Seattle_EDA.ipynb

{
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   "cell_type": "markdown",
   "id": "1adb6868",
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    "id": "1adb6868"
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   "source": [
    "# Project: Exploratory Data Analysis on Seattle Airbnb Data\n",
    "### Project Due on Friday 10th Dec 2021, 11:59 pm EST\n",
    "\n",
    "#### Context\n",
    "Since 2008, guests and hosts have used Airbnb to travel in a more unique, personalized way. As part of the Airbnb Inside initiative, this dataset describes the listing activity of homestays in Seattle, WA.\n",
    "\n",
    "#### Content\n",
    "The following Airbnb data is included in this Seattle dataset:\n",
    "- Listings, including full descriptions and average review score\n",
    "- Reviews, including unique id for each reviewer and detailed comments\n",
    "- Calendar, including listing id and the price and availability for that day\n",
    "\n",
    "**To get the dataset click [here](https://georgiancollege-my.sharepoint.com/:f:/g/personal/katie_tiwari_georgiancollege_ca/Eh6hG8jQX_FBvU_pk5R5wZwB_xn5gS2eBE1vWQ_DmaH1kQ?e=evqomn)**\n",
    "\n",
    "#### Inspiration for questions\n",
    "- Can you describe the vibe of each Seattle neighborhood using listing descriptions?\n",
    "- What are the busiest times of the year to visit Seattle? By how much do prices spike?\n",
    "- Is there a general upward trend of both new Airbnb listings and total Airbnb visitors to Seattle?\n",
    "\n",
    "#### **Note:** Don't discuss the results, limitations, conclusion, reasons as comment in the code cell. Use separate markdown cells instead. If you are not familier with markdown, refer this [link here](https://www.datacamp.com/community/tutorials/markdown-in-jupyter-notebook). Only add code related comments as comment in the code cell. Also Make sure to add comments in the code.\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26a4ee08",
   "metadata": {
    "id": "26a4ee08"
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   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "> In this section of the report, provide a brief introduction to the dataset you've selected for analysis. \n",
    "\n",
    "- Create atleast three complex and meaningful questions(refer inspiration questions to get an idea), then address those questions in the rest of the analysis. If you're not sure what questions to ask, then make sure you familiarize yourself with the dataset, its variables and the dataset context for ideas of what to explore.\n",
    "\n",
    "    1) Which months are the least and most expensive to visit Seattle in?\n",
    "\n",
    "    2) Which neighbourhoods are the most and least expensive to stay in?\n",
    "\n",
    "    3) Which property types are the most expensive?\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f705f76",
   "metadata": {},
   "source": [
    "First of all, let's import the .csv dataset files using pandas into a dataframe and look at the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "adc23424",
   "metadata": {
    "id": "adc23424"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df_listings = pd.read_csv('./Seattle Dataset/listings.csv')\n",
    "df_calendar = pd.read_csv('./Seattle Dataset/calendar.csv')\n",
    "df_reviews = pd.read_csv('./Seattle Dataset/reviews.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1833c1ee",
   "metadata": {
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   "outputs": [
    {
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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>listing_url</th>\n",
       "      <th>scrape_id</th>\n",
       "      <th>last_scraped</th>\n",
       "      <th>name</th>\n",
       "      <th>summary</th>\n",
       "      <th>space</th>\n",
       "      <th>description</th>\n",
       "      <th>experiences_offered</th>\n",
       "      <th>neighborhood_overview</th>\n",
       "      <th>...</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>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>241032</td>\n",
       "      <td>https://www.airbnb.com/rooms/241032</td>\n",
       "      <td>20160104002432</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>Stylish Queen Anne Apartment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Make your self at home in this charming one-be...</td>\n",
       "      <td>Make your self at home in this charming one-be...</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>2</td>\n",
       "      <td>4.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>953595</td>\n",
       "      <td>https://www.airbnb.com/rooms/953595</td>\n",
       "      <td>20160104002432</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>Bright &amp; Airy Queen Anne Apartment</td>\n",
       "      <td>Chemically sensitive? We've removed the irrita...</td>\n",
       "      <td>Beautiful, hypoallergenic apartment in an extr...</td>\n",
       "      <td>Chemically sensitive? We've removed the irrita...</td>\n",
       "      <td>none</td>\n",
       "      <td>Queen Anne is a wonderful, truly functional vi...</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>f</td>\n",
       "      <td>strict</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>6</td>\n",
       "      <td>1.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3308979</td>\n",
       "      <td>https://www.airbnb.com/rooms/3308979</td>\n",
       "      <td>20160104002432</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>New Modern House-Amazing water view</td>\n",
       "      <td>New modern house built in 2013.  Spectacular s...</td>\n",
       "      <td>Our house is modern, light and fresh with a wa...</td>\n",
       "      <td>New modern house built in 2013.  Spectacular s...</td>\n",
       "      <td>none</td>\n",
       "      <td>Upper Queen Anne is a charming neighborhood fu...</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>f</td>\n",
       "      <td>strict</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>2</td>\n",
       "      <td>1.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                           listing_url       scrape_id last_scraped  \\\n",
       "0   241032   https://www.airbnb.com/rooms/241032  20160104002432   2016-01-04   \n",
       "1   953595   https://www.airbnb.com/rooms/953595  20160104002432   2016-01-04   \n",
       "2  3308979  https://www.airbnb.com/rooms/3308979  20160104002432   2016-01-04   \n",
       "\n",
       "                                  name  \\\n",
       "0         Stylish Queen Anne Apartment   \n",
       "1   Bright & Airy Queen Anne Apartment   \n",
       "2  New Modern House-Amazing water view   \n",
       "\n",
       "                                             summary  \\\n",
       "0                                                NaN   \n",
       "1  Chemically sensitive? We've removed the irrita...   \n",
       "2  New modern house built in 2013.  Spectacular s...   \n",
       "\n",
       "                                               space  \\\n",
       "0  Make your self at home in this charming one-be...   \n",
       "1  Beautiful, hypoallergenic apartment in an extr...   \n",
       "2  Our house is modern, light and fresh with a wa...   \n",
       "\n",
       "                                         description experiences_offered  \\\n",
       "0  Make your self at home in this charming one-be...                none   \n",
       "1  Chemically sensitive? We've removed the irrita...                none   \n",
       "2  New modern house built in 2013.  Spectacular s...                none   \n",
       "\n",
       "                               neighborhood_overview  ... review_scores_value  \\\n",
       "0                                                NaN  ...                10.0   \n",
       "1  Queen Anne is a wonderful, truly functional vi...  ...                10.0   \n",
       "2  Upper Queen Anne is a charming neighborhood fu...  ...                10.0   \n",
       "\n",
       "  requires_license license jurisdiction_names instant_bookable  \\\n",
       "0                f     NaN         WASHINGTON                f   \n",
       "1                f     NaN         WASHINGTON                f   \n",
       "2                f     NaN         WASHINGTON                f   \n",
       "\n",
       "  cancellation_policy  require_guest_profile_picture  \\\n",
       "0            moderate                              f   \n",
       "1              strict                              t   \n",
       "2              strict                              f   \n",
       "\n",
       "  require_guest_phone_verification calculated_host_listings_count  \\\n",
       "0                                f                              2   \n",
       "1                                t                              6   \n",
       "2                                f                              2   \n",
       "\n",
       "  reviews_per_month  \n",
       "0              4.07  \n",
       "1              1.48  \n",
       "2              1.15  \n",
       "\n",
       "[3 rows x 92 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_listings.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12689caf",
   "metadata": {},
   "source": [
    "The listings dataset includes the urls, names, summaries, locations etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "497102df",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>listing_id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>t</td>\n",
       "      <td>$85.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>t</td>\n",
       "      <td>$85.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id        date available   price\n",
       "0      241032  2016-01-04         t  $85.00\n",
       "1      241032  2016-01-05         t  $85.00\n",
       "2      241032  2016-01-06         f     NaN"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_calendar.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e9ce502",
   "metadata": {},
   "source": [
    "The calendar dataset includes the listing id, date, availability (true/false) and the price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "93bef85c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>listing_id</th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>reviewer_id</th>\n",
       "      <th>reviewer_name</th>\n",
       "      <th>comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7202016</td>\n",
       "      <td>38917982</td>\n",
       "      <td>2015-07-19</td>\n",
       "      <td>28943674</td>\n",
       "      <td>Bianca</td>\n",
       "      <td>Cute and cozy place. Perfect location to every...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7202016</td>\n",
       "      <td>39087409</td>\n",
       "      <td>2015-07-20</td>\n",
       "      <td>32440555</td>\n",
       "      <td>Frank</td>\n",
       "      <td>Kelly has a great room in a very central locat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7202016</td>\n",
       "      <td>39820030</td>\n",
       "      <td>2015-07-26</td>\n",
       "      <td>37722850</td>\n",
       "      <td>Ian</td>\n",
       "      <td>Very spacious apartment, and in a great neighb...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id        id        date  reviewer_id reviewer_name  \\\n",
       "0     7202016  38917982  2015-07-19     28943674        Bianca   \n",
       "1     7202016  39087409  2015-07-20     32440555         Frank   \n",
       "2     7202016  39820030  2015-07-26     37722850           Ian   \n",
       "\n",
       "                                            comments  \n",
       "0  Cute and cozy place. Perfect location to every...  \n",
       "1  Kelly has a great room in a very central locat...  \n",
       "2  Very spacious apartment, and in a great neighb...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reviews.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b4cd3c5",
   "metadata": {},
   "source": [
    "The reviews dataset includes the listing id, review id, date of review published, reviewer id, name and their comments.\n",
    "\n",
    "Let's take a look at the dataset info, this contains the counts of each variable, their names and datatypes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "23acd0a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3818 entries, 0 to 3817\n",
