Low booking rate listings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring the availability of Airbnb listings and their characteristics\n",
    "## How can we promote earlier booking of listings and also discourage hosts from placing listings that are actually not available for rent?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load datasets\n",
    "source=\"./airbnb_data/\"\n",
    "calendar=pd.read_csv(source+\"calendar.csv\")\n",
    "listings=pd.read_csv(source+\"listings.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>listing_id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "      <th>metro_area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2515</td>\n",
       "      <td>2018-03-05</td>\n",
       "      <td>t</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NYC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2515</td>\n",
       "      <td>2018-03-04</td>\n",
       "      <td>t</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NYC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2515</td>\n",
       "      <td>2018-03-03</td>\n",
       "      <td>t</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NYC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2515</td>\n",
       "      <td>2018-03-02</td>\n",
       "      <td>t</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NYC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2515</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>t</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NYC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id        date available  price metro_area\n",
       "0        2515  2018-03-05         t   69.0        NYC\n",
       "1        2515  2018-03-04         t   69.0        NYC\n",
       "2        2515  2018-03-03         t   69.0        NYC\n",
       "3        2515  2018-03-02         t   69.0        NYC\n",
       "4        2515  2018-03-01         t   69.0        NYC"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calendar.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean dataset\n",
    "### 1) Filter and clean data of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['NYC', 'denver', 'chicago', 'boston', 'dc']\n"
     ]
    }
   ],
   "source": [
    "print(calendar['metro_area'].unique().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>listing_id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "      <th>metro_area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2515</td>\n",
       "      <td>2018-03-05</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NYC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2515</td>\n",
       "      <td>2018-03-04</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NYC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2515</td>\n",
       "      <td>2018-03-03</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NYC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2515</td>\n",
       "      <td>2018-03-02</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NYC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2515</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NYC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   listing_id       date  available  price metro_area\n",
       "0        2515 2018-03-05          1   69.0        NYC\n",
       "1        2515 2018-03-04          1   69.0        NYC\n",
       "2        2515 2018-03-03          1   69.0        NYC\n",
       "3        2515 2018-03-02          1   69.0        NYC\n",
       "4        2515 2018-03-01          1   69.0        NYC"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter only NYC data\n",
    "calendar = calendar[calendar['metro_area']=='NYC']\n",
    "\n",
    "# In 'available' column, change true to 1 and false to 0\n",
    "calendar['available'] = calendar['available'].apply(lambda x:1 if x == 't' else 0)\n",
    "\n",
    "# Convert date column to data type date\n",
    "calendar['date'] = pd.to_datetime(calendar['date'])\n",
    "\n",
    "calendar.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Create table of available days by month and listing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each listing, the availability for the next 365 calendar days are stored in the calendar dataset. Availability means that the listing is made available for rental by the host but has not been booked. We also recognise that there is may be an insignificant number of hosts who may have made the listing available for rental but are not actually accepting bookings due to various reasons. Non-availability could mean that the listing has already been booked or it was not made available for rental by the host. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find start_date for dataset of each listing\n",
    "# start_date = calendar.groupby('listing_id')['date'].min().to_frame()\n",
    "# start_date = start_date.reset_index()\n",
    "# start_date.head()\n",
    "\n",
    "# We can check that all the start dates are in May 2017\n",
    "# np.mean(start_date['date'].dt.month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>listing_id</th>\n",
       "      <th>available_days</th>\n",
       "      <th>total_days</th>\n",
       "      <th>not_available_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>2515</td>\n",
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       "      <td>25</td>\n",
