818581            Sofia                479                     0.0"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "Z9__MBqxQgPu",
        "outputId": "99cb18c4-b197-465d-c8ca-04f42b92f963"
      },
      "source": [
        "#Create scatter plot based on number of reviews and availability in a year\n",
        "popular_host_df.plot.scatter(x='Number of reviews', y='Availability in a year', xlim=(100,1000),ylim=(10,380));"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b1xRlWhFoVm9"
      },
      "source": [
        "##**By Shantanu Houzwala**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XvBdu_Inoh1b"
      },
      "source": [
        "# Answering following Questions:\n",
        "* Find the total count of each room type\n",
        "\n",
        "* Which are the top 25 most used words in listing names?\n",
        "\n",
        "* Find top 10 hosts with most listings\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2iMVmTHDJFg0"
      },
      "source": [
        "#Importing Libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "seU4zi6HJ0Lj",
        "outputId": "d20a348b-8769-4d2f-dd2f-bdde9ef529bc"
      },
      "source": [
        "# Mount Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m4_uMiR5K43i"
      },
      "source": [
        "# Importing data by reading csv file\n",
        "file_path = '/content/drive/MyDrive/Capstone Project (AlmaBetter)/Airbnb EDA Project/Copy of Airbnb NYC 2019.csv'\n",
        "airbnb_df=pd.read_csv(file_path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "8UplLqwmFHqR",
        "outputId": "db3e6807-2206-48d4-b68a-4fb2cc8dc56e"
      },
      "source": [
        "#Understanding the Data\n",
        "airbnb_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>name</th>\n",
              "      <th>host_id</th>\n",
              "      <th>host_name</th>\n",
              "      <th>neighbourhood_group</th>\n",
              "      <th>neighbourhood</th>\n",
              "      <th>room_type</th>\n",
              "      <th>price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2539</td>\n",
              "      <td>Clean &amp; quiet apt home by the park</td>\n",
              "      <td>2787</td>\n",
              "      <td>John</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Kensington</td>\n",
              "      <td>Private room</td>\n",
              "      <td>149</td>\n",
              "      <td>1</td>\n",
              "      <td>9</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>Entire home/apt</td>\n",
              "      <td>225</td>\n",
              "      <td>1</td>\n",
              "      <td>45</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>Private room</td>\n",
              "      <td>150</td>\n",
              "      <td>3</td>\n",
              "      <td>0</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>Entire home/apt</td>\n",
              "      <td>89</td>\n",
              "      <td>1</td>\n",
              "      <td>270</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>Entire home/apt</td>\n",
              "      <td>80</td>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     id  ... availability_365\n",
              "0  2539  ...              365\n",
              "1  2595  ...              355\n",
              "2  3647  ...              365\n",
              "3  3831  ...              194\n",
              "4  5022  ...                0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LhhfYo7ETlq_",
        "outputId": "5faf621c-1b02-47d7-a4ac-a0c901d2b103"
      },
      "source": [
        "airbnb_df.info()"
      ],
      "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 12 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   room_type                       48858 non-null  object\n",
            " 7   price                           48858 non-null  int64 \n",
            " 8   minimum_nights                  48858 non-null  int64 \n",
            " 9   number_of_reviews               48858 non-null  int64 \n",
            " 10  calculated_host_listings_count  48858 non-null  int64 \n",
            " 11  availability_365                48858 non-null  int64 \n",
            "dtypes: int64(7), object(5)\n",
            "memory usage: 4.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZPf_u3KfTs78",
        "outputId": "f60800c6-0b75-4777-9836-bdb7eee29348"
      },
      "source": [
        "airbnb_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(48858, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KXfuEmoK6AWz"
      },
      "source": [
        "## Cleaning the Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vclDCRf6XcR0",
        "outputId": "fb40937b-8623-49cb-a4c7-203a2fff3d84"
      },
      "source": [
        "#Removing the Duplicates if any\n",
        "airbnb_df.duplicated().sum()\n",
        "airbnb_df.drop_duplicates(inplace=True)\n",
        "airbnb_df.info()"
      ],
      "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 12 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   room_type                       48858 non-null  object\n",
            " 7   price                           48858 non-null  int64 \n",
            " 8   minimum_nights                  48858 non-null  int64 \n",
            " 9   number_of_reviews               48858 non-null  int64 \n",
            " 10  calculated_host_listings_count  48858 non-null  int64 \n",
            " 11  availability_365                48858 non-null  int64 \n",
            "dtypes: int64(7), object(5)\n",
            "memory usage: 4.8+ MB\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7jqGRE3lYlbF"
      },
      "source": [
        "# Drop null observations\n",
        "airbnb_non_null_df = airbnb_df.dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9sEdjOWpZcdt",
        "outputId": "58585f7c-0251-4688-a421-80e48b9a8715"
      },
      "source": [
        "airbnb_non_null_df.info()"
      ],
      "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 12 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   room_type                       48858 non-null  object\n",
            " 7   price                           48858 non-null  int64 \n",
            " 8   minimum_nights                  48858 non-null  int64 \n",
            " 9   number_of_reviews               48858 non-null  int64 \n",
            " 10  calculated_host_listings_count  48858 non-null  int64 \n",
            " 11  availability_365                48858 non-null  int64 \n",
            "dtypes: int64(7), object(5)\n",
            "memory usage: 4.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zwk1Khix04SZ"
      },
      "source": [
        "### Finding Total count of each room types"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TNKUKdRLo40M"
      },
      "source": [
        "# Finding unique values from column 'room_type'\n",
        "airbnb_room_type = airbnb_df.room_type.unique()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vDR_QNooqE-7",
