Airbnb project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How important is for an Airbnb listing to be close to the beach in Sydney?\n",
    "## by Pietro Pollo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "- [Business understanding](#intro)\n",
    "- [Data understanding](#gather)\n",
    "- [Prepare data](#assess)\n",
    "- [Data modeling](#analysis)\n",
    "- [Summary](#summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "pd.set_option(\"display.max_columns\", None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Business understanding\n",
    "\n",
    "Here I analyze data from Airbnb listings in Sydney from 7 Dec 2018 to 6 Dec 2019 to understand the consequences and characteristics of properties for being close to a beach. First I assessed which neighborhoods use the word \"beach\" most often in their listing description and whether these listing have different characteristics (e.g. number of bedrooms). Then, I focused in understand how being close to the beach relates to price and availability of listings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='gather'></a>\n",
    "## Data understanding\n",
    "\n",
    "Datasets retrieved from https://www.kaggle.com/tylerx/sydney-airbnb-open-data. Dictionary can be found here (https://docs.google.com/spreadsheets/d/1iWCNJcSutYqpULSQHlNyGInUvHg2BoUGoNRIGa6Szc4/edit#gid=982310896), made by http://insideairbnb.com/.\n",
    "\n",
    "Two files used: \n",
    "- listings is a csv file with Airbnb listings in Sydney from 7 Dec 2018 to 6 Dec 2019\n",
    "- calendar is a csv file with the calendar availability from Airbnb listings in Sydney from 7 Dec 2018 to 6 Dec 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pietr\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3146: DtypeWarning: Columns (43,61,62,87) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "listings = pd.read_csv('listings_dec18.csv')\n",
    "calendar = pd.read_csv('calendar_dec18.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='assess'></a>\n",
    "## Prepare data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Listings dataframe has many columns I am not interested in. Column 'price' is a string and should be of type numeric, and there are some missing values for 'bedrooms' and 'description'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>listing_url</th>\n",
       "      <th>scrape_id</th>\n",
       "      <th>last_scraped</th>\n",
       "      <th>name</th>\n",
       "      <th>summary</th>\n",
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       "      <th>notes</th>\n",
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       "      <th>medium_url</th>\n",
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       "      <th>xl_picture_url</th>\n",
       "      <th>host_id</th>\n",
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       "      <th>host_name</th>\n",
       "      <th>host_since</th>\n",
       "      <th>host_location</th>\n",
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       "      <th>host_response_rate</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_is_superhost</th>\n",
       "      <th>host_thumbnail_url</th>\n",
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       "      <th>host_neighbourhood</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>host_verifications</th>\n",
       "      <th>host_has_profile_pic</th>\n",
       "      <th>host_identity_verified</th>\n",
       "      <th>street</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "      <th>city</th>\n",
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       "      <th>zipcode</th>\n",
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       "      <th>smart_location</th>\n",
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       "      <th>longitude</th>\n",
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       "      <th>property_type</th>\n",
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       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beds</th>\n",
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       "      <th>amenities</th>\n",
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       "      <th>price</th>\n",
       "      <th>weekly_price</th>\n",
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       "      <th>cleaning_fee</th>\n",
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       "      <th>minimum_nights</th>\n",
       "      <th>maximum_nights</th>\n",
       "      <th>calendar_updated</th>\n",
       "      <th>has_availability</th>\n",
       "      <th>availability_30</th>\n",
       "      <th>availability_60</th>\n",
       "      <th>availability_90</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>calendar_last_scraped</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>first_review</th>\n",
       "      <th>last_review</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>review_scores_accuracy</th>\n",
       "      <th>review_scores_cleanliness</th>\n",
       "      <th>review_scores_checkin</th>\n",
       "      <th>review_scores_communication</th>\n",
       "      <th>review_scores_location</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>requires_license</th>\n",
       "      <th>license</th>\n",
       "      <th>jurisdiction_names</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>is_business_travel_ready</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12351</td>\n",
       "      <td>https://www.airbnb.com/rooms/12351</td>\n",
       "      <td>20181207034750</td>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>Sydney City &amp; Harbour at the door</td>\n",
       "      <td>Come stay with Vinh &amp; Stuart (Awarded as one o...</td>\n",
       "      <td>We're pretty relaxed hosts, and we fully appre...</td>\n",
       "      <td>Come stay with Vinh &amp; Stuart (Awarded as one o...</td>\n",
       "      <td>none</td>\n",
       "      <td>Pyrmont is an inner-city village of Sydney, on...</td>\n",
       "      <td>We've a few reasons for the 6.00pm arrival tim...</td>\n",
       "      <td>Our home is centrally located and an easy walk...</td>\n",
       "      <td>We look forward to welcoming you just as we wo...</td>\n",
       "      <td>As much or as little as you like. We live here...</td>\n",
       "      <td>We look forward to welcoming you to stay you j...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/763ad5c8-c...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17061</td>\n",
       "      <td>https://www.airbnb.com/users/show/17061</td>\n",
       "      <td>Stuart</td>\n",
       "      <td>2009-05-14</td>\n",
       "      <td>Sydney, New South Wales, Australia</td>\n",
       "      <td>G'Day from Australia!\\r\\n\\r\\nHe's Vinh, and I'...</td>\n",
       "      <td>within a few hours</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>https://a0.muscache.com/im/users/17061/profile...</td>\n",
       "      <td>Pyrmont</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>t</td>\n",
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       "      <td>Pyrmont, NSW, Australia</td>\n",
       "      <td>Pyrmont</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pyrmont</td>\n",
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       "      <td>2009</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>Pyrmont, Australia</td>\n",
       "      <td>AU</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-33.865153</td>\n",
       "      <td>151.191896</td>\n",
       "      <td>t</td>\n",
       "      <td>Townhouse</td>\n",
       "      <td>Private room</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>{TV,Internet,Wifi,\"Air conditioning\",\"Paid par...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$100.00</td>\n",
       "      <td>$800.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$0.00</td>\n",
       "      <td>$55.00</td>\n",
       "      <td>2</td>\n",
       "      <td>$395.00</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>today</td>\n",
       "      <td>t</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>28</td>\n",
       "      <td>187</td>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>493</td>\n",
       "      <td>2010-07-24</td>\n",
       "      <td>2018-11-21</td>\n",
       "      <td>95.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14250</td>\n",
       "      <td>https://www.airbnb.com/rooms/14250</td>\n",
       "      <td>20181207034750</td>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>Manly Harbour House</td>\n",
       "      <td>Beautifully renovated, spacious and quiet, our...</td>\n",
       "      <td>Our home is a thirty minute walk along the sea...</td>\n",
       "      <td>Beautifully renovated, spacious and quiet, our...</td>\n",
       "      <td>none</td>\n",
       "      <td>Balgowlah Heights is one of the most prestigio...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Balgowlah - Manly bus # 131 or #132  (Bus stop...</td>\n",
       "      <td>Guests have access to whole house except locke...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard Terms and Conditions of Temporary Hol...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/56935671/f...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55948</td>\n",
       "      <td>https://www.airbnb.com/users/show/55948</td>\n",
       "      <td>Heidi</td>\n",
       "      <td>2009-11-20</td>\n",
       "      <td>Sydney, New South Wales, Australia</td>\n",
       "      <td>I am a Canadian who has made Australia her hom...</td>\n",
       "      <td>within a few hours</td>\n",
       "      <td>90%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>https://a0.muscache.com/im/users/55948/profile...</td>\n",
       "      <td>https://a0.muscache.com/im/users/55948/profile...</td>\n",
       "      <td>Balgowlah</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>['email', 'phone', 'reviews', 'jumio', 'offlin...</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Balgowlah, NSW, Australia</td>\n",
       "      <td>Balgowlah</td>\n",
       "      <td>Manly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Balgowlah</td>\n",
       "      <td>NSW</td>\n",
       "      <td>2093</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>Balgowlah, Australia</td>\n",
       "      <td>AU</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-33.800929</td>\n",
       "      <td>151.261722</td>\n",
       "      <td>t</td>\n",
       "      <td>House</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>{TV,Wifi,\"Air conditioning\",Kitchen,\"Pets live...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$471.00</td>\n",
       "      <td>$3,000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$900.00</td>\n",
       "      <td>$100.00</td>\n",
       "      <td>6</td>\n",
       "      <td>$40.00</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>9 months ago</td>\n",
       "      <td>t</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>46</td>\n",
       "      <td>321</td>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>f</td>\n",
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       "      <td>2</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15253</td>\n",
       "      <td>https://www.airbnb.com/rooms/15253</td>\n",
       "      <td>20181207034750</td>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>Stunning Penthouse Apartment In Heart Of The City</td>\n",
       "      <td>Penthouse living in a great central location: ...</td>\n",
       "      <td>A charming two-level, two-bedroom, two-bathroo...</td>\n",
       "      <td>Penthouse living in a great central location: ...</td>\n",
       "      <td>none</td>\n",
       "      <td>The location is really central and there is nu...</td>\n",
       "      <td>The apartment is on the top floor with 4 fligh...</td>\n",
       "      <td>If walking is not your thing I am well service...</td>\n",
       "      <td>Guests have own key and access to use faciliti...</td>\n",
       "      <td>You would be staying with me and my very cute,...</td>\n",
       "      <td>I am fairly easygoing and will try to accomoda...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/3643414/2e...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59850</td>\n",
       "      <td>https://www.airbnb.com/users/show/59850</td>\n",
       "      <td>Morag</td>\n",
       "      <td>2009-12-03</td>\n",
       "      <td>Sydney, New South Wales, Australia</td>\n",
       "      <td>I am originally Scottish but I have made Sydne...</td>\n",
       "      <td>within an hour</td>\n",
       "      <td>100%</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>https://a0.muscache.com/im/pictures/user/730ee...</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/user/730ee...</td>\n",
       "      <td>Darlinghurst</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>['email', 'phone', 'facebook', 'reviews', 'jum...</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Darlinghurst, NSW, Australia</td>\n",
       "      <td>Darlinghurst</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Darlinghurst</td>\n",
       "      <td>NSW</td>\n",
       "      <td>2010</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>Darlinghurst, Australia</td>\n",
       "      <td>AU</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-33.880455</td>\n",
       "      <td>151.216541</td>\n",
       "      <td>f</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>{TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$109.00</td>\n",
       "      <td>$800.00</td>\n",
       "      <td>$3,000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>$10.00</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4 days ago</td>\n",
       "      <td>t</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>41</td>\n",
       "      <td>316</td>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>300</td>\n",
       "      <td>2012-02-23</td>\n",
       "      <td>2018-11-16</td>\n",
       "      <td>88.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>2</td>\n",
       "      <td>3.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20865</td>\n",
       "      <td>https://www.airbnb.com/rooms/20865</td>\n",
       "      <td>20181207034750</td>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>3 BED HOUSE + 1 BED STUDIO Balmain</td>\n",
       "      <td>Hi! We are a married professional couple with ...</td>\n",
       "      <td>HOUSE : _________ * DUCTED AIR CONDITIONING IN...</td>\n",
       "      <td>Hi! We are a married professional couple with ...</td>\n",
       "      <td>none</td>\n",
       "      <td>BALMAIN is an older inner city village / subur...</td>\n",
       "      <td>During your stay guests may use the landline f...</td>\n",
       "      <td>Balmain is well serviced by buses (from Darlin...</td>\n",
       "      <td>Whole house (including studio/flat &amp; courtyard...</td>\n",
       "      <td>We will always be available should you require...</td>\n",
       "      <td>PLEASE ENJOY YOURSELVES WITHOUT MAKING TOO MUC...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/58785124/c...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64282</td>\n",
       "      <td>https://www.airbnb.com/users/show/64282</td>\n",
       "      <td>Fiona</td>\n",
       "      <td>2009-12-19</td>\n",
       "      <td>Sydney, New South Wales, Australia</td>\n",
       "      <td>We are a professional, married couple with 2 c...</td>\n",
       "      <td>within a day</td>\n",
       "      <td>100%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t</td>\n",
       "      <td>https://a0.muscache.com/im/users/64282/profile...</td>\n",
       "      <td>https://a0.muscache.com/im/users/64282/profile...</td>\n",
       "      <td>Balmain/Birchgrove</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>['email', 'phone', 'reviews', 'jumio', 'govern...</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Balmain, NSW, Australia</td>\n",
       "      <td>Balmain/Birchgrove</td>\n",
       "      <td>Leichhardt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Balmain</td>\n",
       "      <td>NSW</td>\n",
       "      <td>2041</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>Balmain, Australia</td>\n",
       "      <td>AU</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-33.859072</td>\n",
       "      <td>151.172753</td>\n",
       "      <td>t</td>\n",
       "      <td>House</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>{TV,Internet,Wifi,\"Air conditioning\",Kitchen,\"...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$450.00</td>\n",
       "      <td>$2,804.00</td>\n",
       "      <td>$7,009.00</td>\n",
       "      <td>$800.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>$0.00</td>\n",
       "      <td>7</td>\n",
       "      <td>365</td>\n",
       "      <td>7 weeks ago</td>\n",
       "      <td>t</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>69</td>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>15</td>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>96.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>strict_14_with_grace_period</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26174</td>\n",
       "      <td>https://www.airbnb.com/rooms/26174</td>\n",
       "      <td>20181207034750</td>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>COZY PRIVATE ROOM, GREAT LOCATION!</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Double bed in decent sized bedroom, in two bed...</td>\n",
       "      <td>Double bed in decent sized bedroom, in two bed...</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Need to be quiet after 10.30pm No Smoking Clea...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://a0.muscache.com/im/pictures/7ce9714f-8...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110561</td>\n",
