Analyzing_the_Real_Estate_Market_in_Canada.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dd012883",
   "metadata": {},
   "source": [
    "# Analysis of the Canadian Real Estate Market"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1fd3822",
   "metadata": {},
   "source": [
    "2021 saw a boom in the canadian real estate market. This continued up until 2022 with many home owners selling their houses for up to 50% over the original price. If you are a home owner, you most likely had a realtor at your door, trying to convince you to sell your house.\n",
    "\n",
    "To address this issue, the mortgage rates were increased in hopes, that it would crash the market by reducing the demand, which would then lead to reduction in house prices. But how successful has this strategy been?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8cd4445",
   "metadata": {},
   "source": [
    "My analysis would focus on the real estate market in **major cities ( > 200,000 population)** in the **Prairie & West Coast regions of Canada**.\n",
    "\n",
    "I will use my analysis to answer the following questions:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a916ff2",
   "metadata": {},
   "source": [
    "- **How effective has the increase in mortgage rate been in crashing the market?**\n",
    "- **What cities (in the Prairie & West Coast region had the highest demand?**\n",
    "- **Where would you advise someone with limited funds to buy a house?**\n",
    "- **How well can we predict the price of a house?**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a9260b7",
   "metadata": {},
   "source": [
    "## Data Collection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd53cc5b",
   "metadata": {},
   "source": [
    "To answer these questions, I obtained data on:\n",
    "   - Houses sold in Major cities in the Prairie & West Coast regions from **Sept 25, 2022 to January 23, 2023**. (*Source: honestdoor.ca*)\n",
    "   - Population of major cities in the Prairie & West Coast regions in 2021 (*Source: Wikipedia*)\n",
    "   - 5 - year fixed mortgage rates from September to January (*Source: ycharts.com*) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c144bdaa",
   "metadata": {},
   "source": [
    "**Read in the datasets**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c8c2a0aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import datetime\n",
    "from matplotlib import dates, pyplot as plt\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ba5f9f83",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get the house price data\n",
    "house_data = pd.read_csv('canada_house_prices.csv').iloc[:,2:]\n",
    "\n",
    "#Get the population data\n",
    "population = pd.read_csv('population.txt', sep =',')\n",
    "\n",
    "#Get the mortgage rates \n",
    "rates = pd.read_csv('mortgage_rates.txt', sep =',')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80b5c234",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86147c6c",
   "metadata": {},
   "source": [
    "**Check out the house data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b5bafa8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22432 entries, 0 to 22431\n",
      "Data columns (total 30 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   id                 22432 non-null  object \n",
      " 1   slug               22432 non-null  object \n",
      " 2   assessmentClass    22432 non-null  object \n",
      " 3   zoning             20049 non-null  object \n",
      " 4   accountNumber      18057 non-null  float64\n",
      " 5   creaListing        127 non-null    object \n",
      " 6   bathroomsTotal     19417 non-null  float64\n",
      " 7   bedroomsTotal      19388 non-null  float64\n",
      " 8   livingArea         19911 non-null  float64\n",
      " 9   lotSizeArea        18504 non-null  float64\n",
      " 10  yearBuiltActual    21841 non-null  float64\n",
      " 11  show               22432 non-null  bool   \n",
      " 12  fireplace          1752 non-null   object \n",
      " 13  garageSpaces       9552 non-null   float64\n",
      " 14  houseStyle         2525 non-null   object \n",
      " 15  livingAreaUnits    615 non-null    object \n",
      " 16  basement           2609 non-null   object \n",
      " 17  building           0 non-null      float64\n",
      " 18  unparsedAddress    22432 non-null  object \n",
      " 19  province           22432 non-null  object \n",
      " 20  city               22432 non-null  object \n",
      " 21  cityName           22432 non-null  object \n",
      " 22  neighbourhood      22364 non-null  object \n",
      " 23  neighbourhoodName  22364 non-null  object \n",
      " 24  postal             22220 non-null  object \n",
      " 25  location           22419 non-null  object \n",
      " 26  closeDate          22432 non-null  object \n",
      " 27  closePrice         22432 non-null  int64  \n",
      " 28  predictedDate      22044 non-null  object \n",
      " 29  predictedValue     22044 non-null  float64\n",
      "dtypes: bool(1), float64(9), int64(1), object(19)\n",
      "memory usage: 5.0+ MB\n"
     ]
    }
   ],
   "source": [
    "house_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aabab8b1",
   "metadata": {},
   "source": [
    "**Observations from the data**\n",
    "\n",
    "- The data contains information on 22,432 houses sold. \n",
    "- There are 30 columns with info such as the no of bathroomsTotal, bedroomsTotal,cityName, Province, closeDate, closePrice etc.\n",
    "- Some columns have null values, which means that some info was not recorded for certain houses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f33b1c4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2022-11-15\n",
       "1        2022-10-27\n",
       "2        2022-12-14\n",
       "3        2022-09-26\n",
       "4        2022-12-27\n",
       "            ...    \n",
       "22427    2023-01-08\n",
       "22428    2023-01-03\n",
       "22429    2023-01-06\n",
       "22430    2022-12-30\n",
       "22431    2022-11-15\n",
       "Name: closeDate, Length: 22432, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert the closeDate column to date object\n",
    "house_data['closeDate'] = pd.to_datetime(house_data['closeDate']).dt.date\n",
    "house_data['closeDate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e365f3bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now lets confirm the houses were sold between september 24, 2022 & January 23, 2023\n",
    "date_min = house_data['closeDate'].min().strftime(\"%B %d,%Y\")\n",
    "date_max = house_data['closeDate'].max().strftime(\"%B %d,%Y\")\n",