      "Data columns (total 92 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   id                                3818 non-null   int64  \n",
      " 1   listing_url                       3818 non-null   object \n",
      " 2   scrape_id                         3818 non-null   int64  \n",
      " 3   last_scraped                      3818 non-null   object \n",
      " 4   name                              3818 non-null   object \n",
      " 5   summary                           3641 non-null   object \n",
      " 6   space                             3249 non-null   object \n",
      " 7   description                       3818 non-null   object \n",
      " 8   experiences_offered               3818 non-null   object \n",
      " 9   neighborhood_overview             2786 non-null   object \n",
      " 10  notes                             2212 non-null   object \n",
      " 11  transit                           2884 non-null   object \n",
      " 12  thumbnail_url                     3498 non-null   object \n",
      " 13  medium_url                        3498 non-null   object \n",
      " 14  picture_url                       3818 non-null   object \n",
      " 15  xl_picture_url                    3498 non-null   object \n",
      " 16  host_id                           3818 non-null   int64  \n",
      " 17  host_url                          3818 non-null   object \n",
      " 18  host_name                         3816 non-null   object \n",
      " 19  host_since                        3816 non-null   object \n",
      " 20  host_location                     3810 non-null   object \n",
      " 21  host_about                        2959 non-null   object \n",
      " 22  host_response_time                3295 non-null   object \n",
      " 23  host_response_rate                3295 non-null   object \n",
      " 24  host_acceptance_rate              3045 non-null   object \n",
      " 25  host_is_superhost                 3816 non-null   object \n",
      " 26  host_thumbnail_url                3816 non-null   object \n",
      " 27  host_picture_url                  3816 non-null   object \n",
      " 28  host_neighbourhood                3518 non-null   object \n",
      " 29  host_listings_count               3816 non-null   float64\n",
      " 30  host_total_listings_count         3816 non-null   float64\n",
      " 31  host_verifications                3818 non-null   object \n",
      " 32  host_has_profile_pic              3816 non-null   object \n",
      " 33  host_identity_verified            3816 non-null   object \n",
      " 34  street                            3818 non-null   object \n",
      " 35  neighbourhood                     3402 non-null   object \n",
      " 36  neighbourhood_cleansed            3818 non-null   object \n",
      " 37  neighbourhood_group_cleansed      3818 non-null   object \n",
      " 38  city                              3818 non-null   object \n",
      " 39  state                             3818 non-null   object \n",
      " 40  zipcode                           3811 non-null   object \n",
      " 41  market                            3818 non-null   object \n",
      " 42  smart_location                    3818 non-null   object \n",
      " 43  country_code                      3818 non-null   object \n",
      " 44  country                           3818 non-null   object \n",
      " 45  latitude                          3818 non-null   float64\n",
      " 46  longitude                         3818 non-null   float64\n",
      " 47  is_location_exact                 3818 non-null   object \n",
      " 48  property_type                     3817 non-null   object \n",
      " 49  room_type                         3818 non-null   object \n",
      " 50  accommodates                      3818 non-null   int64  \n",
      " 51  bathrooms                         3802 non-null   float64\n",
      " 52  bedrooms                          3812 non-null   float64\n",
      " 53  beds                              3817 non-null   float64\n",
      " 54  bed_type                          3818 non-null   object \n",
      " 55  amenities                         3818 non-null   object \n",
      " 56  square_feet                       97 non-null     float64\n",
      " 57  price                             3818 non-null   object \n",
      " 58  weekly_price                      2009 non-null   object \n",
      " 59  monthly_price                     1517 non-null   object \n",
      " 60  security_deposit                  1866 non-null   object \n",
      " 61  cleaning_fee                      2788 non-null   object \n",
      " 62  guests_included                   3818 non-null   int64  \n",
      " 63  extra_people                      3818 non-null   object \n",
      " 64  minimum_nights                    3818 non-null   int64  \n",
      " 65  maximum_nights                    3818 non-null   int64  \n",
      " 66  calendar_updated                  3818 non-null   object \n",
      " 67  has_availability                  3818 non-null   object \n",
      " 68  availability_30                   3818 non-null   int64  \n",
      " 69  availability_60                   3818 non-null   int64  \n",
      " 70  availability_90                   3818 non-null   int64  \n",
      " 71  availability_365                  3818 non-null   int64  \n",
      " 72  calendar_last_scraped             3818 non-null   object \n",
      " 73  number_of_reviews                 3818 non-null   int64  \n",
      " 74  first_review                      3191 non-null   object \n",
      " 75  last_review                       3191 non-null   object \n",
      " 76  review_scores_rating              3171 non-null   float64\n",
      " 77  review_scores_accuracy            3160 non-null   float64\n",
      " 78  review_scores_cleanliness         3165 non-null   float64\n",
      " 79  review_scores_checkin             3160 non-null   float64\n",
      " 80  review_scores_communication       3167 non-null   float64\n",
      " 81  review_scores_location            3163 non-null   float64\n",
      " 82  review_scores_value               3162 non-null   float64\n",
      " 83  requires_license                  3818 non-null   object \n",
      " 84  license                           0 non-null      float64\n",
      " 85  jurisdiction_names                3818 non-null   object \n",
      " 86  instant_bookable                  3818 non-null   object \n",
      " 87  cancellation_policy               3818 non-null   object \n",
      " 88  require_guest_profile_picture     3818 non-null   object \n",
      " 89  require_guest_phone_verification  3818 non-null   object \n",
      " 90  calculated_host_listings_count    3818 non-null   int64  \n",
      " 91  reviews_per_month                 3191 non-null   float64\n",
      "dtypes: float64(17), int64(13), object(62)\n",
      "memory usage: 2.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df_listings.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aeeb7c1",
   "metadata": {},
   "source": [
    "This shows that there are 92 variables, each contains roughly 3000-4000 values, mostly of datatypes int, float (numbers) and objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fe5c81a1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1393570 entries, 0 to 1393569\n",
      "Data columns (total 4 columns):\n",
      " #   Column      Non-Null Count    Dtype \n",
      "---  ------      --------------    ----- \n",
      " 0   listing_id  1393570 non-null  int64 \n",
      " 1   date        1393570 non-null  object\n",
      " 2   available   1393570 non-null  object\n",
      " 3   price       934542 non-null   object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 42.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df_calendar.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81c34277",
   "metadata": {},
   "source": [
    "This shows that the calendar dataset has 4 variables, with id/date/available variables including almost 1400000 entries, while the variable price is only entered 934542 times."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e90205b",
   "metadata": {},
   "source": [
    "Let's now take a look if there are any null values present in the dataset. Based on the initial exploration, we can presume that there are, indeed, null values present in all 3 datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "698f8230",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                    0\n",
       "listing_url                           0\n",
       "scrape_id                             0\n",
       "last_scraped                          0\n",
       "name                                  0\n",
       "                                   ... \n",
       "cancellation_policy                   0\n",
       "require_guest_profile_picture         0\n",
       "require_guest_phone_verification      0\n",
       "calculated_host_listings_count        0\n",
       "reviews_per_month                   627\n",
       "Length: 92, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_listings.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b3fdb736",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "listing_id         0\n",
       "date               0\n",
       "available          0\n",
       "price         459028\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_calendar.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "caa8596a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "listing_id        0\n",
       "id                0\n",
       "date              0\n",
       "reviewer_id       0\n",
       "reviewer_name     0\n",
       "comments         18\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reviews.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5d55563",
   "metadata": {},
   "source": [
    "The analysis further shows that there are, indeed some null values in all 3 datasets. This finding will be worked upon in the data wrangling part of this assignment."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0b1cc45",
   "metadata": {},
   "source": [
    "All in all, the initial analysis shows us what the data contains, what they do not contain and which variables might be beneficial for us to take a closer look at in the search of the answers for questions that we might have."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a033f40c",
   "metadata": {},
   "source": [
    "Based on the analysis, I have chosen these 3 questions to answer in the following parts of the assignment:\n",
    "    \n",
    "    1) Which months are the least and most expensive to visit Seattle in?\n",
    "    2) Which neighbourhoods are the most and least expensive to stay in?\n",
    "    3) Which property types are the most expensive?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beabf01f",
   "metadata": {
    "id": "beabf01f"
   },
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "> In this section of the report, you will load in the data, check for cleanliness, merging multiple files, printing some rows of the data in output, handling missing values, duplicat values, outliers etc. and then trim and clean your dataset for analysis. Make sure that you document your steps carefully and privde the reasoning for every decision taken."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de269763",
   "metadata": {},
   "source": [
    "In the introductory section of this report, I took a closer look at the variables within the dataset and discovered that there are null values present. In this part of the report, I will be further preparing the dataset for the EDA part of this assignment.\n",
    "\n",
    "Using plots, we can look at the distribution of missing values per variable.\n",
    "\n",
    "Let's start with the listings dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f94805e9",
   "metadata": {
    "id": "f94805e9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Missing values per column, %'}>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_missing_listings = df_listings.isnull().mean()*100\n",
    "listings_null = df_missing_listings[df_missing_listings > 0]\n",
    "listings_null.plot.bar(title='Missing values per column, %', figsize=(12, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ac76c94",
   "metadata": {},
   "source": [
    "We can see that there are mostly (90%+) null values in both square_feet and reviews_per_month variables. There are also null values in other variables.\n",
    "\n",
    "Let's take a look at the null values in calendar dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "10ef5005",