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       "      <th>1</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>2595</td>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>2017-05-31</td>\n",
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       "      <td>2017-05-31</td>\n",
       "      <td>3831</td>\n",
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       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>4611</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
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      ],
      "text/plain": [
       "        date  listing_id  available_days  total_days  not_available_days\n",
       "0 2017-05-31        2515               5          30                  25\n",
       "1 2017-05-31        2595              30          30                   0\n",
       "2 2017-05-31        3647              30          30                   0\n",
       "3 2017-05-31        3831               0          30                  30\n",
       "4 2017-05-31        4611               4          30                  26"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sum total number of days in a month with data and number of days which listing is available for\n",
    "availability = calendar.groupby([pd.Grouper(key='date',freq='M'),'listing_id'])['available'].agg(['sum','count']).reset_index()\n",
    "availability['not_available_days'] = availability['count'] - availability['sum']\n",
    "availability = availability.rename({'sum':'available_days','count':'total_days'}, axis=1)\n",
    "availability.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
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   "outputs": [
    {
     "data": {
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       "529789"
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     "execution_count": 267,
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   ],
   "source": [
    "len(availability)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Focus on listings & months with low booking rate\n",
    "We find that a significant percentage of listings and months are available for almost a full month. That is, the listings are shown as available on the calendar on Airbnb but are not booked at all/have extremely low booking rates. We choose to focus on examining why by exploring their characteristics and identifying patterns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique listings: 40753\n",
      "% of listing-months with zero bookings: 29.887936518123254\n",
      "Ave # months/listing with zero bookings: 6.929368517789156\n"
     ]
    }
   ],
   "source": [
    "# Number of unique listing ids in dataset\n",
    "print(\"Number of unique listings: {}\".format(len(availability['listing_id'].unique().tolist())))\n",
    "\n",
    "# Percentage of listing months which have 0 booking days\n",
    "print(\"% of listing-months with zero bookings: {}\".format(len(availability[availability['not_available_days'] == 0])/len(availability)*100))\n",
    "\n",
    "# Create dataset of high-availability months and listings\n",
    "no_booking = availability[availability['not_available_days']==0]\n",
    "print(\"Ave # months/listing with zero bookings: {}\".format(len(no_booking)/len(no_booking['listing_id'].unique().tolist())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>listing_id</th>\n",
       "      <th>available_days</th>\n",
       "      <th>total_days</th>\n",
       "      <th>not_available_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>2595</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>3647</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>5121</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>11943</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>12303</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "         date  listing_id  available_days  total_days  not_available_days\n",
       "1  2017-05-31        2595              30          30                   0\n",
       "2  2017-05-31        3647              30          30                   0\n",
       "7  2017-05-31        5121              30          30                   0\n",
       "47 2017-05-31       11943              29          29                   0\n",
       "51 2017-05-31       12303              29          29                   0"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_booking.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22851"
      ]
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     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "len(no_booking['listing_id'].unique().tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) Create dataset with unique listings and adjustment factor\n",
    "Since the data ends sometime in May 2018, the count of frequency based on booked_days = 0 may result in an additional month being counted. We calculate an adjustment factor, which is 1 if a month needs to be deducted from the frequency, and 0 if no deduction needs to be made. The adjustment factor is 1 when booked_days = 0 in BOTH May 2017 and May 2018.  \n",
    "*The count of available days done in the previous step was done by calendar month, not by 30/31 days from the start date of each listing's data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>listing_id</th>\n",
       "      <th>not_available_days</th>\n",
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0</td>\n",
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       "      <th>4</th>\n",
