Dor_Hotel_Corona_analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Dor - Hotel_Corona_analysis.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/elie-benayoun/GhosTown/blob/master/Dor_Hotel_Corona_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rjnDUSzS1IQH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "from pathlib import Path\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qog3bGyL4YcW",
        "colab_type": "text"
      },
      "source": [
        "missions:\n",
        "1. identify same hotels\n",
        "2. EDA of market before and after corona\n",
        "3. compare the features of before and after"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qzyk4IZH1M0h",
        "colab_type": "text"
      },
      "source": [
        "# Hotel Corona"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WXzO8nHA87w5",
        "colab_type": "text"
      },
      "source": [
        "## Import data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R_IEzCL_6Qk5",
        "colab_type": "text"
      },
      "source": [
        "First mount your drive to this notebook using this command:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iq6Hf-aV5_9B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "outputId": "fd5aeaa1-477e-4466-91bf-f9f97fdf5d0e"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MCWBIxea9H8n",
        "colab_type": "text"
      },
      "source": [
        "Now find your data folder path, and load the dataset from there"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sWULgam36gkf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3f90c08b-7908-4e40-8bb6-55e8746be82c"
      },
      "source": [
        "path_data_dor =  '/content/drive/My Drive/Learning_ML/ITC Program/Hackathon/SETS'\n",
        "os.listdir(path_data_dor) # test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['AB_NYC_08_2019.csv', 'AB_NYC_05_2020.csv']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wcPYJJiz9W2P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# colab output:\n",
        "output_folder_path = '.'\n",
        "output_folder_path = Path(output_folder_path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S3YPzfpV-GG8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "path_data_dor_old = path_data_dor + '/AB_NYC_08_2019.csv'\n",
        "path_data_dor_new = path_data_dor + '/AB_NYC_05_2020.csv'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EmLI_EVT9qgc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "cc0d2f8e-abdb-40b1-ffca-7024e339ed27"
      },
      "source": [
        "df_old = pd.read_csv(path_data_dor_old)\n",
        "df_new = pd.read_csv(path_data_dor_new)\n",
        "display(df_old.head(), df_new.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (61,62,94) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "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>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]"
            ]
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          "metadata": {
            "tags": []
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          "output_type": "display_data",
          "data": {
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              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>listing_url</th>\n",
              "      <th>scrape_id</th>\n",
              "      <th>last_scraped</th>\n",
              "      <th>name</th>\n",
              "      <th>summary</th>\n",
              "      <th>space</th>\n",
              "      <th>description</th>\n",
              "      <th>experiences_offered</th>\n",
              "      <th>neighborhood_overview</th>\n",
              "      <th>notes</th>\n",
              "      <th>transit</th>\n",
              "      <th>access</th>\n",
              "      <th>interaction</th>\n",
              "      <th>house_rules</th>\n",
              "      <th>thumbnail_url</th>\n",
              "      <th>medium_url</th>\n",
              "      <th>picture_url</th>\n",
              "      <th>xl_picture_url</th>\n",
              "      <th>host_id</th>\n",
              "      <th>host_url</th>\n",
              "      <th>host_name</th>\n",
              "      <th>host_since</th>\n",
              "      <th>host_location</th>\n",
              "      <th>host_about</th>\n",
              "      <th>host_response_time</th>\n",
              "      <th>host_response_rate</th>\n",
              "      <th>host_acceptance_rate</th>\n",
              "      <th>host_is_superhost</th>\n",
              "      <th>host_thumbnail_url</th>\n",
              "      <th>host_picture_url</th>\n",
              "      <th>host_neighbourhood</th>\n",
              "      <th>host_listings_count</th>\n",
              "      <th>host_total_listings_count</th>\n",
              "      <th>host_verifications</th>\n",
              "      <th>host_has_profile_pic</th>\n",
              "      <th>host_identity_verified</th>\n",
              "      <th>street</th>\n",
              "      <th>neighbourhood</th>\n",
              "      <th>neighbourhood_cleansed</th>\n",
              "      <th>...</th>\n",
              "      <th>extra_people</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>maximum_nights</th>\n",
              "      <th>minimum_minimum_nights</th>\n",
              "      <th>maximum_minimum_nights</th>\n",
              "      <th>minimum_maximum_nights</th>\n",
              "      <th>maximum_maximum_nights</th>\n",
              "      <th>minimum_nights_avg_ntm</th>\n",
              "      <th>maximum_nights_avg_ntm</th>\n",
              "      <th>calendar_updated</th>\n",
              "      <th>has_availability</th>\n",
              "      <th>availability_30</th>\n",
              "      <th>availability_60</th>\n",
              "      <th>availability_90</th>\n",
              "      <th>availability_365</th>\n",
              "      <th>calendar_last_scraped</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>number_of_reviews_ltm</th>\n",
              "      <th>first_review</th>\n",
              "      <th>last_review</th>\n",
              "      <th>review_scores_rating</th>\n",
              "      <th>review_scores_accuracy</th>\n",
              "      <th>review_scores_cleanliness</th>\n",
              "      <th>review_scores_checkin</th>\n",
              "      <th>review_scores_communication</th>\n",
              "      <th>review_scores_location</th>\n",
              "      <th>review_scores_value</th>\n",
              "      <th>requires_license</th>\n",
              "      <th>license</th>\n",
              "      <th>jurisdiction_names</th>\n",
              "      <th>instant_bookable</th>\n",
              "      <th>is_business_travel_ready</th>\n",
              "      <th>cancellation_policy</th>\n",
              "      <th>require_guest_profile_picture</th>\n",
              "      <th>require_guest_phone_verification</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>calculated_host_listings_count_entire_homes</th>\n",
              "      <th>calculated_host_listings_count_private_rooms</th>\n",
              "      <th>calculated_host_listings_count_shared_rooms</th>\n",
              "      <th>reviews_per_month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2060</td>\n",
              "      <td>https://www.airbnb.com/rooms/2060</td>\n",
              "      <td>20200506054410</td>\n",
              "      <td>2020-05-09</td>\n",
              "      <td>Modern NYC</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Lovely, spacious, sunny 1 BR apartment in 6th ...</td>\n",
              "      <td>Lovely, spacious, sunny 1 BR apartment in 6th ...</td>\n",
              "      <td>none</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://a0.muscache.com/im/pictures/3755/3f9d3...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2259</td>\n",
              "      <td>https://www.airbnb.com/users/show/2259</td>\n",
              "      <td>Jenny</td>\n",
              "      <td>2008-08-18</td>\n",
              "      <td>US</td>\n",
              "      <td>NaN</td>\n",
              "      <td>a few days or more</td>\n",
              "      <td>22%</td>\n",
              "      <td>50%</td>\n",
              "      <td>f</td>\n",
              "      <td>https://a0.muscache.com/im/users/2259/profile_...</td>\n",
              "      <td>https://a0.muscache.com/im/users/2259/profile_...</td>\n",
              "      <td>Washington Heights</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>['reviews']</td>\n",
              "      <td>t</td>\n",
              "      <td>f</td>\n",
              "      <td>New York, NY, United States</td>\n",
              "      <td>Washington Heights</td>\n",
              "      <td>Washington Heights</td>\n",
              "      <td>...</td>\n",
              "      <td>$0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>730</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>730</td>\n",
              "      <td>730</td>\n",
              "      <td>1.0</td>\n",
              "      <td>730.0</td>\n",
              "      <td>3 months ago</td>\n",
              "      <td>t</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-05-09</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2008-09-22</td>\n",
              "      <td>2008-09-22</td>\n",
              "      <td>80.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>f</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>flexible</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2595</td>\n",
              "      <td>https://www.airbnb.com/rooms/2595</td>\n",
              "      <td>20200506054410</td>\n",
              "      <td>2020-05-08</td>\n",
              "      <td>Skylit Midtown Castle</td>\n",
              "      <td>Beautiful, spacious skylit studio in the heart...</td>\n",
              "      <td>- Spacious (500+ft²), immaculate and nicely fu...</td>\n",
              "      <td>Beautiful, spacious skylit studio in the heart...</td>\n",
              "      <td>none</td>\n",
              "      <td>Centrally located in the heart of Manhattan ju...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Apartment is located on 37th Street between 5t...</td>\n",
              "      <td>Guests have full access to the kitchen, bathro...</td>\n",
              "      <td>I am a Sound Therapy Practitioner and Kundalin...</td>\n",
              "      <td>Make yourself at home, respect the space and t...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://a0.muscache.com/im/pictures/f0813a11-4...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2845</td>\n",
              "      <td>https://www.airbnb.com/users/show/2845</td>\n",
              "      <td>Jennifer</td>\n",
              "      <td>2008-09-09</td>\n",
              "      <td>New York, New York, United States</td>\n",
              "      <td>A New Yorker since 2000! My passion is creatin...</td>\n",
              "      <td>within a few hours</td>\n",
              "      <td>96%</td>\n",
              "      <td>35%</td>\n",
              "      <td>f</td>\n",
              "      <td>https://a0.muscache.com/im/pictures/user/50fc5...</td>\n",
              "      <td>https://a0.muscache.com/im/pictures/user/50fc5...</td>\n",
              "      <td>Midtown</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>['email', 'phone', 'reviews', 'offline_governm...</td>\n",
              "      <td>t</td>\n",
              "      <td>t</td>\n",
              "      <td>New York, NY, United States</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Midtown</td>\n",
              "      <td>...</td>\n",
              "      <td>$0.00</td>\n",
              "      <td>3</td>\n",
              "      <td>1125</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1125</td>\n",
              "      <td>1125</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1125.0</td>\n",
              "      <td>2 months ago</td>\n",
              "      <td>t</td>\n",
              "      <td>30</td>\n",
              "      <td>60</td>\n",
              "      <td>90</td>\n",
              "      <td>365</td>\n",
              "      <td>2020-05-08</td>\n",
              "      <td>48</td>\n",
              "      <td>4</td>\n",
              "      <td>2009-11-21</td>\n",
