1_cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting Airbnb Listing Price | Cleaning\n",
    "\n",
    "With the data collected and combined into two files `los-angeles_listings.csv` and `los-angeles_reviews.csv`, it still had to be cleaned. \n",
    "\n",
    "Major cleaning considerations included:\n",
    "- size of data\n",
    "- data types\n",
    "- redundancy in features\n",
    "- data entry errors (e.g capitalization and spelling)\n",
    "- null values\n",
    "- combining listings and reviews into a single dataframe\n",
    "\n",
    "This notebook will cover the data cleaning process for this project, in which I altered the data significantly by dropping redundant/unneccessary columns, optimized datatypes, implemented one hot encoding for categorical variables, and processed the text data.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import multiprocessing as mp\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "sns.set_style('white')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "directory = '/Users/limesncoconuts2/datasets/airbnb/'\n",
    "city = 'los-angeles'\n",
    "listing = pd.read_csv(directory + city + '_listings.csv')\n",
    "review = pd.read_csv(directory + city + '_reviews.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, I started out by getting a sense of the size of both dataframes. `listing` has 750 thousand rows and 106 features while `review` has over 2 million rows but only 6 features. All the data is either of integer, float, or object data type. `listing` is much larger than `review` despite having many fewer rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 750771 entries, 0 to 750770\n",
      "Columns: 106 entries, access to zipcode\n",
      "dtypes: float64(21), object(85)\n",
      "memory usage: 607.2+ MB\n"
     ]
    }
   ],
   "source": [
    "listing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2095642 entries, 0 to 2095641\n",
      "Data columns (total 6 columns):\n",
      "listing_id       int64\n",
      "id               int64\n",
      "date             object\n",
      "reviewer_id      int64\n",
      "reviewer_name    object\n",
      "comments         object\n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 95.9+ MB\n"
     ]
    }
   ],
   "source": [
    "review.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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>access</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>amenities</th>\n",
       "      <th>availability_30</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>availability_60</th>\n",
       "      <th>availability_90</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bed_type</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</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>calendar_last_scraped</th>\n",
       "      <th>calendar_updated</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>city</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>country</th>\n",
       "      <th>country_code</th>\n",
       "      <th>description</th>\n",
       "      <th>experiences_offered</th>\n",
       "      <th>extra_people</th>\n",
       "      <th>first_review</th>\n",
       "      <th>guests_included</th>\n",
       "      <th>has_availability</th>\n",
       "      <th>host_about</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_has_profile_pic</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_location</th>\n",
       "      <th>host_name</th>\n",
       "      <th>host_neighbourhood</th>\n",
       "      <th>host_picture_url</th>\n",
       "      <th>host_response_rate</th>\n",
       "      <th>host_response_time</th>\n",
       "      <th>host_since</th>\n",
       "      <th>host_thumbnail_url</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>host_url</th>\n",
       "      <th>host_verifications</th>\n",
       "      <th>house_rules</th>\n",
       "      <th>id</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>interaction</th>\n",
       "      <th>is_business_travel_ready</th>\n",
       "      <th>is_location_exact</th>\n",
       "      <th>jurisdiction_names</th>\n",
       "      <th>last_review</th>\n",
       "      <th>last_scraped</th>\n",
       "      <th>latitude</th>\n",
       "      <th>license</th>\n",
       "      <th>listing_url</th>\n",
       "      <th>longitude</th>\n",
       "      <th>market</th>\n",
       "      <th>maximum_maximum_nights</th>\n",
       "      <th>maximum_minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "      <th>maximum_nights_avg_ntm</th>\n",
       "      <th>medium_url</th>\n",
       "      <th>minimum_maximum_nights</th>\n",
       "      <th>minimum_minimum_nights</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>minimum_nights_avg_ntm</th>\n",
       "      <th>monthly_price</th>\n",
       "      <th>name</th>\n",
       "      <th>neighborhood_overview</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "      <th>notes</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>number_of_reviews_ltm</th>\n",
       "      <th>picture_url</th>\n",
       "      <th>price</th>\n",
       "      <th>property_type</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>requires_license</th>\n",
       "      <th>review_scores_accuracy</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>room_type</th>\n",
       "      <th>scrape_id</th>\n",
       "      <th>security_deposit</th>\n",
       "      <th>smart_location</th>\n",
       "      <th>space</th>\n",
       "      <th>square_feet</th>\n",
       "      <th>state</th>\n",
       "      <th>street</th>\n",
       "      <th>summary</th>\n",
       "      <th>thumbnail_url</th>\n",
       "      <th>transit</th>\n",
       "      <th>weekly_price</th>\n",
       "      <th>xl_picture_url</th>\n",
       "      <th>zipcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>{Internet,\"Wireless Internet\",\"Air Conditionin...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>365</td>\n",
       "      <td>60</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-07-28</td>\n",
       "      <td>6 months ago</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Arcadia</td>\n",
       "      <td>$10.00</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>安靜舒適的独立大套房. 阳光明媚的大卧室，(附帶冰箱)一个超大衣帽間和浴室. 提供免费无线高...</td>\n",
       "      <td>none</td>\n",
       "      <td>$20.00</td>\n",
       "      <td>2014-09-15</td>\n",
       "      <td>1</td>\n",
       "      <td>t</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100%</td>\n",
       "      <td>t</td>\n",
       "      <td>20130073</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PH</td>\n",
       "      <td>Cici</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a2.muscache.com/ac/users/20130073/prof...</td>\n",
       "      <td>100%</td>\n",
       "      <td>within a day</td>\n",
       "      <td>2014-08-16</td>\n",
       "      <td>https://a2.muscache.com/ac/users/20130073/prof...</td>\n",
       "      <td>1</td>\n",
       "      <td>https://www.airbnb.com/users/show/20130073</td>\n",
       "      <td>['email', 'phone', 'reviews']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3890624</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-11-02</td>\n",
       "      <td>2015-07-28</td>\n",
       "      <td>34.1328</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.airbnb.com/rooms/3890624</td>\n",
       "      <td>-118.025147</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a0.muscache.com/ac/pictures/52102815/8...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2,500.00</td>\n",
       "      <td>安靜舒適的独立大套房,非常适合专业商務人士，学生，实习，渡假遊學...</td>\n",
       "      <td>风景秀丽，豪宅林立，闹中取静，交通方便，华人超市!</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Arcadia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>无合约要求，适合短期旅游，探亲访友，随时拎包入住，</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a0.muscache.com/ac/pictures/52102815/8...</td>\n",
       "      <td>$90.00</td>\n",
       "      <td>House</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.19</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2.015073e+13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>安靜舒適的独立大套房. 阳光明媚的大卧室，(附帶冰箱)一个超大衣帽間和浴室.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CA</td>\n",
       "      <td>South 2nd Avenue, Arcadia, CA 91006, United St...</td>\n",
       "      <td>我們j位於Arcadia市中心,非常适合专业商務人士，学生，渡假遊學...鄰近高速公路,餐厅...</td>\n",
       "      <td>https://a0.muscache.com/ac/pictures/52102815/8...</td>\n",
       "      <td>聚集高尔夫球场，网球场，大型购物中心，影院，图书馆，大型街心公园，跑马场，医院，诊所。非常便...</td>\n",
       "      <td>$650.00</td>\n",
       "      <td>https://a0.muscache.com/ac/pictures/52102815/8...</td>\n",
       "      <td>91006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  access accommodates                                          amenities  \\\n",
