airbnb_berlin_covid19.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mtick\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics.pairwise import pairwise_distances\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Business Understanding\n",
    "\n",
    "Let's try to analyze Data from Inside Airbnb Berlin to answer the following questions:\n",
    "* Is 2020 a year like any other at airbnb?\n",
    "* Can we find traces of COVID-19 in Inside Airbnb's data?\n",
    "* Does COVID-19 infect the data?\n",
    "\n",
    "Read the corresponding blog post at <https://medium.com/@sopka/covid-19-and-inside-airbnb-eb0d2da212ea>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Understanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in calendar data\n",
    "calendar2020 = pd.read_csv('data/2020-01-10/unzipped/calendar.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "      <th>adjusted_price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>2020-01-11</td>\n",
       "      <td>f</td>\n",
       "      <td>$33.00</td>\n",
       "      <td>$33.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>2020-01-12</td>\n",
       "      <td>f</td>\n",
       "      <td>$33.00</td>\n",
       "      <td>$33.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>2020-01-13</td>\n",
       "      <td>f</td>\n",
       "      <td>$33.00</td>\n",
       "      <td>$33.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>2020-01-14</td>\n",
       "      <td>f</td>\n",
       "      <td>$33.00</td>\n",
       "      <td>$33.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>2020-01-15</td>\n",
       "      <td>f</td>\n",
       "      <td>$33.00</td>\n",
       "      <td>$33.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1125.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9252394</th>\n",
       "      <td>41453441</td>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>f</td>\n",
       "      <td>$26.00</td>\n",
       "      <td>$26.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9252395</th>\n",
       "      <td>41453441</td>\n",
       "      <td>2021-01-06</td>\n",
       "      <td>f</td>\n",
       "      <td>$26.00</td>\n",
       "      <td>$26.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9252396</th>\n",
       "      <td>41453441</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>f</td>\n",
       "      <td>$26.00</td>\n",
       "      <td>$26.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9252397</th>\n",
       "      <td>41453441</td>\n",
       "      <td>2021-01-08</td>\n",
       "      <td>f</td>\n",
       "      <td>$26.00</td>\n",
       "      <td>$26.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9252398</th>\n",
       "      <td>41453441</td>\n",
       "      <td>2021-01-09</td>\n",
       "      <td>f</td>\n",
       "      <td>$26.00</td>\n",
       "      <td>$26.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1125.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9252399 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         listing_id        date available   price adjusted_price  \\\n",
       "0              2015  2020-01-11         f  $33.00         $33.00   \n",
       "1              2015  2020-01-12         f  $33.00         $33.00   \n",
       "2              2015  2020-01-13         f  $33.00         $33.00   \n",
       "3              2015  2020-01-14         f  $33.00         $33.00   \n",
       "4              2015  2020-01-15         f  $33.00         $33.00   \n",
       "...             ...         ...       ...     ...            ...   \n",
       "9252394    41453441  2021-01-05         f  $26.00         $26.00   \n",
       "9252395    41453441  2021-01-06         f  $26.00         $26.00   \n",
       "9252396    41453441  2021-01-07         f  $26.00         $26.00   \n",
       "9252397    41453441  2021-01-08         f  $26.00         $26.00   \n",
       "9252398    41453441  2021-01-09         f  $26.00         $26.00   \n",
       "\n",
       "         minimum_nights  maximum_nights  \n",
       "0                   5.0          1125.0  \n",
       "1                   5.0          1125.0  \n",
       "2                   5.0          1125.0  \n",
       "3                   5.0          1125.0  \n",
       "4                   5.0          1125.0  \n",
       "...                 ...             ...  \n",
       "9252394             4.0          1125.0  \n",
       "9252395             4.0          1125.0  \n",
       "9252396             4.0          1125.0  \n",
       "9252397             4.0          1125.0  \n",
       "9252398             4.0          1125.0  \n",
       "\n",
       "[9252399 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(calendar2020) # show tabular overview of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "listing_id        False\n",
       "date              False\n",
       "available         False\n",
       "price              True\n",
       "adjusted_price     True\n",
       "minimum_nights     True\n",
       "maximum_nights     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calendar2020.isnull().any() # check for any NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>adjusted_price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2418809</th>\n",
       "      <td>11563150</td>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2418810</th>\n",
       "      <td>11563150</td>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2418811</th>\n",
       "      <td>11563150</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2418812</th>\n",
       "      <td>11563150</td>\n",
       "      <td>2021-01-06</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2418813</th>\n",
       "      <td>11563150</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2418814</th>\n",
       "      <td>11563150</td>\n",
       "      <td>2021-01-08</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2418815</th>\n",
       "      <td>11563150</td>\n",
       "      <td>2021-01-09</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5822441</th>\n",
       "      <td>25967543</td>\n",
       "      <td>2020-01-10</td>\n",
       "      <td>f</td>\n",
       "      <td>$40.00</td>\n",
       "      <td>$40.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5822990</th>\n",
       "      <td>25969985</td>\n",
       "      <td>2020-01-10</td>\n",
       "      <td>f</td>\n",
       "      <td>$50.00</td>\n",
       "      <td>$50.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9068513</th>\n",
       "      <td>40885548</td>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9068514</th>\n",
       "      <td>40885548</td>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9068515</th>\n",
       "      <td>40885548</td>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9068516</th>\n",
       "      <td>40885548</td>\n",
       "      <td>2021-01-06</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9068517</th>\n",
       "      <td>40885548</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9068518</th>\n",
       "      <td>40885548</td>\n",
       "      <td>2021-01-08</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9068519</th>\n",
       "      <td>40885548</td>\n",
       "      <td>2021-01-09</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         listing_id        date available   price adjusted_price  \\\n",
       "2418809    11563150  2021-01-03         f     NaN            NaN   \n",
       "2418810    11563150  2021-01-04         f     NaN            NaN   \n",
       "2418811    11563150  2021-01-05         f     NaN            NaN   \n",
       "2418812    11563150  2021-01-06         f     NaN            NaN   \n",
       "2418813    11563150  2021-01-07         f     NaN            NaN   \n",