              "      <td>2019-11-04</td>\n",
              "      <td>94.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>f</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>strict_14_with_grace_period</td>\n",
              "      <td>t</td>\n",
              "      <td>t</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3831</td>\n",
              "      <td>https://www.airbnb.com/rooms/3831</td>\n",
              "      <td>20200506054410</td>\n",
              "      <td>2020-05-08</td>\n",
              "      <td>Cozy Entire Floor of Brownstone</td>\n",
              "      <td>Urban retreat: enjoy 500 s.f. floor in 1899 br...</td>\n",
              "      <td>Greetings!      We own a double-duplex brownst...</td>\n",
              "      <td>Urban retreat: enjoy 500 s.f. floor in 1899 br...</td>\n",
              "      <td>none</td>\n",
              "      <td>Just the right mix of urban center and local n...</td>\n",
              "      <td>IF YOU ARE SAME-DAY BOOKING, PLEASE LET ME KNO...</td>\n",
              "      <td>B52 bus for a 10-minute ride to downtown Brook...</td>\n",
              "      <td>You will have the private, exclusive use of an...</td>\n",
              "      <td>We'll be around, but since you have the top fl...</td>\n",
              "      <td>Smoking - outside please; pets allowed but ple...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://a0.muscache.com/im/pictures/e49999c2-9...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4869</td>\n",
              "      <td>https://www.airbnb.com/users/show/4869</td>\n",
              "      <td>LisaRoxanne</td>\n",
              "      <td>2008-12-07</td>\n",
              "      <td>New York, New York, United States</td>\n",
              "      <td>Laid-back bi-coastal actor/professor/attorney.</td>\n",
              "      <td>within a few hours</td>\n",
              "      <td>96%</td>\n",
              "      <td>94%</td>\n",
              "      <td>f</td>\n",
              "      <td>https://a0.muscache.com/im/users/4869/profile_...</td>\n",
              "      <td>https://a0.muscache.com/im/users/4869/profile_...</td>\n",
              "      <td>Clinton Hill</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>['email', 'phone', 'reviews', 'offline_governm...</td>\n",
              "      <td>t</td>\n",
              "      <td>t</td>\n",
              "      <td>Brooklyn, NY, United States</td>\n",
              "      <td>Clinton Hill</td>\n",
              "      <td>Clinton Hill</td>\n",
              "      <td>...</td>\n",
              "      <td>$0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>730</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1125</td>\n",
              "      <td>1125</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1125.0</td>\n",
              "      <td>3 months ago</td>\n",
              "      <td>t</td>\n",
              "      <td>29</td>\n",
              "      <td>45</td>\n",
              "      <td>49</td>\n",
              "      <td>261</td>\n",
              "      <td>2020-05-08</td>\n",
              "      <td>318</td>\n",
              "      <td>63</td>\n",
              "      <td>2014-09-30</td>\n",
              "      <td>2020-04-26</td>\n",
              "      <td>89.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>f</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>moderate</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5099</td>\n",
              "      <td>https://www.airbnb.com/rooms/5099</td>\n",
              "      <td>20200506054410</td>\n",
              "      <td>2020-05-09</td>\n",
              "      <td>Large Cozy 1 BR Apartment In Midtown East</td>\n",
              "      <td>My large 1 bedroom apartment has a true New Yo...</td>\n",
              "      <td>I have a large 1 bedroom apartment centrally l...</td>\n",
              "      <td>My large 1 bedroom apartment has a true New Yo...</td>\n",
              "      <td>none</td>\n",
              "      <td>My neighborhood in Midtown East is called Murr...</td>\n",
              "      <td>Read My Full Listing For All Information. New ...</td>\n",
              "      <td>From the apartment is a 10 minute walk to Gran...</td>\n",
              "      <td>I will meet you upon arrival.</td>\n",
              "      <td>I usually check in with guests via text or ema...</td>\n",
              "      <td>• Check-in time is 2PM. • Check-out time is 12...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://a0.muscache.com/im/pictures/24020910/1...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7322</td>\n",
              "      <td>https://www.airbnb.com/users/show/7322</td>\n",
              "      <td>Chris</td>\n",
              "      <td>2009-02-02</td>\n",
              "      <td>New York, New York, United States</td>\n",
              "      <td>I'm an artist, writer, traveler, and a native ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>78%</td>\n",
              "      <td>f</td>\n",
              "      <td>https://a0.muscache.com/im/pictures/user/26745...</td>\n",
              "      <td>https://a0.muscache.com/im/pictures/user/26745...</td>\n",
              "      <td>Flatiron District</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>['email', 'phone', 'reviews', 'jumio', 'govern...</td>\n",
              "      <td>t</td>\n",
              "      <td>f</td>\n",
              "      <td>New York, NY, United States</td>\n",
              "      <td>Midtown East</td>\n",
              "      <td>Murray Hill</td>\n",
              "      <td>...</td>\n",
              "      <td>$100.00</td>\n",
              "      <td>3</td>\n",
              "      <td>21</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "      <td>3.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6 months ago</td>\n",
              "      <td>t</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-05-09</td>\n",
              "      <td>78</td>\n",
              "      <td>7</td>\n",
              "      <td>2009-04-20</td>\n",
              "      <td>2019-10-13</td>\n",
              "      <td>90.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>f</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>moderate</td>\n",
              "      <td>t</td>\n",
              "      <td>t</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5121</td>\n",
              "      <td>https://www.airbnb.com/rooms/5121</td>\n",
              "      <td>20200506054410</td>\n",
              "      <td>2020-05-07</td>\n",
              "      <td>BlissArtsSpace!</td>\n",
              "      <td>NaN</td>\n",
              "      <td>HELLO EVERYONE AND THANKS FOR VISITING BLISS A...</td>\n",
              "      <td>HELLO EVERYONE AND THANKS FOR VISITING BLISS A...</td>\n",
              "      <td>none</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://a0.muscache.com/im/pictures/2090980c-b...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7356</td>\n",
              "      <td>https://www.airbnb.com/users/show/7356</td>\n",
              "      <td>Garon</td>\n",
              "      <td>2009-02-03</td>\n",
              "      <td>New York, New York, United States</td>\n",
              "      <td>I am an artist(painter, filmmaker) and curato...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>67%</td>\n",
              "      <td>f</td>\n",
              "      <td>https://a0.muscache.com/im/pictures/user/72a61...</td>\n",
              "      <td>https://a0.muscache.com/im/pictures/user/72a61...</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>['email', 'phone', 'facebook', 'reviews', 'off...</td>\n",
              "      <td>t</td>\n",
              "      <td>f</td>\n",
              "      <td>Brooklyn, NY, United States</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>...</td>\n",
              "      <td>$30.00</td>\n",
              "      <td>29</td>\n",
              "      <td>730</td>\n",
              "      <td>29</td>\n",
              "      <td>29</td>\n",
              "      <td>730</td>\n",
              "      <td>730</td>\n",
              "      <td>29.0</td>\n",
              "      <td>730.0</td>\n",
              "      <td>28 months ago</td>\n",
              "      <td>t</td>\n",
              "      <td>30</td>\n",
              "      <td>60</td>\n",
              "      <td>90</td>\n",
              "      <td>365</td>\n",
              "      <td>2020-05-07</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>2009-05-28</td>\n",
              "      <td>2019-12-02</td>\n",
              "      <td>90.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>f</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>strict_14_with_grace_period</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.38</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 106 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     id  ... reviews_per_month\n",
              "0  2060  ...              0.01\n",
              "1  2595  ...              0.38\n",
              "2  3831  ...              4.66\n",
              "3  5099  ...              0.58\n",
              "4  5121  ...              0.38\n",
              "\n",
              "[5 rows x 106 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KB0kw1pBYqAG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 800
        },
        "outputId": "d9af9e37-ac4c-4a02-9bb5-a4a63939d52b"
      },
      "source": [
        "relevant_data = set(df_old.columns).intersection(set(df_new.columns))\n",
        "right = pd.merge(left=df_new[relevant_data], right=df_old[relevant_data], on=['latitude', 'longitude', 'id'], how='right', suffixes=('_after', '_before'))\n",
        "right"
      ],
      "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_after</th>\n",
              "      <th>minimum_nights_after</th>\n",
              "      <th>neighbourhood_after</th>\n",
              "      <th>id</th>\n",
              "      <th>number_of_reviews_after</th>\n",
              "      <th>last_review_after</th>\n",
              "      <th>reviews_per_month_after</th>\n",
              "      <th>longitude</th>\n",
              "      <th>host_name_after</th>\n",
              "      <th>calculated_host_listings_count_after</th>\n",
              "      <th>host_id_after</th>\n",
              "      <th>name_after</th>\n",
              "      <th>price_after</th>\n",
              "      <th>availability_365_after</th>\n",
              "      <th>latitude</th>\n",
              "      <th>room_type_before</th>\n",
              "      <th>minimum_nights_before</th>\n",
              "      <th>neighbourhood_before</th>\n",
              "      <th>number_of_reviews_before</th>\n",
              "      <th>last_review_before</th>\n",
              "      <th>reviews_per_month_before</th>\n",
              "      <th>host_name_before</th>\n",
              "      <th>calculated_host_listings_count_before</th>\n",
              "      <th>host_id_before</th>\n",
              "      <th>name_before</th>\n",
              "      <th>price_before</th>\n",
              "      <th>availability_365_before</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>2595</td>\n",
              "      <td>48.0</td>\n",
              "      <td>2019-11-04</td>\n",
              "      <td>0.38</td>\n",
              "      <td>-73.98377</td>\n",
              "      <td>Jennifer</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2845.0</td>\n",
              "      <td>Skylit Midtown Castle</td>\n",
              "      <td>$225.00</td>\n",
              "      <td>365.0</td>\n",
              "      <td>40.75362</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>1</td>\n",
              "      <td>Midtown</td>\n",
              "      <td>45</td>\n",
              "      <td>2019-05-21</td>\n",
              "      <td>0.38</td>\n",
              "      <td>Jennifer</td>\n",
              "      <td>2</td>\n",
              "      <td>2845</td>\n",
              "      <td>Skylit Midtown Castle</td>\n",
              "      <td>225</td>\n",
              "      <td>355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Clinton Hill</td>\n",
              "      <td>3831</td>\n",
              "      <td>318.0</td>\n",
              "      <td>2020-04-26</td>\n",