        "outputId": "cfd0ba1e-0c0f-4378-e2b1-eb833b4e3db9"
      },
      "source": [
        "airbnb_room_type"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Private room', 'Entire home/apt', 'Shared room'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ClSDZA6LreFm"
      },
      "source": [
        "# Which is the most listed room type?\n",
        "airbnb_roomtype_frequency = dict(airbnb_df.room_type.value_counts())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MY9Kvc2-wHuq",
        "outputId": "d18d29b2-60f5-4684-e587-947b79390cf7"
      },
      "source": [
        "airbnb_roomtype_frequency"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Entire home/apt': 25409, 'Private room': 22326, 'Shared room': 1160}"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "ZzlITkJO1ltz",
        "outputId": "0d0a4ded-7790-4c39-b5db-2aaa438dd7e5"
      },
      "source": [
        "# Creating Dataset \n",
        "\n",
        "room_type = list(airbnb_roomtype_frequency.keys())\n",
        "data = list(airbnb_roomtype_frequency.values())\n",
        "\n",
        "# Creating color parameters\n",
        "\n",
        "colors = ( \"skyblue\", \"red\", \"yellow\",)\n",
        "\n",
        "# Creating explode data\n",
        "\n",
        "explode = (0.03, 0.03, 0.1)\n",
        "\n",
        "# Wedge properties\n",
        "wp = { 'linewidth' : 1, 'edgecolor' : \"pink\" }\n",
        "\n",
        "# Creating autocpt arguments\n",
        "def func(pct, allvalues):\n",
        "    absolute = int(pct / 100.*np.sum(allvalues))\n",
        "    return \"{:.1f}%\\n({:d})\".format(pct, absolute)\n",
        "\n",
        "# Creating Pie Chart\n",
        "\n",
        "# Creating plot\n",
        "fig, airbnb_pie_chart = plt.subplots(figsize =(12, 8))\n",
        "wedges, texts, autotexts = airbnb_pie_chart.pie(data, autopct = lambda pct: func(pct, data),                                              \n",
        "                                                explode = explode,\n",
        "                                                shadow = True,\n",
        "                                                colors = colors,\n",
        "                                                startangle = 0,\n",
        "                                                wedgeprops = wp,\n",
        "                                                textprops = dict(color =\"black\"))\n",
        "\n",
        "# Adding legend\n",
        "airbnb_pie_chart.legend(wedges, room_type,\n",
        "                        title =\"Room Type\",\n",
        "                        loc =\"upper left\",          \n",
        "                        bbox_to_anchor=(1, 0., 0.,1))\n",
        "\n",
        "plt.setp(autotexts, size = 15, weight = \"bold\")\n",
        "airbnb_pie_chart.set_title(\"Count of Listed Rooms\")\n",
        "  \n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Count of Listed Rooms')"
            ]
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eJvCOvolrm42"
      },
      "source": [
        "# Visualizing all listing’s room type proportions from each neighbourhood group using Plotly API library for graph visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "rOToM_5BrtW2",
        "outputId": "565720a5-86fc-466f-bbef-38639416024d"
      },
      "source": [
        "import plotly.offline as pyo\n",
        "import plotly.graph_objs as go\n",
        "\n",
        "#Setting up the color pallete\n",
        "color_dict = {'Private room': '#cc5a49', 'Entire home/apt' : '#4586ac', 'Shared room' : '#C0C0C0'}\n",
        "\n",
        "#Grouping the room type using 'neighbourhood_group' as an index\n",
        "airbnb_types=airbnb_df.groupby(['neighbourhood_group', 'room_type']).size()\n",
        "\n",
        "#Creating function to plot room type proportion on all region area\n",
        "for region in airbnb_df.neighbourhood_group.unique():\n",
        "    \n",
        "    plt.figure(figsize=(24,12))\n",
        "    \n",
        "    airbnb_reg=airbnb_types[region]\n",
        "    labels = airbnb_reg.index\n",
        "    sizes = airbnb_reg.values\n",
        "              \n",
        "    \n",
        "    plot_num = 321\n",
        "    plt.subplot(plot_num)\n",
        "    reg_ch = go.Figure(data = [go.Pie(labels = labels, values = sizes, hole = 0.6)])\n",
        "    reg_ch.update_traces(title = region, textfont_size=15, titlefont_size=25)\n",
        "    reg_ch.update_layout(legend=dict(\n",
        "    yanchor=\"top\",\n",
        "    y=0.99,\n",
        "    xanchor=\"left\",\n",
        "    x=0.10,\n",
        "    ))\n",
        "    reg_ch.show()\n",
        "    \n",
        "   "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>\n",
              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"539a2bcf-c6b0-40e3-9dd8-772c73a560ba\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                \n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
              "                    \n",
              "                if (document.getElementById(\"539a2bcf-c6b0-40e3-9dd8-772c73a560ba\")) {\n",
              "                    Plotly.newPlot(\n",
              "                        '539a2bcf-c6b0-40e3-9dd8-772c73a560ba',\n",
              "                        [{\"hole\": 0.6, \"labels\": [\"Entire home/apt\", \"Private room\", \"Shared room\"], \"textfont\": {\"size\": 15}, \"title\": {\"font\": {\"size\": 25}, \"text\": \"Brooklyn\"}, \"type\": \"pie\", \"values\": [9559, 10132, 413]}],\n",
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,
            "text/plain": [
              "<Figure size 1728x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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,
            "text/plain": [
              "<Figure size 1728x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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,
            "text/plain": [
              "<Figure size 1728x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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,
            "text/plain": [
              "<Figure size 1728x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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,
            "text/plain": [
              "<Figure size 1728x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L6HDp0Arsl91"
      },
      "source": [
        "#Finding Relation between neighbourhood group and availability of rooms"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "tSSM7T2kG2V5",
        "outputId": "d8e25cee-2d01-4d5f-b9e8-ce6d569f1178"
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "sns.set_style('white')\n",
        "\n",
        "ax = sns.boxplot(data=airbnb_df, x='neighbourhood_group',y='availability_365',palette='plasma')\n",
        "\n",
        "# Naming the Chart\n",
        "ax.set_title('Relation between Neighbourhood group & Availability of rooms').set_fontsize('15')\n",
        "\n",
        "# Naming X & Y axis\n",