    "no_of_days = str(house_data['closeDate'].max() - house_data['closeDate'].min()).split(',')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "af663f67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our data contains houses sold from September 24,2022 to January 18,2023, which is a total of 116 days.\n"
     ]
    }
   ],
   "source": [
    "print(f'Our data contains houses sold from {date_min} to {date_max}, which is a total of {no_of_days}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19fc7fe0",
   "metadata": {},
   "source": [
    "**Check out the population data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4a89a4e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8 entries, 0 to 7\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   City        8 non-null      object\n",
      " 1   Province    8 non-null      object\n",
      " 2   Population  8 non-null      int64 \n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 320.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "population.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9cb349dd",
   "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>City</th>\n",
       "      <th>Province</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Winnipeg</td>\n",
       "      <td>Manitoba</td>\n",
       "      <td>749607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Calgary</td>\n",
       "      <td>Alberta</td>\n",
       "      <td>1306784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Edmonton</td>\n",
       "      <td>Alberta</td>\n",
       "      <td>1010899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       City  Province  Population\n",
       "0  Winnipeg  Manitoba      749607\n",
       "1   Calgary   Alberta     1306784\n",
       "2  Edmonton   Alberta     1010899"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f7611ad",
   "metadata": {},
   "source": [
    "**Check out the mortgage rates**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "69633412",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5 entries, 0 to 4\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   date    5 non-null      object \n",
      " 1   rate    5 non-null      float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 208.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "rates.info() \n",
    "rates['date'] = pd.to_datetime(rates['date']).dt.date\n",
    "#rates.set_index(keys='date', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d67b7dff",
   "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>rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-09-01</td>\n",
       "      <td>5.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-10-01</td>\n",
       "      <td>5.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-11-01</td>\n",
       "      <td>5.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  rate\n",
       "0  2022-09-01  5.58\n",
       "1  2022-10-01  5.64\n",
       "2  2022-11-01  5.75"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rates.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2087a8f3",
   "metadata": {},
   "source": [
    "## Now lets use the data to answer some questions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f0e080b",
   "metadata": {},
   "source": [
    "### 1. How effective has the increase in mortgage rate been in crashing the market?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2ede83e",
   "metadata": {},
   "source": [
    "To get an idea of the effect of the increase in mortgage rates on the market, I will look at the monthly number of houses sold & the average monthly prices of houses sold in each city"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a06f4e8a",
   "metadata": {},
   "source": [
    "To get this, I will need to clean the data a bit."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c30d4fd",
   "metadata": {},
   "source": [
    "**Data Preparation**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d75af546",
   "metadata": {},
   "source": [
    "Because we only have data from september 25, we can not fully investigate this month. So I will remove houses sold in september from this dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9c328e31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2022, 10, 1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove all houses sold in september from dataset\n",
    "house_from_oct = house_data[house_data['closeDate'] >= datetime.date(2022,10,1)]\n",
    "house_from_oct = house_from_oct.copy()\n",
    "#confirm\n",
    "house_from_oct['closeDate'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8694ac29",
   "metadata": {},
   "source": [
    "Now since we want to get the data for each month, I will create a new column which will contain the month each house was sold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "132efadd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([datetime.date(2022, 11, 1), datetime.date(2022, 10, 1),\n",
       "       datetime.date(2022, 12, 1), datetime.date(2023, 1, 1)],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create a function to get the first day of the month from each date\n",
    "\n",
    "def get_first(date):\n",
    "    \n",
    "    \"\"\"\n",
    "    Input:\n",
    "    date: date object\n",
    "    output:\n",
    "    This function returns the first day of the month\n",
    "    \"\"\"\n",
    "    return date.replace(day=1)\n",
    "\n",
    "#create another column to hold the month & year that the house was sold.\n",
    "#The first day of the month will be used to represent each month\n",
    "\n",
    "house_from_oct['closeMonth'] = house_from_oct['closeDate'].apply(get_first)\n",
    "house_from_oct['closeMonth'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8e9d2a84",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a function that will get the the number of houses sold & the avergae prices per month\n",
    "\n",
    "def get_city_data(df, city_list):\n",
    "    \n",
    "    \"\"\"\n",
    "    Inputs:\n",
    "    - df: dataframe object; the dataframe you are working with\n",
    "    - city_list: list; the names of the city which you want to get data for\n",
    "    \n",
    "    Outputs:\n",
    "    city_count: dict containing groupby objects of the no of houses sold in each city grouped by month\n",
    "    city_avg: dict containing groupby objects of the average prices of houses sold in each city grouped by month\n",
    "    \"\"\"\n",
    "    \n",
    "    city_count_dict = {}\n",
    "    city_avg_dict = {}\n",
    "    \n",
    "    for city in city_list:\n",
    "        city_data = df[df['cityName'] == city]\n",
    "        city_count = city_data.groupby(by='closeMonth')['closePrice'].count()\n",
    "        city_avg = city_data.groupby(by='closeMonth')['closePrice'].mean()\n",
    "        city_count_dict[city] = city_count\n",
    "        city_avg_dict[city] = city_avg\n",
    "    \n",