              "      <td>4.66</td>\n",
              "      <td>-73.95976</td>\n",
              "      <td>LisaRoxanne</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4869.0</td>\n",
              "      <td>Cozy Entire Floor of Brownstone</td>\n",
              "      <td>$89.00</td>\n",
              "      <td>261.0</td>\n",
              "      <td>40.68514</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>1</td>\n",
              "      <td>Clinton Hill</td>\n",
              "      <td>270</td>\n",
              "      <td>2019-07-05</td>\n",
              "      <td>4.64</td>\n",
              "      <td>LisaRoxanne</td>\n",
              "      <td>1</td>\n",
              "      <td>4869</td>\n",
              "      <td>Cozy Entire Floor of Brownstone</td>\n",
              "      <td>89</td>\n",
              "      <td>194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Midtown East</td>\n",
              "      <td>5099</td>\n",
              "      <td>78.0</td>\n",
              "      <td>2019-10-13</td>\n",
              "      <td>0.58</td>\n",
              "      <td>-73.97500</td>\n",
              "      <td>Chris</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7322.0</td>\n",
              "      <td>Large Cozy 1 BR Apartment In Midtown East</td>\n",
              "      <td>$200.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.74767</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>3</td>\n",
              "      <td>Murray Hill</td>\n",
              "      <td>74</td>\n",
              "      <td>2019-06-22</td>\n",
              "      <td>0.59</td>\n",
              "      <td>Chris</td>\n",
              "      <td>1</td>\n",
              "      <td>7322</td>\n",
              "      <td>Large Cozy 1 BR Apartment In Midtown East</td>\n",
              "      <td>200</td>\n",
              "      <td>129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Private room</td>\n",
              "      <td>29.0</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>5121</td>\n",
              "      <td>50.0</td>\n",
              "      <td>2019-12-02</td>\n",
              "      <td>0.38</td>\n",
              "      <td>-73.95596</td>\n",
              "      <td>Garon</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7356.0</td>\n",
              "      <td>BlissArtsSpace!</td>\n",
              "      <td>$60.00</td>\n",
              "      <td>365.0</td>\n",
              "      <td>40.68688</td>\n",
              "      <td>Private room</td>\n",
              "      <td>45</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>49</td>\n",
              "      <td>2017-10-05</td>\n",
              "      <td>0.40</td>\n",
              "      <td>Garon</td>\n",
              "      <td>1</td>\n",
              "      <td>7356</td>\n",
              "      <td>BlissArtsSpace!</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Private room</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>5178</td>\n",
              "      <td>473.0</td>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>3.53</td>\n",
              "      <td>-73.98493</td>\n",
              "      <td>Shunichi</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8967.0</td>\n",
              "      <td>Large Furnished Room Near B'way</td>\n",
              "      <td>$79.00</td>\n",
              "      <td>272.0</td>\n",
              "      <td>40.76489</td>\n",
              "      <td>Private room</td>\n",
              "      <td>2</td>\n",
              "      <td>Hell's Kitchen</td>\n",
              "      <td>430</td>\n",
              "      <td>2019-06-24</td>\n",
              "      <td>3.47</td>\n",
              "      <td>Shunichi</td>\n",
              "      <td>1</td>\n",
              "      <td>8967</td>\n",
              "      <td>Large Furnished Room Near B'way</td>\n",
              "      <td>79</td>\n",
              "      <td>220</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",
              "      <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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36484087</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-73.99183</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.76392</td>\n",
              "      <td>Private room</td>\n",
              "      <td>4</td>\n",
              "      <td>Hell's Kitchen</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Kat</td>\n",
              "      <td>1</td>\n",
              "      <td>274321313</td>\n",
              "      <td>Spacious Room w/ Private Rooftop, Central loca...</td>\n",
              "      <td>125</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48891</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36484363</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-73.80844</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.69137</td>\n",
              "      <td>Private room</td>\n",
              "      <td>1</td>\n",
              "      <td>Jamaica</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Michael</td>\n",
              "      <td>2</td>\n",
              "      <td>107716952</td>\n",
              "      <td>QUIT PRIVATE HOUSE</td>\n",
              "      <td>65</td>\n",
              "      <td>163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48892</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36485431</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-73.94867</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.81475</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>10</td>\n",
              "      <td>Harlem</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Ilgar &amp; Aysel</td>\n",
              "      <td>1</td>\n",
              "      <td>23492952</td>\n",
              "      <td>Sunny Studio at Historical Neighborhood</td>\n",
              "      <td>115</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48893</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36485609</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-73.99112</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.75751</td>\n",
              "      <td>Shared room</td>\n",
              "      <td>1</td>\n",
              "      <td>Hell's Kitchen</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Taz</td>\n",
              "      <td>6</td>\n",
              "      <td>30985759</td>\n",
              "      <td>43rd St. Time Square-cozy single bed</td>\n",
              "      <td>55</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48894</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36487245</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-73.98933</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.76404</td>\n",
              "      <td>Private room</td>\n",
              "      <td>7</td>\n",
              "      <td>Hell's Kitchen</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Christophe</td>\n",
              "      <td>1</td>\n",
              "      <td>68119814</td>\n",
              "      <td>Trendy duplex in the very heart of Hell's Kitchen</td>\n",
              "      <td>90</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>48895 rows × 27 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       room_type_after  ...  availability_365_before\n",
              "0      Entire home/apt  ...                      355\n",
              "1      Entire home/apt  ...                      194\n",
              "2      Entire home/apt  ...                      129\n",
              "3         Private room  ...                        0\n",
              "4         Private room  ...                      220\n",
              "...                ...  ...                      ...\n",
              "48890              NaN  ...                       31\n",
              "48891              NaN  ...                      163\n",
              "48892              NaN  ...                       27\n",
              "48893              NaN  ...                        2\n",
              "48894              NaN  ...                       23\n",
              "\n",
              "[48895 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MiAfFXUUa0Jt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 800
        },
        "outputId": "aaf65e25-1f76-441e-d3bd-c2f9f70f7682"
      },
      "source": [
        "right[right.isna()==False]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>room_type_after</th>\n",
              "      <th>minimum_nights_after</th>\n",
              "      <th>neighbourhood_after</th>\n",
              "      <th>id</th>\n",
              "      <th>number_of_reviews_after</th>\n",
              "      <th>last_review_after</th>\n",
              "      <th>reviews_per_month_after</th>\n",
              "      <th>longitude</th>\n",
              "      <th>host_name_after</th>\n",
              "      <th>calculated_host_listings_count_after</th>\n",
              "      <th>host_id_after</th>\n",
              "      <th>name_after</th>\n",
              "      <th>price_after</th>\n",
              "      <th>availability_365_after</th>\n",
              "      <th>latitude</th>\n",
              "      <th>room_type_before</th>\n",
              "      <th>minimum_nights_before</th>\n",
              "      <th>neighbourhood_before</th>\n",
              "      <th>number_of_reviews_before</th>\n",
              "      <th>last_review_before</th>\n",
              "      <th>reviews_per_month_before</th>\n",
              "      <th>host_name_before</th>\n",
              "      <th>calculated_host_listings_count_before</th>\n",
              "      <th>host_id_before</th>\n",
              "      <th>name_before</th>\n",
              "      <th>price_before</th>\n",
              "      <th>availability_365_before</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>48.0</td>\n",
              "      <td>2019-11-04</td>\n",
              "      <td>0.38</td>\n",
              "      <td>-73.98377</td>\n",
              "      <td>Jennifer</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2845.0</td>\n",
              "      <td>Skylit Midtown Castle</td>\n",
              "      <td>$225.00</td>\n",
              "      <td>365.0</td>\n",
              "      <td>40.75362</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>1</td>\n",
              "      <td>Midtown</td>\n",
              "      <td>45</td>\n",
              "      <td>2019-05-21</td>\n",
              "      <td>0.38</td>\n",
              "      <td>Jennifer</td>\n",
              "      <td>2</td>\n",
              "      <td>2845</td>\n",
              "      <td>Skylit Midtown Castle</td>\n",
              "      <td>225</td>\n",
              "      <td>355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Clinton Hill</td>\n",
              "      <td>3831</td>\n",
              "      <td>318.0</td>\n",
              "      <td>2020-04-26</td>\n",
              "      <td>4.66</td>\n",
              "      <td>-73.95976</td>\n",
              "      <td>LisaRoxanne</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4869.0</td>\n",
              "      <td>Cozy Entire Floor of Brownstone</td>\n",
              "      <td>$89.00</td>\n",
              "      <td>261.0</td>\n",
              "      <td>40.68514</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>1</td>\n",
              "      <td>Clinton Hill</td>\n",
              "      <td>270</td>\n",
              "      <td>2019-07-05</td>\n",
              "      <td>4.64</td>\n",
              "      <td>LisaRoxanne</td>\n",
              "      <td>1</td>\n",
              "      <td>4869</td>\n",
              "      <td>Cozy Entire Floor of Brownstone</td>\n",
              "      <td>89</td>\n",
              "      <td>194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Midtown East</td>\n",
              "      <td>5099</td>\n",
              "      <td>78.0</td>\n",
              "      <td>2019-10-13</td>\n",
              "      <td>0.58</td>\n",
              "      <td>-73.97500</td>\n",
              "      <td>Chris</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7322.0</td>\n",
              "      <td>Large Cozy 1 BR Apartment In Midtown East</td>\n",
              "      <td>$200.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.74767</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>3</td>\n",