       "0    NaN            2  {Internet,\"Wireless Internet\",\"Air Conditionin...   \n",
       "\n",
       "   availability_30 availability_365 availability_60  availability_90  \\\n",
       "0             30.0              365              60             90.0   \n",
       "\n",
       "  bathrooms  bed_type bedrooms beds calculated_host_listings_count  \\\n",
       "0         1  Real Bed        1    1                              1   \n",
       "\n",
       "  calculated_host_listings_count_entire_homes  \\\n",
       "0                                         NaN   \n",
       "\n",
       "  calculated_host_listings_count_private_rooms  \\\n",
       "0                                          NaN   \n",
       "\n",
       "  calculated_host_listings_count_shared_rooms calendar_last_scraped  \\\n",
       "0                                         NaN            2015-07-28   \n",
       "\n",
       "  calendar_updated cancellation_policy     city cleaning_fee        country  \\\n",
       "0     6 months ago            moderate  Arcadia       $10.00  United States   \n",
       "\n",
       "  country_code                                        description  \\\n",
       "0           US  安靜舒適的独立大套房. 阳光明媚的大卧室，(附帶冰箱)一个超大衣帽間和浴室. 提供免费无线高...   \n",
       "\n",
       "  experiences_offered extra_people first_review guests_included  \\\n",
       "0                none       $20.00   2014-09-15               1   \n",
       "\n",
       "  has_availability host_about host_acceptance_rate host_has_profile_pic  \\\n",
       "0                t        NaN                 100%                    t   \n",
       "\n",
       "    host_id host_identity_verified host_is_superhost  host_listings_count  \\\n",
       "0  20130073                      f                 f                  1.0   \n",
       "\n",
       "  host_location host_name host_neighbourhood  \\\n",
       "0            PH      Cici                NaN   \n",
       "\n",
       "                                    host_picture_url host_response_rate  \\\n",
       "0  https://a2.muscache.com/ac/users/20130073/prof...               100%   \n",
       "\n",
       "  host_response_time  host_since  \\\n",
       "0       within a day  2014-08-16   \n",
       "\n",
       "                                  host_thumbnail_url  \\\n",
       "0  https://a2.muscache.com/ac/users/20130073/prof...   \n",
       "\n",
       "  host_total_listings_count                                    host_url  \\\n",
       "0                         1  https://www.airbnb.com/users/show/20130073   \n",
       "\n",
       "              host_verifications house_rules       id instant_bookable  \\\n",
       "0  ['email', 'phone', 'reviews']         NaN  3890624                f   \n",
       "\n",
       "  interaction is_business_travel_ready is_location_exact jurisdiction_names  \\\n",
       "0         NaN                      NaN                 t                NaN   \n",
       "\n",
       "  last_review last_scraped latitude license  \\\n",
       "0  2014-11-02   2015-07-28  34.1328     NaN   \n",
       "\n",
       "                            listing_url   longitude       market  \\\n",
       "0  https://www.airbnb.com/rooms/3890624 -118.025147  Los Angeles   \n",
       "\n",
       "   maximum_maximum_nights  maximum_minimum_nights  maximum_nights  \\\n",
       "0                     NaN                     NaN          1125.0   \n",
       "\n",
       "   maximum_nights_avg_ntm                                         medium_url  \\\n",
       "0                     NaN  https://a0.muscache.com/ac/pictures/52102815/8...   \n",
       "\n",
       "   minimum_maximum_nights minimum_minimum_nights minimum_nights  \\\n",
       "0                     NaN                    NaN              1   \n",
       "\n",
       "  minimum_nights_avg_ntm monthly_price                                 name  \\\n",
       "0                    NaN     $2,500.00  安靜舒適的独立大套房,非常适合专业商務人士，学生，实习，渡假遊學...   \n",
       "\n",
       "       neighborhood_overview neighbourhood neighbourhood_cleansed  \\\n",
       "0  风景秀丽，豪宅林立，闹中取静，交通方便，华人超市!           NaN                Arcadia   \n",
       "\n",
       "  neighbourhood_group_cleansed                       notes  number_of_reviews  \\\n",
       "0                          NaN  无合约要求，适合短期旅游，探亲访友，随时拎包入住，                 2.0   \n",
       "\n",
       "   number_of_reviews_ltm                                        picture_url  \\\n",
       "0                    NaN  https://a0.muscache.com/ac/pictures/52102815/8...   \n",
       "\n",
       "    price property_type require_guest_phone_verification  \\\n",
       "0  $90.00         House                                f   \n",
       "\n",
       "  require_guest_profile_picture requires_license  review_scores_accuracy  \\\n",
       "0                             f                f                    10.0   \n",
       "\n",
       "   review_scores_checkin  review_scores_cleanliness  \\\n",
       "0                   10.0                        8.0   \n",
       "\n",
       "   review_scores_communication  review_scores_location  review_scores_rating  \\\n",
       "0                         10.0                    10.0                  90.0   \n",
       "\n",
       "   review_scores_value  reviews_per_month     room_type     scrape_id  \\\n",
       "0                  8.0               0.19  Private room  2.015073e+13   \n",
       "\n",
       "  security_deposit smart_location                                   space  \\\n",
       "0              NaN            NaN  安靜舒適的独立大套房. 阳光明媚的大卧室，(附帶冰箱)一个超大衣帽間和浴室.   \n",
       "\n",
       "   square_feet state                                             street  \\\n",
       "0          NaN    CA  South 2nd Avenue, Arcadia, CA 91006, United St...   \n",
       "\n",
       "                                             summary  \\\n",
       "0  我們j位於Arcadia市中心,非常适合专业商務人士，学生，渡假遊學...鄰近高速公路,餐厅...   \n",
       "\n",
       "                                       thumbnail_url  \\\n",
       "0  https://a0.muscache.com/ac/pictures/52102815/8...   \n",
       "\n",
       "                                             transit weekly_price  \\\n",
       "0  聚集高尔夫球场，网球场，大型购物中心，影院，图书馆，大型街心公园，跑马场，医院，诊所。非常便...      $650.00   \n",
       "\n",
       "                                      xl_picture_url zipcode  \n",
       "0  https://a0.muscache.com/ac/pictures/52102815/8...   91006  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listing.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping Columns\n",
    "Looking through the first row of `listing`, it looks like many rows repeat information or are not relevant to this project's analysis. For example, all rows related to location (neighbourhood, city, state) can be removed in favor of just the zip code. Also for this project, I am intentionally not using any non-categorical text data apart from that in `review`. \n",
    "\n",
    "Therefore, I put the names of all columns I did not want to keep into a list called `drop` and used the pandas drop function to remove those columns in place."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop = ['access', 'availability_30','availability_60','availability_90',\n",
    "'calculated_host_listings_count', 'calculated_host_listings_count_entire_homes',\n",
    "'calculated_host_listings_count_private_rooms', 'calculated_host_listings_count_shared_rooms',\n",
    "'calendar_last_scraped', 'calendar_updated', 'city', 'country', 'country_code', 'description',\n",
    "'first_review', 'has_availability', 'host_about', 'host_id', 'host_location', \n",
    "'host_listings_count', 'host_name', 'host_neighbourhood', 'host_picture_url', \n",
    "'host_thumbnail_url', 'host_since', 'host_url', 'host_verifications','house_rules',\n",
    "'interaction', 'jurisdiction_names','last_review', 'last_scraped', 'license', \n",
    "'listing_url', 'market', 'maximum_maximum_nights', 'maximum_minimum_nights',\n",
    "'maximum_nights_avg_ntm', 'medium_url', 'minimum_maximum_nights', 'minimum_minimum_nights',\n",
    "'minimum_nights_avg_ntm', 'monthly_price', 'name', 'neighborhood_overview', 'neighbourhood',\n",
    "'neighbourhood_cleansed', 'neighbourhood_group_cleansed', 'notes', 'number_of_reviews_ltm',\n",
    "'picture_url', 'review_scores_accuracy', 'review_scores_checkin', 'review_scores_cleanliness',\n",
    "'review_scores_communication','review_scores_location','review_scores_value','scrape_id', \n",
    "'security_deposit','smart_location','space', 'state', 'street','summary', 'thumbnail_url',\n",
    "'transit', 'weekly_price', 'xl_picture_url']\n",
    "\n",
    "listing.drop(columns=drop, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inconsistencies in Categorical Data\n",