        "ax.set_ylabel('Availability 365 Days').set_fontsize('15')\n",
        "ax.set_xlabel('Neighbourhood Group').set_fontsize('15')\n",
        "\n",
        "#Adjusting Bar labels\n",
        "ax.set_xticklabels(ax.get_xticklabels(), size = '15')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0, 'Brooklyn'),\n",
              " Text(0, 0, 'Manhattan'),\n",
              " Text(0, 0, 'Queens'),\n",
              " Text(0, 0, 'Staten Island'),\n",
              " Text(0, 0, 'Bronx')]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NqfFHsdQi7YB"
      },
      "source": [
        "# Finding Top 25 most used words from listing names"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aD0EBRgsjS0V"
      },
      "source": [
        "#Creting empty list to store name strings\n",
        "airbnb_names=[]\n",
        "\n",
        "#Getting name string from 'name' column and appending it to the empty list\n",
        "for name in airbnb_df.name:\n",
        "    airbnb_names.append(name)\n",
        "\n",
        "#Setting a function to split name strings into seperate words\n",
        "def split_name(name):\n",
        "    ns = str(name).split()\n",
        "    return ns\n",
        "\n",
        "#Creating empty list to store the count of words\n",
        "names_count = []\n",
        "\n",
        "#Getting name string to append it to the names_count list\n",
        "for n in airbnb_names:\n",
        "    for word in split_name(n):\n",
        "        word = word.lower()\n",
        "        names_count.append(word)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fWIl7c8sohqW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc09dbd4-6b31-45bb-81e8-811ba1647b9d"
      },
      "source": [
        "# Importing 'counter' library to count and generate raw data and count top 25 most used words\n",
        "from collections import Counter\n",
        "\n",
        "# Counting most common words\n",
        "count_words = Counter(names_count).most_common()\n",
        "count_words[:25]\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('in', 16733),\n",
              " ('room', 9389),\n",
              " ('bedroom', 7231),\n",
              " ('private', 6985),\n",
              " ('apartment', 6113),\n",
              " ('cozy', 4634),\n",
              " ('the', 3869),\n",
              " ('to', 3827),\n",
              " ('studio', 3772),\n",
              " ('brooklyn', 3629),\n",
              " ('apt', 3571),\n",
              " ('spacious', 3387),\n",
              " ('1', 3357),\n",
              " ('with', 3092),\n",
              " ('2', 3079),\n",
              " ('of', 2993),\n",
              " ('east', 2967),\n",
              " ('and', 2869),\n",
              " ('manhattan', 2855),\n",
              " ('&', 2820),\n",
              " ('park', 2632),\n",
              " ('sunny', 2536),\n",
              " ('beautiful', 2320),\n",
              " ('near', 2295),\n",
              " ('williamsburg', 2293)]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TqBt_1RA3Zs4",
        "outputId": "1bfc27f5-5409-4931-88a2-9d3e355e099a"
      },
      "source": [
        "# Cleaning the List by removing prepositions\n",
        "items_to_remove = {('in', 16733), ('the', 3869), ('to', 3827), ('of', 2993), ('-', 2272), ('a', 1909)}\n",
        "top_25_cleaned = [e for e in count_words if e not in items_to_remove]\n",
        "top_25 = top_25_cleaned[:25]\n",
        "top_25"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('room', 9389),\n",
              " ('bedroom', 7231),\n",
              " ('private', 6985),\n",
              " ('apartment', 6113),\n",
              " ('cozy', 4634),\n",
              " ('studio', 3772),\n",
              " ('brooklyn', 3629),\n",
              " ('apt', 3571),\n",
              " ('spacious', 3387),\n",
              " ('1', 3357),\n",
              " ('with', 3092),\n",
              " ('2', 3079),\n",
              " ('east', 2967),\n",
              " ('and', 2869),\n",
              " ('manhattan', 2855),\n",
              " ('&', 2820),\n",
              " ('park', 2632),\n",
              " ('sunny', 2536),\n",
              " ('beautiful', 2320),\n",
              " ('near', 2295),\n",
              " ('williamsburg', 2293),\n",
              " ('village', 2055),\n",
              " ('heart', 2044),\n",
              " ('large', 1882),\n",
              " ('loft', 1822)]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jNBCC9CnqBYk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 824
        },
        "outputId": "ea250120-e5bb-4122-eba0-8cf4a05ee77f"
      },
      "source": [
        "#converting the data into DataFrame\n",
        "word_count_df = pd.DataFrame(top_25)\n",
        "word_count_df.rename(columns={0:'Words',1:'Counts'},inplace=True)\n",
        "word_count_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Words</th>\n",
              "      <th>Counts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>room</td>\n",
              "      <td>9389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>bedroom</td>\n",
              "      <td>7231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>private</td>\n",
              "      <td>6985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>apartment</td>\n",
              "      <td>6113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>cozy</td>\n",
              "      <td>4634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>studio</td>\n",
              "      <td>3772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>brooklyn</td>\n",
              "      <td>3629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>apt</td>\n",
              "      <td>3571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>spacious</td>\n",
              "      <td>3387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>3357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>with</td>\n",
              "      <td>3092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2</td>\n",
              "      <td>3079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>east</td>\n",
              "      <td>2967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>and</td>\n",
              "      <td>2869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>manhattan</td>\n",
              "      <td>2855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>&amp;</td>\n",
              "      <td>2820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>park</td>\n",
              "      <td>2632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>sunny</td>\n",
              "      <td>2536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>beautiful</td>\n",
              "      <td>2320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>near</td>\n",
              "      <td>2295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>williamsburg</td>\n",
              "      <td>2293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>village</td>\n",