              "      <td>Murray Hill</td>\n",
              "      <td>74</td>\n",
              "      <td>2019-06-22</td>\n",
              "      <td>0.59</td>\n",
              "      <td>Chris</td>\n",
              "      <td>1</td>\n",
              "      <td>7322</td>\n",
              "      <td>Large Cozy 1 BR Apartment In Midtown East</td>\n",
              "      <td>200</td>\n",
              "      <td>129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Private room</td>\n",
              "      <td>29.0</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>5121</td>\n",
              "      <td>50.0</td>\n",
              "      <td>2019-12-02</td>\n",
              "      <td>0.38</td>\n",
              "      <td>-73.95596</td>\n",
              "      <td>Garon</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7356.0</td>\n",
              "      <td>BlissArtsSpace!</td>\n",
              "      <td>$60.00</td>\n",
              "      <td>365.0</td>\n",
              "      <td>40.68688</td>\n",
              "      <td>Private room</td>\n",
              "      <td>45</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>49</td>\n",
              "      <td>2017-10-05</td>\n",
              "      <td>0.40</td>\n",
              "      <td>Garon</td>\n",
              "      <td>1</td>\n",
              "      <td>7356</td>\n",
              "      <td>BlissArtsSpace!</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Private room</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>5178</td>\n",
              "      <td>473.0</td>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>3.53</td>\n",
              "      <td>-73.98493</td>\n",
              "      <td>Shunichi</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8967.0</td>\n",
              "      <td>Large Furnished Room Near B'way</td>\n",
              "      <td>$79.00</td>\n",
              "      <td>272.0</td>\n",
              "      <td>40.76489</td>\n",
              "      <td>Private room</td>\n",
              "      <td>2</td>\n",
              "      <td>Hell's Kitchen</td>\n",
              "      <td>430</td>\n",
              "      <td>2019-06-24</td>\n",
              "      <td>3.47</td>\n",
              "      <td>Shunichi</td>\n",
              "      <td>1</td>\n",
              "      <td>8967</td>\n",
              "      <td>Large Furnished Room Near B'way</td>\n",
              "      <td>79</td>\n",
              "      <td>220</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",
              "      <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",
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              "    <tr>\n",
              "      <th>48890</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-73.99183</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.76392</td>\n",
              "      <td>Private room</td>\n",
              "      <td>4</td>\n",
              "      <td>Hell's Kitchen</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Kat</td>\n",
              "      <td>1</td>\n",
              "      <td>274321313</td>\n",
              "      <td>Spacious Room w/ Private Rooftop, Central loca...</td>\n",
              "      <td>125</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48891</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36484363</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-73.80844</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.69137</td>\n",
              "      <td>Private room</td>\n",
              "      <td>1</td>\n",
              "      <td>Jamaica</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Michael</td>\n",
              "      <td>2</td>\n",
              "      <td>107716952</td>\n",
              "      <td>QUIT PRIVATE HOUSE</td>\n",
              "      <td>65</td>\n",
              "      <td>163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>Entire home/apt</td>\n",
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              "      <td>Harlem</td>\n",
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              "      <td>Ilgar &amp; Aysel</td>\n",
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              "      <td>23492952</td>\n",
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              "      <td>27</td>\n",
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              "      <td>40.75751</td>\n",
              "      <td>Shared room</td>\n",
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              "      <td>Hell's Kitchen</td>\n",
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              "      <td>43rd St. Time Square-cozy single bed</td>\n",
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              "      <td>Christophe</td>\n",
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              "      <td>23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>48895 rows × 27 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       room_type_after  ...  availability_365_before\n",
              "0      Entire home/apt  ...                      355\n",
              "1      Entire home/apt  ...                      194\n",
              "2      Entire home/apt  ...                      129\n",
              "3         Private room  ...                        0\n",
              "4         Private room  ...                      220\n",
              "...                ...  ...                      ...\n",
              "48890              NaN  ...                       31\n",
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              "48892              NaN  ...                       27\n",
              "48893              NaN  ...                        2\n",
              "48894              NaN  ...                       23\n",
              "\n",
              "[48895 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dBCRws4BgwQB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "2ff8fd87-af32-455b-8412-7690dd22652a"
      },
      "source": [
        "right.room_type_before.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Entire home/apt    25409\n",
              "Private room       22326\n",
              "Shared room         1160\n",
              "Name: room_type_before, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8PXXzZUkg_pX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "6cc5ae6b-30d0-41bc-b343-22fd570674d2"
      },
      "source": [
        "right.room_type_after.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Entire home/apt    17619\n",
              "Private room       15021\n",
              "Shared room          633\n",
              "Hotel room           177\n",
              "Name: room_type_after, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yXcQ-vXjhMx5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "outputId": "4925e9f9-040e-41de-be42-41a5bdddb0d5"
      },
      "source": [
        "right[right=='Hotel']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/ops/array_ops.py:253: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
            "  res_values = method(rvalues)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
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            ],
            "text/plain": [
              "      room_type_after  ...  availability_365_before\n",
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              "[48895 rows x 27 columns]"
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tGkoOPKfBKRw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "b924eb68-8dbe-4447-b1dd-b10c88f0281b"
      },
      "source": [
        "df_old.columns, df_new.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Index(['id', 'name', 'host_id', 'host_name', 'neighbourhood_group',\n",
              "        'neighbourhood', 'latitude', 'longitude', 'room_type', 'price',\n",
              "        'minimum_nights', 'number_of_reviews', 'last_review',\n",
              "        'reviews_per_month', 'calculated_host_listings_count',\n",
              "        'availability_365'],\n",
              "       dtype='object'),\n",
              " Index(['id', 'listing_url', 'scrape_id', 'last_scraped', 'name', 'summary',\n",
              "        'space', 'description', 'experiences_offered', 'neighborhood_overview',\n",
              "        ...\n",
              "        'instant_bookable', 'is_business_travel_ready', 'cancellation_policy',\n",
              "        'require_guest_profile_picture', 'require_guest_phone_verification',\n",
              "        'calculated_host_listings_count',\n",
              "        'calculated_host_listings_count_entire_homes',\n",
              "        'calculated_host_listings_count_private_rooms',\n",
              "        'calculated_host_listings_count_shared_rooms', 'reviews_per_month'],\n",
              "       dtype='object', length=106))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Tmy_uuKBmbL",
        "colab_type": "text"
      },
      "source": [
        "# 1. identify same hotels using id\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MfLwjEStBioZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e87148fa-1aea-41b5-e335-682db0eb9582"
      },
      "source": [
        "df_old['id'].values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([    2539,     2595,     3647, ..., 36485431, 36485609, 36487245])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7FuEKu8dBwjM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7c575a67-7456-42b7-c1d0-cb8fd38300f9"
      },
      "source": [
        "len(df_new['id']),len(df_old['id'])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50246, 48895)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tIdWbohwGj-J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "f9489bd1-8e17-4a68-c913-3d70a8baa6e8"
      },
      "source": [
        "sns.kdeplot(df_new['id'], label=\"new\")\n",
        "sns.kdeplot(df_old['id'], label=\"old\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8b2a5f7668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UzrO7ibIKaTn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "set(df_old['id'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bsZFtIHZDcPt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "692db616-f37e-4876-ef44-734aecf4504d"
      },
      "source": [
        "np.where(df_old['id'].values==df_new['id'].values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([], dtype=int64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MsI-qGnkED3L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        },
        "outputId": "45371c42-8863-4bc8-c433-5bcd8e962aed"
      },
      "source": [
        "same_ids = df_old['id'].eq(df_new['id'])==True\n",
        "df_old['id'][same_ids]\n",
        "display(df_old.loc[df_old['id'][same_ids]],df_new.loc[df_new['id'][same_ids]])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>2595</th>\n",
              "      <td>1350055</td>\n",
              "      <td>Cozy room in Beautiful Williamsburg Appartment</td>\n",
              "      <td>2035472</td>\n",
              "      <td>Alessio</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Williamsburg</td>\n",
              "      <td>40.71449</td>\n",
              "      <td>-73.95905</td>\n",
              "      <td>Private room</td>\n",
              "      <td>85</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2019-02-03</td>\n",
              "      <td>0.32</td>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           id  ... availability_365\n",
              "2595  1350055  ...               89\n",
              "\n",
              "[1 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>listing_url</th>\n",
              "      <th>scrape_id</th>\n",
              "      <th>last_scraped</th>\n",
              "      <th>name</th>\n",
              "      <th>summary</th>\n",
              "      <th>space</th>\n",
              "      <th>description</th>\n",
              "      <th>experiences_offered</th>\n",
              "      <th>neighborhood_overview</th>\n",
              "      <th>notes</th>\n",
              "      <th>transit</th>\n",
              "      <th>access</th>\n",
              "      <th>interaction</th>\n",
              "      <th>house_rules</th>\n",
              "      <th>thumbnail_url</th>\n",
              "      <th>medium_url</th>\n",
              "      <th>picture_url</th>\n",
              "      <th>xl_picture_url</th>\n",
              "      <th>host_id</th>\n",
              "      <th>host_url</th>\n",
              "      <th>host_name</th>\n",
              "      <th>host_since</th>\n",
              "      <th>host_location</th>\n",
              "      <th>host_about</th>\n",
              "      <th>host_response_time</th>\n",
              "      <th>host_response_rate</th>\n",
              "      <th>host_acceptance_rate</th>\n",
              "      <th>host_is_superhost</th>\n",
              "      <th>host_thumbnail_url</th>\n",
              "      <th>host_picture_url</th>\n",
              "      <th>host_neighbourhood</th>\n",
              "      <th>host_listings_count</th>\n",
              "      <th>host_total_listings_count</th>\n",
              "      <th>host_verifications</th>\n",
              "      <th>host_has_profile_pic</th>\n",
              "      <th>host_identity_verified</th>\n",
              "      <th>street</th>\n",
              "      <th>neighbourhood</th>\n",
              "      <th>neighbourhood_cleansed</th>\n",
              "      <th>...</th>\n",
              "      <th>extra_people</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>maximum_nights</th>\n",
              "      <th>minimum_minimum_nights</th>\n",
              "      <th>maximum_minimum_nights</th>\n",
              "      <th>minimum_maximum_nights</th>\n",
              "      <th>maximum_maximum_nights</th>\n",
              "      <th>minimum_nights_avg_ntm</th>\n",
              "      <th>maximum_nights_avg_ntm</th>\n",
              "      <th>calendar_updated</th>\n",
              "      <th>has_availability</th>\n",
              "      <th>availability_30</th>\n",
              "      <th>availability_60</th>\n",
              "      <th>availability_90</th>\n",
              "      <th>availability_365</th>\n",
              "      <th>calendar_last_scraped</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>number_of_reviews_ltm</th>\n",
              "      <th>first_review</th>\n",
              "      <th>last_review</th>\n",
              "      <th>review_scores_rating</th>\n",
              "      <th>review_scores_accuracy</th>\n",
              "      <th>review_scores_cleanliness</th>\n",
              "      <th>review_scores_checkin</th>\n",
              "      <th>review_scores_communication</th>\n",
              "      <th>review_scores_location</th>\n",
              "      <th>review_scores_value</th>\n",
              "      <th>requires_license</th>\n",
              "      <th>license</th>\n",
              "      <th>jurisdiction_names</th>\n",
              "      <th>instant_bookable</th>\n",
              "      <th>is_business_travel_ready</th>\n",
              "      <th>cancellation_policy</th>\n",
              "      <th>require_guest_profile_picture</th>\n",
              "      <th>require_guest_phone_verification</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>calculated_host_listings_count_entire_homes</th>\n",
              "      <th>calculated_host_listings_count_private_rooms</th>\n",
              "      <th>calculated_host_listings_count_shared_rooms</th>\n",
              "      <th>reviews_per_month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2595</th>\n",
              "      <td>1735804</td>\n",
              "      <td>https://www.airbnb.com/rooms/1735804</td>\n",
              "      <td>20200506054410</td>\n",
              "      <td>2020-05-07</td>\n",
              "      <td>Lovely Suite in Historic Brownstone near Subway</td>\n",
              "      <td>This large suite on the top floor of historic ...</td>\n",
              "      <td>Newly renovated, fully furnished, 1 bedroom ap...</td>\n",
              "      <td>This large suite on the top floor of historic ...</td>\n",
              "      <td>none</td>\n",
              "      <td>The apartment is located on a beautiful tree-l...</td>\n",
              "      <td>We own and rent all units in the building. For...</td>\n",
              "      <td>Two short blocks from the subway's J Train, yo...</td>\n",
              "      <td>The apartment includes: - Free Street Parking ...</td>\n",
              "      <td>While we do not share the space with our guest...</td>\n",
              "      <td>No smoking or pets in the building please. Enj...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://a0.muscache.com/im/pictures/63113142/0...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1755097</td>\n",
              "      <td>https://www.airbnb.com/users/show/1755097</td>\n",
              "      <td>Jay</td>\n",
              "      <td>2012-02-15</td>\n",
              "      <td>Brooklyn, New York, United States</td>\n",
              "      <td>NaN</td>\n",
              "      <td>within an hour</td>\n",
              "      <td>100%</td>\n",
              "      <td>98%</td>\n",
              "      <td>f</td>\n",
              "      <td>https://a0.muscache.com/im/pictures/user/60ccd...</td>\n",
              "      <td>https://a0.muscache.com/im/pictures/user/60ccd...</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>['email', 'phone', 'reviews', 'kba']</td>\n",
              "      <td>t</td>\n",
              "      <td>t</td>\n",
              "      <td>Brooklyn, NY, United States</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>...</td>\n",
              "      <td>$50.00</td>\n",
              "      <td>2</td>\n",
              "      <td>365</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1125</td>\n",
              "      <td>1125</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1125.0</td>\n",
              "      <td>6 weeks ago</td>\n",
              "      <td>t</td>\n",
              "      <td>6</td>\n",
              "      <td>17</td>\n",
              "      <td>47</td>\n",
              "      <td>190</td>\n",
              "      <td>2020-05-07</td>\n",
              "      <td>241</td>\n",
              "      <td>37</td>\n",
              "      <td>2014-09-13</td>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>96.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>f</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>strict_14_with_grace_period</td>\n",
              "      <td>f</td>\n",
              "      <td>t</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 106 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           id  ... reviews_per_month\n",
              "2595  1735804  ...               3.5\n",
              "\n",
              "[1 rows x 106 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NgH1OLS8Kg9N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3d05e91a-2176-4b02-ba9b-2ea7cd09a4b1"
      },
      "source": [
        "same_ids = list(set(df_new['id']) & set(df_old['id']))\n",
        "len(same_ids)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "35383"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3TDyFKPBOXTf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "same_ids[0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vO9ldrObOq6f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "17eb2bfc-d8ce-4ab2-b6ba-ac7f9aac4d40"
      },
      "source": [
        "sns.distplot(df_new.availability_365, label=\"new\")\n",
        "sns.distplot(df_old.availability_365, label=\"old\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8b29d09588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_uT_jhWbp_lP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "outputId": "3923e5f0-5959-4217-f83c-afec6a23e365"
      },
      "source": [
        "sns.displot(right.availability_365_after.value_counts(dropna=False))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-115-b27b1929915c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mavailability_365_after\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: module 'seaborn' has no attribute 'displot'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WdCciF_YoTlz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "8f519a2d-15e5-4474-a53c-a75a5925c47d"
      },
      "source": [
        "# PRICE\n",
        "#right.price_after = right.price_after.str.replace('$', '')\n",
        "#right.price_after = (right.price_after.str.replace(',', '')).astype(float)\n",
        "sns.distplot(right.price_after, label=\"new\")\n",
        "sns.distplot(right.price_before, label=\"old\")\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f8b29ccbc18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Hw6fLrMDyN2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "right['survived'] = right['host_id_after'].notnull() * 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KUWQgLWuFhXM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "right['last_review_before_datetime'] = pd.to_datetime(right.last_review_before)\n",
        "right['last_review_after_datetime'] = pd.to_datetime(right.last_review_after)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "70yABcodXADu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "outputId": "7d1d7e41-68b3-4b68-f7ee-80e599b1ca3d"
      },
      "source": [
        "right['last_review_after_datetime'].value_counts(normalize=True,sort=False).sort_index()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2011-09-18    0.000036\n",
              "2011-09-19    0.000036\n",
              "2011-12-10    0.000036\n",
              "2011-12-28    0.000036\n",
              "2012-01-02    0.000036\n",
              "                ...   \n",
              "2020-05-04    0.001038\n",
              "2020-05-05    0.000501\n",
              "2020-05-06    0.000215\n",
              "2020-05-07    0.000358\n",
              "2020-05-08    0.000107\n",
              "Name: last_review_after_datetime, Length: 2000, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 261
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1XAcB07nGaby",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bd3d47e9-3453-44a2-df8f-2339c121ec5c"
      },
      "source": [
        "pd.DataFrame(right.last_review_before_datetime.value_counts(sort=False).sort_index()[-130:-69])['last_review_before_datetime'].sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2276"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 257
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "guq6JGj2nT-t",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d27b76cd-5799-47c0-ba53-8b77106e46fd"
      },
      "source": [
        "pd.DataFrame(right.last_review_before_datetime.value_counts(sort=False).sort_index()[-130:-68]).values.sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2349"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 265
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wMl7HSGjm5D6",
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          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "30a519f0-3f23-4d52-9242-762402e51514"
      },
      "source": [
        "pd.DataFrame(right.last_review_before_datetime.value_counts(sort=False).sort_index()).iloc[-62]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "last_review_before_datetime    64\n",
              "Name: 2019-05-08 00:00:00, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 260
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wzIs-Ar7QwEt",
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        },
        "outputId": "7b0d2ff1-7b54-45ab-b744-cb949d82dec2"
      },
      "source": [
        "pd.DataFrame(right.last_review_before_datetime.value_counts(sort=False).sort_index()).iloc[-130:-69]"
      ],
      "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",
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>last_review_before_datetime</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-03-01</th>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-02</th>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-03</th>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-04</th>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-03-05</th>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-04-26</th>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-04-27</th>\n",