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      "text/plain": [
       "   listing_id  not_available_days\n",
       "0        2515                   0\n",
       "1        2595                   0\n",
       "2        3647                   0\n",
       "3        3831                   0\n",
       "4        4611                   0"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "may_data = no_booking[no_booking['date'].dt.month == 5]\n",
    "may_data = may_data.groupby('listing_id')['not_available_days'].sum().reset_index()\n",
    "may_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "(22851, 2)"
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     "execution_count": 272,
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   ],
   "source": [
    "adjustment = pd.DataFrame(no_booking['listing_id'].unique())\n",
    "adjustment = adjustment.rename({0:'listing_id'},axis=1)\n",
    "adjustment['factor'] = may_data['not_available_days'].apply(lambda x:1 if x==0 else 0)\n",
    "adjustment['factor'] = adjustment['factor'].fillna(0)\n",
    "adjustment.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5) Create dataset of unique listings with count of full-availability months\n",
    "Reminder: full-availability here refers to the entire calendar month being marked as available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "158343\n",
      "22851\n"
     ]
    }
   ],
   "source": [
    "print(len(no_booking))\n",
    "print(len(no_booking['listing_id'].unique().tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "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>listing_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>11943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>12303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    listing_id\n",
       "1         2595\n",
       "2         3647\n",
       "7         5121\n",
       "47       11943\n",
       "51       12303"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Listings with zero-booking months (non-unique listings)\n",
    "no_booking_mth = no_booking.drop(['available_days','not_available_days','total_days','date'], axis=1)\n",
    "no_booking_mth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add column of frequency i.e. number of zero-booking months per unique listing\n",
    "no_booking_mth = no_booking_mth['listing_id'].value_counts().reset_index(name='no_months').rename(columns={'index': 'listing_id'})\n",
    "\n",
    "# Merge no_booking_mth with adjustment\n",
    "no_booking_mth = no_booking_mth.merge(adjustment, on='listing_id', how='inner')\n",
    "\n",
    "# Calculate actual number of months and remove adjustment row\n",
    "no_booking_mth['no_months'] = no_booking_mth['no_months']-no_booking_mth['factor']\n",
    "no_booking_mth = no_booking_mth.drop('factor',axis=1)\n",
    "no_booking_mth.no_months = no_booking_mth.no_months.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "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>listing_id</th>\n",
       "      <th>no_months</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40453</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7208745</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17726161</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6904576</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>238119</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id  no_months\n",
       "0       40453         12\n",
       "1     7208745         12\n",
       "2    17726161         12\n",
       "3     6904576         12\n",
       "4      238119         12"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_booking_mth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22851\n"
     ]
    }
   ],
   "source": [
    "# Check that number of unique listings matches that in low_occ table\n",
    "print(len(no_booking_mth))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot a histogram of the full-availability months and see that a significant number of unique listings have a full year of full-availability months. There could be various reasons why these listings are experiencing a full year of zero bookings. Possible reasons:  \n",
    "1) The quality of the listing could be poor such that no one books them, in which case we can explore reasons for this and characterise such listings to make recommendations to hosts.  \n",
    "2) The listings are outdated and no longer maintained by hosts, in which case it would be helpful to remove them fromt he website so that potential customers do not have to sift through unnecessarily large numbers of listings. Nonetheless, we believe this forms a relatively smaller proportion of all listings.  \n",
    "3) Related to point 2, the hosts are currently staying in their listing and indicate the listing as being available even though it is not. They reject requests to rent. It would be helpful to motivate hosts to update accurate availabilities to enhance customer experience and efficiency of matching customers to listings. Again, we believe this group forms a relatively smaller proportion of all listings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count of listings')"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(no_booking_mth['no_months'])\n",
    "plt.xlabel('Number of months with all days available')\n",
    "plt.ylabel('Count of listings')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export dataset of listings and number of zero-booking months\n",