              "      <td>2055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>heart</td>\n",
              "      <td>2044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>large</td>\n",
              "      <td>1882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>loft</td>\n",
              "      <td>1822</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Words  Counts\n",
              "0           room    9389\n",
              "1        bedroom    7231\n",
              "2        private    6985\n",
              "3      apartment    6113\n",
              "4           cozy    4634\n",
              "5         studio    3772\n",
              "6       brooklyn    3629\n",
              "7            apt    3571\n",
              "8       spacious    3387\n",
              "9              1    3357\n",
              "10          with    3092\n",
              "11             2    3079\n",
              "12          east    2967\n",
              "13           and    2869\n",
              "14     manhattan    2855\n",
              "15             &    2820\n",
              "16          park    2632\n",
              "17         sunny    2536\n",
              "18     beautiful    2320\n",
              "19          near    2295\n",
              "20  williamsburg    2293\n",
              "21       village    2055\n",
              "22         heart    2044\n",
              "23         large    1882\n",
              "24          loft    1822"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z-SITUC4OcxU"
      },
      "source": [
        "### Visualizing the findings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 935
        },
        "id": "BgfE63dyBhLv",
        "outputId": "5e77dcbb-ef14-4d4b-8624-d4779cab37f7"
      },
      "source": [
        "#Setting the figure size\n",
        "sns.set(rc={'figure.figsize':(12,6)})\n",
        "\n",
        "#Setting background colour of chart as white\n",
        "sns.set_style('white')\n",
        "\n",
        "#Plotting the Chart\n",
        "count_viz = sns.barplot(x='Words',y='Counts', data = word_count_df)\n",
        "\n",
        "# Naming the Chart\n",
        "count_viz.set_title('Top 25 Used Words for Listing Names', weight = 'bold').set_fontsize('15')\n",
        "\n",
        "\n",
        "# Naming X & Y axis\n",
        "count_viz.set_ylabel('Count of words', weight = 'bold')\n",
        "count_viz.set_xlabel('Words', weight = 'bold')\n",
        "\n",
        "#Adjusting Bar labels\n",
        "count_viz.set_xticklabels(count_viz.get_xticklabels(),rotation = 90, weight = 'bold', size = '15')\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0, 'room'),\n",
              " Text(0, 0, 'bedroom'),\n",
              " Text(0, 0, 'private'),\n",
              " Text(0, 0, 'apartment'),\n",
              " Text(0, 0, 'cozy'),\n",
              " Text(0, 0, 'studio'),\n",
              " Text(0, 0, 'brooklyn'),\n",
              " Text(0, 0, 'apt'),\n",
              " Text(0, 0, 'spacious'),\n",
              " Text(0, 0, '1'),\n",
              " Text(0, 0, 'with'),\n",
              " Text(0, 0, '2'),\n",
              " Text(0, 0, 'east'),\n",
              " Text(0, 0, 'and'),\n",
              " Text(0, 0, 'manhattan'),\n",
              " Text(0, 0, '&'),\n",
              " Text(0, 0, 'park'),\n",
              " Text(0, 0, 'sunny'),\n",
              " Text(0, 0, 'beautiful'),\n",
              " Text(0, 0, 'near'),\n",
              " Text(0, 0, 'williamsburg'),\n",
              " Text(0, 0, 'village'),\n",
              " Text(0, 0, 'heart'),\n",
              " Text(0, 0, 'large'),\n",
              " Text(0, 0, 'loft')]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "scYfBdC7_gzI"
      },
      "source": [
        "## From the chart above, we see the top 25 words used in the listing name. We can use the word cloud visualization method to help us better understand the chart."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "oKsKw_PS_rfR",
        "outputId": "2126a66c-bd8c-4e8b-ea07-1556c2cdf822"
      },
      "source": [
        "#Importing WordCloud library\n",
        "from wordcloud import WordCloud, ImageColorGenerator\n",
        "text = ' '.join(str(n) for n in word_count_df.Words)\n",
        "\n",
        "#Generating wordcloud image\n",
        "wordcloud = WordCloud(max_font_size=80, colormap='hsv', width=400, height=150).generate(text)\n",
        "plt.figure(figsize=(12,8))\n",
        "\n",
        "#Displaying the image\n",
        "plt.imshow(wordcloud, interpolation='bilinear')\n",
        "plt.axis('off')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xq_W_RLp4qKm"
      },
      "source": [
        "## Finding Top 10 Listing Counts"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qd2K18W2fjBv",
        "outputId": "ab522888-532a-48a6-928a-0c83cfa77f52"
      },
      "source": [
        "airbnb_df['host_id'].count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "48895"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "mQNQ2O9xPeVY",
        "outputId": "296acf53-de48-465e-9db8-14f049201db8"
      },
      "source": [
        "#Creating DataFrame\n",
        "count_host_id = list(airbnb_df['host_id'].value_counts())\n",
        "host_id = list(airbnb_df.host_id)\n",
        "listing_count= list(zip(host_id,count_host_id))\n",
        "count_host_id_df= pd.DataFrame(listing_count)\n",
        "count_host_id_df.rename(columns={0:'Host_Id',1:'Counts'},inplace=True)\n",
        "\n",
        "#Storing top 10 hosts with most listings\n",
        "top_host_id = count_host_id_df.head(10)\n",
        "top_host_id"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Host_Id</th>\n",
              "      <th>Counts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2787</td>\n",
              "      <td>327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2845</td>\n",
              "      <td>232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4632</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4869</td>\n",
              "      <td>103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7192</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>7322</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7356</td>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8967</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>7490</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>7549</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Host_Id  Counts\n",
              "0     2787     327\n",
              "1     2845     232\n",
              "2     4632     121\n",
              "3     4869     103\n",
              "4     7192      96\n",
              "5     7322      96\n",
              "6     7356      91\n",
              "7     8967      87\n",
              "8     7490      65\n",
              "9     7549      52"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "id": "NT5piUvm5OGf",
        "outputId": "8a548062-8ac1-4f54-cf6c-ee7f6e2b3f70"
      },
      "source": [
        "#Setting the figure size\n",