              "      <td>86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-04-28</th>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-04-29</th>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-04-30</th>\n",
              "      <td>117</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>61 rows × 1 columns</p>\n",
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              "            last_review_before_datetime\n",
              "2019-03-01                           23\n",
              "2019-03-02                           16\n",
              "2019-03-03                           25\n",
              "2019-03-04                           19\n",
              "2019-03-05                           15\n",
              "...                                 ...\n",
              "2019-04-26                           55\n",
              "2019-04-27                           86\n",
              "2019-04-28                          121\n",
              "2019-04-29                           80\n",
              "2019-04-30                          117\n",
              "\n",
              "[61 rows x 1 columns]"
            ]
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            "tags": []
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          "execution_count": 255
        }
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          "height": 51
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      },
      "source": [
        "pd.DataFrame(right.last_review_before_datetime.value_counts(sort=False).sort_index()).iloc[-1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "last_review_before_datetime    89\n",
              "Name: 2019-07-08 00:00:00, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 186
        }
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    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "4a173c11-fe55-4ef2-d60b-fc34494de9ff"
      },
      "source": [
        "pd.DataFrame(right.last_review_after_datetime.value_counts(sort=False).sort_index()).iloc[-69]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "last_review_after_datetime    257\n",
              "Name: 2020-03-01 00:00:00, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TaJ9SZAeS14k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2f30815f-76cd-4dc2-aaef-f1cf4191f0ef"
      },
      "source": [
        "pd.DataFrame(right.last_review_after_datetime.value_counts(sort=False).sort_index()[-69:]).last_review_after_datetime.sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6200"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 233
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L6uH_WF3WbSB",
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          "height": 223
        },
        "outputId": "430f9e6e-562f-41f2-b1d8-2891c71a90d4"
      },
      "source": [
        "right.last_review_after_datetime.value_counts(normalize=True,sort=False).sort_index()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2011-09-18    0.000036\n",
              "2011-09-19    0.000036\n",
              "2011-12-10    0.000036\n",
              "2011-12-28    0.000036\n",
              "2012-01-02    0.000036\n",
              "                ...   \n",
              "2020-05-04    0.001038\n",
              "2020-05-05    0.000501\n",
              "2020-05-06    0.000215\n",
              "2020-05-07    0.000358\n",
              "2020-05-08    0.000107\n",
              "Name: last_review_after_datetime, Length: 2000, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 207
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JDZBII2aNHLn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9c1abfc0-a6e6-47f1-b837-aba6215ed05e"
      },
      "source": [
        "len(right.last_review_before_datetime.value_counts(normalize=True,sort=False))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1764"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JY6IG3RBLxFM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "outputId": "b2bb154d-c317-4205-df87-add0c2a19a64"
      },
      "source": [
        "right.last_review_after"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        2019-11-04\n",
              "1        2020-04-26\n",
              "2        2019-10-13\n",
              "3        2019-12-02\n",
              "4        2020-03-15\n",
              "            ...    \n",
              "48890           NaN\n",
              "48891           NaN\n",
              "48892           NaN\n",
              "48893           NaN\n",
              "48894           NaN\n",
              "Name: last_review_after, Length: 48895, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 195
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UUDwxOrxV87w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "outputId": "f2175c7c-c845-402f-d40d-f7081dc8caf0"
      },
      "source": [
        "sns.countplot(right.last_review_before_datetime, label=\"before\", bins=20)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-268-9986f8d77cea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcountplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlast_review_before_datetime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"before\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mcountplot\u001b[0;34m(x, y, hue, data, order, hue_order, orient, color, palette, saturation, dodge, ax, **kwargs)\u001b[0m\n\u001b[1;32m   3557\u001b[0m         \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3558\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3559\u001b[0;31m     \u001b[0mplotter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3560\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3561\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, ax, bar_kws)\u001b[0m\n\u001b[1;32m   1655\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbar_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1656\u001b[0m         \u001b[0;34m\"\"\"Make the plot.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1657\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_bars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbar_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1658\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mannotate_axes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1659\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morient\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"h\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mdraw_bars\u001b[0;34m(self, ax, kws)\u001b[0m\n\u001b[1;32m   1621\u001b[0m             \u001b[0;31m# Draw the bars\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1622\u001b[0m             barfunc(barpos, self.statistic, self.width,\n\u001b[0;32m-> 1623\u001b[0;31m                     color=self.colors, align=\"center\", **kws)\n\u001b[0m\u001b[1;32m   1624\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1625\u001b[0m             \u001b[0;31m# Draw the confidence intervals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1563\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1564\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1565\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1566\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1567\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[1;32m   2395\u001b[0m                 \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'_nolegend_'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2396\u001b[0m                 )\n\u001b[0;32m-> 2397\u001b[0;31m             \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2398\u001b[0m             \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_interpolation_steps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2399\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0morientation\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'vertical'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, props)\u001b[0m\n\u001b[1;32m   1004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1005\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meventson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1006\u001b[0;31m             \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_update_property\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1007\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1008\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1005\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meventson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1006\u001b[0;31m             \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_update_property\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1007\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1008\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36m_update_property\u001b[0;34m(self, k, v)\u001b[0m\n\u001b[1;32m   1000\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1001\u001b[0m                     raise AttributeError('{!r} object has no property {!r}'\n\u001b[0;32m-> 1002\u001b[0;31m                                          .format(type(self).__name__, k))\n\u001b[0m\u001b[1;32m   1003\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'Rectangle' object has no property 'bins'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QOnPbU6ED1Ry",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "8c40655e-a198-4309-c532-93e3ed2cddba"
      },
      "source": [
        "sns.countplot(right.last_review_after_datetime, label=\"after\")\n",
        "\n",
        "plt.xticks(rotation=90)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([   0,    1,    2, ..., 1997, 1998, 1999]),\n",
              " <a list of 2000 Text major ticklabel objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 205
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M3HeCE-fLI9N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "outputId": "fb6f146b-cb0e-4d76-b8ee-374d40a23d1e"
      },
      "source": [
        "display(df_old[df_old.id==same_ids[0]], df_new[df_new.id==same_ids[0]])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [id, name, host_id, host_name, neighbourhood_group, neighbourhood, latitude, longitude, room_type, price, minimum_nights, number_of_reviews, last_review, reviews_per_month, calculated_host_listings_count, availability_365]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>listing_url</th>\n",
              "      <th>scrape_id</th>\n",
              "      <th>last_scraped</th>\n",
              "      <th>name</th>\n",
              "      <th>summary</th>\n",
              "      <th>space</th>\n",
              "      <th>description</th>\n",
              "      <th>experiences_offered</th>\n",
              "      <th>neighborhood_overview</th>\n",
              "      <th>notes</th>\n",
              "      <th>transit</th>\n",
              "      <th>access</th>\n",
              "      <th>interaction</th>\n",
              "      <th>house_rules</th>\n",
              "      <th>thumbnail_url</th>\n",
              "      <th>medium_url</th>\n",
              "      <th>picture_url</th>\n",
              "      <th>xl_picture_url</th>\n",
              "      <th>host_id</th>\n",
              "      <th>host_url</th>\n",
              "      <th>host_name</th>\n",
              "      <th>host_since</th>\n",
              "      <th>host_location</th>\n",
              "      <th>host_about</th>\n",
              "      <th>host_response_time</th>\n",
              "      <th>host_response_rate</th>\n",
              "      <th>host_acceptance_rate</th>\n",
              "      <th>host_is_superhost</th>\n",
              "      <th>host_thumbnail_url</th>\n",
              "      <th>host_picture_url</th>\n",
              "      <th>host_neighbourhood</th>\n",
              "      <th>host_listings_count</th>\n",
              "      <th>host_total_listings_count</th>\n",
              "      <th>host_verifications</th>\n",
              "      <th>host_has_profile_pic</th>\n",
              "      <th>host_identity_verified</th>\n",
              "      <th>street</th>\n",
              "      <th>neighbourhood</th>\n",
              "      <th>neighbourhood_cleansed</th>\n",
              "      <th>...</th>\n",
              "      <th>extra_people</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>maximum_nights</th>\n",
              "      <th>minimum_minimum_nights</th>\n",