    "    return city_count_dict, city_avg_dict\n",
    "\n",
    "city_list = ['Winnipeg', 'Calgary', 'Edmonton', 'Vancouver', 'Surrey', 'Burnaby', 'Regina', 'Saskatoon']\n",
    "\n",
    "cities_count, cities_avg = get_city_data(house_from_oct,city_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46feaae1",
   "metadata": {},
   "source": [
    "**Data Visualization**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5d28a19",
   "metadata": {},
   "source": [
    "Now lets look at what the data says"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d93df792",
   "metadata": {},
   "source": [
    "**How has the mortgage rate changed since September 2022?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "57ae9e68",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax =rates.plot(x= 'date', y ='rate', legend=False,figsize=(8.5,5))\n",
    "ax.set(xlabel='')\n",
    "ax.xaxis.set_major_locator(dates.MonthLocator(interval =1))\n",
    "ax.xaxis.set_major_formatter(dates.DateFormatter('%b-%Y'))\n",
    "pos = ['left','right','top']\n",
    "for char in pos:\n",
    "    ax.spines[char].set_visible(False)\n",
    "    ax.tick_params(left=False, bottom=False)\n",
    "ax.grid(axis='y', ls ='--')\n",
    "ax.yaxis.tick_right()\n",
    "ax.text(dates.date2num(datetime.date(2022, 9, 1))/1.0005,5.95,'5-Year Fixed Mortgage Rates in Canada',size=14, weight='bold')\n",
    "ax.text(dates.date2num(datetime.date(2022, 9, 1))/1.0005,5.92,'Mortgage Rates have been on the increase',size=10)\n",
    "ax.text(dates.date2num(datetime.date(2022, 9, 1))/1.0005,5.5, ' '*132 + 'Source: ycharts.com',color='#8B6A9E',backgroundcolor='#fff0f0',size=10)\n",
    "\n",
    "\n",
    "plt.savefig('mortgage_rates_per_month.png', bbox_inches='tight',  facecolor='w')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab44785a",
   "metadata": {},
   "source": [
    "We can see that the mortgage rate has been increasing since September 2022 till date. A total increase of 5% since september."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7409e168",
   "metadata": {},
   "source": [
    "**Now how has this increase in rates affected the prices & demand for houses?**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2a0171c",
   "metadata": {},
   "source": [
    "Let's look at the number of houses sold from Oct till date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0b49f04c",
   "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>closeMonth</th>\n",
       "      <th>2022-10-01</th>\n",
       "      <th>2022-11-01</th>\n",
       "      <th>2022-12-01</th>\n",
       "      <th>2023-01-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cityName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Burnaby</th>\n",
       "      <td>246</td>\n",
       "      <td>108</td>\n",
       "      <td>94</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calgary</th>\n",
       "      <td>2146</td>\n",
       "      <td>3698</td>\n",
       "      <td>3764</td>\n",
       "      <td>376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edmonton</th>\n",
       "      <td>2297</td>\n",
       "      <td>1858</td>\n",
       "      <td>2163</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Regina</th>\n",
       "      <td>196</td>\n",
       "      <td>162</td>\n",
       "      <td>113</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saskatoon</th>\n",
       "      <td>220</td>\n",
       "      <td>212</td>\n",
       "      <td>158</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Surrey</th>\n",
       "      <td>489</td>\n",
       "      <td>219</td>\n",
       "      <td>169</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vancouver</th>\n",
       "      <td>507</td>\n",
       "      <td>296</td>\n",
       "      <td>217</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Winnipeg</th>\n",
       "      <td>542</td>\n",
       "      <td>418</td>\n",
       "      <td>247</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "closeMonth  2022-10-01  2022-11-01  2022-12-01  2023-01-01\n",
       "cityName                                                  \n",
       "Burnaby            246         108          94           4\n",
       "Calgary           2146        3698        3764         376\n",
       "Edmonton          2297        1858        2163         200\n",
       "Regina             196         162         113          27\n",
       "Saskatoon          220         212         158          58\n",
       "Surrey             489         219         169          14\n",
       "Vancouver          507         296         217          13\n",
       "Winnipeg           542         418         247         118"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(house_from_oct['cityName'], house_from_oct['closeMonth'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "35374b97",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 24 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "fig,((ax1,ax2,ax3),(ax4,ax5,ax6),(ax7,ax8,ax9),(ax10,ax11,ax12),(ax13,ax14,ax15),(ax16,ax17,ax18),(ax19,ax20,ax21),(ax22,ax23,ax24)) = plt.subplots(ncols =3, nrows=8,figsize = (20,20))\n",
    "\n",
    "#winnipeg\n",
    "axes = [ax1,ax2,ax3]\n",
    "\n",
    "for ax in axes:\n",
    "    ax.plot(cities_count['Winnipeg'], color = '#af0b1e', alpha = 0.2, )\n",
    "    ax.set(xlabel='')\n",
    "    pos = ['left','right','top','bottom']\n",
    "    for char in pos:\n",
    "        ax.spines[char].set_visible(False)\n",
    "    ax.tick_params(left=False, bottom=False)\n",
    "    ax.set_yticklabels([])\n",
    "    ax.set_xticklabels([]) \n",
    "ax1.plot(cities_count['Winnipeg'][[0,1]],color='#af0b1e', linewidth=2.5) #oct to nov\n",
    "ax2.plot(cities_count['Winnipeg'][[1,2]],color='#af0b1e', linewidth=2.5)#nove to dec\n",
    "ax3.plot(cities_count['Winnipeg'][[2,3]],color='#af0b1e', linewidth=2.5) #dec to jan\n",
    "ax1.text(dates.date2num(datetime.date(2022, 10, 1)), 450, '542', alpha=0.5, size='large')\n",
    "ax1.text(dates.date2num(datetime.date(2022, 11, 1)), 350, '418', alpha=0.5, size='large')\n",
    "ax1.text(dates.date2num(datetime.date(2022, 11, 1))/1.0012, 450, 'Oct-Nov', color='#af0b1e',weight='bold', rotation=-15, size='x-large', alpha =0.8)\n",
    "ax2.text(dates.date2num(datetime.date(2022, 11, 1)), 320, '418', alpha=0.5, size='large')\n",
    "ax2.text(dates.date2num(datetime.date(2022, 12, 1)), 170, '247', alpha=0.5, size='large')\n",
    "ax2.text(dates.date2num(datetime.date(2022, 12, 1))/1.0012, 300, 'Nov-Dec', color='#af0b1e',weight='bold', rotation=-20, size='x-large', alpha =0.8)\n",
    "ax3.text(dates.date2num(datetime.date(2022, 12, 1)), 170, '247', alpha=0.5, size='large')\n",
    "ax3.text(dates.date2num(datetime.date(2023, 1, 1)), 80, '118', alpha=0.5, size='large')\n",