        "sns.set(rc={'figure.figsize':(10,8)})\n",
        "\n",
        "#Setting background colour of chart as white\n",
        "sns.set_style('white')\n",
        "\n",
        "#Plotting the Chart\n",
        "viz_bar = sns.barplot(x= 'Host_Id', y= 'Counts', color='r', data=top_host_id,\n",
        "                      order=top_host_id.sort_values('Counts',ascending = False).Host_Id)\n",
        "\n",
        "#Setting font size for title\n",
        "sns.set(font_scale = 2)\n",
        "\n",
        "# Naming the Chart\n",
        "viz_bar.set_title('Hosts with most listings in New York')\n",
        "\n",
        "# Naming X & Y axis\n",
        "viz_bar.set_xlabel('Host IDs')\n",
        "viz_bar.set_ylabel('Count of listings')\n",
        "\n",
        "#Adjusting Bar labels\n",
        "viz_bar.set_xticklabels(viz_bar.get_xticklabels(), rotation=45)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0, '2787'),\n",
              " Text(0, 0, '2845'),\n",
              " Text(0, 0, '4632'),\n",
              " Text(0, 0, '4869'),\n",
              " Text(0, 0, '7192'),\n",
              " Text(0, 0, '7322'),\n",
              " Text(0, 0, '7356'),\n",
              " Text(0, 0, '8967'),\n",
              " Text(0, 0, '7490'),\n",
              " Text(0, 0, '7549')]"
            ]
          },
          "metadata": {},
          "execution_count": 138
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GoJaXTqQEVYT"
      },
      "source": [
        "## By Suloy Kumar Mandal"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5qoBvXBNEcjt"
      },
      "source": [
        "# Answering following Questions:\n",
        "* Find the top three hosts based on their turnover\n",
        "\n",
        "* Find total no. of nights spend per location\n",
        "\n",
        "* Total no. of nights spends per room types\n",
        "\n",
        "* Top 10 highest listing neighborhood\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BRIqfbuA7WOZ"
      },
      "source": [
        "# Import pandas and numpy package\n",
        "# Importing matplotlib.pyplot and seaborn\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mSm2DWrc7dNp",
        "outputId": "ba84fceb-5268-4691-8f8f-574febae70f5"
      },
      "source": [
        "# import csv files\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "id": "xlVTx4Dj83TS",
        "outputId": "d9219e45-25ef-471d-d21b-418b96f9111c"
      },
      "source": [
        "# loading csv File\n",
        "airbnb=pd.read_csv( '//content/drive/MyDrive/Colab Notebooks/eda project 1/Copy of Airbnb NYC 2019.csv')\n",
        "airbnb.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>name</th>\n",
              "      <th>host_id</th>\n",
              "      <th>host_name</th>\n",
              "      <th>neighbourhood_group</th>\n",
              "      <th>neighbourhood</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>room_type</th>\n",
              "      <th>price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>last_review</th>\n",
              "      <th>reviews_per_month</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2539</td>\n",
              "      <td>Clean &amp; quiet apt home by the park</td>\n",
              "      <td>2787</td>\n",
              "      <td>John</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Kensington</td>\n",
              "      <td>40.64749</td>\n",
              "      <td>-73.97237</td>\n",
              "      <td>Private room</td>\n",
              "      <td>149</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>2018-10-19</td>\n",
              "      <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>NaN</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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     id  ... availability_365\n",
              "0  2539  ...              365\n",
              "1  2595  ...              355\n",
              "2  3647  ...              365\n",
              "3  3831  ...              194\n",
              "4  5022  ...                0\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "ESI0X6L0Ahrm",
        "outputId": "a0ceb441-4f75-4402-ac9b-d33f9ebd004c"
      },
      "source": [
        "\n",
        "# datavisualization of null value\n",
        "airbnb.isnull()\n",
        "sns.heatmap(airbnb.isnull(),yticklabels=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7e57443a90>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OPYWUM2CBJAc"
      },
      "source": [
        "# after removing last_review , reviews_per_month columns\n",
        "airbnb.drop(['last_review', 'reviews_per_month'], axis = 1,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c2kMh_lTHtoI",
        "outputId": "c5d529fa-00c0-4b03-f4d8-3d7779c88022"
      },
      "source": [
        "#find any dupicated value \n",
        "airbnb.duplicated().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "etQKYEXw885C",
        "outputId": "ef40faee-92ec-45e5-db34-60bcba339eb3"
      },
      "source": [
        "# Returns  booleans if a column value contains nulls\n",
        "airbnb.isnull()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>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>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48890</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48891</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48892</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48893</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48894</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>48895 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          id   name  ...  calculated_host_listings_count  availability_365\n",
              "0      False  False  ...                           False             False\n",
              "1      False  False  ...                           False             False\n",
              "2      False  False  ...                           False             False\n",
              "3      False  False  ...                           False             False\n",
              "4      False  False  ...                           False             False\n",
              "...      ...    ...  ...                             ...               ...\n",
              "48890  False  False  ...                           False             False\n",
              "48891  False  False  ...                           False             False\n",
              "48892  False  False  ...                           False             False\n",
              "48893  False  False  ...                           False             False\n",
              "48894  False  False  ...                           False             False\n",
              "\n",
              "[48895 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "Ka24KEt09CNy",
        "outputId": "d619da4d-ccb5-417e-d763-19673801d2fe"
      },
      "source": [
        "# datavisualization of null value after removing last_review , reviews_per_month columns\n",