    "#no_booking_mth.to_csv(r'no_booking_listings.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge datasets\n",
    "### 1) Merge listings and no_booking_mth to characterise the listings with 0 and 12 full-availability months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accommodates</th>\n",
       "      <th>amenities</th>\n",
       "      <th>availability_30</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bed_type</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>city</th>\n",
       "      <th>has_availability</th>\n",
       "      <th>...</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>room_type</th>\n",
       "      <th>state</th>\n",
       "      <th>weekly_price</th>\n",
       "      <th>zipcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>{\"Cable TV\",\"Wireless Internet\",\"Air condition...</td>\n",
       "      <td>24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>moderate</td>\n",
       "      <td>sunnysidebronx</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Private room</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>{TV,Internet,\"Wireless Internet\",\"Air conditio...</td>\n",
       "      <td>30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>flexible</td>\n",
       "      <td>sunnysidebronx</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Private room</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...</td>\n",
       "      <td>30</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>strict</td>\n",
       "      <td>sunnysidebronx</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>NY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>{TV,Internet,\"Wireless Internet\",\"Air conditio...</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>strict</td>\n",
       "      <td>long island city</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>NY</td>\n",
       "      <td>775.0</td>\n",
       "      <td>10464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>{Internet,\"Wireless Internet\",\"Air conditionin...</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>moderate</td>\n",
       "      <td>sunnysidebronx</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Private room</td>\n",
       "      <td>NY</td>\n",
       "      <td>350.0</td>\n",
       "      <td>10464</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   accommodates                                          amenities  \\\n",
       "0           2.0  {\"Cable TV\",\"Wireless Internet\",\"Air condition...   \n",
       "1           4.0  {TV,Internet,\"Wireless Internet\",\"Air conditio...   \n",
       "2           4.0  {TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...   \n",
       "3           3.0  {TV,Internet,\"Wireless Internet\",\"Air conditio...   \n",
       "4           4.0  {Internet,\"Wireless Internet\",\"Air conditionin...   \n",
       "\n",
       "   availability_30  bathrooms  bed_type  bedrooms  beds cancellation_policy  \\\n",
       "0               24        1.0  Real Bed       1.0   1.0            moderate   \n",
       "1               30        1.0  Real Bed       1.0   1.0            flexible   \n",
       "2               30        3.0  Real Bed       3.0   3.0              strict   \n",
       "3                8        1.0  Real Bed       1.0   1.0              strict   \n",
       "4               17        1.0  Real Bed       1.0   1.0            moderate   \n",
       "\n",
       "               city  has_availability  ...  review_scores_checkin  \\\n",
       "0    sunnysidebronx               NaN  ...                   10.0   \n",
       "1    sunnysidebronx               NaN  ...                    NaN   \n",
       "2    sunnysidebronx               NaN  ...                    NaN   \n",
       "3  long island city               NaN  ...                   10.0   \n",
       "4    sunnysidebronx               NaN  ...                   10.0   \n",
       "\n",
       "   review_scores_cleanliness review_scores_communication  \\\n",
       "0                       10.0                        10.0   \n",
       "1                        NaN                         NaN   \n",
       "2                        NaN                         NaN   \n",
       "3                       10.0                        10.0   \n",
       "4                       10.0                        10.0   \n",
       "\n",
       "   review_scores_location  review_scores_rating review_scores_value  \\\n",
       "0                    10.0                 100.0                10.0   \n",
       "1                     NaN                   NaN                 NaN   \n",
       "2                     NaN                   NaN                 NaN   \n",
       "3                    10.0                  93.0                10.0   \n",
       "4                    10.0                  97.0                10.0   \n",
       "\n",
       "         room_type  state weekly_price  zipcode  \n",
       "0     Private room     NY          NaN    10464  \n",
       "1     Private room     NY          NaN    10464  \n",
       "2  Entire home/apt     NY          NaN    10464  \n",
       "3  Entire home/apt     NY        775.0    10464  \n",
       "4     Private room     NY        350.0    10464  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['accommodates', 'amenities', 'availability_30', 'bathrooms', 'bed_type',\n",