   "metadata": {
    "id": "10ef5005"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Missing values per column, %'}>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_calendar_missing = pd.DataFrame([df_calendar.isnull().mean()*100])\n",
    "df_calendar_missing.plot.bar(title='Missing values per column, %', figsize=(8, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b37e5b2",
   "metadata": {},
   "source": [
    "The variable price contains 30%+ null values. \n",
    "\n",
    "Let's take a look at the reviews dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bf9df108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Missing values per column, %'}>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_reviews_missing = pd.DataFrame([df_reviews.isnull().mean()*100])\n",
    "df_reviews_missing.plot.bar(title='Missing values per column, %', figsize=(8, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6704e959",
   "metadata": {},
   "source": [
    "There are only 0.02% null values in the comments variable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7af9753",
   "metadata": {},
   "source": [
    "Since we have determined our three research questions, we can only focus on variables which are pertinent to our research goal. \n",
    "\n",
    "First, let's remove all rows where price is null (missing) in the calendar dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d7fa9489",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "listing_id    0\n",
       "date          0\n",
       "available     0\n",
       "price         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_calendar=df_calendar.dropna(subset=['price'])\n",
    "df_calendar.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "29ba677e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>934542</td>\n",
       "      <td>934542</td>\n",
       "      <td>934542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>365</td>\n",
       "      <td>1</td>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>t</td>\n",
       "      <td>$150.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>2922</td>\n",
       "      <td>934542</td>\n",
       "      <td>36646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              date available    price\n",
       "count       934542    934542   934542\n",
       "unique         365         1      669\n",
       "top     2017-01-01         t  $150.00\n",
       "freq          2922    934542    36646"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_calendar.describe(include=object)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a688d0c",
   "metadata": {},
   "source": [
    "Availability also does not quite concern us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6e009fe7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>934542</td>\n",
       "      <td>934542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>365</td>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>$150.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>2922</td>\n",
       "      <td>36646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              date    price\n",
       "count       934542   934542\n",
       "unique         365      669\n",
       "top     2017-01-01  $150.00\n",
       "freq          2922    36646"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_calendar=df_calendar.drop(['available'],axis=1)\n",
    "df_calendar.describe(include=object)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69fd820a",
   "metadata": {},
   "source": [
    "Let's check what data types are in each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "68a502e6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "listing_id     int64\n",
       "date          object\n",
       "price         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_calendar.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c166c02b",
   "metadata": {},
   "source": [
    "The date is of datatype object, we need to change the data type to datetime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4e0b06b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_calendar[\"date\"]= pd.to_datetime(df_calendar[\"date\"], format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f15b88b7",
   "metadata": {},
   "source": [
    "We should introduce a new column which would be the number corresponding to a month in a calendar year. This will help us with the analysis later. This is done because to answer the first question, we need to know the prices by month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "85bb3b34",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_calendar[\"month\"]=df_calendar[\"date\"].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "84c2d377",
   "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>price</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>$85.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>$85.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>$85.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-14</td>\n",
       "      <td>$85.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-18</td>\n",
       "      <td>$85.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-19</td>\n",
       "      <td>$85.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-20</td>\n",
       "      <td>$85.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-24</td>\n",
       "      <td>$85.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-25</td>\n",
       "      <td>$85.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-26</td>\n",
       "      <td>$85.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-27</td>\n",
       "      <td>$85.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-28</td>\n",
       "      <td>$85.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>$85.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-02-02</td>\n",
       "      <td>$85.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-02-03</td>\n",
       "      <td>$85.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    listing_id       date   price  month\n",
       "0       241032 2016-01-04  $85.00      1\n",
       "1       241032 2016-01-05  $85.00      1\n",
       "9       241032 2016-01-13  $85.00      1\n",
       "10      241032 2016-01-14  $85.00      1\n",
       "14      241032 2016-01-18  $85.00      1\n",
       "15      241032 2016-01-19  $85.00      1\n",
       "16      241032 2016-01-20  $85.00      1\n",
       "20      241032 2016-01-24  $85.00      1\n",
       "21      241032 2016-01-25  $85.00      1\n",
       "22      241032 2016-01-26  $85.00      1\n",
       "23      241032 2016-01-27  $85.00      1\n",
       "24      241032 2016-01-28  $85.00      1\n",
       "28      241032 2016-02-01  $85.00      2\n",
       "29      241032 2016-02-02  $85.00      2\n",
       "30      241032 2016-02-03  $85.00      2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_calendar.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fc627b0",
   "metadata": {},
   "source": [
    "We also need to remove the \"$\" and \",\" signs from price rows and change the datatype to numeric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "50b6a85d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_calendar['price'] = df_calendar['price'].str.replace(r'\\$', '')\n",
    "df_calendar['price'] = df_calendar['price'].str.replace(r'\\,', '')\n",
    "df_calendar[\"price\"] = pd.to_numeric(df_calendar[\"price\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0337552f",
   "metadata": {
    "scrolled": true
   },
   "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>price</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-14</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-18</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-19</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-20</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-24</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-25</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-26</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    listing_id       date  price  month\n",
       "0       241032 2016-01-04   85.0      1\n",
       "1       241032 2016-01-05   85.0      1\n",
       "9       241032 2016-01-13   85.0      1\n",
       "10      241032 2016-01-14   85.0      1\n",
       "14      241032 2016-01-18   85.0      1\n",
       "15      241032 2016-01-19   85.0      1\n",
       "16      241032 2016-01-20   85.0      1\n",
       "20      241032 2016-01-24   85.0      1\n",
       "21      241032 2016-01-25   85.0      1\n",
       "22      241032 2016-01-26   85.0      1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_calendar.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34f455c1",
   "metadata": {},
   "source": [
    "Now we have everything we need to implement code to answer our first question. But since all 3 questions are concerning price, we have to include the data in the listings dataset in our prepared calendar dataframe. We can do this, since we have our listing_id's available in both datasets.\n",
    "\n",
    "First, let's drop columns that are of no use to us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c4315093",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_listings = df_listings[['id','neighbourhood_cleansed', 'neighbourhood_group_cleansed', 'property_type', 'cleaning_fee']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7f2ccafb",
   "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>neighbourhood_cleansed</th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "      <th>property_type</th>\n",
       "      <th>cleaning_fee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>241032</td>\n",
       "      <td>West Queen Anne</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>953595</td>\n",
       "      <td>West Queen Anne</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>$40.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3308979</td>\n",
       "      <td>West Queen Anne</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>House</td>\n",
       "      <td>$300.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7421966</td>\n",
       "      <td>West Queen Anne</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>278830</td>\n",
       "      <td>West Queen Anne</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>House</td>\n",
       "      <td>$125.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id neighbourhood_cleansed neighbourhood_group_cleansed property_type  \\\n",
       "0   241032        West Queen Anne                   Queen Anne     Apartment   \n",
       "1   953595        West Queen Anne                   Queen Anne     Apartment   \n",
       "2  3308979        West Queen Anne                   Queen Anne         House   \n",
       "3  7421966        West Queen Anne                   Queen Anne     Apartment   \n",
       "4   278830        West Queen Anne                   Queen Anne         House   \n",
       "\n",
       "  cleaning_fee  \n",
       "0          NaN  \n",
       "1       $40.00  \n",
       "2      $300.00  \n",
       "3          NaN  \n",
       "4      $125.00  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_listings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f49c0219",
   "metadata": {},
   "source": [
    "We have to rename the listing_id column name to id, otherwise we won't be able to merge the two dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "72e2d888",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_calendar.rename(columns={'listing_id': 'id'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55d12ac4",
   "metadata": {},
   "source": [
    "Merge the two dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b53dd8d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_l_c = pd.merge(df_calendar, df_listings, on=\"id\", how=\"inner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "40b7164d",
   "metadata": {
    "scrolled": false
   },
   "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>date</th>\n",
       "      <th>price</th>\n",
       "      <th>month</th>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "      <th>property_type</th>\n",
       "      <th>cleaning_fee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1</td>\n",
       "      <td>West Queen Anne</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id       date  price  month neighbourhood_cleansed  \\\n",
