Data Mining Challenge.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "import math\n",
    "import scipy\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 33538 rows and 64 columns in the dataset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3020: DtypeWarning: Columns (30) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "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>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>...</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</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>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22267382</td>\n",
       "      <td>Modern and Cozy Large Studio in Brooklyn</td>\n",
       "      <td>Modern large studio with new amenities and app...</td>\n",
       "      <td>Our place is a little quiet sanctuary in the h...</td>\n",
       "      <td>Modern large studio with new amenities and app...</td>\n",
       "      <td>none</td>\n",
       "      <td>BAM, Barclays, Brooklyn City Point, Fort Green...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Subway: 2,3,4,5,A,C,B,Q,G</td>\n",
       "      <td>Washer/Dryer Dishwasher Internet Gym Roof Top ...</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>flexible</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2473861</td>\n",
       "      <td>Royal Harlem TRIPLEX Home 5 Beds</td>\n",
       "      <td>Harlem is back and so gorgeous! Visit and expl...</td>\n",
       "      <td>Harlem is back and so gorgeous! Visit and expl...</td>\n",
       "      <td>Harlem is back and so gorgeous! Visit and expl...</td>\n",
       "      <td>none</td>\n",
       "      <td>HARLEM is a piece of real NY history overflowi...</td>\n",
       "      <td>HARLEM RESTAURANTS Red Rooster Harlem -- excel...</td>\n",
       "      <td>PUBLIC TRANSPORTATION: Conveniently near all p...</td>\n",
       "      <td>The WHOLE ENTIRE HOUSE</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>3</td>\n",
       "      <td>2.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25079703</td>\n",
       "      <td>Sunny East Village Studio</td>\n",
       "      <td>Clean, hip and well designed sun drenched East...</td>\n",
       "      <td>This is a rare East Village studio with it's h...</td>\n",
       "      <td>Clean, hip and well designed sun drenched East...</td>\n",
       "      <td>none</td>\n",
       "      <td>East Village is one of the last remaining neig...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>You'll have access to the entire space - it's ...</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</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>0.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id                                      name  \\\n",
       "0  22267382  Modern and Cozy Large Studio in Brooklyn   \n",
       "1   2473861          Royal Harlem TRIPLEX Home 5 Beds   \n",
       "2  25079703                 Sunny East Village Studio   \n",
       "\n",
       "                                             summary  \\\n",
       "0  Modern large studio with new amenities and app...   \n",
       "1  Harlem is back and so gorgeous! Visit and expl...   \n",
       "2  Clean, hip and well designed sun drenched East...   \n",
       "\n",
       "                                               space  \\\n",
       "0  Our place is a little quiet sanctuary in the h...   \n",
       "1  Harlem is back and so gorgeous! Visit and expl...   \n",
       "2  This is a rare East Village studio with it's h...   \n",
       "\n",
       "                                         description experiences_offered  \\\n",
       "0  Modern large studio with new amenities and app...                none   \n",
       "1  Harlem is back and so gorgeous! Visit and expl...                none   \n",
       "2  Clean, hip and well designed sun drenched East...                none   \n",
       "\n",
       "                               neighborhood_overview  \\\n",
       "0  BAM, Barclays, Brooklyn City Point, Fort Green...   \n",
       "1  HARLEM is a piece of real NY history overflowi...   \n",
       "2  East Village is one of the last remaining neig...   \n",
       "\n",
       "                                               notes  \\\n",
       "0                                                NaN   \n",
       "1  HARLEM RESTAURANTS Red Rooster Harlem -- excel...   \n",
       "2                                                NaN   \n",
       "\n",
       "                                             transit  \\\n",
       "0                          Subway: 2,3,4,5,A,C,B,Q,G   \n",
       "1  PUBLIC TRANSPORTATION: Conveniently near all p...   \n",
       "2                                                NaN   \n",
       "\n",
       "                                              access        ...          \\\n",
       "0  Washer/Dryer Dishwasher Internet Gym Roof Top ...        ...           \n",
       "1                             The WHOLE ENTIRE HOUSE        ...           \n",
       "2  You'll have access to the entire space - it's ...        ...           \n",
       "\n",
       "  review_scores_communication review_scores_location  review_scores_value  \\\n",
       "0                        10.0                   10.0                 10.0   \n",
       "1                         9.0                    9.0                  9.0   \n",
       "2                        10.0                   10.0                 10.0   \n",
       "\n",
       "  instant_bookable is_business_travel_ready cancellation_policy  \\\n",
       "0                t                        f            flexible   \n",
       "1                t                        f            moderate   \n",
       "2                f                        f            moderate   \n",
       "\n",
       "  require_guest_profile_picture require_guest_phone_verification  \\\n",
       "0                             f                                f   \n",
       "1                             f                                f   \n",
       "2                             f                                f   \n",
       "\n",
       "  calculated_host_listings_count  reviews_per_month  \n",
       "0                              1               0.59  \n",
       "1                              3               2.47  \n",
       "2                              1               0.89  \n",
       "\n",
       "[3 rows x 64 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('train.csv')\n",
    "updated_df = df.drop('price', axis=1)\n",
    "print ('There are {} rows and {} columns in the dataset'.format(*updated_df.shape))\n",
    "updated_df.head(3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         145.0\n",
       "1         175.0\n",
       "2         180.0\n",
       "3          42.0\n",
       "4          80.0\n",
       "5         550.0\n",
       "6         177.0\n",
       "7          70.0\n",
       "8         500.0\n",
       "9         106.0\n",
       "10        175.0\n",
       "11         95.0\n",
       "12         70.0\n",
       "13         59.0\n",
       "14        299.0\n",
       "15        295.0\n",
       "16         89.0\n",
       "17        285.0\n",
       "18         45.0\n",
       "19        119.0\n",
       "20        100.0\n",
       "21         47.0\n",
       "22         80.0\n",
       "23         95.0\n",
       "24        275.0\n",
       "25         70.0\n",
       "26        130.0\n",
       "27         57.0\n",
       "28         50.0\n",
       "29         80.0\n",
       "          ...  \n",
       "33508      98.0\n",
       "33509     100.0\n",
       "33510     200.0\n",
       "33511      60.0\n",
       "33512      75.0\n",
       "33513      35.0\n",
       "33514     105.0\n",
       "33515     109.0\n",
       "33516      60.0\n",
       "33517      75.0\n",
       "33518     160.0\n",
       "33519     200.0\n",
       "33520      87.0\n",
       "33521     125.0\n",
       "33522     105.0\n",
       "33523     995.0\n",
       "33524      89.0\n",
       "33525     135.0\n",
       "33526      95.0\n",
       "33527      42.0\n",
       "33528      85.0\n",
       "33529      65.0\n",
       "33530     140.0\n",
       "33531     265.0\n",
       "33532      75.0\n",
       "33533     110.0\n",
       "33534    1195.0\n",
       "33535      50.0\n",
       "33536      60.0\n",
       "33537     325.0\n",
       "Name: price, Length: 33538, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'name', 'summary', 'space', 'description', 'experiences_offered',\n",
       "       'neighborhood_overview', 'notes', 'transit', 'access', 'interaction',\n",
       "       'house_rules', 'host_id', 'host_name', 'host_since', 'host_location',\n",
       "       'host_about', 'host_response_time', 'host_response_rate',\n",
       "       'host_acceptance_rate', 'host_is_superhost', 'host_neighbourhood',\n",
       "       'host_listings_count', 'host_verifications', 'host_has_profile_pic',\n",
       "       'host_identity_verified', 'neighbourhood_cleansed',\n",
       "       'neighbourhood_group_cleansed', 'city', 'state', 'zipcode', 'market',\n",
       "       'country_code', 'country', 'property_type', 'room_type', 'accommodates',\n",
       "       'bathrooms', 'bedrooms', 'beds', 'bed_type', 'amenities', 'square_feet',\n",
       "       'price', 'guests_included', 'extra_people', 'minimum_nights',\n",
       "       'maximum_nights', 'number_of_reviews', 'first_review', 'last_review',\n",
       "       'review_scores_rating', 'review_scores_accuracy',\n",
       "       'review_scores_cleanliness', 'review_scores_checkin',\n",
       "       'review_scores_communication', 'review_scores_location',\n",
       "       'review_scores_value', 'instant_bookable', 'is_business_travel_ready',\n",
       "       'cancellation_policy', 'require_guest_profile_picture',\n",
       "       'require_guest_phone_verification', 'calculated_host_listings_count',\n",
       "       'reviews_per_month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py:3140: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self[k1] = value[k2]\n"
     ]
    }
   ],
   "source": [
    "# drop all the unnecessary columns\n",
    "feature_to_keep = ['id','description','access','property_type','room_type','accommodates','bathrooms',\n",
    "                  'bedrooms','bed_type','guests_included','extra_people','minimum_nights',\n",
    "                  'maximum_nights','cancellation_policy', 'neighbourhood_group_cleansed','host_is_superhost']\n",
    "\n",
    "new_df = updated_df[feature_to_keep]\n",
    "\n",
    "# remove the dollar sign before \"cleaning_fee\", \"extra_people\", \"price\" and change the datatype to numerical variables\n",
    "feature_to_remove_dollar = ['extra_people']\n",
    "new_df[feature_to_remove_dollar] = new_df[feature_to_remove_dollar].replace('\\$','',regex = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                  0\n",
       "description                       308\n",
       "access                          14234\n",
       "property_type                       0\n",
       "room_type                           0\n",
       "accommodates                        0\n",
       "bathrooms                          60\n",
       "bedrooms                           33\n",
       "bed_type                            0\n",
       "guests_included                     0\n",
       "extra_people                        0\n",
       "minimum_nights                      0\n",
       "maximum_nights                      0\n",
       "cancellation_policy                 0\n",
       "neighbourhood_group_cleansed        0\n",
       "host_is_superhost                   5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py:543: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "# drop 308 rows with missing descriptions\n",
    "new_df.loc[:,'description'] = new_df['description'].dropna()\n",
    "\n",
    "# fill NaN with median value for 'bathrooms', 'bedrooms','price'\n",
    "new_df.loc[:,'bathrooms'] = new_df.loc[:,'bathrooms'].fillna(new_df.loc[:,'bathrooms'].median())\n",
    "new_df.loc[:,'bedrooms'] = new_df.loc[:,'bedrooms'].fillna(new_df.loc[:,'bedrooms'].median())\n",
    "#new_df.loc[:, 'reviews_per_month'] = new_df.loc[:, 'reviews_per_month'].fillna(new_df.loc[:, 'reviews_per_month'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def guests(x):\n",
    "    if x == 1:\n",
    "        return 'one'\n",
    "    elif x == 2:\n",
    "        return 'two'\n",
    "    elif x == 3: \n",
    "        return 'three'\n",
    "    elif x == 4:\n",
    "        return 'four'\n",
    "    else:\n",
    "        return 'many'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "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>description</th>\n",
       "      <th>access</th>\n",
       "      <th>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bed_type</th>\n",
       "      <th>guests_included</th>\n",
       "      <th>extra_people</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "      <th>host_is_superhost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22267382</td>\n",
       "      <td>Modern large studio with new amenities and app...</td>\n",
       "      <td>Washer/Dryer Dishwasher Internet Gym Roof Top ...</td>\n",
       "      <td>Loft</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>two</td>\n",
       "      <td>30.00</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2473861</td>\n",
       "      <td>Harlem is back and so gorgeous! Visit and expl...</td>\n",
       "      <td>The WHOLE ENTIRE HOUSE</td>\n",
       "      <td>House</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>three</td>\n",
       "      <td>19.00</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25079703</td>\n",
       "      <td>Clean, hip and well designed sun drenched East...</td>\n",
       "      <td>You'll have access to the entire space - it's ...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1125</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9342478</td>\n",
       "      <td>Private, spacious, comfortable room in 2-bed f...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4866426</td>\n",
       "      <td>Comfy, quiet and big private room in a three b...</td>\n",
       "      <td>Your room has a very comfortable queen sized b...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19115443</td>\n",
       "      <td>Spacious in size, suites feature a fully equip...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30</td>\n",
       "      <td>1125</td>\n",
       "      <td>super_strict_60</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12132427</td>\n",
       "      <td>This super safe, super quiet, super convenient...</td>\n",
       "      <td>The apartment is located on the ground floor o...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>two</td>\n",
       "      <td>25.00</td>\n",
       "      <td>3</td>\n",
       "      <td>1125</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27551517</td>\n",
       "      <td>Large Sunny room in a 1 BR apartment for 1 ONL...</td>\n",
       "      <td>ALL</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27016367</td>\n",
       "      <td>**We are a event space, The price is for a 5 h...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Guesthouse</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>16</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Queens</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14028380</td>\n",
       "      <td>Beautiful and spacious newly renovated 1 bedro...</td>\n",
       "      <td>There's a separate entryway upstairs to the pr...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>two</td>\n",
       "      <td>25.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20917330</td>\n",
       "      <td>You will have the 1 bed apartment to yourself ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>20.00</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13466663</td>\n",
       "      <td>Moja oferta jest w pobliżu: World Trade Center...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>25.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7497287</td>\n",
       "      <td>Prospect Park South. Metro Church Av on the B ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>19890678</td>\n",
       "      <td>A private room in a large 2-bedroom apartment ...</td>\n",
       "      <td>You have access to balcony and guest room. Two...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>25.00</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>29606554</td>\n",
       "      <td>Amazing two bedroom in the heart of Chelsea. A...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3</td>\n",
       "      <td>1125</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10233048</td>\n",
       "      <td>Beautiful, sunny, spacious bedroom with a view...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>21531318</td>\n",
       "      <td>法拉盛黄金地段,靠近7号地铁,及北方大道与缅街｡餐饮､娱乐､购物､交通都方便｡拎包入住! 干...</td>\n",
       "      <td>跟在您自己的家里一样,您有独立卫生间,无需共用!厨房 客厅方便使用｡</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>two</td>\n",
       "      <td>50.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Queens</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>62925</td>\n",
       "      <td>*NOTE RE: PASSAGE OF NY RESTRICTIONS -- THIS I...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>two</td>\n",
       "      <td>20.00</td>\n",
       "      <td>3</td>\n",
       "      <td>90</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>24730560</td>\n",
       "      <td>We have enjoyed decorating our apartment for y...</td>\n",
       "      <td>There are 2 other private bedrooms in the apar...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4332961</td>\n",
       "      <td>If you are looking for a bohemian and comforta...</td>\n",
       "      <td>Spacious apartment with open access to living ...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>30.00</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>29161249</td>\n",
       "      <td>LOCATION, LOCATION, LOCATION!!! BRYANT PARK SH...</td>\n",
       "      <td>Kitchen, dinning area, washer/dryer, couch in ...</td>\n",
       "      <td>Loft</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>50.00</td>\n",
       "      <td>5</td>\n",
       "      <td>1125</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>29269102</td>\n",
       "      <td>Clean and cozy atmosphere, everything is fairl...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>14798649</td>\n",
       "      <td>Come stay in my quirky nook that I call HOME. ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>two</td>\n",
       "      <td>20.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1125</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>21900790</td>\n",
       "      <td>next to tompkins square park . very convenient...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>27065834</td>\n",
       "      <td>High floor with a great view overlooking all o...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>two</td>\n",
       "      <td>75.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>15898592</td>\n",
       "      <td>Рядом с моим жильем subway, supermarket, bank ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>721762</td>\n",
       "      <td>I AM CURRENTLY LOOKING TO RENT FOR LONGER TERM...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4</td>\n",
       "      <td>365</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>19790151</td>\n",
       "      <td>Only 30 minutes away from the Manhattan includ...</td>\n",
       "      <td>Access to the living room, bathroom and kitche...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>10.00</td>\n",
       "      <td>3</td>\n",
       "      <td>1125</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>25045574</td>\n",
       "      <td>Beautiful private room in NYC loft apartment S...</td>\n",
       "      <td>You have the possibility to cook and hang out ...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>14716403</td>\n",
       "      <td>My place is a few blocks away from a 24-hour o...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Loft</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>30.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33508</th>\n",
       "      <td>25678517</td>\n",
       "      <td>(PLEASE read the whole description of the plac...</td>\n",
       "      <td>Guests can access the kitchen, they're totally...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1125</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33509</th>\n",
       "      <td>29761534</td>\n",
       "      <td>Welcome to Greenpoint! Enjoy a tranquil retrea...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33510</th>\n",
       "      <td>3969366</td>\n",
       "      <td>Cute and comfortable 2 bedroom, 1 bathroom apa...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33511</th>\n",
       "      <td>5762947</td>\n",
       "      <td>纽约之家位于皇后区法拉盛,此处是新生代华人聚集区,周边华语通用,汇聚大江南北各种美食,您完全...</td>\n",
       "      <td>厨房,客厅是公用的</td>\n",
       "      <td>Townhouse</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>two</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Queens</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33512</th>\n",
       "      <td>7713509</td>\n",
       "      <td>The private room is in a 3 rm apt.; in a 4 sto...</td>\n",