       "2418814    11563150  2021-01-08         f     NaN            NaN   \n",
       "2418815    11563150  2021-01-09         f     NaN            NaN   \n",
       "5822441    25967543  2020-01-10         f  $40.00         $40.00   \n",
       "5822990    25969985  2020-01-10         f  $50.00         $50.00   \n",
       "9068513    40885548  2021-01-03         f     NaN            NaN   \n",
       "9068514    40885548  2021-01-04         f     NaN            NaN   \n",
       "9068515    40885548  2021-01-05         f     NaN            NaN   \n",
       "9068516    40885548  2021-01-06         f     NaN            NaN   \n",
       "9068517    40885548  2021-01-07         f     NaN            NaN   \n",
       "9068518    40885548  2021-01-08         f     NaN            NaN   \n",
       "9068519    40885548  2021-01-09         f     NaN            NaN   \n",
       "\n",
       "         minimum_nights  maximum_nights  \n",
       "2418809             1.0          1125.0  \n",
       "2418810             1.0          1125.0  \n",
       "2418811             1.0          1125.0  \n",
       "2418812             1.0          1125.0  \n",
       "2418813             1.0          1125.0  \n",
       "2418814             1.0          1125.0  \n",
       "2418815             1.0          1125.0  \n",
       "5822441             NaN             NaN  \n",
       "5822990             NaN             NaN  \n",
       "9068513             5.0            12.0  \n",
       "9068514             5.0            12.0  \n",
       "9068515             5.0            12.0  \n",
       "9068516             5.0            12.0  \n",
       "9068517             5.0            12.0  \n",
       "9068518             5.0            12.0  \n",
       "9068519             5.0            12.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(16, 7)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ok there are null values, so show some of them...\n",
    "display(calendar2020[calendar2020.isnull().any(axis=1)])\n",
    "calendar2020[calendar2020.isnull().any(axis=1)].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "listing_id        False\n",
       "date              False\n",
       "available         False\n",
       "price             False\n",
       "adjusted_price    False\n",
       "minimum_nights    False\n",
       "maximum_nights    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Since only a few rows have nan values, lets drop the ones specifically from the columns\n",
    "# that are important for our analysis.\n",
    "\n",
    "def drop_nan_calendar_values(calendar):\n",
    "    '''\n",
    "    Description: drops rows with NaN values from the columns price and minimum_nights.\n",
    "    \n",
    "    Arguments:\n",
    "        calendar (pandas.DataFrame): a pandas DataFrame populated with calendar.csv \n",
    "                                     from Inside Airbnb.\n",
    "    \n",
    "    Returns:\n",
    "        DataFrame: processed calender instance.\n",
    "    '''\n",
    "    calendar.dropna(subset=['price'], inplace=True)\n",
    "    if 'minimum_nights' in calendar.columns:\n",
    "        calendar.dropna(subset=['minimum_nights'], inplace=True)\n",
    "    return calendar\n",
    "\n",
    "calendar2020 = drop_nan_calendar_values(calendar2020)\n",
    "calendar2020.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(calendar2020.date[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert date string values to timestamps for better comparison and grouping\n",
    "def convert_dates(calendar):\n",
    "    '''\n",
    "    Description: converts the column date from type str to pandas.TimeStamp\n",
    "    \n",
    "    Arguments:\n",
    "        calendar (pandas.DataFrame): a pandas DataFrame populated with calendar.csv \n",
    "                                     from Inside Airbnb.\n",
    "    \n",
    "    Returns:\n",
    "        pandas.DataFrame: processed calender instance.\n",
    "    '''\n",
    "    calendar.date = calendar.date.apply(pd.Timestamp)\n",
    "    return calendar\n",
    "\n",
    "calendar2020 = convert_dates(calendar2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check the assumption that for every day there is exactly one entry for each listing\n",
    "# so lets first create a grouping consisting of (date,listing_id) pairs\n",
    "cglid2020 = calendar2020.groupby(by=['date','listing_id']) # create pairs grouped by date and listing_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of pairs 9252383\n",
      "number of pairs equals dataframe number of rows? True\n",
      "for every of these pairs there exists exactly 1 entry? True\n"
     ]
    }
   ],
   "source": [
    "count_date_listingid_pairs = cglid2020.price.count().count()\n",
    "print(\"number of pairs\", count_date_listingid_pairs)\n",
    "print(\"number of pairs equals dataframe number of rows?\", count_date_listingid_pairs==calendar2020.shape[0])\n",
    "print(\"for every of these pairs there exists exactly 1 entry?\", cglid2020.price.value_counts().mean()==1.000000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of unique listings ids is 25349\n",
      "ration of pairs and unique listing ids is 364.9999211014241\n"
     ]
    }
   ],
   "source": [
    "# if there is exactly 1 entry for each pair, do we have for each day the amount of unique listing ids?\n",
    "count_unique_listings = calendar2020.listing_id.nunique()\n",
    "print(\"number of unique listings ids is\", count_unique_listings)\n",
    "print(\"ration of pairs and unique listing ids is\", count_date_listingid_pairs / count_unique_listings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2020-01-10     3388\n",
       "2020-01-11    25349\n",
       "2020-01-12    25349\n",
       "2020-01-13    25349\n",
       "2020-01-14    25349\n",
       "              ...  \n",
       "2021-01-05    25347\n",
       "2021-01-06    25347\n",
       "2021-01-07    25347\n",
       "2021-01-08    25347\n",
       "2021-01-09    21971\n",
       "Name: date, Length: 366, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The ratio of pairs and unique listing ids is not an integer, \n",
    "# so there must be an unequal distribution of number of listings per day\n",
    "calendar2020.groupby('date').date.count() # .hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# so there are days where we have no data for some listings\n",
    "# lets define a function to remove those days in our analysis\n",
    "\n",
    "def remove_days_with_less_entries(calendar):\n",
    "    '''\n",
    "    Description: removes all rows where the group of entries with the same date\n",
    "                 are less then 20000\n",
    "    \n",
    "    Arguments:\n",
    "        calendar (pandas.DataFrame): a pandas DataFrame populated with calendar.csv \n",
    "                                     from Inside Airbnb.\n",
    "    \n",
    "    Returns:\n",
    "        pandas.DataFrame: processed calender instance.\n",
    "    '''\n",
    "    calendar = calendar[calendar.groupby('date').listing_id.transform('count') >= 20000]\n",
    "    return calendar.copy()\n",
    "\n",
    "calendar2020 = remove_days_with_less_entries(calendar2020)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot one calendar timeseries which can be seen as the forecast of the overall availability of accommodation. The overall availability is measured by the mean all entries for each day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# before we can plot the mean of available accommodations we need to prepare our data.\n",
    "# we need to convert the categorical column available to integers for which we e.g. can\n",
    "# compute the mean.\n",