       "0  241032 2016-01-04   85.0      1        West Queen Anne   \n",
       "\n",
       "  neighbourhood_group_cleansed property_type cleaning_fee  \n",
       "0                   Queen Anne     Apartment          NaN  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_l_c.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95b39c2f",
   "metadata": {},
   "source": [
    "We need to remove the \"$\" and \",\" signs from the cleaning_fee column and change the datatype to numeric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1b534657",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_l_c['cleaning_fee'] = df_l_c['cleaning_fee'].str.replace(r'\\$', '')\n",
    "df_l_c['cleaning_fee'] = df_l_c['cleaning_fee'].str.replace(r'\\,', '')\n",
    "df_l_c[\"cleaning_fee\"] = pd.to_numeric(df_l_c[\"cleaning_fee\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "16de4fd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                   0\n",
       "date                                 0\n",
       "price                                0\n",
       "month                                0\n",
       "neighbourhood_cleansed               0\n",
       "neighbourhood_group_cleansed         0\n",
       "property_type                      309\n",
       "cleaning_fee                    247995\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_l_c.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "144613ce",
   "metadata": {},
   "source": [
    "Finally, we do have some null values in the cleaning_fee column. Let's assume that it means no cleaning fee included, so we will replace the null values with 0s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7af37739",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_l_c['cleaning_fee'] = df_l_c['cleaning_fee'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7b5a71e4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>date</th>\n",
       "      <th>price</th>\n",
       "      <th>month</th>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "      <th>property_type</th>\n",
       "      <th>cleaning_fee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1</td>\n",
       "      <td>West Queen Anne</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id       date  price  month neighbourhood_cleansed  \\\n",
       "0  241032 2016-01-04   85.0      1        West Queen Anne   \n",
       "\n",
       "  neighbourhood_group_cleansed property_type  cleaning_fee  \n",
       "0                   Queen Anne     Apartment           0.0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_l_c.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59dfcbaa",
   "metadata": {},
   "source": [
    "Our dataframe is now ready to be worked with in the last part of this assignment."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a4a23c6",
   "metadata": {
    "id": "0a4a23c6"
   },
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "> Now you're ready to move on to exploration. Compute statistics and create visualizations with the goal of addressing the research questions that you posed in the Introduction section. Look at one variable at a time, and then follow it up by looking at relationships between variables.\n",
    "\n",
    "- The EDA should include both single-variable (1d) and multiple-variable (2d) explorations. \n",
    "- The project's visualizations should be varied and show multiple comparisons and trends. At least three kinds of plots(eg. histogram, box plot, line plot, bar plot, etc) should be created as part of the explorations.\n",
    "- Please make sure reasoning is provided for each analysis decision, plot, and statistical summary.\n",
    "- Visualizations made in the notebook, should depict the data in an appropriate manner (i.e., has appropriate axis labels, scale, legends, and title) that allows plots to be readily interpreted.\n",
    "\n",
    "### 1) Which months are the least and most expensive to visit Seattle in?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "597517a5",
   "metadata": {
    "id": "9d22dc89"
   },
   "source": [
    "As we have already prepared our data, we can now finally analyse what kind of data we are working with. We can examine the distribution of the data, look for outliers or see if there's correlations. All this gives us further insight and it also helps us make a robust machine learning model in the end.\n",
    "\n",
    "Let's begin with checking the price histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3c6c0316",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_l_c[\"price\"].plot.hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa75aabc",
   "metadata": {},
   "source": [
    "The y-axis shows the count (number of occurences) and the x-axis shows the price. We can see that there's mostly listings under $250.\n",
    "Let's look at the cleaning fee's as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "255c9e86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_l_c[\"cleaning_fee\"].plot.hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47d569b2",
   "metadata": {},
   "source": [
    "The cleaning fee seems more \"randomly\" distributed, however, the most occuring value is 0, which means no cleaning fee.\n",
    "\n",
    "Let's take a look at the average price per month. I am deliberately using the mean function here, since the histogram does resemble gaussian distribution, therefore we can presume that our data is not skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3a1d2aa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "price_by_month = df_l_c.groupby('month', as_index=False)['price'].mean()\n",
    "price_by_month.plot.bar(x='month', y = 'price', figsize=(14,6));\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.xlabel('Month', fontsize = 16)\n",
    "plt.ylabel('Average price', fontsize = 16)\n",
    "plt.title(\"Average $ price per month\", fontsize = 16);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40130263",
   "metadata": {},
   "source": [
    "The months January, February and March are the least expensive. Months June, July and August seem to be the most expensive."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "766b919f",
   "metadata": {},
   "source": [
    "Let us also look at the cleaning fee prices. In this case, I believe using median() function is reasonable, as the data does not resemble gaussian distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fbc064bc",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "cleaning_fee_by_month = df_l_c.groupby('month', as_index=False)['cleaning_fee'].median()\n",
    "cleaning_fee_by_month.plot.bar(x='month', y = 'cleaning_fee', figsize=(14,6));\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.xlabel('Month', fontsize = 16)\n",
    "plt.ylabel('Median of cleaning fee prices', fontsize = 16)\n",
    "plt.title(\"Median $ cleaning fee prices per month\", fontsize = 16);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08683e95",
   "metadata": {},
   "source": [
    "The cleaning fee seems to be lower in the July - December interval by $5."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e66ebe18",
   "metadata": {},
   "source": [
    "After looking at each variable individually, we can also add them together. I assume that the cleaning fee is normally added on top of the standard price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1e185860",
   "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>date</th>\n",
       "      <th>price</th>\n",
       "      <th>month</th>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "      <th>property_type</th>\n",
       "      <th>cleaning_fee</th>\n",
       "      <th>combined_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>241032</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1</td>\n",
       "      <td>West Queen Anne</td>\n",
       "      <td>Queen Anne</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id       date  price  month neighbourhood_cleansed  \\\n",
       "0  241032 2016-01-04   85.0      1        West Queen Anne   \n",
       "\n",
       "  neighbourhood_group_cleansed property_type  cleaning_fee  combined_price  \n",
       "0                   Queen Anne     Apartment           0.0            85.0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_l_c['combined_price'] = df_l_c['price'] + df_l_c['cleaning_fee']\n",
    "df_l_c.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7f7d705",
   "metadata": {},
   "source": [
    "Let's now look if this made any difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c09117a4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "combined_price_by_month = df_l_c.groupby('month', as_index=False)['combined_price'].mean()\n",
    "combined_price_by_month.plot.bar(x = 'month', y = 'combined_price', figsize = (14,6));\n",
    "plt.xticks(fontsize = 16)\n",
    "plt.yticks(fontsize = 16)\n",
    "plt.xlabel('Month', fontsize = 16)\n",
    "plt.ylabel('Average of combined prices', fontsize = 16)\n",
    "plt.title(\"Average $ combined prices per month\", fontsize = 16);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48c98ca6",
   "metadata": {},
   "source": [
    "The histogram of combined prices shows that months June, July and August are still the most expensive, while months January, February and March are the least expensive. Finally, let's take a look at a line plot for better understanding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e3808bba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = df_l_c.groupby('month', as_index=False)['combined_price'].mean()\n",
    "plt.figure(figsize=(20, 10))\n",
    "x = temp['month']\n",
    "y = temp['combined_price']\n",
    "\n",
    "plt.plot(x,y,linewidth=2.5)\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Average Combined Price')\n",
    "plt.title('Month vs Average Combined Price')\n",
    "plt.xticks(x)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3469df89",
   "metadata": {},
   "source": [
    "It is interesting to observe the pricing jump from May to June, this is probably because most people are travelling in the summer months, and so the airbnb owners can justify setting higher prices, since we can assume that there is a high demand. Let's examine if we can see anything more interesting if we plot the price, combined price and cleaning fee on one graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "dbd7e6e6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp1 = df_l_c.groupby('month', as_index=False)['combined_price'].mean()\n",
    "\n",
    "plt.figure(figsize=(20, 10))\n",
    "\n",
    "x = temp1['month']\n",
    "y = temp1['combined_price']\n",
    "\n",
    "temp2 = df_l_c.groupby('month', as_index=False)['price'].mean()\n",
    "\n",
    "z = temp2['price']\n",
    "\n",
    "temp3 = df_l_c.groupby('month', as_index=False)['cleaning_fee'].mean()\n",
    "\n",
    "w = temp3['cleaning_fee']\n",
    "\n",
    "plt.plot(x, y, linewidth=2.5, label = \"Combined Price\")\n",
    "plt.plot(x, z, linewidth=1.5, label = \"Price\")\n",
    "plt.plot(x, w, linewidth=1.5, label = \"Cleaning Fee\")\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Average Combined Price / Price / Cleaning Fee')\n",
    "plt.title('Month vs Average Combined Price / Price / Cleaning Fee')\n",
    "plt.xticks(x)\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99fdb788",
   "metadata": {},
   "source": [
    "It does seem that the cleaning fee acts a constant, with not much of an impact depending on the month. The most expensive month based on combined price as well as normal price is July. The least expensive month is June.\n",
    "\n",
    "This concludes Question 1."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "355e22cc",
   "metadata": {
    "id": "355e22cc"
   },
   "source": [
    "### 2) Which Seattle neighbourhoods are the most and least expensive?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ab2c747",
   "metadata": {},
   "source": [
    "Let's take a look on how many neighbourhoods and districts there are. First, let's see how many unique neighbourhoods there are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "42464b29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['West Queen Anne', 'Adams', 'West Woodland', 'East Queen Anne',\n",
       "       'Wallingford', 'North Queen Anne', 'Green Lake', 'Westlake',\n",
       "       'Mann', 'Madrona', 'University District', 'Harrison/Denny-Blaine',\n",
       "       'Minor', 'Leschi', 'Atlantic', 'Pike-Market', 'Eastlake',\n",