    "\n",
    "It's important to correct inconsistencies in categorical data to prevent more categories being created than necessary. By converting all text in `listing` to lowercase, I immediately got rid of all capitalization-related data entry inconsistencies and could focus on spelling and formatting errors.\n",
    "\n",
    "I used the `value_counts()` function to find such inconsistencies with the `bed_type` column and found that some entries are actually people's names! These entry errors are a vast minority in the data, and I decided to remove those rows from `listing`.\n",
    "\n",
    "I ran `value_counts()` on other categorical variables (omitted because the code wasn't necessary for the final analysis), and found that removing the rows from the `bed_type` analysis got rid of most other inconsistencies in other columns as well. \n",
    "\n",
    "One minor flaw that I found in the `accommodates` column included a string with a '%', which prevented the column from being converted to a numerical datatype. I removed that row from the `listing`.\n",
    "\n",
    "Finally, I fixed minor entry inconsistencies in the `property_type` column in which property types of the same name had slightly different entry formats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "listing = listing.apply(lambda x: x.astype(str).str.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "750771\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "real bed         734244\n",
       "futon              6151\n",
       "pull-out sofa      4127\n",
       "airbed             3616\n",
       "couch              2588\n",
       "jay                  14\n",
       "arya                 14\n",
       "eliza                12\n",
       "vitaly                3\n",
       "newsha                2\n",
       "Name: bed_type, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(listing.bed_type))\n",
    "listing.bed_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "listing = listing[listing.accommodates != '50%']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "listing = listing[(listing.bed_type == 'real bed') |\n",
    "                  (listing.bed_type == 'futon') |\n",
    "                  (listing.bed_type == 'pull-out sofa') |\n",
    "                  (listing.bed_type == 'airbed') |\n",
    "                  (listing.bed_type == 'couch')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "listing['property_type'] = listing['property_type'].str.replace('bed & breakfast', 'bed and breakfast')\\\n",
    "                                                    .str.replace('casa particular \\(cuba\\)', 'casa particular', regex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Amenities Feature\n",
    "\n",
    "One interesting feature in `listing` is the `amenities` row, which is a string containing a list of the amenities offered in a listing. Although the format wasn't machine leaning-compatible, I saw this as a great opportunity to do some feature creation.\n",
    "\n",
    "From the row of `listing` above, you can see that `amenities` consists of strings wrapped in curly braces containing lists of amenities, and some amenity names are contained in quotes. To create a string without these extraneous characters, I removed the characters.\n",
    "\n",
    "After removing the extraneous characters, I created a list of all unique amenities represented in the dataset. I iterated through this list of unique amenities to create new feature-specific boolean columns in `listing` using my function `has_feature()`.\n",
    "\n",
    "The output of `has_feature` were individual boolean columns for each unique feature in the dataset that indicated whether a listing provided that feature. After creating these columns I was able to drop the original `amenities` column from `listing`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "listing['amenities'] = listing.amenities.str.replace('{','').str.replace('}','').str.replace('\"','')\n",
    "listing['amenities'] = listing['amenities'].str.replace('doorman entry', 'doorman')\\\n",
    "                                                    .str.replace('elevator in building', 'elevator')\\\n",
    "                                                    .str.replace('ski in/ski out', 'ski-in/ski-out')\\\n",
    "                                                    .str.replace('smart lock', 'smartlock')\\\n",
    "                                                    .str.replace('ceiling fan', 'ceiling fans')\\\n",
    "                                                    .str.replace('translation missing: en.hosting_amenity_49', '')\\\n",
    "                                                    .str.replace('translation missing: en.hosting_amenity_50', '')\n",
    "listing['amenities'] = listing['amenities'].str.split(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_amenities = []\n",
    "for entry in listing.amenities:\n",
    "    try:\n",
    "        all_amenities.extend(entry)\n",
    "    except TypeError: # if nan value\n",
    "        pass\n",
    "unique_amenities = list(set(all_amenities))\n",
    "unique_amenities.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def has_feature(series, feature):\n",
    "    \"\"\" returns a pandas series that includes True or False based\n",
    "        on whether that entry in the dataframe has that amenity\n",
    "    \"\"\"\n",
    "    col = []\n",
    "    for ls in series:\n",
    "        try:\n",
    "            if feature in ls:\n",
    "                col.append(True)\n",
    "            else:\n",
    "                col.append(False)\n",
    "        except:\n",
    "            col.append(False)\n",
    "    col = pd.Series(col)\n",
    "    return col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new column in listings for each amenity\n",
    "for amenity in unique_amenities:\n",
    "    col_name = 'has_' + amenity.replace(' ', '_')\n",
    "    \n",
    "    # if that listing has that amenity, True, if not, False\n",
    "    try:\n",
    "        listing[col_name] = has_feature(listing.amenities, amenity)\n",
    "    except TypeError: # is a null value\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# drop blank column and old amenities column\n",
    "listing = listing.drop(columns=['has_', 'amenities'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changing Data Types\n",
    "\n",
    "Since machine learning algorithms in scikit-learn only take numeric data, I needed to make sure that all columns in `listing` were either integers, floats, booleans, or categorical. Going manually through the columns, I separated them into lists based on which data type they should be, and used these lists in the `change_datatypes()` function.\n",
    "\n",
    "Additionally, there were special columns that required specific processing because they included a special symbol: prices ($) and percentages (\\%). The columns were put into their own lists as well for `change_datatypes()`.\n",
    "\n",
    "`change_datatypes()` used these lists to run data type conversion functions on `listing`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_datatypes(df):\n",
    "    \"\"\" Changes the datatypes of specified columns in the dataframe,\n",
    "        depending on if the dataframe passed to the function has\n",
    "        listings data or reviews data. Has try/except blocks just in case\n",
    "        a certain column is not found in a dataframe.\n",
    "    \"\"\"\n",
    "    # lists of columns that need to be converted in listings data\n",
    "    to_numeric = ['accommodates', 'availability_365', 'bathrooms', 'bedrooms', \n",
    "                  'beds', 'guests_included','host_total_listings_count', 'id',\n",
    "                  'latitude', 'longitude', 'maximum_nights', 'minimum_nights',\n",
    "                 'number_of_reviews', 'review_scores_rating','reviews_per_month',\n",
    "                  'square_feet', 'zipcode']\n",
    "    to_bool = ['experiences_offered', 'host_has_profile_pic', 'host_identity_verified',\n",
    "               'host_is_superhost','instant_bookable', 'is_business_travel_ready',\n",
    "               'is_location_exact', 'require_guest_phone_verification',\n",
    "               'require_guest_profile_picture', 'requires_license']\n",
    "    to_category = ['bed_type', 'cancellation_policy', 'host_response_time',\n",
    "                   'property_type', 'room_type']\n",
    "    prices = ['cleaning_fee', 'extra_people', 'price',]\n",
    "    percentages = ['host_acceptance_rate', 'host_response_rate']\n",
    "    \n",
    "    ## to integer\n",
    "    for col_name in to_numeric:\n",
    "        try:\n",
    "            df[col_name] = pd.to_numeric(df[col_name], errors='coerce')\n",