       "      <td>https://www.airbnb.com/users/show/110561</td>\n",
       "      <td>Amanda</td>\n",
       "      <td>2010-04-19</td>\n",
       "      <td>Sydney, New South Wales, Australia</td>\n",
       "      <td>Hi everyone,  I look forward to welcoming you ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>https://a0.muscache.com/im/users/110561/profil...</td>\n",
       "      <td>https://a0.muscache.com/im/users/110561/profil...</td>\n",
       "      <td>Bellevue Hill</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['email', 'phone', 'reviews', 'jumio', 'govern...</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>Bellevue Hill, NSW, Australia</td>\n",
       "      <td>Bellevue Hill</td>\n",
       "      <td>Woollahra</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bellevue Hill</td>\n",
       "      <td>NSW</td>\n",
       "      <td>2023</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>Bellevue Hill, Australia</td>\n",
       "      <td>AU</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-33.889087</td>\n",
       "      <td>151.259404</td>\n",
       "      <td>t</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Real Bed</td>\n",
       "      <td>{TV,Wifi,Kitchen,\"Free street parking\",Heating...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$62.00</td>\n",
       "      <td>$400.00</td>\n",
       "      <td>$1,400.00</td>\n",
       "      <td>$0.00</td>\n",
       "      <td>$25.00</td>\n",
       "      <td>1</td>\n",
       "      <td>$15.00</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>a week ago</td>\n",
       "      <td>t</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "      <td>140</td>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>42</td>\n",
       "      <td>2011-04-11</td>\n",
       "      <td>2018-11-23</td>\n",
       "      <td>94.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                         listing_url       scrape_id last_scraped  \\\n",
       "0  12351  https://www.airbnb.com/rooms/12351  20181207034750   2018-12-07   \n",
       "1  14250  https://www.airbnb.com/rooms/14250  20181207034750   2018-12-07   \n",
       "2  15253  https://www.airbnb.com/rooms/15253  20181207034750   2018-12-07   \n",
       "3  20865  https://www.airbnb.com/rooms/20865  20181207034750   2018-12-07   \n",
       "4  26174  https://www.airbnb.com/rooms/26174  20181207034750   2018-12-07   \n",
       "\n",
       "                                                name  \\\n",
       "0                  Sydney City & Harbour at the door   \n",
       "1                                Manly Harbour House   \n",
       "2  Stunning Penthouse Apartment In Heart Of The City   \n",
       "3                 3 BED HOUSE + 1 BED STUDIO Balmain   \n",
       "4                 COZY PRIVATE ROOM, GREAT LOCATION!   \n",
       "\n",
       "                                             summary  \\\n",
       "0  Come stay with Vinh & Stuart (Awarded as one o...   \n",
       "1  Beautifully renovated, spacious and quiet, our...   \n",
       "2  Penthouse living in a great central location: ...   \n",
       "3  Hi! We are a married professional couple with ...   \n",
       "4                                                NaN   \n",
       "\n",
       "                                               space  \\\n",
       "0  We're pretty relaxed hosts, and we fully appre...   \n",
       "1  Our home is a thirty minute walk along the sea...   \n",
       "2  A charming two-level, two-bedroom, two-bathroo...   \n",
       "3  HOUSE : _________ * DUCTED AIR CONDITIONING IN...   \n",
       "4  Double bed in decent sized bedroom, in two bed...   \n",
       "\n",
       "                                         description experiences_offered  \\\n",
       "0  Come stay with Vinh & Stuart (Awarded as one o...                none   \n",
       "1  Beautifully renovated, spacious and quiet, our...                none   \n",
       "2  Penthouse living in a great central location: ...                none   \n",
       "3  Hi! We are a married professional couple with ...                none   \n",
       "4  Double bed in decent sized bedroom, in two bed...                none   \n",
       "\n",
       "                               neighborhood_overview  \\\n",
       "0  Pyrmont is an inner-city village of Sydney, on...   \n",
       "1  Balgowlah Heights is one of the most prestigio...   \n",
       "2  The location is really central and there is nu...   \n",
       "3  BALMAIN is an older inner city village / subur...   \n",
       "4                                                NaN   \n",
       "\n",
       "                                               notes  \\\n",
       "0  We've a few reasons for the 6.00pm arrival tim...   \n",
       "1                                                NaN   \n",
       "2  The apartment is on the top floor with 4 fligh...   \n",
       "3  During your stay guests may use the landline f...   \n",
       "4                                                NaN   \n",
       "\n",
       "                                             transit  \\\n",
       "0  Our home is centrally located and an easy walk...   \n",
       "1  Balgowlah - Manly bus # 131 or #132  (Bus stop...   \n",
       "2  If walking is not your thing I am well service...   \n",
       "3  Balmain is well serviced by buses (from Darlin...   \n",
       "4                                                NaN   \n",
       "\n",
       "                                              access  \\\n",
       "0  We look forward to welcoming you just as we wo...   \n",
       "1  Guests have access to whole house except locke...   \n",
       "2  Guests have own key and access to use faciliti...   \n",
       "3  Whole house (including studio/flat & courtyard...   \n",
       "4                                                NaN   \n",
       "\n",
       "                                         interaction  \\\n",
       "0  As much or as little as you like. We live here...   \n",
       "1                                                NaN   \n",
       "2  You would be staying with me and my very cute,...   \n",
       "3  We will always be available should you require...   \n",
       "4                                                NaN   \n",
       "\n",
       "                                         house_rules  thumbnail_url  \\\n",
       "0  We look forward to welcoming you to stay you j...            NaN   \n",
       "1  Standard Terms and Conditions of Temporary Hol...            NaN   \n",
       "2  I am fairly easygoing and will try to accomoda...            NaN   \n",
       "3  PLEASE ENJOY YOURSELVES WITHOUT MAKING TOO MUC...            NaN   \n",
       "4  Need to be quiet after 10.30pm No Smoking Clea...            NaN   \n",
       "\n",
       "   medium_url                                        picture_url  \\\n",
       "0         NaN  https://a0.muscache.com/im/pictures/763ad5c8-c...   \n",
       "1         NaN  https://a0.muscache.com/im/pictures/56935671/f...   \n",
       "2         NaN  https://a0.muscache.com/im/pictures/3643414/2e...   \n",
       "3         NaN  https://a0.muscache.com/im/pictures/58785124/c...   \n",
       "4         NaN  https://a0.muscache.com/im/pictures/7ce9714f-8...   \n",
       "\n",
       "   xl_picture_url  host_id                                  host_url  \\\n",
       "0             NaN    17061   https://www.airbnb.com/users/show/17061   \n",
       "1             NaN    55948   https://www.airbnb.com/users/show/55948   \n",
       "2             NaN    59850   https://www.airbnb.com/users/show/59850   \n",
       "3             NaN    64282   https://www.airbnb.com/users/show/64282   \n",
       "4             NaN   110561  https://www.airbnb.com/users/show/110561   \n",
       "\n",
       "  host_name  host_since                       host_location  \\\n",
       "0    Stuart  2009-05-14  Sydney, New South Wales, Australia   \n",
       "1     Heidi  2009-11-20  Sydney, New South Wales, Australia   \n",
       "2     Morag  2009-12-03  Sydney, New South Wales, Australia   \n",
       "3     Fiona  2009-12-19  Sydney, New South Wales, Australia   \n",
       "4    Amanda  2010-04-19  Sydney, New South Wales, Australia   \n",
       "\n",
       "                                          host_about  host_response_time  \\\n",
       "0  G'Day from Australia!\\r\\n\\r\\nHe's Vinh, and I'...  within a few hours   \n",
       "1  I am a Canadian who has made Australia her hom...  within a few hours   \n",
       "2  I am originally Scottish but I have made Sydne...      within an hour   \n",
       "3  We are a professional, married couple with 2 c...        within a day   \n",
       "4  Hi everyone,  I look forward to welcoming you ...                 NaN   \n",
       "\n",
       "  host_response_rate  host_acceptance_rate host_is_superhost  \\\n",
       "0               100%                   NaN                 f   \n",
       "1                90%                   NaN                 f   \n",
       "2               100%                   NaN                 f   \n",
       "3               100%                   NaN                 t   \n",
       "4                NaN                   NaN                 f   \n",
       "\n",
       "                                  host_thumbnail_url  \\\n",
       "0  https://a0.muscache.com/im/users/17061/profile...   \n",
       "1  https://a0.muscache.com/im/users/55948/profile...   \n",
       "2  https://a0.muscache.com/im/pictures/user/730ee...   \n",
       "3  https://a0.muscache.com/im/users/64282/profile...   \n",
       "4  https://a0.muscache.com/im/users/110561/profil...   \n",
       "\n",
       "                                    host_picture_url  host_neighbourhood  \\\n",
       "0  https://a0.muscache.com/im/users/17061/profile...             Pyrmont   \n",
       "1  https://a0.muscache.com/im/users/55948/profile...           Balgowlah   \n",
       "2  https://a0.muscache.com/im/pictures/user/730ee...        Darlinghurst   \n",
       "3  https://a0.muscache.com/im/users/64282/profile...  Balmain/Birchgrove   \n",
       "4  https://a0.muscache.com/im/users/110561/profil...       Bellevue Hill   \n",
       "\n",
       "   host_listings_count  host_total_listings_count  \\\n",
       "0                  2.0                        2.0   \n",
       "1                  2.0                        2.0   \n",
       "2                  2.0                        2.0   \n",
       "3                  2.0                        2.0   \n",
       "4                  1.0                        1.0   \n",
       "\n",
       "                                  host_verifications host_has_profile_pic  \\\n",
       "0  ['email', 'phone', 'manual_online', 'reviews',...                    t   \n",
       "1  ['email', 'phone', 'reviews', 'jumio', 'offlin...                    t   \n",
       "2  ['email', 'phone', 'facebook', 'reviews', 'jum...                    t   \n",
       "3  ['email', 'phone', 'reviews', 'jumio', 'govern...                    t   \n",
       "4  ['email', 'phone', 'reviews', 'jumio', 'govern...                    t   \n",
       "\n",
       "  host_identity_verified                         street       neighbourhood  \\\n",
       "0                      t        Pyrmont, NSW, Australia             Pyrmont   \n",
       "1                      t      Balgowlah, NSW, Australia           Balgowlah   \n",
       "2                      t   Darlinghurst, NSW, Australia        Darlinghurst   \n",
       "3                      t        Balmain, NSW, Australia  Balmain/Birchgrove   \n",
       "4                      t  Bellevue Hill, NSW, Australia       Bellevue Hill   \n",
       "\n",
       "  neighbourhood_cleansed  neighbourhood_group_cleansed           city state  \\\n",
       "0                 Sydney                           NaN        Pyrmont   NSW   \n",
       "1                  Manly                           NaN      Balgowlah   NSW   \n",
       "2                 Sydney                           NaN   Darlinghurst   NSW   \n",
       "3             Leichhardt                           NaN        Balmain   NSW   \n",
       "4              Woollahra                           NaN  Bellevue Hill   NSW   \n",
       "\n",
       "  zipcode  market            smart_location country_code    country  \\\n",
       "0    2009  Sydney        Pyrmont, Australia           AU  Australia   \n",
       "1    2093  Sydney      Balgowlah, Australia           AU  Australia   \n",
       "2    2010  Sydney   Darlinghurst, Australia           AU  Australia   \n",
       "3    2041  Sydney        Balmain, Australia           AU  Australia   \n",
       "4    2023  Sydney  Bellevue Hill, Australia           AU  Australia   \n",
       "\n",
       "    latitude   longitude is_location_exact property_type        room_type  \\\n",
       "0 -33.865153  151.191896                 t     Townhouse     Private room   \n",
       "1 -33.800929  151.261722                 t         House  Entire home/apt   \n",
       "2 -33.880455  151.216541                 f     Apartment     Private room   \n",
       "3 -33.859072  151.172753                 t         House  Entire home/apt   \n",
       "4 -33.889087  151.259404                 t     Apartment     Private room   \n",
       "\n",
       "   accommodates  bathrooms  bedrooms  beds  bed_type  \\\n",
       "0             2        1.0       1.0   1.0  Real Bed   \n",
       "1             6        3.0       3.0   3.0  Real Bed   \n",
       "2             2        1.0       1.0   1.0  Real Bed   \n",
       "3             8        2.0       4.0   4.0  Real Bed   \n",
       "4             2        1.0       1.0   1.0  Real Bed   \n",
       "\n",
       "                                           amenities  square_feet    price  \\\n",
       "0  {TV,Internet,Wifi,\"Air conditioning\",\"Paid par...          NaN  $100.00   \n",
       "1  {TV,Wifi,\"Air conditioning\",Kitchen,\"Pets live...          NaN  $471.00   \n",
       "2  {TV,\"Cable TV\",Internet,Wifi,\"Air conditioning...          NaN  $109.00   \n",
       "3  {TV,Internet,Wifi,\"Air conditioning\",Kitchen,\"...          NaN  $450.00   \n",
       "4  {TV,Wifi,Kitchen,\"Free street parking\",Heating...          NaN   $62.00   \n",
       "\n",
       "  weekly_price monthly_price security_deposit cleaning_fee  guests_included  \\\n",
       "0      $800.00           NaN            $0.00       $55.00                2   \n",
       "1    $3,000.00           NaN          $900.00      $100.00                6   \n",
       "2      $800.00     $3,000.00              NaN          NaN                1   \n",
       "3    $2,804.00     $7,009.00          $800.00          NaN                6   \n",
       "4      $400.00     $1,400.00            $0.00       $25.00                1   \n",
       "\n",
       "  extra_people  minimum_nights  maximum_nights calendar_updated  \\\n",
       "0      $395.00               2               7            today   \n",
       "1       $40.00               5              22     9 months ago   \n",
       "2       $10.00               2               7       4 days ago   \n",
       "3        $0.00               7             365      7 weeks ago   \n",
       "4       $15.00               1              60       a week ago   \n",
       "\n",
       "  has_availability  availability_30  availability_60  availability_90  \\\n",
       "0                t                6               16               28   \n",
       "1                t               12               16               46   \n",
       "2                t                9               22               41   \n",
       "3                t                0               17               17   \n",
       "4                t                6               20               50   \n",
       "\n",
       "   availability_365 calendar_last_scraped  number_of_reviews first_review  \\\n",
       "0               187            2018-12-07                493   2010-07-24   \n",
       "1               321            2018-12-07                  1   2016-01-02   \n",
       "2               316            2018-12-07                300   2012-02-23   \n",
       "3                69            2018-12-07                 15   2012-01-03   \n",
       "4               140            2018-12-07                 42   2011-04-11   \n",
       "\n",
       "  last_review  review_scores_rating  review_scores_accuracy  \\\n",
       "0  2018-11-21                  95.0                    10.0   \n",
       "1  2016-01-02                 100.0                    10.0   \n",
       "2  2018-11-16                  88.0                     9.0   \n",
       "3  2018-01-07                  96.0                     9.0   \n",
       "4  2018-11-23                  94.0                    10.0   \n",
       "\n",
       "   review_scores_cleanliness  review_scores_checkin  \\\n",
       "0                        9.0                   10.0   \n",
       "1                       10.0                   10.0   \n",
       "2                        9.0                    9.0   \n",
       "3                        9.0                    9.0   \n",
       "4                       10.0                   10.0   \n",
       "\n",
       "   review_scores_communication  review_scores_location  review_scores_value  \\\n",
       "0                         10.0                    10.0                 10.0   \n",
       "1                          8.0                    10.0                 10.0   \n",
       "2                          9.0                     9.0                  9.0   \n",