      "       'bedrooms', 'beds', 'cancellation_policy', 'city', 'has_availability',\n",
      "       'host_id', 'id', 'instant_bookable', 'latitude', 'longitude',\n",
      "       'metropolitan', 'name', 'price', 'property_type',\n",
      "       'review_scores_checkin', 'review_scores_cleanliness',\n",
      "       'review_scores_communication', 'review_scores_location',\n",
      "       'review_scores_rating', 'review_scores_value', 'room_type', 'state',\n",
      "       'weekly_price', 'zipcode'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(listings.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter only listings in New York\n",
    "listings_new = listings[listings['state']=='NY'] \n",
    "\n",
    "# Drop unnecessary columns\n",
    "listings_new = listings_new.drop(['has_availability','availability_30','name','weekly_price','state'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40738, 24)"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listings_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_booking_list = no_booking_mth.merge(listings_new, left_on='listing_id', right_on='id', how='inner')\n",
    "no_booking_list = no_booking_list.drop(['id'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "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>listing_id</th>\n",
       "      <th>no_months</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>amenities</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bed_type</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>city</th>\n",
       "      <th>...</th>\n",
       "      <th>price</th>\n",
       "      <th>property_type</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>room_type</th>\n",
       "      <th>zipcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40453</td>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>flexible</td>\n",
       "      <td>new york</td>\n",
       "      <td>...</td>\n",
       "      <td>250.0</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>10065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7208745</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{TV,\"Cable TV\",\"Wireless Internet\",\"Air condit...</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>strict</td>\n",
       "      <td>queens</td>\n",
       "      <td>...</td>\n",
       "      <td>60.0</td>\n",
       "      <td>House</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Private room</td>\n",
       "      <td>11365.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17726161</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{TV,\"Cable TV\",\"Wireless Internet\",\"Free parki...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>flexible</td>\n",
       "      <td>jamaica</td>\n",
       "      <td>...</td>\n",
       "      <td>85.0</td>\n",
       "      <td>House</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Private room</td>\n",
       "      <td>11434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6904576</td>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>strict</td>\n",
       "      <td>new york</td>\n",
       "      <td>...</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Private room</td>\n",
       "      <td>10031.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>238119</td>\n",
       "      <td>12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{TV,Internet,\"Wireless Internet\",\"Air conditio...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>strict</td>\n",
       "      <td>brooklyn</td>\n",
       "      <td>...</td>\n",
       "      <td>135.0</td>\n",
       "      <td>House</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Private room</td>\n",
       "      <td>11230</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id  no_months  accommodates  \\\n",
       "0       40453         12           1.0   \n",
       "1     7208745         12           2.0   \n",
       "2    17726161         12           2.0   \n",
       "3     6904576         12           1.0   \n",
       "4      238119         12           2.0   \n",
       "\n",
       "                                           amenities  bathrooms  bed_type  \\\n",
       "0  {TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...        1.0  Real Bed   \n",
       "1  {TV,\"Cable TV\",\"Wireless Internet\",\"Air condit...        1.5  Real Bed   \n",
       "2  {TV,\"Cable TV\",\"Wireless Internet\",\"Free parki...        1.0  Real Bed   \n",
       "3  {TV,\"Cable TV\",Internet,\"Wireless Internet\",\"A...        1.0  Real Bed   \n",
       "4  {TV,Internet,\"Wireless Internet\",\"Air conditio...        2.0  Real Bed   \n",
       "\n",
       "   bedrooms  beds cancellation_policy      city  ...  price property_type  \\\n",
       "0       1.0   1.0            flexible  new york  ...  250.0     Apartment   \n",
       "1       1.0   1.0              strict    queens  ...   60.0         House   \n",
       "2       2.0   2.0            flexible   jamaica  ...   85.0         House   \n",
       "3       1.0   1.0              strict  new york  ...   55.0     Apartment   \n",
       "4       1.0   1.0              strict  brooklyn  ...  135.0         House   \n",
       "\n",
       "   review_scores_checkin  review_scores_cleanliness  \\\n",
       "0                   10.0                        9.0   \n",
       "1                    NaN                        NaN   \n",
       "2                    NaN                        NaN   \n",