    "# later in the analysis we need the day of the year, so we include it in this function for reuse\n",
    "\n",
    "def prepare_columns_for_utilization(calendar):\n",
    "    '''\n",
    "    Description: creates a new column obtainable with converted values from the column\n",
    "                 available with integers instead of booleans and creates a new column\n",
    "                 dayofyear from the column date with the day of year value [1;365] instead\n",
    "                 of a complete date type.\n",
    "    \n",
    "    Arguments:\n",
    "        calendar (pandas.DataFrame): a pandas DataFrame populated with calendar.csv \n",
    "                                     from Inside Airbnb.\n",
    "    \n",
    "    Returns:\n",
    "        pandas.DataFrame: processed calender instance.\n",
    "    '''\n",
    "    # first we transform the categorical values for the available indication from t/f to 0/1\n",
    "    calendar['obtainable'] = calendar.available.map(lambda x: 1 if x == 't' else 0) # obtainable is synonym to available\n",
    "    # when we plot functions for multiple years together we need just the day of the year\n",
    "    calendar['dayofyear'] = calendar.date.dt.dayofyear # .strftime('%j').astype(int)\n",
    "    return calendar\n",
    "\n",
    "calendar2020 = prepare_columns_for_utilization(calendar2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EmoHLR3EfY8o7O4PMn5nJ7OnOcpq7RxiAbnXjCrOnZ9DW0eNK6g465TlZk+FKkhFbQIZh7B8k0zOtADar6lZVDQL3A+cmHqCqz6hqbNjNKmDWYBWKk1L0ZBwRAbgbOG8Y1z1uqCrv7gxy8JxMZlY46xiNXBgiiDjC0NoZjU8sCwSd/1kTsDBPX8yVZBjGUCQjDFXAroTX1fSzhnMClwOPJbzOFpE1IrJKRM5zy8qAFlWN9bhD1Tlh7K4P09mtHDQ3kxmlPkRgd31oRHW1dkYpyPVQWughGoWObsdSiLuSJmseg7mSDMMYhDFd2lNEPgksA96fUDxXVWtE5ADgaRFZD7QOo84rgCsA5syZM5aX2y8x62DOdB+ZGUJFsXfEcxnaOqIU5nkoyndWaWvtiFKY58UfnNwYg3mSDMMYjGQshhpgdsLrWW5ZL0TkVOAbwDmqGoiVq2qN+38r8CxwJNAIFItITJj6rdM973ZVXaaqyyoqKpK43NHR7T7N52Y7b83Mct+IXUktHRGK8j0U5Tt1xWIO3YHJjTHYPAbDMAYjmZ5pNbDQHUWUCVwErEw8QESOBG7DEYW6hPISEclyt8uB44CN6kQ/nwEudA+9BHhktDczFnT5nU47JgyV5b4RWwytHVGK8r0JFoMTgI5ZDFmTMfPZY64kwzAGZ0hhcOMAVwFPAG8BD6jqBhG5XkRio4x+CuQDf+4zLPVQYI2IvI4jBDeo6kZ33zXA1SKyGSfmcOeY3dW+98CW6mBSo3G6/DGLwem0Z1b4aGqL0h2IsqM2xFvbA4PW886OAE+t7kRVqWsKU1bkjVsMze5chsAku5Is+GwYxmAkFWNQ1UeBR/uUfSth+9QBznsZWDzAvq04I57GndUb/Vx7az0rDsvmR1dWDLrOcl83z8xy5y2qbQjz/bsa2V4b4qLTC7nivOJ+z//d31tZ85afBbMy6ehW5s7IoLzYS4YPquucIHY8xjBJweewCYNhGIOwX8x8fmu7k9n03xv87NgzuFuoy6/4vJDpDiWtTBCGGnd00l+ebqO+Zd96VJW3dwSJROHex534+uzpPrweYc6MDHbUOuc3tzsupcLcSYgxeMTWYzAMY1D2C2FITHld3+x06P5gNB5PSKQrEI3HF4D4XIbttSFCYfjgcXlEIvDoS537nFvbGIkHmJ9a7UzrmDPDmSQ3d0YGO/Y4wrCnIUxpoWfSciWZLhiGMRj7hTBsrQlx0Bwn40Z9s/O0/ss/NfOhq6t5/rU+6bD9Sk6Ci6cwz0t+jrB+izPQ6rADsphe6mXX3n3nNry1zTnm5GW58bKKYifwPLcygz2NEbr9UfY0hZlRNqYjhZNGxNZ8NgxjcKa8MHQHotTUh1m+KBsRqHMthk3uIjwP/F9br+O7/L0tBnDcSW+6wlBW5GV6mY89jfu6kt7eESQrQ7jywpJ4mcfjiMxc13LYuTfEnsbIpAqDDVc1DGMwJqd3mkB27Q2jCgtnZ1JS4KGhxbEYYh127HWM7kBviwGgqiKDTbscC6GsyEtlmY9XNnTv09Zb2wIsnJNJaaGXqz9e2quemDBs2x2irinMSUfl7nP+ROD1iLmSDMMYlClvMTS1Oh1/RbGXihIfda4rKRZfaGyN9ArG9mcxHDy3J/FrWZGXGeXOENZAsMcnEworm3YFOXSec+yHjs/nlOV58f2V5T48Aq9vChCJYq4kwzBSlikvDM3upLLiAi8VxV7qW3oLQyTae62EroDG5zDEOHxBVny7MM/DjFKnU9/b1GNtbNvtBKdjwtCXzAxhWqk3vvDPjDLvaG9tRFgSPcMwhmLKC0Os0y8u8DCtxBsfldQV0PiIo4bWhJTY/ug+qSoWzu7p7EUkPoQ1Mc4QCzwfMi+LgaiqyIgL06xpGSO+p9FgabcNwxiK/UAYImRlCDlZHspLfHT5lfauKP6AMme608EnzklwXEm9LYbMPumxp7tP+7UJwvDGloATmC4d2BKocoWossw7ea4kj2MlGYZhDMSUDz63tEcoLnD0b1qJ02nHJprNmZHBqjf98QC0qjqupH6S2z34o6r4aJ6yQi9eDzQ095z3xqYASxZmDTqrOiYGC+dM3mJ1tuazYRhDMeWFobUjSrGbxC42pyA20ayqwofH0zMyKRhSolHIyd63cy8t6rEEPB6hrKgnXrG7IUxja4QlBw7sRgLiOZMWzR/8uPHEciUZhjEUU14YmtsjlBa6wlDi3O5OVxjyczyUFXrjk966+qTcHozyYi8Nrgtq41ZnTsTiBYN3+KcfnYfHA6cmjFaaaLyWEsMwjCHYD2IMUYoLHGEoL/Yi0uNKys3xMH9mBpvdyW5d7gpruUkktytPGOG0t8kRiFgMYSC8XuGMY/Lxeic+eV4MW6jHMIyhmNLCoKq0dEQodl04Pq9QWuhlu2sx5GYJh87PYlttiC5/NJ4WuzBv6KGkFSU+6lsiqCoNLREKcicn99FwsVxJhmEMRer3ZKOgy6+EwsQtBnCe9Ovc+Qe52R4OnZeJqpPOIhZ7mFM59FDS8iIv/oDS6VcaWiOUF0/OvITh4vGIWQyGYQzKlBKGcERZ+44/7tpp7XQtgPye26xI6MBzsz0c4k5Ie2tbgB21IbIyZNAhpzFiQtDQEqGhJX2EQQSLMRiGMShTShh+dHcjX7m5jl8+0AwQT1mRk7BSWmzIKjirtBXmeZlW6mXHnhA794SYPcNZP2EoKkpiwhCmoSVMeVF6CIO5kgzDGIqkhEFEzhSRd0Rks4hc28/+q0Vko4i8ISJPichct3ypiPxLRDa4+z6WcM7vRGSbuxToOhFZOpob6eiK8oKbQjuWEjsYcnrAjIQJaokzk2Ojj2aWOes6b98TYt6M5GYkx2Y/v/R6N83t0bhQpDoWfDYMYyiGFAYR8QK3AmcBi4CLRWRRn8NeA5ap6hLgQeAnbnkX8GlVPQw4E7hJRIoTzvuqqi51/9aN5kZWvdlNOAKHzMukwQ0Kx4QhK0EYlh2aHd/O8PWs0ralJkRdU4S5ScQXACqKfZx9Qj6PPN+BKpQXp8fIX49HiJjJYBjGICRjMawANqvqVlUNAvcD5yYeoKrPqGpsxZtVwCy3/F1V3eRu7wbqgIqxuvhEVm/spqTQw/uOzKU7oHR2K4F+hCExEB2jstyH353DsGBW8rOSrzivOB5bSCdXklkMo6OzO8pTqzt5bm2XxWuMKUkywlAF7Ep4Xe2WDcTlwGN9C0VkBZAJbEko/oHrYvq5iPQ7O0xErhCRNSKypr6+fsBGm9qizCj1xdNO7G0Kx4Whb66j732hnMvOLoq/jrmFABbNT14Y8nI8XP3xUgpyPcyvmpykeMPFZj6Pjo6uKJd8dzc/+G0j372jgS01+67kZxjpzpgGn0Xkk8Ay4Kd9yiuBe4DPqMZXA/g6cAiwHCgFrumvTlW9XVWXqeqyioqBjY32riiFeR6mu77+uuYIoQGE4bgluXzyrB5hmJkgDMnMYUjkmMNzePinVUwvTQ9Xktdj6zGMho3bAjS1RTlthbPQUiBoKmtMPZIRhhpgdsLrWW5ZL0TkVOAbwDmqGkgoLwT+AXxDVVfFylW1Vh0CwG9xXFYjpq0zQmGeh2luB13XPLDF0JeYxZDEYKR+GSxxXqohIra05yiIzZI/fqkjDJGIvZfG1COZx9zVwEIRmY8jCBcBH088QESOBG4DzlTVuoTyTOAh4Peq+mCfcypVtVacXvU84M3R3EhbZ5SCPC8lBR58XqhrisQXw8kaQhiK8j185JQCTpyk5TYnEo9A1CyGEfPuriCV5b54QkRLYW5MRYYUBlUNi8hVwBOAF7hLVTeIyPXAGlVdieM6ygf+7D4971TVc4CPAu8DykTkUrfKS90RSPeKSAUgwDrgCyO9iVBY6fIrRXkePB4n7UVja4SSQufHO1SqChHhixeUjLT5tEI8Fnweiu5AlC3VIQ47IHMfa3BzdYiFszPic11shJcxFUnKMa6qjwKP9in7VsL2qQOc9wfgDwPsOzn5yxyc9tgM5zxHAPJyPHT6owRd/29merj/J4TYBDdVTSsX2Giobw7z8hvdnH1CPp4k/IV3rmzlr8+0s2JRNt/7QkV8WHNHd5Td9WHOem8eXvdZIxwZpCLDSFOmxMzn1k7n15koDF3+KMFwcjGG/YlYx7g/WQ0/v6+Jm//UzKMvd/Yqb+uMEO4TIwhHlP/7t3Pcvzf6+c3DLfF9W9z4wsLZmfEMuRZjMKYiU0IY2uI5kZyYQm620NWtBIJKhi+9gsPjTeyt2F+E4d2dQVa96Sc7S/j1X5tZvbEbcATgM9+r5ePX7aYpYc3vf2/opq0zyg++WME5J+Tzl2fa2VLtCMK7rjAcODsTnzt4zWIMxlRkaglDzGLIdl1JYR0y8Ly/EVsKYqp2aNV1Ibr9PTf35hZngNxNX57OjFIfX/9VPX94rJWX3+imuS1KQ0uEex5vjR//5L+7KM73sHxRNpedU0R+joef/qGJ5vYIm3cFKSvyUlrotRiDMaWZmsLgxhgCIU2LNRImEom7ktK3Q1NVWtr3de4/+HQbl363lqt+tpdmd39tY5jsLGHh7Ax+8ZXpHLckh7v+1sp3ftMAwFGHZLNqfTeqSntXlJff6OLkZbn4vE6Cxa99upQdtSGuvqmON7cGWTjbmcgYizFELMZgTEGmRK8ZF4Zc53ZirqRgSC3w3IeYKykVHnT3NoX5zxv3sr12eLOH//ZCB+dfU8PHr6vhzkdaUFWef62LXz3YwpEHZ7OjNsTDz7YDsKcxzIxSHyJCbraH715Rwc+/PI2sDOGMY/J4/3ty2dsUYXttiOfWdhEKw+nH5MfbOm5JLj+8soKauhC1DWHeuzgHoCfGkApvpGGMMVOi22zriJDhg2x3Sc68bA+BkNLtVzLNYuiFJ4ViDLf9tYX1WwL85ek2/vsTZUmdE44of/xnG3MrM5he6uXeJ9rw+YS/vdDOwXMz+cEXK/jij/fwzk4nHrCnMRyfzxLjiIXZrLxxFgCtHc4j/7/e6OaVDX7mVmbErYIYRx6cza1fm4FH4ICqPhbDFHXJGfs3U6LX7A4oudmeeJA5L8e5reb2iMUY+uBxP/GJnOT24rou7lzZ0uvpurE1wrNru8jJEp5e00WXf98LamqL9IoXAKx5y09dU4TPnlPEj66s4KSjcrn7H600tUX57LnFZGYIB83JZNPOIKrqCsO+zz8ZPiHDJ5QX+zhoTiZ/frqd9VsCnLYit9/BCgtnZ7JgVs+8hniMwUYlGVOQKSEMfYPMudnOdnNbxIaq9sHjdmwTlRajoyvKT//QxL2Pt3HLn5vj5Q0tzip7px+dR3dAeWdHsNd59/2zjY9+vYarfraX9q4ecYittXH4gixEhK98spQDqjI4eG4m7znYycO4cHYmze1RtteG6OzWfoUhkWMOz6a1w2njzAQ30mB4bVSSMYWZGsIQ0l4CEFuAp7k9ahZDHyZ6uOrfXuygvSvKUYdk8/cXOmhqc1w3zW1Oj3rQXCebbXOfYPI/X+mkstzHrr0h7nykJV6+tylCdpbEBxrkZHn49bUz+PmXp8Wf5g+a49T55L+dTPBDCcNxRzipUC46rYDSJNOn26gkYyozdYTB1yMAMVeS353HYPTgGafhqoFglCt+VMtXf1FHTX1PMHnb7iDTS738x0dLiEThiVXO5LFm17e/oMrpxGOCEaO+OczRh2Vz7JIcXnqjOz6Kam9TTzA5hs8rZCfEkg6ak0lVhY/7/9mGxwMLhkiJvnB2Jr+9rpLPnVec9P3aqCRjKjNlhCFx+c687J5tG67am1iMYaQWg6qyemM3L67r6lW+pSbE5l0hXn3bz3d/0xBfPa+hJUJ5sZc5MzI47IBMnl7jCENLu6NMs6f58Hmd9TRidHRH6fIrFSU+jjk8h8bWCJurHbHZ2xhmeungT/WZGcJ1l5cze7qPb15WTtW0odfKmFuZMayJkDYqyZjKTIlecx9XUk7PbZkrqTcyyhjDyhc6uOaWer51ewOd3T2d+SZ3FNCVFxazuTrEqjedGcaOMDhm27FLctlSHaK+JUxLu+MSysn2UFLgpTnBYqhrcuIP00u9HH24Mzw0NmN5b1MkqbUvDpqTyd3fnsmJ7xmfjLk2KsmYykwNYQj3cSVl99yWBZ97Ex+uOsIO7cV13fHt1W/549ubdgUpyvdwzgkF+Lzw9vYAqkpDayS+/OnRhznrba/e4Ke5LUKJm7q6tLCPMDQ72xUlPkoKvJQXe9m1N0xnd5T2ruiQMYOJwBN3JZnFYEw9poYw9LEY8nJ6tk0YehMfrjpIf/abh1t4+Ln2fcojEWXjtgBnn5BPYZ6Hl9/ocSe9uzPIwtmZZGYIC2Zl8vb2IJ1+xR/Q+HrY82dmUFHsZfVbfprbo/H1t0sKPTQlBJ/rmx2LYZq7Gt/Mch+768PsTbAkJhsRweOxGIMxNZn8R68xoK8wZGUIXo9j5psw9CZmMQwkDNGoct8/2wAoyPVwyvK8+L7N1UG6A8rShVm0d0bjeYiiUWV7bYgLDnEsgkPnZfLEqs64SyhmMYgIRyzMYu07fooLvPEn/9JCL5t29QSs65oieDzERwjNrPCxeqOfPY1OfalgMYAT9LYYgzEVmRoWQ595DCLCUW4nZbLQm1iMQQfo0PY29TwC3/jHJqrrejrsjducOMLhC7KomuajrjlCJOIskhSO9HTkh87LojugrHFdTTFhAFh8YBZNbVG21oQoLnC+fiUFXprbI0Tda6prDlNe1JOobma5j8bWCDv3xCyG1BCG2MOHYaQqTa0RrvhR7bDPS0oYRORMEXlHRDaLyLX97L9aRDaKyBsi8pSIzE3Yd4mIbHL/LkkoP0pE1rt1/kJGkRu7r8UAcP5JBQC9JkcZCb7xAR50d+xxhOC6y8rwCNzzaE/m0bqmMBk+p6OvLPMRjTrxgLau3kkMFx/oTDT7v9XOCKREYVhyYHZ8uyQ/5kryEo325Lxq74rGl86EnjW5173rJzND4ivzTTZej8UYjNSmwx9l867h5SKDJIRBRLzArcBZwCLgYhFZ1Oew14BlqroEeBD4iXtuKfBt4GhgBfBtEYmtofm/wOeAhe7fmcO+epf+kuUtX5TNNZ8u5ZIPFo202inJUMHnHW5Cu6MOzeb0o/N4dm1XPJNpQ2uE8iIvIsIMt7OubQjT7i6UVOAmMZxR5mNmhY/Nu0J4PcRjDABzZvg4oCqDaaVeli1yRCJmObS4s48DIe01L2FmhdPWa+/6mVbiTZn1NbxeMYvBSGn8gZE9uCTz6LUC2KyqW1U1CNwPnJt4gKo+o6qxSOQqYJa7fQbwpKo2qWoz8CRwpohUAoWqukqdmUu/B84b0R0AwfC+sQQR4Yxj8ilLcibr/kIs7XZUFVXdZ2LZzj0hSgo9FOZ5OfuEfEJheP4156NNHHo6MyYMjeGEtOc97/X8SmfuwBnvzes1l0REuOMbldz//SqOWOgIQ2xCYixfUiCoZGX2fJ6z3XkIoXDqxBfAXElG6uMPjuwLmowwVAG7El5Xu2UDcTnw2BDnVrnbydY5IKraryvJ6J/E7Kovvt7NhdfW8J837o3PE9i6O8Tc6U5HPK8yg8I8D5vclctik9UAKoq9eDywpyEcd9fFLAaAC04uYP7MDD7zoeIhryk2vDg2L8LfRxjycz0c7KbOSIURSTG8HjFXkpHSBILjZzEkjYh8ElgG/HQM67xCRNaIyJr6+vp99oeceGSveQzGwMRHJUV7EtLVNYf5+q/qeXNLgE27giw6wIkRiAgLZmWweVfImZOQIAxerzC9xEttY5h212IoyOv5Oi09KJs7v1mZlMUWS3rY5Xe+xP6gkp3Z+/M88mDHusjOSo34AjiJ9MxiMFKZ7nF0JdUAsxNez3LLeiEipwLfAM5R1cAQ59bQ424asE4AVb1dVZep6rKKiop99sdSL2SYxZAUkjCPobXDSTJ429dnUJjr4brb6olG4YiFWfHjD5yVydbdQVo7nBXxEgPJleU+ahvC8eBzosUwHGJJDwdyJYGTARVg1rRUciXZcFUjtfGPo8WwGlgoIvNFJBO4CFiZeICIHAnchiMKdQm7ngBOF5ESN+h8OvCEqtYCbSJyjDsa6dPAI8lccENLmJOv3MlLrzt+75gwWOqL5Iil3VZVWjuiFBU48YQvXFBCa0cUjwcOP6C3MITCsPYdd+hpggUwo9zHHtdiyMly1jcYCflujKEj7kqKkt3n81xyYDZ3fnMGZx+fXFrsicBrE9yMFMcfGKcYg6qGgatwOvm3gAdUdYOIXC8i57iH/RTIB/4sIutEZKV7bhPwPRxxWQ1c75YBXAncAWwGttATlxiU2MpcN//Jye0fDDvCYDGG5JAEV1JLR4Rid8joaStyWb4omyMPyiYnIaXIovmOb//vL3QAvYeeVpb5aG6PUtcc7uVGGi45fVxJgaD26zKaPzMTjyd1PmevTXAzUpyRWgxJ2eWq+ijwaJ+ybyVsnzrIuXcBd/VTvgY4POkrddnb6A6dbInQ0R2NWwwmDMnh7eNKis0XEBF++MWKfWYEVk3L4MiDs3jtnQCZGcLs6T2ZSmMjhDbtDMbX2x7ZNQnZWUKXP0o44kyW6+tKSkW8HgibxWCkMCkRfJ4IEmfivvJmd48wWPA5KRIX6mntiPSaSOb1Sny2cSIXnVaIzwv//fHSeH4jSByyGiF/FMIAzsikTn80/kXuG3xORSzGYKQ63SNckyZ1InlJUlMfZsGsDBpbIqx6szs+K9YshuSIxRgiUaWlIxp3JQ3G8kU5PPKzWeT0ce/EJrlBz6znkZKXLXR2a1wY0iFm5PVajMFIbfyBaK/JosmSdhZDTX2Y2dMyOPrwHP69wR8fjmXCkByxlBj+oJP5tDg/ua9AX1EAep1bUjC6+QW5OR66/FG63Qk5aWExWIzBSHH6zglKlrQSBgX2NIapmubjqEOyae+KxidfmTAkR8yVFEtzUZiExTBwXcKRB2dRkOvhotMLR3VdedkeOrt7XEnpsPKezXw2Up1AP3OCkiGtXEmRiBKNOrNuYxOnGtzc/RZjSI5YCKHZXUozWYthIG740jR8Xkadvyg3W2hs7XElZWel/ufp9QjBkCmDkbp0B6Ij+i2llTBE3d9gbrYnPpmqodV58s1MA9dDKhDrwJtdi6F4lC6gkc5d6EtejmMxxIbX9Z3HkIrYzGcj1fEHlZypHmOIuXOzMyU+br4xJgxmMSRFbLhqbNGbxHkJk0luthNj6HElpf7naaOSjFTHH9QRWQxpJQyx9etzsj3xcfNxYUiDJ8xUIObxqakP4/VARUlqCENettAVULrdmZrpIQwQtVFJRgoTCOqIRvillTDEVvjKznQmRPm8JgzDJTZzuKYuzIwyX7/zFiaD3GwPqsTTgI9kiN1EY6OSjFTHP8IYQ+r/+hJIdCWJCAW5HsuuOkxiOhAIaXwOSCoQu5atNc4ExnSxGCzGYKQy+0WMIdGVBD3ZPLOzZESz+/ZHEgcPzUwhYZg7w0m1EcuFlRbzGGxpz1ERiShba4Ko2ns4XnSPMMaQOj1DEsRGJcU6jVgAuqrClzLLPaY6noRHgVSyGKqm+fB60sxisKU9R8zat/3c/Y9W1m8JsHRhFmefkM9Jy/Im+7KmFKq6f8xjiLpPFjkxYXAthlR68k11EgU0lYTB5xVmTc9gR22IDB8pE/sYDCeJnj3tDpc/Pt7KHStbyc8Rzj8xn+de6+YHv23khCNz8XlT/3NPZV59289dK1vIyRIqy32ojmxxq9TpGZIgZnHGniZjefyrKtLqNiaVxP72qEOyJ+9C+mHuDB87akNpEXiG2HDVyb6K9GLn3hB3rGzl5GW5fO1TZU7G3hnt3Hx/M60dUVujfZT88k9N+IPOUsebdoV47+IcTnxP7rDrSaseNRp13EixkTWxyVAzKzIGO81IoLTIS0Wxl8+eW0xeTmp1wIsXZPH8a92sOCy1BGsgfN70jzE8vaaT7bUhLju7eELae36ts8DW588vjo8kjCVybGmPmDCMgvqWMDv3hvn8h4v52GmjS1GTXsKgSk5CICW2CH0qLRCf6uTnePjTD6sm+zL65fyTCjj7hIK0GXqc7jGGvU1hbry3ie6A8sHj8plemlx3EI4ou/aGCIVhQVUG3mG4f55/rYtF8zOpKO5pK5aWpbUjjd/MSabbH+VPT7YD8J4x8ASklzBEe49WmT09g9c3BZhella3YQyAiJCZRsZfOg1XfXdnkMde7uBf67tp7XDGtrd3ReMz4Z9e08XFSSRCDIaUq2/ay8ZtzuixIw/O4idXTUtKHBpawmyuDnHFh4t7lRe5aVlaOmy24EhQVb53VwOr3vRTmOdhQdXof0RJ9agiciZwM+AF7lDVG/rsfx9wE7AEuEhVH3TLTwJ+nnDoIe7+h0Xkd8D7gVZ336Wqum6w6+gbSLnywmJOXpbLnOlp1JsYUwavR9LClbS9NsSVP9lDhlc4+vBsppf68AedlOvvf08uN93fzH1PtHLgrAyWL8oZtK7//UszG7cF+eIFxYRCyh0rW7n94Ra+eEHJkNfx+qYAAO85uPcTbcxiaGlPE5VNMVa96WfVm37OfX8+p63IG5Plb4cUBhHxArcCpwHVwGoRWamqGxMO2wlcCnwl8VxVfQZY6tZTirO+8z8TDvlqTESSIar0ciVlZ3pYelB6+KONqUcsiZ6qpvRw6d/9vYXsTOH335lJaeG+btdrPl3KN3/dwM/+0MQfvzez36f/f63v5qFn21nzlp+PnFLAR05xrIuG1gh/fqodn1dY9WY3372inFnT+n9Qe+1dP3k5woJZvfcX5HkQcVYUNIbPq291k50lXHVhybDceoORjMWwAtisqlsBROR+4FwgLgyqut3dN5jkXwg8pqpdI73YaHRkY3INYzyIDamNKqTCKMst1UH+/lIHhx+QxSnLnTkBu/aGeP61bj51VmG/ogBQVZHBZWcX8e3bG3hlQzfHLslFVVnzlp9gWLn38Tbe3RGkvNjLGcfk8bnziuPnXnlhCVtqQtz3zzbAsQoGEobX3w2w5MDsfYYiez1CYZ6HljSIMbR1RqipC7NwTma/Q2tfXNfFzAofB1RlAjiLTwWUts4ImT6hoztKS0eUp1Z38s6OIAW5Ho4/IpfKch/tXVE6uqJEo8riA7OYX5UZH3k5GLsbwlRV+MZMFCA5YagCdiW8rgaOHkFbFwH/06fsByLyLeAp4FpVDfQ9SUSuAK4AKJ62qN+VxAxjMoj55yORnu2xQFV59W0/W6pDVJb7KC7wsOTAwS3jjq4o3/jfehpaIzzyXAd7GsN84Lh8Hnq2nQwfnPv+gkHPP3ZxDiWFHp5a08WxS3LZuC3INbfUA858lwtPKeCSDxbt8/vzeYVvf7acm+5r4sXXu6lrCvdbfyis7G4Ic8ry/odOFud7krIYIhFl2+4Q+bkeZiQZW2xqjVDfEubguVnxso6uKL9+qJmZZT4OmZeFx00o6Q8oz67totvvpIDPzfYwe7qPkgIv9z7eFp+ZX1bk5ewT8vngcfkU5Xt4d2eQv7/YweP/6iTDBwtnZxIMK5t3hfq9puwsYcWibBpaItz+cEv/x2QKn/pAER85pWDQ+R2768PMrRxbd/qERG1FpBJYDDyRUPx1YA+QCdwOXANc3/dcVb3d3U/ZzCM0HRZwMfYPPN6e9bNhbL6XbZ0Rfvz7Jv61vrtX+bnvz+eMo/O4/eEWuv3Kh07I5+RlufGO+qFn26lviXDz1dO59/FW7lzZyn3/bMMfUE4/Jm9AayGG1yscNj+LTW7Ht36z84x29vH5fObsokHX7Sgt9HL95yv4+HU11Db0LwwNLRFUGXCgSFG+d9AYQ2tHhAefaufl9d1s2x1CBE5ZlssZ783nhde6CISUi08vZM6M3h1ke1eU//r5XnY3hPnmZeWsWJRNJApf+0Udm3YF6ZsDMZYZICdLyM700NEVJRByDqoo9vKZs4uYUerj/1Z38ru/t3LPo63k5Xho64zi88IFJxfgD0TZ0xghO1P59AcKKSnwUpjnIRhWCnI9FOZ5mTPDR2Ge857u3BsiFHL2FeQ6x23YGuCxlzv5zcMtvLCui59cNY383B5RVlUaWiOUFnqpbQxz7JLBY0PDJRlhqAFmJ7ye5ZYNh48CD6lqXD5VtdbdDIjIb+kTn+iPqJoryUgd4hbDGHlAdu4J8bVb6mhqjfDFC4o5eVkeDS1hHnm+g0eec/4AKsu83HhvE7/+azPXX1HBkQdn8+zaLg5fkMXhC7L4wRcr2LQryG8ebqG00Mt/fHTowDDAgbMzeemNbrr9UTZsDTBrmo8vf7w06euvLPexewBhiFkS00v673KKCzzsqA2xaVeQcEQ5dF4WO2pDtLRHCIaV3z/aylvbg8ydkcFXP1nKrrowDz3Tzv+t7iLD51guW2qC/PqaGXT5lX++0klDS5iHn+sgGFaml3i5/o4GPB4nc0IwrHzvCxUsnJ3Brr1hFGfkVn1zmE+d1SOE0ahS1xxhx54Qh87LjHfmpx2dR01diJUvdNDUFuG4I3I58qAsikawVG7fwTM5wLFLcjl2SS7Pru3iR79r4Ku/qOPyc4v4+4sdBIKKzye89Ho3n/5AIaHw2M/lSkYYVgMLRWQ+jiBcBHx8mO1cjGMhxBGRSlWtFSdqdx7w5lCVRKP9L0pvGJNBzFc+2MikZALTe5vC/O2FDlY+305GhvDLr0yPuz3Kirx87VNZXHFeMd+9o4HSQi/fvKyMN7cEuOH3TfziT01cfEYR23aHuOojjgB4PMLBc7P42X9OH9b9LJiVgSpsqQmxYWuAow8f3lNoZbmPf73R3e++vc2Om2jaAHOOZlZk8MK6bq788R4iUSebQU19j8h4PfCNS8t65VP6yCkFbK0JMWe6j9c3Bfjh7xr57A/2EI4o1XXOuScdlctHTy2galoGr7/r5+0dQVraI5yyPC8+cKXcnVPRd7QUOO/ljDJfv26rqmkZSY3GGg0nvieXrIwKvn17PV/7ZT2FeR7Kirzs2BOirMjL7x91YjtjnRZoyNpUNSwiV+G4gbzAXaq6QUSuB9ao6koRWQ48BJQAZ4vId1X1MAARmYdjcTzXp+p7RaQCxwZfB3xh6GtJj7WAjf0Dr9vHRfq4xrfWBGlqi/DHx9t4a3uQC08p4LKzi/YRiEhU+cX9zfzjJccSeO/iHD5/fnG/wdviAi8///L0uNAsPjCbKy8o5rrbGrjh7kZmlHk5adnwUx8ksnCWEzB96Nl2WjqiLFmYNcQZvZlZ5qO5PUq3PxrPgBwjZjFUDLBi4KfOLKSzO0pbZ5R5lRlsqQ7ywePyOXhuJh4PzKvM2OdpvKTAy1GHOGUnL/Oya2+Id3YE2dMY5luXl3HIvKxeHfpxR+Ry3BGje48mg/cuzuHX186gtjHMEQuzyc/xEIko9S0RPn7dbgBmT59gYQBQ1UeBR/uUfSthezWOi6m/c7fjBLD7lp88nAuNkZEKwz8Mgx6L4Z7HW/F6hKbWCDlZwuOrOlGFvBxh6UFZ3Pt4G2s2+jloTiaXn1sUd0f85uEW/vZiB+e+P5+PnVqYVDA1UVyOOyKXm66eBsCi+VmjTkBXUeJlWqmXZ17toqTQw8lHDa8TnenmLKuuD7NwdmavfXXNEYrzPWQNkAcrJ9vDly9O3m3VF49H+MwEpfWYDA6oyoyPdAInJjSjzMfKG2exc0+IigFcdCMl7aYMj+XoD8MYCx55rgOvB/JyPASCyjnvy+fIg7I5aE4m00u9/OOlTv72QjuP/auDrbuDfOuz5eRne1j5QgenLs/lPz828g5xqNFKw0FE+OEXK/jObxq4+IzCATvxgThknmNhrN8c2FcYmsJMSzLlhpE8+TkeFs0fnmWXDGn3SY3FrD7DGAsOqMpgeqmXaz9dxvyqDHKzPUSj+y4z+6Hj8/nQ8fk8u7aLG+5u5JLv1lJW6AyNPOd9gw8jnWgOqMrk99+ZOaJzZ5T5qCzz8tq7fs4/qfd97W2OMGta2nU3+y1p90l5LV+ekSIsmp/Ffd/v4yUd5Pt54ntyOXhOJr/6SzMvvd7N/JkZHHZA5sAnpCFHHpLN82u7iES110S2jq4oRXlm7qcLaScMZjAY6UxluY/vfb6C7kAUj5DSqTRGwtKF2Tz6Uifbd4dYMKtH9PzBqA01TyPSTsLHctq3YUwWOVkDB2LTmUPnO2Lw1vZgr/JAUKfk/U5V0u6TMovBMFKXmeU+CvM8vLWtJ7tNOKKEIzbUPJ1IO2Ewi8EwUhcR4ZB5mby9o8di8AecCYDmSkof0k4YPGl3xYaxf3HI3Ey214bwB51cId3uf8takD6k3SdlBoNhpDZVFU5qjbomZ0p4wF2bPStNlmw10lEYTBkMI6WJ5UPa66bBiLuSLMaQNqSdMFjw2TBSm+nuDOe9rsXgD1qMId1IO2Ewi8EwUpvyYi8egbpmx2LoDliMId1Iu0/KLAbDSG18XqG82MveRteVFIsxmMWQNqSdMJjFYBipz/RSH3XNvYPP5kpKH9JOGGy4qmGkPtNKvT3B56AFn9ONtOtmzZVkGKlPRbGXxtYIqtoTY7CUGGlDUp+UiJwpIu+IyGYRubaf/e8TkbUiEhaRC/vsi4jIOvdvZUL5fBF5xa3zTyKSVJpJcyUZRuqTn+shFIZASM1iSEOGFAYR8QK3AmcBi4CLRWRRn8N2ApcCf+ynim5VXer+nZNQ/mPg56p6INAMXJ7MBZsuGEbqk5/jdC0dXVECQcXrYdQrzBkTRzIWwwpgs6puVdUgcD9wbuIBqrpdVd8Aosk0Kk6u4ZOBB92iu4HzkjnXLAbDSH0Kcl1h6Fb8gahZC2lGMsJQBexKeF1NP2s4D0K2iKwRkVUicp5bVga0qGp4uHVa8NkwUp/83B6LwR9Usi2+kFZMxEI9c1W1RkQOAJ4WkfVAa7Ini8gVwBUABRWLbc1nw0gD4q6k7ijdQSXHLIa0IplutgaYnfB6lluWFKpa4/7fCjwLHAk0AsUiEhOmAetU1dtVdZmqLgNb89kw0oG83N4xBpvcll4kIwyrgYXuKKJM4CJg5RDnACAiJSKS5W6XA8cBG1VVgWeA2AimS4BHkqnTLAbDSH16YgxRJ8ZgwpBWDNnNunGAq4AngLeAB1R1g4hcLyLnAIjIchGpBj4C3CYiG9zTDwXWiMjrOEJwg6pudPddA1wtIptxYg53JnXBZjEYRsqTl91jMXQHLMaQbiQVY1DVR4FH+5R9K2F7NY47qO95LwOLB6hzK86Ip2FhwWfDSH0yM4SsDKGjO0pjW4SqaRMRzjTGirTrZs2VZBjpQX6uh7bOKA0tEaaXmDCkE2nXzXrNlWQYaUF+roede0JEo1BR4p3syzGGQRoKw2RfgWEYyZCfI2zdHQJgmlkMaUXadbMWfDaM9CA/xxNf1tMshvQi7YTBLAbDSA8K8np+rNNKzWJIJ9Kum7VRSYaRHiw/NCe+nZ9jln46kXbdrLmSDCM9OPGo3Pi2kzfTSBfSzr4zV5JhpAcZPuGmq6fFl/Y00oc0FAZ78jCMdGHJgdmTfQnGCEi752+zGAzDMMaXtOtmLfhsGIYxvqRdN2tBLMMwjPEl7YTBMAzDGF/SShjMWDAMwxh/0koYDMMwjPEnrYTBLAbDMIzxJ62EwTAMwxh/khIGETlTRN4Rkc0icm0/+98nImtFJCwiFyaULxWRf4nIBhF5Q0Q+lrDvdyKyTUTWuX9Lh7yOJG/KMAzDGDlDznwWES9wK3AaUA2sFpGVCWs3A+wELgW+0uf0LuDTqrpJRGYCr4rIE6ra4u7/qqo+mOzF2lBVwzCM8SeZlBgrgM3uGs2IyP3AuUBcGFR1u7svmniiqr6bsL1bROqACqBltBduGIZhjA/JuJKqgF0Jr6vdsmEhIiuATGBLQvEPXBfTz0Uka4DzrhCRNSKyJhqNDLdZwzAMY5hMSPBZRCqBe4DPqGrMqvg6cAiwHCgFrunvXFW9XVWXqeoyr9dWgTIMwxhvkhGGGmB2wutZbllSiEgh8A/gG6q6KlauqrXqEAB+i+OyGryuZBs1DMMwRkwywrAaWCgi80UkE7gIWJlM5e7xDwG/7xtkdq0IxIkonwe8OXSFybRqGIZhjIYhhUFVw8BVwBPAW8ADqrpBRK4XkXMARGS5iFQDHwFuE5EN7ukfBd4HXNrPsNR7RWQ9sB4oB74/1LWYLhiGYYw/opo+qytVzDpC66tfn+zLMAzDSCtE5FVVXZbs8Wk189ksBsMwjPEnrYTBMAzDGH/SShhs4rNhGMb4k1bCYBiGYYw/aSUMZjEYhmGMP2klDBZ+NgzDGH/SShhMFgzDMMaftBIGUwbDMIzxJ62EwXTBMAxj/EkrYTBlMAzDGH/SShhMFwzDMMaftBIGwzAMY/xJK2GweQyGYRjjT1oJg2EYhjH+pJUwmMVgGIYx/qSVMBiGYRjjT1LCICJnisg7IrJZRK7tZ//7RGStiIRF5MI++y4RkU3u3yUJ5UeJyHq3zl+4S3wOcR3JXK1hGIYxGoYUBhHxArcCZwGLgItFZFGfw3YClwJ/7HNuKfBt4GhgBfBtESlxd/8v8Dlgoft35lDXUl7sG+oQwzAMY5QkYzGsADar6lZVDQL3A+cmHqCq21X1DSDa59wzgCdVtUlVm4EngTNFpBIoVNVV6qwt+nvgvCEv1iwGwzCMcScZYagCdiW8rnbLkmGgc6vc7ZHUaRiGYYwjKR98FpErRGSNiKypr6+f7MsxDMOY8iQjDDXA7ITXs9yyZBjo3Bp3e8g6VfV2VV2mqssqKiqSbNYwDMMYKckIw2pgoYjMF5FM4CJgZZL1PwGcLiIlbtD5dOAJVa0F2kTkGHc00qeBR0Zw/YZhGMYYM6QwqGoYuAqnk38LeEBVN4jI9SJyDoCILBeRauAjwG0issE9twn4Ho64rAaud8sArgTuADYDW4DHxvTODMMwjBEhzqCg9GDZsmW6Zs2ayb4MwzCMtEJEXlXVZcken/LBZ8MwDGNiSSuLQUTagXeAIqC1n0MGKu9vXznQMEZ1DVVeDoQmoJ1YeezeRlPXcM5JbG+i3s++9zfSupI5Z6LfTxj8OzMe7WfQ/3s60u/gUO0n850Zi3ZijPX7OVT7/b2f43mfQ72fB6tqwQD17Iuqps0fsMb9f/sA+/st72/fWNaVRPmaCWrn9sR7G01dwzknsb2Jej/Hqq5UfD+H+s6MR/sDvacj/Q4OtS+Z78xY3udYv59D1TWc78xEvJ+D/Wb6+0tXV9Lfhlk+knPGsq6JOmey25/Icya7ffs8U7P9iTpnstsf6TlJkW6upDU6jADKRNWVSm1Ze+nf3mS0ae1N7faGez3pZjHcnqJ1pVJb1l76tzcZbVp7U7u9YV1PWlkMhmEYxviTbhaDYRiGMc6YMBiGYRi9mLLCICIREVmX8DdvkGOfFZERBYpEREXkDwmvfSJSLyJ/H0l9w2z7PLf9Q8axjcm8v47xbmO4bY7mu9KnnnH/7Pq09w0R2SAib7i/h6MnoM1ZIvKIu3rjFhG52c23NtDx/yUiuSNoR0XkxoTXXxGR74zwspNpL9a3bBCR10Xkv0VkQvrSifpNTFlhALpVdWnC3/ZxaqcTOFxEctzXp5F89lnA6WxH2PbFwIvu/+G05x3G4aO+P6NfRvTZjQQReS/wIeA9qroEOJXe66SMR5sC/BV4WFUXAgcB+cAPBjntv4BhCwMQAM4XkfIRnDsSYn3LYTi/h7NwVqqcMkxlYdgHd53p50TkVRF5wl1JLsan3KeAN0VkxTCrfhT4oLt9MXBfQpsrRORfIvKaiLwsIge75ZeKyEoReRp4agT3kg8cD1yOk/EWETlRRJ4XkX+Is0b3r2NPMiLSISI3isjrwHsn4P6eF5GlCce9KCJHjOA+T0y0TkTkFhG51N3eLiLfFWe98fVj9fQ9WJtjVP9An91A9/kBEXnb/d7+YgTWWiXQoKoBAFVtUNXdA/0eXKvo5lH8HgBOBvyq+lu3zQjwZeAyEckTkZ+5db8hIv8hIv8PmAk8IyLPDLOtMM6omy/33SEi80Tkabedp0RkjogUiciOhN9GnojsEpGM4d6kqtYBVwBXiYNXRH4qIqvdNj+fcC3XuN/T10XkhuG2lVBPvnsvse/9uQn3+paI/Ma1Zv6Z8EA3LKayMORIjxvpIfdD/yVwoaoeBdxF76eXXFVdipP19a5htnU/cJGIZANLgFcS9r0NnKCqRwLfAn6YsO897vW8f5jtgbO86uOq+i7QKCJHueUrgP/AWZ97AXC+W54HvKKqR6jqi8NsayT3dyfOOuCIyEFAtqq+Psx2k6FBVd+Ds4b4V8ah/vFgoM9uH9z3/DbgLPd7O5JFSf4JzBaRd0XkVyLy/nH+PQAcBryaWKCqbTjrw38WmAcsdS2Ye1X1F8Bu4CRVPWkE7d0KfEJEivqU/xK4O9YO8AtVbQXWAbHf3YdwlgMIjaBdVHUr4AWm4Yh9q6ouB5YDnxNnyYKzcD73o1X1COAnI2nLxQ982P3enwTcKCKxhY8XAre61kwLcMFIGhipCyMd6Ha/2ACIyOHA4cCT7nvoBWoTjr8PQFWfF5FCESlW1ZZkGlLVN8SJYVyM83SdSBFwt4gsBBQnh0qMJ7UnDflwuRi42d2+3339d+Df7hcVEbkP58n0QSAC/GUkDY3w/v4MXCciXwUuA343kraT4K/u/1fpEcFUZ6DPrj8OAbaq6jb39X04T6hJo6odrvicgNOR/An4PuP0e0iCE4FfqZPSn1H8BuKoapuI/B74f0B3wq730vO9uIeeDvlPwMeAZ3Cstl+N9hpcTgeWiMiF7usinM76VOC3qtrlXu9o7lmAH4rI+4AozrLI091921R1nbv9Ko4AD5upLAx9EWCDqg7kRuk7oWO4EzxWAj/D+dKXJZR/D3hGVT/sdq7PJuzrHGYbAIhIKY6pvlhEFOdHrcA/+rnu2Gu/a86PlGHdn6p2iciTOE9JHwUGfCoegjC9LdvsPvsD7v8IY/d9HqrNETPIZ/fIeLUJcVfOs8CzIrIe+BLj+3vYCFyYWCAihcAcYPsw60qWm4C1wG+TOHYlTudaivPdfHqkjYrIATjfvzqcfuY/VPWJPsecMdL6++ETOJbjUaoaEpHt9HxfAgnHRQBzJQ3BO0CFOIE4RCRDRA5L2P8xt/x4HFNwoIyGA3EX8F1VXd+nvIieYO2lw77q/rkQuEdV56rqPFWdDWzDeSJc4ZquHpx7Gq7baCBGcn93AL8AVqtq8wjb3QEsEpEsESkGThlhPanS5kCfnWeANt8BDpCeUXUfG26DInKwa9HFWIqz6NZ4/h6eAnJF5NNuPV7gRhzL8Qng8+IOunA7Z4B2IPkMoH1wn8IfwHHnxHgZN46D06G+4B7bgbN42M3A30f60CQiFcCvgVvUmS38BPDFWLxCRA4SkTzgSeAz4o66SrjnkVAE1LmicBIwdxR19ct+YzGoatA1737h+iF9OE8YG9xD/CLyGo4r5LIR1F+N0wn25Sc4rpZv4jzRjwUXAz/uU/YX4Is4X/ZbgANxzOSHxqLBkdyfqr4qIm0k9wTXC7fTCKjqLhF5AHgTpwN9bdgXn1ptDvTZXYTTqfVqU1W7ReRK4HER6cT5fIdLPvBLV3DCOKsmXoETsB2v34OKyIeBX4nIdTjC9yjw/+E8yR4EvCEiIeA3ON/Z29373D3COAM44nNVwuv/AH7rujTrgc8k7PsTjsvzxGG2kSMi63DemzCOi+p/3H134Lhv1rp+/3rgPFV9XJzBGGtEJEjPe5E0se8nTqzkb67ltwYnzjemWEqMKYSInAh8RVU/NMmXAoCIzMRxXxyiqtFhnnsE8BtVHcmImBExGW0mg4jku3ECwQmyblLVn49je8/ifI9sucQUYiK/n/uTK8mYQFwXwivAN0YgCl/ACX5+czyuLVXaHAafc59QN+C4EW6b3MsxJpqJ/n6axWAYhmH0wiwGwzCMFENEZovIMyKyUZzJav/plpeKyJPipBl5UkRK3PJPiDOhbr04E02PSKjrLhGpE5E3k23fhMEwDCP1CAP/raqLgGOAL4nIIuBa4Ck3zchT7mtwBiy8X1UX4wwhT1x/4XfAmcNp3ITBMAwjxVDVWlVd62634wwvrsKZF3S3e9jdwHnuMS8nDAlfBcxKqOt5YFgT6kwYDMMwUhh3DsuROIM5pqtqbIb6HnpmPCdyOfDYaNrcb+YxGIZhpBviJFz8C/BfbtqP+D53roj2Of4kHGE4fjTtmsVgGIaRgrizp/+Ck2QwlhNsr/Rkwa3EScMRO34JzgS7c1W1cTRtmzAYhmGkGO5kxjuBt1T1fxJ2rQQucbcvwcmxhYjMwUko+Sk3a+/o2rd5DIZhGKmFm6PqBWA9TgZVcFJovIKTOmUOTk6vj6pqk4jcgZNie4d7bFhVl7l13YeT9qMc2At8W1XvHLR9EwbDMAwjEXMlGYZhGL0wYTAMwzB6YcJgGIZh9MKEwTAMw+iFCYNhGIbRCxMGw0gSEfmOiHxlkP3nuYnODCOtMWEwjLHjPMCEwUh7bB6DYQyCiHwDZ4ZpHbALeBVoxVkzORNn/eRPAUuBv7v7WnEmG4GzFGcF0AV8TlXHfH1ewxhrTBgMYwBE5CicXPZH4yScXAv8GvhtLBeNiHwf2KuqvxSR3wF/V9UH3X1PAV9Q1U0icjTwI1U9eeLvxDCGh2VXNYyBOQF4SFW7AERkpVt+uCsIxUA+8ETfE92smMcCf07IiJk13hdsGGOBCYNhDJ/fAeep6usicilOHpq+eIAWVV06cZdlGGODBZ8NY2CeB84TkRwRKQDOdssLgFo3LfInEo5vd/ehqm3ANhH5CDjZMhPX4TWMVMaEwTAGwF1a8U/A6zgrYq12d12Hk+XyJSAxmHw/8FUReU1EFuCIxuUi8jqwAWdZRsNIeSz4bBiGYfTCLAbDMAyjFyYMhmEYRi9MGAzDMIxemDAYhmEYvTBhMAzDMHphwmAYhmH0woTBMAzD6IUJg2EYhtGL/x+TH+Jx7FN7cQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the mean of availability\n",
    "calendar2020.groupby('date').obtainable.mean().plot(label=\"2020\", legend=True, color='royalblue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how it matches the forecast/booking state of a few month ago in December 2019 and with a few more calendar datasets later in 2020...\n",
    "So let's prepare new datasets for these time series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_calendar(filename_or_calendar, drop_na=True):\n",
    "    '''\n",
    "    Description: combines multiple processing functions to manipulate the data of a pandas.DataFrame.\n",
    "                 Called functions:\n",
    "                 * drop_nan_calendar_values\n",
    "                 * convert_dates\n",
    "                 * remove_days_with_less_entries\n",
    "                 * prepare_columns_for_utilization\n",
    "    \n",
    "    Arguments:\n",
    "        filename_or_calendar (str or pandas.DataFrame): a filename to a calendar.csv file from Inside Airbnb\n",