    "        except KeyError:\n",
    "            pass\n",
    "\n",
    "    ## to bool: booleans start out as 't' or 'f' strings\n",
    "    for col_name in to_bool:\n",
    "        try:\n",
    "            df[col_name] = df[col_name] == 't'\n",
    "        except:\n",
    "            pass\n",
    "    \n",
    "    ## to category\n",
    "    for col_name in to_category:\n",
    "        try:\n",
    "            df[col_name] = df[col_name].astype('category')\n",
    "        except KeyError:\n",
    "            pass\n",
    "       \n",
    "    ## change price columns\n",
    "    for col_name in prices:\n",
    "        try:\n",
    "            df[col_name] = pd.to_numeric(df[col_name].str.replace('$',''),\n",
    "                                         downcast='float', errors='coerce')\n",
    "        except:\n",
    "            pass\n",
    "        # rename column\n",
    "        df.rename(columns={col_name: col_name + '_USD'}, inplace=True)\n",
    "\n",
    "    ## change percentage columns\n",
    "    for col_name in percentages:\n",
    "        try:\n",
    "            df[col_name] = pd.to_numeric(df[col_name].str.replace('%',''),\n",
    "                                         downcast='float', errors='coerce')\n",
    "        except:\n",
    "            pass\n",
    "        # rename column\n",
    "        df.rename(columns={col_name: col_name + '_percentage'}, inplace=True)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "listing = change_datatypes(listing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Target Variable Null Values\n",
    "\n",
    "For the final analysis, it wouldn't be useful to have rows of data that do not have values for the target variable `price_USD`. Therefore, I dropped all columns where `price_USD` was null."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop rows where target column is null\n",
    "listing = listing[listing.price_USD.notna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One Hot Encoding Categorical Variables\n",
    "\n",
    "Before one hot encoding categorical variables, I saved and exported a small file containing columns of interest for plotting later in the analysis.\n",
    "\n",
    "Then, I created a list with the names of the columns that I wished to be one hot encoded, and used the pandas `get_dummies()` function to add those encoded columns to the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/limesncoconuts2/datasets/airbnb/for_plotting.csv'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save columns for plotting\n",
    "destination = '/Users/limesncoconuts2/datasets/airbnb/'\n",
    "filename = 'for_plotting.csv'\n",
    "current_dir = os.getcwd() + '/' + filename\n",
    "df_plot = listing[['price_USD', 'property_type', 'accommodates', 'square_feet']]\n",
    "df_plot.to_csv(filename, index=False)\n",
    "shutil.move(os.path.join(current_dir), os.path.join(destination, filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# encode categories\n",
    "encode = ['bed_type','host_response_time','property_type','room_type', 'cancellation_policy']\n",
    "listing = pd.get_dummies(listing, columns=encode, drop_first=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Incorporating Review Data\n",
    "\n",
    "The `review` dataframe contains a row for each review written for an airbnb listing. I aggregated all the review text for each individual listing and added it to `listing` as an additional column.\n",
    "\n",
    "I dropped all columns from the data that were not `listing_id` and `comments`, the two rows that would help me append the review text to the final dataframe.\n",
    "\n",
    "Using the reduced `review` dataframe, I created the `aggregate_reviews()` function that grouped together all review text for each listing. Then I added that grouped data as another feature to the dataframe and renamed the dataframe `df`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "review.drop(columns=['id', 'date', 'reviewer_id', 'reviewer_name'], inplace=True)\n",
    "review.comments.fillna('', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def aggregate_reviews(review):\n",
    "    '''Groups together all review text for each listing and stores in a dataframe'''\n",
    "    review_combined=pd.DataFrame(columns=['id', 'reviews'])\n",
    "    unique_id = list(review.listing_id.unique())\n",
    "    \n",
    "    for i in unique_id:\n",
    "        subset = review[i == review.listing_id]\n",
    "        all_comments = []\n",
    "        for row in subset.iterrows():\n",
    "            review_text = row[1].comments\n",
    "            all_comments.append(review_text)\n",
    "        review_combined = review_combined.append({'id': i, 'reviews': all_comments}, ignore_index=True)\n",
    "    \n",
    "    review_combined.id = pd.to_numeric(review_combined.id, errors='coerce')\n",
    "    return review_combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "cores = mp.cpu_count()\n",
    "df_split = np.array_split(review, cores, axis=0)\n",
    "pool = mp.Pool(cores)\n",
    "review_combined = np.vstack(pool.map(aggregate_reviews, df_split))\n",
    "pool.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "review_combined = pd.DataFrame(review_combined, columns=['id', 'reviews'])\n",
    "review_combined.id = review_combined.id.astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add as a column in the df\n",
    "df = listing.merge(review_combined, on='id', how='left')\n",
    "df.reviews.fillna('', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing Values\n",
    "\n",
    "Because machine learning algorithms can't handle missing values, those missing values needed to be filled in way that makes sense. First I created a list of column names that had null values and weren't booleans and another list of column names that had null values are were booleans.  I filled the nan values differently for different columns:\n",
    "\n",
    "For `zipcode` I filled missing values with the mode.\n",
    "\n",
    "For other non-boolean columns, I filled missing values with the median.\n",
    "\n",
    "For boolean columns, I filled missing values with `False`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "nans = df.isna().sum()\n",
    "nans = nans[nans != 0].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "has_cols = list(nans[11: len(nans)-1].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "nans = [n for n in nans.index if n not in has_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in nans:\n",
    "    if col == 'zipcode':\n",
    "        mode = df.zipcode.mode().values[0]\n",
    "        df[col].fillna(mode, inplace=True)\n",
    "    else:\n",
    "        median = np.nanmedian(df[col].values)\n",
    "        df[col].fillna(median, inplace=True)\n",
    "\n",
    "for col in has_cols:\n",
    "    df[col].fillna(False, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Summary of Data and Save\n",
    "\n",
    "I ended up with a single cleaned dataframe with 1.1 million entries and 353 features of numeric type (except for the review text data, which will be turned into a numeric feature in the machine learning analysis). I saved the dataframe as the csv `df_clean.csv` for later use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1166164 entries, 0 to 1166163\n",
      "Columns: 353 entries, accommodates to reviews\n",
      "dtypes: bool(259), float32(5), float64(16), int64(1), object(1), uint8(71)\n",
      "memory usage: 558.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "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>accommodates</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>cleaning_fee_USD</th>\n",
       "      <th>experiences_offered</th>\n",
       "      <th>extra_people_USD</th>\n",
       "      <th>guests_included</th>\n",
       "      <th>host_acceptance_rate_percentage</th>\n",
       "      <th>host_has_profile_pic</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>host_response_rate_percentage</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>id</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>is_business_travel_ready</th>\n",
       "      <th>is_location_exact</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>maximum_nights</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>price_USD</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>requires_license</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>square_feet</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>has__toilet</th>\n",
       "      <th>has_24-hour_check-in</th>\n",
       "      <th>has_accessible-height_bed</th>\n",
       "      <th>has_accessible-height_toilet</th>\n",
       "      <th>has_air_conditioning</th>\n",
       "      <th>has_air_purifier</th>\n",