              "      <th>maximum_minimum_nights</th>\n",
              "      <th>minimum_maximum_nights</th>\n",
              "      <th>maximum_maximum_nights</th>\n",
              "      <th>minimum_nights_avg_ntm</th>\n",
              "      <th>maximum_nights_avg_ntm</th>\n",
              "      <th>calendar_updated</th>\n",
              "      <th>has_availability</th>\n",
              "      <th>availability_30</th>\n",
              "      <th>availability_60</th>\n",
              "      <th>availability_90</th>\n",
              "      <th>availability_365</th>\n",
              "      <th>calendar_last_scraped</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>number_of_reviews_ltm</th>\n",
              "      <th>first_review</th>\n",
              "      <th>last_review</th>\n",
              "      <th>review_scores_rating</th>\n",
              "      <th>review_scores_accuracy</th>\n",
              "      <th>review_scores_cleanliness</th>\n",
              "      <th>review_scores_checkin</th>\n",
              "      <th>review_scores_communication</th>\n",
              "      <th>review_scores_location</th>\n",
              "      <th>review_scores_value</th>\n",
              "      <th>requires_license</th>\n",
              "      <th>license</th>\n",
              "      <th>jurisdiction_names</th>\n",
              "      <th>instant_bookable</th>\n",
              "      <th>is_business_travel_ready</th>\n",
              "      <th>cancellation_policy</th>\n",
              "      <th>require_guest_profile_picture</th>\n",
              "      <th>require_guest_phone_verification</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>calculated_host_listings_count_entire_homes</th>\n",
              "      <th>calculated_host_listings_count_private_rooms</th>\n",
              "      <th>calculated_host_listings_count_shared_rooms</th>\n",
              "      <th>reviews_per_month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>0 rows × 106 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [id, listing_url, scrape_id, last_scraped, name, summary, space, description, experiences_offered, neighborhood_overview, notes, transit, access, interaction, house_rules, thumbnail_url, medium_url, picture_url, xl_picture_url, host_id, host_url, host_name, host_since, host_location, host_about, host_response_time, host_response_rate, host_acceptance_rate, host_is_superhost, host_thumbnail_url, host_picture_url, host_neighbourhood, host_listings_count, host_total_listings_count, host_verifications, host_has_profile_pic, host_identity_verified, street, neighbourhood, neighbourhood_cleansed, neighbourhood_group_cleansed, city, state, zipcode, market, smart_location, country_code, country, latitude, longitude, is_location_exact, property_type, room_type, accommodates, bathrooms, bedrooms, beds, bed_type, amenities, square_feet, price, weekly_price, monthly_price, security_deposit, cleaning_fee, guests_included, extra_people, minimum_nights, maximum_nights, minimum_minimum_nights, maximum_minimum_nights, minimum_maximum_nights, maximum_maximum_nights, minimum_nights_avg_ntm, maximum_nights_avg_ntm, calendar_updated, has_availability, availability_30, availability_60, availability_90, availability_365, calendar_last_scraped, number_of_reviews, number_of_reviews_ltm, first_review, last_review, review_scores_rating, review_scores_accuracy, review_scores_cleanliness, review_scores_checkin, review_scores_communication, review_scores_location, review_scores_value, requires_license, license, jurisdiction_names, instant_bookable, is_business_travel_ready, cancellation_policy, require_guest_profile_picture, ...]\n",
              "Index: []\n",
              "\n",
              "[0 rows x 106 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4KMbjtkpPvuq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c17faa57-2518-4cb5-d62c-90d5f26f3dee"
      },
      "source": [
        "same_ids = list(set(df_new['id']) & set(df_old['id']))\n",
        "len(same_ids)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "35383"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BD7yLKzqWNgL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "57bc7eaf-89ab-4a84-dd29-10ce34a9f859"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14028018"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I0PYnOlrEzoz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "outputId": "065d7e13-1402-40d2-bb9d-18e4ea05341c"
      },
      "source": [
        "import random\n",
        "\n",
        "same_ids = list(set(df_new['id']) & set(df_old['id']))\n",
        "\n",
        "def show_listings_by_id():\n",
        "  same_id = random.choice(same_ids)\n",
        "  print(\"Old listing:\")\n",
        "  display(df_old[df_old.id == same_id])\n",
        "  print(\"New listing:\")\n",
        "  display(df_new[df_new.id == same_id])\n",
        "\n",
        "show_listings_by_id()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Old listing:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>5509</th>\n",
              "      <td>3987571</td>\n",
              "      <td>Garden Apartment with Private Entry</td>\n",
              "      <td>20670026</td>\n",
              "      <td>Monifa &amp; Saint</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>40.68084</td>\n",
              "      <td>-73.94779</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>115</td>\n",
              "      <td>2</td>\n",
              "      <td>259</td>\n",
              "      <td>2019-06-30</td>\n",
              "      <td>4.43</td>\n",
              "      <td>1</td>\n",
              "      <td>285</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           id  ... availability_365\n",
              "5509  3987571  ...              285\n",
              "\n",
              "[1 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "New listing:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>listing_url</th>\n",
              "      <th>scrape_id</th>\n",
              "      <th>last_scraped</th>\n",
              "      <th>name</th>\n",
              "      <th>summary</th>\n",
              "      <th>space</th>\n",
              "      <th>description</th>\n",
              "      <th>experiences_offered</th>\n",
              "      <th>neighborhood_overview</th>\n",
              "      <th>notes</th>\n",
              "      <th>transit</th>\n",
              "      <th>access</th>\n",
              "      <th>interaction</th>\n",
              "      <th>house_rules</th>\n",
              "      <th>thumbnail_url</th>\n",
              "      <th>medium_url</th>\n",
              "      <th>picture_url</th>\n",
              "      <th>xl_picture_url</th>\n",
              "      <th>host_id</th>\n",
              "      <th>host_url</th>\n",
              "      <th>host_name</th>\n",
              "      <th>host_since</th>\n",
              "      <th>host_location</th>\n",
              "      <th>host_about</th>\n",
              "      <th>host_response_time</th>\n",
              "      <th>host_response_rate</th>\n",
              "      <th>host_acceptance_rate</th>\n",
              "      <th>host_is_superhost</th>\n",
              "      <th>host_thumbnail_url</th>\n",
              "      <th>host_picture_url</th>\n",
              "      <th>host_neighbourhood</th>\n",
              "      <th>host_listings_count</th>\n",
              "      <th>host_total_listings_count</th>\n",
              "      <th>host_verifications</th>\n",
              "      <th>host_has_profile_pic</th>\n",
              "      <th>host_identity_verified</th>\n",
              "      <th>street</th>\n",
              "      <th>neighbourhood</th>\n",
              "      <th>neighbourhood_cleansed</th>\n",
              "      <th>...</th>\n",
              "      <th>extra_people</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>maximum_nights</th>\n",
              "      <th>minimum_minimum_nights</th>\n",
              "      <th>maximum_minimum_nights</th>\n",
              "      <th>minimum_maximum_nights</th>\n",
              "      <th>maximum_maximum_nights</th>\n",
              "      <th>minimum_nights_avg_ntm</th>\n",
              "      <th>maximum_nights_avg_ntm</th>\n",
              "      <th>calendar_updated</th>\n",
              "      <th>has_availability</th>\n",
              "      <th>availability_30</th>\n",
              "      <th>availability_60</th>\n",
              "      <th>availability_90</th>\n",
              "      <th>availability_365</th>\n",
              "      <th>calendar_last_scraped</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>number_of_reviews_ltm</th>\n",
              "      <th>first_review</th>\n",
              "      <th>last_review</th>\n",
              "      <th>review_scores_rating</th>\n",
              "      <th>review_scores_accuracy</th>\n",
              "      <th>review_scores_cleanliness</th>\n",
              "      <th>review_scores_checkin</th>\n",
              "      <th>review_scores_communication</th>\n",
              "      <th>review_scores_location</th>\n",
              "      <th>review_scores_value</th>\n",
              "      <th>requires_license</th>\n",
              "      <th>license</th>\n",
              "      <th>jurisdiction_names</th>\n",
              "      <th>instant_bookable</th>\n",
              "      <th>is_business_travel_ready</th>\n",
              "      <th>cancellation_policy</th>\n",
              "      <th>require_guest_profile_picture</th>\n",
              "      <th>require_guest_phone_verification</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>calculated_host_listings_count_entire_homes</th>\n",
              "      <th>calculated_host_listings_count_private_rooms</th>\n",
              "      <th>calculated_host_listings_count_shared_rooms</th>\n",
              "      <th>reviews_per_month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4830</th>\n",
              "      <td>3987571</td>\n",
              "      <td>https://www.airbnb.com/rooms/3987571</td>\n",
              "      <td>20200506054410</td>\n",
              "      <td>2020-05-07</td>\n",
              "      <td>Garden Apartment with Private Entry</td>\n",
              "      <td>Beautiful historic 17th Century brownstone wit...</td>\n",
              "      <td>Private entrance for rented space.  The entire...</td>\n",
              "      <td>Beautiful historic 17th Century brownstone wit...</td>\n",
              "      <td>none</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Please communicate with host if you have ANY p...</td>\n",
              "      <td>Public transportation is very close. A and C t...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Live and respect the place as if it was your o...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://a0.muscache.com/im/pictures/51685327/a...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20670026</td>\n",
              "      <td>https://www.airbnb.com/users/show/20670026</td>\n",
              "      <td>Monifa &amp; Saint</td>\n",
              "      <td>2014-08-28</td>\n",
              "      <td>New York, New York, United States</td>\n",
              "      <td>Monifa (an accountant) and Saint (a real estat...</td>\n",
              "      <td>within a few hours</td>\n",
              "      <td>70%</td>\n",
              "      <td>100%</td>\n",
              "      <td>t</td>\n",
              "      <td>https://a0.muscache.com/im/users/20670026/prof...</td>\n",
              "      <td>https://a0.muscache.com/im/users/20670026/prof...</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>['email', 'phone', 'facebook', 'reviews', 'jum...</td>\n",
              "      <td>t</td>\n",
              "      <td>t</td>\n",
              "      <td>Brooklyn, NY, United States</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>...</td>\n",
              "      <td>$15.00</td>\n",
              "      <td>2</td>\n",
              "      <td>25</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1125</td>\n",
              "      <td>1125</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1125.0</td>\n",
              "      <td>3 months ago</td>\n",
              "      <td>t</td>\n",
              "      <td>30</td>\n",