    "                                                        or an already loaded file as a pd.DataFrame \n",
    "        drop_na (bool): flag if NaN values should be dropped or not\n",
    "    \n",
    "    Returns:\n",
    "        pandas.DataFrame: processed calender instance.\n",
    "    '''\n",
    "    if isinstance(filename_or_calendar, str):\n",
    "        print('load csv file', filename_or_calendar)\n",
    "        calendar = pd.read_csv(filename_or_calendar)\n",
    "    elif isinstance(filename_or_calendar, pd.DataFrame):\n",
    "        calendar = filename_or_calendar\n",
    "    else:\n",
    "        raise TypeError('argument is not of type ' + str(pd.DataFrame))\n",
    "    if drop_na:\n",
    "        calendar = drop_nan_calendar_values(calendar)\n",
    "    calendar = convert_dates(calendar)\n",
    "    calendar = remove_days_with_less_entries(calendar)\n",
    "    calendar = prepare_columns_for_utilization(calendar)\n",
    "    return calendar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load csv file data/2019-12-11/unzipped/calendar.csv\n"
     ]
    }
   ],
   "source": [
    "calendar2019 = process_calendar('data/2019-12-11/unzipped/calendar.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load csv file data/2020-04-17/unzipped/calendar.csv\n"
     ]
    }
   ],
   "source": [
    "calendar2020_04 = process_calendar('data/2020-04-17/unzipped/calendar.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load csv file data/2020-06-13/unzipped/calendar.csv\n"
     ]
    }
   ],
   "source": [
    "calendar2020_07 = process_calendar('data/2020-06-13/unzipped/calendar.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# and plot all of them together\n",
    "ax = calendar2020.groupby('date').obtainable.mean().plot(label=\"01/2020\", legend=True)\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))\n",
    "calendar2019.groupby('date').obtainable.mean().plot(label=\"12/2019\", legend=True)\n",
    "calendar2020_04.groupby('date').obtainable.mean().plot(label=\"04/2020\", legend=True)\n",
    "calendar2020_07.groupby('date').obtainable.mean().plot(label=\"07/2020\", legend=True)\n",
    "plt.title('availability forecasts')\n",
    "plt.xlabel('date')\n",
    "plt.ylabel('availability %')\n",
    "plt.gcf().savefig('availability_forcast.png', bbox_inches='tight') # https://problemsolvingwithpython.com/06-Plotting-with-Matplotlib/06.04-Saving-Plots/\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that the overall availability in 2020 is less than expected in 2019.\n",
    "On the otherhand a lot of accommodations are booked spontanously so a 1 year prediction will always be inacurate.\n",
    "\n",
    "But if we look at the datasets for January, April and July of 2020 we also see that a 2 month forecast is shifted to the right and there are more accomadations available than expected.\n",
    "\n",
    "Let's make an educated guess: Is COVID19 the reason?\n",
    "\n",
    "Maybe we'll also find hints about covid in the reviews and mark the beginning of covid19 in our diagram..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in reviews\n",
    "reviews2020 = pd.read_csv('data/2020-08-30/unzipped/reviews.csv', converters={'date:': pd.Timestamp})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2020-03-05'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the first occurance of covid in the reviews\n",
    "covid_mindate = reviews2020[reviews2020.comments.str.contains(pat='covid|coronavirus', case=False, regex=True, na=False)].date.min()\n",
    "display(covid_mindate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# and plot all of them together\n",
    "\n",
    "ax = calendar2020.groupby('date').obtainable.mean().plot(label=\"01/2020\", legend=True)\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))\n",
    "calendar2019.groupby('date').obtainable.mean().plot(label=\"12/2019\", legend=True)\n",
    "calendar2020_04.groupby('date').obtainable.mean().plot(label=\"04/2020\", legend=True)\n",
    "calendar2020_07.groupby('date').obtainable.mean().plot(label=\"07/2020\", legend=True)\n",
    "\n",
    "# plot a gray underlay starting at the date covid was first mentioned\n",
    "plt.axvspan(covid_mindate, pd.Timestamp(\"2021-01-01\")-pd.Timedelta(\"1 days\"), color='gray', alpha=0.4)\n",
    "plt.title('availability forecasts')\n",
    "plt.xlabel('date')\n",
    "plt.ylabel('availability %')\n",
    "plt.text(covid_mindate, 0.1, '<- reviews mention covid', fontsize=9)\n",
    "\n",
    "plt.gcf().savefig('covid_min_date.png', bbox_inches='tight') # https://problemsolvingwithpython.com/06-Plotting-with-Matplotlib/06.04-Saving-Plots/\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's find more evidence for our hypothesis that covid19 is the reason that after March 2020 there are more accomodations available as usual.\n",
    "\n",
    "Since airbnb is giving us for each calendar several datasets with a forecast of 1 year, their time intervals overlap. We could try to aggregate all the calendar data for each year...\n",
    "\n",
    "For this we read in all calendar datasets of a year and extract the rows from the time intervals between the scraped dates, which equal to the minimum date of each dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read csv file: data/2020-01-10/unzipped/calendar.csv\n",
      "read csv file: data/2020-02-18/unzipped/calendar.csv\n",
      "read csv file: data/2020-03-17/unzipped/calendar.csv\n",
      "read csv file: data/2020-04-17/unzipped/calendar.csv\n",
      "read csv file: data/2020-05-14/unzipped/calendar.csv\n",
      "read csv file: data/2020-06-13/unzipped/calendar.csv\n",
      "read csv file: data/2020-08-30/unzipped/calendar.csv\n",
      "processing date interval 2020-01-10 00:00:00 - 2020-02-18 00:00:00\n",
      "processing date interval 2020-02-18 00:00:00 - 2020-03-17 00:00:00\n",
      "processing date interval 2020-03-17 00:00:00 - 2020-04-17 00:00:00\n",
      "processing date interval 2020-04-17 00:00:00 - 2020-05-14 00:00:00\n",
      "processing date interval 2020-05-14 00:00:00 - 2020-06-13 00:00:00\n",
      "processing date interval 2020-06-13 00:00:00 - 2020-08-30 00:00:00\n",
      "processing date interval 2020-08-30 00:00:00 - 2021-01-01 00:00:00\n",
      "read csv file: data/2019-01-14/unzipped/calendar.csv\n",
      "read csv file: data/2019-02-06/unzipped/calendar.csv\n",
      "read csv file: data/2019-03-08/unzipped/calendar.csv\n",
      "read csv file: data/2019-04-11/unzipped/calendar.csv\n",
      "read csv file: data/2019-05-14/unzipped/calendar.csv\n",
      "read csv file: data/2019-06-08/unzipped/calendar.csv\n",
      "read csv file: data/2019-07-11/unzipped/calendar.csv\n",
      "read csv file: data/2019-08-12/unzipped/calendar.csv\n",
      "read csv file: data/2019-09-19/unzipped/calendar.csv\n",
      "read csv file: data/2019-10-16/unzipped/calendar.csv\n",
      "read csv file: data/2019-11-12/unzipped/calendar.csv\n",
      "read csv file: data/2019-12-11/unzipped/calendar.csv\n",
      "processing date interval 2019-01-14 00:00:00 - 2019-02-06 00:00:00\n",
      "processing date interval 2019-02-06 00:00:00 - 2019-03-08 00:00:00\n",
      "processing date interval 2019-03-08 00:00:00 - 2019-04-11 00:00:00\n",
      "processing date interval 2019-04-11 00:00:00 - 2019-05-14 00:00:00\n",
      "processing date interval 2019-05-14 00:00:00 - 2019-06-08 00:00:00\n",
      "processing date interval 2019-06-08 00:00:00 - 2019-07-11 00:00:00\n",
      "processing date interval 2019-07-11 00:00:00 - 2019-08-12 00:00:00\n",
      "processing date interval 2019-08-12 00:00:00 - 2019-09-19 00:00:00\n",
      "processing date interval 2019-09-19 00:00:00 - 2019-10-16 00:00:00\n",
      "processing date interval 2019-10-16 00:00:00 - 2019-11-14 00:00:00\n",