       "      <th>has_alfresco_bathtub</th>\n",
       "      <th>has_alfresco_shower</th>\n",
       "      <th>has_amazon_echo</th>\n",
       "      <th>has_apple_tv</th>\n",
       "      <th>has_baby_bath</th>\n",
       "      <th>has_baby_monitor</th>\n",
       "      <th>has_babysitter_recommendations</th>\n",
       "      <th>has_balcony</th>\n",
       "      <th>has_bath_towel</th>\n",
       "      <th>has_bathroom_essentials</th>\n",
       "      <th>has_bathtub</th>\n",
       "      <th>has_bathtub_with_bath_chair</th>\n",
       "      <th>has_bbq_grill</th>\n",
       "      <th>has_beach_chairs</th>\n",
       "      <th>has_beach_essentials</th>\n",
       "      <th>has_beach_view</th>\n",
       "      <th>has_beachfront</th>\n",
       "      <th>has_bed_linens</th>\n",
       "      <th>has_bedroom_comforts</th>\n",
       "      <th>has_bidet</th>\n",
       "      <th>has_body_soap</th>\n",
       "      <th>has_breakfast</th>\n",
       "      <th>has_breakfast_bar</th>\n",
       "      <th>has_breakfast_table</th>\n",
       "      <th>has_brick_oven</th>\n",
       "      <th>has_building_staff</th>\n",
       "      <th>has_buzzer/wireless_intercom</th>\n",
       "      <th>has_cable_tv</th>\n",
       "      <th>has_carbon_monoxide_detector</th>\n",
       "      <th>has_cat(s)</th>\n",
       "      <th>has_ceiling_fans</th>\n",
       "      <th>has_ceiling_fanss</th>\n",
       "      <th>has_ceiling_hoist</th>\n",
       "      <th>has_central_air_conditioning</th>\n",
       "      <th>has_changing_table</th>\n",
       "      <th>has_chef's_kitchen</th>\n",
       "      <th>has_children’s_books_and_toys</th>\n",
       "      <th>has_children’s_dinnerware</th>\n",
       "      <th>has_cleaning_before_checkout</th>\n",
       "      <th>has_coffee_maker</th>\n",
       "      <th>has_computer</th>\n",
       "      <th>has_convection_oven</th>\n",
       "      <th>has_cooking_basics</th>\n",
       "      <th>has_crib</th>\n",
       "      <th>has_day_bed</th>\n",
       "      <th>has_decorative_fireplace</th>\n",
       "      <th>has_dining_area</th>\n",
       "      <th>has_disabled_parking_spot</th>\n",
       "      <th>has_dishes_and_silverware</th>\n",
       "      <th>has_dishwasher</th>\n",
       "      <th>has_dog(s)</th>\n",
       "      <th>has_doorman</th>\n",
       "      <th>has_double_oven</th>\n",
       "      <th>has_dryer</th>\n",
       "      <th>has_dvd_player</th>\n",
       "      <th>has_electric_profiling_bed</th>\n",
       "      <th>has_elevator</th>\n",
       "      <th>has_en_suite_bathroom</th>\n",
       "      <th>has_espresso_machine</th>\n",
       "      <th>has_essentials</th>\n",
       "      <th>has_ethernet_connection</th>\n",
       "      <th>has_ev_charger</th>\n",
       "      <th>has_exercise_equipment</th>\n",
       "      <th>has_extra_pillows_and_blankets</th>\n",
       "      <th>has_family/kid_friendly</th>\n",
       "      <th>has_fax_machine</th>\n",
       "      <th>has_fire_extinguisher</th>\n",
       "      <th>has_fire_pit</th>\n",
       "      <th>has_fireplace_guards</th>\n",
       "      <th>has_firm_mattress</th>\n",
       "      <th>has_first_aid_kit</th>\n",
       "      <th>has_fixed_grab_bars_for_shower</th>\n",
       "      <th>has_fixed_grab_bars_for_toilet</th>\n",
       "      <th>has_flat_path_to_front_door</th>\n",
       "      <th>has_foosball_table</th>\n",
       "      <th>has_formal_dining_area</th>\n",
       "      <th>has_free_parking_on_premises</th>\n",
       "      <th>has_free_parking_on_street</th>\n",
       "      <th>has_free_street_parking</th>\n",
       "      <th>has_front_desk/doorperson</th>\n",
       "      <th>has_full_kitchen</th>\n",
       "      <th>has_game_console</th>\n",
       "      <th>has_games</th>\n",
       "      <th>has_garage_parking</th>\n",
       "      <th>has_garden</th>\n",
       "      <th>has_garden_or_backyard</th>\n",
       "      <th>has_gas_fireplace</th>\n",
       "      <th>has_gas_grill</th>\n",
       "      <th>has_gas_oven</th>\n",
       "      <th>has_gated_property</th>\n",
       "      <th>has_ground_floor_access</th>\n",
       "      <th>has_gym</th>\n",
       "      <th>has_hair_dryer</th>\n",
       "      <th>has_hammock</th>\n",
       "      <th>has_handheld_shower_head</th>\n",
       "      <th>has_hangers</th>\n",
       "      <th>has_hbo_go</th>\n",
       "      <th>has_heat_lamps</th>\n",
       "      <th>has_heated_floors</th>\n",
       "      <th>has_heated_infinity_pool</th>\n",
       "      <th>has_heated_pool</th>\n",
       "      <th>has_heated_towel_rack</th>\n",
       "      <th>has_heating</th>\n",
       "      <th>has_high_chair</th>\n",
       "      <th>has_high-resolution_computer_monitor</th>\n",
       "      <th>has_home_theater</th>\n",
       "      <th>has_host_greets_you</th>\n",
       "      <th>has_hot_tub</th>\n",
       "      <th>has_hot_water</th>\n",
       "      <th>has_hot_water_kettle</th>\n",
       "      <th>has_ice_machine</th>\n",
       "      <th>has_indoor_fireplace</th>\n",
       "      <th>has_infinity_pool</th>\n",
       "      <th>has_internet</th>\n",
       "      <th>has_iron</th>\n",
       "      <th>has_ironing_board</th>\n",
       "      <th>has_jetted_tub</th>\n",
       "      <th>has_keypad</th>\n",
       "      <th>has_kitchen</th>\n",
       "      <th>has_kitchenette</th>\n",
       "      <th>has_lake_access</th>\n",
       "      <th>has_lap_pool</th>\n",
       "      <th>has_laptop_friendly_workspace</th>\n",
       "      <th>has_library</th>\n",
       "      <th>has_lock_on_bedroom_door</th>\n",
       "      <th>has_lockbox</th>\n",
       "      <th>has_long_term_stays_allowed</th>\n",
       "      <th>has_lounge_area</th>\n",
       "      <th>has_lounge_chairs</th>\n",
       "      <th>has_luggage_dropoff_allowed</th>\n",
       "      <th>has_media_room</th>\n",
       "      <th>has_memory_foam_mattress</th>\n",
       "      <th>has_microwave</th>\n",
       "      <th>has_mini_fridge</th>\n",
       "      <th>has_misting_system</th>\n",
       "      <th>has_mobile_hoist</th>\n",
       "      <th>has_mountain_view</th>\n",
       "      <th>has_mudroom</th>\n",
       "      <th>has_murphy_bed</th>\n",
       "      <th>has_nan</th>\n",
       "      <th>has_natural_gas_barbeque</th>\n",
       "      <th>has_nespresso_machine</th>\n",
       "      <th>has_netflix</th>\n",
       "      <th>has_office</th>\n",
       "      <th>has_other</th>\n",
       "      <th>has_other_pet(s)</th>\n",
       "      <th>has_outdoor_kitchen</th>\n",
       "      <th>has_outdoor_parking</th>\n",
       "      <th>has_outdoor_seating</th>\n",
       "      <th>has_outlet_covers</th>\n",
       "      <th>has_oven</th>\n",
       "      <th>has_pack_’n_play/travel_crib</th>\n",
       "      <th>has_paid_parking_off_premises</th>\n",
       "      <th>has_paid_parking_on_premises</th>\n",
       "      <th>has_parking</th>\n",
       "      <th>has_patio_or_balcony</th>\n",
       "      <th>has_pets_allowed</th>\n",
       "      <th>has_pets_live_on_this_property</th>\n",
       "      <th>has_piano</th>\n",
       "      <th>has_pillow-top_mattress</th>\n",
       "      <th>has_ping_pong_table</th>\n",
       "      <th>has_play_room</th>\n",
       "      <th>has_pocket_wifi</th>\n",
       "      <th>has_pond</th>\n",
       "      <th>has_pool</th>\n",
       "      <th>has_pool_cover</th>\n",
       "      <th>has_pool_house</th>\n",
       "      <th>has_pool_table</th>\n",
       "      <th>has_pool_toys</th>\n",
       "      <th>has_pool_with_pool_hoist</th>\n",
       "      <th>has_printer</th>\n",
       "      <th>has_private_bathroom</th>\n",
       "      <th>has_private_entrance</th>\n",
       "      <th>has_private_gym</th>\n",
       "      <th>has_private_hot_tub</th>\n",
       "      <th>has_private_living_room</th>\n",
       "      <th>has_private_pool</th>\n",
       "      <th>has_projector</th>\n",
       "      <th>has_projector_and_screen</th>\n",
       "      <th>has_propane_barbeque</th>\n",
       "      <th>has_putting_green</th>\n",
       "      <th>has_rain_shower</th>\n",
       "      <th>has_refrigerator</th>\n",
       "      <th>has_roll-in_shower</th>\n",
       "      <th>has_roll-in_shower_with_chair</th>\n",
       "      <th>has_room-darkening_shades</th>\n",
       "      <th>has_safety_card</th>\n",
       "      <th>has_satellite_tv</th>\n",
       "      <th>has_sauna</th>\n",
       "      <th>has_security_cameras</th>\n",
       "      <th>has_security_system</th>\n",
       "      <th>has_self_check-in</th>\n",
       "      <th>has_shampoo</th>\n",
       "      <th>has_shared_gym</th>\n",
       "      <th>has_shared_hot_tub</th>\n",
       "      <th>has_shared_pool</th>\n",