    "ax3.text(dates.date2num(datetime.date(2023, 1, 1))/1.0012, 160, 'Dec-Jan', color='#af0b1e',weight='bold', rotation=-15, size='x-large', alpha =0.8)\n",
    "\n",
    "#Now we will add subtitle & title\n",
    "ax1.text(dates.date2num(datetime.date(2022, 10, 1)),900,'The Increase in Mortgage Rates May Have Crashed the Demand',size=22, weight='bold')\n",
    "ax1.text(dates.date2num(datetime.date(2022, 10, 1)),780,'Number of Houses Sold in Each City (Oct 2022-Jan 2023)',size=18)\n",
    "ax1.text(dates.date2num(datetime.date(2022, 10, 1))/1.002,200,'Winnipeg',size=16, rotation=90, weight = 'bold')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#calgary\n",
    "axes = [ax4,ax5,ax6]\n",
    "\n",
    "for ax in axes:\n",
    "    ax.plot(cities_count['Calgary'], color = '#af0b1e', alpha = 0.2)\n",
    "    ax.set(xlabel='')\n",
    "    pos = ['left','right','top','bottom']\n",
    "    for char in pos:\n",
    "        ax.spines[char].set_visible(False)\n",
    "    ax.tick_params(left=False, bottom=False)\n",
    "    ax.set_yticklabels([])\n",
    "    ax.set_xticklabels([]) \n",
    "ax4.plot(cities_count['Calgary'][[0,1]],color='#af0b1e', linewidth=2.5) #oct to nov\n",
    "ax5.plot(cities_count['Calgary'][[1,2]],color='#af0b1e', linewidth=2.5)#nove to dec\n",
    "ax6.plot(cities_count['Calgary'][[2,3]],color='#af0b1e', linewidth=2.5) #dec to jan\n",
    "ax4.text(dates.date2num(datetime.date(2022, 10, 1)), 1700, '2146', alpha=0.5, size='large')\n",
    "ax4.text(dates.date2num(datetime.date(2022, 11, 1)), 3850, '3698', alpha=0.5, size='large')\n",
    "#ax4.text(dates.date2num(datetime.date(2022, 11, 1))/1.002, 2800, 'Oct-Nov', color='#af0b1e',weight='bold', rotation=5, size='x-large')\n",
    "ax5.text(dates.date2num(datetime.date(2022, 11, 1)), 3850, '3698', alpha=0.5, size='large')\n",
    "ax5.text(dates.date2num(datetime.date(2022, 12, 1)), 3950, '3764', alpha=0.5, size='large')\n",
    "#ax5.text(dates.date2num(datetime.date(2022, 12, 1))/1.0013, 2800, 'Nov-Dec', color='#af0b1e',weight='bold', rotation=0, size='x-large')\n",
    "ax6.text(dates.date2num(datetime.date(2022, 12, 1)), 3850, '3764', alpha=0.5, size='large')\n",
    "ax6.text(dates.date2num(datetime.date(2023, 1, 1)), 400, '376', alpha=0.5, size='large')\n",
    "#ax6.text(dates.date2num(datetime.date(2023, 1, 1))/1.0005, 1500, 'Dec-Jan', color='#af0b1e',weight='bold', rotation=10, size='x-large')\n",
    "ax4.text(dates.date2num(datetime.date(2022, 10, 1))/1.002,1000,'Calgary',size=16, rotation=90, weight = 'bold')\n",
    "\n",
    "\n",
    "#Edmonton\n",
    "axes = [ax7,ax8,ax9]\n",
    "\n",
    "for ax in axes:\n",
    "    ax.plot(cities_count['Edmonton'], color = '#af0b1e', alpha = 0.2)\n",
    "    ax.set(xlabel='')\n",
    "    pos = ['left','right','top','bottom']\n",
    "    for char in pos:\n",
    "        ax.spines[char].set_visible(False)\n",
    "    ax.tick_params(left=False, bottom=False)\n",
    "    ax.set_yticklabels([])\n",
    "    ax.set_xticklabels([]) \n",
    "ax7.plot(cities_count['Edmonton'][[0,1]],color='#af0b1e', linewidth=2.5) #oct to nov\n",
    "ax8.plot(cities_count['Edmonton'][[1,2]],color='#af0b1e', linewidth=2.5)#nove to dec\n",
    "ax9.plot(cities_count['Edmonton'][[2,3]],color='#af0b1e', linewidth=2.5) #dec to jan\n",
    "ax7.text(dates.date2num(datetime.date(2022, 10, 1)), 1700, '2296', alpha=0.5, size='large')\n",
    "ax7.text(dates.date2num(datetime.date(2022, 11, 1)), 1500, '1858', alpha=0.5, size='large')\n",
    "#ax7.text(dates.date2num(datetime.date(2022, 11, 1))/1.0012, 2100, 'Oct-Nov', color='#af0b1e',weight='bold',rotation=-10, size='x-large')\n",
    "ax8.text(dates.date2num(datetime.date(2022, 11, 1)), 1500, '1858', alpha=0.5, size='large')\n",
    "ax8.text(dates.date2num(datetime.date(2022, 12, 1)), 2300, '2163', alpha=0.5, size='large')\n",
    "#ax8.text(dates.date2num(datetime.date(2022, 12, 1))/1.0013, 2000, 'Nov-Dec', color='#af0b1e',weight='bold', rotation=10, size='x-large')\n",
    "ax9.text(dates.date2num(datetime.date(2022, 12, 1)), 2300, '2163', alpha=0.5, size='large')\n",
    "ax9.text(dates.date2num(datetime.date(2023, 1, 1)), 250, '200', alpha=0.5, size='large')\n",
    "#ax9.text(dates.date2num(datetime.date(2023, 1, 1))/1.0011, 800, 'Dec-Jan', color='#af0b1e',weight='bold', rotation=-45, size='x-large')\n",
    "ax7.text(dates.date2num(datetime.date(2022, 10, 1))/1.002,100,'Edmonton',size=16, rotation=90, weight = 'bold')\n",
    "\n",
    "\n",
    "#Vancouver\n",
    "axes = [ax10,ax11,ax12]\n",
    "\n",
    "for ax in axes:\n",
    "    ax.plot(cities_count['Vancouver'], color = '#af0b1e', alpha = 0.2)\n",
    "    ax.set(xlabel='')\n",
    "    pos = ['left','right','top','bottom']\n",
    "    for char in pos:\n",
    "        ax.spines[char].set_visible(False)\n",
    "    ax.tick_params(left=False, bottom=False)\n",
    "    ax.set_yticklabels([])\n",
    "    ax.set_xticklabels([]) \n",
    "ax10.plot(cities_count['Vancouver'][[0,1]],color='#af0b1e', linewidth=2.5) #oct to nov\n",
    "ax11.plot(cities_count['Vancouver'][[1,2]],color='#af0b1e', linewidth=2.5)#nove to dec\n",
    "ax12.plot(cities_count['Vancouver'][[2,3]],color='#af0b1e', linewidth=2.5) #dec to jan\n",
    "ax10.text(dates.date2num(datetime.date(2022, 10, 1)), 400, '507', alpha=0.5, size='large')\n",
    "ax10.text(dates.date2num(datetime.date(2022, 11, 1)), 200, '296', alpha=0.5, size='large')\n",
    "#ax10.text(dates.date2num(datetime.date(2022, 11, 1))/1.0012, 350, 'Oct-Nov', color='#af0b1e',weight='bold',rotation=-25, size='x-large')\n",
    "ax11.text(dates.date2num(datetime.date(2022, 11, 1)), 200, '296', alpha=0.5, size='large')\n",
    "ax11.text(dates.date2num(datetime.date(2022, 12, 1)), 100, '217', alpha=0.5, size='large')\n",
    "#ax11.text(dates.date2num(datetime.date(2022, 12, 1))/1.0013, 270, 'Nov-Dec', color='#af0b1e',weight='bold', rotation=-10, size='x-large')\n",
    "ax12.text(dates.date2num(datetime.date(2022, 12, 1)), 100, '217', alpha=0.5, size='large')\n",
    "ax12.text(dates.date2num(datetime.date(2023, 1, 1)), 5, '13', alpha=0.5, size='large')\n",
    "#ax12.text(dates.date2num(datetime.date(2023, 1, 1))/1.0011, 80, 'Dec-Jan', color='#af0b1e',weight='bold', rotation=-20, size='x-large')\n",
    "ax10.text(dates.date2num(datetime.date(2022, 10, 1))/1.002,0,'Vancouver',size=16, rotation=90, weight = 'bold')\n",
    "\n",
    "\n",
    "#Surrey\n",
    "axes = [ax13,ax14,ax15]\n",
    "\n",
    "for ax in axes:\n",
    "    ax.plot(cities_count['Surrey'], color = '#af0b1e', alpha = 0.2)\n",
    "    ax.set(xlabel='')\n",
    "    pos = ['left','right','top','bottom']\n",
    "    for char in pos:\n",
    "        ax.spines[char].set_visible(False)\n",
    "    ax.tick_params(left=False, bottom=False)\n",
    "    ax.set_yticklabels([])\n",
    "    ax.set_xticklabels([]) \n",