      "processing date interval 2019-11-14 00:00:00 - 2019-12-11 00:00:00\n",
      "processing date interval 2019-12-11 00:00:00 - 2020-01-01 00:00:00\n",
      "read csv file: data/2018-04-12/unzipped/calendar.csv\n",
      "read csv file: data/2018-05-14/unzipped/calendar.csv\n",
      "read csv file: data/2018-06-09/unzipped/calendar.csv\n",
      "read csv file: data/2018-07-10/unzipped/calendar.csv\n",
      "read csv file: data/2018-08-14/unzipped/calendar.csv\n",
      "read csv file: data/2018-09-12/unzipped/calendar.csv\n",
      "read csv file: data/2018-10-10/unzipped/calendar.csv\n",
      "read csv file: data/2018-11-07/unzipped/calendar.csv\n",
      "read csv file: data/2018-12-10/unzipped/calendar.csv\n",
      "processing date interval 2018-04-12 00:00:00 - 2018-05-14 00:00:00\n",
      "processing date interval 2018-05-14 00:00:00 - 2018-06-09 00:00:00\n",
      "processing date interval 2018-06-09 00:00:00 - 2018-07-10 00:00:00\n",
      "processing date interval 2018-07-10 00:00:00 - 2018-08-14 00:00:00\n",
      "processing date interval 2018-08-14 00:00:00 - 2018-09-12 00:00:00\n",
      "processing date interval 2018-09-12 00:00:00 - 2018-10-10 00:00:00\n",
      "processing date interval 2018-10-10 00:00:00 - 2018-11-07 00:00:00\n",
      "processing date interval 2018-11-07 00:00:00 - 2018-12-10 00:00:00\n",
      "processing date interval 2018-12-10 00:00:00 - 2019-01-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import itertools\n",
    "import os.path\n",
    "from pandas.tseries.offsets import DateOffset\n",
    "\n",
    "\n",
    "def pairwise(iterable):\n",
    "    \"s -> (s0,s1), (s1,s2), (s2, s3), ...\"\n",
    "    # from https://docs.python.org/3/library/itertools.html\n",
    "    a, b = itertools.tee(iterable)\n",
    "    next(b, None)\n",
    "    return zip(a, b)\n",
    "\n",
    "def truncate_calendars(calendars):\n",
    "    '''\n",
    "    Description: computes the overlapping time intervals of the datasets and concats them together in a\n",
    "                 single dataset.\n",
    "    \n",
    "    Arguments:\n",
    "        filenames ([pandas.DataFrame]): list of Inside Airbnb calendar.csv files of one year.\n",
    "    \n",
    "    Returns:\n",
    "        ([pandas.DataFrame], int): tuple of the combined dataset and the corresponding year\n",
    "    '''\n",
    "    combined_calendar = None  # return value\n",
    "    # sorting date values in calendar map\n",
    "    sorted_dates = sorted(calendars)\n",
    "    # creating interval tuples from sorted date list\n",
    "    date_pairs = list(pairwise(sorted_dates))\n",
    "    # get the latest year from all dates\n",
    "    year = sorted_dates[len(sorted_dates)-1].year\n",
    "    end_of_year_plus_one_day = pd.Timestamp(str(year+1)) #ciso8601.parse_datetime(str(year+1) + \"-01-01 00:00:00\")\n",
    "    # add closing interval to the end of the year\n",
    "    # todo: check if last interval already is a the end of the year\n",
    "    date_pairs.append(\n",
    "        (sorted_dates[len(sorted_dates)-1], end_of_year_plus_one_day))\n",
    "    delta_t_one_day = pd.Timedelta(\"1 days\")\n",
    "    for (i_start, i_end) in date_pairs:  # loop interval pairs\n",
    "        print(\"processing date interval {} - {}\".format(i_start, i_end))\n",
    "        calendar = calendars[i_start]  # reference to the actual dataframe\n",
    "        # only take values for date in the interval [i_start;i_end-1_day]\n",
    "        trunc_cal = calendar[calendar.date < i_end - delta_t_one_day]\n",
    "        # and concatenate to the return value\n",
    "        combined_calendar = pd.concat([combined_calendar, trunc_cal])\n",
    "    return (combined_calendar, year)\n",
    "\n",
    "def combine_calendars_for_year(filenames, drop_na=True):\n",
    "    '''\n",
    "    Description: reads in a list of calandar files and removes overlapping time intervals and processes \n",
    "                 the resulting dataset with the process_calendar function and saves it to the filesystem\n",
    "                 under the filename data/cal_combined_{year}.csv.\n",
    "    \n",
    "    Arguments:\n",
    "        filenames ([str]): list of filenames of existing Inside Airbnb calendar.csv files of one year.\n",
    "    \n",
    "    Returns:\n",
    "        (pd.DataFrame): new calendar instance.\n",
    "    '''\n",
    "    cals = {} # \"cals -> {mindate: calender_dataframe, ...}\"\n",
    "    \n",
    "    # preprocessing: read in all datasets stored under the minimum date as the map-key.\n",
    "    for filename in filenames:\n",
    "        print(\"read csv file: {}\".format(filename))\n",
    "        calendar = pd.read_csv(filename)\n",
    "        calendar = convert_dates(calendar)\n",
    "        # drop any row date > 3 month, because airbnb scrape intervals are shorter\n",
    "        # it is premature optimization to save some memory\n",
    "        cal_min_date = calendar.date.min()\n",
    "        mindate_plus_3month = cal_min_date + DateOffset(months=3)\n",
    "        calendar = calendar[calendar.date < mindate_plus_3month]\n",
    "        cals[calendar.date.min()] = calendar\n",
    "    \n",
    "    # further slice up the calendars to fit into each time interval between scraped days\n",
    "    combined_calendar, year = truncate_calendars(cals)\n",
    "    combined_calendar = process_calendar(combined_calendar, drop_na)\n",
    "    combined_calendar.to_csv(\"data/cal_combined_{}.csv\".format(year), index=False)    \n",
    "    return combined_calendar\n",
    "        \n",
    "# only do this once. takes a lot of time. time for ☕.\n",
    "# subsequent runs of the notebook could just read in the created file\n",
    "if not os.path.isfile('data/cal_combined_2020.csv'):\n",
    "    calendar2020 = combine_calendars_for_year(sorted(glob.glob('data/2020-*/unzipped/calendar*')), drop_na=True)\n",
    "else:\n",
    "    calendar2020 = process_calendar('data/cal_combined_2020.csv')\n",
    "\n",
    "if not os.path.isfile('data/cal_combined_2019.csv'):\n",
    "    calendar2019 = combine_calendars_for_year(sorted(glob.glob('data/2019-*/unzipped/calendar*')), drop_na=True)\n",
    "else:\n",
    "    calendar2019 = process_calendar('data/cal_combined_2019.csv')\n",
    "    \n",
    "if not os.path.isfile('data/cal_combined_2018.csv'):\n",
    "    calendar2018 = combine_calendars_for_year(sorted(glob.glob('data/2018-*/unzipped/calendar*')), drop_na=False)\n",
    "else:\n",
    "    calendar2018 = process_calendar('data/cal_combined_2018.csv', drop_na=False)\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "def predict_linear_regression(series):\n",
    "    '''\n",
    "    Description: computes a linear regression for 1d (x,y) value pairs.\n",
    "    \n",
    "    Arguments:\n",
    "        series (pandas.Series): pandas Series with number values. The index will be used as the x-values.\n",
    "    \n",
    "    Returns:\n",
    "        ([number],[number]): tuple of two lists for x and y values.\n",
    "    '''\n",
    "    linear_regression = LinearRegression()\n",
    "    X = series.index.values.reshape(-1,1)\n",
    "    Y = series.values.reshape(-1,1)\n",
    "    linear_regression.fit(X,Y)\n",
    "    return (X,linear_regression.predict(X))\n",
    "\n",
    "# group by day of year to plot function for different years comparable together in 1 plot\n",
    "cg2020 = calendar2020.groupby('dayofyear')\n",
    "cg2019 = calendar2019.groupby('dayofyear')\n",
    "cg2018 = calendar2018.groupby('dayofyear')\n",
    "ax = cg2020.obtainable.mean().plot(label=\"2020\", legend=True, color='royalblue')\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))\n",
    "# also plot the corresponding linear regression line\n",