        "sns.heatmap(airbnb.isnull(),yticklabels=False)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7e565d5550>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rJBaEFTV9IcK",
        "outputId": "0adf841e-4508-4cd4-9388-f6419bde2b5e"
      },
      "source": [
        "# drop null value from the dataset\n",
        "non_null_file = airbnb.dropna()\n",
        "non_null_file.info()"
      ],
      "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 14 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  calculated_host_listings_count  48858 non-null  int64  \n",
            " 13  availability_365                48858 non-null  int64  \n",
            "dtypes: float64(2), int64(7), object(5)\n",
            "memory usage: 5.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vtHcGnA1JEbI"
      },
      "source": [
        "# Which are the top three hosts based on their turnover?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "FyrPW-mbJKaO",
        "outputId": "a9990143-2655-46cc-f1b5-407a0338594e"
      },
      "source": [
        "# copy of the non_null_file\n",
        "new=non_null_file.copy()\n",
        "new.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>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>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2539</td>\n",
              "      <td>Clean &amp; quiet apt home by the park</td>\n",
              "      <td>2787</td>\n",
              "      <td>John</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Kensington</td>\n",
              "      <td>40.64749</td>\n",
              "      <td>-73.97237</td>\n",
              "      <td>Private room</td>\n",
              "      <td>149</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>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>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>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>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>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     id  ... availability_365\n",
              "0  2539  ...              365\n",
              "1  2595  ...              355\n",
              "2  3647  ...              365\n",
              "3  3831  ...              194\n",
              "4  5022  ...                0\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jnGFJAgqJTBG",
        "outputId": "ce953b1f-41f5-410c-c6e3-08fc53bac1c3"
      },
      "source": [
        "new['price'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    48858.000000\n",
              "mean       152.740309\n",
              "std        240.232386\n",
              "min          0.000000\n",
              "25%         69.000000\n",
              "50%        106.000000\n",
              "75%        175.000000\n",
              "max      10000.000000\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cP2ysiF6JYWX",
        "outputId": "843a8ae0-7490-4318-c923-29c321260e0e"
      },
      "source": [
        "# replace whose price less than 50\n",
        "new.loc[new['price']<50,'price'] = new.loc[(new['price']>50) & (new['price']<80),'price'].median()\n",
        "new['price'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    48858.000000\n",
              "mean       155.369008\n",
              "std        239.104348\n",
              "min         50.000000\n",
              "25%         69.000000\n",
              "50%        106.000000\n",
              "75%        175.000000\n",
              "max      10000.000000\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "-9jnngghJd2n",
        "outputId": "4a9f5602-c623-45e3-d97e-cae86c389395"
      },
      "source": [
        "# after removing price less than 100\n",
        "new.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>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>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2539</td>\n",
              "      <td>Clean &amp; quiet apt home by the park</td>\n",
              "      <td>2787</td>\n",
              "      <td>John</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Kensington</td>\n",
              "      <td>40.64749</td>\n",
              "      <td>-73.97237</td>\n",
              "      <td>Private room</td>\n",
              "      <td>149</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>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>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>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>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>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     id  ... availability_365\n",
              "0  2539  ...              365\n",
              "1  2595  ...              355\n",
              "2  3647  ...              365\n",
              "3  3831  ...              194\n",
              "4  5022  ...                0\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VRDvpP38Jodf",
        "outputId": "5232f8e2-ac57-4d0f-a3f8-03fd7ee36953"
      },
      "source": [
        "#  find the maximum price across different host name\n",
        "top_host=new.groupby(['host_name','host_id'])['price'].sum().reset_index()\n",
        "top_host.rename(columns={'price':'total_price'},inplace=True)\n",
        "top_host.head()\n",
        "#top_host.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>host_name</th>\n",
              "      <th>host_id</th>\n",
              "      <th>total_price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>'Cil</td>\n",
              "      <td>45354224</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(Ari) HENRY LEE</td>\n",
              "      <td>40605120</td>\n",
              "      <td>140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(Email hidden by Airbnb)</td>\n",
              "      <td>5610823</td>\n",
              "      <td>261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(Email hidden by Airbnb)</td>\n",
              "      <td>7580102</td>\n",
              "      <td>389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(Email hidden by Airbnb)</td>\n",
              "      <td>11495251</td>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  host_name   host_id  total_price\n",
              "0                      'Cil  45354224          120\n",
              "1           (Ari) HENRY LEE  40605120          140\n",
              "2  (Email hidden by Airbnb)   5610823          261\n",
              "3  (Email hidden by Airbnb)   7580102          389\n",
              "4  (Email hidden by Airbnb)  11495251          200"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "JOtlX9XwKhYn",
        "outputId": "760176c0-c8b9-4f50-9dd8-709e2c7e59bc"
      },
      "source": [
        "# find top three host best on their turnover\n",
        "top_3=top_host.sort_values('total_price',ascending=False).iloc[:3,:3]\n",
        "top_3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>host_name</th>\n",
              "      <th>host_id</th>\n",