    "ax13.plot(cities_count['Surrey'][[0,1]],color='#af0b1e', linewidth=2.5) #oct to nov\n",
    "ax14.plot(cities_count['Surrey'][[1,2]],color='#af0b1e', linewidth=2.5)#nove to dec\n",
    "ax15.plot(cities_count['Surrey'][[2,3]],color='#af0b1e', linewidth=2.5) #dec to jan\n",
    "ax13.text(dates.date2num(datetime.date(2022, 10, 1)), 350, '489', alpha=0.5, size='large')\n",
    "ax13.text(dates.date2num(datetime.date(2022, 11, 1)), 150, '219', alpha=0.5, size='large')\n",
    "#ax13.text(dates.date2num(datetime.date(2022, 11, 1))/1.0012, 300, 'Oct-Nov', color='#af0b1e',weight='bold',rotation=-35, size='x-large')\n",
    "ax14.text(dates.date2num(datetime.date(2022, 11, 1)), 150, '219', alpha=0.5, size='large')\n",
    "ax14.text(dates.date2num(datetime.date(2022, 12, 1)), 80, '169', alpha=0.5, size='large')\n",
    "#ax14.text(dates.date2num(datetime.date(2022, 12, 1))/1.0013, 200, 'Nov-Dec', color='#af0b1e',weight='bold', rotation=-5, size='x-large')\n",
    "ax15.text(dates.date2num(datetime.date(2022, 12, 1)), 80, '169', alpha=0.5, size='large')\n",
    "ax15.text(dates.date2num(datetime.date(2023, 1, 1)), 5, '14', alpha=0.5, size='large')\n",
    "#ax15.text(dates.date2num(datetime.date(2023, 1, 1))/1.0011, 50, 'Dec-Jan', color='#af0b1e',weight='bold', rotation=-20, size='x-large')\n",
    "ax13.text(dates.date2num(datetime.date(2022, 10, 1))/1.002,150,'Surrey',size=16, rotation=90, weight = 'bold')\n",
    "\n",
    "\n",
    "#Burnaby\n",
    "axes = [ax16,ax17,ax18]\n",
    "\n",
    "for ax in axes:\n",
    "    ax.plot(cities_count['Burnaby'], color = '#af0b1e', alpha = 0.2)\n",
    "    ax.set(xlabel='')\n",
    "    pos = ['left','right','top','bottom']\n",
    "    for char in pos:\n",
    "        ax.spines[char].set_visible(False)\n",
    "    ax.tick_params(left=False, bottom=False)\n",
    "    ax.set_yticklabels([])\n",
    "    ax.set_xticklabels([]) \n",
    "ax16.plot(cities_count['Burnaby'][[0,1]],color='#af0b1e', linewidth=2.5) #oct to nov\n",
    "ax17.plot(cities_count['Burnaby'][[1,2]],color='#af0b1e', linewidth=2.5)#nove to dec\n",
    "ax18.plot(cities_count['Burnaby'][[2,3]],color='#af0b1e', linewidth=2.5) #dec to jan\n",
    "ax16.text(dates.date2num(datetime.date(2022, 10, 1)), 180, '246', alpha=0.5, size='large')\n",
    "ax16.text(dates.date2num(datetime.date(2022, 11, 1)), 70, '108', alpha=0.5, size='large')\n",
    "#ax16.text(dates.date2num(datetime.date(2022, 11, 1))/1.0012, 150, 'Oct-Nov', color='#af0b1e',weight='bold',rotation=-35, size='x-large')\n",
    "ax17.text(dates.date2num(datetime.date(2022, 11, 1)), 70, '108', alpha=0.5, size='large')\n",
    "ax17.text(dates.date2num(datetime.date(2022, 12, 1)), 50, '94', alpha=0.5, size='large')\n",
    "#ax17.text(dates.date2num(datetime.date(2022, 12, 1))/1.0013, 105, 'Nov-Dec', color='#af0b1e',weight='bold', rotation=-2.5, size='x-large')\n",
    "ax18.text(dates.date2num(datetime.date(2022, 12, 1)), 60, '94', alpha=0.5, size='large')\n",
    "ax18.text(dates.date2num(datetime.date(2023, 1, 1)), -10, '4', alpha=0.5, size='large')\n",
    "#ax18.text(dates.date2num(datetime.date(2023, 1, 1))/1.0011, 30, 'Dec-Jan', color='#af0b1e',weight='bold', rotation=-20, size='x-large')\n",
    "ax16.text(dates.date2num(datetime.date(2022, 10, 1))/1.002,100,'Burnaby',size=16, rotation=90, weight = 'bold')\n",
    "\n",
    "#Regina\n",
    "axes = [ax19,ax20,ax21]\n",
    "\n",
    "for ax in axes:\n",
    "    ax.plot(cities_count['Regina'], color = '#af0b1e', alpha = 0.2)\n",
    "    ax.set(xlabel='')\n",
    "    pos = ['left','right','top','bottom']\n",
    "    for char in pos:\n",
    "        ax.spines[char].set_visible(False)\n",
    "    ax.tick_params(left=False, bottom=False)\n",
    "    ax.set_yticklabels([])\n",
    "    ax.set_xticklabels([]) \n",
    "ax19.plot(cities_count['Regina'][[0,1]],color='#af0b1e', linewidth=2.5) #oct to nov\n",
    "ax20.plot(cities_count['Regina'][[1,2]],color='#af0b1e', linewidth=2.5)#nove to dec\n",
    "ax21.plot(cities_count['Regina'][[2,3]],color='#af0b1e', linewidth=2.5) #dec to jan\n",
    "ax19.text(dates.date2num(datetime.date(2022, 10, 1)), 170, '196', alpha=0.5, size='large')\n",
    "ax19.text(dates.date2num(datetime.date(2022, 11, 1)), 130, '162', alpha=0.5, size='large')\n",
    "#ax19.text(dates.date2num(datetime.date(2022, 11, 1))/1.0012, 180, 'Oct-Nov', color='#af0b1e',weight='bold',rotation=-10, size='x-large')\n",
    "ax20.text(dates.date2num(datetime.date(2022, 11, 1)), 130, '162', alpha=0.5, size='large')\n",
    "ax20.text(dates.date2num(datetime.date(2022, 12, 1)), 90, '113', alpha=0.5, size='large')\n",
    "#ax20.text(dates.date2num(datetime.date(2022, 12, 1))/1.0013, 130, 'Nov-Dec', color='#af0b1e',weight='bold', rotation=-15, size='x-large')\n",
    "ax21.text(dates.date2num(datetime.date(2022, 12, 1)), 70, '113', alpha=0.5, size='large')\n",
    "ax21.text(dates.date2num(datetime.date(2023, 1, 1)), 10, '27', alpha=0.5, size='large')\n",
    "#ax21.text(dates.date2num(datetime.date(2023, 1, 1))/1.0011, 50, 'Dec-Jan', color='#af0b1e',weight='bold', rotation=-30, size='x-large')\n",
    "ax19.text(dates.date2num(datetime.date(2022, 10, 1))/1.002,100,'Regina',size=16, rotation=90, weight = 'bold')\n",
    "\n",
    "\n",
    "#Saskatoon\n",
    "axes = [ax22,ax23,ax24]\n",
    "\n",
    "for ax in axes:\n",
    "    ax.plot(cities_count['Saskatoon'], color = '#af0b1e', alpha = 0.2)\n",
    "    ax.set(xlabel='')\n",
    "    pos = ['left','right','top','bottom']\n",
    "    for char in pos:\n",
    "        ax.spines[char].set_visible(False)\n",
    "    ax.tick_params(left=False, bottom=False)\n",
    "    ax.set_yticklabels([])\n",
    "    ax.set_xticklabels([]) \n",
    "ax22.plot(cities_count['Saskatoon'][[0,1]],color='#af0b1e', linewidth=2.5) #oct to nov\n",
    "ax23.plot(cities_count['Saskatoon'][[1,2]],color='#af0b1e', linewidth=2.5)#nove to dec\n",
    "ax24.plot(cities_count['Saskatoon'][[2,3]],color='#af0b1e', linewidth=2.5) #dec to jan\n",
    "ax22.text(dates.date2num(datetime.date(2022, 10, 1)), 190, '220', alpha=0.5, size='large')\n",
    "ax22.text(dates.date2num(datetime.date(2022, 11, 1)), 180, '212', alpha=0.5, size='large')\n",
    "#ax22.text(dates.date2num(datetime.date(2022, 11, 1))/1.0012, 215, 'Oct-Nov', color='#af0b1e',weight='bold',rotation=-5, size='x-large')\n",
    "ax23.text(dates.date2num(datetime.date(2022, 11, 1)), 180, '212', alpha=0.5, size='large')\n",
    "ax23.text(dates.date2num(datetime.date(2022, 12, 1)), 130, '158', alpha=0.5, size='large')\n",
    "#ax23.text(dates.date2num(datetime.date(2022, 12, 1))/1.0013, 180, 'Nov-Dec', color='#af0b1e',weight='bold', rotation=-15, size='x-large')\n",
    "ax24.text(dates.date2num(datetime.date(2022, 12, 1)), 120, '158', alpha=0.5, size='large')\n",
    "ax24.text(dates.date2num(datetime.date(2023, 1, 1)), 30, '58', alpha=0.5, size='large')\n",