       "       'South Lake Union', 'Lawton Park', 'Briarcliff', 'Belltown',\n",
       "       'International District', 'Central Business District',\n",
       "       'First Hill', 'Yesler Terrace', 'Pioneer Square', 'Gatewood',\n",
       "       'Arbor Heights', 'Alki', 'North Admiral', 'Crown Hill',\n",
       "       'Fairmount Park', 'Genesee', 'Interbay', 'Industrial District',\n",
       "       'Mid-Beacon Hill', 'South Beacon Hill', 'Greenwood', 'Holly Park',\n",
       "       'Fauntleroy', 'North Beacon Hill', 'Mount Baker', 'Brighton',\n",
       "       'South Delridge', 'View Ridge', 'Dunlap', 'Rainier Beach',\n",
       "       'Columbia City', 'Seward Park', 'North Delridge', 'Maple Leaf',\n",
       "       'Ravenna', 'Riverview', 'Portage Bay', 'Bryant', 'Montlake',\n",
       "       'Broadway', 'Loyal Heights', 'Victory Heights', 'Matthews Beach',\n",
       "       'Whittier Heights', 'Meadowbrook', 'Olympic Hills', 'Roosevelt',\n",
       "       'Lower Queen Anne', 'Wedgwood', 'North Beach/Blue Ridge',\n",
       "       'Cedar Park', 'Bitter Lake', 'Sunset Hill', 'Haller Lake',\n",
       "       'Pinehurst', 'North College Park', 'Phinney Ridge', 'Windermere',\n",
       "       'Laurelhurst', 'Southeast Magnolia', 'High Point', 'Seaview',\n",
       "       'Georgetown', 'South Park', 'Roxhill', 'Highland Park',\n",
       "       'Madison Park', 'Stevens', 'Broadview', 'Fremont'], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_l_c[\"neighbourhood_cleansed\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "86fb6777",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Queen Anne', 'Ballard', 'Other neighborhoods', 'Cascade',\n",
       "       'Central Area', 'University District', 'Downtown', 'Magnolia',\n",
       "       'West Seattle', 'Interbay', 'Beacon Hill', 'Rainier Valley',\n",
       "       'Delridge', 'Seward Park', 'Northgate', 'Capitol Hill',\n",
       "       'Lake City'], dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_l_c[\"neighbourhood_group_cleansed\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3001f948",
   "metadata": {},
   "source": [
    "There are quite a lot of neighbourhoods, therefore I will only be listing the 10 most and least expensive neighbourhoods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "87229812",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df_l_c.groupby('neighbourhood_cleansed')['combined_price'].mean().sort_values(ascending=False)[:10]).plot(kind=\"bar\", figsize=(16,8));\n",
    "plt.title(\"10 Most expensive average listing prices of Seattle neighbourhoods\");\n",
    "plt.xlabel('Neighbourhood');\n",
    "plt.ylabel('Average $ Price');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "304054b5",
   "metadata": {},
   "source": [
    "The histogram shows 10 most expensive neighbourhoods in descending order. Portage Bay has the largest average listing price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7ee9e080",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df_l_c.groupby('neighbourhood_cleansed')['combined_price'].mean().sort_values(ascending=False)[-10:]).plot(kind=\"bar\", figsize=(16,8));\n",
    "plt.title(\"10 Least expensive average listing prices of Seattle neighbourhoods\");\n",
    "plt.xlabel('Neighbourhood');\n",
    "plt.ylabel('Average $ Price');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43327f67",
   "metadata": {},
   "source": [
    "The histogram shows 10 least expensive neighbourhoods in descending order. Georgetown has the lowest average listing price."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0699b391",
   "metadata": {},
   "source": [
    "However, since we have used mean in the previous peek at the data, it would also be nice to compare if there is any difference when we look at the median vs mean prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ca00a2ce",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(18,8))\n",
    "fig.suptitle(\"Average vs Median Price by Most Expensive Neighbourhoods\", fontsize=20)\n",
    "rank = df_l_c.groupby('neighbourhood_cleansed')['combined_price'].mean().sort_values(ascending=False)[:10].index\n",
    "sns.barplot(x='combined_price', y='neighbourhood_cleansed', data=df_l_c, hue_order=None, order=rank, ax=ax1)\n",
    "ax1.set_ylabel(\"Neighborhood\")\n",
    "ax1.set_xlabel(\"Average Price\")\n",
    "rank = df_l_c.groupby('neighbourhood_cleansed')['combined_price'].median().sort_values(ascending=False)[:10].index\n",
    "sns.barplot(x='combined_price', y='neighbourhood_cleansed', data=df_l_c, estimator=np.median, hue_order=None, order=rank, ax=ax2)\n",
    "ax2.set_ylabel(\"\")\n",
    "ax2.set_xlabel(\"Median Price\")\n",
    "sns.despine(left=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18671836",
   "metadata": {},
   "source": [
    "There are differences, it would be beneficial to compare the two groups using grouped bar plots. Needless to say, some bars will be missing, because there are neighbourhoods missing from either the Median price or Average price top 10 samples. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50fba4ae",
   "metadata": {},
   "source": [
    "Let's only pick 5 from each median and mean sampled neighbourhood data, since they are not present in both samples, it would be cluttered otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4cb33a1d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Average and Median Prices by Most Expensive Neighbourhoods'}, xlabel='neighbourhood_cleansed', ylabel='combined_price'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp1 = df_l_c.groupby('neighbourhood_cleansed', as_index=False)['combined_price'].mean().sort_values(ascending=False, by='combined_price')\n",
    "\n",
    "plt.figure(figsize=(20, 10))\n",
    "plt.title(\"Average and Median Prices by Most Expensive Neighbourhoods\", fontsize=20)\n",
    "x = temp1['neighbourhood_cleansed']\n",
    "y = temp1['combined_price']\n",
    "\n",
    "temp2 = df_l_c.groupby('neighbourhood_cleansed', as_index=False)['combined_price'].median().sort_values(ascending=False, by='combined_price')\n",
    "\n",
    "z = temp2['neighbourhood_cleansed']\n",
    "w = temp2['combined_price']\n",
    "\n",
    "temp1['type']=\"Mean\"\n",
    "temp2['type']=\"Median\"\n",
    "\n",
    "res = pd.concat([temp1[:5], temp2[:5]])\n",
    "sns.barplot(x=\"neighbourhood_cleansed\",y=\"combined_price\", data=res, hue='type')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab504b7d",
   "metadata": {},
   "source": [
    "Based on the output, we can say that the most expensive neighbourhood is Portage Bay (based on the mean value of combined price of listings). Briarcliff is the most expensive neighbourhood based on the median value of combined price of listings.\n",
    "\n",
    "Let's do the same with the least expensive neighbourhoods.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a2dad77f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(18,8))\n",
    "fig.suptitle(\"Average vs Median Price by Least Expensive Neighbourhoods\", fontsize=20)\n",
    "rank = df_l_c.groupby('neighbourhood_cleansed')['combined_price'].mean().sort_values(ascending=False)[-10:].index\n",
    "sns.barplot(x='combined_price', y='neighbourhood_cleansed', data=df_l_c, hue_order=None, order=rank, ax=ax1)\n",
    "ax1.set_ylabel(\"Neighborhood\")\n",
    "ax1.set_xlabel(\"Average Price\")\n",
    "rank = df_l_c.groupby('neighbourhood_cleansed')['combined_price'].median().sort_values(ascending=False)[-10:].index\n",
    "sns.barplot(x='combined_price', y='neighbourhood_cleansed', data=df_l_c, estimator=np.median, hue_order=None, order=rank, ax=ax2)\n",
    "ax2.set_ylabel(\"\")\n",
    "ax2.set_xlabel(\"Median Price\")\n",
    "sns.despine(left=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "94198e87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Average and Median Prices by Least Expensive Neighbourhoods'}, xlabel='neighbourhood_cleansed', ylabel='combined_price'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp1 = df_l_c.groupby('neighbourhood_cleansed', as_index=False)['combined_price'].mean().sort_values(ascending=False, by='combined_price')\n",
    "\n",
    "plt.figure(figsize=(20, 10))\n",
    "plt.title(\"Average and Median Prices by Least Expensive Neighbourhoods\", fontsize=20)\n",
    "x = temp1['neighbourhood_cleansed']\n",
    "y = temp1['combined_price']\n",
    "\n",
    "temp2 = df_l_c.groupby('neighbourhood_cleansed', as_index=False)['combined_price'].median().sort_values(ascending=False, by='combined_price')\n",
    "\n",
    "z = temp2['neighbourhood_cleansed']\n",
    "w = temp2['combined_price']\n",
    "\n",
    "temp1['type']=\"Mean\"\n",
    "temp2['type']=\"Median\"\n",
    "\n",
    "res = pd.concat([temp1[-5:], temp2[-5:]])\n",
    "sns.barplot(x=\"neighbourhood_cleansed\",y=\"combined_price\", data=res, hue='type')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f8611bf",
   "metadata": {},
   "source": [
    "Based on the output, we can say that the least expensive neighbourhood is Georgetown (based on the mean value of combined price of listings). Roxhill is the least expensive neighbourhood based on the median value of combined price of listings.\n",
    "\n",
    "This concludes Question 2."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa519840",
   "metadata": {
    "id": "fa519840"
   },
   "source": [
    "###  3) Which property types are the most expensive?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "78e9dadc",
   "metadata": {
    "id": "78e9dadc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OO/H444+z/fbbs//++09p/+677zJixAj+9Kc/sfHGG3PggQcyZswYnnjiCR599FEgJUQrrbQSDz/8MAMHDuTYY4+t+tzzzTcfDz/8MHvvvfeUhOaEE05g3XXX5YEHHuDOO+/kkEMO4eOPP+bcc89l9tln5/HHH+eII47goYceavN1tRXDe++9x6hRozj44IOnecz222/PPvvsw2OPPcY999zDwgsvzLBhw3juuee4//77efTRR3nooYe46667GvlRT8PJlJmZmZlZixsxYgRbbLEF8803HwD9+vVj9OjRbLfddgDsuOOO3H333VPab7zxxkhi2WWXZcEFF2TZZZdlpplmYplllmHcuHEAzDTTTGy99dYA7LDDDtM8vtzPfvYzAFZeeeUpjx02bBgnn3wyK6ywAoMGDeKzzz7j5Zdf5q677mKHHXYAYLnllmO55ZZr83W1FUNpe7kPP/yQ1157jc033xyAPn36MPvsszNs2DCGDRvGiiuuyEorrcTTTz/Nc8891+Zz1yP3OVOSbgUGAydExO/Kts8DnApsBswGjAYOjIgnKh7fB/g9sAMwN/AocGhEzHiqWUVnV2ap5EotZmZmZtZREdHuGknl+2eddVYgJSul70v3J02a1O7jy5Ue36tXrymPjQj++c9/stRSS9V9nHqUP3aOOeaYbn9EVH1cRHD44Yez5557Nvzc1eSaTEnaFli+ynYBQ4HFgf2Ad4HDgTslrRARr5Y1vxDYEDgEeBHYB7hN0poR8WjXvgIzMzMzs+k1+wL5euutx+abb86BBx7IvPPOy8SJE/n+97/PVVddxY477sgVV1zBWmut1aFjTp48mWuvvZZtttmGK6+8skOPHzx4MGeddRZnnXUWknjkkUdYccUVWXvttbniiitYZ511ePLJJ3n88cc7NYa55pqLRRddlBtuuIHNNtuMzz//nK+++orBgwdz5JFHsv3229O3b19ee+01evfuzQILLFD3a6omt2RK0tzAn4ADgSsrdm8CrAWsGxF3Zu1HA2OB3wD7Z9uWB7YDhkTExdm2UcAY4LjsOGZmZmZm3doyyyzDEUccwcCBA+nVqxcrrrgif/7znxkyZAinnnoq888/PxdffHGHjjnHHHMwZswYVl55Zb72ta9x9dVX1/3YI488kgMOOIDllluOiGDAgAH861//Yu+992bXXXdlueWWY4UVVmC11Vbr9Bguu+wy9txzT4466ih69+7NNddcw/rrr89TTz3FmmuuCaTCFpdffvkMJ1Oq1RXW1SSdDywZEetJCsqG+Um6ENggIhapeMzfgEERsVh2/0jgSGDuiPikrN2xwGHAXBHxeVtxrLLKKtGR2vYe5mdmZmZmlZ566im++93v5h1Gt9O3b18++uijpj5ntd+lpIciYpXKtrkUoJC0FrAT8MsaTZYBnqyyfQzQX1LfsnZjyxOpsnazAN/shHDNzMzMzMym0/RhfpJ6A+cBp0XEMzWa9QPGVdk+Mfs6D/BR1u7dNtr1qxHDHsAeAP37968rbjMzMzMz6xqrr746n38+7YCyyy67rOm9Uh2Vx5ypQ0nV+U5oo42AauMPK0t/1NtuGhFxPnA+pGF+bbU1MzMzM6tHPRX1rLr77rsv7xCA2tUAa2nqMD9J/YEjSPOcZpU0d1aIgrL7vUg9S9V6lebJvpZ6o9prN7HKPjMzMzOzTtWnTx8mTJjQ4ZNxK46IYMKECfTp06fuxzS7Z2oJoA9weZV9v85uK5LmPK1fpc3SwMsRUervGwNsLmn2inlTSwNfAM93VuBmZmZmZrUsuuiivPrqq4wfPz7vUGwG9OnTh0UXXbTu9s1Oph4F1qmy/U5SgnUhKQEaCuwqaWBEjAKQNBewMdOWUR8KHAtsCfwtazczsDUwrL1KfmZmZmZmnaF3794svvjieYdhTdbUZCoi3gNGVm7Pxpa+FBEjs/tDgdHA5ZIOYeqivQL+UHa8RyVdDZyRFbYYC+xNWux3+y58KWZmZmZm1sPlUhq9PRExGdgIuB04B7ge+ApYJyJeqWi+K3AxcDxwM/AN0hpVDzcvYjMzMzMz62nyqOY3nYiYruxJREwEhmS3th77KXBQdjMzMzMzM2uKQvZMmZmZmZmZFZ2TKTMzMzMzswY4mTIzMzMzM2uAkykzMzMzM7MGOJkyMzMzMzNrgJMpMzMzMzOzBjiZMjMzMzMza4CTKTMzMzMzswY4mTIzMzMzM2uAkykzMzMzM7MGOJkyMzMzMzNrgJMpMzMzMzOzBjiZMjMzMzMza4CTKTMzMzMzswY4mTIzMzMzM2uAkykzMzMzM7MGOJkyMzMzMzNrgJMpMzMzMzOzBjiZMjMzMzMza4CTKTMzMzMzswY4mTIzMzMzM2uAkykzMzMzM7MGOJkyMzMzMzNrgJMpMzMzMzOzBjiZMjMzMzMza0BTkylJgyWNkPSmpM8lvSrpH5KWLmszQFLUuM1dcbw+kk6V9IakTyWNlrR2M1+TmZmZmZn1TDM3+fn6AQ8B5wDjgf7AYcC9kpaNiJfK2p4EDK14/IcV9y8ENgQOAV4E9gFuk7RmRDza+eGbmZmZmZklTU2mIuLvwN/Lt0m6H3ga2AI4vWzXixFxb61jSVoe2A4YEhEXZ9tGAWOA44BNOjd6MzMzMzOzqYowZ2pC9vXLDj5uk+wxV5c2RMQk4CpgsKRZOyc8MzMzMzOz6eWSTEnqJWkWSd8CzgPeJCVB5U6SNEnS+5KGSlq2Yv8ywNiI+KRi+xhgFuCbXRK8mZmZmZkZzZ8zVXIfsHL2/fPAuhHxdnb/c1KCNYw0r+o7wG+BeyStFhFPZe36Ae9WOfbEsv1VSdoD2AOgf//+M/AyzMzMzMysp8prmN+OwBqkOU8fALdLGgAQEW9ExF4RcV1E/Cci/gqsDQRwRNkxlG2rpPaePCLOj4hVImKV+eeffwZfipmZmZmZ9US5JFMR8VRE3JcVpFgP6Euq6ler/SvA3cCqZZsnUr33aZ6y/WZmZmZmZl0i9wIUEfEeaahfe3OcKnuixgCLS5q9ot3SwBfZMc3MzMzMzLpE7smUpAVJ86JeaKNNf+AHpLlWJUOB3sCWZe1mBrYGhkXE510SsJmZmZmZGU0uQCHpeuBh4HHSXKlvAwcCk8jWmJJ0OinJG00qQLEUcDgwGTixdKyIeFTS1cAZknoDY4G9gcWB7Zv0kszMzMzMrIdqdjW/e4GtgINJ5ctfAUYCJ0XEuKzNGFJStAswJ/AOMAI4NiKeqTjersAJwPHA3MBjwAYR8XAXvgYzMzMzM7PmJlMRcQpwSjttLgIuqvN4nwIHZTczMzMzM7OmyX3OlJmZmZmZWStyMmVmZmZmZtYAJ1NmZmZmZmYNcDJlZmZmZmbWACdTZmZmZmZmDXAyZWZmZmZm1gAnU2ZmZmZmZg1wMmVmZmZmZtYAJ1NmZmZmZmYNcDJlZmZmZmbWACdTZmZmZmZmDXAyZWZmZmZm1gAnU2ZmZmZmZg1wMmVmZmZmZtYAJ1NmZmZmZmYNcDJlZmZmZmbWACdTZmZmZmZmDXAyZWZmZmZm1gAnU2ZmZmZmZg1wMmVmZmZmZtYAJ1NmZmZmZmYNcDJlZmZmZmbWACdTZmZmZmZmDXAyZWZmZmZm1gAnU2ZmZmZmZg1oajIlabCkEZLelPS5pFcl/UPS0hXt5pF0gaR3JH0sabikZascr4+kUyW9IelTSaMlrd28V2RmZmZmZj1Vs3um+gEPAfsC6wOHA8sA90paDECSgKHABsB+wM+B3sCdkhatON6FwO7AUcBGwBvAbZJW6PJXYmZmZmZmPdrMzXyyiPg78PfybZLuB54GtgBOBzYB1gLWjYg7szajgbHAb4D9s23LA9sBQyLi4mzbKGAMcFx2HDMzMzMzsy5RhDlTE7KvX2ZfNwFeLyVSABHxPnATsGnZ4zbJHnN1WbtJwFXAYEmzdmXQZmZmZmbWs+WSTEnqJWkWSd8CzgPeJCVBkIb9PVnlYWOA/pL6lrUbGxGfVGk3C/DNzo/czMzMzMwsyatn6j7gc+BZYDnSkL63s339gHerPGZi9nWeOtv1q/XkkvaQ9KCkB8ePH9/R2M3MzMzMzHJLpnYE1iDNefoAuF3SgGyfgKjyGFW5X0+76UTE+RGxSkSsMv/889cdtJmZmZmZWUkuyVREPBUR92UFKdYD+gKHZbsnUr1XqdQj9W6d7SZW2WdmZmZmZtYpci9AERHvAc8zdY7TGNJ8qEpLAy9HxEdl7RaXNHuVdl9kxzQzMzMzM+sSuSdTkhYEvgO8kG0aCiwiaWBZm7mAjbN9lLXrDWxZ1m5mYGtgWER83sWhm5mZmZlZD9bUdaYkXQ88DDxOmiv1beBAYBJpjSlISdJo4HJJh5CG9R1Omgv1h9KxIuJRSVcDZ0jqTVqHam9gcWD7prwgMzMzMzPrsZqaTAH3AlsBB5PKl78CjAROiohxABExWdJGwGnAOUAfUnK1TkS8UnG8XYETgOOBuYHHgA0i4uGufiFmZmZmZtazNTWZiohTgFPqaDcRGJLd2mr3KXBQdjMzMzMzM2ua3OdMmZmZmZmZtSInU2ZmZmZmZg1wMmVmZmZmZtYAJ1NmZmZmZmYNcDJlZmZmZmbWgLqTKSWbSDpN0sWSFsu2D5T09a4L0czMzMzMrHjqKo0uaR7gFmB10mK7cwJnAS8BuwMTgf27KEYzMzMzM7PCqbdn6lTgG8APgPkAle0bDqzXyXGZmZmZmZkVWr2L9m4K/DoiRkvqVbHvZVKiZWZmZmZm1mPU2zPVF3itxr4+TNtTZWZmZmZm1u3Vm0w9A6xfY99A4InOCcfMzMzMzKw11DvM72zgbEnvA1dm2+aWtCuwL7BHVwRnZmZmZmZWVHUlUxHxV0lLAscCx2WbbwcmA3+IiCu6KD4zMzMzM7NCqrdniog4TNK5wI+BBYAJwO0R8WJXBWdmZmZmZlZUdSdTABHxEnBBF8ViZmZmZmbWMuoqQCFpV0nH1Nh3jKSdOzUqMzMzMzOzgqu3mt+vSMP6qnkbOKBTojEzMzMzM2sR9SZT3wTG1Nj3FLBk54RjZmZmZmbWGupNpiYB89XYN38nxWJmZmZmZtYy6k2m7gf2qrFvL+CBzgnHzMzMzMysNdRbze8EYLik+0jV/F4DFgF2A1YilUs3MzMzMzPrMepdtHeUpC2AM4DzynaNA34eESM7PTIzMzMzM7MC68iivTcCN0paCpgXeCcinu2yyMzMzMzMzAqsQ4v2AkTEM10RiJmZmZmZWSupmUxJ2gm4OSImZN+3KSIu7dTIzMzMzMzMCqytnqlLgDVIi/Ve0s5xAmg3mcrmXW0LrAIsALwMXAecGBEfZm0GAGNrHGKeiHiv7Hh9gN8DOwBzA48Ch0bEXe3FYmZmZmZmNiPaSqYWB94o+74z/JqUQP0WeBVYETgGWEfS9yNiclnbk4ChFY//sOL+hcCGwCHAi8A+wG2S1oyIRzspZjMzMzMzs+nUTKYi4iUASb2BFYDHI6JWj1G9No6I8WX3R0maCPwNGASMKNv3YkTcW+tAkpYHtgOGRMTF2bZRwBjgOGCTGYzVzMzMzMyspnYX7Y2IL4F/AANm9MkqEqmS0oK/i3TwcJsAXwJXlx1/EnAVMFjSrA0FaWZmZmZmVod2k6nMi6Q5Tl1hYPb1qYrtJ0maJOl9SUMlLVuxfxlgbER8UrF9DDAL8M0uiNXMzMzMzAyoP5n6A3CEpPk788klLUIakjc8Ih7MNn9OWhh4T2Ad0jyrZYF7JH237OH9gHerHHZi2f5az7uHpAclPTh+fLXOMjMzMzMzs7bVu87UuqTkZKyke0mFKaJsf0TEzh15Ykl9gRuBScCuZQd6A9irrOl/JN1K6nE6glS5D0AVMVC2vU0RcT5wPsAqq6xS7RhmZmZmZmZtqjeZWos0P2k8sGR2K9ehhCQraT4UWAIYGBGvttU+Il6RdDewatnmiUD/Ks3nKdtvZmZmZmbWJepKpiKis0qjl6oD/hNYDfhRRDxR70OZNmkbA2wuafaKeVNLA18Az3dGvGZmZmZmZtXUO2eqU0iaCbgCWA/YtK3S5xWP6w/8ALivbPNQoDewZVm7mYGtgWER8XlnxW1mZmZmZlap3mF+SOoF7ASsSSpj/hpwD3BZRHxV52HOJiU/JwAfS1qjbN+rEfGqpNNJSd5o0rDCpYDDgcnAiaXGEfGopKuBM7LerrHA3qQFhrev93WZmZmZmZk1oq6eKUmLkYbVXQhsQCqTvgFwEfBktr8eP8m+HkFKlspvu2X7xpDmaJ0H3A4cA/wXWD0inqk43q7AxcDxwM3AN4ANIuLhOuMxMzMzMzNrSL09U38B5gLWioh7Shsl/QC4BjiLtIhumyJiQB1tLiIlae2KiE+Bg7KbmZmZmZlZ09Q7Z2pd4PDyRAogIv4L/Dbbb2ZmZmZm1mPUm0x9BLxdY9/bwCc19pmZmZmZmXVL9SZTlzPtQrrl9gQu7ZxwzMzMzMzMWkO9c6aeB7aU9ARpjai3gAWBLYA5gX9LGlJqnM17MjMzMzMz67bqTabOzr4uCixTZf85Zd8HdRaQMDMzMzMza1X1JlOLd2kUZmZmZmZmLaauZCoiXurqQMzMzMzMzFpJvQUozMzMzMzMrIyTKTMzMzMzswbUO2fKupEBh93cpccfd/KGXXp8MzMzM7MicM+UmZmZmZlZA5xMmZmZmZmZNaBDw/wkzQesAcwL3BQREyX1Ab6IiMldEaCZmZmZmVkR1dUzpeRU4FVgKGlR3gHZ7huBI7okOjMzMzMzs4Kqd5jf4cC+wHHA6oDK9t0EbNTJcZmZmZmZmRVavcP8dgOOi4iTJPWq2Pc8sGTnhmVmZmZmZlZs9fZMLQLcW2PfF8AcnROOmZmZmZlZa6g3mXoN+F6NfcsDYzsnHDMzMzMzs9ZQbzJ1DXCUpB+UbQtJ3wYOBq7q9MjMzMzMzMwKrN5k6hjgaeAu4Lls2zXAE9n9kzs9MjMzMzMzswKrqwBFRHwqaRCwHTCYVHRiAvB74IqImNRVAZqZmZmZmRVR3Yv2RsRXwGXZzczMzMzMrEerd5ifmZmZmZmZlamrZ0rSWCBq7J4MvA88BPw5Ip7spNjMzMzMzMwKq96eqVFAL2BhUhn0e7OvXyclZC8BGwMPSPp+F8RpZmZmZmZWKPUmU/8h9T4tHhHrRcR2EbEesDjwAfBv4JvAY8CxXRKpmZmZmZlZgdSbTB0KHBcRb5ZvjIg3gOOBQyPiY+BMYLVaB5G0haR/SnpJ0qeSnpF0kqQ5K9rNI+kCSe9I+ljScEnLVjleH0mnSnojO95oSWvX+ZrMzMzMzMwaVm8y9Q3g8xr7PgMWyb5/DZiljeP8GvgK+C2wAXAusDdwu6SZACQJGJrt3w/4OdAbuFPSohXHuxDYHTgK2Ah4A7hN0gp1vi4zMzMzM7OG1Fsa/SngYEnDImJKUiWpDylBeirb9HXgrTaOs3FEjC+7P0rSROBvwCBgBLAJsBawbkTcmT3PaNIcrd8A+2fblietezUkIi7Oto0CxgDHZccxMzMzMzPrEvUmU78B/gW8LOkW4G1gAeCnwNzZV4DvA8NqHaQikSp5IPta6t3aBHi9lEhlj3tf0k3ApmTJVNbuS+DqsnaTJF0FHCZp1vLEz8zMzMzMrDPVNcwvIoYDKwF3AGuTht+tDQwHVoiIO7J2+0fEHh2MYWD2tdS7tQxQrbz6GKC/pL5l7cZGxCdV2s1CKohhZmZmZmbWJertmSIi/kcaVtdpJC1CGpI3PCIezDb3A8ZVaT4x+zoP8FHW7t022vVr43n3APYA6N+/f4fjNjMzMzMzq7cARafLephuBCYBu5bvovoCwapyv55204mI8yNilYhYZf75568zYjMzMzMzs6nq7pmStACwLbAU0Kdid0TELzpwrD6kin1LAAMj4tWy3ROp3qs0T/b13bJ21bqV5inbb2ZmZmZm1iXqSqYkLQXcC/QC5gDeISU8vUjJzfv1PqGk3sA/SetR/SginqhoMgZYv8pDlwZejoiPytptLmn2inlTSwNfAM/XG5OZmZmZmVlH1TvM71TgfmBB0jC6nwCzAbsBnwCb13OQbC2pK4D1gE0j4t4qzYYCi0gaWPa4uYCNs33l7XoDW5a1mxnYGhjmSn5mZmZmZtaV6h3mtyqwF1MX7p0pIiYBF0maDzgDWKeO45xNSn5OAD6WtEbZvlez4X5DgdHA5ZIOIfV8HU5K4v5QahwRj0q6Gjgj6+0aS1oAeHFg+zpfl5mZmZmZWUPq7ZnqC0yMiMmkIX3zle17kJRs1eMn2dcjSAlT+W03gOw5NgJuB84Brge+AtaJiFcqjrcrcDFwPHAz8A1gg4h4uM54zMzMzMzMGlJvz9Q4YKHs+2dIvUu3Zvc3At6r5yARMaDOdhOBIdmtrXafAgdlNzMzMzMzs6apt2fqduDH2fd/BHaV9IykMcCvgIu6IjgzMzMzM7Oiqrdn6nBgVoCI+IekT0mFHmYHzgT+2jXhmZmZmZmZFVO7yZSkXsB3gNdL2yLiJuCmLozLrKYBh93c5c8x7uQNu/w5zMzMzKy11TPML0hFJlbs4ljMzMzMzMxaRrs9UxExWdIrpMV6zawTuHfNzMzMrPXVW4DiPOAASbN0ZTBmZmZmZmatot4CFHMCSwIvSroVeIM0/K8kIuLozg7OzMzMzMysqOpNpn5b9n21tZ8CcDJl1oN09VBFD1M0MzOzoqsrmYqIeocDmpmZmZmZ9QhOkszMzMzMzBpQdzKlZBNJp0m6WNJi2faBkr7edSGamZmZmZkVT13D/CTNA9wCrA58QCpIcRbwErA7MBHYv4tiNDMzMzMzK5x6C1CcCnwD+AHwAPBF2b7hwCGdHJeZWZdzEQ0zMzObEfUmU5sCv46I0ZJ6Vex7mZRomZmZmZmZ9Rj1zpnqC7xWY18fQJ0TjpmZmZmZWWuot2fqGWB90pC+SgOBJzotIjMzq5uHKpqZmeWn3mTqbOBsSe8DV2bb5pa0K7AvsEdXBGdmZmZmZlZU9S7a+1dJSwLHAsdlm28HJgN/iIgruig+MzMzMzOzQqq3Z4qIOEzSucCPgQWACcDtEfFiVwVnZmZmZmZWVPWuM9UrIr6KiJeAC7o4JjMzMzMzs8Krt5rf65LOkLRyl0ZjZmZmZmbWIupNpq4DdgDul/Q/SYdJ6t+FcZmZmZmZmRVaXclUROwNLAz8HHgKOBp4UdIISbtImrMLYzQzMzMzMyucenumiIgvI+KGiPg5sBDwS9KcqwuAN7ooPjMzMzMzs0Kqu5pfuYh4X9K/gXmBJUi9VmZmZmZmZj1G3T1TAJLmlDRE0ghgLHAE8B9g4w4cY1FJZ0kaLekTSSFpQEWbAdn2are5K9r2kXSqpDckfZodd+2OvC4zMzMzM7OOqrc0+obAjqSkqQ9wF7AHcE1EfNjB5/wmsBXwECkRW7+NticBQyu2VT7fhcCGwCHAi8A+wG2S1oyIRzsYm5mZmZmZWV3qHeZ3E/AMcAJweUS8PAPPeVdELAggaTfaTqZejIh7a+2UtDywHTAkIi7Oto0CxgDHAZvMQJxmZmZmZmY11TvMb/WI+G5EnFiZSEkaKOmiep8wIiZ3KMK2bQJ8CVxddvxJwFXAYEmzduJzmZmZmZmZTVFvafQHyu9L+qak4ySNBe4kDdvrCidJmiTpfUlDJS1bsX8ZYGxEfFKxfQwwC2lIoZmZmZmZWaeruwCFpK9J2kPS3aQhf0cA7wJ7A1/v5Lg+B84D9gTWAX4NLAvcI+m7Ze36ZTFUmli2fzrZ63hQ0oPjx4/vvKjNzMzMzKzHaDOZkjSTpJ9Kuoq0ltT/AQOAs7MmB0TEeRHxQWcGFRFvRMReEXFdRPwnIv4KrA0EKYmbEmK2bbrQ2zn++RGxSkSsMv/883de4GZmZmZm1mPUTKYknQa8Rio+sTFwPbAB0B84inYSls4WEa8AdwOrlm2eSPXep3nK9puZmZmZmXW6tqr5HUTq9bkF2CUiJpR2SKrWG9QMlT1RY4DNJc1eMW9qaeAL4PlmBmdmZmZmZj1HW8P8LiKt6bQh8Iykv0harTlhTU9Sf+AHwH1lm4cCvYEty9rNDGwNDIuIz5sapJmZmZmZ9Rg1e6YiYjdJ+wI/A3YG9gL2lvQsachfw71TkrbIvl05+/oTSeOB8RExStLppERvNDAeWAo4HJgMnFgW46OSrgbOkNQbGEsqiLE4sH2j8ZmZmZmZmbWnzUV7I+Iz4ErgSkkLAzsBOwKHZU1OlnQOcG3Wtl7XVNw/J/s6ChhEGr63N7ALMCfwDjACODYinql47K6kxYSPB+YGHgM2iIiHOxCPmZnlZMBhN3fp8cedvGGXHt/MzHquNpOpchHxBnAKcIqkVUm9VVsDlwJnMbXoQz3Haq/a3kWkYYb1HOtT0vyug+p9fjMzMzMzsxlV9zpT5SLigYjYl7S+1BakHiUzMzMzM7Meo+6eqWoi4kvguuxmZmbW43T1MEXwUEUzs6JqqGfKzMzMzMysp3MyZWZmZmZm1gAnU2ZmZmZmZg2YoTlTZmZm1vo878vMrDFOpszMzKzleb0yM8uDkykzMzOzAnBCaNZ6PGfKzMzMzMysAU6mzMzMzMzMGuBkyszMzMzMrAFOpszMzMzMzBrgAhRmZmZm1ilcRMN6GvdMmZmZmZmZNcDJlJmZmZmZWQOcTJmZmZmZmTXAyZSZmZmZmVkDnEyZmZmZmZk1wMmUmZmZmZlZA5xMmZmZmZmZNcDJlJmZmZmZWQOcTJmZmZmZmTXAyZSZmZmZmVkDnEyZmZmZmZk1wMmUmZmZmZlZA5qeTElaVNJZkkZL+kRSSBpQpd08ki6Q9I6kjyUNl7RslXZ9JJ0q6Q1Jn2bHXbspL8bMzMzMzHqsPHqmvglsBbwL/KdaA0kChgIbAPsBPwd6A3dKWrSi+YXA7sBRwEbAG8BtklboiuDNzMzMzMwAZs7hOe+KiAUBJO0GrF+lzSbAWsC6EXFn1nY0MBb4DbB/tm15YDtgSERcnG0bBYwBjsuOY2ZmZmZm1uma3jMVEZPraLYJ8Hopkcoe9z5wE7BpRbsvgavL2k0CrgIGS5q1U4I2MzMzMzOrUNQCFMsAT1bZPgboL6lvWbuxEfFJlXazkIYUmpmZmZmZdbqiJlP9SHOqKk3Mvs5TZ7t+1Q4uaQ9JD0p6cPz48TMUqJmZmZmZ9UxFTaYERI3tjbSbRkScHxGrRMQq888/f4MhmpmZmZlZT1bUZGoi1XuVSj1S79bZbmKVfWZmZmZmZjOsqMnUGNJ8qEpLAy9HxEdl7RaXNHuVdl8Az3ddiGZmZmZm1pMVNZkaCiwiaWBpg6S5gI2zfeXtegNblrWbGdgaGBYRnzcnXDMzMzMz62nyWGcKSVtk366cff2JpPHA+IgYRUqSRgOXSzqENKzvcNJcqD+UjhMRj0q6GjhDUm/SOlR7A4sD2zflxZiZmZmZWY+USzIFXFNx/5zs6yhgUERMlrQRcFq2rw8puVonIl6peOyuwAnA8cDcwGPABhHxcBfFbmZmZmZmlk8yFRFtVtvL2kwEhmS3ttp9ChyU3czMzMzMzJqiqHOmzMzMzMzMCs3JlJmZmZmZWQOcTJmZmZmZmTXAyZSZmZmZmVkDnEyZmZmZmZk1wMmUmZmZmZlZA5xMmZmZmZmZNcDJlJmZmZmZWQOcTJmZmZmZmTXAyZSZmZmZmVkDnEyZmZmZmZk1wMmUmZmZmZlZA5xMmZmZmZmZNcDJlJmZmZmZWQOcTJmZmZmZmTXAyZSZmZmZmVkDnEyZmZmZmZk1wMmUmZmZmZlZA5xMmZmZmZmZNWDmvAMwMzMzMyuKAYfd3KXHH3fyhl16fGsu90yZmZmZmZk1wMmUmZmZmZlZA5xMmZmZmZmZNcDJlJmZmZmZWQNcgMLMzMzMrJvo6gIa4CIa5QrbMyVpkKSocnuvot08ki6Q9I6kjyUNl7RsTmGbmZmZmVkP0Qo9U/sDD5Tdn1T6RpKAocDiwH7Au8DhwJ2SVoiIV5sZqJmZmZmZ9RytkEw9FRH31ti3CbAWsG5E3AkgaTQwFvgNKREzMzMzMzPrdIUd5lenTYDXS4kUQES8D9wEbJpbVGZmZmZm1u21QjJ1haSvJE2QdKWk/mX7lgGerPKYMUB/SX2bE6KZmZmZmfU0RR7m9z5wOjAK+ABYEfgtMFrSihHxNtAPGFflsROzr/MAH1XulLQHsAdA//79K3ebmZmZmZm1q7DJVEQ8AjxStmmUpLuA+0lzoX4HCIgqD1c7xz4fOB9glVVWqfZ4MzMzMzOzNrXCML8pIuJh4Flg1WzTRFLvVKV5sq/vNiMuMzMzMzPreVoqmcqU90aNIc2bqrQ08HJETDfEz8zMzMzMrDO0VDIlaRXg28B92aahwCKSBpa1mQvYONtnZmZmZmbWJQo7Z0rSFaT1oh4G3iMVoDgceA04K2s2FBgNXC7pEKYu2ivgD00O2czMzMzMZtCAw27u8ucYd/KGnXKcwiZTpJLn2wL7AbMDbwLXAUdHxDsAETFZ0kbAacA5QB9ScrVORLySS9RmZmZmZtYjFDaZioiTgJPqaDcRGJLdzMzMzMzMmqKl5kyZmZmZmZkVhZMpMzMzMzOzBjiZMjMzMzMza4CTKTMzMzMzswY4mTIzMzMzM2uAkykzMzMzM7MGOJkyMzMzMzNrgJMpMzMzMzOzBjiZMjMzMzMza4CTKTMzMzMzswY4mTIzMzMzM2uAkykzMzMzM7MGOJkyMzMzMzNrgJMpMzMzMzOzBjiZMjMzMzMza4CTKTMzMzMzswY4mTIzMzMzM2uAkykzMzMzM7MGOJkyMzMzMzNrgJMpMzMzMzOzBjiZMjMzMzMza4CTKTMzMzMzswY4mTIzMzMzM2uAkykzMzMzM7MGOJkyMzMzMzNrQMsnU5K+IelaSe9L+kDSdZL65x2XmZmZmZl1by2dTEmaHRgBfAfYGdgR+BZwp6Q58ozNzMzMzMy6t5nzDmAG7Q4sASwVEc8DSHoceA7YE/hjjrGZmZmZmVk31tI9U8AmwL2lRAogIsYC/wU2zS0qMzMzMzPr9lo9mVoGeLLK9jHA0k2OxczMzMzMehBFRN4xNEzSF8AfI+Kwiu3HA4dFRNVhjJL2APbI7i4FPNOFYc4HvNOFx28Gv4b8tXr84NdQFH4NxdDqr6HV4we/hqLwayiGVn8NzYh/sYiYv3Jjq8+ZAqiWDarNB0ScD5zfNeFUBCI9GBGrNOO5uopfQ/5aPX7waygKv4ZiaPXX0Orxg19DUfg1FEOrv4Y842/1YX7vAv2qbJ8n22dmZmZmZtYlWj2ZGkOaN1VpaeB/TY7FzMzMzMx6kFZPpoYCa0haorRB0gDgB9m+ImjKcMIu5teQv1aPH/waisKvoRha/TW0evzg11AUfg3F0OqvIbf4W70AxRzAY8CnwO9I86d+D8wJLBcRH+UYnpmZmZmZdWMt3TMVER8D6wLPApcBVwBjgXWdSJmZmZmZWVdq6Z4pMzMzMzOzvLR0z5SZmZmZmVlenEyZmZmZFZykEZK+U2PftyWNaHZMPZmkvpIWk9Q771g6QlL/WjFLmllS/2bH1OqcTFm34g+b4pA0h6T9JV0r6U5J38q2b1Prd2QGU/6Od8qKDLU0SbNI+pOkVfOOpSeT9JWk1WrsW1nSV82OqQGDgLlq7JsTGNi8UBoj6ShJX6+xb2FJRzU7po6StJGkh4H3gReAZbPtF0jaLtfg6jMWWLHGvuWz/dYBTqY6WTf5R9FP0o6SfivpuIrbsXnH145BtPiHTXcg6RvA48CpwLeAtUk/f4B1gF/nFFqHdIekT9JFkhavsW8xSRc1O6Y6LAlcArwp6W+S1s05noZFxBfAnsBseccyoyQtIekwSedk76vy24V5x9cOtbGvF6kacCuoFeeSQCsU3joaWLTGvq9n+wtL0mbAjcA7wKFMex49Ftg5h7A6qq2/hd7A5GYF0l3MnHcA3dDRwK3A61X2lf5RHNfUiDpA0vrAP4FaV4SDgv+zo/U/bKYhaT5gDWBe4KaImCipD/BFRBT1n97pwOekROp14IuyfaOAY3KIqRH/k/QWcCcwArgzIl7IOaaO2gX4P6pfbZyP9OE/pJkBtSciFpO0DrAT8DNgB0mvkaq2XhYRT+caYMc9Qrp6fVfegTRK0qbANaSTx7dJf9/lCpmMSJqJqSePM2X3y80G/IR0clw4knYFds3uBnC+pA8rms0GfA+4o5mxNaitE/l5mP59VTRHAxdHxG6SZgb+ULbvSeCX+YTVNklzA/3KNi1SvkZrZjbS58GbzYqru3Ay1fla/R/FH0kf/PsAT0fElznH065u+GEDgCSR/lHvB8xCem2rAhNJV8buJq2rVkQ/BvaIiJcl9arY9xqwSA4xNWIwqSdtHeAcoFd2Uj+ClGDdGREv5xhfvWqd6C5EWqevcCLiTuBOSb8ENiclVr8BDpP0APA34KqIeDfHMOt1MPB3SS8BN0drltE9HhgJbB8R43OOpS6SjgZKo0EC+G8bzc/p+ogaMhkoDUFUxf2SCcC5wClNjKtukgaRlrEp2VPSRhXNZgM2BMY0KaxGfZf0fwim/7/6LumiZxH9ipQIRna7tkY7UfwL5sCUCyV7AFsC3wD6VDSJiFisGbE4meoE3ewfxQDgwIh4Iu9AOqDlP2xqOBzYl9STeTtwX9m+m4AdKW4yNQtQmdCWfA0ofJIOEBG3k372SOpLGq64LrAe6ecfFPD/qKTNSQlIybGSKq+8zwb8EHioaYE1ICI+Ba4ErpS0ELBDdjsb+JOkf0XEFnnGWIdrSO/7G4FJkt5m2hOxpn3oz4AlgINbJZHKjMy+ipRUXQi8WtHmc+B/wL+aF1b9IuJvpAsHSLoT2LsFe2YHAr/Lvg+mXvws9wXp97B/s4Jq0AekHv1qBgBF/fu4ARhH+lu4iHRxpHKUxefA/yLi8aZG1rg/AAeROgAeYNoRME1VuJOAFtWd/lE8QhqO2DK6yYdNNbsBx0XESVV6d54nDVssqseBn5OGvFb6CQU/ga9hEaA/sBhTx/w/mV84bepPSpRKVmD6XvHPgXtISXtLiIg3gdMknQscCxzItEljUd1BQYfBdcDTFPeqe1URMYo0rBhJAVwQEa/lG1VjJM1CSsj7k34XLSMijiX9vSJpMrBGRNyfb1QNux04XNK/mXrBMCTNSrr4+e/cImtDRDwGPAZT/hb+FRET8o1qhu0A/D4icu9JczLVCbrZP4qDgEskPRsRo/MOpqMiYp28Y+hEiwD31tj3BbXntRXBqcC1aaQiV2bbls7mXfwC2CSvwDpC0hBST9Q6wMLAM6QhfnsBIwv8YTQWWDEi3pM0Ftgs+zBtWdmw1/VIw/02J73/nwMuzTOuekTELnnH0Al+A5wh6b6IeDHvYDoq+5yehqSlScO2RkdEtXnOhRERX2SFZCblHcuMiIhWL3x2BHA/6bPgFtJFksOA5UjJ7ma5RVan7AJ0dzAzBZmH6mSqk3WDfxQPka6i3i3pY+C9iv2FH44iaS7gp6QreNXG0BZ1aFyl10jzvO6ssq/Q5Usj4rpsrsvJTC1ucCnpSt6+EVGtx6qILgA+IQ0R/WNEvJFzPPW6nlS05AFST9qs+YbTOEnfIw2p3J6U0L4PXAH8rRUv+LSwY0g9U09Jeo40d7NcRERhq6VK+gswc0Tsld3/GXA1qZLfB5J+HBEP5BljHW4H1idd0GlZ2ZIHvyANm56XNL/2OUnbAI8WeWRJRIyTtBLpAvpg0pSCtUmjMI4qelJeImlnYFtqnycVeeRLybWk30Huc+HVmvNgW4OkBZj+TUqRJ6xL+iNwAGm439NUGYMaEdWGMRaCpB+Q5hPNXaNJRETlkLlCknQKKRHZjNRD9SWwMvAx6cP0/IgobGVImPKhuSawAGne2j0RUWsuVeFIOp3UK7Uc6cLCKNLPfkREPJVjaG2S9D6wRUTc3oq95ZIWBLYj9UItR5oHOYw0nPfGiCh6IZ/pSFoROJJ04jU3sFpEPCzpROCuol9gkDSSdoYqFnlkgKQXgGMj4tLs/hOk4dJHkaqPfhERlXOdC0XSD4HLSXPwbgDeoOJ3UvRew2zZjJGkodJPky4Yrpr9LZwH9IqI3XIMsduTdCQpGXwyu033/7TI53klkmYnXVgbD9xGKgAyjYhoyoUHJ1OdLKsucjxpXZG5q7Up8sm8pInAWUUYg9qIrMpXL2B34IlsjZeWJGk20gnk94GXSJNbXyRVrbkHGNzKr6+VSJqHlFQNIg37+y6pPPSIiNg+x9CqUlqcegBpCMROwM3UnhgdEfGLJoVWF0lfkv6OnyQlUJdHxFv5RtU4SWsBw0l/v8NJcytWyU4gjwe+FxGb5Rhit5eNtPhJRNwlaVHgZbKLDFnBqAsjYsF8o2xbdmGkpOrJW5HPLwAk/YOUQG3A1GUzSn8L2wHHRMS384yxo1ppuCiApHHA9RFxYN6xzAhJSwLXkS2aXCZIhTaadvHcw/w63wGksuKnkJKqE0hXVbfPvp6cW2T1CQoyBrVB3wW2iohWLHAwjYj4NKsUuR2pK/t5Uu/O74ErIqKwY+ezuVH9IuLi7P5iwFWkD9HbgF0iomXW/MrKb1+ntOr9Y8A2pPk725D+totmb+BPpF6QAFajdqWjIl5RO5s0jO+R9hpK2jQibmxCTDPiZNL7fjNSkrhv2b6HSQmvda1Pgb7Z9wNJVdkezO5/xNRFxYus8L0FdWjpZTNqDBf9B2n9tVYZLjovaQRPq7uYVFnxV9QYSdUsTqY6366kUtZnkJKp68uuPg4jjU8tsmtI1dZyH4PaoJdp4fkhlSLiK7KFSvOOpYN+R3ovlfyRNKzjfNL8l2OAXzc/rI6R9HWmFqBYl/T3Oxl4lFRko9p8ttxFxDOkeYOlq9kbt9Iwv4g4oL02kn5Oep8tR0pQimwl4GcREVklrXLvAPPnEFO7JK0NPBwRH2Xftykiinwh7mFgH0kvky543h5TFz1fnDRkrtC6SeGAVl824ydkBccyx5ISk9Jw0aOBQg8XJQ1XX54Wn3sHrALsFBG11sxqGidTnW8J4MGI+ErSJNJaLkTEl5LOAM4inUgW1b9Ja7d8jTShMrcxqA06lrSo5x0R8UHewcwISfMBs5fPsZO0J1nvTkQUcl2UzJKk8uil4Yo/Jf3Tu0bSU6Ry3IVPpkhr0gTwBGmOwghgVIu9t9YhLcvQUiQdSBqu25+0HsrvIuKm7KT+LNLfwWukyopF9xkwe419paIaRTSSVMjk/uz7Wr2YyvYVOak9gvSZ9hhp/mP5+2Yz0mu0rtfqy2YsRFqviWy46DLALyLiCUl/Jq1lVnQHkEZaTCBVJKwsJkPZhYYie5kce6PKOZnqfO8ztejE68BSTF11fWagXx5BdcD12ddfZLeSKWNQKfYH5kbAgsBYSaOpXnFq5+aH1ZCLSCfzv4RpJo2+C/xS0nYRcXWO8bWlD2lYDaQ5XzOTemYhlZRtlbXMtgTujIjpPmxaRbbWTqkq3kDS/6AJpKIHhVwnS9KvSQsyPkNaTHUA8E9Jh5OGUL9LOiE4r0XmDd4NHCCpfDhiKTH5BcW9QlyeiBe2uEQ9IuIBSf2B7wDPVVwQOZ9UZr/QJF3UTpPCzX+sotWXzegOw0Wfzb5eXGN/IRejr+J44FBJI/KeNtAKP6xW8wiwNGl8/G3AsZI+Ja0NcQJpqEGRtfQHJrAW6R/BB6QrRpWKOD+kllXIFiPO7AWcGBG/y66AHUQq7VtE40i/i1HApsBDEVG6+r4Axb0SP42I+GfeMcwoSTMDl5DK4KpsV0i6kjR/7as8YmvDrqR5CNtGViUpS6T+QPofOrjFEtwjSRfVHiOV8w1g56x66srAqjnGVlMpEa/8vlVFxMdU6fmIiJtzCKcR6zL9Z1g/0gn8e0y/lEnhdINlM1p+uChpKkornQvVMpg0fWBcdvG8ciRV0y6eu5pfJ5P0Y2CJiDhP0kKksbQrZ7tfAjaNiMdzC9BahqTPgB9FxN1Zr8JjwHey9TjWBa6LiLlzDbIGSb8CTiPFvAKwd0T8Ndt3GrBSRKybX4T1k7QsaRz8QGAeUm/nSOC4ovbslJP0e+BQUq/m5cCbpKEqO5Be10lFq96Zvfc3Kz+xyoa9vp1tH5pbcA3K1qY5lVQUpBdp7t1/gIPqKbRRFNk6ft8jFQp4DXiylYa9SlqeNGKk2rIlhV8Aupps6Ov/Adu3ynupVZfNkLQqaYji3KTkdZ3SOV3W8/xJRGybW4A9SFaVsK0kJiJiiabE4mSqayn1ZS9JGi//VEQUfXKlFYSk14CDI+IqSfsDh0TEN7J9g4FrImKuXINsg6TtyRaOLT9JydYSuTsiCl9UI/vgHEUa2jGUqYnIxqT5kGsXvXKkpLHARVFlsWpJRwG7RsTizY+stmprY2WVv74krUlT6J95WyT1IfUmvBcRn+QdT0dk75eDScOcSr2cHwKnRsTxuQVWB0lzk5YIWJOpw9ah7GSs6GXF2yJpd2DniFgr71i6uywRnG64qKQNs23P1nxwwUjqS6ru97rPTxvnYX5dLBui8nzecdQrW5+mLRER6zUlmAaphVdXrzAcOCa7In8wqQBCyXdIPZ2FFRFXkBbUq9y+Zw7hNOok0lpH65VfNZU0J+n3cxKwfk6x1evrwOga++4hTcwvopmydftKSie6qtjeKpOlAYiIz0jzaVuKpGNJwxUvIC1z8BZpfuq2pOHsM0fEMflF2K4TSZ8HPyT1CG5OGm48hJRgbZNfaJ3iRWDFvIOopp5KkOUKXhWyOwwXJVtb7ThSVT9IQ40flnQBaf3EK2s+uAAkzQLcCxwWEcPaa9/VnEx1AUkLk05+yyd7jwT+GBFv5hhaPWZi+m7TeUnDIsYzdeJiIdVYXb00IXQd4EdAq6yu/hvSsKyTgAeYthzr9qRJ7da11gB2rBx+EhEfSjqFaee0FdXrwA9IyV+l71PcE/v/1th+X8X9lpgsLWkJYCtSdcLKIWatUDhgd+D0iDikbNsYYISk94E9KHal2sGk/6H3ZvdfzXo4R0o6l7RWTUuu95XNi9yFVLCoiEZS3xydwhe5ktTue6Tow0UlbQb8k7QEzqGkuaglY4GdmVocpJAi4gtJi5PqEeSu8B9ArUbSt0lXveYhnQw8TxoW9CtgJ0k/jIjCVg2KiEHVtmcrTd9AurpXZKcDnwPfYurq6iWjKPaH/TQi4i3SAofV/IhUbrmQsmFabX54tsiQmvZOAFphnPQVwBHZ7+QK0gTphUhX4o8gVccrmu4yQRqYsoj1NaSLVW+T/keVa4XX+jVSUaVqbiUtFF1kCwMvZsuWfMa0VdeuI/W2FVqNkSOzAN8mXfQs6jIBrV7YqtwlNbaX/w0XOpkizZW9OCJ2yxLx8mTqSbIKwi3gdtLIkNyroTqZ6nynkCrJrR4R40obJS1GKg19CvCzfEJrXES8IOlk0gTqQg4lyLT06ur1aoEJ39VOhucl/eObldofSEVzH/BbScMrhvnNQbqid2/NRxbHMaT1745l2osJIl19PHb6h+Sr4MPFGnE86er89hExPudYGnUfaShQtR7OVZm+x7Bo3iQVDYA0RHpN0u8E4Js5xFMXSftFxFnZ3WojRz4kSwYjYmQzY6tXd6gEWaba/NJ5ScuybEcq7FN03yWNfIHp30/vkl5PKzgLuDxLCG8gXSic5vVExIvNCMTJVOdbB9irPJECiIiXJB0DnJNHUJ1kPOkKWJG1+urqU7TymiK1ToazBPcmWqQ0OvBb0gnXS5L+xdRenQ1JBSgG5RZZnSJiErCdpBNI8wj7kSoSjiLNp3oEWC6/CKcn6TbSmndDI6KowxA7YglSMZmWSqQq5qbtD1yfLUZ/DVPnTG1Fmne0afMj7JC7SQnUv4DLgKMlDSANE9qZVGCmiM6QtCUwpNbIEWueiKg2V/kl0nwjkZYs2a65UXXYB8B8NfYNIJ3rtYJSkn4QcGCNNk0ZAeNkqvO1dTL/Yba/5UjqR3rDvpB3LO1o9dXVy7X8miKVsiE25wB/Ac7IOZx2RcT9ktYAjiLNuSglIiOA30fEE3nG1xERMYY0x2UKSd+l+npseRtHKnZwtqQHSVcdb4iIp/IMagY8Tetc7S03iWn/B4m0PtDJFe1E+t9b5HOKY5m6WPippN/H1qRKu0OB/XKKqz3rAX8FHpN0NGneWisMC60pW+rjF1QvUV/4Ildt+A/pPKnobgcOl/Rvpp6vhqRZgX2Bf+cWWcfsmncAJUX+x9eqHgX2k/Tv8gpT2RWLX2b7Cysro1z5j3oW0hVISIlKkbX66upTRMSAatvL1xRpakCdZ1ZSUtISsjVEtsg7jp6kVPFR0pqkHo+dgOMlPU9KrK6PiFYYYlnyG1IPw33NGnbSSVp67pqkTYBREfF+RLxAdjEwKwF9cHYrtIgYma11dyxpzvIWkoZExP9yDq0hklYn9SiMI81tfpw0x7w/qYBGy1Q/rmIN4KO8g6hG0ovA5hHxGGmu7P3AM8AtpL/xw0gjFL4GbJZTmB0SEYUpAOV1pjqZpA1IwwheAK5m6rCgLUn/ODYsQhnHWiRdwvQfnp+RurGvyT6QCk3SXqQrp3My7Vooh0TE+bkF1omKvqaIpP5VNs9Cqq54KvBKqyza251J+jnwj1YoBiLpO6Ry1puS5ui8TepRuB64o2hrpEiqLO/8TVJvyHOk3s1yEREDmxJYDyLpK2DNrId5yvd5x9WobOHnC4ClSRcVvqhoEhGxc7Pj6ghJd5CGiO5IGna/SkQ8nC1EfxmpemruBQVqydZaq1T6bNsQ+EtE1BpylpvKtfskLUpK0AczdeHkW4GjIuKV3AJtQNZZsTRTq2c/1ezeWydTXSBLqI4nFWoolfp8CDgyImpVQ7JO1Kqrq9dL0nqk+SRz5B1LNW1U8xPpQsMmRR2yVcdctXKFnbdWj1ZKpspJWoipidUg4POI+FquQVWQNJIO9OpERHeqeFYIWcn2LSLi9moLQbcaSXORhkfvQrpQWy2ZWqLJYXWIpPGkOWq3koaRrh4RD2T79gZ2iYjVcwyxTdn7qNLnpAvOVwEnRURltc7cdYf3fzWSdiOdb89ftvlt4HcRcWGz4vAwvy4QEbcCt0qandR9/W6rrXTf6rJF9apVnWp5LbCmCKQJ6bV6OB+IiK+aH1Ldqs1Vq6WQV6OyNY3qsVCXBjKDsoUZ9yb1PD1Z2p6t13cucG52gvmTnEKsqTsWC8h+Hz+h9lyX3zc/qjY9BJxX1kt4ZHYyX02hL4xkQxbPIc3x2r2ZJ4qdrDfwcURMljSRVLK+5BlSD09hRcRM7bcqrEJ+XjVK0vbA+aT1si4nVexciDQF4nxJn0TE35sSi3umOoekOUkLYPYGRkbER5KWIpUiXo6UKZ8VEdflF2V1Wbf1BRHxeo0u7HJF/MCcRpZsrAl8g+k/8ImIjvQ85KaeNUUi4q/NjcpaQT3rfJWakv6mC9szJelTYHBEVA6baxnZQp83R8SEKvv6ARu1wEKfXydVxBtAem+VhlBPeZ8V7X2UfQb/CfgOsBjwDtP35pRERFQbnpwrSfOTCvZsAdxM+r/fshUuJT1EKqJxZfYZ9wFTl4v5G/D9iFgytwC7qewz4VbS30B7Cj9cFEDSY8DjEbFjlX2XActGxApNicXJ1IzLFuodTlrDSKTseGNSRRQBLwJLknqpBkdEoXpMyrt/a3Rhlyv6iddKpDkUizL1w75coeMvV2OYUKl3p7BripTLThTXZOpY5nsjonK+iHUySR36ICzSRN5Kkh4G/hwRl+QdS6Pamq8jaWXg/qL/X5J0BWne18+Bl4HVSSWUh5Cq4q1fo2x0IbTqMCdJE4DJwAERcUXe8cyobImYr0fEHpJ+REoQvwS+AvoC+0fE2TmG2K5s1NEQYCBTP9tGApcUdRRS9v5/k+kXDK+m8MNFAZQW39602vQZSYNJFWBna0YsHubXOX5POsldn1To4ETS5NBHSL/oz7I/vn+RKqYUKpkq77Zu8S5sSFXuPiJVo3ma2lchC6/VhwlJOp5ULWsWpia2n0s6LSKOzC+yxkhagOo9nS/nEE6bipwcNeAo4ExJD0ULlaKvUO3CTskcpLkjRfdD4NdAqVdkcqT1FI/K1o/7M8Vea2o9oGYFPEl9I6KIldhGAPtExNt5B9IZomwNwogYni098XPS8MVbi1ygC6bM1xxJGiHyEilBWYLUc7ifpEER8VZ+EbZps1a7mNCOD0kXzqtZlNrLFHU6J1Od4wfAYRFxB6QVy0nrufwyIj4DiIhPJJ1FGudvXWdpYKuIuCXvQHoySQeQFry9kGnHMu8A/FbS+Ij4c34R1kdp0dLjgT2BuWs0K3SPQjdwKOmK9SOSxjH9KveFrIQnaQVgpbJNG2fr65SbDdiGVOGv6OYFXs/munxMGmlRMoK0Pk2RbR4Rd1bbIakvcBvps7xQImLLvGPoShHxCOnCc6v4A+m9/8OI+G9po6TvA/8ETiHNabau92/gREnPRsR/ShuzJTWOp4nrZTmZ6hwLMe1itqXvK8c1v8G0FUcKrZWuxJd5lnSlt1vI1hc5mjScYB5SSeWRFH/B2L2AMytKxD4DjJL0EWnNtcInU8ABwD6kD8jjgRNIQ262z75WLl5qne8r2uhRKLBNSX+7kJK/I2q0m0BaA6/oXgXmy75/gTQSozTKYjXS6Iwi21XSmxFxYvnGrPLrraQ5tmbt+QlwaHkiBRAR90j6Hf5MaKbfkNb2GinpNaYuRbQoab2y3zQrECdTnWMm0gd+Sen7yvkuhZ+gllXGOpM0Bn7WGs2KfCX+t8Ap2eKYRU762iVpVdLihp+S1tMp9e5sDGwoae2IeCjHENsygDQWvpqbSRXaWsGupIVLzyAlU9dna6IcDwwjLTRpXaiFh7ueAVzC1HmzmzP9ou2fA281e02UBt1JuqhzA3AecHbW+/Ylaa2a83KLrD5bAjdKeiMiLoYpc19uBRYH1s4zuJ4iqwh5OLAt6f9n5XlGRESRz037Mv2F8pJXs/3WBBHxZvY/aFfS328/0mLQo2jy/LUiv2FbzSJl5Yh7lW17r6xNrbGdRXI2afzyhcAT1DdZsTAi4lZJg4DnJD0LvDt9k+INCarhJOBJYL0oWyMrqxw5PNu/fk6xtWcCqcRttfmBy2T7W8ESwIMR8ZWkSaRhWUTEl5LOAM4iVew0m0ZEvA+8D5DNCxkM/Djb/TIwIiIezCm8RvyOdLJCRJybVU3dmjTX5Q+kiw6FlX027A78NSuPfgdpGNCSwMBogQXpu4lTSb39/wauo8XOMUgjLHYkJeGVdiDN1S6cbjAffjqS+pCGUr8NXEPqmXqoNL2mmZxMdZ5rq2y7oeJ+aQHfIhsMHFL0ajq1SDqM1LU7nlRytcjrGbVnDdJq8NNMooyIDyWdQiojW1TXA7/PKlFdlSUfM5OuDh9HsWMv9z5Th7q+TlpfpzS8Y2ayk0vrWpIWIRUzWZs0d2fjiHgym5s3OiLuyzO+WrK4LyUtLFxZhCIkjQJ2iohCrhmXzRncEBibrfM1paxyRJwFnJUNRR6QxwlMR0XEpVkBgX+QLlT1BwZFRCvMWesutgCOjogT8g6kQacBl0paELiSqUPLtgF+REq0rAtJmpV0AWd3pu/Z/EzSucBvI6JpBcicTHWOXfMOoBOJdOWlVR1AGm6ybxR7Ydh6tJd4FzkxPxxYnpQ0XZQtztiP1Gt7N2k4Zit4hFTU5Lbsdmy27tEk0vyph3OMrUeQtAzwH9KFkdHAiqQKkZDWDloN2C6f6GqTNDdpfmM/UhXXoaQhKJCGwW5KuvBzp6RVI+K9ZsdYhx1IC8Uu20abD4ErJe3RrAUy65Ulg5VOI40S2YZU4e/ZUruIaG9pEJtxfUl/xy0pIi7PhoceB1xQtust0hpgV+YTWc8gSaTK2OsCNwK3kHr6RZr3uBFwIOlz+6dNi6s1hmpbs2QVBydHxK/yjqURkj4glf+stuBtS5E0HPgasG7FML85SNWz3o+Iog7zK/3T25BUUrkfqXjGKODfLTJHBEk/BpaIiPOyK9o3AStnu18iLX3weG4B9gCSbgXmJPWaf0Za7mCVbO7alsApRVwTRdLJpKpeq9daf0nS4qQTy0si4rAmhlcXScOAZyJiv3banQksFREbNCey+rSzeHXlSJGiz9XpFiRdDjxfXiK9FWUJ+FJM/Wx7xsl418v+5/8d2DIirq/R5mek3uetIuK6psTVIuc01iSSNiZNnB5FyvinW2C1yImKpKtJK2K36hCCKSStRrqy/RnpSkxpOMGGpLk7gyLigdwC7IGyBHFJ0jyRpyLiy5xD6vay6o/bRsRN2XpGXzI1mVqbtDbN7PlGOT1JzwDnRsQZ7bQ7kHRFe6mmBNYBkt4Bdo2Im9pptzFwcUTM11a7ZssWiK37JCciju26aHqusvnkkCoaXwpcQe1zjBebFJq1GEnXAZ9FRJujEST9HZglIn7ejLh8FcYq3Zh9XZxp10oIpl7JK3I1vzOAS9I5L7cyfQGKlvlHHRH3ZxPXjyJdlS9dARtB8UujT9GiJfarynrUns87jh6mrau985GqXRZRf6CeapsPUdyqkHNS5X9oFe9mbQul1Xs/upHnmTapFalwz9EV7VrhHKNU9finpL/bys+2iIjfNz+qHmNFUjGc9vyLVIG3KZxMWaV18g5gBpWKA/ye2tWlCv2Pulw2hGyLvOPoqG5QYh+ArOejlsmkAhVPu4eqS91PmpdarXdkK6b+zRfNx9RXoGQeoGklfDvoHdK8tLvbadefsuIUZhWGUOw5vnWT9APS/6K5azQJ0vmHdY35SXOk2vMysEAXxzKFkymbRkSMyjuGGdTS/7QlHdWB5kW+AtbSJfbLjKT999Mnkv4cEbUWZbUZ83tgeDZ/50rS7+NHkn5FWrupqOsD3U+q7HVjO+12ytoW0d3AzqQhWW3ZhfYTrtxlaxz9hDTXxT0KzXMp01aFnE5ZVcg2h5QWwBmkQjK7A080s2KcAWmIfT3nE19QZURMV/GcKbMCySZMVyoNsZxue0QUsndH0tvAsa1aYr8kmwtyFvAYafmDt4AFST0iywFHAqsDewCHR8RpOYXarUnakHQSs2TZ5nHAPhHx7zxiao+k9UlDjf9IlTK92Yn9iaTKUz+JiGHNj7JtktYkJUl/Bg6t8hp6k6rj7QOsFRH3Nj/K+kj6Oum1DGDa/6lTToKK+v+01UnaiawqZESMrdFmAOnCW+GqQpbL5nBuFRG35B1LT5SdI/2c9JnclhWBfzTrb9rJlCFpBPDLiHg6+74tERHrNSOuRpS/lir7vg38X0Ss2/zI6pNNsC83M2lOyOpUKcNd1PLv2aKY20ZEtUV7W4akC4EvI2KvKvvOI01w3VXSn4EfR8R3mx5kDyLpm6ShGxMiovBLOEj6LalnbQJwO9OWRv8xac2soyOiaWP7Oypby+t00msYRqpiCWn4X+k1HBwRZ+YSYJ0kXQF8k3Qi9jLpf+p40miGrYH1a1VdtBnT6lUhy0n6H3BErUpy1rXaqdA5TVOaeMHZw/wMpu31mIm236jVekiKZBAwV419cwIDmxdKx1UmR1khDYCvipo41XAVsDHQ0skUaRjZ1jX2XQtcnX1/K6l3yrpQRDxPCxUAiYgTJY0mrSe1GakKJ6QLJHcBpxa5OipARJwh6WHSWlmbM+1rGAmcHBH/ySm8jvgh8GvS4tuQlgAZBxyVXcT6M2ntL+t8K5F6+NszHNi+i2OZUccCh0m6IyI+yDuYHqiQ67o6mTIiYp2y7wflGEpnqZUMLgl81MxAehJJ5T1+w4AzJM1JC5bYL9OL9L65vcq+bzK1iMbntO68sMKT9C1SBac1gUWA14B7gOOzBKuwIuJO0sK8vUi9OALeaaWLIxFxF3BXtrZOqfz5hFZ6DaSf/esRMVnSx6TCHyUjgH3zCatHaOmqkJIurdi0IDA2u1BS+dkWEbFzcyLreSLib3nHUI2TKWt5knZl6tWKAM6X9GFFs9mA7wF3NDO2HmY405bQF61bYr/kFuDEbNjiDRHxVXZSvDlwAnBz1m4Z4IWcYuzWJA0i/R4+Jf28S/PWNga2lrRBKxTOyRKPt/OOY0Zki5K26mt4lamJ4AvA+kztOV+NtJ6fdY1Wrwq5NhULPAMfkP7vV/LcmR7IyZRNR9LMpCvA36D6+kAXNT2otk0GSldIVXG/ZAJwLnBKE+PqaVq9rH41+wHXA9cAkyS9S7qiPTPpxKA0B+B9UjEB63ynA48AgyNiSs9y1us5LNu/Sk6xWeu4kzTM+wbgPOBsSSuQFoEenG2zrtHSVSEjYkDeMVixuQCFTUPSSqSTx0VpsQpyAJLuBPauVoCiFVSsFA+p9+YZ0lj+MZXtW2UB4laXVWZbHVgYeAO4NyKqDf2zTibpU2DriBhaZd9mwN8jYrbpHmhWRtJ8QL+IeDa7vx9pPuTspDmPx0WEe6e6QHeqCmlWjXumrNL/keYVbQY8TarV3xKyUsNfIw0VaMlkiulXii+5oUb7wia23UlWtrpwpat7iFeBWWrsm4U0f8qsTRHxDmVDyCLiLOorimAzKCJGSzqY1Iu8fVbdr1ZVyMInUpLmAH5BGv43L6mc+3OStgEebdWLudY4J1NWaWladA2FiPhC0uLApLxjmQGFrFTTCEk7A9uSkttqC2QuOf2jikvSAlQf9lrPauzWuFOAYyWNjogpiZOkRYCj8fBK64CsiMbSpJPgByPi45xD6hG6S1VISd8gxbso6aLt95haNGMd4EfAbrkEZ7lxMmWVngXmyDuIGXA7aWJxK1SKm05RK9V0lKQjSSVknwQepUUr3UmaCziTNBxo1hrN3DvYtQaSTlZekHQvUwtQrJF9PygrUgGupGVtkLQPKQGfN9u0KvCwpBuAERHx57xi6wm6SVXI00mfZ98ildkvH70zCjgmh5gsZ06mrNJvgVMk3deiV9zPAi7PimjcQJrfMs2wOc8zaopfAGdGxIF5BzKDziYt8nkh8AQtmhS2uLVIBWXeIA0JWizb/kb29YdlbT0J2KqStDvpwshFpCG7/yjb/R/S37mTqSZo8aqQPyYN63s5q+xa7jXS0g3WwziZsmlExK3ZVd7nJD3L9GtDREQUeeHbUonkg4BaJ/LuSeh68wI35R1EJxgMHBIRZ+cdSE8VEYvnHYN1CwcBp0fEoVVOgp8GDskhJms9swCVS6+UfI1UHdJ6GCdTNg1JhwG/AcaT1lFope536EZzjlrcKGB5WnS4ZRmRqimaWWtbHLitxr6PgbmbF4q1sMdJvZi3Vtn3E+Ch5oZjReBkyiodQFpvY98WG8cMdJ85R93AAcB1kiaQFlytXCW+NNSj6K4iLQ47vL2G1rWyid+11r5r9aTdut47wIAa+5bCVSGtPqcC10oCuDLbtrSkTUnD2zfJKzDLj9eZsmlI+gDYzCcnNiMklRKlWv9gIiIKfzFH0sbAGaSetlpJof9WulC29toVwGqlTdnXyL4v9Np3VgySziX1HKxLKsv9JbAy8AppDaSbI+Lg/CK0ViFpL+BkUmGc0v+jD0lDws/PLTDLjZMpm4akq4HHI+KEvGNpVFbCelvS1cZqJbl/0fyoehZJx9BOMYCIOLY50TSuLCms5BP5JpE0gvS3fDI11r6LiFGV28zKSZoXuIfUu3kfaY2ge4DvkIohfD8i3s8vQmsl2VpT3wfmByYA90RErblU1s05mbJpZCuVXwJcShoTXFmAotDV8CQtBdxLKjIxB2loR7/s/rvA+xGxRH4RWiuR1G6xFZ/Idy1JHwK7RMQ/847FWpukOUlDkAcDC5BOgm8F/hQRH+QYmpm1MCdTNo2KK/FV3xxFvhIvaShpPaDNSJOKVyFNGN2JtO7RRhHxWG4B9hCSvuNV4K0zSHoK+E1EdIfqkJYDSbOQFn++MiIeyDsea23ZguEHk3o3+wGbRMSTkg4ARkfEfXnGZ81X+DkL1nRDaO21WlYF9mLqekAzRcQk4CJJ85Hmv6yTU2w9yf8kvQXcSarod2dEvJBzTNaaTgQOlTQiIj7OOxhrPRHxhaQ9gevzjsVam6RlSOuSfQWMBlYklUuHtAbeasB2+URneXEyZdOIiEvyjmEG9QUmRsRkSe8zdZV1gAeBo/IJq8cZTEpa1wHOAXpJeo0ssSIlV4VfFDqbr9OWiIj1mhJMDxURl0n6DjBO0r1UX/tu5xxCs