    "plt.plot(*predict_linear_regression(cg2020.obtainable.mean()), color='royalblue')\n",
    "cg2019.obtainable.mean().plot(label=\"2019\", legend=True, color='darkorange')\n",
    "plt.plot(*predict_linear_regression(cg2019.obtainable.mean()), color='darkorange')\n",
    "cg2018.obtainable.mean().plot(label=\"2018\", legend=True, color='green')\n",
    "plt.plot(*predict_linear_regression(cg2018.obtainable.mean()), color='green')\n",
    "\n",
    "plt.title('aggregated calendar data')\n",
    "plt.xlabel('day of year')\n",
    "plt.ylabel('availability %')\n",
    "plt.gcf().savefig('day_of_year_availability.png', bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The overall availability plotted for the two years before 2020 clearly show the same pattern. A simple linear regression line indicates that the year 2020 is definetly different. Apart from a greater gradient we can visually see that the availability of accommodation is up to 25% where the years before it is about 15%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's try to find out if in 2020 people have given priority to amenities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in listing data which contains the list of amenities\n",
    "listings2020 = pd.read_csv('data/2020-08-30/unzipped/listings.csv')\n",
    "listings2019 = pd.read_csv('data/2019-12-11/unzipped/listings.csv')\n",
    "listings2018 = pd.read_csv('data/2018-12-10/unzipped/listings.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "def convert_to_float(value):\n",
    "    '''\n",
    "    Description: converts a string value to a float number.\n",
    "    \n",
    "    Arguments:\n",
    "        value (str): a string representing a valid fload number.\n",
    "    \n",
    "    Returns:\n",
    "        (float): converted float value\n",
    "    '''\n",
    "    if isinstance(value, float):\n",
    "        return value\n",
    "    else:\n",
    "        return float(value.replace('$','').replace(',',''))\n",
    "    \n",
    "def combine_amenities_and_availability(listings, calendar, json_format=True):\n",
    "    '''\n",
    "    Description: creates a new dataset with from the combination of Inside Airbnb listing.csv and calendar.csv datasets.\n",
    "                 From the listings dataset it derives boolean columns for the types of accommodation.\n",
    "                 It then adds the availability and price values from the calendar dataset.\n",
    "    \n",
    "    Arguments:\n",
    "        listings (pandas.DataFrame): dataset of Inside Airbnb listings.csv file.\n",
    "        calendar (pandas.DataFrame): dataset of Inside Airbnb calendar.csv file.\n",
    "        json_format (bool): flag indicating if the amenities are encoded in json_format\n",
    "    \n",
    "    Returns:\n",
    "        (pandas.DataFrame): the combined new dataset for analyzing amenities.\n",
    "    '''\n",
    "    amenities = listings[['id','amenities']] # we only analize amenites, drop the rest\n",
    "    # amenities of an accomodation/listing are encoded in one cell value. We need to extract the information...\n",
    "    # After extractions every amenity is marked for existence in a new boolean column\n",
    "    if json_format:\n",
    "        amenities = pd.concat([amenities,\n",
    "          # in 2020 the data is in valid json format\n",
    "          amenities.amenities.apply(lambda x: ','.join(json.loads(x))).str.get_dummies(sep=',')\n",
    "          ],axis=1)\n",
    "    else:\n",
    "        amenities = pd.concat([amenities,\n",
    "          # before 2020 the data contains some weird array syntax, hard to parse, so we use string regexes\n",
    "          amenities.amenities.str.replace('[{}\"]','',regex=True).str.get_dummies(sep=',')\n",
    "          ],axis=1)            \n",
    "    # now the actual merge with the obtainale value of the calendar dataframe\n",
    "    amenities = pd.merge(amenities, calendar[['listing_id','obtainable','price']], left_on='id', right_on='listing_id', how='left')\n",
    "    amenities.drop(columns=['amenities','listing_id'], axis=1, inplace=True)\n",
    "    amenities.dropna(subset=['obtainable','price'], inplace=True)\n",
    "    amenities.price = amenities.price.apply(convert_to_float)\n",
    "    return amenities\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#amenities2020 = combine_amenities_and_availability(listings2020[listings2020.id.isin(covid_listings)], calendar2020)\n",
    "#amenities2019 = combine_amenities_and_availability(listings2019[listings2019.id.isin(covid_listings)], calendar2019, json_format=False)\n",
    "#amenities2018 = combine_amenities_and_availability(listings2018[listings2018.id.isin(covid_listings)], calendar2018, json_format=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in amenities\n",
    "amenities2020 = combine_amenities_and_availability(listings2020, calendar2020)\n",
    "amenities2019 = combine_amenities_and_availability(listings2019, calendar2019, json_format=False)\n",
    "amenities2018 = combine_amenities_and_availability(listings2018, calendar2018, json_format=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_correlation_matrix(amenities):\n",
    "    '''\n",
    "    Description: computes the correlation matrix of the given numeric dataset and a corresponding\n",
    "                 boolean matrix which can be used to hide the upper triangular part of the matrix during plotting.\n",
    "    \n",
    "    Arguments:\n",
    "        amenities (pandas.DataFrame): a dataset containing amenities with numeric encoding\n",
    "    \n",
    "    Returns:\n",
    "        (pandas.DataFrame,[[int]]): the combined new dataset for analyzing amenities and the corresponding\n",
    "                                    boolean mask.\n",
    "    '''\n",
    "    no_id = amenities.drop(columns=['id'])\n",
    "    # corr = 1-pairwise_distances(no_id.T, metric = \"hamming\")\n",
    "    # corr = pd.DataFrame(corr, index=no_id.columns, columns=no_id.columns)\n",
    "    corr = no_id.corr()\n",
    "    # mask the upper triangle of the correlation matrix. \n",
    "    mask = np.triu(np.ones_like(corr, dtype=bool))\n",
    "    return (corr,mask)\n",
    "\n",
    "def plot_and_save_correlation(corr,mask, filename):\n",
    "    '''\n",
    "    Description: plots a correlation matrix and saves the plot to the filesystem.\n",
    "    \n",
    "    Arguments:\n",
    "        corr (pandas.DataFrame): dataset containing a correlation matrix.\n",
    "        mask ([[int]]): helper matrix to hide the upper triangular part of the matrix in the plot.\n",
    "        filename: name of resulting plot file.\n",
    "    \n",
    "    Returns:\n",
    "        None\n",
    "    '''\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    svm = sns.heatmap(corr, mask=mask, annot=True)\n",
    "    figure = svm.get_figure()\n",
    "    figure.savefig(filename, bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the correlation matrix for 2020\n",
    "cor_columns2020 = ['id', 'Air conditioning', 'Bathroom essentials', 'Coffee maker', 'Cooking basics', 'Long term stays allowed', 'Stove', 'TV', 'Toiletries', 'Wifi', 'price', 'obtainable']\n",
    "plot_and_save_correlation(*compute_correlation_matrix(amenities2020[cor_columns2020]),'corr2020.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the correlation matrix for 2019\n",
    "cor_columns2019 = ['id', 'Air conditioning', 'Bathroom essentials', 'Coffee maker', 'Cooking basics', 'Long term stays allowed', 'Stove', 'TV', 'Toilet paper', 'Wifi', 'price', 'obtainable']\n",
    "plot_and_save_correlation(*compute_correlation_matrix(amenities2019[cor_columns2019]),'corr2019.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the end."
   ]
  }
 ],
 "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}