              "      <th>total_price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>33209</th>\n",
              "      <td>Sonder (NYC)</td>\n",
              "      <td>219517861</td>\n",
              "      <td>82795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4856</th>\n",
              "      <td>Blueground</td>\n",
              "      <td>107434423</td>\n",
              "      <td>70331</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31216</th>\n",
              "      <td>Sally</td>\n",
              "      <td>156158778</td>\n",
              "      <td>37097</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          host_name    host_id  total_price\n",
              "33209  Sonder (NYC)  219517861        82795\n",
              "4856     Blueground  107434423        70331\n",
              "31216         Sally  156158778        37097"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 726
        },
        "id": "Tuh3vMTCK7f3",
        "outputId": "c0c8ef90-4c62-4d9c-e4d6-212d9742308b"
      },
      "source": [
        "sns.set(rc={'figure.figsize':(8,10)})\n",
        "sns.set_style('white')\n",
        "abc= sns.barplot(x='host_name',y='total_price',data = top_3)\n",
        "abc.set_title('three hosts based on their turnover', Weight='bold')\n",
        "abc.set_ylabel('host name', Weight='bold')\n",
        "abc.set_xlabel('total price', Weight='bold')\n",
        "\n",
        "abc.set_xticklabels(abc.get_xticklabels(),Weight='bold',rotation = 45,size='15')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0, 'Sonder (NYC)'), Text(0, 0, 'Blueground'), Text(0, 0, 'Sally')]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u42SUmiYL2Um"
      },
      "source": [
        "# Find total no. nights spend per location"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "NrlDrWVpMAmf",
        "outputId": "23af7b55-2fbb-4ca5-fac0-88bef275573e"
      },
      "source": [
        "#  find the maximum(minimum nights) spending by customer across different neighourhood group\n",
        "total_nights=new.groupby('neighbourhood_group')['minimum_nights'].sum().reset_index()\n",
        "final_nights=total_nights.sort_values('minimum_nights',ascending=True)\n",
        "final_nights.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>neighbourhood_group</th>\n",
              "      <th>minimum_nights</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Staten Island</td>\n",
              "      <td>1074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bronx</td>\n",
              "      <td>3441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Queens</td>\n",
              "      <td>19305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>87458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Manhattan</td>\n",
              "      <td>116571</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  neighbourhood_group  minimum_nights\n",
              "4       Staten Island            1074\n",
              "0               Bronx            3441\n",
              "3              Queens           19305\n",
              "1            Brooklyn           87458\n",
              "2           Manhattan          116571"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "XvEYBwOsIba-",
        "outputId": "ac0e1679-6895-4e16-c022-e67eb57fc7ea"
      },
      "source": [
        "# visualization of total no. nights spend per location using seaborn\n",
        "sns.set(rc={'figure.figsize':(10,5)})\n",
        "sns.set_style('white')\n",
        "abc= sns.barplot(x='neighbourhood_group',y='minimum_nights',data = final_nights)\n",
        "abc.set_title('find total no. nights spend per location', Weight='bold')\n",
        "abc.set_ylabel('minimum night', Weight='bold')\n",
        "abc.set_xlabel('neighbourhood group', Weight='bold')\n",
        "\n",
        "abc.set_xticklabels(abc.get_xticklabels(),Weight='bold',rotation = 45,size='15')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0, 'Staten Island'),\n",
              " Text(0, 0, 'Bronx'),\n",
              " Text(0, 0, 'Queens'),\n",
              " Text(0, 0, 'Brooklyn'),\n",
              " Text(0, 0, 'Manhattan')]"
            ]
          },
          "metadata": {},
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PC0DTzjmRvI8"
      },
      "source": [
        "# Total no. of nights spend per room types\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i3JOLNe4R8Z0",
        "outputId": "b15918b6-34eb-4737-8a23-49041f36350c"
      },
      "source": [
        "# find unique value of room types\n",
        "list(new['room_type'].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Private room', 'Entire home/apt', 'Shared room']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "ZwRHQ6VKSNU7",
        "outputId": "575b657a-a4c6-448b-93c2-6332e9fa89c2"
      },
      "source": [
        "#  find the maximum(minimum nights) spending by customer across different room type\n",
        "total_room=new.groupby(  'room_type')['minimum_nights'].sum().reset_index()\n",
        "room_types=total_room.sort_values('minimum_nights',ascending=True)\n",
        "room_types.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>room_type</th>\n",
              "      <th>minimum_nights</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Shared room</td>\n",
              "      <td>3721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Private room</td>\n",
              "      <td>80160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>143968</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         room_type  minimum_nights\n",
              "2      Shared room            3721\n",
              "1     Private room           80160\n",
              "0  Entire home/apt          143968"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m9Tk0Bd9KQ85"
      },
      "source": [
        "# create dataset\n",
        "labels=list(room_types['room_type'])\n",
        "sizes=list(room_types['minimum_nights'])\n",
        "# create color parameter\n",
        "colors=['green','pink','yellow']\n",
        "# create explode\n",
        "explode = (0.07, 0.07, 0.07)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "EpwKLcYPKY0L",
        "outputId": "71f7736c-e8ae-463f-bf43-f86ada109730"
      },
      "source": [
        "#creating pie chart\n",
        "plt.pie(sizes,explode=explode,labels=labels,colors=colors,autopct='%1.1f%%',shadow=True)\n",
        "plt.title('total no. of nights spend per room types', fontsize=20)\n",
        "plt.axis(\"equal\")  \n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HR-rqEy8IQgl"
      },
      "source": [
        "# Top ten Highest listing neighourhood"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "heC8DmoBRWSK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "outputId": "d8afae83-33e4-43b2-b9a0-3101380a7e02"
      },
      "source": [
        "# find listing value of all neighourhood in dataset\n",
        "neighour=new['neighbourhood'].value_counts().sort_values(ascending=False).reset_index()\n",