       "3                         10.0                    10.0                  9.0   \n",
       "4                         10.0                    10.0                  9.0   \n",
       "\n",
       "  requires_license license  jurisdiction_names instant_bookable  \\\n",
       "0                f     NaN                 NaN                f   \n",
       "1                f     NaN                 NaN                f   \n",
       "2                f     NaN                 NaN                t   \n",
       "3                f     NaN                 NaN                f   \n",
       "4                f     NaN                 NaN                f   \n",
       "\n",
       "  is_business_travel_ready          cancellation_policy  \\\n",
       "0                        f  strict_14_with_grace_period   \n",
       "1                        f  strict_14_with_grace_period   \n",
       "2                        f  strict_14_with_grace_period   \n",
       "3                        f  strict_14_with_grace_period   \n",
       "4                        f                     moderate   \n",
       "\n",
       "  require_guest_profile_picture require_guest_phone_verification  \\\n",
       "0                             t                                t   \n",
       "1                             f                                f   \n",
       "2                             f                                f   \n",
       "3                             t                                t   \n",
       "4                             f                                f   \n",
       "\n",
       "   calculated_host_listings_count  reviews_per_month  \n",
       "0                               2               4.83  \n",
       "1                               2               0.03  \n",
       "2                               2               3.63  \n",
       "3                               1               0.18  \n",
       "4                               1               0.45  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(36662, 96)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 36662 entries, 0 to 36661\n",
      "Data columns (total 96 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   id                                36662 non-null  int64  \n",
      " 1   listing_url                       36662 non-null  object \n",
      " 2   scrape_id                         36662 non-null  int64  \n",
      " 3   last_scraped                      36662 non-null  object \n",
      " 4   name                              36650 non-null  object \n",
      " 5   summary                           35642 non-null  object \n",
      " 6   space                             25052 non-null  object \n",
      " 7   description                       36116 non-null  object \n",
      " 8   experiences_offered               36662 non-null  object \n",
      " 9   neighborhood_overview             21617 non-null  object \n",
      " 10  notes                             14907 non-null  object \n",
      " 11  transit                           22096 non-null  object \n",
      " 12  access                            21668 non-null  object \n",
      " 13  interaction                       20263 non-null  object \n",
      " 14  house_rules                       21429 non-null  object \n",
      " 15  thumbnail_url                     0 non-null      float64\n",
      " 16  medium_url                        0 non-null      float64\n",
      " 17  picture_url                       36662 non-null  object \n",
      " 18  xl_picture_url                    0 non-null      float64\n",
      " 19  host_id                           36662 non-null  int64  \n",
      " 20  host_url                          36662 non-null  object \n",
      " 21  host_name                         36656 non-null  object \n",
      " 22  host_since                        36656 non-null  object \n",
      " 23  host_location                     36608 non-null  object \n",
      " 24  host_about                        19258 non-null  object \n",
      " 25  host_response_time                18778 non-null  object \n",
      " 26  host_response_rate                18778 non-null  object \n",
      " 27  host_acceptance_rate              0 non-null      float64\n",
      " 28  host_is_superhost                 36656 non-null  object \n",
      " 29  host_thumbnail_url                36656 non-null  object \n",
      " 30  host_picture_url                  36656 non-null  object \n",
      " 31  host_neighbourhood                24844 non-null  object \n",
      " 32  host_listings_count               36656 non-null  float64\n",
      " 33  host_total_listings_count         36656 non-null  float64\n",
      " 34  host_verifications                36662 non-null  object \n",
      " 35  host_has_profile_pic              36656 non-null  object \n",
      " 36  host_identity_verified            36656 non-null  object \n",
      " 37  street                            36662 non-null  object \n",
      " 38  neighbourhood                     30595 non-null  object \n",
      " 39  neighbourhood_cleansed            36662 non-null  object \n",
      " 40  neighbourhood_group_cleansed      0 non-null      float64\n",
      " 41  city                              36633 non-null  object \n",
      " 42  state                             36520 non-null  object \n",
      " 43  zipcode                           36488 non-null  object \n",
      " 44  market                            36597 non-null  object \n",
      " 45  smart_location                    36662 non-null  object \n",
      " 46  country_code                      36662 non-null  object \n",
      " 47  country                           36662 non-null  object \n",
      " 48  latitude                          36662 non-null  float64\n",
      " 49  longitude                         36662 non-null  float64\n",
      " 50  is_location_exact                 36662 non-null  object \n",
      " 51  property_type                     36662 non-null  object \n",
      " 52  room_type                         36662 non-null  object \n",
      " 53  accommodates                      36662 non-null  int64  \n",
      " 54  bathrooms                         36643 non-null  float64\n",
      " 55  bedrooms                          36653 non-null  float64\n",
      " 56  beds                              36623 non-null  float64\n",
      " 57  bed_type                          36662 non-null  object \n",
      " 58  amenities                         36662 non-null  object \n",
      " 59  square_feet                       245 non-null    float64\n",
      " 60  price                             36662 non-null  object \n",
      " 61  weekly_price                      3651 non-null   object \n",
      " 62  monthly_price                     2213 non-null   object \n",
      " 63  security_deposit                  23094 non-null  object \n",
      " 64  cleaning_fee                      26124 non-null  object \n",
      " 65  guests_included                   36662 non-null  int64  \n",
      " 66  extra_people                      36662 non-null  object \n",
      " 67  minimum_nights                    36662 non-null  int64  \n",
      " 68  maximum_nights                    36662 non-null  int64  \n",
      " 69  calendar_updated                  36662 non-null  object \n",
      " 70  has_availability                  36662 non-null  object \n",
      " 71  availability_30                   36662 non-null  int64  \n",
      " 72  availability_60                   36662 non-null  int64  \n",
      " 73  availability_90                   36662 non-null  int64  \n",
      " 74  availability_365                  36662 non-null  int64  \n",
      " 75  calendar_last_scraped             36662 non-null  object \n",
      " 76  number_of_reviews                 36662 non-null  int64  \n",
      " 77  first_review                      24725 non-null  object \n",
      " 78  last_review                       24725 non-null  object \n",
      " 79  review_scores_rating              23536 non-null  float64\n",
      " 80  review_scores_accuracy            23491 non-null  float64\n",
      " 81  review_scores_cleanliness         23511 non-null  float64\n",
      " 82  review_scores_checkin             23469 non-null  float64\n",
      " 83  review_scores_communication       23505 non-null  float64\n",
      " 84  review_scores_location            23468 non-null  float64\n",
      " 85  review_scores_value               23465 non-null  float64\n",
      " 86  requires_license                  36662 non-null  object \n",
      " 87  license                           76 non-null     object \n",
      " 88  jurisdiction_names                0 non-null      float64\n",
      " 89  instant_bookable                  36662 non-null  object \n",
      " 90  is_business_travel_ready          36662 non-null  object \n",
      " 91  cancellation_policy               36662 non-null  object \n",
      " 92  require_guest_profile_picture     36662 non-null  object \n",
      " 93  require_guest_phone_verification  36662 non-null  object \n",
      " 94  calculated_host_listings_count    36662 non-null  int64  \n",
      " 95  reviews_per_month                 24725 non-null  float64\n",
      "dtypes: float64(22), int64(13), object(61)\n",
      "memory usage: 26.9+ MB\n"
     ]
    }
   ],
   "source": [
    "#listings\n",
    "listings.head()\n",
    "listings.shape\n",
    "listings.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calendar's column 'date' should be of type datetime, 'available' and 'price' should be of type numeric. Except for the variable \"price\", there are no missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14250</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>t</td>\n",
       "      <td>$470.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12351</td>\n",
       "      <td>2019-08-17</td>\n",
       "      <td>t</td>\n",
       "      <td>$110.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12351</td>\n",
       "      <td>2019-08-16</td>\n",
       "      <td>t</td>\n",
       "      <td>$110.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12351</td>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>t</td>\n",
       "      <td>$110.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12351</td>\n",
       "      <td>2019-08-14</td>\n",
       "      <td>t</td>\n",
       "      <td>$110.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id        date available    price\n",
       "0       14250  2019-12-06         t  $470.00\n",
       "1       12351  2019-08-17         t  $110.00\n",
       "2       12351  2019-08-16         t  $110.00\n",
       "3       12351  2019-08-15         t  $110.00\n",
       "4       12351  2019-08-14         t  $110.00"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(13381265, 4)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13381265 entries, 0 to 13381264\n",
      "Data columns (total 4 columns):\n",
      " #   Column      Dtype \n",
      "---  ------      ----- \n",
      " 0   listing_id  int64 \n",
      " 1   date        object\n",
      " 2   available   object\n",
      " 3   price       object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 408.4+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "listing_id          0\n",
       "date                0\n",
       "available           0\n",
       "price         9810841\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calendar \n",
    "calendar.head()\n",
    "calendar.shape\n",
    "calendar.info()\n",
    "calendar.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Listings dataframe**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Get only variables of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "key_columns = ['id',\n",
    "               'description',\n",
    "               'neighbourhood_cleansed',\n",
    "               'room_type',\n",
    "               'price',\n",
    "               'accommodates',\n",
    "               'bedrooms', \n",
    "               'amenities']\n",
    "key_listings = listings.copy()[key_columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) Filter listings for the entire place (not only a room)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "key_listings = key_listings[key_listings['room_type'] == 'Entire home/apt']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) Rename the variable \"neighbourhood_cleansed\" to become easier to recall it in the future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "key_listings.rename(columns={'neighbourhood_cleansed': 'neighborhood'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4) Fix variable \"price\" by transforming it to float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "key_listings['price'] = key_listings['price'].replace('\\$', '', regex=True).replace(',', '', regex=True).astype('float')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5) Fill null values of variable \"description\" by making them a blank space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "key_listings['description'] = key_listings['description'].fillna('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6) Fill null values of variable \"bedrooms\" with its median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_bedrooms = np.median(key_listings[key_listings['bedrooms'].notnull()]['bedrooms'])\n",
    "key_listings['bedrooms'] = key_listings['bedrooms'].fillna(median_bedrooms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7) Re-check the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 22918 entries, 1 to 36660\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   id            22918 non-null  int64  \n",
      " 1   description   22918 non-null  object \n",
      " 2   neighborhood  22918 non-null  object \n",
      " 3   room_type     22918 non-null  object \n",
      " 4   price         22918 non-null  float64\n",
      " 5   accommodates  22918 non-null  int64  \n",
      " 6   bedrooms      22918 non-null  float64\n",
      " 7   amenities     22918 non-null  object \n",
      "dtypes: float64(2), int64(2), object(4)\n",
      "memory usage: 1.6+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "id              0.0\n",
       "description     0.0\n",
       "neighborhood    0.0\n",
       "room_type       0.0\n",
       "price           0.0\n",
       "accommodates    0.0\n",
       "bedrooms        0.0\n",
       "amenities       0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key_listings.info()\n",
    "key_listings.isnull().sum()/key_listings.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Calendar dataframe**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Rename variable \"listing_id\" to match listings dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "calendar.rename(columns={'listing_id': 'id'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) Fix variable \"date\" by transforming it to datetime type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "calendar['date'] = pd.to_datetime(calendar['date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) Fix variable \"available\" by transforming it to numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "calendar['available'] = np.where(calendar['available'] == 't', 1, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4) Fix variable \"price\" by cleaning it and transforming it to numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "calendar['price'] = calendar['price'].replace('\\$', '', regex=True).replace(',', '', regex=True).astype('float')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5) Create variables \"month\" and \"season\" (based on months)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "calendar['month'] = calendar['date'].dt.month\n",
    "calendar['season'] = np.select([calendar['month'].isin([12, 1, 2]), \n",
    "                                calendar['month'].isin([6, 7, 8]),\n",
    "                                calendar['month'].isin([3, 4, 5, 9, 10, 11])],\n",
    "                               ['summer', 'winter', 'other'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6) Re-check the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13381265 entries, 0 to 13381264\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Dtype         \n",
      "---  ------     -----         \n",
      " 0   id         int64         \n",
      " 1   date       datetime64[ns]\n",
      " 2   available  int32         \n",
      " 3   price      float64       \n",
      " 4   month      int64         \n",
      " 5   season     object        \n",
      "dtypes: datetime64[ns](1), float64(1), int32(1), int64(2), object(1)\n",
      "memory usage: 561.5+ MB\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.338126e+07</td>\n",
       "      <td>1.338126e+07</td>\n",
       "      <td>3.570424e+06</td>\n",
       "      <td>1.338126e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.852100e+07</td>\n",
       "      <td>2.668226e-01</td>\n",
       "      <td>2.608456e+02</td>\n",
       "      <td>6.526027e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.473819e+06</td>\n",
       "      <td>4.422989e-01</td>\n",
       "      <td>4.255012e+02</td>\n",
       "      <td>3.447851e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.235100e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.400000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.170458e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.000000e+01</td>\n",
       "      <td>4.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.027378e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.600000e+02</td>\n",