       "      <td>kitchen bathroom livingroom private room rooft...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>60.00</td>\n",
       "      <td>3</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33513</th>\n",
       "      <td>12249461</td>\n",
       "      <td>This room is very cosy, and near to the Subway...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33514</th>\n",
       "      <td>12177276</td>\n",
       "      <td>Probably the safest neighborhood in the whole ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>25.00</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33515</th>\n",
       "      <td>3174292</td>\n",
       "      <td>Hello! My name is Nicole and I welcome you to ...</td>\n",
       "      <td>Guest will have access to the shared living ro...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33516</th>\n",
       "      <td>8207453</td>\n",
       "      <td>Sunny, breezy, single occupancy room for rent ...</td>\n",
       "      <td>The main entrance door is best, sorry no eleva...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>20.00</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33517</th>\n",
       "      <td>17647138</td>\n",
       "      <td>1 Adult Close to everything Bus ,Train, Uber P...</td>\n",
       "      <td>Close to everything Bus ,Train, Uber Pick up a...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33518</th>\n",
       "      <td>2182899</td>\n",
       "      <td>Our sunny and spacious New York apartment is s...</td>\n",
       "      <td>Kitchen, communal rooftop terrace with incredi...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>two</td>\n",
       "      <td>20.00</td>\n",
       "      <td>3</td>\n",
       "      <td>1125</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33519</th>\n",
       "      <td>14553862</td>\n",
       "      <td>My place is close to Central Park, Riverside P...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>two</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33520</th>\n",
       "      <td>1832120</td>\n",
       "      <td>Haven on Bedford Avenue. Spacious and lovely p...</td>\n",
       "      <td>Wireless, Netflix and HBO. All kitchenwares an...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>15.00</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33521</th>\n",
       "      <td>8841646</td>\n",
       "      <td>Last minute Christmas / New Years Special ! A ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33522</th>\n",
       "      <td>2498576</td>\n",
       "      <td>Victorian elegance at its finest with this tur...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>four</td>\n",
       "      <td>20.00</td>\n",
       "      <td>30</td>\n",
       "      <td>1125</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33523</th>\n",
       "      <td>25185813</td>\n",
       "      <td>Wonderful home in Manhattan NYC with 5 beds. 3...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>50.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33524</th>\n",
       "      <td>8354416</td>\n",
       "      <td>One large room with half bathroom and lofted s...</td>\n",
       "      <td>The kitchen and main bathroom with shower/basi...</td>\n",
       "      <td>Loft</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>40.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1125</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Queens</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33525</th>\n",
       "      <td>10232344</td>\n",
       "      <td>Extremely clean studio apartment with queen be...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33526</th>\n",
       "      <td>16667077</td>\n",
       "      <td>Experience a New Yorker's life in Upper West S...</td>\n",
       "      <td>Kitchen, Living Room, both bathrooms. The whol...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33527</th>\n",
       "      <td>13219269</td>\n",
       "      <td>My place is pretty quiet - even for New York. ...</td>\n",
       "      <td>Guest may use the kitchen for a coffee and lig...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Airbed</td>\n",
       "      <td>one</td>\n",
       "      <td>25.00</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33528</th>\n",
       "      <td>5050799</td>\n",
       "      <td>Sunny comfortable Prospect Heights/Crown Heigh...</td>\n",
       "      <td>This is your own apartment with full kitchen, ...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5</td>\n",
       "      <td>1125</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33529</th>\n",
       "      <td>3698367</td>\n",
       "      <td>Want an affordable way to visit NYC?An incredi...</td>\n",
       "      <td>Guests will have access to the entire apartmen...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33530</th>\n",
       "      <td>14506036</td>\n",
       "      <td>Large one bedroom studio with private bathroom...</td>\n",
       "      <td>Private Studio and bathroom with (sometimes) s...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>two</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33531</th>\n",
       "      <td>12848383</td>\n",
       "      <td>Comfortable and roomy ground-floor apartment, ...</td>\n",
       "      <td>Exclusive access to entire apartment and backy...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>many</td>\n",
       "      <td>25.00</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33532</th>\n",
       "      <td>21766709</td>\n",
       "      <td>Stay at this gorgeous room in a 4 Beds, 4 bath...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30</td>\n",
       "      <td>1125</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33533</th>\n",
       "      <td>3958032</td>\n",
       "      <td>Private room in cozy, sun-drenched &amp; plant fil...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>25.00</td>\n",
       "      <td>3</td>\n",
       "      <td>1125</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33534</th>\n",
       "      <td>7987209</td>\n",
       "      <td>This is a beautiful four bedroom and three bat...</td>\n",
       "      <td>Access to the unit is through elevators.</td>\n",
       "      <td>Serviced apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33535</th>\n",
       "      <td>796128</td>\n",
       "      <td>Room for rent in duplex house located in Astor...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180</td>\n",
       "      <td>365</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Queens</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33536</th>\n",
       "      <td>17281121</td>\n",
       "      <td>Very spacious bright beautiful apartment in Wa...</td>\n",
       "      <td>Living room, bedroom, shared kitchen and bathr...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7</td>\n",
       "      <td>1125</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33537</th>\n",
       "      <td>17414910</td>\n",
       "      <td>One of a kind, Immense 3bed/2 bath duplex Loft...</td>\n",
       "      <td>The entire home will be for you and your group...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>four</td>\n",
       "      <td>25.00</td>\n",
       "      <td>5</td>\n",
       "      <td>120</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33538 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                                        description  \\\n",
       "0      22267382  Modern large studio with new amenities and app...   \n",
       "1       2473861  Harlem is back and so gorgeous! Visit and expl...   \n",
       "2      25079703  Clean, hip and well designed sun drenched East...   \n",
       "3       9342478  Private, spacious, comfortable room in 2-bed f...   \n",
       "4       4866426  Comfy, quiet and big private room in a three b...   \n",
       "5      19115443  Spacious in size, suites feature a fully equip...   \n",
       "6      12132427  This super safe, super quiet, super convenient...   \n",
       "7      27551517  Large Sunny room in a 1 BR apartment for 1 ONL...   \n",
       "8      27016367  **We are a event space, The price is for a 5 h...   \n",
       "9      14028380  Beautiful and spacious newly renovated 1 bedro...   \n",
       "10     20917330  You will have the 1 bed apartment to yourself ...   \n",
       "11     13466663  Moja oferta jest w pobliżu: World Trade Center...   \n",
       "12      7497287  Prospect Park South. Metro Church Av on the B ...   \n",
       "13     19890678  A private room in a large 2-bedroom apartment ...   \n",
       "14     29606554  Amazing two bedroom in the heart of Chelsea. A...   \n",
       "15     10233048  Beautiful, sunny, spacious bedroom with a view...   \n",
       "16     21531318  法拉盛黄金地段,靠近7号地铁,及北方大道与缅街｡餐饮､娱乐､购物､交通都方便｡拎包入住! 干...   \n",
       "17        62925  *NOTE RE: PASSAGE OF NY RESTRICTIONS -- THIS I...   \n",
       "18     24730560  We have enjoyed decorating our apartment for y...   \n",
       "19      4332961  If you are looking for a bohemian and comforta...   \n",
       "20     29161249  LOCATION, LOCATION, LOCATION!!! BRYANT PARK SH...   \n",
       "21     29269102  Clean and cozy atmosphere, everything is fairl...   \n",
       "22     14798649  Come stay in my quirky nook that I call HOME. ...   \n",
       "23     21900790  next to tompkins square park . very convenient...   \n",
       "24     27065834  High floor with a great view overlooking all o...   \n",
       "25     15898592  Рядом с моим жильем subway, supermarket, bank ...   \n",
       "26       721762  I AM CURRENTLY LOOKING TO RENT FOR LONGER TERM...   \n",
       "27     19790151  Only 30 minutes away from the Manhattan includ...   \n",
       "28     25045574  Beautiful private room in NYC loft apartment S...   \n",
       "29     14716403  My place is a few blocks away from a 24-hour o...   \n",
       "...         ...                                                ...   \n",
       "33508  25678517  (PLEASE read the whole description of the plac...   \n",
       "33509  29761534  Welcome to Greenpoint! Enjoy a tranquil retrea...   \n",
       "33510   3969366  Cute and comfortable 2 bedroom, 1 bathroom apa...   \n",
       "33511   5762947  纽约之家位于皇后区法拉盛,此处是新生代华人聚集区,周边华语通用,汇聚大江南北各种美食,您完全...   \n",
       "33512   7713509  The private room is in a 3 rm apt.; in a 4 sto...   \n",
       "33513  12249461  This room is very cosy, and near to the Subway...   \n",
       "33514  12177276  Probably the safest neighborhood in the whole ...   \n",
       "33515   3174292  Hello! My name is Nicole and I welcome you to ...   \n",
       "33516   8207453  Sunny, breezy, single occupancy room for rent ...   \n",
       "33517  17647138  1 Adult Close to everything Bus ,Train, Uber P...   \n",
       "33518   2182899  Our sunny and spacious New York apartment is s...   \n",
       "33519  14553862  My place is close to Central Park, Riverside P...   \n",
       "33520   1832120  Haven on Bedford Avenue. Spacious and lovely p...   \n",
       "33521   8841646  Last minute Christmas / New Years Special ! A ...   \n",
       "33522   2498576  Victorian elegance at its finest with this tur...   \n",
       "33523  25185813  Wonderful home in Manhattan NYC with 5 beds. 3...   \n",
       "33524   8354416  One large room with half bathroom and lofted s...   \n",
       "33525  10232344  Extremely clean studio apartment with queen be...   \n",
       "33526  16667077  Experience a New Yorker's life in Upper West S...   \n",
       "33527  13219269  My place is pretty quiet - even for New York. ...   \n",
       "33528   5050799  Sunny comfortable Prospect Heights/Crown Heigh...   \n",
       "33529   3698367  Want an affordable way to visit NYC?An incredi...   \n",
       "33530  14506036  Large one bedroom studio with private bathroom...   \n",
       "33531  12848383  Comfortable and roomy ground-floor apartment, ...   \n",
       "33532  21766709  Stay at this gorgeous room in a 4 Beds, 4 bath...   \n",
       "33533   3958032  Private room in cozy, sun-drenched & plant fil...   \n",
       "33534   7987209  This is a beautiful four bedroom and three bat...   \n",
       "33535    796128  Room for rent in duplex house located in Astor...   \n",
       "33536  17281121  Very spacious bright beautiful apartment in Wa...   \n",
       "33537  17414910  One of a kind, Immense 3bed/2 bath duplex Loft...   \n",
       "\n",
       "                                                  access       property_type  \\\n",
       "0      Washer/Dryer Dishwasher Internet Gym Roof Top ...                Loft   \n",
       "1                                 The WHOLE ENTIRE HOUSE               House   \n",
       "2      You'll have access to the entire space - it's ...           Apartment   \n",
       "3                                                    NaN           Apartment   \n",
       "4      Your room has a very comfortable queen sized b...           Apartment   \n",
       "5                                                    NaN           Apartment   \n",
       "6      The apartment is located on the ground floor o...           Apartment   \n",
       "7                                                    ALL           Apartment   \n",
       "8                                                    NaN          Guesthouse   \n",
       "9      There's a separate entryway upstairs to the pr...           Apartment   \n",
       "10                                                   NaN           Apartment   \n",
       "11                                                   NaN           Apartment   \n",
       "12                                                   NaN           Apartment   \n",
       "13     You have access to balcony and guest room. Two...           Apartment   \n",
       "14                                                   NaN           Apartment   \n",
       "15                                                   NaN           Apartment   \n",
       "16                    跟在您自己的家里一样,您有独立卫生间,无需共用!厨房 客厅方便使用｡           Apartment   \n",
       "17                                                   NaN               House   \n",
       "18     There are 2 other private bedrooms in the apar...           Apartment   \n",
       "19     Spacious apartment with open access to living ...           Apartment   \n",
       "20     Kitchen, dinning area, washer/dryer, couch in ...                Loft   \n",
       "21                                                   NaN               House   \n",
       "22                                                   NaN           Apartment   \n",
       "23                                                   NaN           Apartment   \n",
       "24                                                   NaN           Apartment   \n",
       "25                                                   NaN           Apartment   \n",
       "26                                                   NaN           Apartment   \n",
       "27     Access to the living room, bathroom and kitche...           Apartment   \n",
       "28     You have the possibility to cook and hang out ...           Apartment   \n",
       "29                                                   NaN                Loft   \n",
       "...                                                  ...                 ...   \n",
       "33508  Guests can access the kitchen, they're totally...           Apartment   \n",
       "33509                                                NaN           Apartment   \n",
       "33510                                                NaN           Apartment   \n",
       "33511                                          厨房,客厅是公用的           Townhouse   \n",
       "33512  kitchen bathroom livingroom private room rooft...           Apartment   \n",
       "33513                                                NaN               House   \n",
       "33514                                                NaN           Apartment   \n",
       "33515  Guest will have access to the shared living ro...           Apartment   \n",
       "33516  The main entrance door is best, sorry no eleva...           Apartment   \n",
       "33517  Close to everything Bus ,Train, Uber Pick up a...           Apartment   \n",
       "33518  Kitchen, communal rooftop terrace with incredi...           Apartment   \n",
       "33519                                                NaN           Apartment   \n",
       "33520  Wireless, Netflix and HBO. All kitchenwares an...           Apartment   \n",
       "33521                                                NaN           Apartment   \n",
       "33522                                                NaN               House   \n",
       "33523                                                NaN           Apartment   \n",
       "33524  The kitchen and main bathroom with shower/basi...                Loft   \n",
       "33525                                                NaN           Apartment   \n",
       "33526  Kitchen, Living Room, both bathrooms. The whol...           Apartment   \n",
       "33527  Guest may use the kitchen for a coffee and lig...           Apartment   \n",
       "33528  This is your own apartment with full kitchen, ...           Apartment   \n",
       "33529  Guests will have access to the entire apartmen...           Apartment   \n",
       "33530  Private Studio and bathroom with (sometimes) s...           Apartment   \n",
       "33531  Exclusive access to entire apartment and backy...           Apartment   \n",
       "33532                                                NaN           Apartment   \n",
       "33533                                                NaN           Apartment   \n",
       "33534           Access to the unit is through elevators.  Serviced apartment   \n",
       "33535                                                NaN               House   \n",
       "33536  Living room, bedroom, shared kitchen and bathr...           Apartment   \n",
       "33537  The entire home will be for you and your group...           Apartment   \n",
       "\n",
       "             room_type  accommodates  bathrooms  bedrooms  bed_type  \\\n",
       "0      Entire home/apt             2        1.0       1.0  Real Bed   \n",
       "1      Entire home/apt             8        1.0       3.0  Real Bed   \n",
       "2      Entire home/apt             2        1.0       0.0  Real Bed   \n",
       "3         Private room             1        1.0       1.0  Real Bed   \n",
       "4         Private room             2        1.0       1.0  Real Bed   \n",
       "5      Entire home/apt             6        2.5       2.0  Real Bed   \n",
       "6      Entire home/apt             4        1.0       1.0  Real Bed   \n",
       "7         Private room             1        1.0       1.0  Real Bed   \n",
       "8      Entire home/apt            16        2.0       0.0  Real Bed   \n",
       "9      Entire home/apt             4        1.0       1.0  Real Bed   \n",
       "10     Entire home/apt             2        1.0       1.0  Real Bed   \n",
       "11        Private room             2        1.0       1.0  Real Bed   \n",
       "12        Private room             2        1.0       1.0  Real Bed   \n",
       "13        Private room             2        1.0       1.0  Real Bed   \n",
       "14     Entire home/apt             5        1.0       2.0  Real Bed   \n",
       "15        Private room             2        1.0       1.0  Real Bed   \n",
       "16        Private room             2        1.0       1.0  Real Bed   \n",
       "17     Entire home/apt             4        2.0       2.0  Real Bed   \n",
       "18        Private room             1        1.0       1.0  Real Bed   \n",
       "19     Entire home/apt             6        1.0       2.0  Real Bed   \n",
       "20     Entire home/apt             1        1.0       0.0  Real Bed   \n",
       "21        Private room             2        1.0       1.0  Real Bed   \n",
       "22     Entire home/apt             2        1.0       1.0  Real Bed   \n",
       "23     Entire home/apt             2        1.0       0.0  Real Bed   \n",
       "24     Entire home/apt             2        1.0       1.0  Real Bed   \n",
       "25        Private room             2        1.0       1.0  Real Bed   \n",
       "26     Entire home/apt             2        1.0       2.0  Real Bed   \n",
       "27        Private room             2        1.0       1.0  Real Bed   \n",
       "28        Private room             1        1.0       0.0  Real Bed   \n",
       "29        Private room             2        1.0       1.0  Real Bed   \n",
       "...                ...           ...        ...       ...       ...   \n",
       "33508     Private room             2        2.0       1.0  Real Bed   \n",
       "33509     Private room             1        1.0       1.0  Real Bed   \n",
       "33510  Entire home/apt             4        1.0       2.0  Real Bed   \n",
       "33511     Private room             2        1.0       1.0  Real Bed   \n",
       "33512     Private room             2        1.0       1.0  Real Bed   \n",
       "33513     Private room             1        0.5       1.0  Real Bed   \n",
       "33514      Shared room             2        1.0       1.0  Real Bed   \n",
       "33515     Private room             2        1.0       1.0  Real Bed   \n",
       "33516     Private room             1        1.0       1.0  Real Bed   \n",