              "      <td>58</td>\n",
              "      <td>88</td>\n",
              "      <td>361</td>\n",
              "      <td>2020-05-07</td>\n",
              "      <td>292</td>\n",
              "      <td>44</td>\n",
              "      <td>2014-09-20</td>\n",
              "      <td>2020-03-01</td>\n",
              "      <td>96.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>f</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>t</td>\n",
              "      <td>f</td>\n",
              "      <td>moderate</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4.26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 106 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           id  ... reviews_per_month\n",
              "4830  3987571  ...              4.26\n",
              "\n",
              "[1 rows x 106 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0PMJGe7AS3WI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d12ee571-f3c5-4eda-fabd-923d11a725b7"
      },
      "source": [
        "df_old.columns.tolist(),df_new.columns.tolist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(['id',\n",
              "  'name',\n",
              "  'host_id',\n",
              "  'host_name',\n",
              "  'neighbourhood_group',\n",
              "  'neighbourhood',\n",
              "  'latitude',\n",
              "  'longitude',\n",
              "  'room_type',\n",
              "  'price',\n",
              "  'minimum_nights',\n",
              "  'number_of_reviews',\n",
              "  'last_review',\n",
              "  'reviews_per_month',\n",
              "  'calculated_host_listings_count',\n",
              "  'availability_365'],\n",
              " ['id',\n",
              "  'listing_url',\n",
              "  'scrape_id',\n",
              "  'last_scraped',\n",
              "  'name',\n",
              "  'summary',\n",
              "  'space',\n",
              "  'description',\n",
              "  'experiences_offered',\n",
              "  'neighborhood_overview',\n",
              "  'notes',\n",
              "  'transit',\n",
              "  'access',\n",
              "  'interaction',\n",
              "  'house_rules',\n",
              "  'thumbnail_url',\n",
              "  'medium_url',\n",
              "  'picture_url',\n",
              "  'xl_picture_url',\n",
              "  'host_id',\n",
              "  'host_url',\n",
              "  'host_name',\n",
              "  'host_since',\n",
              "  'host_location',\n",
              "  'host_about',\n",
              "  'host_response_time',\n",
              "  'host_response_rate',\n",
              "  'host_acceptance_rate',\n",
              "  'host_is_superhost',\n",
              "  'host_thumbnail_url',\n",
              "  'host_picture_url',\n",
              "  'host_neighbourhood',\n",
              "  'host_listings_count',\n",
              "  'host_total_listings_count',\n",
              "  'host_verifications',\n",
              "  'host_has_profile_pic',\n",
              "  'host_identity_verified',\n",
              "  'street',\n",
              "  'neighbourhood',\n",
              "  'neighbourhood_cleansed',\n",
              "  'neighbourhood_group_cleansed',\n",
              "  'city',\n",
              "  'state',\n",
              "  'zipcode',\n",
              "  'market',\n",
              "  'smart_location',\n",
              "  'country_code',\n",
              "  'country',\n",
              "  'latitude',\n",
              "  'longitude',\n",
              "  'is_location_exact',\n",
              "  'property_type',\n",
              "  'room_type',\n",
              "  'accommodates',\n",
              "  'bathrooms',\n",
              "  'bedrooms',\n",
              "  'beds',\n",
              "  'bed_type',\n",
              "  'amenities',\n",
              "  'square_feet',\n",
              "  'price',\n",
              "  'weekly_price',\n",
              "  'monthly_price',\n",
              "  'security_deposit',\n",
              "  'cleaning_fee',\n",
              "  'guests_included',\n",
              "  'extra_people',\n",
              "  'minimum_nights',\n",
              "  'maximum_nights',\n",
              "  'minimum_minimum_nights',\n",
              "  'maximum_minimum_nights',\n",
              "  'minimum_maximum_nights',\n",
              "  'maximum_maximum_nights',\n",
              "  'minimum_nights_avg_ntm',\n",
              "  'maximum_nights_avg_ntm',\n",
              "  'calendar_updated',\n",
              "  'has_availability',\n",
              "  'availability_30',\n",
              "  'availability_60',\n",
              "  'availability_90',\n",
              "  'availability_365',\n",
              "  'calendar_last_scraped',\n",
              "  'number_of_reviews',\n",
              "  'number_of_reviews_ltm',\n",
              "  'first_review',\n",
              "  'last_review',\n",
              "  'review_scores_rating',\n",
              "  'review_scores_accuracy',\n",
              "  'review_scores_cleanliness',\n",
              "  'review_scores_checkin',\n",
              "  'review_scores_communication',\n",
              "  'review_scores_location',\n",
              "  'review_scores_value',\n",
              "  'requires_license',\n",
              "  'license',\n",
              "  'jurisdiction_names',\n",
              "  'instant_bookable',\n",
              "  'is_business_travel_ready',\n",
              "  'cancellation_policy',\n",
              "  'require_guest_profile_picture',\n",
              "  'require_guest_phone_verification',\n",
              "  'calculated_host_listings_count',\n",
              "  'calculated_host_listings_count_entire_homes',\n",
              "  'calculated_host_listings_count_private_rooms',\n",
              "  'calculated_host_listings_count_shared_rooms',\n",
              "  'reviews_per_month'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xgGn6ya2Tfpk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7bd45c59-ff75-43a4-a5ed-69eadbcf3a81"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['id',\n",
              " 'listing_url',\n",
              " 'scrape_id',\n",
              " 'last_scraped',\n",
              " 'name',\n",
              " 'summary',\n",
              " 'space',\n",
              " 'description',\n",
              " 'experiences_offered',\n",
              " 'neighborhood_overview',\n",
              " 'notes',\n",
              " 'transit',\n",
              " 'access',\n",
              " 'interaction',\n",
              " 'house_rules',\n",
              " 'thumbnail_url',\n",
              " 'medium_url',\n",
              " 'picture_url',\n",
              " 'xl_picture_url',\n",
              " 'host_id',\n",
              " 'host_url',\n",
              " 'host_name',\n",
              " 'host_since',\n",
              " 'host_location',\n",
              " 'host_about',\n",
              " 'host_response_time',\n",
              " 'host_response_rate',\n",
              " 'host_acceptance_rate',\n",
              " 'host_is_superhost',\n",
              " 'host_thumbnail_url',\n",
              " 'host_picture_url',\n",
              " 'host_neighbourhood',\n",
              " 'host_listings_count',\n",
              " 'host_total_listings_count',\n",
              " 'host_verifications',\n",
              " 'host_has_profile_pic',\n",
              " 'host_identity_verified',\n",
              " 'street',\n",
              " 'neighbourhood',\n",
              " 'neighbourhood_cleansed',\n",
              " 'neighbourhood_group_cleansed',\n",
              " 'city',\n",
              " 'state',\n",
              " 'zipcode',\n",
              " 'market',\n",
              " 'smart_location',\n",
              " 'country_code',\n",
              " 'country',\n",
              " 'latitude',\n",
              " 'longitude',\n",
              " 'is_location_exact',\n",
              " 'property_type',\n",
              " 'room_type',\n",
              " 'accommodates',\n",
              " 'bathrooms',\n",
              " 'bedrooms',\n",
              " 'beds',\n",
              " 'bed_type',\n",
              " 'amenities',\n",
              " 'square_feet',\n",
              " 'price',\n",
              " 'weekly_price',\n",
              " 'monthly_price',\n",
              " 'security_deposit',\n",
              " 'cleaning_fee',\n",
              " 'guests_included',\n",
              " 'extra_people',\n",
              " 'minimum_nights',\n",
              " 'maximum_nights',\n",
              " 'minimum_minimum_nights',\n",
              " 'maximum_minimum_nights',\n",
              " 'minimum_maximum_nights',\n",
              " 'maximum_maximum_nights',\n",
              " 'minimum_nights_avg_ntm',\n",
              " 'maximum_nights_avg_ntm',\n",
              " 'calendar_updated',\n",
              " 'has_availability',\n",
              " 'availability_30',\n",
              " 'availability_60',\n",
              " 'availability_90',\n",
              " 'availability_365',\n",
              " 'calendar_last_scraped',\n",
              " 'number_of_reviews',\n",
              " 'number_of_reviews_ltm',\n",
              " 'first_review',\n",
              " 'last_review',\n",
              " 'review_scores_rating',\n",
              " 'review_scores_accuracy',\n",
              " 'review_scores_cleanliness',\n",
              " 'review_scores_checkin',\n",
              " 'review_scores_communication',\n",
              " 'review_scores_location',\n",
              " 'review_scores_value',\n",
              " 'requires_license',\n",
              " 'license',\n",
              " 'jurisdiction_names',\n",
              " 'instant_bookable',\n",
              " 'is_business_travel_ready',\n",
              " 'cancellation_policy',\n",
              " 'require_guest_profile_picture',\n",
              " 'require_guest_phone_verification',\n",
              " 'calculated_host_listings_count',\n",
              " 'calculated_host_listings_count_entire_homes',\n",
              " 'calculated_host_listings_count_private_rooms',\n",
              " 'calculated_host_listings_count_shared_rooms',\n",
              " 'reviews_per_month']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U8wu3HfYSuIg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "d0b87acb-0460-4fa2-d9cc-6e43b59d4e2f"
      },
      "source": [
        "df_new[df_old.columns.tolist()]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-79-73c19d94736f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_new\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf_old\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2804\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2805\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2806\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2807\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2808\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1551\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1552\u001b[0m         self._validate_read_indexer(\n\u001b[0;32m-> 1553\u001b[0;31m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraise_missing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1554\u001b[0m         )\n\u001b[1;32m   1555\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1644\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"loc\"\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1645\u001b[0m                 \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1646\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{not_found} not in index\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1648\u001b[0m             \u001b[0;31m# we skip the warning on Categorical/Interval\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['neighbourhood_group'] not in index\""
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xEQCQoFv1KBp",
        "colab_type": "text"
      },
      "source": [
        "# EDA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RjYJC5Iv1MJ4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# img = plt.imread('./New_York_City_.png', 0)\n",
        "\n",
        "sns.scatterplot(df['longitude'], df['latitude'], )\n",
        "# plt.imshow(img)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}