       "      <td>7.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.524418e+07</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.800000e+02</td>\n",
       "      <td>1.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.059387e+07</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.500000e+04</td>\n",
       "      <td>1.200000e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id     available         price         month\n",
       "count  1.338126e+07  1.338126e+07  3.570424e+06  1.338126e+07\n",
       "mean   1.852100e+07  2.668226e-01  2.608456e+02  6.526027e+00\n",
       "std    8.473819e+06  4.422989e-01  4.255012e+02  3.447851e+00\n",
       "min    1.235100e+04  0.000000e+00  1.400000e+01  1.000000e+00\n",
       "25%    1.170458e+07  0.000000e+00  9.000000e+01  4.000000e+00\n",
       "50%    2.027378e+07  0.000000e+00  1.600000e+02  7.000000e+00\n",
       "75%    2.524418e+07  1.000000e+00  2.800000e+02  1.000000e+01\n",
       "max    3.059387e+07  1.000000e+00  1.500000e+04  1.200000e+01"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calendar.info()\n",
    "calendar.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='analysis'></a>\n",
    "## Listings from which Sydney neighborhoods use the word \"beach\" most often in their description?\n",
    "\n",
    "Here I classify neighborhoods into three categories: coast, inner or center. Coast neighborhoods are the ones that used the word beach most often (more than 50% of their listings). Inner neighborhoods are the other ones (use the word beach in less than 50% of their listings). Center refers to central Sydney, which would be classified as inner but receives a special category given its importance.\n",
    "\n",
    "Although this classification is arbitrary, note that there is approximately a 20% difference between the coast neighboorhood with the least use of the word beach (Woollahra) and the inner neighborhood with the most use of it (Rockdale). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "key_listings['beach'] = key_listings['description'].str.contains('beach', case=False)\n",
    "\n",
    "beach_neighborhood = key_listings.groupby('neighborhood', as_index=False).apply(lambda df: pd.Series({'beach': df.beach.sum(), 'not_beach': (~df.beach).sum()}))\n",
    "beach_neighborhood['total_listings'] = beach_neighborhood['beach'] + beach_neighborhood['not_beach']\n",
    "beach_neighborhood['proportion_beach'] = beach_neighborhood['beach'] / (beach_neighborhood['beach'] + beach_neighborhood['not_beach'])\n",
    "beach_neighborhood.sort_values('proportion_beach', ascending=False, inplace=True)\n",
    "beach_neighborhood['category_beach'] = np.where(beach_neighborhood['proportion_beach'] > 0.5, 'coast', 'inner')\n",
    "beach_neighborhood.loc[beach_neighborhood['neighborhood'] == 'Sydney', 'category_beach'] = 'center'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='proportion_beach', ylabel='neighborhood'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Proportion of listings using the word beach'),\n",
       " Text(0, 0.5, ''),\n",
       " Text(0.5, 1.0, 'Proportion of listings using the word beach by neighborhood')]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2c22efe7040>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='total_listings', ylabel='neighborhood'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Number of listings'),\n",
       " Text(0, 0.5, 'Neighborhood'),\n",
       " Text(0.5, 1.0, 'Number of listings by neighborhood')]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2c22efe72b0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-19-add0d4972e5f>:31: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['#003f5c', '#ffa600', '#bc5090']\n",
    "custom_palette = sns.set_palette(sns.color_palette(colors))\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, sharey=False, figsize=(20,8))\n",
    "sns.barplot(ax = ax[1],\n",
    "            y='neighborhood',\n",
    "            x='proportion_beach',\n",
    "            hue='category_beach',\n",
    "            palette=custom_palette,\n",
    "            dodge=False,\n",
    "            data=beach_neighborhood)\n",
    "ax[1].set(xlabel = 'Proportion of listings using the word beach',\n",
    "          ylabel = '',\n",
    "          title = 'Proportion of listings using the word beach by neighborhood')\n",
    "ax[1].legend(loc='lower right', title='Neighborhood category')\n",
    "\n",
    "sns.set_palette(sns.color_palette(colors))\n",
    "\n",
    "sns.barplot(ax = ax[0],\n",
    "            y='neighborhood',\n",
    "            x='total_listings',\n",
    "            hue='category_beach',\n",
    "            palette=custom_palette,\n",
    "            dodge=False,\n",
    "            data=beach_neighborhood)\n",
    "ax[0].set(xlabel = 'Number of listings',\n",
    "          ylabel = 'Neighborhood',\n",
    "          title = 'Number of listings by neighborhood')\n",
    "ax[0].legend(loc='lower right', title='Neighborhood category')\n",
    "\n",
    "fig.show()\n",
    "\n",
    "#plt.savefig('fig_1.png', bbox_inches='tight', dpi=300);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Is there a difference in the size of places listed for each neighborhood category?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filters only entire homes/apts\n",
    "key_listings2 = pd.merge(key_listings, beach_neighborhood, how='left', on=['neighborhood'])\n",
    "#key_listings2 = key_listings2[key_listings2['room_type'] == 'Entire home/apt']\n",
    "\n",
    "\n",
    "listings_beach_bedrooms = key_listings2.groupby(['category_beach', 'bedrooms']).count()\n",
    "proportions_bedrooms = (listings_beach_bedrooms / listings_beach_bedrooms.groupby(level=[0]).transform('sum')).reset_index()\n",
    "proportions_bedrooms['bedrooms'] = proportions_bedrooms['bedrooms'].astype('int64')\n",
    "proportions_bedrooms = proportions_bedrooms[proportions_bedrooms['bedrooms'] < 10]\n",
    "\n",
    "listings_beach_accommodates = key_listings2.groupby(['category_beach', 'accommodates']).count()\n",
    "proportions_accommodates = (listings_beach_accommodates / listings_beach_accommodates.groupby(level=[0]).transform('sum')).reset_index()\n",
    "proportions_accommodates = proportions_accommodates[proportions_accommodates['accommodates'] < 20]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bedrooms', ylabel='id'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Number of bedrooms'),\n",
       " Text(0, 0.5, 'Proportion of listings'),\n",
       " Text(0.5, 1.0, 'Proportion of listings by number of bedrooms and category')]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2c22efe72e0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='accommodates', ylabel='id'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Number of accommodations'),\n",
       " Text(0, 0.5, ''),\n",
       " Text(0.5, 1.0, 'Proportion of listings by number of accommodations and category')]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2c22f5a0f70>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-21-d9a086eb02a4>:29: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['#003f5c', '#ffa600', '#bc5090']\n",
    "custom_palette = sns.set_palette(sns.color_palette(colors))\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, sharey=True, figsize=(14,8))\n",
    "sns.barplot(ax = ax[0],\n",
    "            data=proportions_bedrooms,\n",
    "            y='id',\n",
    "            x='bedrooms',\n",
    "            hue='category_beach',\n",
    "            hue_order = ['coast', 'inner', 'center'])\n",
    "ax[0].set(xlabel = 'Number of bedrooms',\n",
    "          ylabel = 'Proportion of listings',\n",
    "          title = 'Proportion of listings by number of bedrooms and category')\n",
    "ax[0].legend(loc='upper right', title='Category')\n",
    "\n",
    "sns.set_palette(sns.color_palette(colors))\n",
    "\n",
    "sns.barplot(ax = ax[1],\n",
    "            data=proportions_accommodates,\n",
    "            y='id',\n",
    "            x='accommodates',\n",
    "            hue='category_beach',\n",
    "            hue_order = ['coast', 'inner', 'center'])\n",
    "ax[1].set(xlabel = 'Number of accommodations',\n",
    "          ylabel = '',\n",
    "          title = 'Proportion of listings by number of accommodations and category')\n",
    "ax[1].legend(loc='upper right', title='Category')\n",
    "\n",
    "fig.show()\n",
    "\n",
    "#plt.savefig('fig_2.png', bbox_inches='tight', dpi=300);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Does listing price change according to neighborhood category?\n",
    "\n",
    "I use descriptive statistics (mean price per day) and a linear regression to answer this question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_bedroom = key_listings2.groupby(['category_beach', 'bedrooms'], as_index=False).mean()[['category_beach', 'bedrooms', 'price']]\n",
    "\n",
    "price_accommodates = key_listings2.groupby(['category_beach', 'accommodates'], as_index=False).mean()[['category_beach', 'accommodates', 'price']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2c22f914430>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2c22f3b26a0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 447.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 447.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g1 = sns.relplot(data=key_listings2[key_listings2['bedrooms'] < 9],\n",
    "            y='price',\n",
    "            x='bedrooms',\n",
    "            style = 'category_beach',\n",
    "            hue='category_beach',\n",
    "            hue_order = ['coast', 'inner', 'center'],\n",
    "            kind='line',\n",
    "            markers=True,\n",
    "            dashes=False)\n",
    "g1.set(xlabel = 'Number of bedrooms',\n",
    "          ylabel = 'Average price per day',\n",
    "          title = 'Average price per day by number of bedrooms and category')\n",
    "g1._legend.set_title('Category')\n",
    "leg1 = g1._legend\n",
    "leg1.set_bbox_to_anchor([0.32, 0.9])  # coordinates of lower left of bounding box\n",
    "\n",
    "#plt.savefig('fig_3.png', bbox_inches='tight', dpi=300)\n",
    "\n",
    "g2 = sns.relplot(data=key_listings2[key_listings2['accommodates'] < 20],\n",
    "            y='price',\n",
    "            x='accommodates',\n",
    "            style = 'category_beach',\n",
    "            hue='category_beach',\n",
    "            hue_order = ['coast', 'inner', 'center'],\n",
    "            kind='line',\n",
    "            markers=True,\n",
    "            dashes=False)\n",
    "g2.set(xlabel = 'Number of accommodations',\n",
    "           ylabel = 'Average price per day',\n",
    "          title = 'Average price per day by number of accommodations and category')\n",
    "g2._legend.set_title('Category')\n",
    "leg2 = g2._legend\n",
    "leg2.set_bbox_to_anchor([0.33, 0.9])  # coordinates of lower left of bounding box\n",
    "\n",
    "#plt.savefig('fig_4.png', bbox_inches='tight', dpi=300);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>price</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>beach_x</th>\n",
       "      <th>beach_y</th>\n",
       "      <th>not_beach</th>\n",
       "      <th>total_listings</th>\n",
       "      <th>proportion_beach</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_beach</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>center</th>\n",
       "      <td>1.903001e+07</td>\n",
       "      <td>217.013473</td>\n",
       "      <td>3.648703</td>\n",
       "      <td>1.388178</td>\n",
       "      <td>0.126979</td>\n",
       "      <td>754.000000</td>\n",
       "      <td>5184.000000</td>\n",
       "      <td>5938.000000</td>\n",
       "      <td>0.126979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coast</th>\n",
       "      <td>1.697086e+07</td>\n",
       "      <td>339.694920</td>\n",
       "      <td>4.521057</td>\n",
       "      <td>2.175813</td>\n",
       "      <td>0.882082</td>\n",
       "      <td>1894.566806</td>\n",
       "      <td>187.868605</td>\n",
       "      <td>2082.435411</td>\n",
       "      <td>0.882082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inner</th>\n",
       "      <td>1.962625e+07</td>\n",
       "      <td>220.681635</td>\n",
       "      <td>4.548676</td>\n",
       "      <td>2.004385</td>\n",
       "      <td>0.119453</td>\n",
       "      <td>57.071917</td>\n",
       "      <td>380.520610</td>\n",
       "      <td>437.592528</td>\n",
       "      <td>0.119453</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          id       price  accommodates  bedrooms   beach_x  \\\n",
       "category_beach                                                               \n",
       "center          1.903001e+07  217.013473      3.648703  1.388178  0.126979   \n",
       "coast           1.697086e+07  339.694920      4.521057  2.175813  0.882082   \n",
       "inner           1.962625e+07  220.681635      4.548676  2.004385  0.119453   \n",
       "\n",
       "                    beach_y    not_beach  total_listings  proportion_beach  \n",
       "category_beach                                                              \n",
       "center           754.000000  5184.000000     5938.000000          0.126979  \n",
       "coast           1894.566806   187.868605     2082.435411          0.882082  \n",
       "inner             57.071917   380.520610      437.592528          0.119453  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key_listings2.groupby(['category_beach']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Function to perform linear regressions efficiently and return R2 and coefficients:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lm(X, y):\n",
    "    \n",
    "    '''\n",
    "    Runs a linear regression on the data provided.\n",
    "\n",
    "    Parameters:\n",
    "        X: Dataframe containing independent variables.\n",
    "        y: Dependent variable.\n",
    "\n",
    "    Returns:\n",
    "        Coefficient of determination (R2) and a dataframe with the coefficients values.   \n",
    "    '''\n",
    "    \n",
    "    cat_names = list(X.select_dtypes(include = \"object\").columns)\n",
    "    \n",
    "    for focal_col in cat_names:\n",
    "        try:\n",
    "            X = pd.concat([X.drop([focal_col], axis = 1),\n",
    "                           pd.get_dummies(X[focal_col],\n",
    "                                          dummy_na = False,\n",
    "                                          drop_first = True,\n",
    "                                          prefix = focal_col,\n",
    "                                          prefix_sep = \"_\")],\n",
    "                          axis = 1)\n",
    "        except:\n",
    "            continue\n",
    "\n",
    "\n",
    "    #Split into train and test\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .30, random_state=42)\n",
    "       \n",
    "    #Predict and score the model\n",
    "    lm_model = LinearRegression(normalize=True) # Instantiate\n",
    "    lm_model.fit(X_train, y_train) #Fit\n",
    "\n",
    "    y_test_preds = lm_model.predict(X_test) \n",
    "\n",
    "    #Rsquared\n",
    "    rsquared_score = r2_score(y_test, y_test_preds) #r2_score\n",
    "\n",
    "    coefs_df = pd.DataFrame()\n",
    "    coefs_df['est_int'] = X_train.columns\n",
    "    coefs_df['coefs'] = lm_model.coef_\n",
    "    coefs_df['abs_coefs'] = np.abs(lm_model.coef_)\n",
    "    coefs_df = coefs_df.sort_values('abs_coefs', ascending=False)\n",
    "    \n",
    "    return(rsquared_score, coefs_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.26026342953375814,\n",
       "                 est_int      coefs  abs_coefs\n",
       " 0              bedrooms  89.489677  89.489677\n",
       " 3  category_beach_inner -73.861232  73.861232\n",
       " 1          accommodates  28.903571  28.903571\n",
       " 2  category_beach_coast  27.191672  27.191672)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm(X=key_listings2[['category_beach', 'bedrooms', 'accommodates']], y=key_listings2['price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Does availability differ from winter to season depending on neighboor category?\n",
    "\n",
    "I use descriptive statistics (mean proportional difference between seasons) and a linear regression to answer this question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>season</th>\n",
       "      <th>id</th>\n",
       "      <th>category_beach</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>summer</th>\n",
       "      <th>winter</th>\n",
       "      <th>summer_prop</th>\n",
       "      <th>winter_prop</th>\n",
       "      <th>delta_prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14250</td>\n",
       "      <td>coast</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "      <td>46</td>\n",
       "      <td>92</td>\n",
       "      <td>0.511111</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.488889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20865</td>\n",
       "      <td>inner</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.212077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38073</td>\n",
       "      <td>inner</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>46</td>\n",
       "      <td>92</td>\n",
       "      <td>0.511111</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.488889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39348</td>\n",
       "      <td>coast</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>90</td>\n",
       "      <td>0.144444</td>\n",
       "      <td>0.978261</td>\n",
       "      <td>-0.833816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44545</td>\n",