       "33517     Private room             1        1.0       1.0  Real Bed   \n",
       "33518  Entire home/apt             2        1.0       1.0  Real Bed   \n",
       "33519  Entire home/apt             4        1.0       1.0  Real Bed   \n",
       "33520     Private room             2        1.0       1.0  Real Bed   \n",
       "33521  Entire home/apt             2        1.0       1.0  Real Bed   \n",
       "33522     Private room             2        1.0       1.0  Real Bed   \n",
       "33523  Entire home/apt            10        2.0       3.0  Real Bed   \n",
       "33524     Private room             2        0.5       1.0  Real Bed   \n",
       "33525  Entire home/apt             2        1.0       0.0  Real Bed   \n",
       "33526     Private room             2        2.0       1.0  Real Bed   \n",
       "33527     Private room             2        1.0       1.0    Airbed   \n",
       "33528  Entire home/apt             2        1.0       1.0  Real Bed   \n",
       "33529     Private room             2        1.0       1.0  Real Bed   \n",
       "33530  Entire home/apt             4        1.0       1.0  Real Bed   \n",
       "33531  Entire home/apt             7        2.0       3.0  Real Bed   \n",
       "33532     Private room             1        1.0       1.0  Real Bed   \n",
       "33533     Private room             2        1.0       1.0  Real Bed   \n",
       "33534  Entire home/apt             8        3.0       4.0  Real Bed   \n",
       "33535     Private room             1        1.0       1.0  Real Bed   \n",
       "33536     Private room             2        1.0       1.0  Real Bed   \n",
       "33537  Entire home/apt             8        2.0       3.0  Real Bed   \n",
       "\n",
       "      guests_included extra_people  minimum_nights  maximum_nights  \\\n",
       "0                 two        30.00               7              12   \n",
       "1               three        19.00               3              30   \n",
       "2                 one         0.00               2            1125   \n",
       "3                 one         0.00               3            1125   \n",
       "4                 one        14.00               1              90   \n",
       "5                 one         0.00              30            1125   \n",
       "6                 two        25.00               3            1125   \n",
       "7                 one         0.00               2               2   \n",
       "8                 one         0.00               1               1   \n",
       "9                 two        25.00               1            1125   \n",
       "10                one        20.00               6              30   \n",
       "11                one        25.00               1            1125   \n",
       "12                one         0.00               1            1125   \n",
       "13                one        25.00               2              15   \n",
       "14                one         0.00               3            1125   \n",
       "15                one         0.00               1            1125   \n",
       "16                two        50.00               1            1125   \n",
       "17                two        20.00               3              90   \n",
       "18                one         0.00               2              60   \n",
       "19                one        30.00               2              24   \n",
       "20                one        50.00               5            1125   \n",
       "21                one         0.00               1              90   \n",
       "22                two        20.00               2            1125   \n",
       "23                one         0.00               5               9   \n",
       "24                two        75.00               1            1125   \n",
       "25                one         0.00               7              30   \n",
       "26                one         0.00               4             365   \n",
       "27                one        10.00               3            1125   \n",
       "28                one         0.00               7              12   \n",
       "29                one        30.00               1            1125   \n",
       "...               ...          ...             ...             ...   \n",
       "33508             one         0.00               2            1125   \n",
       "33509             one         0.00               2            1125   \n",
       "33510             one         0.00               1            1125   \n",
       "33511             two        10.00               1            1125   \n",
       "33512             one        60.00               3            1125   \n",
       "33513             one         0.00               1            1125   \n",
       "33514             one        25.00               2              15   \n",
       "33515             one         8.00               1            1125   \n",
       "33516             one        20.00               5              30   \n",
       "33517             one         0.00               5               8   \n",
       "33518             two        20.00               3            1125   \n",
       "33519             two        10.00               1            1125   \n",
       "33520             one        15.00               2               7   \n",
       "33521             one         0.00               1            1125   \n",
       "33522            four        20.00              30            1125   \n",
       "33523             one        50.00               1            1125   \n",
       "33524             one        40.00               2            1125   \n",
       "33525             one         0.00               1            1125   \n",
       "33526             one         0.00               1              60   \n",
       "33527             one        25.00               3              21   \n",
       "33528             one         0.00               5            1125   \n",
       "33529             one         0.00               1            1125   \n",
       "33530             two         0.00               1              30   \n",
       "33531            many        25.00               2              29   \n",
       "33532             one         0.00              30            1125   \n",
       "33533             one        25.00               3            1125   \n",
       "33534             one         0.00               3             365   \n",
       "33535             one         0.00             180             365   \n",
       "33536             one         0.00               7            1125   \n",
       "33537            four        25.00               5             120   \n",
       "\n",
       "               cancellation_policy neighbourhood_group_cleansed  \\\n",
       "0                         flexible                     Brooklyn   \n",
       "1                         moderate                    Manhattan   \n",
       "2                         moderate                    Manhattan   \n",
       "3                         flexible                     Brooklyn   \n",
       "4                         flexible                     Brooklyn   \n",
       "5                  super_strict_60                    Manhattan   \n",
       "6                         moderate                    Manhattan   \n",
       "7      strict_14_with_grace_period                    Manhattan   \n",
       "8      strict_14_with_grace_period                       Queens   \n",
       "9                         moderate                     Brooklyn   \n",
       "10                        flexible                     Brooklyn   \n",
       "11     strict_14_with_grace_period                    Manhattan   \n",
       "12                        flexible                     Brooklyn   \n",
       "13     strict_14_with_grace_period                     Brooklyn   \n",
       "14     strict_14_with_grace_period                    Manhattan   \n",
       "15                        flexible                    Manhattan   \n",
       "16                        flexible                       Queens   \n",
       "17     strict_14_with_grace_period                     Brooklyn   \n",
       "18     strict_14_with_grace_period                     Brooklyn   \n",
       "19                        moderate                    Manhattan   \n",
       "20     strict_14_with_grace_period                    Manhattan   \n",
       "21                        flexible                     Brooklyn   \n",
       "22                        moderate                     Brooklyn   \n",
       "23                        flexible                    Manhattan   \n",
       "24                        flexible                    Manhattan   \n",
       "25     strict_14_with_grace_period                     Brooklyn   \n",
       "26     strict_14_with_grace_period                    Manhattan   \n",
       "27     strict_14_with_grace_period                     Brooklyn   \n",
       "28                        moderate                    Manhattan   \n",
       "29                        flexible                     Brooklyn   \n",
       "...                            ...                          ...   \n",
       "33508  strict_14_with_grace_period                    Manhattan   \n",
       "33509                     flexible                     Brooklyn   \n",
       "33510                     flexible                    Manhattan   \n",
       "33511                     moderate                       Queens   \n",
       "33512                     flexible                     Brooklyn   \n",
       "33513                     flexible                     Brooklyn   \n",
       "33514  strict_14_with_grace_period                    Manhattan   \n",
       "33515                     moderate                     Brooklyn   \n",
       "33516                     flexible                     Brooklyn   \n",
       "33517                     flexible                     Brooklyn   \n",
       "33518  strict_14_with_grace_period                    Manhattan   \n",
       "33519                     flexible                    Manhattan   \n",
       "33520                     moderate                     Brooklyn   \n",
       "33521                     flexible                    Manhattan   \n",
       "33522  strict_14_with_grace_period                Staten Island   \n",
       "33523                     flexible                    Manhattan   \n",
       "33524                     moderate                       Queens   \n",
       "33525                     flexible                    Manhattan   \n",
       "33526  strict_14_with_grace_period                    Manhattan   \n",
       "33527  strict_14_with_grace_period                     Brooklyn   \n",
       "33528                     moderate                     Brooklyn   \n",
       "33529                     flexible                     Brooklyn   \n",
       "33530                     moderate                    Manhattan   \n",
       "33531  strict_14_with_grace_period                     Brooklyn   \n",
       "33532  strict_14_with_grace_period                    Manhattan   \n",
       "33533  strict_14_with_grace_period                    Manhattan   \n",
       "33534  strict_14_with_grace_period                    Manhattan   \n",
       "33535                     flexible                       Queens   \n",
       "33536                     moderate                    Manhattan   \n",
       "33537  strict_14_with_grace_period                    Manhattan   \n",
       "\n",
       "      host_is_superhost  \n",
       "0                     f  \n",
       "1                     f  \n",
       "2                     f  \n",
       "3                     f  \n",
       "4                     t  \n",
       "5                     f  \n",
       "6                     f  \n",
       "7                     f  \n",
       "8                     f  \n",
       "9                     f  \n",
       "10                    f  \n",
       "11                    t  \n",
       "12                    f  \n",
       "13                    f  \n",
       "14                    f  \n",
       "15                    f  \n",
       "16                    f  \n",
       "17                    t  \n",
       "18                    f  \n",
       "19                    f  \n",
       "20                    f  \n",
       "21                    f  \n",
       "22                    f  \n",
       "23                    f  \n",
       "24                    f  \n",
       "25                    f  \n",
       "26                    f  \n",
       "27                    t  \n",
       "28                    f  \n",
       "29                    f  \n",
       "...                 ...  \n",
       "33508                 f  \n",
       "33509                 f  \n",
       "33510                 f  \n",
       "33511                 f  \n",
       "33512                 f  \n",
       "33513                 f  \n",
       "33514                 t  \n",
       "33515                 f  \n",
       "33516                 f  \n",
       "33517                 f  \n",
       "33518                 f  \n",
       "33519                 f  \n",
       "33520                 t  \n",
       "33521                 f  \n",
       "33522                 f  \n",
       "33523                 f  \n",
       "33524                 f  \n",
       "33525                 f  \n",
       "33526                 f  \n",
       "33527                 f  \n",
       "33528                 f  \n",
       "33529                 f  \n",
       "33530                 f  \n",
       "33531                 f  \n",
       "33532                 f  \n",
       "33533                 f  \n",
       "33534                 f  \n",
       "33535                 f  \n",
       "33536                 f  \n",
       "33537                 f  \n",
       "\n",
       "[33538 rows x 16 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "saved = new_df['guests_included'].apply(guests)\n",
    "new_df['guests_included'] = saved\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Apartment                 27060\n",
       "House                      2471\n",
       "Townhouse                  1086\n",
       "Loft                       1035\n",
       "Condominium                 822\n",
       "Serviced apartment          437\n",
       "Guest suite                 163\n",
       "Other                        92\n",
       "Bed and breakfast            68\n",
       "Boutique hotel               68\n",
       "Resort                       65\n",
       "Guesthouse                   37\n",
       "Hostel                       32\n",
       "Hotel                        29\n",
       "Villa                        19\n",
       "Bungalow                     19\n",
       "Tiny house                    9\n",
       "Boat                          5\n",
       "Aparthotel                    5\n",
       "Tent                          3\n",
       "Cottage                       3\n",
       "Camper/RV                     3\n",
       "Timeshare                     1\n",
       "Island                        1\n",
       "Cabin                         1\n",
       "Houseboat                     1\n",
       "Casa particular (Cuba)        1\n",
       "Train                         1\n",
       "Chalet                        1\n",
       "Name: property_type, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['property_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py:189: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n",
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    }
   ],
   "source": [
    "# merge small catergories in property_type into one category \"Other\"\n",
    "Other = ['Serviced apartment','Guest suite','Other','Boutique hotel','Bed and breakfast','Resort','Hotel','Guesthouse',\n",
    "        'Hostel','Bungalow','Villa','Tiny house','Aparthotel','Boat', 'Tent', 'Cottage','Camper/RV','Casa particular (Cuba)',        \n",
    "'Island',                        \n",
    "'Timeshare',                     \n",
    "'Chalet',                        \n",
    "'Cabin',                         \n",
    "'Houseboat',                     \n",
    "'Train', 'Cave', 'Nature lodge', 'Earth house', 'Castle']\n",
    "new_df['property_type'].loc[new_df['property_type'].isin(Other)] = \"Other\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Apartment      27060\n",
       "House           2471\n",
       "Townhouse       1086\n",
       "Other           1064\n",
       "Loft            1035\n",
       "Condominium      822\n",
       "Name: property_type, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['property_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Entire home/apt    17536\n",
       "Private room       15235\n",
       "Shared room          767\n",
       "Name: room_type, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['room_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Real Bed         32907\n",
       "Futon              251\n",
       "Pull-out Sofa      193\n",
       "Airbed             130\n",
       "Couch               57\n",
       "Name: bed_type, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['bed_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "# merge small catergories in bed_type into one category \"No Bed\"\n",
    "Other = ['Futon','Pull-out Sofa','Airbed','Couch']\n",
    "new_df['bed_type'].loc[new_df['bed_type'].isin(Other)] = \"No Bed\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Real Bed    32907\n",
       "No Bed        631\n",
       "Name: bed_type, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['bed_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "strict_14_with_grace_period    15330\n",
       "flexible                       10345\n",
       "moderate                        7746\n",
       "super_strict_60                   81\n",
       "super_strict_30                   34\n",
       "strict                             1\n",
       "long_term                          1\n",
       "Name: cancellation_policy, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['cancellation_policy'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "# merge small catergories in cancellation_policy into one category \"Other\"\n",
    "Other = ['super_strict_60','super_strict_30','strict','long_term']\n",
    "new_df['cancellation_policy'].loc[new_df['cancellation_policy'].isin(Other)] = \"Other\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>id</th>\n",
       "      <th>description</th>\n",
       "      <th>access</th>\n",
       "      <th>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bed_type</th>\n",
       "      <th>guests_included</th>\n",
       "      <th>extra_people</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "      <th>host_is_superhost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22267382</td>\n",
       "      <td>Modern large studio with new amenities and app...</td>\n",
       "      <td>Washer/Dryer Dishwasher Internet Gym Roof Top ...</td>\n",
       "      <td>Loft</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>two</td>\n",
       "      <td>30.00</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2473861</td>\n",
       "      <td>Harlem is back and so gorgeous! Visit and expl...</td>\n",
       "      <td>The WHOLE ENTIRE HOUSE</td>\n",
       "      <td>House</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>three</td>\n",
       "      <td>19.00</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25079703</td>\n",
       "      <td>Clean, hip and well designed sun drenched East...</td>\n",
       "      <td>You'll have access to the entire space - it's ...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1125</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9342478</td>\n",
       "      <td>Private, spacious, comfortable room in 2-bed f...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4866426</td>\n",
       "      <td>Comfy, quiet and big private room in a three b...</td>\n",
       "      <td>Your room has a very comfortable queen sized b...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19115443</td>\n",
       "      <td>Spacious in size, suites feature a fully equip...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30</td>\n",
       "      <td>1125</td>\n",
       "      <td>Other</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12132427</td>\n",
       "      <td>This super safe, super quiet, super convenient...</td>\n",
       "      <td>The apartment is located on the ground floor o...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>two</td>\n",
       "      <td>25.00</td>\n",
       "      <td>3</td>\n",
       "      <td>1125</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27551517</td>\n",
       "      <td>Large Sunny room in a 1 BR apartment for 1 ONL...</td>\n",
       "      <td>ALL</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27016367</td>\n",
       "      <td>**We are a event space, The price is for a 5 h...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>16</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Queens</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14028380</td>\n",
       "      <td>Beautiful and spacious newly renovated 1 bedro...</td>\n",