       "      <th>has_shower_chair</th>\n",
       "      <th>has_single_level_home</th>\n",
       "      <th>has_ski-in/ski-out</th>\n",
       "      <th>has_smart_tv</th>\n",
       "      <th>has_smartlock</th>\n",
       "      <th>has_smoke_detector</th>\n",
       "      <th>has_smoking_allowed</th>\n",
       "      <th>has_soaking_tub</th>\n",
       "      <th>has_sonos_sound_system</th>\n",
       "      <th>has_sound_system</th>\n",
       "      <th>has_stair_gates</th>\n",
       "      <th>has_stand_alone_steam_shower</th>\n",
       "      <th>has_standing_valet</th>\n",
       "      <th>has_steam_oven</th>\n",
       "      <th>has_step-free_access</th>\n",
       "      <th>has_stove</th>\n",
       "      <th>has_suitable_for_events</th>\n",
       "      <th>has_sun_deck</th>\n",
       "      <th>has_sun_loungers</th>\n",
       "      <th>has_swimming_pool</th>\n",
       "      <th>has_table_corner_guards</th>\n",
       "      <th>has_tennis_court</th>\n",
       "      <th>has_terrace</th>\n",
       "      <th>has_toaster</th>\n",
       "      <th>has_toilet_paper</th>\n",
       "      <th>has_touchless_faucets</th>\n",
       "      <th>has_tv</th>\n",
       "      <th>has_video_games</th>\n",
       "      <th>has_walk-in_shower</th>\n",
       "      <th>has_warming_drawer</th>\n",
       "      <th>has_washer</th>\n",
       "      <th>has_washer_/_dryer</th>\n",
       "      <th>has_waterfront</th>\n",
       "      <th>has_well-lit_path_to_entrance</th>\n",
       "      <th>has_wet_bar</th>\n",
       "      <th>has_wheelchair_accessible</th>\n",
       "      <th>has_wide_clearance_to_bed</th>\n",
       "      <th>has_wide_clearance_to_shower</th>\n",
       "      <th>has_wide_doorway</th>\n",
       "      <th>has_wide_entryway</th>\n",
       "      <th>has_wide_hallway_clearance</th>\n",
       "      <th>has_wifi</th>\n",
       "      <th>has_window_guards</th>\n",
       "      <th>has_wine_cellar</th>\n",
       "      <th>has_wine_cooler</th>\n",
       "      <th>has_wine_storage</th>\n",
       "      <th>has_wireless_internet</th>\n",
       "      <th>bed_type_couch</th>\n",
       "      <th>bed_type_futon</th>\n",
       "      <th>bed_type_pull-out sofa</th>\n",
       "      <th>bed_type_real bed</th>\n",
       "      <th>host_response_time_nan</th>\n",
       "      <th>host_response_time_within a day</th>\n",
       "      <th>host_response_time_within a few hours</th>\n",
       "      <th>host_response_time_within an hour</th>\n",
       "      <th>property_type_apartment</th>\n",
       "      <th>property_type_barn</th>\n",
       "      <th>property_type_bed and breakfast</th>\n",
       "      <th>property_type_boat</th>\n",
       "      <th>property_type_boutique hotel</th>\n",
       "      <th>property_type_bungalow</th>\n",
       "      <th>property_type_bus</th>\n",
       "      <th>property_type_cabin</th>\n",
       "      <th>property_type_camper/rv</th>\n",
       "      <th>property_type_campsite</th>\n",
       "      <th>property_type_casa particular</th>\n",
       "      <th>property_type_castle</th>\n",
       "      <th>property_type_cave</th>\n",
       "      <th>property_type_chalet</th>\n",
       "      <th>property_type_condominium</th>\n",
       "      <th>property_type_cottage</th>\n",
       "      <th>property_type_dome house</th>\n",
       "      <th>property_type_dorm</th>\n",
       "      <th>property_type_earth house</th>\n",
       "      <th>property_type_entire floor</th>\n",
       "      <th>property_type_farm stay</th>\n",
       "      <th>property_type_guest suite</th>\n",
       "      <th>property_type_guesthouse</th>\n",
       "      <th>property_type_hostel</th>\n",
       "      <th>property_type_hotel</th>\n",
       "      <th>property_type_house</th>\n",
       "      <th>property_type_houseboat</th>\n",
       "      <th>property_type_hut</th>\n",
       "      <th>property_type_in-law</th>\n",
       "      <th>property_type_island</th>\n",
       "      <th>property_type_lighthouse</th>\n",
       "      <th>property_type_loft</th>\n",
       "      <th>property_type_minsu (taiwan)</th>\n",
       "      <th>property_type_nan</th>\n",
       "      <th>property_type_nature lodge</th>\n",
       "      <th>property_type_other</th>\n",
       "      <th>property_type_parking space</th>\n",
       "      <th>property_type_plane</th>\n",
       "      <th>property_type_resort</th>\n",
       "      <th>property_type_serviced apartment</th>\n",
       "      <th>property_type_tent</th>\n",
       "      <th>property_type_timeshare</th>\n",
       "      <th>property_type_tiny house</th>\n",
       "      <th>property_type_tipi</th>\n",
       "      <th>property_type_townhouse</th>\n",
       "      <th>property_type_train</th>\n",
       "      <th>property_type_treehouse</th>\n",
       "      <th>property_type_vacation home</th>\n",
       "      <th>property_type_villa</th>\n",
       "      <th>property_type_yurt</th>\n",
       "      <th>room_type_nan</th>\n",
       "      <th>room_type_private room</th>\n",
       "      <th>room_type_shared room</th>\n",
       "      <th>cancellation_policy_luxury_moderate</th>\n",
       "      <th>cancellation_policy_luxury_no_refund</th>\n",
       "      <th>cancellation_policy_luxury_super_strict_95</th>\n",
       "      <th>cancellation_policy_moderate</th>\n",
       "      <th>cancellation_policy_nan</th>\n",
       "      <th>cancellation_policy_no_refunds</th>\n",
       "      <th>cancellation_policy_strict</th>\n",
       "      <th>cancellation_policy_strict_14_with_grace_period</th>\n",
       "      <th>cancellation_policy_super_strict_30</th>\n",
       "      <th>cancellation_policy_super_strict_60</th>\n",
       "      <th>reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>False</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3890624.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>34.132812</td>\n",
       "      <td>-118.025147</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.19</td>\n",
       "      <td>700.0</td>\n",
       "      <td>91006.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[Very friendly host and nice room.  Really enj...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   accommodates  availability_365  bathrooms  bedrooms  beds  \\\n",
       "0           2.0             365.0        1.0       1.0   1.0   \n",
       "\n",
       "   cleaning_fee_USD  experiences_offered  extra_people_USD  guests_included  \\\n",
       "0              10.0                False              20.0                1   \n",
       "\n",
       "   host_acceptance_rate_percentage  host_has_profile_pic  \\\n",
       "0                            100.0                  True   \n",
       "\n",
       "   host_identity_verified  host_is_superhost  host_response_rate_percentage  \\\n",
       "0                   False              False                          100.0   \n",
       "\n",
       "   host_total_listings_count         id  instant_bookable  \\\n",
       "0                        1.0  3890624.0             False   \n",
       "\n",
       "   is_business_travel_ready  is_location_exact   latitude   longitude  \\\n",
       "0                     False               True  34.132812 -118.025147   \n",
       "\n",
       "   maximum_nights  minimum_nights  number_of_reviews  price_USD  \\\n",
       "0          1125.0             1.0                2.0       90.0   \n",
       "\n",
       "   require_guest_phone_verification  require_guest_profile_picture  \\\n",
       "0                             False                          False   \n",
       "\n",
       "   requires_license  review_scores_rating  reviews_per_month  square_feet  \\\n",
       "0             False                  90.0               0.19        700.0   \n",
       "\n",
       "   zipcode  has__toilet  has_24-hour_check-in  has_accessible-height_bed  \\\n",
       "0  91006.0        False                 False                      False   \n",
       "\n",
       "   has_accessible-height_toilet  has_air_conditioning  has_air_purifier  \\\n",
       "0                         False                  True             False   \n",
       "\n",
       "   has_alfresco_bathtub  has_alfresco_shower  has_amazon_echo  has_apple_tv  \\\n",
       "0                 False                False            False         False   \n",
       "\n",
       "   has_baby_bath  has_baby_monitor  has_babysitter_recommendations  \\\n",
       "0          False             False                           False   \n",
       "\n",
       "   has_balcony  has_bath_towel  has_bathroom_essentials  has_bathtub  \\\n",
       "0        False           False                    False        False   \n",
       "\n",