       "      <td>center</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.011111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "season     id category_beach  bedrooms  accommodates  summer  winter  \\\n",
       "0       14250          coast       3.0             6      46      92   \n",
       "1       20865          inner       4.0             8      23       4   \n",
       "2       38073          inner       0.0             2      46      92   \n",
       "3       39348          coast       1.0             2      13      90   \n",
       "4       44545         center       1.0             2       1       0   \n",
       "\n",
       "season  summer_prop  winter_prop  delta_prop  \n",
       "0          0.511111     1.000000   -0.488889  \n",
       "1          0.255556     0.043478    0.212077  \n",
       "2          0.511111     1.000000   -0.488889  \n",
       "3          0.144444     0.978261   -0.833816  \n",
       "4          0.011111     0.000000    0.011111  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#joining info from key_listings2 to calendar\n",
    "joined_calendar = pd.merge(calendar, key_listings2[['id', 'category_beach', 'bedrooms', 'accommodates']], on=['id'])\n",
    "\n",
    "#grouping by season and id to get availability in each\n",
    "available_season = joined_calendar.groupby(['id', 'season'], as_index=False).sum()[['id', 'season', 'available']]\n",
    "available_season = pd.merge(available_season, joined_calendar[['id', 'category_beach', 'bedrooms', 'accommodates']], on=['id']).drop_duplicates()\n",
    "\n",
    "#we want to compare summer with winter\n",
    "available_season = available_season[available_season['season'].isin(['summer', 'winter'])]\n",
    "\n",
    "#pivotting\n",
    "pivot_season = available_season.pivot(index = ['id', 'category_beach', 'bedrooms', 'accommodates'], columns = 'season', values = 'available')\n",
    "pivot_season.reset_index(inplace=True)\n",
    "\n",
    "#making proportions\n",
    "pivot_season['summer_prop'] = pivot_season['summer']/90 #number of summer days\n",
    "pivot_season['winter_prop'] = pivot_season['winter']/92 #number of winter days\n",
    "pivot_season['delta_prop'] = pivot_season['summer_prop'] - pivot_season['winter_prop']\n",
    "\n",
    "pivot_season.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2c20f11a430>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2c22fbef070>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 447.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pEXkTYIrIQSLydWZJmkJEXioim0Rki4hcUWT5K0XkSRF5QkQeEZHTy902ICAgIGDvUo6B+RBaKC6Pzn8eRGtlzQgRMdEqpy9DyxhcWtDxGc1f0bLlR6PrF3yvgm0DAgICAvYi5RiYlyulPqOUOqHw+ixaG2imnAhsUUptLegN/Qx45egVlFIptSfNLcEe19yU2wYEBAQE7F3KMTCfKvOzSlnBWKHL3RSpsyEirxaR59Fzcd5RybYBAQEBAXuPkhMtC5LV/wCsEJHrRi2qQUvuz5Ried/FaprcAtwiIi9B17o+r9xtAUTkPcB7AFavnqxMQ0BAQEDAbDLZCKYNLe+cQ8vID79+g651PVN2M7ZIz+jiVhNQSv0NOEBEllSyrVLqO0qp45VSxzc1Nc281RXQ0dHBG9/4Rg444AAOO+ww/uEf/oEXXnih6LoDAwN885vfnNf2BQQEBMwlJQ2MUurvSqkb0bWybxz1+qVSqn8Wjv0wcFCh4E0YeCPjajCIyIEiIoX/j0UXBuotZ9u9jVKKV7/61Zx11lm8+OKLPPvss3zpS1+is7Oz6PrzZWBcdzYGnwEBAQFTU04M5kQR+bOIvCAiW0Vkm4hsnemBC5XTPoguQvYccLNS6hkReZ+IvK+w2mvQadJPoLPG3qA0RbedaZtmkzvuuINQKMT73ve+kc+OPvpojjnmGM4991yOPfZYjjzySH79618DcMUVV/Diiy9y9NFH84lP6CKb1157LSeccAIbN27kqqv2iBhfc801bNiwgfPPP59LL72UL39Z11B64oknOPnkk9m4cSOvfvWr6e/X/YCzzjqLT3/605x55pl88YtfZN26dTiOriM2NDTE2rVrR94HBAQEzBpKqUlf6AJTLwOWoutsNwKNU223EF/HHXecmi++9rWvqY9+9KMTPnccRw0ODiqllOru7lYHHHCA8n1fbdu2TR1++OEj6/3pT39S7373u5Xv+8rzPPXyl79c3XXXXerhhx9WRx11lMpkMmpoaEgdeOCB6tprr1VKKXXkkUeqO++8Uyml1JVXXqk+8pGPKKWUOvPMM9X73//+kX1ffvnl6pZbblFKKfXtb39bffzjH5+LUxAQEFA+e/35OBevctSUB5VSf5g7E7d/oZTi05/+NH/7298wDIPW1taibrPbbruN2267jWOOOQaAVCrF5s2bSSaTvPKVryQWiwFw0UUXATA4OMjAwABnnnkmAG9729t43eteN7K/N7zhDSP/v+td7+I///M/edWrXsUNN9zAd7/73Tn7vgEBAfsv5RiYO0TkWnTt+uF6ziilHpuzVu0DHH744fz85z+f8PmPf/xjuru7efTRRwmFQqxdu5ZcLjdhPaUUn/rUp3jve9875vP//u//nlZ7EonEyP+nnXYa27dv56677sLzPI444ohp7TMgICBgMsqJwZwEHA98CfhK4fXluWzUvsA555xDPp8fMzp4+OGH2bFjB0uXLiUUCnHHHXewY8cOAKqrq0km9xSdvPDCC7n++utJpVIAtLa20tXVxemnn85vf/tbcrkcqVSK3/1Ol+qpra2lvr6eu+++G4Af/vCHI6OZYrz1rW/l0ksv5e1vf/usf/eA/QsnmSPTPht5PwH7GlOOYJRSZ89HQ/Y1RIRbbrmFj370o/z7v/870WiUtWvXcvXVV/PhD3+Y448/nqOPPpoNGzYA0NjYyGmnncYRRxzBy172Mq699lqee+45TjnlFACqqqr40Y9+xAknnMDFF1/MUUcdxZo1azj++OOprdUVrW+88Ube9773kclkWL9+PTfccEPJ9r35zW/ms5/9LJdeeuncn4yAfZpc9xD5/jTRJTUYIXNvNydgAVGy4JiIvEUp9SMR+Xix5Uqp/5rTls0Bxx9/vHrkkUf2djNmTCqVoqqqikwmw0te8hK+853vcOyxx1a0j5///Of8+te/5oc//OEctTJgf8B3PPqf2oVSULWmkeiS6r3dpMXKPllwbLIRzLDTPrhiFhjvec97ePbZZ8nlcrztbW+r2Lh86EMf4g9/+AO///3v56iFAfsLdjKLAqxEmGzHIJHGKgpT1wICpi6ZLCJRpdTEKPQiZF8ZwQQELBQGN7Xjex5mJIQ9kKX24GZC1dG93azFyD5plcvJIntaRDqBu4G/AfeqiQXIAgIC9jO8vIOTyhOu0ynzRsQk2zkYGJiAEabMIlNKHQhcCjwFvAL4e2FmfUBAwH6MPZhFRj1BzGgIeyiLlw9UIQI0UxoYEVkJnAacARwDPAP87xy3KyAgYAGjlCLXNYQZC498JiKICPm+1F5sWcBCohwX2U60uOSXlFLvm2rlgICAfR8va+PnXUIFRYlhrESYbGeS6NJaDLOcaXYB+zLlXAHHADcBbxKR+0XkJhF55xy3K2Ae2b59Oz/5yU/2djMCFhH5/gyYE+PSYhoo38MZyu6FVi0AlAJvn8iJmhXKicH8HbgRuAG4HTgTuHKO27VP8OM/38vaN3wU4+zLWPuGj/LjP9+7t5tUlMDABFSC8hW5niRmPFx0uRnVKctTZajuk9i9qOSLe7sVC4ZyYjCPAPcDr0YrK79EKbV2jtu16Pnxn+/lPV++nh2dvSgFOzp7ec+Xr5+xkbnpppvYuHEjRx11FJdddhk7duzg3HPPZePGjZx77rns3LkTgN/+9recdNJJHHPMMZx33nkjgpp33XUXRx999EjpgGQyyRVXXMHdd9/N0UcfPW2ts4D9BzedR3l+SReYGbFwM3m8jD3PLdv7OD2tZFqDJNthypkH06SU6p6n9swpszkP5qNf/xFPbNlRcvkDz24h70ws7hUJWZx82IFFtzn6wDV89UNvKbnPZ555hksuuYR7772XJUuW0NfXx9ve9jZe+9rX8ra3vY3rr7+e3/zmN/zqV7+iv7+furo6RITvfe97PPfcc3zlK1/hoosu4oorruC0004jlUoRjUa55557+PKXv8ytt95a+YkI2O9Ibu/BSWaw4pGS67jpPKGaONVrl8xjy/YuyvcZevD3SKyOmqNPr3TzfXIeTDkusn3CuMw3xYzLZJ+Xw+23385rX/talizRN21DQwP3338/b3rTmwC47LLLuOeeewDYvXs3F154IUceeSTXXnstzzyj67GddtppfPzjH+e6665jYGAAyyonzyMgQOO7Hvm+FGa0uHtsGDMWxu5L4c/gel9suIP95HuH9nYzFhTB02WaTDbSAFj7ho+yo7N3wudrljVy59c+M61jKqWmlOEYXv6hD32Ij3/841x88cXceeedXH311YCunPnyl7+c3//+95x88sn85S9/mVZbAvZP3FQeUIgxxXVoCIhOBogtrZmfxu1lMjvbMCx/bzdjQRHkEc4RX3zX64hHxvby4pEwX3zX60psMTXnnnsuN998M7292nD19fVx6qmn8rOf/QzQtWZOP10PzQcHB1mxYgWgVZaHefHFFznyyCP5l3/5F44//nief/75CaUCAgJKke1JYkZCZa1rxgvBfn/fD/Y7qRxOfwdmtLTbcH+k5AhGRC6ZbEOl1C9nvzn7Dm8+/zQAPvO9/2NnVy+rlzbyxXe9buTz6XD44Yfzmc98hjPPPBPTNDnmmGO47rrreMc73sG1115LU1PTiET/1Vdfzete9zpWrFjBySefzLZt2wD46le/yh133IFpmhx22GG87GUvwzAMLMviqKOO4vLLL+djH/vYzE9AwD6HZ7s4Q1lCNeVJwRiWiZu2cVI5wjWxqTdYxGTa+jBIghnI5IxmMrn+4WIiS4FT0SnKAGcDdyqlJjVAC5FA7DIgYPrkepKkd/WVbWAAvJyDETKpPXj5HLZs7+Km8ww8tZkwm/HcEGZVEOQfpuQIRin1dgARuRU4TCnVXni/HPjG/DQvICBgoZDrHsKIVha2NaMh7IEMbtbGik2eGLBYyXQMYhhZUEHEYTzlnJG1w8alQCdw8By1JyAgYAHiZm3cjI0ZrjwvSEyDfO++qU/mZvLYA2lMox/MfdsNOB3KuVruFJE/AT8FFPBG4I45bVVAQMCCQisnT6+HbiXCZLuTxJprMax9q6RytnMQwwTJpSHcAOx/k0snY0oDo5T6YCHgf0bho+8opW6Z22YFBAQsFEaUk+PlZY+NRwwDlMIezBJtrJrl1u093KxNvi9NKJaHQH6sKGWNdwsZY0HWWEDAfoibsfFdDysx/RiKFQ+RbR8g0pDYZ0oqZzuHkJCJOIMg0zO++zrlaJFdIiKbRWRQRIZEJCki+810Vdf18P1g8lTA/ovdn0KsmQWwjZCFl3dx0/lZatXexcs55HtTOnEh3wNWEH8pRjlXzX8CFyulapVSNUqpaqXU/jE1F9jZ1UPv0MIJUJ566ql7uwkB+xHK88n1pLBiM++hG2GTXNe+0TfNdA0iliB+HnwHJBBFKUY5BqZTKfXcnLdkgeK4PoPpzPQ27rwDfn8kZDvG/j8D7rvvvhltPxWuu/9oRwVMjVNQTp5ugH80ZixEfiCDl1/c15iXd8j3pLTYp5uCfcTlNxeUc9U8IiL/KyKXFtxll0w1y39fYyA5DQPTeQfc9QoYeh7ue/Oe/5++ZkZtqarSQdI777yTs846i9e+9rVs2LCBN7/5zSP1N9auXctVV13Fsccey5FHHsnzzz8PQDqd5h3veAcnnHACxxxzDL/+9a8B+MEPfsDrXvc6LrroIi644IIZtS9g3yLfk8SIzE7vXERAhHz/wvEITIdc1xBiiNZby/eAsW/O75kNyrlyaoAMMPrJo9iPgv4DqSIG5tGPwsATpTfqfRi8wnZddwKFOM6L34WhZ4pvU3c0HPfVstv1+OOP88wzz9DS0sJpp53GvffeO6JFtmTJEh577DG++c1v8uUvf5nvfe97fPGLX+Scc87h+uuvZ2BggBNPPJHzzjsPgPvvv58nn3yShoaGso8fsG/jOx72QAargpn7U6FLKg8Ra6pBFmFJZS/vku1KajUD3wd7AEL7TcSgYspJU377fDRkIZPN2+Rth0i4Aj903UY9YnGGGDEuGFB90Ky168QTT2TlypUAHH300Wzfvn3EwFxyiR5kHnfccfzyl7ovcNttt/Gb3/yGL3/5ywDkcrmRAmXnn39+YFwCxmAnsyiY1awvwzRwPR97KEukPjFr+50vcj3JPaMXJw34IIvPUM4XUxoYEYkC7wQOB0a6Mkqpd8xhuxYUecchm7fHGpipRhrDLjJGZaAZFiw9E0745qy0KxLZo9xqmuaY+MnwstGfK6X4xS9+wSGHHDJmPw8++CCJxOK72QPmlnx3ErNCaZhyMKMW2c6hRWdgPNsl1zWIVVW47+yhwLhMQTln54dAM3AhcBewEtivtN09X5HJVzhD99EPg1/YxoxpP61vw66fz34Dy+TCCy/k61//+kis5vHHH99rbQlY2Hh5ByeVL1uavxLMSAg3ncPNLK6U5VxPEkT2JDzY3YE8zBSUY2AOVEpdCaSVUjcCLweOnI2Di8hLRWSTiGwRkSuKLH+ziDxZeN0nIkeNWrZdRJ4SkSdEZE4lkiMhi/5kurKNzv4zHPAeiDTByTfB+nfq/0/737lpZBlceeWVOI7Dxo0bOeKII7jyyiv3WlsCFjZaGmbu9i8hk2zP4umn+o5LrnNwz2RTzwE3DUZQ/2UySsr1j6wg8pBS6kQR+RvwAaADeEgptX5GBxYxgReA84HdwMPApUqpZ0etcyrwnFKqX0ReBlytlDqpsGw7cLxSqqfcY05Hrn/Tzna2tnVRk4hy2pGHTL1BQMAiRynFwDOtiGVghOZGO0z5CjeVo/6IlRihhT+HJNMxSLa9n9BwXRt7AAafLeiP7cHLJAO5/lGU00f5jojUA58FfgM8C/zHLBz7RGCLUmqrUsoGfga8cvQKSqn7lFL9hbcPoN1z8044ZJLK5oMZ/QH7BV7Wxss7c2ZcoFBSGSE/MM05ZvOI73hk2gewEqNGK/ZAIA9TBlMaGKXU95RS/Uqpvyml1iulliqlvj0Lx14B7Br1fnfhs1K8E/jD6KYBt4nIoyLynlIbich7ROQREXmku7t7mk0VlNLZZAEB+zr5gcy8pBCb8dCiKKmc70shsOecKCAXyMOUw95MgSg2JCx6pYnI2WgD8y+jPj5NKXUs8DLgH0XkJcW2VUp9Ryl1vFLq+Kamphm1Npt3pr99QMAiQPmKXE8SMz73kwcNy8S3PdzUwpUi9l2PTPsg5mihTy8Hyg7kYcpgbxqY3cCqUe9XAm3jVxKRjcD3gFcqpXqHP1dKtRX+dgG3oF1uc4YhQiq7cG+EgIDZwE3nUY6HMU+TII2ISWYB65Pl+9Io3x97PtwU+2jIZNaZ9CoSEaMQaJ8LHgYOEpF1IhJGFzL7zbjjr0YrBlymlHph1OcJEake/h+tMvD0HLUT0JlkfZVmkgUELDJyfSmMcIWxFzcDud6p1yuCFQvjDGbxcgvPO+B7fiH2Mm40l+8BM8geK4dJDYxSyge+MhcHVkq5wAeBPwHPATcrpZ4RkfeJyPsKq30OaAS+OS4deRlwj4j8HXgI+J1S6o9z0c5houEQA6nAwATsu/iuR743hRmt0D2W2gHpbSUc3FMjppBbgCWV7f40yvXHVuEclocxZk8+Z1+mHCfibSLyGuCXaqqc5gpRSv0e+P24z7416v93Ae8qst1W4Kjxn88llmliOx624xJeBGmVpRgYGOAnP/kJH/jAB/Z2UwIWGG4qr4PZRgXuHzcNTh8g2nUUqrxipRUPk+se0iWVF4g+mfJ80m39WFXjjK2XIZCHKZ9yztLHgf8D7P2x4Nh4Fnsm2cDAAN/8ZmVSNUqpIEV7PyA3HeXkTBtIRAe889Nzk4lpoDyFPbhwUpbzAxmU640dvQDYg4FxqYBy0pSrlVKGUiq0PxYcG4uqyMDc/fbv8tdXf23kdffbvzvjFtx0001s3LiRo446issuu4zu7m5e85rXcMIJJ3DCCSdw7733AnD11Vfzjne8g7POOov169dz3XXXAXDFFVfw4osvcvTRR/OJT3wCgGuvvZYTTjiBjRs3ctVVVwGwfft2Dj30UD7wgQ9w7LHHsmvXruINCtgn8GwXeyhbmYFxs5DvBisBVhxynTBNJ4cZD5HrGGSWnSTTQnk+mbZ+Xe9lPHZPIA9TAeWIXQrwZmCdUuoaEVkFLFdKPTTnrVtghCyLgVSG5sY6Xvj+XSS3TT6vxh43icweyPDoZ0trkVWva+Lgd55ZcvkzzzzDF7/4Re69916WLFlCX18fH/zgB/nYxz7G6aefzs6dO7nwwgt57jldH+7555/njjvuIJlMcsghh/D+97+ff//3f+fpp5/miSeeALTC8ubNm3nooYdQSnHxxRfzt7/9jdWrV7Np0yZuuOGGikc8AYsPZyiLiFSmnJxt1yMXEcAC5YKThHDl/U8zbGEPZHAzNqHE3g2g5wcz+LaLNT5V23N0QkO4fu80bBFSTnflm2hJ4HOAa4AU8A3ghDls14IkGrb2aqD/9ttv57WvfS1LliwBoKGhgb/85S88++yIug5DQ0Mkk1rj6eUvfzmRSIRIJMLSpUvp7OycsM/bbruN2267jWOOOQaAVCrF5s2bWb16NWvWrOHkk0+eh28WsLfJdQ9hVKKc7OYg1wGhuj2fSViPaKZhYGBPSeXQuhnMV5shyldk24pkjoGONwVURDlX1ElKqWNF5HGAgi7YflnCLRIK0TeUQik16UhjmL+++msTPjvuX1877eMrpSb0MH3f5/777ycWmzhsn0zOf/Q+P/WpT/He9753zOfbt28PJPz3E9ysjZuxCdfFy98o1wFijo1HWHFtYBJrwahcZsaMhcn3p4mvqMcM751EGnswg5d3CMeKnAunP5CHqZByolVOQZhSAYhIE2OKnOw/iAi+Kj8OM/6GregGLsK5557LzTffTG+vDqb29fVxwQUX8D//8z8j6wy7vkpRXV09MsIBLeF//fXXk0rpNNHW1la6urpm1M6AxYVWTq4gcO3Z2j1mVY/9XAxQvnaTTQPtotOTG/cGSikybQPFVQwUeq6PFaQnV0I53YTr0DPll4rIF4HXooUv91uyeZt4dGo/8Rk3vHtWj3v44Yfzmc98hjPPPBPTNDnmmGO47rrr+Md//Ec2btyI67q85CUv4Vvf+lbJfTQ2NnLaaadxxBFH8LKXvYxrr72W5557jlNOOQWAqqoqfvSjH2Gacyd0GLBwUEqR6x7CjFfQM8916bhLsWwqI6pHN5G6abXHTETIdg0Sa6qe95LKzpCe8BmuKxLEH5GHqTwNe39mSrl+ABHZAJyL1kf4q1Lqublu2FwwXbn+3d191FXp0UfPYJJD17SwetmSuWhiQMC84qTzDD7fVv7o2nOh/xGwqoprcSkFzgA0nADm9Nxc9kCGmgOXEa6d2Yi/EpRSDD7fhlJgFsuky/VAcvOUAf5Arn8s5XYROoG7gfuAmIgcO3dNWthEw6HKi48FBCxQ7P4UUoksf76QOVlK6HE4RugMTLtNZlSrLM8nbjKHk7GLGxcI5GGmSTlpytcAlwMvskcMQqGzyvY7IiGdqhwQsNhRnk+uJ42VKNM95nuQ2alHL5NhxrSbLDq9Ub4ZDemU5ayNFZv7fCKlFOm2AaxoifMwLA8T2k+n/82AcsawrwcOKBQF2+8JWRb9qQyO6xKyFq9kTECAk86jPA8pt+xvvkcH8aeSqTejYPeBmwdrer1+sQzyPUmsVY3T2r4S3FQeN5UjXF/CJRfIw0ybcs7Y00DdHLdjUSFIUBsmYNGT702VP3Pf9yG9qwKtMZmRm8yKR8h2J/Edb9r7KJdMez9mbJJRXCAPM23Kubr+DXhcRJ4G8sMfKqUunrNWLXh0qnJNIpCMCFic+I6H3Z/Gqikz7dbuA7+CLCorrlOZY8um1b5hwU17IEO0qXqKtaePk8rhJPPFM8eGyQfyMNOlHANzI/AfwFPsp/NfxhOyTAbTWZY11O7tpgQETAs7mUVBedIwSkF6R2VKyUak4CbLTru0sBUPk+kcJLKkqjIJmwrIdAxiRCZJcvAc8NIQbpiT4+/rlGNgepRS1815SxYRkVBQGyZgcZPvTmKWKw2T7wcvr0UtK0EMyPeBtaLyBgJGyMRN53FTeULVsz/B0U3ncQYypWMveiX20QzieaEcx+KjIvJvInKKiBw7/Jrzli1gIiGLwVRmQSi/BgRUipd3cFJ5zEgZ2WOKQubYNOakWAnItU+7EBmAEbHIds5NynKmfWDqGFQgDzMjyunCHFP4O1r1cL9NUwYwDAPX98nbDtHIfinLFrCI0dIwZa7sDOgsqum4iCQEfnLahcigkLI8mMXLO+UZxDJxM3nsqUYvgTzMjJnSwCilzp6Phiw2BCGTtwMDE7CoUEqR6xrCLGd+iQLSO2cW4BZTFyKbpoEREcTQJZUTLbMnk5/tnCL2AuBlQTmBPMwMKGeiZR3wVmDt6PWVUh+es1YtAkSETC5PQ01w8QUsHrysXVoteDxuUr/CM5iLYiV0IbLE6j2z/CveRZhcV5LYstkpqexmbfJ9aUK1UxhON0UQf5kZ5bjIfg88QJBFNoZIyKQ/lWbl0rmfCBYQMFvkBzLli0imd4Exw/RcKRQic1MQml66sZgGyvewBzNEG2beoct2DiEhc+rMtFxvIA8zQ8oxMFGl1MfnvCWLjEgoxEAykIzZX1G+AqXmXfF3JihfketJFpejH4+TArsfIrPQgZKQnksyTQMDYEbD5DoHidQnZpSy7OUc8r0pQrVTxFV8X8efAnmYGVHO3fFDEXm3iCwXkYbh15y3bIETDlmkczaeFwzq9kcyHQMMbu7AdyYWcVuouOk8yvHKczNlds9e792KazeZP/1Z+WbEws04eJmZKVZlugYRq4zS0F6aQB5m5pQzgrGBa4HPMFbscv1cNWqxoAoz+qviQZbJ/oTvuOQ6B0GEoS2dVB+wbK9VYKyEXF8KI1yGcrKb0fNXyqg9H1p2ML6/Z5+G4eF0vjB2JTG1hpmbhHBdha3egxEyyHYnqU5Mz/B5eYd8T6q8OTWBPMysUM5d8XHgQKVUz1w3ZrEhQCafDwzMfka2W1dsDFVHcdM2Qy90UHNQc2mp9wWA7/k6sF1VxsM50wZGqKygvO+bPPDhn468P/m6S4uvaER0obIZGBgzFibfmyLeUjctg57rGkIMY0SGZlICeZhZoRwT/QwQBBuKYJkmQ5ns3m5GwDwyPHqxCg9qKxFGKZ+hzR14C1gA1U3mdMxoqoerm4N819SS/AV8u0y3lxnXoyJv+i5FMQQxwO6vXEXDy7tku5NYiTLiT56j5/6UqzIdUJJyugEe8ISI3MFYscv9Ok0ZCrVhgkD/fkWuNwUwpoa9FY/gZmwGN3VQe3AzZqm6InuRXE+yvBFWrl1nfpUxevFsD3JlGhgRwAdnEMzpJw6Y8TDZziGiTTXljUQK5HqSI3NqpsRNz0h9IGAP5RiYXxVeAeOIhENB8bH9CN/xyLYPYBWJAVjxMG7WZuCFdmoPap6XQlnl4tku9lCW0FTKyZ6tFZBDdWXtt+OBbhpPXs2JX3kDbsbFCBsYhkdJk2PGdLA/On0DY1gmbiqPk8yWXVLZswujzuoyRyR2HxgL5/dbzJQzk//G+WjIYsQ0DFzPI287RMILr9caMLvkenXspVRqshUL4+UcBl/QI5mFYmScIe3GnXreR6cObJcR3HZSDh33dZHtvIcDX7eOXb/bRc/f+9j4wUMJ15T43kZUa3t5NpjTPzdmNES2c7BsA5PrSYIhY0adJVFo5YFpKkAHjGXKMy4i20Rk6/jXfDRuMaCAbD4o9rmv4zse2Y7BoqOX0ZjREIZlMLipHTeTn3Td+SLXnZy8oBbouEO2Fazy5qq03tWB8nxWntMCwPLTloJStN/XVXojEW28ZlCIDPQ5dpI53OzU991IzKyc2AuMkodZuAkbi4lygvzHAycUXmcA1wE/mstGLSaGNckC9m1yvamyJ1aa0RBG2GRwUztOeu8aGTdr42byU2dd5bp0b0mmTmPOdufofryXpuOWEG3UBjdSF6HxyAa6H+/FSU2S7GDGtBtuhohpkC/EwyajWMxsUtwU5T0WA8phyjOplOod9WpVSn2V/VhJeTyRkEV/MqgNsy/jOx6ZErGXUpiREEY0xNCmdpxUbg5bNzlaOXmK29x3IVt+OeTdf23DDBu0vKR5zOfLT1+G8hTt908yijEiOojuzuycWImwLqnslk4y0L/b1KPOMeR6AnmYWaQcF9mxo17Hi8j7gLmrYbrIiIStoPjYPk6uN4VQOvZSCjNsYcZDDL7QgZOcfyOjlCLXPYQZn8I9luvRVSvLcAsN7UgxsHmI5actIxQfu360IULD4fV0P9qLk54kHVnnGpfzFSbZhQFKYQ+UTrLJ91X4u/keuIM6VhQwK5Rz5r8y6vVvwHHA62fj4CLyUhHZJCJbROSKIsvfLCJPFl73ichR5W47X4Qti1Q2j+8HkjH7Ir7rke0YwCzXhz8OI2RhxcMMvtBOfnB+Mw7djI1vuxjWJG4v39MFxcrQCVNKsevPrYRrQiw7sanoOi2nL8N3fDofnGQUYyUg2zbjVGArHiLbMVi08J/v6lFnRb+blykY2kBBebbYa/VgRMQEvgGcD+wGHhaR3yilnh212jbgTKVUv4i8DPgOcFKZ284LIoJSOtCfiAU9n32NfG8KFDOSiTdCJlZVhOSWLjigiUhdhaWHp4ndn0JCU8RU7D7tIitj9NL39ACZ9izrXrkaI1T8fMSaotQfWkfnwz00n7IUK1Zkv8OFyLx05WWYR2GELOx0tmhJ5XxfGuWryn63QB5m1inHRfYREakRzfdE5DERuWAWjn0isEUptVUpZQM/A145egWl1H1KqeGx9APAynK3nVcEsgt4FnfA9JhWL7gEhmViVYVJvthFfhoz0StFeT65njTWZNljSkF6R1mxF9/12X1HG/HmGI1HTq5R1nLGMnzbp/Oh7tIrialn9s8QI2yS6x4a21bPL8TMKvzdAnmYWaccc/0OpdQQcAGwFHg78O+zcOwVwK5R73cXPivFO4E/VLqtiLxHRB4RkUe6uye54EtguAMY/uT+c0OEVHbvBXID5oZp9YInQRuZCMkXO0eym+YKJ51Hed7kAf58H/h2WZMKOx/uwR50WHVey5TzaeLLYtQdXEPnQz14+RJBeCsB2Q5t5GaAGQuR70/j5ffEfOz+NMr1J3cNjsezC/IwC2Pu0r5COXfO8NX0D8ANSqm/j/psJhTbR9GrTUTORhuYf6l0W6XUd5RSxyuljm9qKu43ngzD7iXidky6TjQcoi85tw+MgPnFdz0ybQMjmmOzhWGZWDUxUtu69QTAOSLfm8KYTBpGKR17KcNF5WZd2u/ppPbAamrWlZff0/KSZrycR+fDJTRyxdLzTdyZ3TciAoZBvl/vR3k+6bZ+rKoKDUUgDzMnlGNgHhWR29AG5k8iUs3sVLbcDawa9X4l0DZ+JRHZCHwPeKVSqreSbWcDAaJOu5YbL0EkZAWSMfsYevTiz9roZTSGaRCqiZLc3kO2a2jqDSrEdzzsgfTkmmj2oJ5UWIagY9vdnXh5j5XntpTdhsTyOLUHVtP5YJfWLCvGcCGyGWIltD6Z8nzyAxmU61U2eoFAHmaOKOfueSdwBXCCUioDhNFuspnyMHCQiKwTkTDwRuA3o1cQkdXAL4HLlFIvVLLtbGJ5Q5jeYOnlponteNiLqPhUQGl8z9ejl1mIvZRCTINwTZT0zl6yHaWvrelgJ7MofxJpGAVkdmiF4ynI9efperiHJUc1EF9aWXyi5Yxm3IxH96O9xVcYKUQ2s/6qYRojxiXT1o8Vr3DUqdDuQitI0pltyplo6SulHlNKDRTe9yqlnpzpgZVSLvBB4E/Ac8DNSqlnROR9hbk2AJ8DGoFvisgTIvLIZNvOtE0l2yoWIXtyNxkEkjH7Cvbw6KXSXnCFiGkQqo2S3t1HpmOgaLrtdMj3JDGLZW8N4wzpksjm1A/U1tvbEVNYcdbyittRtTJBzboq2u/vwneKGJGRQmQzH8WZ0RCZtn58x8OYKnNuPIE8zJyxV8+oUur3wO/HffatUf+/C3hXudvOFZ4RJ+Z0kfcPQJUcRuvqlrVV5QnwBSxM/GEf/hyOXkYjRsHItPajPEW8pW5mNefzDk4yT7huktFGZldZYo6p1jR9zw7QcsYywtXTE3NdfkYzm27aQvfjvcXnzsxCITLQJZXtgUxls/aHcZME8jBzQ3BWy0Lf8KY7UHKNkBXEYfYF7L40ypv70ctoxDAI18TItg+Qae2f0UhGS8NMsoKTAntgSveYUopdf2nDSlg0n7J02u2pWVNF1eoE7fd14btFRjFmTKsX+zN3L4fr4pWPXgByvYE8zBxRloEREVNEWkRk9fBrrhu20PDNGBF7d8nlOpMskIxZzPieT7p9/kYvoxFDCNXGyHYMktk9PSOjlCLXNYQ5WZmA9O6yHqYDLwyR2plmxZnNmJGZGduWM5pxkg49fy8y70W05Av27Cc7lEUgDzOnlDPR8kNAJ/Bn4HeF161z3K4FhzJiGN4Q4hcvkRwJWaQy2UAyZhFj96dRTpmjl/xgQcNr9o4vhhCqi5HtGiS9qxflV7ZzL2vj5Z3SvXg3A07flOWQfU+x+69tRBsjNB0z/eJgw9SsqyKxIq5HMV6R72RFITd1jHNO8DIUMiL2zvH3ccoZwXwEOEQpdbhS6sjCa+NcN2xBIgaWUzwjRkTwlSJnBzP6FyMVzZ/I9cPQMzD0PCQ3z6jO/HhE9Egm150ktbMyI5MfyEwu7Jhp1anBU9DzeC+53jwrz22pqCxxKUSEljOWYQ/Y9D5VZBRjxHSNGG8vJMnYg2WVKAiYHuUYmF3A7OZRLlJ8o4pIfteks4+DTLLFSb4/Xd78iVwfDD2rxSEjS8DuhYEnwJm9SZPDRibfmyS1owflTT0qVr4i15PEjJcwkG4O8t1Tjl68vEfr3zqoXp2g7uCa6TS/KLUH1hBvjtF+b+dEoykCyIwLkU2LQB5mTinHwGwF7hSRT4nIx4dfc92whYgyQojKY3rFHyYiQjq3MKoYBpSPGpn3MkVsItcLQ89BuHbPSCBUp9Nb+5/U8Y1ZSjUWEcJ1cfL9aZJlGBk3nUc5XumJodk23c4pXEHt93Xhpl1Wnr9iRtls4xkexeT7bPqeKSLVP0uFyCoikIeZc8oxMDvR8Zcwug7M8Gv/REJYTnEp8mg4FBQfW4TkBzL47hSy9rmeUcZlXHa/GdVptumdMPjMjItpjSZcG8MeyJDc3o0/iZHJ9aUwwqViL3kd45hi9GIP2XQ+0EXD4XVUtcx+un3dIbXEmqK03dM5MYnBjIIz80JkFeGmmR3Vq4BSlCPX/3kAEUkopfb7p6dnJgjb7eSjayc8aIJMssWH8nwyrf2Tj15yXTD0gjYipSbjiQmRBp0GPPAEVB0E0ZkHyEEbGWcoS3JrF9XrmiYYQt/zyfelCZXSTct16GytKaToW+/qQClYeXblkyrLQURYfsYytv5yB/3PDdJwWN24FQqFyKy5Of4E7D4wpje/J6A8yskiO0VEnkXPmEdEjhKRb855yxYqYgIeljsxLGWZJnnbwXEDyZjFQn4gg+9MMnrJdk5tXEYTqtICkkPPQXLrrMzvAAjVxHDTeZJbuyaUCXaTOVCqeEDec7TryZrc6ZDpzNLzRB9LT1hCpH7u5oQ0HFpHtDFC290dE0cxVhxy7fMjOjksD1OGmkHA9CnHRfZV4EKgF6CgpvySOWzTgkcZUUJ2KW1NCWrDLBJ07GWS0UumQ2eJlWtchpEQhBv1yGfgqYIrZuaEqqO4aZvkli58Z4+RyfUkMUspJ+c6C22aPHlh91/bMKMmLacvq6hNRiRGfPUqEBnzfynEEJafvoxsV46BF8bNfTHC2kXmzYMXwMsA5RVaC5g+ZU20VErtGvdRCXnU/QPfiBNy+xC/WEBfBZlki4T8YKa0dlWmHVJbKjcuw4jskT/p/7s2VrPQMw/VRHFzDkMvduI7Lp7tYg9li0vz+y5kd09ZUGxwa5LBF5O0nL6seAXKEhiRGPEVjZi9txJZvmLk/1DD5AXJGo+oJ1IfLj6KMYxZKUQ2JU4SVBB/mWvKSlMWkVMBJSJhEflnCu6y/RYRFGC5E2+EkGUymC4+GTNg4aB8pWMvxdJ607sh9SKE62fewzVjEKrR+xt6flbmeoSqI3i2x+ALnbqkMyWUk3MFKfxJvoNSit1/aSNcF2bpCUsqakd02RKk47fIU1cSdp/FuPulyIvfIVw7uTtODGH5acvItGcZfHFcRqZVpd2Ss5SNV5J8b+AemwfKMTDvA/4RXTFyN3B04f1+jW8mCOdbJ3weCYUYSAWB/oWOPZjBt92xoxeFNi7p7QXjUtytFFp2MGbToSOv0LKDJz+YWBBp1L3m/ie0FtgMCSXCKM8jvasXs1hZZN/TkvxTZI71PtlPpjPLyrOXY1iVSRPmttwHGHDi95C7L4bUFvwTf0i2berKsY0b6wnXhiaOYsQCZc+aW7EovqcVnMuohRMwM8rJIusB3jwPbVlUKCOC5fRieGl8c09VwEjIoj+VQSk1q/MIAmaPkdHLaM0xhU4zzuyEcMOkGVe+b/DAh3868v7k6y4t78ChavDzMPAMxFdAfLV2CU0TKxHBiIaKz32xe0F5k45efMen9c524stjNBxeV9Gxxeki3v8/cOB1cP+ex4Ps+BHmksvxc5MLvxqmwfJTl7HjD7tJbk+NrZQpIT0pdArX3rQJ5GHmjXKyyA4Wkb+KyNOF9xtF5LNz37SFjy8mlju2t2YYRiAZs8CxBzMFza7Cw1cB6R2Q3TWlcQFwszMIQRoRPTrKtsPgU+DOzJ1a1Lj4vjaWocldVZ0PdWMPOayqdFKlN0R89yeRwz8JPfeBGUGdcyfqoA8jPXdN6SIbZsnRDYSqLNru7hy7wIrrBIm50vUL5GHmjXK6T98FPgU4AIViY2+cy0YtFnyzSrvJxpdTVoFkzEJlwuhFoV1imd0Qqp/SuAy+OIRvj/29y5FyGYMY2sj4rk4AyHXPbmqu3Qe+PanumJN2ab+3k7qDaqhZU8FIwc8Rb/00htNKZlcrudi5eKfeirP5V4CHd+L/km0tPhF5PIZl0HzqUpI7UiR3pvYsEFOPvtzZk98ZQyAPM2+UE8GMK6UeGtfDCSZ6AIiFoVxML4ln1e75WIR0NkdDzRwN8QOmzfDoJRyL60ByaruWUQk3TOkycbMu2367i8PWHc7J171RF2NMO2Q6UmS3DVJ/SO2k20/ASoByYWgTRAcgsQ7MGSYVKKXdfFZi0tXa7u7As31WnttSwb5dYm2fx8w+Q7blKrzYRrzePpzePsQ6ntDmy3D7UvhLyq+o3nTsEtrv7aLt7k4OefOo+8UIa8MbrvCcTsWwPEy4YXb3G1CUckYwPSJyAIU+loi8Fphn0aCFi2+ECDljpcYjIZOBdFB8bKExPHox4+GCcdmqJ/aVYVwAdvxhN27awenchNf9PH7v83i9z/PiTbez5f+20fVoT+WNEkvPmZkt0cz8gJ5LMkkAO9ebp/vRHpqOaSTWVGYmlVJEO75CKH0/uWUfxa0+a+zi8ErcxMmEBn6jR09lYoYMmk9uYmhrklTrqMC+GQe7Z9Ymqo4wl8kDARMox8D8I/BtYIOItAIfRWeWBQC+kSBkd+ia3gUioRADycDALDSGRy9myNRpw9mOgltsauPS+3Q/fc8M0PKSZhLL9+h0hRIhNrz1QGoPqGHH73fTemd75cXCRGZHNFNRyBybXEds9+1tiGmw4szmsncd6fku4aE/kG98G07dK4uuY9e/BsPrJ5S8s/w2A0uPW4IZM8fGYsTQMZhZVKkGtCEPxC3njSkNjFJqq1LqPKAJ2KCUOl0ptWPum7ZIEANRCmuU1Hg4ZJHO5/Eq9c0HzBkjo5eoBckteq5FmSMXe8hmxx92k1gRZ/lpE2e6m2GTg96wjiVHN9B2dyfbb91VvLDWVIyIZu6YnmimM6h76JPM70juStP//CDNpy4lVFWeDle47/+I9P0Eu/Yi8o2Xl1zPix+HF15DuP/nFRlIM2LSfFITg5uHSLeP6phZUf07zRaBPMy8U04W2Ysi8mPgMmDV3Ddp8eGbMULOROmYINC/cHCSWbxsHjO/TafARhrLMi5KKbb9dhfKU6x/5ZqSBbjEENa+YhUtZyyj54k+tty8Fc+eRraZmLptbg4G/q7rz5SDAjK7Jh29KKXY9ZdWQlUWzSc3lbVba+ivRLu/gVP1EnLLPjr5ORPBrrsEM/8CZu6Z8tpdYOkJTZgRg/Z7RhkUI6YrcHqzlJHpZXTMK5CHmTfKcZEdhnaRNQJfFpGtInLL3DZrceGbcSy3H/FH9TgVZPJBbZiFgFKKzK5eTG+HdpFUEODteqSHoa1JVp3XQrRx8ol5IsKKs5az5uUrGXwxyfM3bcFJT/PhGKrSxiL5HCS36cmBk+Em9Qhmkuyo/ucGSe/OsOKs5ZilpP1HYaYfJtb+b7ixo8ku/0xZqb1O7fkoI0G4/xdTrjsaK2qy7MQm+p8fJNNVSN0eKUQ2S/UOnSRlqmMFzBLlnG0PnaLsAT7QCZSXh7hfYWA5e4K8lmkylAkkYxYCzkAKt+d5TElWZFxyvTl2/6WN2gOqaTqufOn9pccu4cDXrSPXneO5GzaT65tmR0NCEGrQgpUDT04eoE7vBqO068f3fHbf3kasKcqSo6Y+B0b2eeKtV+JH1pBZ8a/lz3o34ti1L8dK3oWUqJtUimUnNWGEx41izJhOxJgNAnmYeaccAzOEVlTeBrxNKXWKUuq9c9qqRYhvJgjbbSO+50jICgL9CwDlOWQ2PYxppHQgvdztfMXWX+1EQgZrL1pdsSpD/SG1HHLZgXg5j+du2EyqbZrXQjmimW4anP5JU5O7H+0l32+z8ryWkm6+YQx7J/HWf0FZ9WRW/ieYlaXb23WvBhThgd9UtJ0Vs1h6/BL6nhkg21PwBphRPfKYaSGyQB5mr1COgbkU+BvwAeBnIvJ5ETl3bpu1+FBGGMPPYPh6wlgkHGIgFRiYvYrv4Ox8CDc5gFlV2byH9ns6SbdlWPuylYSrp1eUqmplgg2XH4QRNth00xYGtgxNvVEpRkQzt04Uzcy0gpTOjHJzLm1/66BmXRW1B0whROn2EN/1CUBIr7wWZVVeNE2Fl+NWnUpo8LdaGqcCmk9uwrCE9ntHB/eNmeu3uWnd+ZtDeRgjEqPq4EP2HGMWNOcWO+Vkkf1aKfUJ4L3A74HLgVvnuF2LEzEJ2Vo6xjQMXM8jH0jG7B08G9X7CJnWbszquoo2TbdnaLu7g4Yj6mk4fHLp+amILYly2NsPItoYYfPPttL9RO/0dyZWoWpmUo9m7EEtNZPrmXT00n5vF27WY+W5LZOPxLwk8V2fRLwhMiv/AxVeOe2m2nWvwfAGCSVvr2i7UCJE03FL6H2qn1x/wTjNRiEyZ27lYYZLF1h9fyCybLlWhtj+I3D3705mOVlkvxCRF4GvAQngrcDM7rp9FM+s1tlkSgdkFUEm2V7Bs6HvUZyBARw3UVZAexjf8dl6yw6sRIg1L10xK80JVem5MjXrqtn+2120/a1IHZSKdlgNZhgGnobkJl32t4ThyA/adD7YTeOR9WPm70zAzxNv/QyGvZPMin/Fjx4y/fYBXvwYvPA6Heyv8Ls2n7IUMUaNYowweNlCkbBpku8Ba+7kYXTpgluRp68k7DwFfz0btnxrv5/YWY6L7GvAwUqpC5VS/6qUuov5KWq6+BATUS6mp10hhgjp3L6TSebZLl5+gasEeXnoexjlpMj0R7GilfVad9/RTq43z7qLV1dUfGsqzIjJQW9cR+OR9bTe1cGO3+9G+TO4jYZFMz17Ukn+1jt0gHzF2ZPUuVcusfYvYGafIrv8M3iJ46bfrmFEsOtfg5nfgpl9qqJNw9Uhmo5ppPfv/eQHCx00MadfiMzNawM1iTbbTMns2o0XXoc68XvIPa/SxepO/RlEy0sH31cpq2SyUmp8juT9c9GYfQFlRAjb+qYOW9Y+FYdJ7+qj/+ldZDoGKxd4nA+8PPQ9Al4O163GzbgVjV6GtiXpfLCbpScsoXZ9eYrAlWCYButeuZrlpy2l+7FetvzfNjxnBudRDB2XKTF6Sbdn6H2qn2UnNRGpLRGjUYpo538TSt1LbumHcGvOnn57xuHUnIcyqitOWQZoPnUpAB33FTLRrATkplkVdB5GEYZkMGuWIn//9J4Pt90YuMhKLRCRZhE5DoiJyDEicmzhdRYwuRbFfoxvJLCcbsS3iYStfab4mJdzsAcyhKqjZNr6GXiuDXtoAaVheznofQi8HCpUS7ojixkp37i4OZdtv9lJtDFSmQBkhYgIK89pYfVLVzDwwhCbfrgFJzP7o0KlFLv/2oYVM4uqDwwT6fk+4cHfkW+8DKf+ktlthBHFrnsFVupuxKlsRn6kNsySoxrofrwXO+no+JNvg5uaeuPx2H1zLg8TXxqF7nvAjOCf/xhs+GfouiNwkU2y7ELgy8BK4L+ArxReHwM+Pcl2+zeF3qTpDhC2LFLZPP5c1bWYR3I9ScQ0ENMgXBsDUxh6oYOhF7vw8ns5kcHNauPi2xCuw027uGm3IgOz84+t2EmHda9agxmafGBvRGLEV68CkTH/V8KyE5o48HVryXRkef6GzeT7Z9eVOvhikqFtKVpe0lzSTRjq/yWRvh9h176CfOM7ZvX4w9gF3bLwwK8q3rb5tKUoX9Fxf2EUIyEdS6kEpfTk2jmU5zfsncjd/4DrJXBP/g2ZHgVHfh7O/EPgIiu1QCl1o1LqbOBypdTZo16vVEr9ch7buOjwzTgRezcigtoHasP4jkeuewgrsceHbYYtwvVx3HSO/mdayXQM4O8Nt5mbgb6HtOpuuE7P2u/MYlYQe+l7boDep/ppOWMZVS2TD86Hs4XM3luJLF8x8n+oofK8l/oNdRzylgNwMi7P/WDzWB2uGaB8xe6/tBFpCJecIGoN3U606+s4VadPLQEzk7aEmnGrTic08DvwK5vLEq2P0HhkPd2P9mhFBCumJ51W0mHzsoXKnnOXQRbpuR6UIpdZTvLZZ3U1Tyu+3xsXKC8Gc6+IfF9E/gAgIoeJyDtn4+Ai8lIR2SQiW0TkiiLLN4jI/SKSF5F/Hrdsu4g8JSJPiMgjs9Ge2UIZUQxvCMPLoFBk93YPf4bk+9OAIEXK+1qJCKHqCJm2AQaebSU/kJ5ZhlQluBnofVAXfCvUDXHTLk6q/NGLnXTY8btdxJfHWH761OrC0aW1SPuvkaeuJOw8iXH3S5EXv1N2FcfxVK+u4tDLD0IM4fmbtjC4debqwT1/7yPbnWPl2S1FK16a6UeItX8JL3Yk2eVXzrk2l13/Ggx/iNDQXyretuX0ZfieouP+bt3OSguRzbE8jJF7nlDyTuyGN6CsILl2POWc+RuAPwHDjukX0JL9M0JETOAbwMvQemeXishh41brAz6MdtUV42yl1NFKqeNn2p5SuCkHZU/jgSkGptuDZRgMpRdQrKJClOeT6RjAjJfOwBFDu82MkEFyS5d2m+Xm2Ki6aeh5ABAd6IaKRy9KKbb/bhee47P+VWswzMnniES6v4t5x0mAQp30A+SeSyC1BXX8t8hvm37eS6wpyqFvP4hIXZjNP32RnienmS0FeLZH653tJFbGqT90YrEuI7dJS8CEV5NZ8cV5mdnuxTbiRQ4g3P/LilOWo41RGg6ro+vRHtyMu6cQWbnke+ZUHiba/V18s5Z8/evn7BiLmXIMzBKl1M1oHTKUUi5al2ymnAhsKZQDsIGfAWMKTSilupRSD1Mo17w38LMuXn/lrh/PrCKSb9WSMenFG+izh7Iox8Owpn5oGyHtNvOyefqfbSXd1j83bjMnpY2LmGPqzrsZD6eC2EvP430Mbh5i1bktxJaUeAj5OcK9P6Z666VE+n6M2/RK1PKLkCc+uWednT8j7t1DfOdHMNMPTauWS7gmzIa3HUTV6iq2/Xon7fd2Tmsk2PlAN07KZdV5KyZMqjTs3cR3/wvKqiWz6j/BnP1MuaIMpyzbWzGzT1S8+fLTl+HbPp0PdVdWiMz3wJk7eRgz/QhW5lHshreAOXkF0f2VcgxMWkQa2VPR8mRgNuRNVwC7Rr3fXfisXBRwm4g8KiLvKbWSiLxHRB4RkUe6uyvo+YzCTyr8fKV110OIyhM3s4s2VVkpRbZjADNWWQaOFddus1znEAPPtJLvnyW3mZOCoReg+14wLK04PIpMZ6Zs45Lry7PztlZq1lWx9IQlE1dQDqH+X1G19U1Ee76LGzuC1JrvIUdehXT8YSRbSB38caT3Afx178VwWkns/iSJHe/FSt6lXXcVYEVNDr50PQ2H17H79nZ2/qm1orkyTsqh/f4u6jfUUr1q7ANP3F7iuz8BKDIrr0VZRb7zHOJUn4tv1kwrZTm+NEb9hlo6H+rGzSt9XsspROamgTmSh1GKaPd38K1lI4kMARMpx/n6ceA3wAEici+68NhrZ+HYxX71Sp5Cpyml2kRkKfBnEXleKfW3CTtU6jvAdwCOP/746T3llMLtdwg3V9gTkhBRr4cepxnbcQmHFlcdCjedx03bhOsrz0oXwyBUE8V3XIa2dhGuiZFY2YBVobHCd7WbI70d7P5CieE6bWBG4aRdnLRLuHrq/Stfse03OxGDiUKWyiM09FcivTdgOO24saPILvk8XvxIQE+oCzWcQ/jUV5Jt68ZccjnhAz5EtrULf/1PCQ3eRrjvp8TbrsILr8JueBNOzfllxzkMy2D9q9cQqg7p0UjSYf2r12BYU/cFW//WgXJ9Vp4zLs3aSxHf/UnE7Se96qv44b1Q1smI4NReRLjvp4jdjgpPMvGzCMtPX0b/84N0PdxNyyk1uhBZZIqYhz0wZ/ElK3UXZv4Fss1XBBUyJ6EcLbLHgDOBU9F6ZIcrpZ6chWPvZmwBs5XAxKpdpdvVVvjbBdyCdrnNDWHBT3r4dmU9Us9MELY7EOUuykyyXNcQRmRmN6gRsojUxfFyDgPPtZFu7cd3y/CwOkkY3ASdt0Pf4+A7EF2qtbiMiW3KdGYwQuWNXjru7yK1K82al63cMwFRKazkPSS2v4tYx5dQRhXplf9BZtVXR4zL8HpObx/prTvwc5k9/+f1THGn7uWk191IZvlVIGFiHf9B1dY3E+r/ZdlZVCLC6vNXsOqCFvqfH2TTj1/EzU7uEsr25Oh+rJem45aMrVszLAGT30FmxTX4sQ1ltWEumEnKcmJ5nNqDauh4sBvPDZdXiMyeI3kY5RLp/j5eeK3uPASUpBwtsig60H4N8HngHwufzZSHgYNEZJ2IhIE3okdKUyIiCRGpHv4fuAB4ehbaNILv+3T0DWCt3sCSE88i1tiAN1BhKEhMwMPyBxedgfFyDvn+DGZsduQ1rHiYUHWUXNcQ/c+WcJv5LmQ7oPs+PWktsxNCtRBbOmmlRifj4qScsmRhMh1ZWu/soP7QWhqO0D1gM/M48Z3/SLzts6BcMsuvIr3m23iJk6bnXhETt+Zs0mu+R2bFf+CHmoh1XUfV1jcS7v0xeOVNFmw+aSkHXLKGdGuG536wZY9sShF2/7UNM2zQcsaoTDjlEWv/Ilb272SXfwovcULl32UWUaGluNVnEh78HfiVJ760nLEML+vR9VgvUxYic/N68u0cyMOEBv+I6ewi3/TuOU1/3hcop3t6E5AEvl54fynwQ+B1MzmwUsoVkQ+iM9RM4Hql1DMi8r7C8m+JSDPwCFAD+CLyUXTG2RLgloJrwwJ+opT640zaMxrf93lq225e+Zn/ZkdHD2ual/CLqz5AonUrvpNFppiIN+Z7GlGq6aY/maa5sW62mjjn5HpTiCkV10GZDDGk4DbzSL7YRai24DazclpyPrNLB2ZD1Xq0UiaZjgxGGZIwvuuz9dc7sOIma/9hFWZ+E9Hu72FlHsG3msgu+wRO7YWz51YRwa06CbfqJMzMk0T6fkS057u6vn3dq7DrXztlamvD4fVYCYstN2/juRs2c/Cl64kvG9srT+5IMfDCECvOXk4oUWi7UkQ7v0oo9TdySz+IW7MwKmzYdZcQSt5BaOjPOHUXV7Rt1YoENeur6Xigm6XHrsfMtUO0RCzJ1an1s46fI9L7A9zoEbiJU2d///sYMlXwVUT+rpQ6aqrPFgPHH3+8euSRqafMdPQNcPIHPs+Ojj2zhtc0L+Gv13yQ9PbHCS2pwOeqFH6ui6GqUzhl4xHTafa84zsefU/tIlQdKTr3ZXYO4uIOduMPtRGrc4gtjWHE6iruEToZl8EtQ4Rrpv5Ndv2ljY77uzjktVUsq7mJUOpv+GYtdsNbsOsunpeUXSO3mUjfj3USgISwa1+h51CESsu5AGQ6s7zw0634tseBr19HzVqdAaaU4rnrN2OnHDZ+4FCMQucn0nMDkd4byTe8Wfe0FwpKkdjxXlA26bU3VDxCTO5M8fyNW1h1QQvNx1hQfyxYRRwqQ1v0CGeSMgbTIdz7U6I93ya96jq8+MYJy71MErOqjpqjT69013NXqGYvUs7T4/FC5hgAInIScO/cNWnvk7fdMcYFYEdHD75l4Q64KLeCWIwIlhnCTrYtGsmY/IBOq55146LQPcvUNuh7BMvdRqjKIJdK0L/dJDfoVpxtlunMYoSnbmdyR4qO+7to3rCTFcZ7sDKPkGu8nNS6n2A3vG7eKh360YPItlxNet2NONXnEh74tc5Ua/93jPyOktvFl8U49O0HEa4J88JPttL7TD8Afc8MkG7LsPKs5SPGJdT/KyK9N2LX/gP5Je+al+9VNiLY9Zdg2tsxM49WvHn16iqq11TRcV+XzlR2BiauNCIPM8vzX7wkkb4f4yROLmpcAiZS0hcgIk+hHwkh4K0isrPwfg3w7Pw0b+8QCVusaV4yYQRj+C4i4A66hBrLH8UoK0Ek20rOdohHF3bJVuUrsu2DWIlZzIzxXZ3Rk9mtZ98bIbCqdXkDdLax7/qkdqTJV+dJLI+XJZXvZlycpDPl6MVP97Ltls3EqlIcfNi3setfi93wJpRVNytfbzr44dXklv8L+SWXE+67mfDgrYSG/oRb9RLyjW/Gjx48YZtIbZgNbzuQLTdvI3HQEVCToP54j+NOOIFwjYHb+QJW8k6iXV/DSZxKbtnH57SC43Rxqs8h0v1twv2/JJuofI50yxnL2PSjF+l5Os/S4zogNk6BwcvoVOZZjo9E+n4Kfpr8kgU0IlzgTHYXv2LeWrHAWFpXw6+/+LExMZj/u/L9uO3bkIiB1+9i1YWQyWZ+j0IZESzVSTbdSzw6d0q9s4EzlMV33JkbGIVWvs13a/0opXSgPlK8dLFhGYRrw7g5j4HNQ8SaosSaopOm56a7siO99qJ4KSJ9P+PFPynyqeM46qJt5A6+ARUqP74z16jQMvLLPoTdeBnh/p8THriFUOou3PgJ5BvfghfbOMZIWDGLg998AFIb5+FP3Dzy+cnXXYqZfoxY+xfxYoeTbfncnEvATBsjjFN3EeHeHyJ2KypcWWG36rVVVK1M0H5/H0uODGO4mbFJIE5y1g2ruD2E+3+h5/NED5jVfe/LlLwClVKlx+v7OIZhcOS6lTzwzasYHBzi2d1d3HX3vZy/qgoxBAV4Qy5WffkZKkpCZAdboXHhGhilFJmOwZlljnmuTiHNtOmepITAqtG1S8rAipqoiEGuN0+u3ybRHCNSF0aMsQ8MN+PiDJUYvfg5wv23EOn7CT27VtO+7d20nBwjfNS7F2ylPGXVkW96F/mGNxIe+DXh/p+T2PUR3NgR2A1vxk2cPPLQNCwDMz721hXlEGv7LH5oBZkV/wbG3MmjzAZ23cWEe39MeOBX5Jf+Y0XbiggtZyzjhZ9upfeZDE0n9401MPmeWf/+kZ4bQXnkl8yN6vS+ytypwC1yDMOguaEOtXsTV339er771z3JARI1cPsclFf+48oI15Lrf6Hi2d3ziZuxcdN5zEiFBkahZ9knt+qCX8kX9cMw3KAzwso0LsOICKGqEGbEILU7zdDWpNahGkWmOzdx9KJcQgO/pmrrm4n2fJuMHMOzj7+b+LIYy885qLLvtLcwq7Ab30xq/c/ILv0IhtNNvPVTJHa8C2vo9pFy3OMRrx9lVJNZee38ScDMAGUtwa0+i/Dg78GvXOmi5oBq4stjtN8/hMq075mi7bt6BDOLMTXD3kVo8Hc4dRehwgu3g7gQWaBj6IXFeeuX8N8PbGN7X4q1DYVRjFJ4KRertryHcTgcJZXp0LGIEm6ivU2uc7CsgPkII6OV1j0laUPlj1amwjANwjVhvLx2m0WXRIgvjeG7PvagvWf0onys5O1Ee67HcNpwY0eQafwcm35Xg5cfYt1lq4uqCk8XpRRO0kEpXd53/OhqVjAiOPWvxqm7iNDQXwj3/YR4+xfwelZgN1wKyw/h1G++Bd91MPxBzFiczKovo0KLRyLerr+EUPKvhAb/hFP/6oq21aOYZrbcvI3ep/tZcnpKB/PcDLMtDxPpuR4kTL7xslnb5/5CORMtPygi+7UO9dnrdK79nzftERqQiInb55atFWWZBrZn4qQWpufRyzvkBzJT644Nj1aGtkDfw4XRijHt0Uo5mBGTUE0IJ2fi0kCqLYdVnSC+ehVW+kES299FvP1fUUaMzIp/I7Pq63RtWcnApkFWnr2c+NLZm82tlMIZcog1RaleGcdOOfgzKXs8FWLh1L6U9NofkGn5AhgJYulfEQ3tJDr4Q2LZW4kmBgj1/RJz+eKaOeDFDseLHkp44JfTGtnXHVxDbFmU9vuTqGxBZ3CW5WGM3CZCyTuwG16PshZmx3AhU87ToBl4WERuLtRvWXhpKXPM0kSEY1Y0cNvzrSOfiSng6VFMubiSwB7aqWVPFhi5nhRiTDGxMj8A/Y/BwJP6Rg7VQrh+XlJ8zWic2oOaCQ38jkhzCzXr6zF7fkU8tgNUnszyK0mv+S5u1SnkB212/LGV6jUJlp08ez16bVxsYkujxJtjRBuj1K2vxrM93NxsCIxPghi41S/RCgMn/h903408fRUSrUXufwuy9bvTrkmzN8nXX4Jp78LMVF7SSURoOb2ZXJ9D3xPbdCGyWZaH0XL8NeTr3zBr+9yfKEeL7LPAQcD3gcuBzSLyJRHZr1IpLtjQwuaeJFt796i4StjA7a1g7oYItu1AvneOWjk9fNcj2zU0eeaYk4ShZ3TqZ7hBuyPmYLRSiuiyJUjHrchTVxJVT2HcczGy9Xr8de8jve5GPVNdDJTSQpYA6y5ePWtKBEop7CGb2LIY8ebYyH5DVSHqDqxBBF11ca4RId02hF3/KtTJP0bufT2ktuCf+EOybdNTC9+buNVn4ZsNulbMNKg/tJbokijt9/Wjct2zKg9jph/FyjyC3XBZIMc/Tcp6Qij9BO0ovFygHvi5iPznHLZtQXHuwS0I49xkloCr8FLl9V4t0yDpmJBeWG4yuz8DSpWeWOlmYfAZsKrmbULieDK7duPGjkCd+D3k3tcUHqo3kenMjXGJdD7YTXJHmtUXriBSNzttVb7CHnJILI8TXxabYLTMiEntAdVYcQt7KD/nFT2NcJRQFOTxj418Jjt+hJlY2JljRZEQdt3FhNIPYNi7pl5//OYiHPmJf+DQj1yMkWjCXHEKoWUT5xBVjFJEe4bl+CuTtAnYQzkxmA+LyKPAf6Jn8B+plHo/cBzwmjlu34JhaVWUY1c2ctsoAwMgEcHrc8p6qIRDFoM5AbtPP7QXAMpXZNoHSo9e3Lw2LkZkrxkX/DzRwR8SSiSQv3965GPZ8eMxD9VsV5bdt7dTd3ANS46aHX+58nVAP7E8TnzpROMyjGEZ1KypItYUwx5yKqrjUinDo7kxNWl67lqULjJAZ2dhEeq/ZVrbm7EIj135Cx76p5t54MP/i+/PfIKllboLM7eJ/JLL9951vw9QVkVL4BKl1IVKqf9TSjkASimf/Wwy5vmHtPBiT5ItPUMjn4lloGyFn5k6SBkyLVLZPAoDcl1z2dSycZJ6YmXRipWeC8nndWDfnAPZ8zIQu53Ezg8SXn8Bque+kg9V3/PZ+qudmFGTta9YNSuuMW1cbBIr4sSXTj06EENILI9TvTKOk3TwK5EUqoDMrt3kYufgnXormS6X3JLL8U76OdnWhXFNVYqyGnFqziE8+AfwZl791Uk69D7VP30jP0aO/4IZt2d/phwDs278pEsR+SGAUuq5OWnVAuW8g5dPcJMBSFhwe+0pRzGGISgUeRWFzPZpldadbTLtJSZW+h4kN2mf9rjKkfOFlXqAqh3vwXDayOzYRi52fsmHattdnWQ6s6x9+SpCiZn74H1PYadsqlYmSpdTLkG0MUrtAdV4uTkK/k9Wk2Yv4NkeXn5m39OuuwRRWcJDsyOKvvVXO3jyG8/R+XA3XoVZfiNy/EveFcjxz5By8vkOH/1GREy0e2y/Y0lVlONWNfLnTW2879RDRnrJEjLw0i4q6yPxKS5IBXnfIOqmdL3wcO08tLw4umJljnDduForSkFqK7hJCNXNf8OUT6T3RsK9N+FH1pNp+YKWE+ntw+ntAxh5sAIkd6Vpv6+TJUc3UH/IzM+n7ynclEP1qiqiDdNzj4SqQtQeWENyRwon4xCKz35dkr2N8hVO2sEMm/iOjxEypj0nyI9twI0eTqj/l9h1r64ogcQwPE6+7tKR92Joxen2+zrZ+cdW2v7WwbITm1h6/JKpNe78fEGO/3DcqtOm9V0C9lDyVxSRT4lIEtgoIkOFVxLoAn49by1cYFxwSAtbe1O82DO2JriEDJy+qbOIRIRsztaCj9mOuWpmWWS7hibWUVHoJIRc194xLt4Q8dYriPTeiFNzAenV35hUq8qzPbb9egfh2jCrL6hM06oYvufjphyqViembVyGsaKF4H/Mwk5OPcJdTLgZFyftEF8Wp+6gGuLNMZzUzLLo7PpLMJ1WrPSDFW3ndL6A1/3cyMvtfIH6Q2o59PKD2PC2A0msSNB6Zwd/v+5Zdv65FXuodOG28MAtGG4P+ab3LEih0MVGSQOjlPo3pVQ1cK1SqqbwqlZKNSqlPjWPbVxQnHvwcgxhQrDfCBuorIefndxVELIMkrmcVhPO7NSuqL2Al3fI96UmTqzMtGrV4/D8Tyozci9QteM9mJnHyS77OLnmK6bUlNr1lzby/TbrL16NGZmZO8N3fZy0S/WaBNH62QnsDgf/ow0R7OTcBv/nA9/xyQ/aWHGT+oNriS+NIoYQbYhgRkw8e/rXs1t9Jr61ZNopy+MREapXV3HwG9dz+HsOoe7gWjof7ObJrz/Htt/sJNs9roS1lyTS+2OcxEl48cU1aXWhMplc/wal1PPA/4nIseOXK6Uem9OWLVAaE1GOX7WE2za18f7TDhkbTLYM3D6X8IrSD7qwZZFM5XRded8Fpx8iJaryzSG53hRiGGPbn+uG9DY9eXLU56FlB4/JzDEMD6fzhVltT2jgd0S7vooy60mvug4/duiU2wxsGaL70V6aT2mies3M4kS+6+NmXWrWVhEpo3hZJYghJFrimBGTdGsGK2FNqhK9EFFK4aYdxDSoWVelJXJGXSNiCFUrEwy8OKhdZdMqNW1h172SaM/3MfI78CNrZq398WUxDnj1Glae3UzHA930PN5Lz9/7qDukluWnLqVqZYJI388QPxnI8c8ikzkk/wl4N/CVIssUcM6ctGgRcMGGFv71tid5oXuIQ5bu8fkbEQM/7eHnfYxI8QdIyDJJ5XK4nodlxXSZ4Hk2ML7rke0cIlQ16kGaH4ChzRCumxDY9H2TBz7805H3x//762i9vZ1wbYhwTZhwTYhwbRgrOo0RhJ8n2vU1woO/x40fR3b5lWXVaXEzLtt/u5NYU5QVZy2v/Lijm+D6uBmX6jkwLsOICLElUayoydD2FCqkZjzimi/cnIvv+LqEwpLSJRRCCYtovR6pTTfm5NReRKT3JsIDt5Bb9tEZtLo4kboIa166kpYzmul6uJvOh3sY2DRI9eow69c+iRx4Hn70wFk/7v7KZHL97y78PXv+mrM4OOeg5fzbn5/itk1tYwwMAKbg9juEmyd3seQdBytSBdlOqLHBnJsHWzHsgXETK50UDD1XmJ0/dd6H7/l03N85QT7KjGhxyjGGZ9z70Q8ncdqJt16FmX+BfMNbyC95e1lZO0optv9+N27G4+BLD5jRaMB3fNycS+36akJVcx+IX0zBf9/1cdMuoWqLmjVVZRWBizfHsQcH8T2FUWa9pNEoqw6n+lxCg38kt+Sdc6YMHUpYrDhrOc2nLqX7sV4679vK33e+k/jzFs2n9dNwWN3ciJjuZ0zmIrtksg2VUrPjKF2ENMQjnLB6Cbc938YHT98w1lUQEfykh9/gl1YmVroscyIaLbzpgfj8yICPTKyMFwyam4PBZ/U8F6O4kfPH+dXD1WGO+9RROCkHe8jBHrSxhxzyQzb2oIM9ZJNuy06Q2AewEhaR2jCRRJK4PEg0sQaj5R2YVUcSTvmEqqZ2r/Q93U//cwOsPGc58ebpz8/xHB8/71K7voZQYv6Exa2oSe36apK7UthJm1BVaNYkbWYDpZT+7USoWpMgUhsuu31myCCxPEaqNTNlpdFS2PWXEB76I+HBP2A3vH5a+ygXM2zScqzNATVX0tbzTnY+fRRbb9lB6x3tNJ+ylCVHNUxe1C5gUia7qy6aZJkC9lsDA9pN9oU//Z1NXUNsWLZnFCMiKBO8fgdjWfFRjGkYpHM5GmqqdLA/vW3eDIyTzOLbnjYwng1Dz+pRS4n65V7ew+3NcNyXXjvSwzcMDzGkMCoJw8riOk2+42sDNGRjD9nkB7Uxcnu3k+vMMJA5Ht8Nw+MAmwEK+w2NGvXsGQE1nHQsiEXd8Q7HnXjiSJng6eDZHr7tU7O+hlC8DOPiu+Dn9bwg5QECgv5rVVVc/90IGdSsrSbdniHbkyNcPbGo2t7Ay+s5LdHGCPFlsalHh0oV9L9k5BxE6iPkevN4toc5PkuxDPzowbixIwkP3IJd/5o5n4sS6fk+hmVSd+oF1JxRz8ALQ7Tf28mOP+ym9a4Olp24pLwU54AJTOYie/t8NmSxcc5By/nibU9y26bWMQYG0GWVh1zMhlDR3k/YskhmChksVkynBLtpsOZeUC/bMYgZLSQYDD2vs9hCpd0QbXd30HH/Uxz6zoOpatHzZcrNEzJCBtHGCNHGgqH1hoi1f5VQ+gHsmgvILv0Ynh0aGfXYQw75wmjIHrRJ7kxhDzkjxaSOPfwYHrvyZyP7Hz33oRI828N3fGrXa/2wiSvkC8YkX/hAabmQcB3EV2tXohnXhibfq7Pucl1oY5MYW11xEhZS8N/3dBDfilnUHjiF0VWevl7dnDay4XrI90E0AiL6e61IMPji0LQD/nb9a4i3XY2VfmBO56MMy/HnGy9DWQ0IUH9ILXUH15DaqedXtd7ZQft9XSw9rpFlJzZNe2S2PzKZi+wtSqkficjHiy1XSv3X3DVr4VMXC3PSGu0m+9AZh451k4mgTMEbcDGaJl6MoZA2MEopvZ2YOhZTvX5O2+ym8zipHOGaKCRf0CWNJ5nrku3J0flgN0uObhgxLtPFyG0m3nYl4vSQXfoxnLqLERGsmK4zX8rVpXw14ooragwqxMt7+G7BuEQNrQnn57TBFbQxs6ogslQbFCuh3YdmiZhaqBqq1upCV3ah+FquW+/IjOvtJ3nADgf/jbBBamd63oP/SincrAs+VK2IE6mPFB9J+baO1SlXX6/RZqhdrks2mGEYeAZyHfqcoWMckYYI9pA9rTiTW3U6vtVEuP8Xc2pgot3fKyrHLyJUr6miek0VmY4s7fd30fFAN50P9tC4sZ7mU5ZWrPCwPzLZHTvcnV6cCnrzwAUbVnD1H5/guc5BDmuuG7NMIgbugINVbyHjeqWmIbi+j+N6hEOWrgKZ2QFV6+Z0cle2K6k1x9Jbwe7XPc8SKKXY+adWjJDBynNmlqUVGvwD0c7/Qpl1ZFZfhxc7rOxtR7viZuQLVy5eJovv2tSujWIZ/WCb+gEZW1Z4UMa0UTCm8YC34voVX6lHPvYAZFsh362LaRmRQhJF8X1HasKYBxgkd6ZxMm55brsZ4tkeXs4lUh8h3hzHHH1+ldIdkOEKkVZcX5+RJcWrllat0/O6lD+yLL4shj1gTy/gLxZ23auI9nwXI78NP7JuZl+2CGb6MazMw+SaPgBm6TT3eHMhxfksneLc/UQvPU8UUpxPW0rVikDKvxSTuci+Xfj7+flrzuLi7AOb+VdDuG1T20QDU3AVuIMuocaJoxgRIWc72sAYIf3AdwZHeoCzjZd3yfenCBlderQ0xUTKgReGGNqaZPWFK6av7eXniXZ9nfDgrbjxY8ku/1xZKcilGC8JYhjeRHedUqBs8BxQzsioxLUNlFlD7cbVWFV1BWMSnRuDbka00Yot08XlnEGt2pBt1yMAI6Rjb8bY28+KWfMS/B8t8VK7vmZP9pzvateXnwcEIo1QdYDuiEzl9rPikFijR3CFkuAzDfg7tS/XkkH9vyTX/E8Vbz8pI3L8S7HrXlnWJpH6CGtetpKWlyyj86Eeuh4ppDivrWL5qUupWR/0xcczZTdJRNYDXwNORjsQ7gc+ppTaOsdtW/DUxsKctKaJ2za18ZGXHDrhYSARA6/fxaoN6doxo5cBOdumJlFwDRlhyLbNmYHJ9SaRfBfCLm1cJnlw+Y7PrttaiTVFaTpuenN0xOkg3vq5Qgrym8kveceMg7XjJ3d6ytOuG9/WsSQRQCAUh+gS/RA3I7h5A6myqD2oGTMyz2nBRkj3+iNLoOZQcIcg26VHN35enxOrasQFNxL8b8uQ68sRqprd4L+bcfE9n/iyOLElEUTZOo6kPF2oK7Z8z4jOqPBcJdZBemdhX/q3nknAX1l1ODXnERq6jVzTu8Gsqaw9k2Cl/oaZe55s879ULMcfSoRYefZylhdSnDse7OaFn2zlqM9eRGR5FCNske0awoyECNfuHRXyhUI54/CfAN8AXl14/0bgp8BJc9WoxcQFG1q46g9P8EzHAEcsH+tyEkNQgJd0serH3qyWaZLM5FhaX0gQCFXrYHH1wRN6tjPFdz1yu3Zg+TshWj+lkGDHA13kB2wOueyAac1lMNMPEWv7VwSPTMu/4lafPt2mT0R5ekQAOvvNqtbxEiuhRyRmZIzxdDN5Pfv8oGXzb1zGY5h6NBCuh5qDtZhorgeyuyE3CBhgJRArRmJFHDNqkm7LYMVnHvz3XR8n4xKuDlG7VDDNJNhD+vxVHahHK6HqmVUptWJQtR7S2/X+YMYBf7vuNYQHf0948PfYDW+cfttGo1wiPd+bsRy/GTFpPmUpS09YQu/T/VixCA/98/+NLD/1228HAgMzFaKU+uGo9z8SkQ/OVYMWG2cf2Mw1BTfZeAMDIFEDt8/BrLGQUQ/rcMgkmR0lry6mfnjafRBdOqtttLvaUENbkIb6KSdS5gdt2u/ppP7QOmrWVjjkVz7h3h8R6b0BP7yOzIov4IdXzqDl4/evtCsxsU6fI3Nyg+GmbcQwqDmoGTOywFJMRXQsI1SjkzvctM7EyuqMNEGI1cUwQglSu9KokDmt4L9SCjeZB3LULDcIV3tIpEmPVML1s1q/HtBusvR27W4rdJRGAv6DdsXuVj96AG7saML9t2DXv7asicBTERr8I6a9i0zLNbOSAm1YBk1HN2JWL9xJs3uLydSUG0SkAbhDRK4QkbUiskZEPgn8bv6auLCpiYY5Ze1S/rypraharhgCSo9iRhMyTbJ5B88bNR3eikO68rKxk6HyQ2Q2PYxVXd4s/V1/1iKeq86vcF6OlyTW+mmivdfj1JxHes03Zte4gDa+8ZUQXzGlcXFSee1uOngBGpdiWAlIrIIlp8Cys6HuKDCriESGqF1po5w0TiY/9X6G8fO4yV6cvm6idR71Rx5IZO2pSPN50Hicnnc128YF9Aiy6gCd5DCK+LIYKJ0OXSl2/Wsw3E6s1P0zb9+IHP9huFWzOLIOKMpkd96j6JjLcLf7vaOWKeCauWrUgsEeQMQgUl3D+mM2su3vT6P8icWLLjikhbu3dvJ0+wBHthQZxURGjWIK/nQRwVcK23WIDafAmgnId+mJaxVO3CuKm8HZ9QC+CmGFp36YDG1P0v/cACvObCZSW35QVqcgfw5xusku/QhO3atmP3juDOhRS2LNniuy1KrJPGbUouaApRihRWBcxmNGIb5cv3wHyx6grqqV5Jbt2L2DhKqjSCg+tsOgfH3d+Hk9pyUfJtS4lpp1a7CqJ4+5zTrxVbqe0KhRzEjAvy1DuLqygL9bdQq+tUynLFefMaOmDcvxZ5d/dtbPiWF4nPy1N4BhYISje98luwCYLIts9vMCFxOZ3bD7VzStuZDEhhWEev5E48oT6dk5cYRx5oHNhEyD2za1FjcwpqByCi/lYtXsueh0oN8hFikYmOEgda5b92ZngpeHvkfJdtuYsanTKJWv2PnHViJ1YZpPLd9Fp1OQ/xtl1pBZ/TW82OFTb1QpzpAOhFetn/Kh4KZyWNEw1QcsxQgtDjHJSTFCEG3CiDZRU384qe07ybftJqSGEHHRTgidGqysWly1HGJVVG1oJtKQ2DsSNGYYqg/SE3mjTSMfR+oj5PpsvLxXmbtPLOz6VxPt/hZG7kX86AHTa1dBjt9NnIgXP3p6+5gEp/MFvEwSs6qOxNHB6AjKK5mMiBwhIq8XkbcOv+a6YXsVewB23wJPfoZ69RSR+y/C2PodGpqLZ1TVREOcuraJP29qxy9RVEqiJm6vO6YeiCEG6ey44kehal3wayb4LvQ/gZvK4OQjZd3MXY/0kO3Oser8lvICyr5NtOMrxDr+Ay92BOk1350b4+Km9UO2+pAp56c4yRxmLEz1gfuIcRmHhEJUHbie+CFH44QOx68+Qs8/qT0CL340jlpDpGUl9UeuJdpYtXf1zeIr9O/m7ylCpgP+cby8V3HxNbv25SiJEh74xbSbFOn7X8RPkgvk+OeNKZ8kInIV8PXC62zgP4GLZ+PgIvJSEdkkIltE5IoiyzeIyP0ikheRf65k2xkRroMD3g2n/hS59zWQ2oJ7/A3sfPbFkptcsGEFHcksT7X1F10upoCr8NJ7YjHhkDU20A/aPeImwUkyLZQPA0+BPUA2VVpafTRO2qX1znZq1ldTV0bJYXE6Sez6MOHB35JveBOZlf+JskpP2pw2Xk5/n5pDp465DGWxEhGq1y/Vk0n3UUSEeHMt1Qcuw82HcKnHzliIZVF7aAtVq5YsDLegEdIZkfbY+yEU1wH/YkKok2JW49ScT2joL4g7UHFzxO0l3P9/ONXn4kcPqnj7gOlRzgjmtcC5QEdBn+woYMbl/kTERKc/vww4DLhURMZP8e4DPgx8eRrbzox8Dzz6kZG35s6bqK8vrcJ15gHLCJvGhEqXo9GxGHek9xayTFLZXJEVTS27USlKweDzkO3AM+rJ99uYsakftrvvaMN3fFZfuKJor9eIxIivXgUiWN5mqlpCGG4XmZZrCqVl5+CB5tvawNQdBtbk8ShnKEeoOrbPG5fRROoS1G1YjmGZVK1qpPaQFkKJ2anCOWvElus5Jv7YUXpimY4HVhrwt+svQZRNaLDyHKNI702gXHJL3lHxtgHTpxwDk1VK+YArIjVAFzAbolknAluUUluVUjbwM2DMlFqlVJdS6mFgfLHvKbedEcMuMjOCe96j+Ad9BOm9n6ZlcardJ4puUhUJcdo6nU1W0k1mCdgKL60NlWUaOK6P447rzYVqIbWDCQVXpiK1VSszR5vI9+e18OAUbpJ0W4aex/tYemJTUW0lIxIjvqIRs/dW4nUpYuuOgPQ27GN/PeOAa0mUq3WvajZMKQBqD2YJ1UapWteEYe5fsupWPELdhhaiS2sWhBLzBAwLag6B/NhRjFEI+LuZ8bf15PiRdbjxYwkP/EpfI2Ui9m5CA7fi1F2ECq+o6JgBM6OcO/IREakDvovOLHsMeGgWjr0CGB0x3134bFa3FZH3iMgjIvJId3d3eXsP18H6d8I5d7BzZ45d/sXkjv0x+UeuZl3m36m37yi62QUbWuhK5fh7a1/JXUtY8PqckVGMQpGzx90shqVlTsalek5KZnchqLoU34dsd35KPSulFDv+uJtQlcWKlzQXXSe6bAnScSvy1JVYZhq5/zJk6/VYjbOcgjzSKF9/75qDIFI36ar2QJZwXYLqNfufcVk0RJt1OrQ3dqQeqYtgRS28fLna3BqdstyNlbqn/Cb0XA8SIt94WUXHCpg5U96VSqkPKKUGlFLfAs4H3jZLUv7FulzljpnL3lYp9R2l1PFKqeObmpqKrVIcKw7RJuxMmo4XNrPpoSfZrN5OyjyC1bn/x7LczdolNYqXHNBMxJrCTRYy8PMKP6tHJ4YIeadIT86MaqNRDtku6H9Sy5GIgT1oa6XmKXq1vU/2k27NsPKclpKJALnNd4GEUSd+D+59PaS24J/4Q7JtZRrrSlBKz3VJrB2TfTRxNYU9mCHSUEX1mkYkMC4LF8PUMTR7aMzH0w34u4mT8UMthPvLC/YbuRcIJW/Hrn8tymqsqOkBM2eyiZbHjn8BDYBV+H+m7AZG5+KuBEo/mWdv22njS5yt8SvoC51Fs/1zVuX+35iheiJscfq6ZfxlUxueX/qmkZDg9mojYJkGyXR24kpWldYm86dwI9gD0P8oROrBsFC+Itudm7I4kpvz2PXXNhIr4jRuLB6gDw3+kUTvl6HxOOTvn97T/h0/wkzMgVS50w+xFXoyZRGU5+PlHOyBDJHGaqoC47I4iC6FUGLCKCYUt4g0VhjwFxO77tVY2acwclMXm4t2fxffqCE/WzIzARUx2VPoK5MsU8A5Mzz2w8BBIrIOaEVrnL1pHradGWKxK/p+bFlCs/1zLL+fHfGP44sOXF6woYW/bm7nidY+jltVvMdkhA28tIvK+YQsi6GigX4DXU65F2LF3Vc4Keh9WMuNFModO2kXz/amnMzWdncHbtrl4EvXT4zT+HZBBfm3+C/5A9JzP5gR/PMfQ3b8COm5i/ABH8LpLe0KrBhnQItwVq0FAd/z8W0X3/H1yEbAME2sqgixZbVEGqoWZtwhYCJi6FFM38MTJhAnlg5L+vtluznt2pcS6fm+VlleXjqB1Mw8XpDjf/+kcvwBc8dkEy3PnssDK6XcgqbZnwATuF4p9YyIvK+w/Fsi0gw8AtQAvoh8FDhMKTVUbNu5bO8YROiMvh7HWMLK3Hc4MH0VW+NX4BoNnLF+GVHL5LZNrSUNDGhXmdvnEG6JMJjO4vs+hjHuBrOq9JyYYgbGyxVu2PCYmzbbnZ1StTbbnaProW6ajmkksXysDLs4ncTbrsLMPU++4VLynRFCjecQPvWVZNu6MZdcTviAD5Ft7Zr6PJWJnxvC96P41kpkKIcCjJBJqCpKqCqKGQthRqyFkX4bMD0iS3TyipsZI/0/HPBP7a5A0t+sxql9KaHB35Fvem/xFHmliHZ/B99qwq571ex8h4CKmayi5TlKqdtF5JJiy5VSv5zpwZVSvwd+P+6zb436vwPt/ipr2/mmL3wOjtHAmsx/cVD6M2yNfxrCqzh9/VL+sqmdT55zJGaJXrYRNvDTHiqvUEqRd1xikXE3mBUvlFMee1Pi2dD3qO7Zh/b0zNysi5NyJ71RlVLsvK0VI2yy4uyxhcTM9MPE2q9BlEem5ZqRLDGnt29ktOLnMjMaufiuj+/4KNfXOkReDgkLoZYjCNXWYEbDmGFrn5wouV8jorMCex6cUFsmUlf5DH+77tWEB35FaPC32I0T531bqbsxc8+Rbf5kxXL8AbPHZGPSMwt/LyryesUct2vRkLSO5sXE5xE8DkpfScJ9hgs2rKA3k+ex3b2Tb2wJbr+DguKBfgDMQr33Ar4HA0/qGe7hsZMis725CdUzxzOwaZChrUlWnNlMKFHoXyifcO9NxHd/EmU2kl7zrVlJQfZdXxu9pI1deClPEaq2SKyMU7c2TP3BERpOPpvq9SuINlYTSkQC47KvEm7Qxcjc1JiPxRCqWioL+PuRNbjxEwj3/3piyvKIHP+aGcnxB8ycyVxkVxX+zkbG2D5N1lzH5sQXWZ/5EuszX+RVq9/H50Imt21q44TVpQt2SVjwUx4SgWzeoa6YmzhcreXPE2sABYNP60mg47KsPMcn32/vqU5YBN/x2fnnNmJNUZYeX2iXlyTW/iVC6ftxqs8j2/xPYFSmsquUQnlqz8hEACVYEYNwTQgrbmFFTIywsUdZwLfBzmj14CnmugTsI4joeTE992v37yisuEW0MUJ+oHxJf7v+NcRbr8BK3oVbc+7I56HBP2HaOwty/IFbdW9S1tkXkZcDhwMjzn6l1BfmqlGLEcdoYkviGtZmvsxBzv/wP2eeySfvM/iXc4/AGh9bKSAiKFMwU4rBVIbljXUTVzLCOlNspOxuW9F6Mfn+vC7TPMnEyvb7u7ALhcTEkIIK8lWI00l26Ydx6l49pZikUgrlamPiez6CoFBYEZNwbZhQwsIMGxhhs3SxMt/VEiL1x89ZBc+ABUq4HiJNWgopNLbeUHxpjHwFAX83cSJeaAXh/l/uMTAjcvyHBnL8C4BySiZ/C4ijdci+h5aOmY2JlvscnlSxNf4ZVmW/wTsPuIt8rp3Hdx3DCWtKZIEBEtEGZmgoU3rHRgiGntMFqaLLJiz2PZ2abE6SmpwfsGm/t5P6w3QhsdDgH4l2/ldBBfm6soQqnZSNAqyIRaQ+jBXXxsSMmOVndClfj8Bqj4TY7BZWC1gkVB8M3fdOMDA64B8ntTtdXsBfDJz6S4h2fR0j+zx+bENBjr+b7PJPz2+JgoCilJMXeKpS6q1Av1Lq88ApjJ2DEjAKJSF2xj5Mq3URHzj8BY5WX8dQRdKQC4gIpmWQ77VxvBLzAUI1BePSVPSmsYdslM+k5Y13/aUVEWHVuU0FFeR/x4sdXrYKspt1saIWDYfWU3dQDYnlcSK1YayYVYFxUboUQfVBULW6vG0C9j3CtToz0h6csChSpzsu5c7wt2teijLiWmV5WI4/fgJe/JjZbnXANChLi6zwNyMiLWhdsP27VsxUiEFP/DL+Z/P5HFO3hfXpz2P5E2+mkdWjBirpk03ZJVYw9Q1ZpLyrUopsVw4rWjowPrQtSf9zg7ScEqdh8J8KKsiXkll5bVkqyL7jo3xF1erEpEZsSvKFOjfVgZrtfk/1geDnJ6hhVBzwNxM4zW8ndOR7iPZ+D4kvg1OuD0YvC4RyDMytBS2ya9E6ZNuBn85hm/YZnOpX8OrbXkLY28mB6c8S9oqLDejYiSLZNYmbrARuYWKlESr+U/qeYscfW4nUKtY3fxKjUIs83/TesgKgyle4GZeaNVVTzq+ZlHyf9r3XHBbc/AF6VB5boWOL47DiFrEl0bJm+BuRGNZRH0YyOwivOR116s2Y/bcTapiD8hEBFVOOFtk1BS2yXwBrgA1Kqc/NfdMWP6etX8pf29Zy5dNvxiDLQZkribubiq5rxkwG2lN65noFZLomn1jZ9Ug3uZ4cBx/xfYxwbcUpyE7SIbEiPml22pTYg3ruQ93GKYuGBexHVK3X2YRFRiqxJp1P5HuT3w9ajPUP8PQXUIk1yH2vR178DuHa6km3C5gfyik49ncR+bSIHKCUyiulSvt6AsYQsUzOOrCZbz9u8Hz0C3hSxQGZL1DrPDhh3XDYIpvPk+srHa8Zz/DEylKT05yhftru3E5j83PUHdJMes038cPlh8+clEOkPky0cQYT1dy0HrHUH6tVBwIChglVQXx10VHMcMDfTU8+isns2o1d9wrUyT9B5lqMNaBiynGRXQy4wM0i8rCI/LOIBBHaMrlgwwoGsjb37LbYHL+GrLmONdn/Ykl+rAiBZZrkxCPTlcN3yxvFZPvyJSdWGrnNdN76W3zXYPW5VeRaPlPR/BYvr91uiZb49Evvejn9ajheS7YHBIynal1hFDPxmi8n4G+Eo4SiII9/dOSzORNjLQPfV9glJ03vf5TjItuhlPpPpdRxaEHJjcC2OW/ZPsIpa5uoClv8eVMbnlHDi/HPMWidwIr8D2jJ3ThyY4kIGGC7HvmBEsH+UXiOj92XxypSsTI0+Ee8x/+d9hc30nxcGGv9RRXFPXzXx7c9qtdUlVVyufhOHHCGtHEJBe6KgBJYcV2eoUjto5GAf650wH+4XtGwGKs6+ONajHUvucgG0hk6+wMnzzBlPT1EZK2IfBJdOXID8Mk5bdU+xLCb7K+b23E8HyVhdsQ+Tnf4ZTTZv2NN9quIKhgUBSoEmc7slOVk8/32xIqVvk204ytE2/+DTU+8kVDCYPnZU6cgj0YphZt2qFqdmDQzbfKdeDqoX3+MlgYJCJiMqnX6mlETRypW3CLWFC3pKsvs2k0udg7eqbeS6XLJLbkc76Sfz6oYa7m4nktn3+CkpTr2N8qJwTwI/BKtWvw6pdSJSqnJpPwDxnHBhhaGcg4P7ij4hcWgLfp2WiNvpc59gAMy12D6SUQE23NRvsIeyJfcn55YmR0zsVKcThK7Pkx48Lfs6v4gyZ4mVp63smzxwGGcpENsWYxI7TTjLsNzXWoPK11mICBgNGYUqg8oWcE1tjQ6UsJhAkrh9PaR3rpjRIg1vXUHfr5IjaU5pncoRS5wj42hnBHM25RSxyql/k0ptXXOW7QPcvKaJqoi1oRKlz2RV7A99jFi3lYOzFxJQrrJ2HmsuEW6M4cq0RMaFo0cnpNiph8hsePdGPYuhhqvYdvDh1C1MkHjkZWlajoZl1CVRXzpDOIluS6dHZRYM/19BOx/xAthXX/iSMWwygv4700cR49e4pFAuXk05cRgnheRl4vIJ0Xkc8Ov+WjcvkLYMjn7wOXcXnCTjWYwdApb41di+YMcbn8elXoOwzRQrk9+cGIsRilFtrNQsVL5hHt/SHz3J0ZUkHc+th437bL6pSsqCs57tnZPVK+aQSGvfA/EV2hBw2CuS0AlmBGoKj2KidSHCcUt3Fx5M/znm66BIcQwMIpWc99/KcdF9i3gDcCHAAFeh54PE1ABF2xoIZV3uX/7xPTJtLWBLYl/xZcwh9rXYAzdgxW3yHRmJ4xi3LSrM7yMNLHWzxLt+T5u9bmk13yT9GATXQ9303TsxEJik6F8hZf3qFlbVXLC5pTk+yFUB7WHF6pxBgRUSHyVvnaKjGJEhMSKOH4Fkv7zRd6x6R4cIjG+nlNAoEU2X5y8ponqSIjbNrUWXZ43V7Al8UUy0kKi/XNEUrfi2z720NhRTKY7R4htVO14L1b6QbJLP0x2+WdQEmXnn3YXLSQ2GUop7KRNVUucUHya0uZOUvdA648BI5BHD5gmZljLCNn9RRdbMYvY0tIB/71FV/8QIdOcfjr/PkygRTZPhEyDcw5q5s7NHdhu8WG+a9TxlPEpctHjiHX+F4nsD8h0ZEd6bG7Wxej8HTUdHwJlk1l9HU79JSBC//ODDG1LseKs5ooMhZt2iDZGiTRM03fsZnT2T8NxwUTKgJkTX6nVw/3iwfJYUxQMKXuu2FyTzefpG0oTjwbXfjECLbJ55IINK0jZxd1kI5hxdldfgV17EbGBnxBP3YiyGsF3UFtvoPqIE/HiR5Ne850RFWTP8dn151ZiS6MsPa50gbPxuFkXM2qRWD7NyZReXhuYxhMmlMENCJgWhqXl/EuMYgzLINESK0unbD7o6BskZJkQxF6KEmiRzSMnrl5CTTQ0IZtsNCHTJJN3yS37OPnVnyZy7AeQ9ltQg88SOvRyVGY3zsbvoqw980s67uvEHnRY/dKVZQfofVcrJFevmqZCsu9qjbGG47RwYUDAbBFr0anLfvEJx5HahRHwT2dzDKYyxILYS0kqisbuT1pk9mCWbOcgLWs2csipr2D1YSfNeJ8h0+Dcg5Zz55YO8iXcZJZlksrqwl7mIe9E9dyPPP15PRv+/suQrd8nXLfngZ4fyNN+XxcNh9dRs6ZYzeWJKF/hpl2qViUqniejd+DpjLH6jRAtf8QUEFAWhgnVh5TMKBsJ+Nt7L+CvlKKtd4BIeAYisPsBQUS2BF7e4b73/WDk/fFfed2s7Pf8Q1q45amd3Leti7MPmhiMN0RQ+LieR2bXbiLLLyZ80sHIPa8AwD//sTFCfrv+3KYLiZ3XUnYbnJRDfHmMSDlVA/9/e+cdJllZ5f/PuZVzdY7T3ZOHARRhzAkQUVgVA64BV8V1UQyoq7uG3Z9x3cVVllV3FwMGQFBRDKzrooIKuuQ8zMAww+TQ09MzHStX3fP7495ueno6VE9XdZr38zz9VNWNp6qr7rnvec/5nvGMFFLG1zkpyQZDNQg2gSfkaNl5jtUV84acCv/s4dzslL6Pk+FMllQ2SyJiQsNTYfJJy8RO2/T/4hCZjcPY6eMfmj+7o55kyD9lmAyEfKH4tJDfwx9+es0YIb+B7UP0PTFAy4uaymsxCxRSBfwJ/6gc+ozJ9kBkuVNMaTBUC8vj3MTkByfdZL4m/FVt9vUeMaGxMph0BCMip0+1o6o+WHlzFi4ScOYpUvcMkrp3EF9rgMCqEP7OIJa/fD89Eib738f3ki2UCPqODVEJkC8WXSG//x4V8pNdP3CE/FZ+kFzPYXb/Zi+BGj/Nz2so69ylXAnLaxFtO45J/VLWmXMJNUFinSmkNFSfYCP4IpOOYkYm/Id3pfAn5u5i359Kk8sXiUeMQvh0TBUim0pvTIGzK2zLgsIT8PGCb15MIZvBLhZRKZF8bQPF/gK5bRly2zIM394PHsHfGSS4KoSvPVDWJPu561q56dFd3Lmjh7PXHBsm81gWqWyO9J69+GrPxv+CC8jsP4Sn/p34V36QzL4eeu47RLY3x+o3LS9L8dgu2ZTyJZKr4uUrJKvtKCLbeWciv+ZZzo/eFFIa5gKxIH4SHLlvQgcDzoR/LpajmC0dvzjrDLBtmwO9/SYtuUwmdTCqetZcGrLQ8CdCQIhdf7qHgV3bCdY4WVvepA/vBh/hM2IUewrktqXJbc+S355BghaBFUECK8N4G32TjhLOWFZHTdgJk03kYHxeD+lsblTIr3D4CMComF9huMD+O7pJrIqRWD19BpeqUhwuOgrJoTKm3ey841hUnbqE8DLHwZhRi2GuCdSDL+Gkw0+QCi8iRFrC9G0dxBOwql7s2DeUolAqmvBYmZQ1yS8ipwDrgdHbCFW9tlpGLQZEBF+TH1+Tn8jzlPzeHLmn0mS3pMluTmPFPARWhRxnkzz6Y/ZaFuesbuFXm/eSKRQJ+cat91gMpXPYto1lHTta2Pv7A9gFpePc8vTGCsMFQo1BgjVTFFOqQnEYShnnbjG+3hmteIx4n2EeEXHmYnrvmbTWyhvyEp6DCf+iXeTAkX4igflpZrYYmdbBiMhngDNxHMyvgfOAPwMntIMZi3iEQGeQQGfQkXfZmSW3LU3moWEyDw3jrfc5zmZFCCvsDOPPXdfGTx7Zxf9t7+GcteMzwAQECqUiAevoO6XhfSl6HzlC8/MbCdZN/0Uvpov4Il7CTZPEi+0iFPqdcFigEZKngr/GhMEMCwd/rdNXqDgM3olT8UMNQbJ9eeySjeWpznf3yEAKtRVPlY6/FClnBHMh8EzgIVW9WESagKura9bixfJbBNeECa4JU0qVyG/PkN2WIXX3IKl7BvG1BQisDPGsjhpqw35+s2X/BA4GUCVfKBLw+ccsUnbfsg9f1Evri5umtaVUsFEgOpFCcjHt/GDFB9FVEGox1fiGhYmIo9B96K5JHYzltQjVB8j0ZLGilXcAhWKRg32DhMzcy4wox8FkVNUWkaKIxIEewOSoloEn4iF0apTQqVGKfW5ywFNucoBX+OeaNfxg5z7S2QLh4NFDexGLTK5AbMw1v/eRI6T2p1nx2o5pCyTVVkqZIomVcTwjCslaciftC+BPOlX4/jonJdRgWMj4ayDY4AirTtKC2x/zkTmYrcrpDw0MAU4CjqF8ynEw97taZN8GHgCGgXuradRSxFvjw/tsH+ENMYoH8+S2ZVi+TflseCUDP+7BXh0lsCqEt8FJDvB5PaSyT3e1LGaL7L3tANFlEWpPmbqRmKpSGMoTaYvgi3idNM/CkHMnGO5wJu4n+ZFOd1yjGGuYN2Jr4ND/Tfrd9QQ9iE8qHibLFQoc6hskFjZzLzNlWgejqu9zn35DRG4B4qr6aHXNWrqICL7mAL7mAMHnxvmHq+/mVYFGTtoiZDensOIeAitD+FYEyYSf1mLaf3s3xXSRjreumPYiX0wVCdT4CcZzkB0EbwSSpzjV0dbMJ0Ft2+ZQ/xBP7jmA5bFY295CXSJqnI1hbvEnnDbc+X7n+ThEhEDST/ZwDitSOQfT0z+A18jxHxflZpG14Qhdet3XL1HVO2Z7chF5JfBVwANcraqXj1sv7vrzgTTwzpECTxHZCQwBJaCoqhtma89c4/VZ1KyO8bGNT/D7vzkXz/7iUckBUufB/tiL8Qb8tL16JcveYOGPCIWDT056zGImh4c0kboAEmiFSIfTCOw4fhwjjmXLnv0Mp3MkoiHsks29jz9FPBJidXszDcnYhJluBkNViK2Gnj+BTpw2H4j7yRyqXJgsk89xZGC4zKJKG4uJBTpPVMrJIvsSTkfLzTgXc3AKLWflYETEA/wn8HJgL3CfiNysqpvHbHYesNr9ey5wlfs4wlmq2jsbO+abc9e18qOHdvCnPYd45Ulto8kBuacyZLam0KLFPZ/88ej2z/vaWyY+UCmNncugOYvYM0/BqmmftDhtOsY6llQmRzwSoqn26TvGcDBANp/nwSd3EA4GWLOsmcZkwmTXGKqPL+aEeHOHnHnEcXhCHiyvYJf0+FTCx9F9eACf18vkcvyKZWcQuwBYpDx1WP7yG/4tdcoZwbwWWKuquek2nCHPAbap6nYAEfkRcAGOIxvhAuBadSRT7xaRpIi0qOqBCtsyb5zWVktDJMBvt+zjlSc54pGeiIfwM6LYq714IlNMwGsRiimwS6gvTsHTSvz0lXhqylNVHo9t2/T0DbJlzwHSWcexNNYcG4oACPr9BP1+svkCD2/bTdDnZc2yFpprk8bRGKpLbCVk9jm1W+NGMSJCoCbghslmVxOTzmQZSKUnFLS07CyW7aiel7xJCoF6Sp4IOc3h9xgJmRHKcTDbAR9QaQfTBuwZ83ovR49OJtumDTiAM4r6rYgo8E1V/VaF7ZsTLBHOWdvKTY/sIpUvEvE//S/xWNbEcqR2DgopsCwItkCwicKwTbgzSeA4nMuxjiU8qWMZT9DvI+j3kSsUeHT7HrbsPsDq9iZa6mrwek12mqEKeCNOskrmAASOTXgJxP1kemYXJlNV9h8ZIOB72kmJ5rFKWUApeWPk/a2UPDFUjGT/ZJTjYNLAwyJyG2OcjKpeNstzTzTmHN/cYaptXqiq+0WkEfidiDwx0byQiFwCXALQ0dExG3urxrlrW/nhgzu4fVs3569vH13u9Xgo2nk3LKaAYknOiVPG1zipm5aHwlAOXzxEuDk5o/Pats3BvgGe3NNNOpufcsQyHQGfj8akj0KxyKad+3hiTzer25poa6hxQwwGQwWJLof0bqdAeFxRsCfoQWYZJhvOZhnOZEhE/FilISy1KVkhcoFllLwJVEw9TDmU88u/2f2rNHuBZWNetwPjNewn3UZVRx57ROTnOCG3YxyMO7L5FsCGDRvmpzvRNDyzrZbGaJDfbtl/lIPxeS0ef+AOTlmWRLQIwWZKwUZIPnPU9ZZyBcQSYl31ZXezLJVsevoHeGL3AbK5gutYKtOV0uf10pCMUyyV2LL7AE/uOcCqtmbaGmpMcyZD5fCGnbYR6d0QqDtqlVhCsDZA9nAeKzLzmxstFTh4cA9Rj41Vgry/BduTwLZM6GumlJOmfI2I+IE17qItqlqowLnvA1aLyHJgH/Bm4K3jtrkZ+IA7P/NcYEBVD4hIBLBUdch9fi7w+QrYNC9YIrx8bSs3PryT4VyBaGDkQqx4SgPktYlAzTPAe/SkvV2yKWUKJNe1Yvmm/yEd5VjyBeLhEPFwdX40Xo+H+mSMYqnE1n3dbNt3kOWtDSxrqCW4iIUCM7k8Q+kMPX1On5Lm2iTJaNiEA+eDaBekdjoFxHL05++faTaZ2o66heYZSucZLMWJJNsoWGEmn+A3TEc5WWRnAtcAO3E+6WUi8o7ZpimralFEPgD8BidN+buquklE3uuu/waO9tn5wDacUN3F7u5NwM/dvHQvcIOq3jIbe+abV6xr5foHtnP7tm7+4uRl7iRijrS3nWxwJYFxzkVVKQxmiHU14I1MLUhZKjmhsC17HMeSiFTPsYzH6/FQn4hRsm227z/EU/sO0tXSQGdT/aJQpC0UiwymMhweTNF9pI9UNo/ghAQVZU/PESxLaKqJ01pfQzIawV+GszdUAE/QmfAf3nHMKMYb9CAeQW2dfGSv6oi72lknzOavx/bX8+ThXnxxP7Zl/o+zpZxP8ArgXFXdAiAia4AfAmfM9uSq+mscJzJ22TfGPFfg/RPstx1HH23JcGpLDS3xEL/dsp/XrE1gW0HS4ZMoZiCdK5IYV7xcGMwSaooTrJ+8Ir9Usuk+0s+Tew6QyRdIRsNz5ljG47Es6hNRbNtmT88Rduw/RGdzPZ1NdURCC6dCulSyGc5k6RtKceBwP/2pNILg9VhEggEak+NsjThzWQOpDAeODCBAQzJGa30NNdHIoh6tLQrCHY6DsYswxiGIJQSTAbL9eXzhcZe5UhZKaee5r9aZz/HFwPLSe2SAfFGJBI1zqQTlfIq+EecCoKpPipi0iUojIrx8TRM/fGgXh+0XEIx2AhZ+b57BVIaW+uTotsV0Dl84QKS1dsJjjXUs2ULRGbEskN7hlmVRG4tg2zb7e/vYdbCX9oZaljc3EJ0HKQ5VJZXJMZjO0H1kgN7+QUqqeCyLcMBPQyI2bQW3ZVlEQ0GioaBzvGyeR7btRhVq4xHa6muojUcJB03rg4rjCThircPbjhnF+BN+MofdMJmdd0Jg2G4tzRqnz4zn6UtZsVhi18FeYgvohmexU64W2XeA69zXF+FokhkqhuIpDfOK1Q1ce/9Ofr8jw/nPdDJj/D4vQ5mnY8l2oYjaSnR5PTKu3qRUsjlwuI+te7vJFookI+EF41jGY1kWNbEIqkpP3wB7ew7TUlfDitbGqreizebyDLrzKAf7BikUS4ASCvipiUVmpUwgIkSCASKuM8nk8jy2Yy8KxMNB2hvqqItH58WZLlnC7TD81DGjGG9AkdIwmlXEF4ZIl1Oc6Z34sz/UP0ShZBP1mPm0SlGOg7kUJ0x1Gc4czB3Af1XTqBMJ0SKe0hAFXyMrV5xMc2ILtz2+hfOfuR5wQkv5YpFisYTHEorDORJrWvAEjr7zckYs3eSKC9uxjEdESEYdR3NkaJj9G/toTCZY1dZIMhapyDkKxSJD6SyHB4c5cPjpeRS/z0s0FMBbxQtKKOAfnWvK5gs8sXs/tirhgJ9lDXXUJ2PEwkGjczUbPH5HCHPwcadvTGEI7DxiBQi2dZFNhfDFa6acqy8Uiuw+2DtvIeSlSjlZZDng39w/QwWxSmlEbbLBVRS9TsHYy9av4cf3PMhgJkt8zFA9Vyjiy5UIt9fhizs/gqMdS4FkJEIiujgcy3hEhEQkTCICQ+kMdz62lfpkjFVtTdTEIjO6ANu2zVDamUfpPuLMo6iCz+OZeB5ljhgpSgXIF4ps23+QLXsPEPR5aW+soyERIx4JGW234yHcBqntjhBmuN3pb+RL4I/myWw5MG0iWPeRARQjx19pJnUwInKjqv6liGzk2AJIVPUZVbVsSVPCWxyi6E2SC3QcVbR1zvo1XH/X/dyxZRuvOu2U0eWpvmEa2+oJNcUpFkscONzHk3u7yReLi9qxTEQsHCIWDjGcyXLP5qeoiUVY3d5EbXxiBWdVJZ3NMZA6eh7FEotI0E99fPp5lLnG7/NS53NUF4qlEru6e9m27yA+j4e2hhqaahIkImEju1MulhfqngeW/6j+Rt5wAPF40JJ9TEh5hFy+yJ5DfSQiJmxZaaYawXzIfXzVXBhyomDZaSy7SDbYRdFbx/hbq3UtTbQmE9y6+clRB+MrwXA+T0drkr09h0cdS000QtJbmTDSQmRk4jyVzXHP40+RiIRZs6yZuniUfKHIYDrDof4hDvYNkCsUEajIPMpc4/V4qHHDgcVSif29/ezs7sVjWbTWJU2tTbl4jw1viSUEaiPk+1OTpvPv6zmM1xIs0ya84kzqYMYISr5PVT8+dp2rsPzxY/cyTI6NtzhM0RMmG149aVWwiPCy9Wu44e4HGEhniAeDeBX6ox7ueOxJiqUSyWh4STuW8YxMmmdyee5/Yjsej4eSbQMQ8HmJBAMTChIuRrweD0l3NFpyVa1Ham0aknHaG0ytzUwJ1ETI9g5NuC6TzdPdN0hNbGl8fxYa5XxLX86xzuS8CZYZJkE0h1XKkgu0UvA1MbGC5dO8bP0arrvzPm7fso1Xr15DsDXJgKXEQ4F50fUK+X34/T5sVSwR8vkCmXwlxBxmaIc7YV6y7RMiVu6xLOKREPFIyJ1XyvDAk06tTV08RnujqbUpB2/Yj1gWatvIuO/Nnp4j+LweTLV+dZhqDuZS4H3AShEZ28EyBvxftQ1bGiie4hC25ScbWkfJU96oY21zI+01CW57bAuvedapeGrC1M3THELI7yNTKHD+J69gV3cvnc313PT5y5zl8+Bk4MSciB1fa5POPV1r05CM0dFUR20sasJoEyAeC39NmMJA+qgw2XA6y6GBQWorlK1oOJapfqk3AK8Gfuk+jvydoapvmwPbFjWiebzFAYq+ejLh8p0LOGGys9et4f5dexiKzG+rVr/fxxs+/TV2dTt93XZ19/KGT38NvxGunDdGam0aknEakjFS2RwPPLmT3z+0mc0799E/lMIRwTCMEKyJYBft0deqsLunl6DPhxm9VI+p5mAGRGQIOFVVd82hTYseqzQMCJngakremcvfqypnr1jOtXfdxx8f28LrXjy33aB7+gbZvGsfm3bu45JXnzXqXEbY1d1LT98gX/7R/7CqrYnV7U2samtaMvMgiwkRGR3ZlGyn0HZXdy/BgI/lzQ001sSNggDgjQQQS0bDZEOpDH2DaWrjx9ecz1AeUwb0VdUWkUdEpENVd8+VUYuXIt7iEAVvHflA+3H3jNB0nnVrO+horON3DzxWVQfTP5xm8859bN61j8279rN55z56B5wJUY9l8cYzn0Nnc/1RTqazuZ5UJssfH36cX/z5aVGHxmScVe1NrG5rYnV7M6vamuhqrq9qIaPhaTyWRTLqjJTzhSJP7j3A47v3UxuL0NlUT10iesL25nHCZBEKg2k8oQA7ug8RDpq5q2pTzretBdgkIvcCqZGFqvqaqlm1CLHsNGIXyQZXUPTWcrzDbs0VEb8XT0OMc844me/f8if6hlKjaayzIZXN8cRux4ls3rmfzbv2sa+3D3DuhDub6njOSStY39nG+s5W1ixroSYa5qbPXzYaJhudg/H5+N1XPs7hwWG27TvI1r3dbN13kG17D3Lv49splkqAkxW1oqXhGMdTN0lNi6EyOHU2jhBqOpvj4W27EBHa6mtoa6glcQIWdAZrIuQODzNYGCaVyVITq+zoJRwLkeysR3weuo/005iMn3Cf8XhkulitiLx0ouWqentVLKoiGzZs0Pvvv39G+zzxp98xsGs7wZqJhSWd9ONBit4YuUAXKscfjtCSDdkC3q46JOBl695u3vKF/+KTF72aN7zk2TM6Vq5QYOvebjbt3D86QtnZ3Tsam2+pSzqOpKuVk7vaWNfRSnQSkb+RLDJVRcrIIiuWSux0CwfHOp6e/sHRbZLRMKvbmljV3jzqeJa3NIxWuk9lx3xnsy1WbNtmKJMlVygS9HnpaKqnqSZxwuii2SWbI4/sYuOBg3i9Fn5v5eYRw7EQ+ZifN3zu66M3Yr/84kc4dXl7uU5mSd5tTetgAESkCRi5wt2rqj1VtapKVNrBWHYWsXPkAsso+uqZLv14OnQ4i9WcwJN0amRUlS/+4Je874Jz6GxpmPSiWiyV2L7/kBvm2sfmnfvYuvfgaK1IXTzK+s5W1ne1cVJnGyd3tVVkRDRTBlJp1+kcHHU+T+3vIeu+H0uEjqY6VrU1uXM7jvNpqUsSDvjJFAoTjqSMk5k5hWKRwXSWkm2TiIToam6gPhFb8vU1Tz34FFu37aGm7vhag09G3YpGzvnkV44JJd/9X5+huTZZziGWpIMpp+HYXwJfBv6I8yF8XUT+TlV/WmXbFjA2nuIwthUgGz4J25r95LadyWPFgliJp+8mwwE/H3vTX3DRF696+qL6ucsYSKW587GtbN65j0279rFldze5gnORjYaCrO9s5a/OfaE7QmmjqSa+IMJRiUiYM9Ys54w1y0eXlWybfYf6nK6Xew+ydd9Bnth9gFsf2DS6TSQY4KbPf4j3/Nt3j8lm+/XlH6N3YIhIMEDQ75uT97kURlI+r5c6d4I7k8vz6FO7ERGaaxO0N9QuOjWEcigWS+xMDRKp4MhlhJIwYTJMrlCs+LkWE+XcrvwD8OyRUYuINAC3AiekgxHN4ymlyPtayAdacJpxzg4tlBARPE1HOwK/3zfqXMC9qH7ma1z5/ov4f9+9iYDPx7qOFl7/kg2c3NXGSZ2tLGuoXVQXBo9l0dFUR0dTHS87/eTR5alsju37e9i69yBb93UTjwQn/AEf6h/ilX//ZcCZRwr5fYQCfsJBP+FAgHDATyjodx4DfiLBgLPefR0O+AkHA+7jsduMd1oLsS5otowUsKoqRwZT7O/tw+f10tlcT3NtgtgSURje19tHwQsRn3c03FsJbFX29fZPmAwTWOIjwuko591b40Jih5ltLGhRolilFCBkQusoeSbvJDmjo6qiuQLejlrEe/THaqtOeFFd0dLAD//f+1je0rBkM7QiwQCnrljGqSuWAZCIhif8AUeCfj7x1leRzubJ5PKkc3nS2ZzzmHOW9Q+n2d/bN2Z9fjR8OB0jTmvECX3zoxfz3iu/f8xI6tYrPrFoHcwIIjKqHDB2Hi0WCtLVXE9DMk5gkdY/OVl13dQkYpAbRlN5CM7+vagq/3bL79mTHub6f7j0qGjDL7/4ERqT8QpYv3gpx8HcIiK/wWmTDPAmxrU5XvJoEW9xgIK3nnygDa1kQ89UHk99FCt8bMqkJTLxRTUUZHV7c+VsWATk84VJs9kufOlzZnQsVaVQLB3ljDJjHFIqm3Nej3Va7rY1sciETr/7SD9fvO5mTlnezqkrlrGmvWlRpwR7PZ7RCvdsvsCmHftQ9tFYE6ejsY6aWGRRKT3v7unFtm28Hg92LEhpMDv9TmVw9R138dP7H+GtzzuDWMbmN//0t1g+D7FY2GSRUV4/mL8TkdcDL8KZg/mWqv686pYtMLLBlaM9WyqFZgtIyIdVO/GE+2QX1fwiv1M+HjL5AiG/j1uv+ETZ2WyTISL4fV78Pu+osGS5TDaSyheKPPDkDm6511FV8nu9rOts4ZSudnck1k5TTWJBzIXNlJE+NqrKUDrDfU9sx+v1sKyxlpbaJPFIaEG/r2wuz7a9B0eLKiXkA5FZh8l+ct9DfOeOu/mLZ57MB895CZmhDIf3HyZWE2X1S59ZKfMXNeVmkTUDzwVs4D5V7a62YdXgeLLINt3zMN27DhOtmyxN+fhwUpKLeJfXIv7J/fxMU4QN1WVkDmaybLaDfQNs3L6XTTv2snHHHh7ftX90orc+EePU5e2csqKdU5a3s76zbbTb5WKjWCoxlM5SKJWIBP10NjVQn4gSWYD97DfvdOq9xmqOFff3o5kiEji+UeZvNj7OZ37xv7x4zUr+5Y2vxuuOVNJDaWI1UTbM3MEsXA89C8qpg3k38Gng9zgfwkuBz6vqd6tvXmU5Hgfz2P1bOLjzINGaysy5gBOiYTiP1RbHE18aE6gnEjNx+sVSia17D7Jx+x427nAcz+6ew4CT4LCyrZFTlzsjnFOWL6OjcXElaYBTczWcyVKylVgoSGdTHXWJ2IKQqEllstzx6Bbq49GjPld7OEdpXz8ySY+Yqbhz2w7+7se/5BnLWrnyLa9z9cwcjIM5mnLc998Bz1LVwwAiUgfcCSw6B7NQ0EwBKxk0zmWRkpnBKNLr8XBSZysndbbyl2c9F4D+4RSP7djHYzv2sHH7Xm6591FuuuM+AGLhIKd0OaOcU5cv4+TlbRNqvC2kVOmAz0fAvchm8wU279qPrUo8EqKjoXZenc1T+3vwez3HOG0JOSKXMw2TPbpnP5/8yX+zsqGOL//lBUc