       "3                    9.0                        9.0   \n",
       "4                   10.0                       10.0   \n",
       "\n",
       "  review_scores_communication  review_scores_location review_scores_rating  \\\n",
       "0                        10.0                    10.0                100.0   \n",
       "1                         NaN                     NaN                100.0   \n",
       "2                         NaN                     NaN                  NaN   \n",
       "3                         9.0                     9.0                 90.0   \n",
       "4                        10.0                     9.0                100.0   \n",
       "\n",
       "   review_scores_value        room_type  zipcode  \n",
       "0                 10.0  Entire home/apt    10065  \n",
       "1                  NaN     Private room  11365.0  \n",
       "2                  NaN     Private room  11434.0  \n",
       "3                  9.0     Private room  10031.0  \n",
       "4                  9.0     Private room    11230  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_booking_list.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Month 0: 1839\n",
      "8.052721460787318\n",
      "Month 1: 2645\n",
      "11.582081709506504\n",
      "Month 2: 1390\n",
      "6.08661382843631\n",
      "Month 3: 1231\n",
      "5.390375268205106\n",
      "Month 4: 1469\n",
      "6.432543679117223\n",
      "Month 5: 1554\n",
      "6.8047466830144065\n",
      "Month 6: 1086\n",
      "4.755440732145203\n",
      "Month 7: 1178\n",
      "5.158295748128038\n",
      "Month 8: 1905\n",
      "8.341726146166309\n",
      "Month 9: 2125\n",
      "9.30507509742961\n",
      "Month 10: 1827\n",
      "8.000175154354777\n",
      "Month 11: 3054\n",
      "13.373034987082367\n",
      "Month 12: 1534\n",
      "6.717169505626834\n"
     ]
    }
   ],
   "source": [
    "for i in range(13):\n",
    "    print(\"Month {}: {}\".format(i,len(no_booking_list[no_booking_list['no_months']==i])))\n",
    "    print(len(no_booking_list[no_booking_list['no_months']==i])/len(no_booking_list)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export merged dataset\n",
    "#no_booking_list.to_csv(r'full_mth_avail_listings.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Merge mth_full_availability (from no_booking) with listings to understand characteristics of remaining supply for each month "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "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>date</th>\n",
       "      <th>listing_id</th>\n",
       "      <th>available_days</th>\n",
       "      <th>total_days</th>\n",
       "      <th>not_available_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>2595</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>3647</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>5121</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>11943</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>12303</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  listing_id  available_days  total_days  not_available_days\n",
       "1  2017-05-31        2595              30          30                   0\n",
       "2  2017-05-31        3647              30          30                   0\n",
       "7  2017-05-31        5121              30          30                   0\n",
       "47 2017-05-31       11943              29          29                   0\n",
       "51 2017-05-31       12303              29          29                   0"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_booking.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>date</th>\n",
       "      <th>listing_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>2595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>3647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>5121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>11943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>12303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  listing_id\n",
       "1  2017-05-31        2595\n",
       "2  2017-05-31        3647\n",
       "7  2017-05-31        5121\n",
       "47 2017-05-31       11943\n",
       "51 2017-05-31       12303"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mth_full_availability = no_booking.drop(['available_days','total_days','not_available_days'], axis=1)\n",
    "mth_full_availability.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>listing_id</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>amenities</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bed_type</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>city</th>\n",
       "      <th>...</th>\n",
       "      <th>price</th>\n",
       "      <th>property_type</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>room_type</th>\n",
       "      <th>zipcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>2595</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{TV,Internet,\"Wireless Internet\",\"Air conditio...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>strict</td>\n",
       "      <td>new york</td>\n",
       "      <td>...</td>\n",
       "      <td>225.0</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>10018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>2595</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{TV,Internet,\"Wireless Internet\",\"Air conditio...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>strict</td>\n",