       "   has_bathtub_with_bath_chair  has_bbq_grill  has_beach_chairs  \\\n",
       "0                        False          False             False   \n",
       "\n",
       "   has_beach_essentials  has_beach_view  has_beachfront  has_bed_linens  \\\n",
       "0                 False           False           False           False   \n",
       "\n",
       "   has_bedroom_comforts  has_bidet  has_body_soap  has_breakfast  \\\n",
       "0                 False      False          False          False   \n",
       "\n",
       "   has_breakfast_bar  has_breakfast_table  has_brick_oven  has_building_staff  \\\n",
       "0              False                False           False               False   \n",
       "\n",
       "   has_buzzer/wireless_intercom  has_cable_tv  has_carbon_monoxide_detector  \\\n",
       "0                         False         False                          True   \n",
       "\n",
       "   has_cat(s)  has_ceiling_fans  has_ceiling_fanss  has_ceiling_hoist  \\\n",
       "0       False             False              False              False   \n",
       "\n",
       "   has_central_air_conditioning  has_changing_table  has_chef's_kitchen  \\\n",
       "0                         False               False               False   \n",
       "\n",
       "   has_children’s_books_and_toys  has_children’s_dinnerware  \\\n",
       "0                          False                      False   \n",
       "\n",
       "   has_cleaning_before_checkout  has_coffee_maker  has_computer  \\\n",
       "0                         False             False         False   \n",
       "\n",
       "   has_convection_oven  has_cooking_basics  has_crib  has_day_bed  \\\n",
       "0                False               False     False        False   \n",
       "\n",
       "   has_decorative_fireplace  has_dining_area  has_disabled_parking_spot  \\\n",
       "0                     False            False                      False   \n",
       "\n",
       "   has_dishes_and_silverware  has_dishwasher  has_dog(s)  has_doorman  \\\n",
       "0                      False           False       False        False   \n",
       "\n",
       "   has_double_oven  has_dryer  has_dvd_player  has_electric_profiling_bed  \\\n",
       "0            False       True           False                       False   \n",
       "\n",
       "   has_elevator  has_en_suite_bathroom  has_espresso_machine  has_essentials  \\\n",
       "0         False                  False                 False            True   \n",
       "\n",
       "   has_ethernet_connection  has_ev_charger  has_exercise_equipment  \\\n",
       "0                    False           False                   False   \n",
       "\n",
       "   has_extra_pillows_and_blankets  has_family/kid_friendly  has_fax_machine  \\\n",
       "0                           False                    False            False   \n",
       "\n",
       "   has_fire_extinguisher  has_fire_pit  has_fireplace_guards  \\\n",
       "0                   True         False                 False   \n",
       "\n",
       "   has_firm_mattress  has_first_aid_kit  has_fixed_grab_bars_for_shower  \\\n",
       "0              False               True                           False   \n",
       "\n",
       "   has_fixed_grab_bars_for_toilet  has_flat_path_to_front_door  \\\n",
       "0                           False                        False   \n",
       "\n",
       "   has_foosball_table  has_formal_dining_area  has_free_parking_on_premises  \\\n",
       "0               False                   False                         False   \n",
       "\n",
       "   has_free_parking_on_street  has_free_street_parking  \\\n",
       "0                       False                    False   \n",
       "\n",
       "   has_front_desk/doorperson  has_full_kitchen  has_game_console  has_games  \\\n",
       "0                      False             False             False      False   \n",
       "\n",
       "   has_garage_parking  has_garden  has_garden_or_backyard  has_gas_fireplace  \\\n",
       "0               False       False                   False              False   \n",
       "\n",
       "   has_gas_grill  has_gas_oven  has_gated_property  has_ground_floor_access  \\\n",
       "0          False         False               False                    False   \n",
       "\n",
       "   has_gym  has_hair_dryer  has_hammock  has_handheld_shower_head  \\\n",
       "0    False           False        False                     False   \n",
       "\n",
       "   has_hangers  has_hbo_go  has_heat_lamps  has_heated_floors  \\\n",
       "0        False       False           False              False   \n",
       "\n",
       "   has_heated_infinity_pool  has_heated_pool  has_heated_towel_rack  \\\n",
       "0                     False            False                  False   \n",
       "\n",
       "   has_heating  has_high_chair  has_high-resolution_computer_monitor  \\\n",
       "0         True           False                                 False   \n",
       "\n",
       "   has_home_theater  has_host_greets_you  has_hot_tub  has_hot_water  \\\n",
       "0             False                False         True          False   \n",
       "\n",
       "   has_hot_water_kettle  has_ice_machine  has_indoor_fireplace  \\\n",
       "0                 False            False                  True   \n",
       "\n",
       "   has_infinity_pool  has_internet  has_iron  has_ironing_board  \\\n",
       "0              False          True     False              False   \n",
       "\n",
       "   has_jetted_tub  has_keypad  has_kitchen  has_kitchenette  has_lake_access  \\\n",
       "0           False       False        False            False            False   \n",
       "\n",
       "   has_lap_pool  has_laptop_friendly_workspace  has_library  \\\n",
       "0         False                          False        False   \n",
       "\n",
       "   has_lock_on_bedroom_door  has_lockbox  has_long_term_stays_allowed  \\\n",
       "0                     False        False                        False   \n",
       "\n",
       "   has_lounge_area  has_lounge_chairs  has_luggage_dropoff_allowed  \\\n",
       "0            False              False                        False   \n",
       "\n",
       "   has_media_room  has_memory_foam_mattress  has_microwave  has_mini_fridge  \\\n",
       "0           False                     False          False            False   \n",
       "\n",
       "   has_misting_system  has_mobile_hoist  has_mountain_view  has_mudroom  \\\n",
       "0               False             False              False        False   \n",
       "\n",
       "   has_murphy_bed  has_nan  has_natural_gas_barbeque  has_nespresso_machine  \\\n",
       "0           False    False                     False                  False   \n",
       "\n",
       "   has_netflix  has_office  has_other  has_other_pet(s)  has_outdoor_kitchen  \\\n",
       "0        False       False      False             False                False   \n",
       "\n",
       "   has_outdoor_parking  has_outdoor_seating  has_outlet_covers  has_oven  \\\n",
       "0                False                False              False     False   \n",
       "\n",
       "   has_pack_’n_play/travel_crib  has_paid_parking_off_premises  \\\n",
       "0                         False                          False   \n",
       "\n",
       "   has_paid_parking_on_premises  has_parking  has_patio_or_balcony  \\\n",
       "0                         False        False                 False   \n",
       "\n",
       "   has_pets_allowed  has_pets_live_on_this_property  has_piano  \\\n",
       "0             False                           False      False   \n",
       "\n",
       "   has_pillow-top_mattress  has_ping_pong_table  has_play_room  \\\n",
       "0                    False                False          False   \n",
       "\n",
       "   has_pocket_wifi  has_pond  has_pool  has_pool_cover  has_pool_house  \\\n",
       "0            False     False     False           False           False   \n",
       "\n",
       "   has_pool_table  has_pool_toys  has_pool_with_pool_hoist  has_printer  \\\n",
       "0           False          False                     False        False   \n",
       "\n",
       "   has_private_bathroom  has_private_entrance  has_private_gym  \\\n",
       "0                 False                 False            False   \n",
       "\n",
       "   has_private_hot_tub  has_private_living_room  has_private_pool  \\\n",
       "0                False                    False             False   \n",
       "\n",
       "   has_projector  has_projector_and_screen  has_propane_barbeque  \\\n",