    "#ax24.text(dates.date2num(datetime.date(2023, 1, 1))/1.0011, 90, 'Dec-Jan', color='#af0b1e',weight='bold', rotation=-30, size='x-large')\n",
    "ax22.text(dates.date2num(datetime.date(2022, 10, 1))/1.002,100,'Saskatoon',size=16, rotation=90, weight = 'bold')\n",
    "ax22.text(dates.date2num(datetime.date(2022, 10, 1))/1.002,-2.9, ' '*290 + 'Source: Honestdoor.ca.',color='#f0f0f0',backgroundcolor='#4d4d4d',size=12)\n",
    "\n",
    "plt.savefig('houses_sold_per_city.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9f81c66",
   "metadata": {},
   "source": [
    "**Observation**\n",
    "\n",
    "- From the plot above we can see that the number of houses sold in most of the cities has been reducing since October 2022.\n",
    "- However, the major cities in Alberta, Calgary & Alberta showed a different trend. \n",
    "- In Calgary, the number of houses sold kept increasing even with the increase in mortgage rate from October to December. However, there was drastic reduction in the number reduced by appoximately 90% in January. \n",
    "- Unlike other cities, Edmonton hasn't had a steady trend. The no of houses sold reduced by approximately 19% from Ocober to November but later increased by 14.1% from November to December and finally increased by just a little over by 90% from December to January.\n",
    "- Overall, the no of houses sold in all the cities has reduced by at least 70% over the last  months."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7af972a1",
   "metadata": {},
   "source": [
    "**Conclusion**: There is evidence to show that the increase in rates has led to a reduction in demand for houses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dcfc3bd",
   "metadata": {},
   "source": [
    "**Can we say the same for the Prices?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f0ae5665",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot the Average prices for each city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e59ac373",
   "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>Oct-2022</th>\n",
       "      <th>Nov-2022</th>\n",
       "      <th>Dec-2022</th>\n",
       "      <th>Jan-2023</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cityName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Burnaby</th>\n",
       "      <td>1305339.0</td>\n",
       "      <td>1105072.0</td>\n",
       "      <td>1049663.0</td>\n",
       "      <td>667100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calgary</th>\n",
       "      <td>550025.0</td>\n",
       "      <td>568786.0</td>\n",
       "      <td>561302.0</td>\n",
       "      <td>491524.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edmonton</th>\n",
       "      <td>404685.0</td>\n",
       "      <td>449809.0</td>\n",
       "      <td>444928.0</td>\n",
       "      <td>346480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Regina</th>\n",
       "      <td>313344.0</td>\n",
       "      <td>301715.0</td>\n",
       "      <td>266673.0</td>\n",
       "      <td>309581.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saskatoon</th>\n",
       "      <td>385900.0</td>\n",
       "      <td>372042.0</td>\n",
       "      <td>408113.0</td>\n",
       "      <td>365531.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Surrey</th>\n",
       "      <td>1421484.0</td>\n",
       "      <td>971818.0</td>\n",
       "      <td>958983.0</td>\n",
       "      <td>849857.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vancouver</th>\n",
       "      <td>1572062.0</td>\n",
       "      <td>1375918.0</td>\n",
       "      <td>1353510.0</td>\n",
       "      <td>1035015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Winnipeg</th>\n",
       "      <td>368699.0</td>\n",
       "      <td>365617.0</td>\n",
       "      <td>355540.0</td>\n",
       "      <td>330470.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Oct-2022   Nov-2022   Dec-2022   Jan-2023\n",
       "cityName                                             \n",
       "Burnaby    1305339.0  1105072.0  1049663.0   667100.0\n",
       "Calgary     550025.0   568786.0   561302.0   491524.0\n",
       "Edmonton    404685.0   449809.0   444928.0   346480.0\n",
       "Regina      313344.0   301715.0   266673.0   309581.0\n",
       "Saskatoon   385900.0   372042.0   408113.0   365531.0\n",
       "Surrey     1421484.0   971818.0   958983.0   849857.0\n",
       "Vancouver  1572062.0  1375918.0  1353510.0  1035015.0\n",
       "Winnipeg    368699.0   365617.0   355540.0   330470.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_avg = pd.crosstab(house_from_oct['cityName'], house_from_oct['closeMonth'], aggfunc ='mean', values = house_from_oct['closePrice'])\n",
    "grouped_avg=grouped_avg.round(0)\n",
    "grouped_avg.columns = ['Oct-2022', 'Nov-2022', 'Dec-2022', 'Jan-2023']\n",
    "grouped_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3a27bb7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zf3tC1hDw32rzz2bjyX5Tvk92/ulFdt5p+nxuRjZI0pltLGMrOu8cVq3PyI7z98l+a3Uju0jRdMwuRtZDas02lrF0/ppM9t3/GfCllNLEiLiIrDEN4OsRsU5K6dFC3t0L/08kazCrSxXHTUeP97srpCur6njPP5NNv6UXJztPfKGQ5GqyniotpJQ+jIinyRooIIsZjq+Udo6p4cpEuevIBvn0tUrT7y3l+wLNW1muK80vdk0aS97Fgqw/+2uFeW/SvLVoYbIPYNP8jym0iFD5Smz5Hp+u+d/lgSVaWe8DWlsG2RdQcd4nFFqLgP0r1GFAYX75iv1JNL8HZdfS/FNr2F8DSnnHFub1ILswMbGU5vw28ieyD0LXCttvYCnd0EKaL9L8qk8CfkGhKw3Zj87/K7xfjOZXpZ8q7dslyb54m+a/UThuyvd2LVfaLl2AjYEf1rAt9yst81mgT2H+gaX5M+rT3vapoQ7lZVT9Ki3n1NL8e8i705FdQDi9NP/BUv6Kx1Rh/qhSmuJntvhZv7BC3mXIfnQtV5h2Uml5PynlOao0f796yqtiPyxC9kXc3nZ/l1LXSbJzYbmlZ4fC/FXJRistzt+ymuOIlueijym0+JB1xS/XcUAN693mMVxhv7/UtF3JLsKMLs3/dh3nsrZe5Xr1BFZtZbnlVp4rC/OClr0g/kKpJZ/sYscX2/mc7l6Yf1xp3ogatkO5hb4jYwGMLS6jwvw2P9+FdP8spJtAoRsf2QWfpnnjKXWrbqd+55Tq0KIFGzi8lOb+dpb53fJno43PxSRKt7uQfW5/WJp2SSlfxc8SWcBeTPdb8u94su/hUaX5pxby9q1wLJ1UmL8C2QW84vydC/OLrdYfUbgti+x7sNiS9jnQr53j+BSaf69VvV8rbJcg61VR8fOXpymf2y+tsJzy/rulirLXpHlvxU+BNausd0+yC17FMlv0QpjVL9pv4V2Q7GJwpV4rS5XW/6HS/Er7vupzWIVjum9p/tDS/IGl+atT4Ral/Ji5spS3XylNud6PUujqy8zfrCvSfCTkCwtplqX5b9a6x+Ko5rih+XkzUeH2H1r2rjmyirL/r5TnpUrbN0+7WIVtOGM7UOHWi1L+ywvpP5zdn4v2XlU9hzcfKW9AYdJLKaWHAFJKj5P9AGzyrYhYpelNSul9ClfNgS2bniMVERuQBURNLkwpNV01XIfmo5pNBYZFxFX51f2RNG+p60b246M1d6SUmo3Cl/KWoJTSa8AGEXFFRLwQ2XM1P4/sOYXDS8spXoXZrDTvipTSfwrvzyfritma7Uvv1wL+VljHH5bml0fmrMWKMfPZix+StbIXRz58jyy4aM0HwOA0s/WM4v9t2I7mI4XenFI6o7CfSSl9mlK6uJBmM2a2dJHn/2Nhu5xH81Fyl2Fma/UrpfKHRcQeEfH1iFg8pTQtpXRPqtwK1JptS+9PTSm9XXg/nOZX6or1mduU1+VXKaUPIf/1m32ZfViYv37eKtwZivtmq4g4LCK+F9mzvedPKb2ZUro4pfR6IV3xM5KA7zYdB/mxMKBURvEz0pHy2pRvq2+RXV1u6/jvDfw5mj/ncVOat3zdnVK6prDs/5Ldf9va+rSlfC76S35ublr2RWQXAGaX05q2a8pacu8szV+mZZbOl1KaCHSPiPMi4on8eYHT8vPg6FLy4rl9HZq3Sr8L/DilNKm0/DtTSi+0UYUHU0qXFd5fX5pfy3YoP0t2Tj5bdljh/57kLb4RsRTNeyVcUuX3RJNii/mHqXILdq3rXU6fCv/vUJr365RSs2M1pfR+jd