5F8OxlONg9gJju/UMAXuqY87SR/MlxGPhaazciMiwuEhGI7zo1DW86NQ1gFNhv+vg4dFRzmM79vCd/7kd240udDbVu8kDzijnGSuWLdhU6ZH5GoBsPu84G1tJRsMsa3SczVyFBQdTGfYd6qMheexvTTwWEvWj2SKUGSbbdvAQH/3Rz2mIR7nyra8nugBGaAudckJk1wKn4sj2K3ABcC/wJICq/luVbawY8x0iU1vRdB5fVy1SgRRJw9Illc3x+K59bNy+13E62/dwZMiRAvzFP32YD/3HD45JNLj1ik8wMJyeL5OnJJPLk8rksFGSkTAdTXXUxaNVbZZ2/xM7GEpniEcmjhTMJEy2v2+AS77/I0Tgm+98M63JiZUATIjsaMpx3U+5fyP80n00t+AzJZPH0xA1zsUwLZFggA1rV7Bh7QrAmbc7cLifjTv20lZfM2Gq9ELuATNSzAmOs9m4fQ+qUBuPsKyxjtpYZTtz9g2l6OkboKl2ckmYcsNkh4dTXHb9T8kXi1z1jjdN6lwMx1JOmvLnAEQk5rzU4apbtQTRTAEJ+7FqTM+U46FYKpErFMkXihRLJWLh0JTCmEsNEaG1vobW+ppJU6XLLR6db8Y6m3Q2x6NP7R51NiOdOWdT0KmqPLFr/7QinuWEyYazOT5yw8/oHU7x9bddyMrG+uO260Rk2nQVETlFRB4CHsOR7X9ARE6ebj/D02jRBlU8zXHEWpIj4YpQsm2y+TwDqTS9A0P09A1yqH+IQ/1DpHN5oqEAXS31rO9qQ1Xp6RukfziNvUgurJVipD6qs9m52HU21/Odv383H/zqtdzz+FPT7L2wCI/pzJkrFHlk225+/9Bm7nt8OwcO95E7jjml3oEh+ofTRMqYI7HiQSiWJlyXLRT4ux//gqcOHeZfLnw1p7a3ztiWE51yQmTfAv5WVf8AICJnAt8GXlA9s5YOqopm8njak4ivPFmXkbv1XL7g3JUKzuwXAqKgzh2t12Ph9XjGPC582RhVJV90RiL5YgnbtlFAcN5PPBKiNhYlHgkRCvgJ+LwE/f5jqsaXNdYxmMqwt+cwe3v7UDdrKXACZPVMVHS688Ahtu/v4QNfvZZ3n/9S3v2qM/EsonTnkTbQkWAAVSWdy/PItt0o0JiM0VZfS208Oq3as23bPL5rH7EyWxBIyBlJjQ+TFW2bf7zpf3h49z4+97rzef6q5ZMdwjAF5TiYyIhzAVDVP4rI3Gu9L1I0XcCqDeOJHfuFH+9InAstoxXm7fW1RMOO8KLf66VYKlEoliiUSuQLRVLZPJlcblTOZPRub+SH4v5ojnZC1pw4ovEhrZEpTEGIhgLUJaLEQiEiIUcFOeDzzUgqXkRIRMMkomFWL2uhp2+A7Qd6GBjOEPB7iYcXdnX5bBmfKl0Ti3DNJ9/D5Tf8im//zx95eNsuvvDXF1KfWHxTpeOdzXDGaZgG0JCMsayxjmQ0MuH35WDfAKlMnsaa8jTAxGshkQCaL4Jb8Gyr8s///Vv+vHU7HzvvbM49ZV3l3twJRjm/6O0i8v+A69zXbwN2VM+kpYPmi4jfg9aESGVzT19s3SHJiCNprU8SD4cI+v2EAr5JNawCTH13PqKxVSg6o4OCO1JI5wqkszky+fyoHYDjiBTgaEfk8zqjoenugEu2TaFYJFcoUiiW3Elm56Ie8HtJRELE65NEg8HR9NVAFST1/T4v7Y11tDXU0j+cZk/PYfa7nToTkfCS73EyQijg53MXv54z1nTxpR/+iov+6Sq++O4LRxMFFiMiQjQUJBoKjjqbB57ciQg0JhO0N9RQE4vg83oplWye2LWfRHRm9WUSD6L7B8DvqCx//dY7+PWjm/mblz6fCzecVp03doJQzi/vXcDngJ+5r+8ALq6aRYuYkQtuoViiWChBtgDtCfyFAslomHiZjuR4GauxNdUQ07Zt1xE5o6FC0RlFpVxxx0zeqczOF0pOSA5AR+N0jDgRr8ciGgqM6lGNOJGJQlpzgYhQE4tQE4uwdlkL3UecUU1/Kk3Q5yMWDi7pUc0Ir3nh6azvauPj3/wx77vyGi559Vm867yXLDqFgPGMdzZD6QwPPDmACDTVJAgHAmQLBeITFKZOhRX2YYtzg3bdnffxw7sf4MJnn8a7Xvy8Kr2TE4cpr3Ai4gF+oqrnzJE9i4KjHIltMzIi8Xk9RIMB6pMxAjmbxPJGkm21C05V17IsAn5r2kydsY4o775fy5LjCmnNNQG/j87mejqa6ugbSrHrYC/dhwfweIR4OLTg/ieVZlVbE9d96j38y/X/zTdu/j0Pbd3FF971hlHBx8XOWGdj2zYDw2kOHO4fbaI2o2N5PUjYzy/ue4T/+v2fOffktfztK846IW5Gqs2UvzJVLYlIWkQSqjpQ6ZOLyCuBr+J07bpaVS8ft17c9ecDaeCdqvpgOftWkky+QGHY6QUD4PNao44kEggQ8DsXW5/XmdsoDOXwNQaJdTQs6i9puY5oISMi1Maj1MajZDvydB8ZYMeBHvqG04T8PqKhpTuqCQcDfP5db+D0NV18+Ue/5qIvXsUX//qNnL6ma75NqyiWZRELh2bVGO0Pu3byr7/9A89f1cX/u+CVWEv0OzHXlHMblwU2isjvgNTIQlW9bDYndkdH/wm8HEeO5j4RuVlVN4/Z7Dxgtfv3XOAq4Lll7lsRkpEwnc11xJKxYxzJRNiFIghEO2qX7IVrsRIM+OlqaRgd1ezs7qWnfxBLLJLR0KLIwpspIsLrXryBk7va+cS3fsylV36f977mbN7xihct+pBZpbj38af49A9v5uSWJv75Da/CtwS/B/NFOQ7mf9y/SvMcYJuqbgcQkR/hyNCMdRIXANeqM3t8t4gkRaQF6Cpj34pQE48QLijeMuQk1FaKw3kSa5qxFnD46ETHsizqEjHqEjHS2RzdhwfY0X2IfKFAOBggugAl52fLmmXNXPup9/DFH9zMf/7iVh7atovPX/x6ktETOyF00859fOyqH9LRWMcVb3kdwROxWW8VKaeS/xoR8QPrcGZ5t6hqvgLnbuNo0cy9OKOU6bZpK3NfAETkEuASgI6OjtlZPA2FoSyhliQ+o5K8aAgHA6xoa6Szud4d1Ryip38Qj2WRiCytUU00FOSf3/1GzljTxRU3/q+bZfZGTlvVOd+mzQs7uw9x2deuIxkN8x8fejsJ8VHqHpyyP5NhZpRTyX8+jhbZ14D/ALaJyHkVOPdE8aPxYkqTbVPOvs5C1W+p6gZV3dDQ0DBDE8unmMnjDQcINxudosWIx2NRn4yxYd0KXvKMtaxobWAonaWnb5BUNjff5lUMEeHClz6H7338b/B6PLzniu9x7W/+fMKpIXQfGeD9/34tHkv4zw+/g4Zk3GlbvnDl3BYl5YwH/w04S1XPVNWXAmcBV1bg3HuBZWNetwP7y9ymnH3nDLtYQvMlYl31yCLqU26YmEgoyKq2Zs561nrOWNtFwOelp3+QI0Mpt45p8bOuo5Xr//FSXnraOr72s9/y0f+6gYHUwlRirjT9wyk+8NVrGM5k+fqH3s6yxjoAxOfBCvvQ/NL4Hy8Eyrka9qjqtjGvtwM9FTj3fcBqEVnuhuDeDNw8bpubgbeLw/OAAVU9UOa+c4KqUhjOEemsx2NUkpcUHo9FY02C565fxYtPXUtHYx0DqQwH+wZIL4FRTTQU5EuXvImPvel87tr8FBf901Vs3L60Wz2lsjku+/oPOHC4nyvffxFrl7UctV4SIXSkENkwa8oJNm4SkV8DN+IMIN+Ik7X1egBV/dlUO0+GqhZF5APAb3BSjb+rqptE5L3u+m8Av8ZJUd6Gk6Z88VT7Ho8ds6U4nCNYGyNQe2JPli51ouEgaztaWNnaSO/AEDsOHOLQwOCoRpwIqDL6aFmCxxI8loVlWXgs66jXCwUR4c1nP49TlrfzqW/fyLu//B0ue8O5vPVlz19yWZD5QpG/+8YP2bL7AP/63jdPmK5thf2UFnDbg8VGOQ3HvjfFalXVd1XWpOpxPA3HUvuOkDs8PGEWWSlXRIs2yZNascoUsjQsHUolm2KpRMm2KZZs97FEseQU4mbzRVdKpzCqy5YrFN35DnFnEhVBXMUeR7LHcULiOiX3z2NVXbxyMJXhc9f8nNsfeYIzTzuJT7/9tZM261pslGybT337J9z24CY++87X86rnnzbptoVdR8DWssVpx2Iajh1NOVlkRhZmAtS2KWXyJNYZ53Ki4vFYxyWJY9v2006p9LRTGlGIyBdKTzslV08uk887GnKjoqGWo9SNUhuLVCTbLR4J8ZVL38INt93F1276LW/74lVcfsmbWN/VNutjzyeqyuU3/IrbHtzER974yimdC4CVDFHqHjouB2M4GpOPd5wUBrOE22rwlVEfYzCMxXLDZDMtlVJVSuNGSn1DKTbt3EtjMl6R0JuIcNE5L+AZK5bxyW/fyLv+9Wo+fOEreNNZz120IbP/+uVt/PxP93PxeS/honOm7zJihf2UTDpZRVg4weBFRDGdwxcLEmo0KcmGuUNE8Ho9BPw+IqEgiWiYrpYGVrc30ztQ2Uazp65YxvX/eCnPX7+Sr/z413ziWzcynMlW9BxzwfW33sn3/vcOXvfiDbzvgpeVtY/4PEjQhxZMNtlsMQ5mhtjFElq0iXY2mO6UhgXBqrYmWutrOFxhJ5OIhLnifW/lstefyx8ffpy3ffEbPLF73qoBZsyv7nqYK39yC2efvp5PvPVVMxqBWfGgSVeuAOUUWgZE5K0i8ikR+fTI31wYt9BQVYpDOaJdDXgm6eFtMMw1lmVxclcb8UiI/uHK1rJYlsXbX/EivvnRi8kXilz8pW/z09vvZbrkoPnmjkee4AvX/oLnrFvBP73rwhknSFgRv5MOaJgV5Xzqv8TR+SriiF2O/J1wFIeyBJtiBGpMSrJhYeH1enjW6k68HqsqoazTVnVy/T9eyrPXLufyG37Fp67+yYJVOHho604++e0bWdvRwpcvfctxtZUQvxcJek2YbJaU88m3q+orq27JAqeULWD5vIRba+fbFINhQgJ+H2esWc5dm7aS9RQIVrjNQk0swr9/4G1c85s/c9Uvb2PL7gN882PvYlVbE7Yqlgj5ca2c54KQ34ff78NWJV8o8IPf3UlLXZKvffBtRILHn4RjJUKUeoZNNtksKMfB3Ckip6rqxqpbs0BRW7GLRZLrWrCMFIxhARMNB9mwbgV3b9qGx5KKN1azLIuLz3sJz1i5jJvuuB+f18M5H72cXd29dDbXc9PnLiPg85Kbo2r4kN9HplDg/E9eMWrD9z7+N3gtD+Ggf1bHtiKm6HK2lFNouRlYBewAcrjtG1X1GdU3r7Icb6Hl8M5e4qubCTXGq2SZwVBZ9vce4aGtu2lIxqpWoBkNBXjF33+ZXd29o8s6m+u58v0X8cbPfB2vx4PP6/65z70eD36f89o7Zrnz2um15PdOsN7d11nvHX19wQtP58LPfv0YG2694hMMVGA+qrDjsFMTO0UPqLGYQsujKef2phLKyYsYIdiUINgQm29DDIayaa2vJZMr8MTu/TTVJKpSwyKWddSFHXBGEU31vOMVL3LabZdKRz0WR58XncdCieFMbnSb4th9iiWKpafbdU/EOWecMqENlUpCsJJumKxMB2M4mkkdjIjEVXUQGJpDexYcwfooYlmLtsjMcOKyorWRbL7Anp7DNCQrP/q2ROhsrj9m9BCPhHjfa8+p6LlU1VU6ONphNSRjE9pQqd+rFTFFl7NhqrHzDe7jA8D97uMDY16fEHgCPiMFY1iUiAjrOlqpT8Q4MlT5xM98vsBNn7+MzuZ6wLmw3/T5y8hXYZJfRPB6PIQCfuKREHXxKM21CVCtqg3i9yJ+LzrJCMowNdPOwSwljmcOxmBY7OQLRe59/CkKxVLFxStHMrjUFeqczyyyatlQ6ktT6hnCKkMWyszBHI2pFjQYljh+n5fT13Rx16atpLM5wrNI3R1PZh4cylzb4Ej4V+3wSxqTc2swnACEgwE2rF1BKuuqMhvKRgIjYbITq610JTAOxmA4QUhEw5y+ppO+4aXT+nmusJIhdJ5HaouRshyMiLxIRC52nzeIyPLqmmUwGKpBY02Ck7va6R0YXvB6YgsJK+IHM4CZMeWIXX4G+DjwSXeRD/hBNY0yGAzVo7O5npVtjfT0D863KYsHvwfxe6YMkxWLJTK5woJqiT3flPNJvA54Da7AparuB0zVocGwiFnT3kxLXZLDgyd0mVvZiAhWIoTmj56/Ktk2g6k0fcMp8sUSy1sa6GismycrFx7lZJHlVVVFRAFExEgJGwyLHMuyOHX5Mu57Yjv9w2mS0fB8m7TgsaIBSr3D2GqTzuYplEr4PB5a62uojUcJBwPYuQKe41BvXqqU80ncKCLfBJIi8jfAu4BvV9csg8FQbUYk/u/avI3hTJZoKDjfJi1YVJWhYp5cJotVKtBUl6Q+GScaCpjGg1MwrYNR1a+IyMuBQWAt8GlV/V3VLTMYDFUnGPDz7LUruPOxrXirIPG/2Ellc6SyWSyxaKqJ0/CsJL6hHMGkCeSUw7QOxs0Y+9OIUxGRkIh0qerOahtnMBiqjyPxv5y7N23Dm4ji9ZzY0kjZfJ7hdA5FqY1HWd3eRF08ht/npZjOMfD4gfk2cdFQTojsJ8ALxrwuucueXRWLDAbDnFMbj/LMVR08vG0Xjcn4CZcJlS8UGUxnsdUmHg6xvquV+mScUODonjKekB/xe7CLJSyjsDwt5TgYr6rmR16oal5EZtfJx2AwLDjaGmrJ5PI8uaebxpr4klcQL5ZKDKYyFEs2oYCf1W1NNNbEiYYnn4sSEYINMTLdA1gx42CmoxwHc0hEXqOqNwOIyAVA7zT7GAyGRcjKtiYyuTz7evuqIvE/35Rsm+FMlly+iM/roaOpnubaBPFIqGyH6o+HyOzrr66hS4RyHMx7getF5D9wFD/3AG+vqlUGg2FeEBHWd7WTyRfoG0pRE1v8k9mqSiqbI53L4xGhtb6G1voaktHwcYUCPSE/4jNhsnIoJ4vsKeB5IhLFkfc3lVkGwxLG47E4bVUnd2/eylA6QyxcWYn/uSKdzZHK5gBHImd9Vxs10QjeWToFESFQHyXbM2jCZNNQThZZAHgD0AV4R4aRqvr5qlpmMBjmDb/Py4a1K7hz01Yyufwxk90LlWy+wHAmi61KbSzCqjEZYJUkkAiT6R6o6DGXIuV86r8EBnA6Weaqa47BYFgohIMBNqxZzl2btuH1WPi8C69CXVXJ5PKkc3lsVWKhIOs7J84AqySesB/L68Eu2VieEyvjbiaU841pV9VXVt0Sg8Gw4EjGIpy+pov7t2ynIRnHswDSl0u2TSqTI1soIDgp1stbGqiJRYjMkRqBiBCoi5I9NIgVMwoIk1GOg7lTRE5V1Y1Vt8ZgMCw4mmodif9NO/fSVJOYl/TlXKHAcCZHybbxeTw01yZoqk2QiIQrHv4qF38yTKa7f17OvVgo5z/zIuCdIrIDJ0QmgKrqM473pCJSC/wYZ15nJ/CXqto3wXavBL4KeICrVfVyd/lngb8BDrmbfkpVf3289hgMhqnpbK4nky+ws/sQjXOQvqyqpHN5Mtk8Nkok6GdlayP1iRixcHBBFIJ6R7LJTJhsUspxMOdV4byfAG5T1ctF5BPu64+P3UBEPMB/Ai8H9gL3icjNqrrZ3eRKVf1KFWwzGAzjEBHWLmsmnc1xeHCYuni04ucolkoMZ3LkC0XEgvp4jFVtTSSjYcLBQMXPN1vEEoJ1MbK9g1hREyabiHLSlHeJyIuA1ar6PRFpAGb77boAONN9fg3wR8Y5GOA5wDZV3Q4gIj9y99uMwWCYcyzL4hkrl3Hv49sZSKVJRGYv8Z/NF0hlndCX3+ulpTZBY02CRDS0IJMKxuNPhskcNNlkk1FOmvJngA04Ssrf4+mOli+cxXmbVPUAgKoeEJHGCbZpwynqHGEv8Nwxrz8gIm8H7gc+OlGIzbX/EuASgI6OjlmYbDAYfF4vp6/p4q7HtpLK5ojMcGRh27YT+srlUSAWCrKmvZnaeJRoKLAgQl8zwRvyI14LLdmICZMdQzm3CK8DngU8CE5HSxGZtqOliNwKNE+w6h/KtG2imcSRJuJXAV9wX38BuAKnT82xO6h+C/gWwIYNG0wTcoNhloQCfp590ojEv0XAN7XEvxP6ypIvlrBEaEjGWLOsmWQ0smjqayZDLCFYGyF7JIUvsvDCePNN1Tpaquo5k60TkYMi0uKOXlqAngk22wssG/O6HdjvHvvgmGN9G/hVOTYZDIbKEAuHOGNNF/c+vp26CST+s/k8w5k8ttoEfF5a62toqkkQD4dmXUm/0PAnI2QOGoGTiZivjpY3A+8ALncffznBNvcBq91+NPuANwNvBRhxTu52rwMem6U9BoNhhtQn4zxj5TIeeWo39YkY2XyBdC4HKsSjIdZ1tFAbjxANBZe0MrM3HBgNkxmOZkoHI8634sfAOirb0fJyHMf118Bu4I3u+Vpx0pHPV9WiiHwA+A1OmvJ3VXWTu/+/ishpOCGyncB7ZmmPwWA4Dtob68jkC2zf30NTTZx1Ha0kIiGCizz0NROcbDInTGbSlY9GVKeelhCRB1T1jDmyp6ps2LBB77///vk2w2BYcti2vegm6CtJYTjLwJYDeII+PCE/8RUT5S1NyZIc4pXzjbhbREz3SoPBMCknsnMBN5vM40Ftk0c0lnK+FWfhOJmnRORREdkoIo9W2zCDwWBYLIjHIlAboZTJT7/xCcR8VfIbDAbDkiJQEyG198h8m7GgmHYEo6q7cNKFz3afp8vZz2AwGE4kvGE/3tCJk9xQDtM6CreS/+PAJ91FI5X8BoPBYHARj4W/dvG3mK4k5YxEXge8BkiBU8kPTFvJbzAYDCcawdooln/ha6jNFVWr5DcYDIYTDV8siCdswmQjlDOCGV/Jfyuzr+Q3GAyGJYkptnyaSUcwIhJQ1ZyqfkVEXk5lK/kNBoPBsMSZKkR2F3C6iFynqn8FGKdiMBgMhrKZysH4ReQdwAtE5PXjV6rqz6pnlsFgMBgWO1M5mPcCFwFJ4NXj1ilgHIzBYDAYJmUqB9OiqpeKyENu0y6DwWAwGMpmqnSHkcLK986FIQaDwWBYWkw1gjksIn8AlovIzeNXquprqmeWwWAwGBY7UzmYvwBOB67D6XlvMBgMBkPZTOpgVDWPI9P/AlU9NIc2GQwGg2EJMFWh5b+r6oeB747IxIzFhMgMBoPBMBVThciucx+/MheGGAwGg2FpMVWI7AH38XYRaXCfm1CZwWAwGMpCVCfuIS0iAnwG+AAgOCnNReDrqvr5ObOwgojIIWDXDHerB3qrYM5MWQh2LAQbwNix0GyAhWHHQrABjs+OXlV9ZTWMmU+mCpF9GHgh8GxV3QEgIiuAq0TkI6p65RzYV1FUtWGm+4jI/aq6oRr2LDY7FoINxo6FZ8NCsWMh2LCQ7FgITFVo+XbgLSPOBUBVtwNvc9cZDAaDwTApUzkYn6oeM8xz52F81TPJYDAYDEuBqRxM/jjXLTUWig7bQrBjIdgAxo6xLAQbYGHYsRBsgIVjx7wz1SR/CUhNtAoIqqoZxRgMBoNhUiZ1MAaDwWAwzAbTPNpgMBgMVcE4mEkQke+KSI+IPDaPNiwTkT+IyOMisklEPjRPdgRF5F4RecS143PzYYdri0dEHhKRX82jDTtFZKOIPCwi98+jHUkR+amIPOF+R54/x+df634GI3+DIvLhubRhjC0fcb+bj4nID0UkOA82fMg9/6b5+hwWGiZENgki8hJgGLhWVU+ZJxtacBq/PSgiMeAB4LWqunmO7RAgoqrDIuID/gx8SFXvnks7XFv+FtgAxFX1VXN9fteGncCGibIs59iOa4A/qerVIuIHwqraP0+2eIB9wHNVdabFzLM9dxvOd3K9qmZE5Ebg16r6/Tm04RTgR8BzcJKgbgEuVdWtc2XDQsSMYCZBVe8AjsyzDQdU9UH3+RDwONA2D3aoqg67L33u35zfmYhIO04biavn+twLDRGJAy8BvgOO+vl8OReXlwFPzbVzGYMXCImIFwgD++f4/CcBd6tqWlWLwO3A6+bYhgWHcTCLBBHpAp4F3DNP5/eIyMNAD/A7VZ0PO/4d+HvAnodzj0WB34rIAyJyyTzZsAI4BHzPDRleLSKRebIF4M3AD+fjxKq6D0eUdzdwABhQ1d/OsRmPAS8RkToRCQPnA8vm2IYFh3EwiwARiQI3AR9W1cH5sEFVS6p6GtAOPMcNCcwZIvIqoGdEhHWeeaGqng6cB7zfDafONV6choBXqeqzcEoKPjEPduCG514D/GSezl8DXAAsB1qBiIi8bS5tUNXHgS8Bv8MJjz2Co914QmMczALHnfO4CbheVX823/a4YZg/AnMtzPdC4DXu/MePgLNF5AdzbAMAqrrffewBfo4Td59r9gJ7x4wkf4rjcOaD84AHVfXgPJ3/HGCHqh5S1QLwM+AFc22Eqn5HVU9X1ZfghNdP6PkXMA5mQeNOrn8HeFxV/20e7WgQkaT7PITzg35iLm1Q1U+qaruqduGEY36vqnN6lwogIhE34QI3JHUuTnhkTlHVbmCPiKx1F70MmNPkjzG8hXkKj7nsBp4nImH3N/MynPnKOUVEGt3HDuD1zO9nsiCYSk35hEZEfgicCdSLyF7gM6r6nTk244XAXwEb3fkPgE+p6q/n2I4W4Bo3U8gCblTVeUsTnmeagJ871zG8wA2qess82fJB4Ho3RLUduHiuDXDnG14OvGeuzz2Cqt4jIj8FHsQJSz3E/Mi13CQidUABeL+q9s2DDQsKk6ZsMBgMhqpgQmQGg8FgqArGwRgMBoOhKhgHYzAYDIaqYByMwWAwGKqCcTAGg8FgqArGwRimRURURK4Y8/pjIvLZCh37+yJyYSWONc153ugqDv+h2ueaT0TkzOmUpkXkNBE5f8zr14jIvKgAGJY2xsEYyiEHvF5E6ufbkLG4dTnl8tfA+1T1rGrZs4g4DUcrCwBVvVlVL58/cwxLFeNgDOVQxClc+8j4FeNHICIy7D6eKSK3i8iNIvKkiFwuIhe5fWU2isjKMYc5R0T+5G73Knd/j4h8WUTuE5FHReQ9Y477BxG5Adg4gT1vcY//mIh8yV32aeBFwDdE5Mvjto+KyG0i8qC73wVj1r3dPfcjInKdu6xJRH7uLntERF7gLv9b95yPidsLRES6xOnVcrW7/HoROUdE/k9EtorIc9ztPisi14jIb8XpNfN6EflX155bXLkgRORlrrDlRnH6FQXc5a90z/NnnAryEfufIyJ3uvvcKU7/Fj/weeBN4vRweZOIvFNE/sPdp9P9PB51HzvG/J+/5h5n+8j/XERaROQO91iPiciLp/4qGU4oVNX8mb8p/3D64sSBnUAC+BjwWXfd94ELx27rPp4J9OOoAARweoV8zl33IeDfx+x/C87Nzmocja0gcAnwj+42AeB+HDHDM3GEHZdPYGcrjmxIA06V/e9x+ueAo5+2YYJ9vDi9ZQDqgW2AACcDW4B6d12t+/hjHNFRAI/7eZyB4+wiQBTYhKN83YXjnE91398DwHfd418A/MI9zmdx+pn4gGcCaeA8d93Pgde6n8keYI27/Frgw2OWr3aPeyPwK3ebOOB1n58D3OQ+fyfwH2M+g9HXwH8D73Cfv2uMjd/HEbO0gPXANnf5R4F/GPN5xOb7+2r+Fs6fGcEYykIdFedrgctmsNt96vS0yQFPASMS6htxLr4j3KiqtjrNmbYD63A0vt7uSuTcA9ThXEQB7lXVHROc79nAH9URPSwC1+P0TJkKAf5ZRB4FbsXpt9MEnA38VN2mYqo60hvobOAqd1lJVQdwRkc/V9WUOn1zfgaM3MnvUNWNqmrjOJ7bVFUn+Az+Vx2hxo04F+oR+ZmR7da6x3rSXX6N+97Wucu3uscdKwCaAH4iTlfWK3Gc5nQ8H7jBfX6d+95G+IX7f9rsfkYA9wEXu3Nyp6rTt8hgAEyIzDAz/h1nLmNs35Ei7vdIRATwj1mXG/PcHvPa5mgdvPF6RYpz4f+gqp7m/i3Xp3t8pCaxT8p8H2O5CGfEc4Y67QgO4owKZAK7JmOq85b7GeQAXEdUcJ3F2O2mOsdkdn4B+IM6HVlfjfO+ZsrYY499L+LaeweOo9sHXCcibz+OcxiWKMbBGMrGvYu/EcfJjLATJ0QETtjHdxyHfqOIWO68zAqc0NRvgEvHzD+skekbat0DvFRE6t0EgLfgdBacigROn5mCiJwFdLrLbwP+UhzxQkSkdszyS91lHnE6S94BvFYcNd8ITifDP5X97svjCaBLRFa5r/8K5709ASwfM6f1lnHvbZ/7/J1jlg8BsUnOcyeOWjU4zvfPUxklIp04n9+3cZS/56tlgGEBYhyMYaZcgTNXMcK3cS7q9wLPZfLRxVRswblY/i/wXlXN4rRF3gw86IZ4vsk06t+qegD4JPAHnIZPD6rqL6c59/XABhG5H+eC+oR7rE3AF4HbReQRYKRdwoeAs0RkI86cysnqtLX+PnAvjpO7WlUfKv/tT4/7mVyME/LaiDOy+Ya7/BLgf9xJ/rEti/8V+BcR+T+csNsIfwDWj0zyjzvVZTghr0dxnNiHpjHtTOBhEXkIeAPw1eN6g4YliVFTNhgMBkNVMCMYg8FgMFQF42AMBoPBUBWMgzEYDAZDVTAOxmAwGAxVwTgYg8FgMFQF42AMBoPBUBWMgzEYDAZDVfj/qHZLtUmwYnEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 447.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g3 = sns.relplot(data=pivot_season[pivot_season['bedrooms'] < 6],\n",
    "            y='delta_prop',\n",
    "            x='bedrooms',\n",
    "            style ='category_beach',\n",
    "            hue='category_beach',\n",
    "            hue_order = ['coast', 'inner', 'center'],\n",
    "            kind='line',\n",
    "            markers=True,\n",
    "            dashes=False)\n",
    "g3.set(xlabel = 'Number of bedrooms',\n",
    "          ylabel = 'Difference in proportional availability between summer and winter',\n",
    "          title = 'Season availability by number of bedrooms and category')\n",
    "g3._legend.set_title('Category')\n",
    "leg3 = g3._legend\n",
    "leg3.set_bbox_to_anchor([0.29, 0.9])  # coordinates of lower left of bounding box\n",
    "\n",
    "#plt.savefig('fig_5.png', bbox_inches='tight', dpi=300);\n",
    "\n",
    "g4 = sns.relplot(data=pivot_season[pivot_season['accommodates'] < 10],\n",
    "            y='delta_prop',\n",
    "            x='accommodates',\n",
    "            style ='category_beach',\n",
    "            hue='category_beach',\n",
    "            hue_order = ['coast', 'inner', 'center'],\n",
    "            kind='line',\n",
    "            markers=True,\n",
    "            dashes=False)\n",
    "g4.set(xlabel = 'Number of accommodations',\n",
    "          ylabel = 'Difference in proportional availability between summer and winter',\n",
    "          title = 'Season availability by number of accommodations and category');\n",
    "g4._legend.set_title('Category')\n",
    "leg4 = g4._legend\n",
    "leg4.set_bbox_to_anchor([0.33, 0.9])  # coordinates of lower left of bounding box\n",
    "\n",
    "#plt.savefig('fig_6.png', bbox_inches='tight', dpi=300);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>season</th>\n",
       "      <th>id</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>summer</th>\n",
       "      <th>winter</th>\n",
       "      <th>summer_prop</th>\n",
       "      <th>winter_prop</th>\n",
       "      <th>delta_prop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category_beach</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>center</th>\n",
       "      <td>1.903001e+07</td>\n",
       "      <td>1.388178</td>\n",
       "      <td>3.648703</td>\n",
       "      <td>29.576794</td>\n",
       "      <td>21.046986</td>\n",
       "      <td>0.328631</td>\n",
       "      <td>0.228772</td>\n",
       "      <td>0.099859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coast</th>\n",
       "      <td>1.697129e+07</td>\n",
       "      <td>2.175740</td>\n",
       "      <td>4.520837</td>\n",
       "      <td>23.438287</td>\n",
       "      <td>22.603742</td>\n",
       "      <td>0.260425</td>\n",
       "      <td>0.245693</td>\n",
       "      <td>0.014733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inner</th>\n",
       "      <td>1.962625e+07</td>\n",
       "      <td>2.004385</td>\n",
       "      <td>4.548676</td>\n",
       "      <td>32.801263</td>\n",
       "      <td>23.245922</td>\n",
       "      <td>0.364458</td>\n",
       "      <td>0.252673</td>\n",
       "      <td>0.111785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "season                    id  bedrooms  accommodates     summer     winter  \\\n",
       "category_beach                                                               \n",
       "center          1.903001e+07  1.388178      3.648703  29.576794  21.046986   \n",
       "coast           1.697129e+07  2.175740      4.520837  23.438287  22.603742   \n",
       "inner           1.962625e+07  2.004385      4.548676  32.801263  23.245922   \n",
       "\n",
       "season          summer_prop  winter_prop  delta_prop  \n",
       "category_beach                                        \n",
       "center             0.328631     0.228772    0.099859  \n",
       "coast              0.260425     0.245693    0.014733  \n",
       "inner              0.364458     0.252673    0.111785  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_season.groupby(['category_beach']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0143281068771125,\n",
       "                 est_int     coefs  abs_coefs\n",
       " 2  category_beach_coast -0.069463   0.069463\n",
       " 3  category_beach_inner  0.025472   0.025472\n",
       " 0              bedrooms -0.022537   0.022537\n",
       " 1          accommodates  0.010375   0.010375)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Linear regression\n",
    "lm(X=pivot_season[['category_beach', 'bedrooms', 'accommodates']], y=pivot_season['delta_prop'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='summary'></a>\n",
    "## Summary\n",
    "\n",
    "Listings in neighborhoods close to a beach have similar number of bedrooms and accommodations than listings in inner neighborhoods. Yet, they are rented for a higher price and are less available in summer (compared to winter) than inner listings."
   ]
  }
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