       "0          False                     False                 False   \n",
       "\n",
       "   has_putting_green  has_rain_shower  has_refrigerator  has_roll-in_shower  \\\n",
       "0              False            False             False               False   \n",
       "\n",
       "   has_roll-in_shower_with_chair  has_room-darkening_shades  has_safety_card  \\\n",
       "0                          False                      False            False   \n",
       "\n",
       "   has_satellite_tv  has_sauna  has_security_cameras  has_security_system  \\\n",
       "0             False      False                 False                False   \n",
       "\n",
       "   has_self_check-in  has_shampoo  has_shared_gym  has_shared_hot_tub  \\\n",
       "0              False         True           False               False   \n",
       "\n",
       "   has_shared_pool  has_shower_chair  has_single_level_home  \\\n",
       "0            False             False                  False   \n",
       "\n",
       "   has_ski-in/ski-out  has_smart_tv  has_smartlock  has_smoke_detector  \\\n",
       "0               False         False          False                True   \n",
       "\n",
       "   has_smoking_allowed  has_soaking_tub  has_sonos_sound_system  \\\n",
       "0                False            False                   False   \n",
       "\n",
       "   has_sound_system  has_stair_gates  has_stand_alone_steam_shower  \\\n",
       "0             False            False                         False   \n",
       "\n",
       "   has_standing_valet  has_steam_oven  has_step-free_access  has_stove  \\\n",
       "0               False           False                 False      False   \n",
       "\n",
       "   has_suitable_for_events  has_sun_deck  has_sun_loungers  has_swimming_pool  \\\n",
       "0                    False         False             False              False   \n",
       "\n",
       "   has_table_corner_guards  has_tennis_court  has_terrace  has_toaster  \\\n",
       "0                    False             False        False        False   \n",
       "\n",
       "   has_toilet_paper  has_touchless_faucets  has_tv  has_video_games  \\\n",
       "0             False                  False   False            False   \n",
       "\n",
       "   has_walk-in_shower  has_warming_drawer  has_washer  has_washer_/_dryer  \\\n",
       "0               False               False        True               False   \n",
       "\n",
       "   has_waterfront  has_well-lit_path_to_entrance  has_wet_bar  \\\n",
       "0           False                          False        False   \n",
       "\n",
       "   has_wheelchair_accessible  has_wide_clearance_to_bed  \\\n",
       "0                      False                      False   \n",
       "\n",
       "   has_wide_clearance_to_shower  has_wide_doorway  has_wide_entryway  \\\n",
       "0                         False             False              False   \n",
       "\n",
       "   has_wide_hallway_clearance  has_wifi  has_window_guards  has_wine_cellar  \\\n",
       "0                       False     False              False            False   \n",
       "\n",
       "   has_wine_cooler  has_wine_storage  has_wireless_internet  bed_type_couch  \\\n",
       "0            False             False                   True               0   \n",
       "\n",
       "   bed_type_futon  bed_type_pull-out sofa  bed_type_real bed  \\\n",
       "0               0                       0                  1   \n",
       "\n",
       "   host_response_time_nan  host_response_time_within a day  \\\n",
       "0                       0                                1   \n",
       "\n",
       "   host_response_time_within a few hours  host_response_time_within an hour  \\\n",
       "0                                      0                                  0   \n",
       "\n",
       "   property_type_apartment  property_type_barn  \\\n",
       "0                        0                   0   \n",
       "\n",
       "   property_type_bed and breakfast  property_type_boat  \\\n",
       "0                                0                   0   \n",
       "\n",
       "   property_type_boutique hotel  property_type_bungalow  property_type_bus  \\\n",
       "0                             0                       0                  0   \n",
       "\n",
       "   property_type_cabin  property_type_camper/rv  property_type_campsite  \\\n",
       "0                    0                        0                       0   \n",
       "\n",
       "   property_type_casa particular  property_type_castle  property_type_cave  \\\n",
       "0                              0                     0                   0   \n",
       "\n",
       "   property_type_chalet  property_type_condominium  property_type_cottage  \\\n",
       "0                     0                          0                      0   \n",
       "\n",
       "   property_type_dome house  property_type_dorm  property_type_earth house  \\\n",
       "0                         0                   0                          0   \n",
       "\n",
       "   property_type_entire floor  property_type_farm stay  \\\n",
       "0                           0                        0   \n",
       "\n",
       "   property_type_guest suite  property_type_guesthouse  property_type_hostel  \\\n",
       "0                          0                         0                     0   \n",
       "\n",
       "   property_type_hotel  property_type_house  property_type_houseboat  \\\n",
       "0                    0                    1                        0   \n",
       "\n",
       "   property_type_hut  property_type_in-law  property_type_island  \\\n",
       "0                  0                     0                     0   \n",
       "\n",
       "   property_type_lighthouse  property_type_loft  property_type_minsu (taiwan)  \\\n",
       "0                         0                   0                             0   \n",
       "\n",
       "   property_type_nan  property_type_nature lodge  property_type_other  \\\n",
       "0                  0                           0                    0   \n",
       "\n",
       "   property_type_parking space  property_type_plane  property_type_resort  \\\n",
       "0                            0                    0                     0   \n",
       "\n",
       "   property_type_serviced apartment  property_type_tent  \\\n",
       "0                                 0                   0   \n",
       "\n",
       "   property_type_timeshare  property_type_tiny house  property_type_tipi  \\\n",
       "0                        0                         0                   0   \n",
       "\n",
       "   property_type_townhouse  property_type_train  property_type_treehouse  \\\n",
       "0                        0                    0                        0   \n",
       "\n",
       "   property_type_vacation home  property_type_villa  property_type_yurt  \\\n",
       "0                            0                    0                   0   \n",
       "\n",
       "   room_type_nan  room_type_private room  room_type_shared room  \\\n",
       "0              0                       1                      0   \n",
       "\n",
       "   cancellation_policy_luxury_moderate  cancellation_policy_luxury_no_refund  \\\n",
       "0                                    0                                     0   \n",
       "\n",
       "   cancellation_policy_luxury_super_strict_95  cancellation_policy_moderate  \\\n",
       "0                                           0                             1   \n",
       "\n",
       "   cancellation_policy_nan  cancellation_policy_no_refunds  \\\n",
       "0                        0                               0   \n",
       "\n",
       "   cancellation_policy_strict  \\\n",
       "0                           0   \n",
       "\n",
       "   cancellation_policy_strict_14_with_grace_period  \\\n",
       "0                                                0   \n",
       "\n",
       "   cancellation_policy_super_strict_30  cancellation_policy_super_strict_60  \\\n",
       "0                                    0                                    0   \n",
       "\n",
       "                                             reviews  \n",
       "0  [Very friendly host and nice room.  Really enj...  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/limesncoconuts2/datasets/airbnb/df_clean.csv'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = 'df_clean.csv'\n",
    "current_dir = os.getcwd() + '/' + filename\n",
    "df.to_csv(filename, index=False)\n",
    "shutil.move(os.path.join(current_dir), os.path.join(destination, filename))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}