8XWaERSzJzZFXIgs5j8/MsKaXJZAOOFbX1eZ9Edi9eU71eJfsuLNosL3sJmvcI+Ag4pnDevILmx14AW7dR9vN53Yvf27Xs15kFZa3qF5P1qmjyGM1b4StmrWJaqpCmWPYGZI03Tb8dE7BPSumJdspuOrbvZGYLFmS/WVsb3bm15ZxX/A4rvMr7ssPyz83HwOkR8Z/Innn8WX7ee5vmv53baxXvzHNYNV4G9orsWeKvRfYc2qaLMuXfxO3V/aBU6JFZ+M3/Ctn9+k12i5nPgt6V5r9Z/9iRlWhSx3HTGcf7Tyn0miX7Hb996ljvqgOBJyOirZ5i7xX+75G3GM81qu3SvBfND4ArSvOvoHnT9V5kP5ybXEB2EEHWVXgXshN1sVvUdLKuP03KXYH75q+2tNp9mDa6rOQnmv3bWXaTYpC4Qmles2G+U0opIkaT3adcSbm+32un7LbWrx6jya7gvdZGmkebAqMarVx6f08Vecrr+WVmdi1vK89/gPvJBh/bIp++C4Wh3yPiJbL7kX6TUmqrC3dR39L71vbzqhXqM6vcnVIaUJ4YEX3JvjBa07f0vrwuH+ddc9dqWmSepzO60p9M1rq+INmX5G8K8z6OiPuAC1JKxeHsi8dCkHWXbEsxfUfKa1dK6T1gn4gYQvbjfiOyZ0WuTfPzZJB1m7ojf9+3tKhKjwUoT6v2M18+F1XqKv80M7usz2rlY39i6X1XOu6VlFLfahLmt8X8ieoeE1U8t5fPW4+myl3U29OZ2+Ht0vsVa69O50gp3ZKf85puKTiArOvyj2i+rWv9sbhY4f9JraR5p/R+6YqpZip3Oy+eyzry/VSt8v55ucIxVMvn/cVUeGRPrnzRpuk80JfmP46XoHl39UraKvve1AmPnMx/AP+N5r91/kP2hI/y74sJpfeVfjyXp5XzFMvemuxCf1Oe6WTjsvyp7VpD3ohzK81/y11Idjtdrb5H5c9u+WJ9h0XEALIeIdUEHIu2M39WnsubyY+Pe8huwalGW3X/lOz3YGvOYOZvw25kg7ieSfPuzPenyo/vqUqNx82E0vt6j/ehNI/DxpP1GGt1fVJKE7KsMR/ZRft1yW4VbdoffYGREbFB04W7kvI5ezFmPn5zjquqhZfmV+IA9ouI15teNL9/B7KrM8WT7Siat4DtkV/lKz6D6qbU/D6cjly9XriNeRWfVxUR69Ey2H2O7CrW1bT8AoxW/ofK97S0dZ9LretYz9WSj8nW52qyK1uXkHUt+Q7ZaGpPtpO/ted9taemK1Kt5KnGwjCjlXJrsqvFt5Jd0SpamSwI+XdELFblsjuyDnOrzlyXShfMWn3GZ8ruBV6LrBVzDNkPjibdyUb1/WseSHbUjHPArC4vpfRBSumqlNLBKaV1yYLq8r0+xSvQs/I4qmbZs7NF8L3S+9n+fPZ8jIThNA/AxpGNHN10LmyWpZX/oeP7qjO3wwOl95vm4wvMKcX7/r4aEd8iu6WkyaiUUq29CiYU/m/tx+zDpfd9I2LZiikz32wj/5z+TNZSXi2f6dny+6kWEdGbrKWrGOzeBmyS9wQse670vtI+Lk8r52kqeyDZaNtNv6GmADumlEZUSl/K+zWywKkYtJyUUtq32OI9lxlO89+LE8i2ddN5r5YgpN5zWPl3QlvPAR9M82D3c7LB2v5BVu/yM7TbOs7HtbV/UkoP0/z3/f4R0Z+ZF/uhjtbdDhw3nXK8R8T8EfEHmge7LwPfTCk9Uk5fSUrp85TSOymlm8huCSnu8/VoeYG9SfmcPaGa8maXdgPeiNiQlq0CXyDb8E2vXqX5fclaVoAZQUhxIKsNya4IF7tKFpvdoWUr1RUppWjnNbSNVWntwP9W6f15KaXVU0rbpZR2pvWHkUPLK3LNmvrzoH8NWldex9XbW8c2ltWed1NKO+evH6SUBqWUjk8p3dXKlZqyjp7YXyq9/3YVecrb5dQq9v0lTYlT1m35opTSlimlL5Adr+uTdTFvshzw/SrXoVyfZvs038/9S2nGVrns2a29delGyx4JxeP8s8L/Xyhe2IqIPhXyNpNSGpNS+llK6ctkV1VXJhvYp3ixqzio09jC/5+S3UPZ1nHQt87y2hQRrbYmpZTG0fK2gOL2anPbtzJtbJVVe7X0vnw8wuwdtGpu0J9s4I0mjwErpJS2ys/tB7aRt3ze+lpEtBUQzA7Xkf1Qb7IE2XgIrZrFAfFlNB9E5Tdk59kmtQxW1WRc4f9F8osWzaSURtNykKCDKy0sPyftUpp8TeH/jnw/QXWB6tjS+5UrHENfLb0v5ylaNSIWKk0r93xqOg+MpXkdH6ziO3RgG2XXO0DVimSPICp28R4JbJ2yrt2VPEw2LkqTNSqs//ql9y1a6CMbAHMEMwOv94BNU0rXVVHvzckabJqCtKbnPZe7olctpdS3mu+ujsq75haPi6bxb76bn/fKn4fOVr4NoRwflC9AFZV/j/8opbRBSmnHvO619MCo5pg9o/D/qjSPQybQfIDdqnXwuCmvW7NjO/9tVvx9MIVS63v++biK5oPEPgps2IGLj00+oOUFktYGayzu68mp4wNTzhLVtPDu3cFll/NdQvMff8WRvt4kGymx6BGaX1XcKSJadGeMiEUi4gcRcXN5XpUWKL2f0eUovw/m6Dby3l56v1tEFEdc3o/m3VzLyvdCnFO4j2CGiPhKRJwYEfu1say51fU0P/FsFRG/yLtMANmPsogYXEjzT5r/sNs/IlqcJCOiV0TsHRGXF6atEBGH5F17gRktcQ/T/F5yaH+E1SY3lN4fkd+fNaN+NL8X/S2y43duVF6XEyKieD/PcTQfMfvfKaViF8LiZ7IbeYtO3hXpfGaOettCRAyMiC2bfsSmlD5LKb1MdvW2+MOzuF+Kn5EFgXPL94VEZv2IOCsivl+Y3pHy2nNiRDwYEYMiomeF+TuX3j9T+P8Omv+AGxAR2xXquzLZFe6i8v5qzT9L73eNiBk/pvMWjtWqXFajKJ/bPyUfWT4/JiqONpl7lGzQxCZLABdGRHk0+a0jYrZ0E88vqJxRmnxiRBxTDmwjYuGI2J8aRp/NFc+7vdoKmFN2n2DxnvPiPaPv0vJ8W43yebO1W1nKT2T4RUQ0exJARCxHNvJ2cZ89R/Nbsq4pLefYiGg2FkhELBkR5adJlLsWt2iNyc+b/y5MWhg4vukiYR78ltejrc/7omQjzzfVazmyhoOiO1op++sRMbj4vZsvY6HIxjX4a768Tpefhx6g+fnnlJTSwJSPKF1Jyro4F2836U723PSm5X6LQsMK2Tl9VGH+fBFxNtmtLU3GkrV0lXtLVKr3bmTdgpu+H6cAO6WUzm8911yhfN6bRj7eRH7snUB9vQXbU+4NsE9T2RFxNC0v8hS19Xt8XZp3N+4M19O8lbR4Drs0tbyFoF11HDd30rxx4dv5Md7k5zQfA+TK4sWiPHa4nebjEtxGNkBk8UJipTr/OiI2j6znbXF6F7InSxTPoZ/T+m1zxYB87vsNnNoecasrWXTfNOrW58DyraTtQ/ORzT6kNBIYWZeESqN/ndjKMveokPYFsuD4JrJRz2Y8yLmUd2gp38BWyvh2hTKaHi0ykZbPgb2klL/8nK+PyZ5Z+0iF5Saaj9K8INl9dcX5H5E9+uQaspP3O4V5Q9vaX6V6DSgtd2y1eVvJf0kbaQeW0g4tzT+vwnZ4laxr4f1Nx1gpz7EV8jxFdoK6hWyk5KbjbWwh31qF9K+RjZJ5LdnQ7eVnsu5Q5bZYiOykWMz7AdnJ5fEK9RxUy/apsg7lZYxqJV3fcn1K85ek5bPgmrp5ltdxOrBZKf9FFdb3tQrbtunVt5D3GmYe40/n2+96Wj5j9/FCnt60HLl4Atln7FqyVoMJhXkD6ymviv1wYWn7jCZ7dMuNZD+4yuu/VSl/+Vl708l+mN5Jy+dljirlbfM4ouXomFPy7fRwK/tmQA3rXWvZfUvzh5bmVzwfV3ku+ojsKnZrryXyfN0rbNPn8331Gi3P7WNL5e5SYZt9kK/rHcwcIXdADdupb1v7uIptMT/ZMVyu1wSyix7Xkj16Zmor6zS2mK/C8svPhH+O7ALRVcBeFdJ/ocI2TmRjJNR0jmtl+/y0jbSVzkUv59vn7sI2aHpNBL5SWsYCwBMVljM6P04eIzu3lT+LB5XST8rTXwVcXEi3GS2fUvA82fm2fF57B1iyrXN5oW630/IpC89TeCYq2edmWilN0/fuDfm+Lp63i+fqgaV8NX9nFZY1obSs92j9szu4lHf5Cuv5INkP+fL+3bqU95AK2+7eNspeopB3HVqeH55tI2/V59LOeNH+KM3l76JX833+Qv6+2bqV8ra57yscl+XPxp4Vtvu7FY6DptfAQt7jSvM+yff1PWTHcnmfDCyV3eo2aWNb/rSVevXvwH6p67ghG+S1mHdqvv7l0d4/oPTscWb+3ml6TSf7PqhU7nmlvI8z8zx2X76sO2kefzS9rmxl3ef65/C2t/N+WFrR+9pJXw7+9ijN37LCxvuc0o+jUp6fk12Zr3RAFl/TSvmGtvXBaOdAaXpNIbu6UZx2SSnvCpR+RJQO9PJQ498o5V+e1oPj8uvYGj54A0p5q/rwt5H/kjbSDiylHVqa3zQqY5vrV8oTZCOBlk8elV7/LeRbq8pteSswfw3bY2VaXpwov6YDx9S6faosv7yMUa2k69vWds3TrEvzR361duy3eNg6WZfl8g+QGecHWv5g7lvIe00V+2UKsHmpzK/SMkht7bVHveW1sx8uqLIen1N4hEgh/3xUvgBUfv0L6FXj52xFmj+uq/gaS8vz84A6jr9y2aNa2+/5/KGl+a2ej6s4F7X3Kh5zh7SRrvx4mxbnSLIWtNYu5rTYjlVsp76l+RU/x+1sjy5krVbVfC++VMo7tji/lfVtbVnDWqlP+VFCn1N4VFMH1q94DvlbG+nmy4+rarbDU8CXW1nOMrT8QVl+jSrlWZrWz4PjS2kHkZ1n2lr+axQeadjKsfI4rf9W+ABYp8K6/Yjmj6Bp67V8Id/A0ryhre2HKvZnLZ/dSyrk/ybZ81hbyzMNOKBCvqE1ll08bwyoMW/V57POeJFdSCmW/1xp/o60/tvpPNo4D7S37yscl+XPxoJtHKdvkQVcFbcd2QW0SheOUz79921t99K8sVVuy4VoGdjdW03eCsuq+7ghu8WqfKGq+HqPrItyOd+oGsodW8r7eJX5bgF6trLuO5TSfnV2fiaqebXXpXnv0vv2+rOXB2wZWHp/Gy3ve/1nSmlsawtMKf2W7F6sM8i+CCeSBRYfkV19vorsXqx6uuP8APgl2dWvz8hGM7uG7H6TNu8ZSNmjAdYjGyTgdbIv31fIBvRYn5ZdR94s5X8N+DpZa/a1+TKm5vV4h6wF9Eyy0WBPZh6Usntq/4/si+sisgsBk8nW8W2yq/HHlfKklNIQsgEMfk92VftDsn0/iSz4vIzsGWPrFbK+QPYD40Kyq/Nvkm3PT/P/byXrYrN1qmHUyZTSS3ldfkp2df0dspPSR2QDKZxP9pzDth7tNFdI2aOzvgIMITu+3yNbl0lkJ74zyK5ujqiQ90WyB89fQ3bFdipZt90jgE1ofWRVyB6pcRzZPngxzz+dbL+OJvsMrZVSanarQMoGVFuDrOv4rWTHzKfM3KejyD4bG6aU/lxvee04kGygtxPJWvnGkJ0vppEd08+QHePfSCkdU86cssEgDiC7V2kk2b2IH+fr8hbZVfjdyLohlQcLaVPKHrWwHtnn5U2yz9erwNlkV57L9/k2vJTSWWQttf8mC1w/JLsws2NK6TdtZG3Kfx7Zvc+nkd0v9QHZvn6P7PxyFq0MlDOr5OfTo8lG1f0lWSv+W8w8z71BdswfRst74tpb9nlkQe9jVD+wzW/JfuA0ae9RTe0pPp5lm1ZuHWj6LA0luwh3AtlFonfIjvsPyT5bl5HdZrBmSumZVpbzJtl30y5krdmvkh0rU8i+y6+l+fgPpGyE/03IWpPH08b9gvl59MtkF3AfIztHNh1D95Ltp6+k5o80rGQC2YjwJ5J9z00lazm7jCxYfrRC2VeQdSU+nqzn2vtk58ApZAHE9Xn5K6e2n9Iwx6SU7iMbR+YUsl59k8n2z0tk3/Nr5sft/5LyWBnNuqymlP5ONijjKLLfKB+T/X7eN//+mWVSdqvDZmSf46bvodfy92vScmTxYt73yboVX0j2PV/Mux6l9eyk+n5Cy7F6OjL+QKdIKZ1D1nBzEVmPlU/IjvknyX7nrJaq6JJfoyPI4oz7yc5/H5GdoyaQ9YC5kKxhYMuUPeqvkuJtH/9K7Q+EO9s1PQBdHZTfSL5Ian6fY9O8zcmuiDRdWHgxpdTWPb2SJM0zInvSQfF+0R+klK5qLX0Vy+tO1gLVNKjlfimlOfYDVJqb5GPL3E3zQQhPrnSBVdWJiOHMvB/+PWC5PBBWFSKiF9mF1qYxH3ZOKZWfgjDHVfscXrVuWeC5iHiQrNVxHNnN6l8ha5UtjqzsCUmSNE/LH9+xFVkXxGJPsBfIWkk7LGXPAj+OmS29h0fERamNAY6kRhcRPyR73OJ6NH9e9ce0/TQRVRARu5B1z16d5uewsw12a/ZzZga7D86NwS7Ywlu3iFiV7Eu+LVOBw1NKZ8+GKkmSNMvko36Xb3n4DNgipTSqE5Y/P1lXuqbHaw1KhUfPSf9rImIopVu/yLru75ZSqnY0f+UiYhQtH0P2JPD11IHRmf9XRcRiZD1ymm49+cYs6HLdKWzhrd9bwFFkw+P3IxtVtivZvcbPk91DcVF+D6gkSY1kPNn9zSd01g+dfHyFr7SbUPrf0jSGyQtko7L/PqX0+pyt0jxvGtl9wtcAvzbYrU1