       "      <td>new york</td>\n",
       "      <td>...</td>\n",
       "      <td>225.0</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>10018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-07-31</td>\n",
       "      <td>2595</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{TV,Internet,\"Wireless Internet\",\"Air conditio...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>strict</td>\n",
       "      <td>new york</td>\n",
       "      <td>...</td>\n",
       "      <td>225.0</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>10018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>2595</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{TV,Internet,\"Wireless Internet\",\"Air conditio...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>strict</td>\n",
       "      <td>new york</td>\n",
       "      <td>...</td>\n",
       "      <td>225.0</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>10018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-09-30</td>\n",
       "      <td>2595</td>\n",
       "      <td>2.0</td>\n",
       "      <td>{TV,Internet,\"Wireless Internet\",\"Air conditio...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>strict</td>\n",
       "      <td>new york</td>\n",
       "      <td>...</td>\n",
       "      <td>225.0</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>10018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  listing_id  accommodates  \\\n",
       "0 2017-05-31        2595           2.0   \n",
       "1 2017-06-30        2595           2.0   \n",
       "2 2017-07-31        2595           2.0   \n",
       "3 2017-08-31        2595           2.0   \n",
       "4 2017-09-30        2595           2.0   \n",
       "\n",
       "                                           amenities  bathrooms  bed_type  \\\n",
       "0  {TV,Internet,\"Wireless Internet\",\"Air conditio...        1.0  Real Bed   \n",
       "1  {TV,Internet,\"Wireless Internet\",\"Air conditio...        1.0  Real Bed   \n",
       "2  {TV,Internet,\"Wireless Internet\",\"Air conditio...        1.0  Real Bed   \n",
       "3  {TV,Internet,\"Wireless Internet\",\"Air conditio...        1.0  Real Bed   \n",
       "4  {TV,Internet,\"Wireless Internet\",\"Air conditio...        1.0  Real Bed   \n",
       "\n",
       "   bedrooms  beds cancellation_policy      city  ...  price property_type  \\\n",
       "0       0.0   1.0              strict  new york  ...  225.0     Apartment   \n",
       "1       0.0   1.0              strict  new york  ...  225.0     Apartment   \n",
       "2       0.0   1.0              strict  new york  ...  225.0     Apartment   \n",
       "3       0.0   1.0              strict  new york  ...  225.0     Apartment   \n",
       "4       0.0   1.0              strict  new york  ...  225.0     Apartment   \n",
       "\n",
       "   review_scores_checkin  review_scores_cleanliness  \\\n",
       "0                    9.0                        9.0   \n",
       "1                    9.0                        9.0   \n",
       "2                    9.0                        9.0   \n",
       "3                    9.0                        9.0   \n",
       "4                    9.0                        9.0   \n",
       "\n",
       "  review_scores_communication  review_scores_location review_scores_rating  \\\n",
       "0                         9.0                    10.0                 97.0   \n",
       "1                         9.0                    10.0                 97.0   \n",
       "2                         9.0                    10.0                 97.0   \n",
       "3                         9.0                    10.0                 97.0   \n",
       "4                         9.0                    10.0                 97.0   \n",
       "\n",
       "   review_scores_value        room_type  zipcode  \n",
       "0                  9.0  Entire home/apt    10018  \n",
       "1                  9.0  Entire home/apt    10018  \n",
       "2                  9.0  Entire home/apt    10018  \n",
       "3                  9.0  Entire home/apt    10018  \n",
       "4                  9.0  Entire home/apt    10018  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mthly_availability = mth_full_availability.merge(listings_new, left_on='listing_id', right_on='id', how='inner')\n",
    "mthly_availability = mthly_availability.drop(['id'], axis=1)\n",
    "mthly_availability.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export merged dataset\n",
    "mthly_availability.to_csv(r'mthly_availability.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Trend of average prices per month - does it correlate with available supply of listings?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find average price per month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of high-availability months is not highly correlated to any particular feature of the listing, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = no_booking_list.corr()\n",
    "ax = sns.heatmap(\n",
    "    corr, \n",
    "    vmin=-1, vmax=1, center=0,\n",
    "    cmap=sns.diverging_palette(20, 220, n=200),\n",
    "    square=True\n",
    ")\n",
    "ax.set_xticklabels(\n",
    "    ax.get_xticklabels(),\n",
    "    rotation=45,\n",
    "    horizontalalignment='right'\n",
    ");"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}