       "      <td>There's a separate entryway upstairs to the pr...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>two</td>\n",
       "      <td>25.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20917330</td>\n",
       "      <td>You will have the 1 bed apartment to yourself ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>20.00</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13466663</td>\n",
       "      <td>Moja oferta jest w pobliżu: World Trade Center...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>25.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7497287</td>\n",
       "      <td>Prospect Park South. Metro Church Av on the B ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>19890678</td>\n",
       "      <td>A private room in a large 2-bedroom apartment ...</td>\n",
       "      <td>You have access to balcony and guest room. Two...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>25.00</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>29606554</td>\n",
       "      <td>Amazing two bedroom in the heart of Chelsea. A...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3</td>\n",
       "      <td>1125</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10233048</td>\n",
       "      <td>Beautiful, sunny, spacious bedroom with a view...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>21531318</td>\n",
       "      <td>法拉盛黄金地段,靠近7号地铁,及北方大道与缅街｡餐饮､娱乐､购物､交通都方便｡拎包入住! 干...</td>\n",
       "      <td>跟在您自己的家里一样,您有独立卫生间,无需共用!厨房 客厅方便使用｡</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>two</td>\n",
       "      <td>50.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Queens</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>62925</td>\n",
       "      <td>*NOTE RE: PASSAGE OF NY RESTRICTIONS -- THIS I...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>two</td>\n",
       "      <td>20.00</td>\n",
       "      <td>3</td>\n",
       "      <td>90</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>24730560</td>\n",
       "      <td>We have enjoyed decorating our apartment for y...</td>\n",
       "      <td>There are 2 other private bedrooms in the apar...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4332961</td>\n",
       "      <td>If you are looking for a bohemian and comforta...</td>\n",
       "      <td>Spacious apartment with open access to living ...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>30.00</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>29161249</td>\n",
       "      <td>LOCATION, LOCATION, LOCATION!!! BRYANT PARK SH...</td>\n",
       "      <td>Kitchen, dinning area, washer/dryer, couch in ...</td>\n",
       "      <td>Loft</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>50.00</td>\n",
       "      <td>5</td>\n",
       "      <td>1125</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>29269102</td>\n",
       "      <td>Clean and cozy atmosphere, everything is fairl...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>14798649</td>\n",
       "      <td>Come stay in my quirky nook that I call HOME. ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>two</td>\n",
       "      <td>20.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1125</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>21900790</td>\n",
       "      <td>next to tompkins square park . very convenient...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>27065834</td>\n",
       "      <td>High floor with a great view overlooking all o...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>two</td>\n",
       "      <td>75.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>15898592</td>\n",
       "      <td>Рядом с моим жильем subway, supermarket, bank ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>721762</td>\n",
       "      <td>I AM CURRENTLY LOOKING TO RENT FOR LONGER TERM...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4</td>\n",
       "      <td>365</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>19790151</td>\n",
       "      <td>Only 30 minutes away from the Manhattan includ...</td>\n",
       "      <td>Access to the living room, bathroom and kitche...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>10.00</td>\n",
       "      <td>3</td>\n",
       "      <td>1125</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>25045574</td>\n",
       "      <td>Beautiful private room in NYC loft apartment S...</td>\n",
       "      <td>You have the possibility to cook and hang out ...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>14716403</td>\n",
       "      <td>My place is a few blocks away from a 24-hour o...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Loft</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>30.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33508</th>\n",
       "      <td>25678517</td>\n",
       "      <td>(PLEASE read the whole description of the plac...</td>\n",
       "      <td>Guests can access the kitchen, they're totally...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1125</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33509</th>\n",
       "      <td>29761534</td>\n",
       "      <td>Welcome to Greenpoint! Enjoy a tranquil retrea...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33510</th>\n",
       "      <td>3969366</td>\n",
       "      <td>Cute and comfortable 2 bedroom, 1 bathroom apa...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33511</th>\n",
       "      <td>5762947</td>\n",
       "      <td>纽约之家位于皇后区法拉盛,此处是新生代华人聚集区,周边华语通用,汇聚大江南北各种美食,您完全...</td>\n",
       "      <td>厨房,客厅是公用的</td>\n",
       "      <td>Townhouse</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>two</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Queens</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33512</th>\n",
       "      <td>7713509</td>\n",
       "      <td>The private room is in a 3 rm apt.; in a 4 sto...</td>\n",
       "      <td>kitchen bathroom livingroom private room rooft...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>60.00</td>\n",
       "      <td>3</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33513</th>\n",
       "      <td>12249461</td>\n",
       "      <td>This room is very cosy, and near to the Subway...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33514</th>\n",
       "      <td>12177276</td>\n",
       "      <td>Probably the safest neighborhood in the whole ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>25.00</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33515</th>\n",
       "      <td>3174292</td>\n",
       "      <td>Hello! My name is Nicole and I welcome you to ...</td>\n",
       "      <td>Guest will have access to the shared living ro...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33516</th>\n",
       "      <td>8207453</td>\n",
       "      <td>Sunny, breezy, single occupancy room for rent ...</td>\n",
       "      <td>The main entrance door is best, sorry no eleva...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>20.00</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33517</th>\n",
       "      <td>17647138</td>\n",
       "      <td>1 Adult Close to everything Bus ,Train, Uber P...</td>\n",
       "      <td>Close to everything Bus ,Train, Uber Pick up a...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33518</th>\n",
       "      <td>2182899</td>\n",
       "      <td>Our sunny and spacious New York apartment is s...</td>\n",
       "      <td>Kitchen, communal rooftop terrace with incredi...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>two</td>\n",
       "      <td>20.00</td>\n",
       "      <td>3</td>\n",
       "      <td>1125</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33519</th>\n",
       "      <td>14553862</td>\n",
       "      <td>My place is close to Central Park, Riverside P...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>two</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33520</th>\n",
       "      <td>1832120</td>\n",
       "      <td>Haven on Bedford Avenue. Spacious and lovely p...</td>\n",
       "      <td>Wireless, Netflix and HBO. All kitchenwares an...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>15.00</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33521</th>\n",
       "      <td>8841646</td>\n",
       "      <td>Last minute Christmas / New Years Special ! A ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33522</th>\n",
       "      <td>2498576</td>\n",
       "      <td>Victorian elegance at its finest with this tur...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>four</td>\n",
       "      <td>20.00</td>\n",
       "      <td>30</td>\n",
       "      <td>1125</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33523</th>\n",
       "      <td>25185813</td>\n",
       "      <td>Wonderful home in Manhattan NYC with 5 beds. 3...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>50.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33524</th>\n",
       "      <td>8354416</td>\n",
       "      <td>One large room with half bathroom and lofted s...</td>\n",
       "      <td>The kitchen and main bathroom with shower/basi...</td>\n",
       "      <td>Loft</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>40.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1125</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Queens</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33525</th>\n",
       "      <td>10232344</td>\n",
       "      <td>Extremely clean studio apartment with queen be...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33526</th>\n",
       "      <td>16667077</td>\n",
       "      <td>Experience a New Yorker's life in Upper West S...</td>\n",
       "      <td>Kitchen, Living Room, both bathrooms. The whol...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33527</th>\n",
       "      <td>13219269</td>\n",
       "      <td>My place is pretty quiet - even for New York. ...</td>\n",
       "      <td>Guest may use the kitchen for a coffee and lig...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>25.00</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33528</th>\n",
       "      <td>5050799</td>\n",
       "      <td>Sunny comfortable Prospect Heights/Crown Heigh...</td>\n",
       "      <td>This is your own apartment with full kitchen, ...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5</td>\n",
       "      <td>1125</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33529</th>\n",
       "      <td>3698367</td>\n",
       "      <td>Want an affordable way to visit NYC?An incredi...</td>\n",
       "      <td>Guests will have access to the entire apartmen...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33530</th>\n",
       "      <td>14506036</td>\n",
       "      <td>Large one bedroom studio with private bathroom...</td>\n",
       "      <td>Private Studio and bathroom with (sometimes) s...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>two</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33531</th>\n",
       "      <td>12848383</td>\n",
       "      <td>Comfortable and roomy ground-floor apartment, ...</td>\n",
       "      <td>Exclusive access to entire apartment and backy...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>many</td>\n",
       "      <td>25.00</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33532</th>\n",
       "      <td>21766709</td>\n",
       "      <td>Stay at this gorgeous room in a 4 Beds, 4 bath...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30</td>\n",
       "      <td>1125</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33533</th>\n",
       "      <td>3958032</td>\n",
       "      <td>Private room in cozy, sun-drenched &amp; plant fil...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>25.00</td>\n",
       "      <td>3</td>\n",
       "      <td>1125</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33534</th>\n",
       "      <td>7987209</td>\n",
       "      <td>This is a beautiful four bedroom and three bat...</td>\n",
       "      <td>Access to the unit is through elevators.</td>\n",
       "      <td>Other</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33535</th>\n",
       "      <td>796128</td>\n",
       "      <td>Room for rent in duplex house located in Astor...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180</td>\n",
       "      <td>365</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Queens</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33536</th>\n",
       "      <td>17281121</td>\n",
       "      <td>Very spacious bright beautiful apartment in Wa...</td>\n",
       "      <td>Living room, bedroom, shared kitchen and bathr...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7</td>\n",
       "      <td>1125</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33537</th>\n",
       "      <td>17414910</td>\n",
       "      <td>One of a kind, Immense 3bed/2 bath duplex Loft...</td>\n",
       "      <td>The entire home will be for you and your group...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>four</td>\n",
       "      <td>25.00</td>\n",
       "      <td>5</td>\n",
       "      <td>120</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33538 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                                        description  \\\n",
       "0      22267382  Modern large studio with new amenities and app...   \n",
       "1       2473861  Harlem is back and so gorgeous! Visit and expl...   \n",
       "2      25079703  Clean, hip and well designed sun drenched East...   \n",
       "3       9342478  Private, spacious, comfortable room in 2-bed f...   \n",
       "4       4866426  Comfy, quiet and big private room in a three b...   \n",
       "5      19115443  Spacious in size, suites feature a fully equip...   \n",
       "6      12132427  This super safe, super quiet, super convenient...   \n",
       "7      27551517  Large Sunny room in a 1 BR apartment for 1 ONL...   \n",
       "8      27016367  **We are a event space, The price is for a 5 h...   \n",
       "9      14028380  Beautiful and spacious newly renovated 1 bedro...   \n",
       "10     20917330  You will have the 1 bed apartment to yourself ...   \n",
       "11     13466663  Moja oferta jest w pobliżu: World Trade Center...   \n",
       "12      7497287  Prospect Park South. Metro Church Av on the B ...   \n",
       "13     19890678  A private room in a large 2-bedroom apartment ...   \n",
       "14     29606554  Amazing two bedroom in the heart of Chelsea. A...   \n",
       "15     10233048  Beautiful, sunny, spacious bedroom with a view...   \n",
       "16     21531318  法拉盛黄金地段,靠近7号地铁,及北方大道与缅街｡餐饮､娱乐､购物､交通都方便｡拎包入住! 干...   \n",
       "17        62925  *NOTE RE: PASSAGE OF NY RESTRICTIONS -- THIS I...   \n",
       "18     24730560  We have enjoyed decorating our apartment for y...   \n",
       "19      4332961  If you are looking for a bohemian and comforta...   \n",
       "20     29161249  LOCATION, LOCATION, LOCATION!!! BRYANT PARK SH...   \n",
       "21     29269102  Clean and cozy atmosphere, everything is fairl...   \n",
       "22     14798649  Come stay in my quirky nook that I call HOME. ...   \n",
       "23     21900790  next to tompkins square park . very convenient...   \n",
       "24     27065834  High floor with a great view overlooking all o...   \n",
       "25     15898592  Рядом с моим жильем subway, supermarket, bank ...   \n",
       "26       721762  I AM CURRENTLY LOOKING TO RENT FOR LONGER TERM...   \n",
       "27     19790151  Only 30 minutes away from the Manhattan includ...   \n",
       "28     25045574  Beautiful private room in NYC loft apartment S...   \n",
       "29     14716403  My place is a few blocks away from a 24-hour o...   \n",
       "...         ...                                                ...   \n",
       "33508  25678517  (PLEASE read the whole description of the plac...   \n",
       "33509  29761534  Welcome to Greenpoint! Enjoy a tranquil retrea...   \n",
       "33510   3969366  Cute and comfortable 2 bedroom, 1 bathroom apa...   \n",
       "33511   5762947  纽约之家位于皇后区法拉盛,此处是新生代华人聚集区,周边华语通用,汇聚大江南北各种美食,您完全...   \n",
       "33512   7713509  The private room is in a 3 rm apt.; in a 4 sto...   \n",
       "33513  12249461  This room is very cosy, and near to the Subway...   \n",
       "33514  12177276  Probably the safest neighborhood in the whole ...   \n",
       "33515   3174292  Hello! My name is Nicole and I welcome you to ...   \n",
       "33516   8207453  Sunny, breezy, single occupancy room for rent ...   \n",
       "33517  17647138  1 Adult Close to everything Bus ,Train, Uber P...   \n",
       "33518   2182899  Our sunny and spacious New York apartment is s...   \n",
       "33519  14553862  My place is close to Central Park, Riverside P...   \n",
       "33520   1832120  Haven on Bedford Avenue. Spacious and lovely p...   \n",
       "33521   8841646  Last minute Christmas / New Years Special ! A ...   \n",
       "33522   2498576  Victorian elegance at its finest with this tur...   \n",
       "33523  25185813  Wonderful home in Manhattan NYC with 5 beds. 3...   \n",
       "33524   8354416  One large room with half bathroom and lofted s...   \n",
       "33525  10232344  Extremely clean studio apartment with queen be...   \n",
       "33526  16667077  Experience a New Yorker's life in Upper West S...   \n",
       "33527  13219269  My place is pretty quiet - even for New York. ...   \n",
       "33528   5050799  Sunny comfortable Prospect Heights/Crown Heigh...   \n",
       "33529   3698367  Want an affordable way to visit NYC?An incredi...   \n",
       "33530  14506036  Large one bedroom studio with private bathroom...   \n",
       "33531  12848383  Comfortable and roomy ground-floor apartment, ...   \n",
       "33532  21766709  Stay at this gorgeous room in a 4 Beds, 4 bath...   \n",
       "33533   3958032  Private room in cozy, sun-drenched & plant fil...   \n",
       "33534   7987209  This is a beautiful four bedroom and three bat...   \n",
       "33535    796128  Room for rent in duplex house located in Astor...   \n",
       "33536  17281121  Very spacious bright beautiful apartment in Wa...   \n",
       "33537  17414910  One of a kind, Immense 3bed/2 bath duplex Loft...   \n",
       "\n",
       "                                                  access property_type  \\\n",
       "0      Washer/Dryer Dishwasher Internet Gym Roof Top ...          Loft   \n",
       "1                                 The WHOLE ENTIRE HOUSE         House   \n",
       "2      You'll have access to the entire space - it's ...     Apartment   \n",
       "3                                                    NaN     Apartment   \n",
       "4      Your room has a very comfortable queen sized b...     Apartment   \n",
       "5                                                    NaN     Apartment   \n",
       "6      The apartment is located on the ground floor o...     Apartment   \n",
       "7                                                    ALL     Apartment   \n",
       "8                                                    NaN         Other   \n",
       "9      There's a separate entryway upstairs to the pr...     Apartment   \n",
       "10                                                   NaN     Apartment   \n",
       "11                                                   NaN     Apartment   \n",
       "12                                                   NaN     Apartment   \n",
       "13     You have access to balcony and guest room. Two...     Apartment   \n",
       "14                                                   NaN     Apartment   \n",
       "15                                                   NaN     Apartment   \n",
       "16                    跟在您自己的家里一样,您有独立卫生间,无需共用!厨房 客厅方便使用｡     Apartment   \n",
       "17                                                   NaN         House   \n",
       "18     There are 2 other private bedrooms in the apar...     Apartment   \n",
       "19     Spacious apartment with open access to living ...     Apartment   \n",
       "20     Kitchen, dinning area, washer/dryer, couch in ...          Loft   \n",
       "21                                                   NaN         House   \n",
       "22                                                   NaN     Apartment   \n",
       "23                                                   NaN     Apartment   \n",
       "24                                                   NaN     Apartment   \n",
       "25                                                   NaN     Apartment   \n",
       "26                                                   NaN     Apartment   \n",
       "27     Access to the living room, bathroom and kitche...     Apartment   \n",
       "28     You have the possibility to cook and hang out ...     Apartment   \n",
       "29                                                   NaN          Loft   \n",
       "...                                                  ...           ...   \n",
       "33508  Guests can access the kitchen, they're totally...     Apartment   \n",
       "33509                                                NaN     Apartment   \n",
       "33510                                                NaN     Apartment   \n",
       "33511                                          厨房,客厅是公用的     Townhouse   \n",
       "33512  kitchen bathroom livingroom private room rooft...     Apartment   \n",
       "33513                                                NaN         House   \n",