        "#top ten neighourhood with listing value\n",
        "top_10=neighour[:10]\n",
        "top_10\n",
        "\n",
        "#m.rename(columns={'index': 'newName1', 'neighourhood': 'newName2'}, inplace=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>neighbourhood</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Williamsburg</td>\n",
              "      <td>3163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>3141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Harlem</td>\n",
              "      <td>2204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bushwick</td>\n",
              "      <td>1942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Hell's Kitchen</td>\n",
              "      <td>1528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>East Village</td>\n",
              "      <td>1489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Upper West Side</td>\n",
              "      <td>1482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Upper East Side</td>\n",
              "      <td>1405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Crown Heights</td>\n",
              "      <td>1265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Midtown</td>\n",
              "      <td>986</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                index  neighbourhood\n",
              "0        Williamsburg           3163\n",
              "1  Bedford-Stuyvesant           3141\n",
              "2              Harlem           2204\n",
              "3            Bushwick           1942\n",
              "4      Hell's Kitchen           1528\n",
              "5        East Village           1489\n",
              "6     Upper West Side           1482\n",
              "7     Upper East Side           1405\n",
              "8       Crown Heights           1265\n",
              "9             Midtown            986"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "6R-ZBkejEKn3",
        "outputId": "777aec79-b565-42c5-8144-44fb8a146933"
      },
      "source": [
        "#make copy of top_10\n",
        "final_10=top_10.copy()\n",
        "#rename that dataframe\n",
        "final_10.rename(columns={'index':'neighourhood','neighbourhood':'listing_value' },inplace=True)\n",
        "\n",
        "final_10"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>neighourhood</th>\n",
              "      <th>listing_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Williamsburg</td>\n",
              "      <td>3163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>3141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Harlem</td>\n",
              "      <td>2204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bushwick</td>\n",
              "      <td>1942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Hell's Kitchen</td>\n",
              "      <td>1528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>East Village</td>\n",
              "      <td>1489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Upper West Side</td>\n",
              "      <td>1482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Upper East Side</td>\n",
              "      <td>1405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Crown Heights</td>\n",
              "      <td>1265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Midtown</td>\n",
              "      <td>986</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         neighourhood  listing_value\n",
              "0        Williamsburg           3163\n",
              "1  Bedford-Stuyvesant           3141\n",
              "2              Harlem           2204\n",
              "3            Bushwick           1942\n",
              "4      Hell's Kitchen           1528\n",
              "5        East Village           1489\n",
              "6     Upper West Side           1482\n",
              "7     Upper East Side           1405\n",
              "8       Crown Heights           1265\n",
              "9             Midtown            986"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 924
        },
        "id": "r5QLrjCiGZtP",
        "outputId": "0e67977d-0783-46f9-92d0-6eb90ba4a641"
      },
      "source": [
        "# data visualizing with barplot\n",
        "sns.set(rc={'figure.figsize':(15,10)})\n",
        "sns.set_style('white')\n",
        "#Plotting the Chart\n",
        "abc= sns.barplot(x='neighourhood', y='listing_value',data = final_10)\n",
        "abc.set_title('find total no. nights spend per location', Weight='bold')\n",
        "# Naming X & Y axis\n",
        "abc.set_ylabel('listing value', Weight='bold')\n",
        "abc.set_xlabel('neighourhood', Weight='bold')\n",
        "#Adjusting Bar labels\n",
        "\n",
        "abc.set_xticklabels(abc.get_xticklabels(),Weight='bold',rotation = 45,size='15')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0, 'Williamsburg'),\n",
              " Text(0, 0, 'Bedford-Stuyvesant'),\n",
              " Text(0, 0, 'Harlem'),\n",
              " Text(0, 0, 'Bushwick'),\n",
              " Text(0, 0, \"Hell's Kitchen\"),\n",
              " Text(0, 0, 'East Village'),\n",
              " Text(0, 0, 'Upper West Side'),\n",
              " Text(0, 0, 'Upper East Side'),\n",
              " Text(0, 0, 'Crown Heights'),\n",
              " Text(0, 0, 'Midtown')]"
            ]
          },
          "metadata": {},
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I4G9lfLbdsIp"
      },
      "source": [
        "#Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aG168Rgxdu7c"
      },
      "source": [
        "\n",
        "*   Manhattan is the most focused place in New York for hosts to do their business\n",
        "*   Customers pay highest amount in Brooklyn,Queens and Manhattan that is 10,000 and lowest amount is 10$.\n",
        "\n",
        "*   For the three types of room type (i.e. Entire home, Shared room, & Private room) average price of entire home is around $157, for shared room is around 60, and for private room is around 75.\n",
        "* 'Entire home/apt' room type has the highest number of listing of 52% and ‘Shared Room’ is the least listed room type at only 2.4% in total. \n",
        "* People stay for longer duration of time in Private rooms in Brooklyn and Manhattan.\n",
        "*Words such as ‘bedroom’, ‘cozy’, ‘private’, ‘apartment’ and ‘spacious’ are used          more frequently than words such as ‘park’, ‘near’, ‘village’ and ‘heart’.\n",
        "*Count of listing by top 10 hosts is almost 2.5%(1270 listings) of the whole dataset.\n",
        "*Top three host base on their turnover are Sonder(nyc),Red awning, Henry and best host is Sonder(nyc)\n",
        "*More customer preferred Manhattan location for night stay then Brooklyn\n",
        "*63.2% customer  spend night in Entire home and 1.6% spend night in Shared room"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OnoPGmXQr73H"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}