9byCLAscFfegVhLOx14ivQZ9xnTzuG8h9QDaj2MkymrSqnu59KkLuwJwFPRGmNCxwELZd8/A2zJ1PUgNgLea35IPU9E3A7cDiCpL2k4xLrAesCOpN7PVvj/MxPT99TOSyqIMB54tukR9TCSdgEOJ10JXonpC1C0wv8ly9/BwN8lvUSq3Of3jTViLWDbiPioyuLPbzH1/MN6kFY4mbEmk7QbcDypSk3J25J+FxEX5hRWvW4HfgxcA/wRuErSWsAkUtWmlq1S2MIWAfqThkAsmm17Mr9w6hcRg6ptl7QkcANpCJp1rWNJw7N+ERHv5RyLta5rgK8BNwKTJL3NtIl4RMRiuURmraSt9RHnAz5tViBWHE6mbBqStgfOB+4ALgfeJF1p2R44X9InEfH3HENsz+GkAhRExD8kfQpsDcwOnAn8NcfYegxJQ0g9UesAC5N6CUeQ5rONjIgJOYY3wyLiBUknkxZwXDHveLq5eYFznEjZDLoD92LajLsf2BWoVhBnK+C/zQ3HisDV/Gwakh4jrTO1Y5V9lwHLRsQKTQ/MWkpWFfIT4FzgjxHxRs4hdTpJ6wPXR8QcecfSnUm6FfhXRPwl71jMrGfLlssYTpr7eyVwIeki7jLANsDarubX8ziZsmlI+gzYNCJuq7JvMHBDRMzW/Mg6RtJcwPdIQ8xeA570OiLNI+l0Uq/UcqR5aqNIPVMjIuKpHEPrFJL6kT5Ivx4Ry+UdT3eWrR33D+AP1F77rq2hN2ZmnUbShqTKwEuWbR4H7BMR/84jJsuXh/lZpQ+ZOq+l0qLZ/kKTdBRpsnFfQNnmDyWdGhHH5xdZzxERBwNImoeUVA0C9gb+nM1VGBER2+cXYX0kjWX6oUGzAAtm3/+8uRH1SKXk+9Ia+1ulmInlJEvIe0fEk9n9+YGDSBd73gbOi4h7cwzRWkBWcOJ7wP0R8S1J3yRb/Dkinsk3OsuTe6ZsGpIuJZX83CIi/lO2fU3gOmBYkcsQSzoWOBK4ALiKVF1nQWBb0hpav4+IY3ILsIeSNIBUyW+b7GsUefHnEkmXMH0y9RnwEnCN187qepKOoZ25LhFxbHOisVYi6evAv4Dls01PkhZ0vx1YHHiHVDRgEvD9iHgohzCtRUiaibSG5YYRMSzveKw4nEzZNCQtRFqHY0nS8Lg3SAUoFgWeJ40Hfiu/CNsm6XXgiog4pMq+04DtIuLrzY+sZ8lOYkoFKNYlVfObDDzK1EV8b615ADOzGSTpAmBD4HekURWHk3qWPwY2ioi3JS1MSrhejYhNcwvWWoKkF4GDI8ILQNsUTqZsOpJmJ/Xi/JC0ztRE0pyXSyLikzxja4+kj0lzvoZX2fcj4EYXDOh6WQGKAJ4gTdQdAYxqlXlrkuYEvg/0JlUf/CgbKnQMU4cGnRUR1+UXpZm1RdI44ISI+Gt2f2XgAWDLiPhnWbttgVMjotYQdzMAJB0K/BT4cURUrnlnPZTHmdt0soTpL9mt1dwHrEqqtlNp1Wy/db0tSb1PE/MOpKMkfZv0/lmENOfuTUkbA//O7r8ILAtcI2lwtcTdOpekWYCfkBZL7lOxOyLi982PylrAIsD/yu6Pyb6+VNFuHF5s1eozJ2nkzotZpdE3mH69sqNzicxy454p61YkfY+0wOf5pEUaS3OmtgJ2BzYFni61dxUwqyTpatLaUXuThgadSDqJ/x+p1/OzrPf2X8DkiPhRbsH2ANmQ0buBAaSTllJRmSkfXq0w/86aL+shXyMi7s/u9wK+BFaJiIfL2q0O3OP3kVWTDe3bPCIey95TbWmJ+cDWudwzZbUqltUUEUt0YTgz6vHs68nZrZxIw85KXAWsC0laFjgaGAjMQxouOhI4rlRVq6B+ABwWEXcASNqPdEX7lxHxGaTeW0lnkdbRsq51KjAeWBt4GVg9uz+EtCD3+vmFZi1gpqxwAECvKtvKt5tVMwCYFSAiZmq7qfVEPpE0SPOhypOp9Ui9Of9las/OD4A3SavIF9lxeJX73ElalfS++hQYSnrvLARsDGwoae0CV85aCCiv0lf6/vWKdm8A8zclop7th8CvmfrznxwR44Cjsp6GP5N6nM2q+W+VbR7ubWadxsmUERG7lL6XtAfpyu/3I+LVsu3fAG4DRjc9wA5oq+y5pEHAThExpFnx9GAnkcoQrxcRU9Ymywo7DM/2F7VHYSbgq7L7pe8rk3Qn7c0xL/B6REzOCszMU7ZvBLBvPmFZC3DJfOss/n9vNTmZskqHAL8tT6QAIuKVbL2XE4G/5hFYI7JF9XYCdiSV5/6UNDzIutYawI7liRRARHwo6RTgb/mEVbdFJJWGs/Yq2/ZeWRtX/mqOV0lrAUHqJVyfqQVmViOt+2U2Ha8/Zp3oWEnv1NEuirwWp3UNJ1NWaVFqn5x8TqqOVGiSvkaaS7ETsGa2+THSHKq/5xVXD9PeVbyiX+W7tsq2Gyrui+K/ju7gTtK8uxuA84CzJa1AKiQwONtmZtaVViCdA7XHnwk9kKv52TQkPURa0HD90mT7bPtspFXjZ4uIlfOKr5ZsMvEGpARqE1L55NeB64B9gHUi4q78IuxZJA0HvgasWzHMbw7S0Kz3I6KQw/wkdeiqYkQUvZetpUmaD+gXEc9m9/cjXSyZHbiVVNDEvVNm1iUqq0KaVXIyZdOQtB5wM/ABcAtTC1D8lHRy/JOIGJFfhNOTdBqwPbAAqVftBtIwsuHAXKQqcoOcTHWtivKxq5Eq931GKiH+Bqmww4bAbKTfxwN5xWrFll0c2RAYW6vyY1YtckBE3NTU4MysR3EyZe1xiUebRlYOekVSL9QPgf2yr8OA5YuWSGUOIiVStwD9I2L7iBiWrSHlqwXNM4Cp5WPvJ82bGkEainUQqedwBOlDyYmUtWUH0pDcj9to8yFwpaRtmxOSmZnZ9NwzZS1P0gXAFkzthboKuDQi7s/mT72Le6a6nK/eWWeRNAx4JiL2a6fdmcBSEbFBcyIzs57Gn23WHvdMWcuLiN1IQ8h2AB4C9gJGS3oKOBT3TjWTf9bWGVYi9Ya3ZziwShfHYj2AJBfksqoiYiYnUtYW90zZdCQNBLYllRLvU7E7ImK95kdVP0kLM7Uc+tLZ5nuBc4BrPVm9a2RX724FXD7WZoikz0lrlN3dTru1gDsiYtbmRGbdQdajeVtE3JLd7wNcFxE/zTcyM2tFTqZsGpL2BM4FJgDPUaUUaESs0+y4GiVpVWBnUvWveUlV5OZp+1HWiCyZepM6y8dGxBLtN7OeSNJrwG8i4op22m0HnBoRhV+ywYpD0vOkOZ5PkuYFHw+sHBF984zLzFqTkymbhqRngfuBIRHxRd7xdBZJvYGNgZ0iYrOcw+mWPK7cOoukq4F52iufn82tejcitm5OZNZdSPo6KYnamXTxcEMXxjGzRnjOlFVaBLi4OyVSABHxZURc50TKrCWcAawn6U+SZqncKal3NlRrXeBPzQ7OWoOkZSVtU2P3h8C3gfeAObKvZmYd5gmXVukhYAngjrwDMbOeKSJGSzoYOB3YPuuBeinbvRjwY9Kw3YMj4t6cwrTiO4W0ePtV5RslzUsqcLIg8APgBOAw4BfNDtDMWp+TKau0P3CFpGdcStzM8hIRZ0h6mHSSuzlpsWeAT0kLQp8cEf/JKTxrDasAu5dvyAoUDQfmBAZGxAuS/kFK3M3MOszJlFW6ibRe052SPiGt0VQuImKx5odlRRcRHjZsnSq7oHOXpJmA+bLNEyLiqxzDstYxK2UFcSQNII26mAlYOyLGZbvGA/M3Ozgz6x6cTFmlO/BaQWZWIBExGXg77zis5TwEHC3pDdL8qDNJydUPI+LVsnYr4/eXmTXI1fzMzMys25G0PGntuwWyTa8DAg4tld2XtAJp/tT1EbFnHnGaWWtzMmVmZmbdkqS5gR8Ck0kjL1YnDWefCHwAfIe0Pt5qEfFmTmGaWQtzMmXTkbQscDQwEJiH9KEzEvh9RDyRY2hmZmYzRNK3gQOAxYExwGlOpMysUU6mbBqSVgVGkSpmDSVdsVuItODtbKRJuw/lF6GZmZmZWTE4mbJpSBpOqua3XkR8WLZ9TlI52fcjYv284jMzMzMzKwqXMrZKawAnlSdSANn9U4A1c4nKzMzMzKxgnExZpfa6Kt2VaWZmZmaGh/lZhWyY39eAdSuG+c0BjMDD/MzMzMzMACdTVkHSaqTKfZ8B/wLeIBWg2BCYHRgYEQ/kFqCZmZmZWUE4mbLpSFoOOIq0Nkc/Umn0Ubg0upmZmZnZFE6mDEkzkXqexkbEkzXaLAsMiIibmhqcmZlZAyRd1IHmERG/6LJgzKzbmjnvAKwQdgDOAZZto82HwN8l7R4Rf29OWGZmZg1bl2mLJs1NmhM8CZgAzEs6D3ofeLfZwZlZ9+BqfgYpmbo4IsbWahAR44ALgZ2bFZSZmVmjImJARCweEYsDOwIfAdsAs0XEwqSF6LfNtu+QX6Rm1sqcTBnASsCwOtoNB1bp4ljMzMw62x9Jayj+IyK+AoiIryLiauBk4Iw8gzOz1uVkygDmpL4hDu9mbc3MzFrJssDzNfY9B3yvibGYWTfiZMoA3gEWq6Nd/6ytmZlZK3kT2KrGvm2At5oYi5l1Iy5AYQB3k+ZCXdFOu12ytmZmZq3kDOBPkhYGriElTwuSEqzBwAG5RWZmLc2l0Q1Ja5KSpD8Dh0bEFxX7ewOnAfsAa0XEvc2P0szMrHGSfgEcDSxatvkV4NiI6EgZdTOzKZxMGQCSDgBOJ5WLHQa8lO1aDPgxqYTswRFxZi4BmpmZzSBJIiVTCwNvAK+GT4TMbAY4mbIpJK0NHAYMJJWMBfgUGAmcHBH/ySk0MzMzM7PCcTJl05E0EzBfdndCqYysmZlZq5K0InAksDZpAd/VIuJhSScCd0XErXnGZ2atydX8bDoRMTki3s5uTqTMzKylSVoLGA18B7iSac9/JgN75RGXmbU+J1NmZmbW3Z0M3AYsAxxUse9h0uL1ZmYd5tLoZmZm1t2tBPwsIkJS5fyGd4D5c4jJzLoB90yZmZlZd/cZMHuNfQsD7zcxFjPrRpxMmZmZWXd3N3CApF5l20o9VL8ARjQ/JDPrDjzMz8zMzLq7I4H/Ao8B15ISqZ0l/RFYGVg1x9jMrIW5NLqZmZl1e5JWAk4llUbvRari9x/goIh4JM/YzKx1OZkyMzOzHkNSH6Af8F5EfJJ3PGbW2pxMmZmZmZmZNcAFKMzMzKxbkjSnpMGSNpLUN9u2lKS/SxojaaSkn+Udp5m1LvdMmZmZWbcj6dvAcGARQMCbwMbAv7P7LwJLAvMAgyNieE6hmlkLczJlZmZm3Y6kq4EVgb2BD4ETgaWA/wGbRsRnkmYH/gVMjogf5RasmbUsJ1NmZmbW7Uh6FTgsIi7P7n8XGENKpG4qa7c5cG5ELJRPpGbWyjxnyszMzLqjhYAXyu6Xvn+9ot0bwPxNicjMuh0nU2ZmZtYdzQR8VXa/9H3lkBwP0TGzhs2cdwBmZmZmXWQRSUtk3/cq2/ZeWZtFmxuSmXUnnjNlZmZm3Y6kyUzf66Ra2yKiF2ZmHeSeKTMzM+uOds07ADPr/twzZWZmZmZm1gAXoDAzMzMzM2uAkykzMzMzM7MGOJkyMzMzMzNrgJMpMzNriKRdJEXZ7UNJj0naV1LLFTiSNEDSMWWltGf0eCMrfj61bgM64/nMzKz5Wu7DzszMCmdL4FVgruz7s4AFgKPyDKoBA4CjgbuBFzvheL8k/UxKjgRWBTapaPdGJzyXmZnlwMmUmZnNqEcj4vns+2GSvgkcQI1kSlJvYFIUpJysJAG9O/u4EfG/iucZD3wREfd29nOZmVk+PMzPzMw62wPAnJIWyIbOhaRfSvqDpNeBz4G5lRwo6RlJX0h6Q9JfJJX35pA9/gRJR0h6VdKnku6StELlE0v6maR7JX0i6T1J10jqX9FmnKTLJQ2R9DTwBbAhcGfW5PayIXiDJP1L0sNVnmtxSZMl7dnID6ne45YNp1xb0g2SPpI0QdLZkmareOzskk6RNDb7mY7Nfm7+vDcz6wL+52pmZp1tceAr4KOybUcA3wb2ADYHPgNOAP4I3A5sDPwB2AW4ucrJ/07AT4F9szYLAndI6ldqIGkv4J/A/4AtgD2B7wGjJM1Zcbx1gIOAY4ENSMP69sn27Q+smd0eBs4BVpS0WsUx9gA+Bq5s9ydSXUePeznwPPAz4E/A7sC5pZ3ZPLXbgN2AM4GfABeQhhee2mCMZmbWBg/zMzOzGdUrO5GfE9iKdLJ/U0R8kkbQAfAWsHlpaF+WBB0E/C0i9s3a3JYNhbsM2AgYWvYcswHrR8TH2ePvA54DDgSOlNQXOAW4OCKGlB6UtXsW+AVwRtnx5gFWjog3y9rOk337VPlQPEm3kpKtPYH7s229gV2BKyLiw479uKbo6HFviYhfZ98PkxTAcZJOjIhngW2BtYCBEXFX1u6O7HdwtKRTIuLtBmM1M7Mq3DNlZmYz6mngS2AiqbflCmBIRZsbKuZIrQHMSuptKXcVMAkYWLH9llIiBRAR44B7Sb1HZF/nAq6QNHPpRiqM8TSwdsXx7i1PpNoSEZOB84BtJH0t27wZqXfsvHqO0UnH/UfF/atIn+Olnq0NgJeAeyp+BsNIc8LWaDRWMzOrzsmUmZnNqM1JVeq+A8wRETtFxMSKNpUV6/pV2x4Rk4AJZftL3qryvG8Bi2TfL5B9HU5K7MpvywLzthNPey4kfWbumN3fC7g/Ih7p4HFm5LiVP4PS/fKfwWJM//rvz/ZX/gzMzGwGeZifmZnNqCfLqvnVUlm5r5RsLQSMKW3MelLmJSVU5RascswFgdey70vtdyk/XpnKIXMdqiQYERMkXQPsKek20pyr3TpyjE447oJM+9pKP5Pyn8FY0lDLasbNWLRmZlbJyZSZmeXhXlJVv22AO8q2b036bBpV0f6nkuYomzM1gDRs7eRs/z2khOmbEfG3BmP6PPs6W4395wCjSUUdPiANs+sM9R53K2BE2f1tgMlM7Xm6Ffg58FFEPN1JsZmZWRucTJmZWdNFxERJfwQOl/QxcAvwXeB40qK5N1c85FNS0YVTSXOtjiUlHn/KjveBpEOAsyXND/wbeJ80BG4gMDIi2qu69yxpvtYQSRNJydUzpUIQEXFvVsp8beCsiPhkhn4ImQ4c96fZ6x9Gmid1NHBpVnwC0ly1XUlFJ04HHgNmAZYkLRS8WWfFbGZmiZMpMzPLyxHAeNI8oV+ShqldChyeFWcodympXPhfgPlIa1ltUz43KyLOk/QKcAiwHanowmvAXcCj7QWTDbnbFziU1DPWizTsbmRZs2uBlZiBwhM11HPcHYCDgb1Ja2P9FShV9yMivpQ0GDiMVF59cdLP7AVScvpFJ8dsZtbjqSAL0JuZmVWVlQA/ISJ+V4BY/gtMjogfNuu4knYBLga+VcfcNDMzayL3TJmZmbVB0qykXqMfAd8HNi3ycc3MrHmcTJmZmbVtYVKBi/eAEyNiaNvNcz+umZk1iYf5mZmZmZmZNcCL9pqZmZmZmTXAyZSZmZmZmVkDnEyZmZmZmZk1wMmUmZmZmZlZA5xMmZmZmZmZNeD/AaR7rf+bHRmuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "price_by_month = df_l_c.groupby('property_type', as_index=False)['combined_price'].mean().sort_values(ascending=False, by=\"combined_price\")\n",
    "price_by_month.plot.bar(x='property_type', y = 'combined_price', figsize=(14,6));\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.xlabel('Property Type', fontsize = 16)\n",
    "plt.ylabel('Average price', fontsize = 16)\n",
    "plt.title(\"Average $ price for property\", fontsize = 16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "30983053",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "price_by_month = df_l_c.groupby('property_type', as_index=False)['combined_price'].median().sort_values(ascending=False, by=\"combined_price\")\n",
    "price_by_month.plot.bar(x='property_type', y = 'combined_price', figsize=(14,6));\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.xlabel('Property Type', fontsize = 16)\n",
    "plt.ylabel('Average price', fontsize = 16)\n",
    "plt.title(\"Median $ price for property\", fontsize = 16);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d6e777b",
   "metadata": {},
   "source": [
    "If we look at the plots, the first one contains mean values of combined price, while the second one contains median values by property types. Based on the two plots, we can say that the Condominium and Boats are the most expensive property types. Since the values of mean and median are significantly different, we should believe median values more, since these should represent the data better. Based on this, I would assume that the Condominium property type is the most expensive.\n",
    "\n",
    "This concludes Question 3."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19ce85ed",
   "metadata": {
    "id": "19ce85ed"
   },
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "> At the end, summarize your findings and the results that have been performed. \n",
    "- The Conclusion should reflect on the steps taken during the data exploration, summarize the main findings in relation to the questions provided at the beginning of the analysis accurately.\n",
    "- The conclusion points out where additional research can be done or where additional information could be useful and **should have at least 1 limitation explained clearly.**\n",
    "- The analysis should not state or imply that one change causes another based solely on a correlation. You can refer to this video on [Correlation does not mean Causation](https://youtu.be/VMUQSMFGBDo)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9f0c078",
   "metadata": {},
   "source": [
    "In the Introduction part of this assignment, we have examined the data from a high level, understood the different variables contained within the data and chose 3 research questions:\n",
    "\n",
    "    1) Which months are the least and most expensive to visit Seattle in?\n",
    "    2) Which neighbourhoods are the most and least expensive to stay in?\n",
    "    3) Which property types are the most expensive?\n",
    "    \n",
    "In the Data wrangling part of this assignment, we have further examined the data, cleaned and merged different variables in our new dataframe and we used this dataframe further in the EDA part of this assignment to answer the research questions.\n",
    "\n",
    "In the EDA part of this assignment, we have looked at the three research questions by analysing various plots and results of our findings. We have found that the dataset has often different median and mean values of the price variable. This affected our research, since we had to plot the data for each case and preferably make an informed decision based on which values we believed more.\n",
    "\n",
    "One limitation would be the fact that square_feet has 90%+ missing values, which essentially makes any data analysis using this variable impossible.\n",
    "\n",
    "There is a clear limitation when it comes to removing outliers. I believe that the data is correct, therefore I haven't made much effort removing the outliers and looking at different results. I don't think it would be helpful though, as the data was supplied by Airbnb and probably correct. \n",
    "\n",
    "Another limitation is the amount of missing prices from the Calendar dataset. We have lost roughly 460 000 entries, since we had to remove the rows which contained null values.\n",
    "\n",
    "However, I have attempted my best at answering the three research questions within the EDA part of this assignment, and here are the answers to conclude:\n",
    "\n",
    "    1) June, July and August are the most expensive months to visit Seattle in. January, February and March are the least expensive months to visit Seattle in.\n",
    "    2) Portege Bay and Briarcliff are the most expensive neighbourhood to stay in. Roxhill is the least expensive neighbourhood to stay in.\n",
    "    3) Condominium is the most expensive property type."
   ]
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