       "33514                                                NaN     Apartment   \n",
       "33515  Guest will have access to the shared living ro...     Apartment   \n",
       "33516  The main entrance door is best, sorry no eleva...     Apartment   \n",
       "33517  Close to everything Bus ,Train, Uber Pick up a...     Apartment   \n",
       "33518  Kitchen, communal rooftop terrace with incredi...     Apartment   \n",
       "33519                                                NaN     Apartment   \n",
       "33520  Wireless, Netflix and HBO. All kitchenwares an...     Apartment   \n",
       "33521                                                NaN     Apartment   \n",
       "33522                                                NaN         House   \n",
       "33523                                                NaN     Apartment   \n",
       "33524  The kitchen and main bathroom with shower/basi...          Loft   \n",
       "33525                                                NaN     Apartment   \n",
       "33526  Kitchen, Living Room, both bathrooms. The whol...     Apartment   \n",
       "33527  Guest may use the kitchen for a coffee and lig...     Apartment   \n",
       "33528  This is your own apartment with full kitchen, ...     Apartment   \n",
       "33529  Guests will have access to the entire apartmen...     Apartment   \n",
       "33530  Private Studio and bathroom with (sometimes) s...     Apartment   \n",
       "33531  Exclusive access to entire apartment and backy...     Apartment   \n",
       "33532                                                NaN     Apartment   \n",
       "33533                                                NaN     Apartment   \n",
       "33534           Access to the unit is through elevators.         Other   \n",
       "33535                                                NaN         House   \n",
       "33536  Living room, bedroom, shared kitchen and bathr...     Apartment   \n",
       "33537  The entire home will be for you and your group...     Apartment   \n",
       "\n",
       "             room_type  accommodates  bathrooms  bedrooms  bed_type  \\\n",
       "0      Entire home/apt             2        1.0       1.0  Real Bed   \n",
       "1      Entire home/apt             8        1.0       3.0  Real Bed   \n",
       "2      Entire home/apt             2        1.0       0.0  Real Bed   \n",
       "3         Private room             1        1.0       1.0  Real Bed   \n",
       "4         Private room             2        1.0       1.0  Real Bed   \n",
       "5      Entire home/apt             6        2.5       2.0  Real Bed   \n",
       "6      Entire home/apt             4        1.0       1.0  Real Bed   \n",
       "7         Private room             1        1.0       1.0  Real Bed   \n",
       "8      Entire home/apt            16        2.0       0.0  Real Bed   \n",
       "9      Entire home/apt             4        1.0       1.0  Real Bed   \n",
       "10     Entire home/apt             2        1.0       1.0  Real Bed   \n",
       "11        Private room             2        1.0       1.0  Real Bed   \n",
       "12        Private room             2        1.0       1.0  Real Bed   \n",
       "13        Private room             2        1.0       1.0  Real Bed   \n",
       "14     Entire home/apt             5        1.0       2.0  Real Bed   \n",
       "15        Private room             2        1.0       1.0  Real Bed   \n",
       "16        Private room             2        1.0       1.0  Real Bed   \n",
       "17     Entire home/apt             4        2.0       2.0  Real Bed   \n",
       "18        Private room             1        1.0       1.0  Real Bed   \n",
       "19     Entire home/apt             6        1.0       2.0  Real Bed   \n",
       "20     Entire home/apt             1        1.0       0.0  Real Bed   \n",
       "21        Private room             2        1.0       1.0  Real Bed   \n",
       "22     Entire home/apt             2        1.0       1.0  Real Bed   \n",
       "23     Entire home/apt             2        1.0       0.0  Real Bed   \n",
       "24     Entire home/apt             2        1.0       1.0  Real Bed   \n",
       "25        Private room             2        1.0       1.0  Real Bed   \n",
       "26     Entire home/apt             2        1.0       2.0  Real Bed   \n",
       "27        Private room             2        1.0       1.0  Real Bed   \n",
       "28        Private room             1        1.0       0.0  Real Bed   \n",
       "29        Private room             2        1.0       1.0  Real Bed   \n",
       "...                ...           ...        ...       ...       ...   \n",
       "33508     Private room             2        2.0       1.0  Real Bed   \n",
       "33509     Private room             1        1.0       1.0  Real Bed   \n",
       "33510  Entire home/apt             4        1.0       2.0  Real Bed   \n",
       "33511     Private room             2        1.0       1.0  Real Bed   \n",
       "33512     Private room             2        1.0       1.0  Real Bed   \n",
       "33513     Private room             1        0.5       1.0  Real Bed   \n",
       "33514      Shared room             2        1.0       1.0  Real Bed   \n",
       "33515     Private room             2        1.0       1.0  Real Bed   \n",
       "33516     Private room             1        1.0       1.0  Real Bed   \n",
       "33517     Private room             1        1.0       1.0  Real Bed   \n",
       "33518  Entire home/apt             2        1.0       1.0  Real Bed   \n",
       "33519  Entire home/apt             4        1.0       1.0  Real Bed   \n",
       "33520     Private room             2        1.0       1.0  Real Bed   \n",
       "33521  Entire home/apt             2        1.0       1.0  Real Bed   \n",
       "33522     Private room             2        1.0       1.0  Real Bed   \n",
       "33523  Entire home/apt            10        2.0       3.0  Real Bed   \n",
       "33524     Private room             2        0.5       1.0  Real Bed   \n",
       "33525  Entire home/apt             2        1.0       0.0  Real Bed   \n",
       "33526     Private room             2        2.0       1.0  Real Bed   \n",
       "33527     Private room             2        1.0       1.0    No Bed   \n",
       "33528  Entire home/apt             2        1.0       1.0  Real Bed   \n",
       "33529     Private room             2        1.0       1.0  Real Bed   \n",
       "33530  Entire home/apt             4        1.0       1.0  Real Bed   \n",
       "33531  Entire home/apt             7        2.0       3.0  Real Bed   \n",
       "33532     Private room             1        1.0       1.0  Real Bed   \n",
       "33533     Private room             2        1.0       1.0  Real Bed   \n",
       "33534  Entire home/apt             8        3.0       4.0  Real Bed   \n",
       "33535     Private room             1        1.0       1.0  Real Bed   \n",
       "33536     Private room             2        1.0       1.0  Real Bed   \n",
       "33537  Entire home/apt             8        2.0       3.0  Real Bed   \n",
       "\n",
       "      guests_included extra_people  minimum_nights  maximum_nights  \\\n",
       "0                 two        30.00               7              12   \n",
       "1               three        19.00               3              30   \n",
       "2                 one         0.00               2            1125   \n",
       "3                 one         0.00               3            1125   \n",
       "4                 one        14.00               1              90   \n",
       "5                 one         0.00              30            1125   \n",
       "6                 two        25.00               3            1125   \n",
       "7                 one         0.00               2               2   \n",
       "8                 one         0.00               1               1   \n",
       "9                 two        25.00               1            1125   \n",
       "10                one        20.00               6              30   \n",
       "11                one        25.00               1            1125   \n",
       "12                one         0.00               1            1125   \n",
       "13                one        25.00               2              15   \n",
       "14                one         0.00               3            1125   \n",
       "15                one         0.00               1            1125   \n",
       "16                two        50.00               1            1125   \n",
       "17                two        20.00               3              90   \n",
       "18                one         0.00               2              60   \n",
       "19                one        30.00               2              24   \n",
       "20                one        50.00               5            1125   \n",
       "21                one         0.00               1              90   \n",
       "22                two        20.00               2            1125   \n",
       "23                one         0.00               5               9   \n",
       "24                two        75.00               1            1125   \n",
       "25                one         0.00               7              30   \n",
       "26                one         0.00               4             365   \n",
       "27                one        10.00               3            1125   \n",
       "28                one         0.00               7              12   \n",
       "29                one        30.00               1            1125   \n",
       "...               ...          ...             ...             ...   \n",
       "33508             one         0.00               2            1125   \n",
       "33509             one         0.00               2            1125   \n",
       "33510             one         0.00               1            1125   \n",
       "33511             two        10.00               1            1125   \n",
       "33512             one        60.00               3            1125   \n",
       "33513             one         0.00               1            1125   \n",
       "33514             one        25.00               2              15   \n",
       "33515             one         8.00               1            1125   \n",
       "33516             one        20.00               5              30   \n",
       "33517             one         0.00               5               8   \n",
       "33518             two        20.00               3            1125   \n",
       "33519             two        10.00               1            1125   \n",
       "33520             one        15.00               2               7   \n",
       "33521             one         0.00               1            1125   \n",
       "33522            four        20.00              30            1125   \n",
       "33523             one        50.00               1            1125   \n",
       "33524             one        40.00               2            1125   \n",
       "33525             one         0.00               1            1125   \n",
       "33526             one         0.00               1              60   \n",
       "33527             one        25.00               3              21   \n",
       "33528             one         0.00               5            1125   \n",
       "33529             one         0.00               1            1125   \n",
       "33530             two         0.00               1              30   \n",
       "33531            many        25.00               2              29   \n",
       "33532             one         0.00              30            1125   \n",
       "33533             one        25.00               3            1125   \n",
       "33534             one         0.00               3             365   \n",
       "33535             one         0.00             180             365   \n",
       "33536             one         0.00               7            1125   \n",
       "33537            four        25.00               5             120   \n",
       "\n",
       "               cancellation_policy neighbourhood_group_cleansed  \\\n",
       "0                         flexible                     Brooklyn   \n",
       "1                         moderate                    Manhattan   \n",
       "2                         moderate                    Manhattan   \n",
       "3                         flexible                     Brooklyn   \n",
       "4                         flexible                     Brooklyn   \n",
       "5                            Other                    Manhattan   \n",
       "6                         moderate                    Manhattan   \n",
       "7      strict_14_with_grace_period                    Manhattan   \n",
       "8      strict_14_with_grace_period                       Queens   \n",
       "9                         moderate                     Brooklyn   \n",
       "10                        flexible                     Brooklyn   \n",
       "11     strict_14_with_grace_period                    Manhattan   \n",
       "12                        flexible                     Brooklyn   \n",
       "13     strict_14_with_grace_period                     Brooklyn   \n",
       "14     strict_14_with_grace_period                    Manhattan   \n",
       "15                        flexible                    Manhattan   \n",
       "16                        flexible                       Queens   \n",
       "17     strict_14_with_grace_period                     Brooklyn   \n",
       "18     strict_14_with_grace_period                     Brooklyn   \n",
       "19                        moderate                    Manhattan   \n",
       "20     strict_14_with_grace_period                    Manhattan   \n",
       "21                        flexible                     Brooklyn   \n",
       "22                        moderate                     Brooklyn   \n",
       "23                        flexible                    Manhattan   \n",
       "24                        flexible                    Manhattan   \n",
       "25     strict_14_with_grace_period                     Brooklyn   \n",
       "26     strict_14_with_grace_period                    Manhattan   \n",
       "27     strict_14_with_grace_period                     Brooklyn   \n",
       "28                        moderate                    Manhattan   \n",
       "29                        flexible                     Brooklyn   \n",
       "...                            ...                          ...   \n",
       "33508  strict_14_with_grace_period                    Manhattan   \n",
       "33509                     flexible                     Brooklyn   \n",
       "33510                     flexible                    Manhattan   \n",
       "33511                     moderate                       Queens   \n",
       "33512                     flexible                     Brooklyn   \n",
       "33513                     flexible                     Brooklyn   \n",
       "33514  strict_14_with_grace_period                    Manhattan   \n",
       "33515                     moderate                     Brooklyn   \n",
       "33516                     flexible                     Brooklyn   \n",
       "33517                     flexible                     Brooklyn   \n",
       "33518  strict_14_with_grace_period                    Manhattan   \n",
       "33519                     flexible                    Manhattan   \n",
       "33520                     moderate                     Brooklyn   \n",
       "33521                     flexible                    Manhattan   \n",
       "33522  strict_14_with_grace_period                Staten Island   \n",
       "33523                     flexible                    Manhattan   \n",
       "33524                     moderate                       Queens   \n",
       "33525                     flexible                    Manhattan   \n",
       "33526  strict_14_with_grace_period                    Manhattan   \n",
       "33527  strict_14_with_grace_period                     Brooklyn   \n",
       "33528                     moderate                     Brooklyn   \n",
       "33529                     flexible                     Brooklyn   \n",
       "33530                     moderate                    Manhattan   \n",
       "33531  strict_14_with_grace_period                     Brooklyn   \n",
       "33532  strict_14_with_grace_period                    Manhattan   \n",
       "33533  strict_14_with_grace_period                    Manhattan   \n",
       "33534  strict_14_with_grace_period                    Manhattan   \n",
       "33535                     flexible                       Queens   \n",
       "33536                     moderate                    Manhattan   \n",
       "33537  strict_14_with_grace_period                    Manhattan   \n",
       "\n",
       "      host_is_superhost  \n",
       "0                     f  \n",
       "1                     f  \n",
       "2                     f  \n",
       "3                     f  \n",
       "4                     t  \n",
       "5                     f  \n",
       "6                     f  \n",
       "7                     f  \n",
       "8                     f  \n",
       "9                     f  \n",
       "10                    f  \n",
       "11                    t  \n",
       "12                    f  \n",
       "13                    f  \n",
       "14                    f  \n",
       "15                    f  \n",
       "16                    f  \n",
       "17                    t  \n",
       "18                    f  \n",
       "19                    f  \n",
       "20                    f  \n",
       "21                    f  \n",
       "22                    f  \n",
       "23                    f  \n",
       "24                    f  \n",
       "25                    f  \n",
       "26                    f  \n",
       "27                    t  \n",
       "28                    f  \n",
       "29                    f  \n",
       "...                 ...  \n",
       "33508                 f  \n",
       "33509                 f  \n",
       "33510                 f  \n",
       "33511                 f  \n",
       "33512                 f  \n",
       "33513                 f  \n",
       "33514                 t  \n",
       "33515                 f  \n",
       "33516                 f  \n",
       "33517                 f  \n",
       "33518                 f  \n",
       "33519                 f  \n",
       "33520                 t  \n",
       "33521                 f  \n",
       "33522                 f  \n",
       "33523                 f  \n",
       "33524                 f  \n",
       "33525                 f  \n",
       "33526                 f  \n",
       "33527                 f  \n",
       "33528                 f  \n",
       "33529                 f  \n",
       "33530                 f  \n",
       "33531                 f  \n",
       "33532                 f  \n",
       "33533                 f  \n",
       "33534                 f  \n",
       "33535                 f  \n",
       "33536                 f  \n",
       "33537                 f  \n",
       "\n",
       "[33538 rows x 16 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/scipy/sparse/sparsetools.py:21: DeprecationWarning: `scipy.sparse.sparsetools` is deprecated!\n",
      "scipy.sparse.sparsetools is a private module for scipy.sparse, and should not be used.\n",
      "  _deprecated()\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "\n",
    "import json\n",
    "import pyLDAvis.gensim\n",
    "pyLDAvis.enable_notebook()\n",
    "\n",
    "from gensim.corpora.dictionary import Dictionary\n",
    "from gensim.models.tfidfmodel import TfidfModel\n",
    "from gensim.models.ldamodel import LdaModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_text(corpus):\n",
    "    processed_corpus = []\n",
    "    english_words = set(nltk.corpus.words.words())\n",
    "    english_stopwords = set(stopwords.words('english'))\n",
    "    wordnet_lemmatizer = WordNetLemmatizer()\n",
    "    tokenizer = RegexpTokenizer(r'[A-Za-z|!]+')\n",
    "    for row in corpus:\n",
    "        sentences = []\n",
    "        word_tokens = tokenizer.tokenize(row)\n",
    "        word_tokens_lower = [t.lower() for t in word_tokens]\n",
    "        word_tokens_lower_english = [t for t in word_tokens_lower if t in english_words or not t.isalpha()]\n",
    "        word_tokens_no_stops = [t for t in word_tokens_lower_english if not t in english_stopwords]\n",
    "        word_tokens_no_stops_lemmatized = [wordnet_lemmatizer.lemmatize(t) for t in word_tokens_no_stops]\n",
    "        for word in word_tokens_no_stops_lemmatized:\n",
    "            if len(word) >= 2:\n",
    "                sentences.append(word)\n",
    "        processed_corpus.append(sentences)\n",
    "    return processed_corpus\n",
    "\n",
    "def pipline(processed_corpus):\n",
    "    dictionary = Dictionary(processed_corpus)\n",
    "    doc_term_matrix = [dictionary.doc2bow(listing) for listing in processed_corpus]\n",
    "    return dictionary, doc_term_matrix\n",
    "\n",
    "def lda_topic_model(doc_term_matrix,dictionary,num_topics = 3, passes = 2):\n",
    "    LDA = LdaModel\n",
    "    ldamodel = LDA(doc_term_matrix,num_topics = num_topics, id2word = dictionary, passes = passes)\n",
    "    return ldamodel\n",
    "\n",
    "def topic_feature(ldamodel, doc_term_matrix, df, new_col, num_topics):\n",
    "    docTopicProbMat = ldamodel[doc_term_matrix]\n",
    "    docTopicProbDf = pd.DataFrame(index = df.index, columns = range(0,num_topics))\n",
    "    for i,doc in enumerate(docTopicProbMat):\n",
    "        for topic in doc:\n",
    "            docTopicProbDf.iloc[i,topic[0]] = topic[1]\n",
    "    docTopicProbDf = docTopicProbDf.fillna(0)\n",
    "    docTopicProbDf[new_col] = docTopicProbDf.idxmax(axis=1)\n",
    "    df_topics = docTopicProbDf[new_col]\n",
    "    df_new = pd.concat([df,df_topics],axis = 1)\n",
    "    return df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package words to /home/y1do/nltk_data...\n",
      "[nltk_data]   Package words is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('words')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to /home/y1do/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus_description = new_df['description'].astype(str)\n",
    "\n",
    "# use nlp package to process the text in description\n",
    "processed_corpus_description = preprocess_text(corpus_description)\n",
    "\n",
    "# generate the doc_term_matrix for lda model\n",
    "#dictionary_description, doc_term_matrix_description = pipline(processed_corpus_description)\n",
    "\n",
    "# lda model for topic modeling\n",
    "#ldamodel_description = lda_topic_model(doc_term_matrix_description,dictionary_description)\n",
    "\n",
    "# add the topic feature to the dataframe\n",
    "#final_df = topic_feature(ldamodel_description,doc_term_matrix_description,new_df,new_col = 'description_topic', num_topics =3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate the doc_term_matrix for lda model\n",
    "dictionary_description, doc_term_matrix_description = pipline(processed_corpus_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lda model for topic modeling\n",
    "ldamodel_description = lda_topic_model(doc_term_matrix_description,dictionary_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the topic feature to the dataframe\n",
    "final_df = topic_feature(ldamodel_description,doc_term_matrix_description, new_df,new_col = 'description_topic', num_topics =4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "review_median = df['reviews_per_month'].median()\n",
    "final_df['reviews_per_month'] = df['reviews_per_month'].fillna(review_median)\n",
    "final_df['access'] = df['access']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def match(x):\n",
    "    matches = [\"full\", \"whole\", \"entire\", \"WHOLE\", \"ENTIRE\"]\n",
    "    if any(a in x for a in matches):\n",
    "        return True\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "saved = df['access'].fillna('None')\n",
    "df['access'] = saved\n",
    "str_saved = df['access'].apply(match)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>id</th>\n",
       "      <th>description</th>\n",
       "      <th>access</th>\n",
       "      <th>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bed_type</th>\n",
       "      <th>guests_included</th>\n",
       "      <th>extra_people</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>description_topic</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>new_access</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22267382</td>\n",
       "      <td>Modern large studio with new amenities and app...</td>\n",
       "      <td>Washer/Dryer Dishwasher Internet Gym Roof Top ...</td>\n",
       "      <td>Loft</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>two</td>\n",
       "      <td>30.00</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "      <td>2</td>\n",
       "      <td>0.59</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2473861</td>\n",
       "      <td>Harlem is back and so gorgeous! Visit and expl...</td>\n",
       "      <td>The WHOLE ENTIRE HOUSE</td>\n",
       "      <td>House</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>three</td>\n",
       "      <td>19.00</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "      <td>2</td>\n",
       "      <td>2.47</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25079703</td>\n",
       "      <td>Clean, hip and well designed sun drenched East...</td>\n",
       "      <td>You'll have access to the entire space - it's ...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1125</td>\n",
       "      <td>moderate</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>f</td>\n",
       "      <td>2</td>\n",
       "      <td>0.89</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9342478</td>\n",
       "      <td>Private, spacious, comfortable room in 2-bed f...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3</td>\n",
       "      <td>1125</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>0.77</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4866426</td>\n",
       "      <td>Comfy, quiet and big private room in a three b...</td>\n",
       "      <td>Your room has a very comfortable queen sized b...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>flexible</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>t</td>\n",
       "      <td>2</td>\n",
       "      <td>3.14</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id                                        description  \\\n",
       "0  22267382  Modern large studio with new amenities and app...   \n",
       "1   2473861  Harlem is back and so gorgeous! Visit and expl...   \n",
       "2  25079703  Clean, hip and well designed sun drenched East...   \n",
       "3   9342478  Private, spacious, comfortable room in 2-bed f...   \n",
       "4   4866426  Comfy, quiet and big private room in a three b...   \n",
       "\n",
       "                                              access property_type  \\\n",
       "0  Washer/Dryer Dishwasher Internet Gym Roof Top ...          Loft   \n",
       "1                             The WHOLE ENTIRE HOUSE         House   \n",
       "2  You'll have access to the entire space - it's ...     Apartment   \n",
       "3                                                NaN     Apartment   \n",
       "4  Your room has a very comfortable queen sized b...     Apartment   \n",
       "\n",
       "         room_type  accommodates  bathrooms  bedrooms  bed_type  \\\n",
       "0  Entire home/apt             2        1.0       1.0  Real Bed   \n",
       "1  Entire home/apt             8        1.0       3.0  Real Bed   \n",
       "2  Entire home/apt             2        1.0       0.0  Real Bed   \n",
       "3     Private room             1        1.0       1.0  Real Bed   \n",
       "4     Private room             2        1.0       1.0  Real Bed   \n",
       "\n",
       "  guests_included extra_people  minimum_nights  maximum_nights  \\\n",
       "0             two        30.00               7              12   \n",
       "1           three        19.00               3              30   \n",
       "2             one         0.00               2            1125   \n",
       "3             one         0.00               3            1125   \n",
       "4             one        14.00               1              90   \n",
       "\n",
       "  cancellation_policy neighbourhood_group_cleansed host_is_superhost  \\\n",
       "0            flexible                     Brooklyn                 f   \n",
       "1            moderate                    Manhattan                 f   \n",
       "2            moderate                    Manhattan                 f   \n",
       "3            flexible                     Brooklyn                 f   \n",
       "4            flexible                     Brooklyn                 t   \n",
       "\n",
       "   description_topic  reviews_per_month  new_access  \n",
       "0                  2               0.59       False  \n",
       "1                  2               2.47        True  \n",
       "2                  2               0.89        True  \n",
       "3                  0               0.77       False  \n",
       "4                  2               3.14       False  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['new_access'] = str_saved\n",
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bath_match(x):\n",
    "    if x < 3:\n",
    "        return 'not many'\n",
    "    else:\n",
    "        return 'many'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bed_match(x):\n",
    "    if x < 5:\n",
    "        return 'not many'\n",
    "    else:\n",
    "        return 'many'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def acc_match(x):\n",
    "    if x < 8:\n",
    "        return 'not many'\n",
    "    else:\n",
    "        return 'many'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "bath_saved = df['bathrooms'].apply(bath_match)\n",
    "final_df['bath'] = bath_saved\n",
    "\n",
    "bed_saved = df['bedrooms'].apply(bed_match)\n",
    "final_df['bed'] = bed_saved\n",
    "\n",
    "acc_saved = df['accommodates'].apply(acc_match)\n",
    "final_df['acc'] = acc_saved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_corpus_description = df['space'].astype(str)\n",
    "\n",
    "# use nlp package to process the text in description\n",
    "s_processed_corpus_description = preprocess_text(s_corpus_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate the doc_term_matrix for lda model\n",
    "s_dictionary_description, s_doc_term_matrix_description = pipline(s_processed_corpus_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lda model for topic modeling\n",
    "s_ldamodel_description = lda_topic_model(s_doc_term_matrix_description, s_dictionary_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the topic feature to the dataframe\n",
    "the_final_df = topic_feature(s_ldamodel_description,s_doc_term_matrix_description, final_df,new_col = 'space_topic', num_topics =3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_corpus_description = df['name'].astype(str)\n",
    "\n",
    "# use nlp package to process the text in description\n",
    "n_processed_corpus_description = preprocess_text(n_corpus_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate the doc_term_matrix for lda model\n",
    "n_dictionary_description, n_doc_term_matrix_description = pipline(n_processed_corpus_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lda model for topic modeling\n",
    "n_ldamodel_description = lda_topic_model(n_doc_term_matrix_description, n_dictionary_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the topic feature to the dataframe\n",
    "name_final_df = topic_feature(n_ldamodel_description,n_doc_term_matrix_description, the_final_df,new_col = 'name_topic', num_topics =3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum_corpus_description = df['summary'].astype(str)\n",
    "\n",
    "# use nlp package to process the text in description\n",
    "sum_processed_corpus_description = preprocess_text(sum_corpus_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate the doc_term_matrix for lda model\n",
    "sum_dictionary_description, sum_doc_term_matrix_description = pipline(sum_processed_corpus_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lda model for topic modeling\n",
    "sum_ldamodel_description = lda_topic_model(sum_doc_term_matrix_description, sum_dictionary_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the topic feature to the dataframe\n",
    "summary_final_df = topic_feature(sum_ldamodel_description,sum_doc_term_matrix_description, name_final_df,new_col = 'summary_topic', num_topics =3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accommodates(x):\n",
    "    if x >= 7:\n",
    "        return True\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>id</th>\n",
       "      <th>description</th>\n",
       "      <th>access</th>\n",
       "      <th>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bed_type</th>\n",
       "      <th>guests_included</th>\n",
       "      <th>...</th>\n",
       "      <th>description_topic</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>new_access</th>\n",
       "      <th>bath</th>\n",
       "      <th>bed</th>\n",
       "      <th>acc</th>\n",
       "      <th>space_topic</th>\n",
       "      <th>name_topic</th>\n",
       "      <th>summary_topic</th>\n",
       "      <th>acc_lot?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22267382</td>\n",
       "      <td>Modern large studio with new amenities and app...</td>\n",
       "      <td>Washer/Dryer Dishwasher Internet Gym Roof Top ...</td>\n",
       "      <td>Loft</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>two</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.59</td>\n",
       "      <td>False</td>\n",
       "      <td>not many</td>\n",
       "      <td>not many</td>\n",
       "      <td>not many</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2473861</td>\n",
       "      <td>Harlem is back and so gorgeous! Visit and expl...</td>\n",
       "      <td>The WHOLE ENTIRE HOUSE</td>\n",
       "      <td>House</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>three</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2.47</td>\n",
       "      <td>True</td>\n",
       "      <td>not many</td>\n",
       "      <td>not many</td>\n",
       "      <td>many</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25079703</td>\n",
       "      <td>Clean, hip and well designed sun drenched East...</td>\n",
       "      <td>You'll have access to the entire space - it's ...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>one</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.89</td>\n",
       "      <td>True</td>\n",
       "      <td>not many</td>\n",
       "      <td>not many</td>\n",
       "      <td>not many</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id                                        description  \\\n",
       "0  22267382  Modern large studio with new amenities and app...   \n",
       "1   2473861  Harlem is back and so gorgeous! Visit and expl...   \n",
       "2  25079703  Clean, hip and well designed sun drenched East...   \n",
       "\n",
       "                                              access property_type  \\\n",
       "0  Washer/Dryer Dishwasher Internet Gym Roof Top ...          Loft   \n",
       "1                             The WHOLE ENTIRE HOUSE         House   \n",
       "2  You'll have access to the entire space - it's ...     Apartment   \n",
       "\n",
       "         room_type  accommodates  bathrooms  bedrooms  bed_type  \\\n",
       "0  Entire home/apt             2        1.0       1.0  Real Bed   \n",
       "1  Entire home/apt             8        1.0       3.0  Real Bed   \n",
       "2  Entire home/apt             2        1.0       0.0  Real Bed   \n",
       "\n",
       "  guests_included   ...    description_topic  reviews_per_month  new_access  \\\n",
       "0             two   ...                    2               0.59       False   \n",
       "1           three   ...                    2               2.47        True   \n",
       "2             one   ...                    2               0.89        True   \n",
       "\n",
       "       bath       bed       acc  space_topic  name_topic  summary_topic  \\\n",
       "0  not many  not many  not many            2           2              2   \n",
       "1  not many  not many      many            1           2              0   \n",
       "2  not many  not many  not many            1           2              0   \n",
       "\n",
       "  acc_lot?  \n",
       "0    False  \n",
       "1     True  \n",
       "2    False  \n",
       "\n",
       "[3 rows x 26 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accom_saved = df['accommodates'].apply(accommodates)\n",
    "summary_final_df['acc_lot?'] = accom_saved\n",
    "summary_final_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df.groupby(['host_neighbourhood']).mean()['price'].sort_values()\n",
    "\n",
    "really_cheap = data.loc[(data.values >= 0) & (data.values < 50)].index.tolist()\n",
    "pretty_cheap = data.loc[(data.values >= 50) & (data.values < 75)].index.tolist()\n",
    "cheap = data.loc[(data.values >= 75) & (data.values < 100)].index.tolist()\n",
    "under_110 = data.loc[(data.values >= 100) & (data.values < 110)].index.tolist()\n",
    "under_120 = data.loc[(data.values >= 110) & (data.values < 120)].index.tolist()\n",
    "under_130 = data.loc[(data.values >= 120) & (data.values < 130)].index.tolist()\n",
    "under_140 = data.loc[(data.values >= 130) & (data.values < 140)].index.tolist()\n",
    "under_150 = data.loc[(data.values >= 140) & (data.values < 150)].index.tolist()\n",
    "under_160 = data.loc[(data.values >= 150) & (data.values < 160)].index.tolist()\n",
    "under_180 = data.loc[(data.values >= 160) & (data.values < 180)].index.tolist()\n",
    "under_190 = data.loc[(data.values >= 180) & (data.values < 190)].index.tolist()\n",
    "under_200 = data.loc[(data.values >= 190) & (data.values < 200)].index.tolist()\n",
    "under_210 = data.loc[(data.values >= 200) & (data.values < 210)].index.tolist()\n",
    "under_220 = data.loc[(data.values >= 210) & (data.values < 220)].index.tolist()\n",
    "under_230 = data.loc[(data.values >= 220) & (data.values < 230)].index.tolist()\n",
    "under_240 = data.loc[(data.values >= 230) & (data.values < 240)].index.tolist()\n",
    "under_250 = data.loc[(data.values >= 240) & (data.values < 250)].index.tolist()\n",
    "under_260 = data.loc[(data.values >= 250) & (data.values < 260)].index.tolist()\n",
    "under_270 = data.loc[(data.values >= 260) & (data.values < 270)].index.tolist()\n",
    "under_280 = data.loc[(data.values >= 270) & (data.values < 280)].index.tolist()\n",
    "expensive_300 = data.loc[(data.values >= 280) & (data.values < 400)].index.tolist()\n",
    "expensive_400 = data.loc[(data.values >= 400) & (data.values < 500)].index.tolist()\n",
    "expensive_500 = data.loc[(data.values >= 500) & (data.values < 600)].index.tolist()\n",
    "expensive_600 = data.loc[(data.values >= 600) & (data.values < 700)].index.tolist()\n",
    "really_expensive = data.loc[data.values >= 700].index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [],
   "source": [
    "def host_cat(x):\n",
    "    if x in really_cheap:\n",
    "        return 'really cheap'\n",
    "    elif x in pretty_cheap:\n",
    "        return 'pretty cheap'\n",
    "    elif x in cheap:\n",
    "        return 'cheap'\n",
    "    elif x in under_110:\n",
    "        return 'under 110'\n",
    "    elif x in under_120:\n",
    "        return 'under 120'\n",
    "    elif x in under_130:\n",
    "        return 'under 130'\n",
    "    elif x in under_140:\n",
    "        return 'under 140'\n",
    "    elif x in under_150:\n",
    "        return 'under 150'\n",
    "    elif x in under_160:\n",
    "        return 'under 160'\n",
    "    elif x in under_180:\n",
    "        return 'under 180'\n",
    "    elif x in under_190:\n",
    "        return 'under 190'\n",
    "    elif x in under_200:\n",
    "        return 'under 200'\n",
    "    elif x in under_110:\n",
    "        return 'under 210'\n",
    "    elif x in under_220:\n",
    "        return 'under 220'\n",
    "    elif x in under_230:\n",
    "        return 'under 230'\n",
    "    elif x in under_240:\n",
    "        return 'under 240'\n",
    "    elif x in under_250:\n",
    "        return 'under 250'\n",
    "    elif x in under_260:\n",
    "        return 'under 260'\n",
    "    elif x in under_270:\n",
    "        return 'under 270'\n",
    "    elif x in under_280:\n",
    "        return 'under 280'\n",
    "    elif x in expensive_300:\n",
    "        return 'expensive_300'\n",
    "    elif x in expensive_400:\n",
    "        return 'expensive_400'\n",
    "    elif x in expensive_500:\n",
    "        return 'expensive_500'\n",
    "    elif x in expensive_600:\n",
    "        return 'expensive_600'\n",
    "    else:\n",
    "        return 'really expensive'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [],
   "source": [
    "host_saved = df['host_neighbourhood'].apply(host_cat)\n",
    "summary_final_df['host_cat'] = host_saved\n",
    "#summary_final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df.groupby(['neighbourhood_cleansed']).mean().price.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [],
   "source": [
    "under_50 = data.loc[(data.values >= 0) & (data.values < 50)].index.tolist()\n",
    "under_100 = data.loc[(n_data.values >= 50) & (data.values < 100)].index.tolist()\n",
    "under_125 = data.loc[(data.values >= 100) & (data.values < 125)].index.tolist()\n",
    "under_150 = data.loc[(data.values >= 125) & (data.values < 150)].index.tolist()\n",
    "under_175 = data.loc[(data.values >= 150) & (data.values < 175)].index.tolist()\n",
    "under_200 = data.loc[(data.values >= 175) & (data.values < 200)].index.tolist()\n",
    "under_225 = data.loc[(data.values >= 200) & (data.values < 225)].index.tolist()\n",
    "under_250 = data.loc[(data.values >= 225) & (data.values < 250)].index.tolist()\n",
    "under_275 = data.loc[(data.values >= 250) & (data.values < 275)].index.tolist()\n",
    "under_300 = data.loc[(data.values >= 275) & (data.values < 300)].index.tolist()\n",
    "under_400 = data.loc[(data.values >= 300) & (data.values < 400)].index.tolist()\n",
    "over_400 = data.loc[(data.values >= 400) & (data.values < 800)].index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [],
   "source": [
    "def n_cat(x):\n",
    "    if x in under_50:\n",
    "        return 'under 50'\n",
    "    elif x in under_100:\n",
    "        return 'under 100'\n",
    "    elif x in under_125:\n",
    "        return 'under 125'\n",
    "    elif x in under_150:\n",
    "        return 'under 150'\n",
    "    elif x in under_175:\n",
    "        return 'under 175'\n",
    "    elif x in under_200:\n",
    "        return 'under 200'\n",
    "    elif x in under_225:\n",
    "        return 'under 225'\n",
    "    elif x in under_250:\n",
    "        return 'under 250'\n",
    "    elif x in under_275:\n",
    "        return 'under 275'\n",
    "    elif x in under_300:\n",
    "        return 'under 300'\n",
    "    elif x in under_400:\n",
    "        return 'under 400'\n",
    "    else:\n",
    "        return 'over 400'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_saved = df['neighbourhood_cleansed'].apply(n_cat)\n",
    "summary_final_df['n_cat'] = n_saved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "guests_included\n",
       "1     130.441134\n",
       "2     145.416110\n",
       "3     196.831155\n",
       "4     241.356634\n",
       "5     278.460432\n",
       "6     333.238636\n",
       "7     286.906250\n",
       "8     421.814433\n",
       "9     538.000000\n",
       "10    454.440000\n",
       "11    624.000000\n",
       "12    706.750000\n",
       "14    413.500000\n",
       "15    300.000000\n",
       "16    589.222222\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['guests_included']).mean().price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "def guest_classify(x):\n",
    "    if (x >= 1) and (x < 4):\n",
    "        return 'pretty cheap'\n",
    "    elif (x >= 4) and (x < 8):\n",
    "        return 'medium'\n",
    "    else:\n",
    "        return 'expensive'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pretty cheap    31169\n",
       "medium           2207\n",
       "expensive         162\n",
       "Name: guests_included, dtype: int64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "guest_saved = df['guests_included'].apply(guest_classify)\n",
    "summary_final_df['guest_cat'] = guest_saved\n",
    "guest_saved.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "number_of_reviews\n",
       "0    169.699608\n",
       "1    139.106537\n",
       "2    130.456785\n",
       "3    138.150798\n",
       "4    134.669811\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['number_of_reviews']).mean().price.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reviews_num(x):\n",
    "    if x < 170:\n",
    "        return 'three_digit'\n",
    "    else:\n",
    "        return 'two_digit'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "review_saved = df['number_of_reviews'].apply(reviews_num)\n",
    "summary_final_df['review_cat'] = review_saved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "reviews_per_month\n",
       "0.01    179.333333\n",
       "0.02    138.504132\n",
       "0.03    123.884932\n",
       "0.04    135.406439\n",
       "0.05    126.687500\n",
       "0.06    127.200521\n",
       "0.07    123.778495\n",
       "0.08    145.285714\n",
       "0.09    155.651099\n",
       "0.10    137.375810\n",
       "0.11    143.649351\n",
       "0.12    142.863636\n",
       "0.13    142.531987\n",
       "0.14    139.676375\n",
       "0.15    139.442231\n",
       "0.16    128.295833\n",
       "0.17    131.920705\n",
       "0.18    128.134615\n",
       "0.19    129.487365\n",
       "0.20    132.008333\n",
       "0.21    136.173709\n",
       "0.22    136.945946\n",
       "0.23    138.040359\n",
       "0.24    143.200000\n",
       "0.25    145.204545\n",
       "0.26    137.872928\n",
       "0.27    133.691011\n",
       "0.28    138.814815\n",
       "0.29    129.412621\n",
       "0.30    146.840237\n",
       "           ...    \n",
       "6.46     66.000000\n",
       "6.47    108.333333\n",
       "6.49     58.500000\n",
       "6.50     75.000000\n",
       "6.51     74.333333\n",
       "6.52    125.200000\n",
       "6.53    149.666667\n",
       "6.55    116.400000\n",
       "6.56    163.000000\n",
       "6.57     70.000000\n",
       "6.58     54.000000\n",
       "6.59     35.000000\n",
       "6.60     89.333333\n",
       "6.61     74.000000\n",
       "6.62     90.000000\n",
       "6.63    133.000000\n",
       "6.64     58.000000\n",
       "6.65     75.000000\n",
       "6.67    146.000000\n",
       "6.68     90.000000\n",
       "6.69     58.000000\n",
       "6.70     30.000000\n",
       "6.71     80.333333\n",
       "6.72     86.500000\n",
       "6.73    133.333333\n",
       "6.74     69.500000\n",
       "6.75     94.000000\n",
       "6.76     80.000000\n",
       "6.77    102.250000\n",
       "6.78    299.000000\n",
       "Name: price, Length: 670, dtype: float64"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['reviews_per_month']).mean().price.head(670)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rpm_cat(x):\n",
    "    if (x >= 0.00) & (x < 4.97):\n",
    "        return 'high'\n",
    "    elif (x >= 4.97) & (x < 6.5):\n",
    "        return 'medium'\n",
    "    else:\n",
    "        return 'low'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "rpm_saved = summary_final_df['reviews_per_month'].apply(reviews_num)\n",
    "summary_final_df['rpm_cat'] = rpm_saved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "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>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>114.266917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.5</th>\n",
       "      <td>74.793103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>99.790140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.5</th>\n",
       "      <td>77.513158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>108.450643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.5</th>\n",
       "      <td>99.803478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>140.459173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.5</th>\n",
       "      <td>133.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>161.900199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.5</th>\n",
       "      <td>154.565891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>181.722308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.5</th>\n",
       "      <td>229.234375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>224.684433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.5</th>\n",
       "      <td>321.297030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>248.882543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.5</th>\n",
       "      <td>248.813187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>280.546875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.5</th>\n",
       "      <td>315.382022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>312.048346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.5</th>\n",
       "      <td>353.567901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>322.944681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.5</th>\n",
       "      <td>388.755556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>359.737179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.5</th>\n",
       "      <td>372.861111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>392.473118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.5</th>\n",
       "      <td>423.394737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>394.869048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.5</th>\n",
       "      <td>515.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>435.915254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.5</th>\n",
       "      <td>564.842105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>476.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.5</th>\n",
       "      <td>500.764706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.0</th>\n",
       "      <td>541.652174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.5</th>\n",
       "      <td>506.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.0</th>\n",
       "      <td>596.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.5</th>\n",
       "      <td>480.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.0</th>\n",
       "      <td>566.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.5</th>\n",
       "      <td>482.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.0</th>\n",
       "      <td>625.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.5</th>\n",
       "      <td>563.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.0</th>\n",
       "      <td>758.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.5</th>\n",
       "      <td>562.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23.0</th>\n",
       "      <td>530.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23.5</th>\n",
       "      <td>372.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24.0</th>\n",
       "      <td>579.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25.0</th>\n",
       "      <td>617.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25.5</th>\n",
       "      <td>1210.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26.5</th>\n",
       "      <td>616.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27.0</th>\n",
       "      <td>497.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27.5</th>\n",
       "      <td>690.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28.0</th>\n",
       "      <td>400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29.0</th>\n",
       "      <td>499.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32.0</th>\n",
       "      <td>847.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            price\n",
       "0                \n",
       "2.0    114.266917\n",
       "2.5     74.793103\n",
       "3.0     99.790140\n",
       "3.5     77.513158\n",
       "4.0    108.450643\n",
       "4.5     99.803478\n",
       "5.0    140.459173\n",
       "5.5    133.266667\n",
       "6.0    161.900199\n",
       "6.5    154.565891\n",
       "7.0    181.722308\n",
       "7.5    229.234375\n",
       "8.0    224.684433\n",
       "8.5    321.297030\n",
       "9.0    248.882543\n",
       "9.5    248.813187\n",
       "10.0   280.546875\n",
       "10.5   315.382022\n",
       "11.0   312.048346\n",
       "11.5   353.567901\n",
       "12.0   322.944681\n",
       "12.5   388.755556\n",
       "13.0   359.737179\n",
       "13.5   372.861111\n",
       "14.0   392.473118\n",
       "14.5   423.394737\n",
       "15.0   394.869048\n",
       "15.5   515.833333\n",
       "16.0   435.915254\n",
       "16.5   564.842105\n",
       "17.0   476.875000\n",
       "17.5   500.764706\n",
       "18.0   541.652174\n",
       "18.5   506.285714\n",
       "19.0   596.700000\n",
       "19.5   480.444444\n",
       "20.0   566.750000\n",
       "20.5   482.500000\n",
       "21.0   625.181818\n",
       "21.5   563.250000\n",
       "22.0   758.363636\n",
       "22.5   562.800000\n",
       "23.0   530.000000\n",
       "23.5   372.500000\n",
       "24.0   579.200000\n",
       "25.0   617.428571\n",
       "25.5  1210.000000\n",
       "26.5   616.000000\n",
       "27.0   497.500000\n",
       "27.5   690.000000\n",
       "28.0   400.000000\n",
       "29.0   499.000000\n",
       "32.0   847.500000"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined = df['accommodates'] + df['bedrooms'] + df['bathrooms']\n",
    "com_df = pd.DataFrame(combined)\n",
    "com_df['price'] = df['price']\n",
    "com_df.groupby([0]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {},
   "outputs": [],
   "source": [
    "def combined_cat(x):\n",
    "    if x <= 3:\n",
    "        return 'one'\n",
    "    elif (x > 3) & (x <= 5):\n",
    "        return 'two'\n",
    "    elif (x > 5) & (x <= 7):\n",
    "        return 'three'\n",
    "    elif (x > 7) & (x <= 10):\n",
    "        return 'four'\n",
    "    elif (x > 10) & (x <= 12):\n",
    "        return 'four'\n",
    "    elif (x > 12) & (x <= 15):\n",
    "        return 'four'\n",
    "    else:\n",
    "        return 'else'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_saved = com_df[0].apply(combined_cat)\n",
    "summary_final_df['combined_cat'] = combined_saved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_to_keep = [\n",
    "'accommodates','bathrooms','bedrooms','maximum_nights','minimum_nights','property_type', 'reviews_per_month',\n",
    "                'bed_type','room_type','extra_people','new_access','cancellation_policy', 'acc_lot?', 'host_cat', 'n_cat',\n",
    "                'neighbourhood_group_cleansed', 'description_topic', 'space_topic','name_topic', 'summary_topic', \n",
    "    'host_is_superhost', 'guest_cat', 'review_cat']\n",
    "model_df = summary_final_df[cols_to_keep]\n",
    "\n",
    "# convert strings to dummies\n",
    "categorical_feats = ['property_type','room_type','bed_type', 'new_access','cancellation_policy',\n",
    "                     'neighbourhood_group_cleansed',\n",
    "                     'description_topic', 'space_topic','name_topic', 'summary_topic', 'acc_lot?', \n",
    "                     'host_cat', 'n_cat', 'host_is_superhost', 'guest_cat', 'review_cat']\n",
    "model_df = pd.get_dummies(model_df,columns = categorical_feats,drop_first = False)\n",
    "\n",
    "# separate the target variable \"yield\" from the dataset\n",
    "target = df['price']\n",
    "X_df = model_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 17337 rows and 64 columns in the dataset\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3020: DtypeWarning: Columns (30) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "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>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>...</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</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>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19307997</td>\n",
       "      <td>Super Lux 2BR in Downtown Manhattan</td>\n",
       "      <td>Prepare to be WOWED! This spectacularly bright...</td>\n",
       "      <td>Top of the line Wolf and Sub-Zero appliances, ...</td>\n",
       "      <td>Prepare to be WOWED! This spectacularly bright...</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20176193</td>\n",
       "      <td>Vintage Eclectic Brownstone Pad in Brooklyn</td>\n",
       "      <td>Ideal for romantic, creative types, this is an...</td>\n",
       "      <td>Not your typical New York abode, my apartment ...</td>\n",
       "      <td>Ideal for romantic, creative types, this is an...</td>\n",
       "      <td>none</td>\n",
       "      <td>Bed Stuy is a diverse historic neighborhood wi...</td>\n",
       "      <td>This is an actual unique living experience whe...</td>\n",
       "      <td>Close to buses and subways there is also free ...</td>\n",
       "      <td>Entrance hallway, living room, bedroom, kitche...</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</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>1.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19485371</td>\n",
       "      <td>Spacious Harlem Hideaway</td>\n",
       "      <td>Postive Vibes . This is our Harlem tree house,...</td>\n",
       "      <td>The private room is very spacious and cozy. Th...</td>\n",
       "      <td>Postive Vibes . This is our Harlem tree house,...</td>\n",
       "      <td>none</td>\n",
       "      <td>You are in a Cultural Haven full of restaurant...</td>\n",
       "      <td>We also keep cucumber water in the fridge feel...</td>\n",
       "      <td>Train, uber or a taxi. (Extremely taxi accessi...</td>\n",
       "      <td>Private Room, Kitchen And Bathroom</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>flexible</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id                                         name  \\\n",
       "0  19307997          Super Lux 2BR in Downtown Manhattan   \n",
       "1  20176193  Vintage Eclectic Brownstone Pad in Brooklyn   \n",
       "2  19485371                     Spacious Harlem Hideaway   \n",
       "\n",
       "                                             summary  \\\n",
       "0  Prepare to be WOWED! This spectacularly bright...   \n",
       "1  Ideal for romantic, creative types, this is an...   \n",
       "2  Postive Vibes . This is our Harlem tree house,...   \n",
       "\n",
       "                                               space  \\\n",
       "0  Top of the line Wolf and Sub-Zero appliances, ...   \n",
       "1  Not your typical New York abode, my apartment ...   \n",
       "2  The private room is very spacious and cozy. Th...   \n",
       "\n",
       "                                         description experiences_offered  \\\n",
       "0  Prepare to be WOWED! This spectacularly bright...                none   \n",
       "1  Ideal for romantic, creative types, this is an...                none   \n",
       "2  Postive Vibes . This is our Harlem tree house,...                none   \n",
       "\n",
       "                               neighborhood_overview  \\\n",
       "0                                                NaN   \n",
       "1  Bed Stuy is a diverse historic neighborhood wi...   \n",
       "2  You are in a Cultural Haven full of restaurant...   \n",
       "\n",
       "                                               notes  \\\n",
       "0                                                NaN   \n",
       "1  This is an actual unique living experience whe...   \n",
       "2  We also keep cucumber water in the fridge feel...   \n",
       "\n",
       "                                             transit  \\\n",
       "0                                                NaN   \n",
       "1  Close to buses and subways there is also free ...   \n",
       "2  Train, uber or a taxi. (Extremely taxi accessi...   \n",
       "\n",
       "                                              access        ...          \\\n",
       "0                                                NaN        ...           \n",
       "1  Entrance hallway, living room, bedroom, kitche...        ...           \n",
       "2                 Private Room, Kitchen And Bathroom        ...           \n",
       "\n",
       "  review_scores_communication review_scores_location  review_scores_value  \\\n",
       "0                         NaN                    NaN                  NaN   \n",
       "1                        10.0                   10.0                 10.0   \n",
       "2                        10.0                   10.0                 10.0   \n",
       "\n",
       "  instant_bookable is_business_travel_ready cancellation_policy  \\\n",
       "0                f                        f            flexible   \n",
       "1                f                        f            flexible   \n",
       "2                t                        f            flexible   \n",
       "\n",
       "  require_guest_profile_picture require_guest_phone_verification  \\\n",
       "0                             f                                f   \n",
       "1                             f                                f   \n",
       "2                             f                                f   \n",
       "\n",
       "  calculated_host_listings_count  reviews_per_month  \n",
       "0                              1                NaN  \n",
       "1                              1               1.48  \n",
       "2                              1               0.37  \n",
       "\n",
       "[3 rows x 64 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv('test.csv')\n",
    "print ('There are {} rows and {} columns in the dataset'.format(*test_df.shape))\n",
    "test_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:10: DeprecationWarning: invalid escape sequence \\$\n",
      "<>:10: DeprecationWarning: invalid escape sequence \\$\n",
      "<>:10: DeprecationWarning: invalid escape sequence \\$\n",
      "<ipython-input-97-69ab648beaf6>:10: DeprecationWarning: invalid escape sequence \\$\n",
      "  test_new_df.loc[:,test_feature_to_remove_dollar] = test_new_df.loc[:,test_feature_to_remove_dollar].replace('\\$','',regex = True)\n",
      "/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py:543: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "# drop all the unnecessary columns\n",
    "test_feature_to_keep = ['id','description','property_type','room_type','accommodates','bathrooms',\n",
    "                  'bedrooms','bed_type','guests_included','extra_people','minimum_nights',\n",
    "                  'maximum_nights','reviews_per_month', 'cancellation_policy','neighbourhood_group_cleansed','host_is_superhost']\n",
    "\n",
    "test_new_df = test_df[test_feature_to_keep]\n",
    "\n",
    "# remove the dollar sign before \"cleaning_fee\", \"extra_people\", \"price\" and change the datatype to numerical variables\n",
    "test_feature_to_remove_dollar = ['extra_people']\n",
    "test_new_df.loc[:,test_feature_to_remove_dollar] = test_new_df.loc[:,test_feature_to_remove_dollar].replace('\\$','',regex = True)\n",
    "test_new_df.loc[:,test_feature_to_remove_dollar] = test_new_df.loc[:,test_feature_to_remove_dollar].apply(pd.to_numeric,errors = \"coerce\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                 0\n",
       "description                      159\n",
       "property_type                      0\n",
       "room_type                          0\n",
       "accommodates                       0\n",
       "bathrooms                         36\n",
       "bedrooms                          23\n",
       "bed_type                           0\n",
       "guests_included                    0\n",
       "extra_people                       0\n",
       "minimum_nights                     0\n",
       "maximum_nights                     0\n",
       "reviews_per_month               3632\n",
       "cancellation_policy                0\n",
       "neighbourhood_group_cleansed       0\n",
       "host_is_superhost                  3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_new_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop 159 rows with missing descriptions\n",
    "test_new_df.loc[:,'description'] = test_new_df['description'].dropna()\n",
    "\n",
    "# fill NaN with median value for 'bathrooms', 'bedrooms','price'\n",
    "test_new_df.loc[:,'bathrooms'] = test_new_df.loc[:,'bathrooms'].fillna(test_new_df.loc[:,'bathrooms'].median())\n",
    "test_new_df.loc[:,'bedrooms'] = test_new_df.loc[:,'bedrooms'].fillna(test_new_df.loc[:,'bedrooms'].median())\n",
    "test_new_df.loc[:,'reviews_per_month'] = test_new_df.loc[:,'reviews_per_month'].fillna(test_new_df.loc[:,'reviews_per_month'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Apartment                 13932\n",
       "House                      1281\n",
       "Townhouse                   541\n",
       "Loft                        509\n",
       "Condominium                 470\n",
       "Serviced apartment          230\n",
       "Guest suite                  83\n",
       "Other                        59\n",
       "Bed and breakfast            45\n",
       "Resort                       38\n",
       "Boutique hotel               37\n",
       "Hostel                       31\n",
       "Guesthouse                   22\n",
       "Hotel                        13\n",
       "Bungalow                     11\n",
       "Villa                        10\n",
       "Tiny house                    6\n",
       "Aparthotel                    4\n",
       "Cabin                         3\n",
       "Camper/RV                     2\n",
       "Boat                          2\n",
       "Cottage                       2\n",
       "Castle                        1\n",
       "Nature lodge                  1\n",
       "Casa particular (Cuba)        1\n",
       "Cave                          1\n",
       "Tent                          1\n",
       "Earth house                   1\n",
       "Name: property_type, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_new_df['property_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py:189: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n",
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# merge small catergories in property_type into one category \"Other\"\n",
    "test_Other = ['Serviced apartment','Bed and breakfast','Resort','Hotel','Guesthouse','Other','Hostel', 'Guest suite', 'Boutique hotel', \n",
    "'Bungalow','Villa','Tiny house','Aparthotel','Boat', 'Tent', 'Cottage','Camper/RV','Casa particular (Cuba)', 'Nature lodge',       \n",
    "'Island','Castle', 'Earth house','Cabin', 'Cave']\n",
    "\n",
    "test_new_df['property_type'].loc[test_new_df['property_type'].isin(test_Other)] = \"Other\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Apartment      13932\n",
       "House           1281\n",
       "Other            604\n",
       "Townhouse        541\n",
       "Loft             509\n",
       "Condominium      470\n",
       "Name: property_type, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_new_df['property_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Entire home/apt    8982\n",
       "Private room       7964\n",
       "Shared room         391\n",
       "Name: room_type, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_new_df['room_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Real Bed         16982\n",
       "Pull-out Sofa      118\n",
       "Futon              113\n",
       "Airbed              89\n",
       "Couch               35\n",
       "Name: bed_type, dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_new_df['bed_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "# merge small catergories in bed_type into one category \"No Bed\"\n",
    "Other = ['Futon','Pull-out Sofa','Airbed','Couch']\n",
    "test_new_df['bed_type'].loc[test_new_df['bed_type'].isin(Other)] = \"No Bed\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Real Bed    16982\n",
       "No Bed        355\n",
       "Name: bed_type, dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_new_df['bed_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "strict_14_with_grace_period    7934\n",
       "flexible                       5406\n",
       "moderate                       3936\n",
       "super_strict_60                  44\n",
       "super_strict_30                  16\n",
       "strict                            1\n",
       "Name: cancellation_policy, dtype: int64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_new_df['cancellation_policy'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "# merge small catergories in cancellation_policy into one category \"Other\"\n",
    "Other = ['super_strict_60','super_strict_30','strict','long_term']\n",
    "test_new_df['cancellation_policy'].loc[test_new_df['cancellation_policy'].isin(Other)] = \"Other\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "strict_14_with_grace_period    7934\n",
       "flexible                       5406\n",
       "moderate                       3936\n",
       "Other                            61\n",
       "Name: cancellation_policy, dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_new_df['cancellation_policy'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_corpus_description = test_new_df['description'].astype(str)\n",
    "\n",
    "# use nlp package to process the text in description\n",
    "test_processed_corpus_description = preprocess_text(test_corpus_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate the doc_term_matrix for lda model\n",
    "test_dictionary_description, test_doc_term_matrix_description = pipline(test_processed_corpus_description)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lda model for topic modeling\n",
    "test_ldamodel_description = lda_topic_model(test_doc_term_matrix_description, test_dictionary_description)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the topic feature to the dataframe\n",
    "test_final_df = topic_feature(test_ldamodel_description, test_doc_term_matrix_description, test_new_df, new_col = 'description_topic', num_topics =4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "review_median = test_df['reviews_per_month'].median()\n",
    "test_final_df['reviews_per_month'] = test_df['reviews_per_month'].fillna(review_median)\n",
    "test_final_df['access'] = test_df['access']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_saved =test_df['access'].fillna('None')\n",
    "test_df['access'] = saved\n",
    "test_str_saved = test_df['access'].apply(match)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_final_df['new_access'] = test_str_saved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_bath_saved = test_df['bathrooms'].apply(bath_match)\n",
    "test_final_df['bath'] = test_bath_saved\n",
    "\n",
    "test_bed_saved = test_df['bedrooms'].apply(bed_match)\n",
    "test_final_df['bed'] = test_bed_saved\n",
    "\n",
    "test_acc_saved = test_df['accommodates'].apply(acc_match)\n",
    "test_final_df['acc'] = test_acc_saved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_s_corpus_description = test_df['space'].astype(str)\n",
    "\n",
    "# use nlp package to process the text in description\n",
    "test_s_processed_corpus_description = preprocess_text(test_s_corpus_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate the doc_term_matrix for lda model\n",
    "test_s_dictionary_description, test_s_doc_term_matrix_description = pipline(test_s_processed_corpus_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lda model for topic modeling\n",
    "test_s_ldamodel_description = lda_topic_model(test_s_doc_term_matrix_description, test_s_dictionary_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the topic feature to the dataframe\n",
    "test_the_final_df = topic_feature(test_s_ldamodel_description,test_s_doc_term_matrix_description, test_final_df,new_col = 'space_topic', num_topics =3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_n_corpus_description = test_df['name'].astype(str)\n",
    "# use nlp package to process the text in description\n",
    "test_n_processed_corpus_description = preprocess_text(test_n_corpus_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate the doc_term_matrix for lda model\n",
    "test_n_dictionary_description, test_n_doc_term_matrix_description = pipline(test_n_processed_corpus_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lda model for topic modeling\n",
    "test_n_ldamodel_description = lda_topic_model(test_n_doc_term_matrix_description, test_n_dictionary_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the topic feature to the dataframe\n",
    "test_name_final_df = topic_feature(test_n_ldamodel_description,test_n_doc_term_matrix_description, test_the_final_df,new_col = 'name_topic', num_topics =3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_sum_corpus_description = test_df['summary'].astype(str)\n",
    "# use nlp package to process the text in description\n",
    "test_sum_processed_corpus_description = preprocess_text(test_sum_corpus_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate the doc_term_matrix for lda model\n",
    "test_sum_dictionary_description, test_sum_doc_term_matrix_description = pipline(test_sum_processed_corpus_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lda model for topic modeling\n",
    "test_sum_ldamodel_description = lda_topic_model(test_sum_doc_term_matrix_description, test_sum_dictionary_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the topic feature to the dataframe\n",
    "test_summary_final_df = topic_feature(test_sum_ldamodel_description,test_sum_doc_term_matrix_description, test_name_final_df,new_col = 'summary_topic', num_topics =3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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>id</th>\n",
       "      <th>description</th>\n",
       "      <th>property_type</th>\n",
       "      <th>room_type</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bed_type</th>\n",
       "      <th>guests_included</th>\n",
       "      <th>extra_people</th>\n",
       "      <th>...</th>\n",
       "      <th>description_topic</th>\n",
       "      <th>access</th>\n",
       "      <th>new_access</th>\n",
       "      <th>bath</th>\n",
       "      <th>bed</th>\n",
       "      <th>acc</th>\n",
       "      <th>space_topic</th>\n",
       "      <th>name_topic</th>\n",
       "      <th>summary_topic</th>\n",
       "      <th>acc_lot?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19307997</td>\n",
       "      <td>Prepare to be WOWED! This spectacularly bright...</td>\n",
       "      <td>Condominium</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>not many</td>\n",
       "      <td>not many</td>\n",
       "      <td>not many</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20176193</td>\n",
       "      <td>Ideal for romantic, creative types, this is an...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>Entrance hallway, living room, bedroom, kitche...</td>\n",
       "      <td>True</td>\n",
       "      <td>not many</td>\n",
       "      <td>not many</td>\n",
       "      <td>not many</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19485371</td>\n",
       "      <td>Postive Vibes . This is our Harlem tree house,...</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>Private Room, Kitchen And Bathroom</td>\n",
       "      <td>True</td>\n",
       "      <td>not many</td>\n",
       "      <td>not many</td>\n",
       "      <td>not many</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id                                        description property_type  \\\n",
       "0  19307997  Prepare to be WOWED! This spectacularly bright...   Condominium   \n",
       "1  20176193  Ideal for romantic, creative types, this is an...     Apartment   \n",
       "2  19485371  Postive Vibes . This is our Harlem tree house,...     Apartment   \n",
       "\n",
       "         room_type  accommodates  bathrooms  bedrooms  bed_type  \\\n",
       "0  Entire home/apt             4        2.0       2.0  Real Bed   \n",
       "1  Entire home/apt             2        1.0       2.0  Real Bed   \n",
       "2     Private room             1        1.0       1.0  Real Bed   \n",
       "\n",
       "   guests_included  extra_people   ...     description_topic  \\\n",
       "0                1           0.0   ...                     2   \n",
       "1                1           0.0   ...                     0   \n",
       "2                1          20.0   ...                     0   \n",
       "\n",
       "                                              access  new_access      bath  \\\n",
       "0                                                NaN       False  not many   \n",
       "1  Entrance hallway, living room, bedroom, kitche...        True  not many   \n",
       "2                 Private Room, Kitchen And Bathroom        True  not many   \n",
       "\n",
       "        bed       acc  space_topic name_topic  summary_topic acc_lot?  \n",
       "0  not many  not many            2          2              1    False  \n",
       "1  not many  not many            2          0              0    False  \n",
       "2  not many  not many            2          0              2    False  \n",
       "\n",
       "[3 rows x 26 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_accom_saved = test_df['accommodates'].apply(accommodates)\n",
    "test_summary_final_df['acc_lot?'] = test_accom_saved\n",
    "test_summary_final_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_host_saved = test_df['host_neighbourhood'].apply(host_cat)\n",
    "test_summary_final_df['host_cat'] = test_host_saved\n",
    "#summary_final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_n_saved = test_df['neighbourhood_cleansed'].apply(n_cat)\n",
    "test_summary_final_df['n_cat'] = test_n_saved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_guest_saved = test_df['guests_included'].apply(guest_classify)\n",
    "test_summary_final_df['guest_cat'] = test_guest_saved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_review_saved = test_df['number_of_reviews'].apply(reviews_num)\n",
    "test_summary_final_df['review_cat'] = test_review_saved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_cols_to_keep = ['accommodates','bathrooms','bedrooms','extra_people','maximum_nights','minimum_nights','property_type',\n",
    "                'bed_type','room_type','cancellation_policy', 'reviews_per_month', 'new_access', 'acc_lot?', 'host_cat', 'n_cat',\n",
    "                     'neighbourhood_group_cleansed', 'description_topic', 'space_topic', 'name_topic', 'summary_topic', \n",
    "                     'host_is_superhost', 'guest_cat', 'review_cat']\n",
    "test_model_df = test_summary_final_df[test_cols_to_keep]\n",
    "\n",
    "# convert strings to dummies\n",
    "test_categorical_feats = ['property_type','room_type','bed_type', 'new_access','cancellation_policy', 'acc_lot?',\n",
    "                           'description_topic', 'space_topic', 'name_topic', 'summary_topic', 'host_cat', 'n_cat',\n",
    "                          'host_is_superhost', 'neighbourhood_group_cleansed', 'guest_cat', 'review_cat']\n",
    "test_model_df = pd.get_dummies(test_model_df,columns = test_categorical_feats,drop_first = False)\n",
    "\n",
    "test_X_df = test_model_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17337, 85)\n",
      "(33538, 85)\n"
     ]
    }
   ],
   "source": [
    "print(test_X_df.shape)\n",
    "print(X_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_X_df = test_X_df[['accommodates', 'bathrooms', 'bedrooms', 'maximum_nights',\n",
    "       'minimum_nights', 'reviews_per_month', 'extra_people',\n",
    "       'property_type_Apartment', 'property_type_Condominium',\n",
    "       'property_type_House', 'property_type_Loft', 'property_type_Other',\n",
    "       'property_type_Townhouse', 'room_type_Entire home/apt',\n",
    "       'room_type_Private room', 'room_type_Shared room', 'bed_type_No Bed',\n",
    "       'bed_type_Real Bed', 'new_access_False', 'new_access_True',\n",
    "       'cancellation_policy_Other', 'cancellation_policy_flexible',\n",
    "       'cancellation_policy_moderate',\n",
    "       'cancellation_policy_strict_14_with_grace_period',\n",
    "       'neighbourhood_group_cleansed_Bronx',\n",
    "       'neighbourhood_group_cleansed_Brooklyn',\n",
    "       'neighbourhood_group_cleansed_Manhattan',\n",
    "       'neighbourhood_group_cleansed_Queens',\n",
    "       'neighbourhood_group_cleansed_Staten Island', 'description_topic_0',\n",
    "       'description_topic_1', 'description_topic_2', 'space_topic_0',\n",
    "       'space_topic_1', 'space_topic_2', 'name_topic_0', 'name_topic_1',\n",
    "       'name_topic_2', 'summary_topic_0', 'summary_topic_1', 'summary_topic_2',\n",
    "       'acc_lot?_False', 'acc_lot?_True', 'host_cat_cheap',\n",
    "       'host_cat_expensive_300', 'host_cat_expensive_400',\n",
    "       'host_cat_expensive_500', 'host_cat_pretty cheap',\n",
    "       'host_cat_really cheap', 'host_cat_really expensive',\n",
    "       'host_cat_under 110', 'host_cat_under 120', 'host_cat_under 130',\n",
    "       'host_cat_under 140', 'host_cat_under 150', 'host_cat_under 160',\n",
    "       'host_cat_under 180', 'host_cat_under 190', 'host_cat_under 200',\n",
    "       'host_cat_under 220', 'host_cat_under 230', 'host_cat_under 240',\n",
    "       'host_cat_under 250', 'host_cat_under 260', 'host_cat_under 270',\n",
    "       'host_cat_under 280', 'n_cat_over 400', 'n_cat_under 100',\n",
    "       'n_cat_under 125', 'n_cat_under 150', 'n_cat_under 175',\n",
    "       'n_cat_under 200', 'n_cat_under 225', 'n_cat_under 250',\n",
    "       'n_cat_under 275', 'n_cat_under 300', 'n_cat_under 400',\n",
    "       'n_cat_under 50', 'host_is_superhost_f', 'host_is_superhost_t',\n",
    "       'guest_cat_expensive', 'guest_cat_medium', 'guest_cat_pretty cheap',\n",
    "       'review_cat_three_digit', 'review_cat_two_digit']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean squared error: 95.419\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([179.99845461, 257.48651766, 155.90748882, ...,  20.10803818,\n",
       "        52.01927556, 364.71535913])"
      ]
     },
     "execution_count": 508,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error,r2_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "#seed = 42\n",
    "#X_train,X_test,y_train,y_test = train_test_split(X_df,target,random_state=seed)\n",
    "linreg = LinearRegression(n_jobs=5).fit(X_df, target)\n",
    "y_pred_linreg = linreg.predict(X_df)\n",
    "print(\"Root Mean squared error: %.3f\" %np.sqrt(mean_squared_error(target,y_pred_linreg)))\n",
    "#rmse_lr = np.sqrt(mean_squared_error(target,y_pred_linreg))\n",
    "#print(X_df.columns)\n",
    "#print(test_X_df.columns)\n",
    "y_pred_linreg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean squared error: 33.199\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([149.35 , 187.36 , 165.5  , ...,  87.065,  59.47 , 367.85 ])"
      ]
     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rf_reg = RandomForestRegressor(n_estimators=100).fit(X_df, target)\n",
    "y_pred_rf = rf_reg.predict(X_df)\n",
    "print(\"Root Mean squared error: %.3f\" %np.sqrt(mean_squared_error(target,y_pred_rf)))\n",
    "#rmse_rf = np.sqrt(mean_squared_error(y_test,y_pred_linreg))\n",
    "y_pred_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([434.7409996 , 127.41651705,  68.85274452, ...,  78.2630413 ,\n",
       "        44.86192879, 144.18402125])"
      ]
     },
     "execution_count": 530,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "de_reg = GradientBoostingRegressor(n_estimators=220).fit(X_df, target)\n",
    "y_pred_dt = de_reg.predict(test_X_df)\n",
    "#print(\"Root Mean squared error: %.3f\" %np.sqrt(mean_squared_error(target,y_pred_dt)))\n",
    "y_pred_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean squared error: 6.273\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([145., 175., 180., ...,  50.,  60., 325.])"
      ]
     },
     "execution_count": 502,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "t_reg = DecisionTreeRegressor().fit(X_df, target)\n",
    "y_pred_t = t_reg.predict(X_df)\n",
    "print(\"Root Mean squared error: %.3f\" %np.sqrt(mean_squared_error(target,y_pred_t)))\n",
    "y_pred_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_test = pd.DataFrame()\n",
    "output_test['Id'] = test_df.id\n",
    "output_test['Predicted'] = y_pred_dt\n",
    "output_test.to_csv('test_pred_38.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}