KaQLNexzMtWzhlSRJkiQ1pPZGaZYkSZIkaZ5kwCtJkiRJakgGvJIkSZKkhmTAK0mSJElqSAa8kiRJkqSG9D8R8A4bPCINGzxi1Tldj1oMGzxi1LDBI348p+shSZIkSfOqdp/DO2zwiG8BvyF7APx0YAxwyJDhgx6exXVrWMMGjxgF/HnI8EEXzum6SJIkSVKjarOFd9jgEYsCNwDnAF8AlgWOB6Z2dkWGDR7RbvCt+riNJUmSJP0viZRSqzOHDR6xLvDPIcMHLdbK/PmAo4F9gW7ALcDPhgwfNHHY4BEDyFoxlyukHwv8eMjwQf8cNnjEUOArwCfAdsChwN+BM4Dv5su7e8jwQTvkebcBfg30BZ4B9hsyfNCT1azksMEjErA/8AugN3A5cOCQ4YNSO+vQF3gZGAicCHQHfjtk+KCTCut/eJ53MeCOvF7vDxs8YiHgQmArYH7gBWAb4CDgSOAzYBpwyZDhgw4cNnjE5mQXFpYG/gSsAfxpyPBBF7ZVx7we2wGnkF2QeBzYf8jwQWMK2/z3wO7AasDCQ4YPmlbYNtuTXcRYGXgXGDxk+KBbhg0eMShft+Xy6acNGT7oD9Vsb0mSJEmaG7R3D+/zwPRhg0eMHDZ4xFbDBo9YvDR/YP7ahCxg6gGcW0P52wNXkQWLl5EFet3Juk8vCfwWYNjgEesAFwM/BXoBfwCuGzZ4RNd8/nnDBo84r52ytgHWA9YEfkgWVFe7Dt8iCxY3BX41bPCIfvn0g4AdgG8DywAfAMPzeXsDPYHl8zrvB0wZMnzQMcC9ZAF3jzzY7Q1cDRxLFpC/CHyzUH6rdRw2eMSXgL8AhwBLADcB1w8bPGLBQv5dga2BxUrB7vrApcBhZPtgY2BsPvudfJstCgwCfpvvB0mSJEmaJ7TZxXXI8EGT8nt4jwAuAPoMGzziJmDfIcMHjSNrNTxzyPBBLwEMGzziKGB03jpYjQeGDB90TZ53MbLW0F5Dhg/6IJ9/d/53X+APQ4YPeih/P3LY4BFHA18nawU+oIqyTh0yfNAEYMKwwSPuAtYiaymtZh2OHzJ80BTgiWGDRzxBFjSPIQvADxwyfNDred6hwKvDBo/Yk6wFtxewat4S/Ugbdfse8MyQ4YOuypdzFllrdJO26rgLcOOQ4YNuz+cNAw4GvgGMyvOfPWT4oNcqlLsPcHFTXuCNphlDhg+6sZDu7mGDR9wGbAQ82sZ6SJIkSdJco917OvOusQMBhg0esTrwZ+AsslbDZYBXCslfyZe5VJXlF4Ow5YH3C8Fu0YrA3sMGj/hZYdqCefnVervw/8dkraRQ3Tq0lndF4B/DBo/4vDB/ep73T2TrdEUezP8ZOGbI8EGfVajbMhS2Rd7V+rXS/Nbq2GzekOGDPs/zLltIXynYJa/fTZVmDBs8YivgOOBLZD0BugNPtbIcSZIkSZrr1DSI0ZDhg54dNnjEJWQtmwBvkgV9TVYguy91HFkg1r1pxrDBI+Yn63JbVLyB+DXgC8MGj1gsb4mlNO+kpntnO1lb67BcxRzN6/V/Q4YPuq+V+ccDx+f3At8EPAdcRPP1BniLLPgEYNjgEVF8304d3yS737ec941C+tZu1H4NWKU8Me8qfjWwF3DtkOGDPhs2eMQ1QLSyHEmSJEma67QZ8OYtulsDVw4ZPuj1YYNHLE/WsvtgnuQvwBHDBo+4mWxgo5PztNOGDR7xPLDQsMEjtgZuIxt0qWtrZQ0ZPuitfDnnDRs8YjAwGdhwyPBB95B1p/7HsMEj/gn8myyQHgDcM2T4oA87uO5N2lqH9vKeD5w0bPCIvYcMH/TKsMEjlgC+MWT4oGuHDR6xCTCebICtSWRdnKfn+caR3Yvb5Ebg3GGDR+wIXAcMBvpUWce/AkcOGzxiU+Aesu7MU4H7q1j3i4Dbhg0ecQNwF9mAWYuQBctd87Km5a29WwCjq1imJEmSJM0V2hu06kNgA+ChYYNHfEQW6I5m5v2lF5N13b2HbDTjT4CfAeQjCB9ANlLxG8BHwOvtlNd07+uzZIMmHZIv6z9k9/GeSzYw1H/Ju1kDDBs84vxhg0ec386yW9PqOlThd2QB6m3DBo/4kGz7bJDP60M2INcksvt97ybr1tyUb+dhg0d8MGzwiLOHDB80HvgBcCrwHvBFoNhq3NZ2fg7Yg2yE5/HAtsC2Q4YP+rRShYcNHvH0sMEjds/z/pt8QCpgYl7HFfOLCAcBfyXb3rvl6ylJkiRJ84w2H0skSZIkSdK8qr0WXkmSJEmS5kkGvJIkSZKkhmTAK0mSJElqSAa8kiRJkqSGZMArSZIkSWpIBrySJEmSpIZkwCtJkiRJakgGvJIkSZKkhmTAK0mSJElqSAa8kiRJkqSGZMArSZIkSWpIBrySJEmSpIZkwCtJkiRJakgGvJIkSZKkhmTAK0mSJElqSAa8kiRJkqSGZMArSZIkSWpIBrySJEmSpIZkwCtJkiRJakgGvJIkSZKkhmTAK0mSJElqSAa8kiRJkqSGZMArSZIkSWpIBrySJEmSpIZkwCtJkiRJakgGvJIkSZKkhmTAK0mSJElqSAa8kiRJkqSGZMArSZIkSWpIBrySJEmSpIZkwCtJkiRJakgGvJIkSZKkhmTAK0mSJElqSHNVwBsRG0fEdRHxRkSkiBhYRZ6IiEMi4tmImBoRb0XEqaU0346IRyLik4h4KSL2q7CcnSLimXwZz0TE96soe4WIuD4iPoqI8RFxdkQsWEqzRkTcHRFT8vX6VURErfWTJEmSJNVmrgp4gR7AaOBgYEqVec4ADgCOAPoB3wPuaZoZESsBNwH3A2sDpwDnRMROhTQbAlcClwFr5X//FhEbtFZoRMwP3AgsAmwE7ArsnNenKc2iwO3AOGA94CDgMODQWuonSZIkSapdpJTmdB0qiojJwIEppUvaSLMaWYD81ZTSmFbSnAbsmFL6YmHahUD/lNKG+fsrgS+klDYvpPkn8G5KaddWlrsVWcC7YkrptXzaHsCFwJIppUkRsT9wGrBUSmlKnuZYYH9guZRSqqZ+kiRJkqTadZnTFajT9sBLwJYRcSNZi/XdwGEppXfyNBsCt5Xy3QrsHRELpJQ+y9OcUyHNgW2UvSEwpinYLeTpCnwNuCtPc29TsFtIcyLQF3i5yvq1Ze68YiFJkiRJs0e0NmNu69Jcq5WBFYEfAQOBPYHVgesjomnd+pB1KS4aRxbs924nTZ82yq6UZzwwvZCvteVSRZpi/SRJkiRJNZrXW3jnI2tR3TOl9DxAROwJPEd2z+xDebpyK2hUmF4pTXutp63Nb2+5HUkjSZIkSarBvN7C+xYwrSnYzb0ATANWyN+/TcuW2iXzNO+1k6bc8lpUKU9vYP5CvtaWSxVpivWTJEmSJNVoXg947wO6RMQqhWkrk7Vcv5K/fwDYrJRvc+A/hftjH8inldPc30bZDwD9ImK5Up6pwCOFNBtFxEKlNG8CY2uonyRJkiSpRnNVwBsRPSJirYhYi6xuK+TvV2glyz+BR4GLI2LtiFgbuJisK/N/8jTnA8tFxFkR0S8ifkx2v++wwnJ+B3wnIo6KiNUj4ihgE+CsNqp7G/A0cGle9mbA6cAFKaVJeZrLgY+BSyLiKxGxI3AkcGaaOTx2NfWTJEmSJNVornosUUQMIBvduGxkSmlgRAwFjkspRSHP0sDZwJZkz+69HTg0pTSukObbwG+B/mStq6ellM4vlb0z8GuyFuIXgWNSSn8vzB8IjABWSimNzaetAJwHfCcv+3JgSEppaiHfGsBwYH3gA7IA94RCwFtV/dow9+xASZIkqUF8/vnnvP7663z00Udzuir/8xZYYAGWXHJJFl100daStDpK81wV8LYnIkYCS6eUtpgDZR8P7AysmVKaNrvLb8O8swMlSZKkecQ777zD1KlTWXbZZZlvvrmqY+z/lJQSU6ZM4Y033mCppZZqLeid9x9LFBFB1pLa1rNxZ6XvAQfOZcGuJEmSpFlgwoQJLLXUUga7c1hE0L17d5ZddlneeeedmvPPM48lyrsALz8Hy19vTpUtSZIkafaaPn06CyywwJyuhnLdunXjs89qH9PXyxWSJEmSVEHWyVRzg47ui3mmhVeSJElSx/U98saa84w9detZUBNp9rGFV5IkSZLUkGzhlSRJkqR2dKSFvBYdaU2/5JJLOOOMM3jxxRdZdNFF+f73v88pp5zCYost1ma+iOCFF15g1VVXrTh/6tSpHHDAAfzzn//k/fffZ9VVV+Xkk09mq622mpHmjjvuYPDgwbz66qtssMEGXHLJJay44ooAnH766YwcOZJXXnmF3r17c8ABB3DYYYcB2ejXBx98MHfffTcfffQRX/nKVzjzzDPZYIMNal7/atjCK0mSJEnzmDPOOIMjjjiC008/nYkTJ/Lggw/yyiuvsPnmm/Ppp5/Wtexp06ax/PLLc/fddzNx4kROPPFEfvjDHzJ27FgAxo8fz4477siJJ57I+++/z7rrrssuu+wyI39KiUsvvZQPPviAW265hXPPPZcrrrgCgMmTJ7PeeuvxyCOP8P7777P33nuz9dZbM3ny5Lrq3BoDXkmSJEmah0yaNInjjjuOc845hy233JIFFliAvn378te//pVXXnmFP//5z0yfPp2TTz6ZVVZZhUUWWYSvfe1rvPbaa2y88cYArLnmmvTo0YMrr7yyxfIXXnhhhg4dSt++fZlvvvnYZpttWGmllXjkkUcA+Pvf/07//v35wQ9+wEILLcTQoUN54oknePbZZwE4/PDDWWeddejSpQurrbYa22+/Pffddx8AK6+8MoceeihLL700888/Pz/5yU/49NNPee6552bJtrJLsyRJkiTNQ+6//34++eQTdtxxx2bTe/TowVZbbcXtt9/Oe++9x1/+8hduuukmvvSlL/Hkk0/SvXt37rnnHiKCJ554otUuzWXjxo3j+eefp3///gA8/fTTrLnmmjPmL7zwwqyyyio8/fTTrL766s3yppS49957+elPf1px2Y8//jiffvpp1XWplS28kiRJkjQPGT9+PL1796ZLl5btl0svvTTjx4/nwgsv5Ne//jWrrbYaEcGaa65Jr169ai7rs88+Y/fdd2fvvfeeEcxOnjyZnj17NkvXs2dPPvzwwxb5hw4dyueff86gQYNazJs0aRJ77rknxx13XIvldRYDXkmSJEmah/Tu3Zvx48czbdq0FvPeeustevfuzWuvvcYqq6xS1fK22morevToQY8ePbjssstmTP/888/Zc889WXDBBTn33HNnTO/RoweTJk1qtoxJkyaxyCKLNJt27rnncumll3LjjTfStWvXZvOmTJnCtttuy9e//nWOOuqoqurZEQa8kiRJkjQP2XDDDenatSt///vfm03/6KOPuPnmm9l0001ZfvnlefHFF6ta3s0338zkyZOZPHkyu+++O5B1Rd5nn30YN24cV199NQsssMCM9P379+eJJ55oVu6LL744o8szwMUXX8ypp57KHXfcwXLLLdesvKlTp7LDDjuw7LLL8oc//KHm9a+FAa8kSZIkzUN69uzJcccdx89+9jNuueUWPvvsM8aOHcsPfvADlltuOfbcc09+/OMf88tf/pIXXniBlBJPPvkk7733HgBLLbUUL730Uptl7L///owZM4brr7+ebt26NZv3/e9/n9GjR3P11VfzySefcMIJJ/DVr351Rpfnyy67jKOPPprbb7+dlVdeuVnezz77jJ133plu3bpx6aWXMt98szYkddAqSZIkSWpHR56TOysdfvjh9OrViyFDhsx4Du8OO+zAZZddRteuXTn00EOZOnUqW2yxBePHj2f11VfnH//4B5DdV7v33nszZcoU/vjHP/LDH/6w2bJfeeUV/vCHP9C1a1f69OkzY/of/vAHdt99d5ZYYgmuvvpqDjzwQPbYYw822GCDGY8dAjj22GN57733WG+99WZM22OPPTj//PO5//77ueGGG+jWrVuz5wXffPPNbLTRRp2+nSKl1OkL1WzlDpQkSVK7+h55Y8155rYgb3YaM2YM/fr1m9PVUEEb+yRay2OXZkmSJElSQzLglSRJkiQ1JANeSZIkSVJDMuCVJEmSJDUkA15JkiRJUkMy4JUkSZIkNSQDXkmSJElSQzLglSRJkiQ1JANeSZIkSVJD6jKnKyBJkiRJc72hPWfx8idWnbRv375MmTKFl156iYUXXhiACy+8kD//+c+MGjWq06p0+umnM3LkSF555RV69+7NAQccwGGHHTZj/tixYxk0aBAPPfQQK6ywAueeey6bbbYZADfeeCOnnHIKo0ePZqGFFmLbbbflzDPPZJFFFgFgyJAhXHvttbz99tssu+yyHH300ey1116dVvcmtvBKkiRJ0jxm2rRp/O53v5ulZaSUuPTSS/nggw+45ZZbOPfcc7niiitmzN91111Ze+21ee+99zjppJPYeeedeffddwGYOHEixx57LG+++SZjxozh9ddfbxYsL7zwwlx//fVMnDiRkSNHcvDBB3P//fd3+joY8EqSJEnSPOawww5j2LBhTJgwocW8+++/n/XWW4+ePXuy3nrrzQgkr7jiCtZdd91maX/729+y3XbbVSzj8MMPZ5111qFLly6sttpqbL/99tx3330APP/88zz66KMcf/zxdOvWjZ122ok11liDq6++GoDddtuNLbfcku7du7P44ouz7777zsgLcPzxx7P66qsz33zzscEGG7DRRhvxwAMPdMamacaAV5IkSZLmMeuuuy4DBgxg2LBhzaa///77bL311hx00EG89957HHrooWy99da89957bLfddjz33HO88MILM9Jffvnl7Lbbbu2Wl1Li3nvvpX///gA8/fTTrLzyyjO6KAOsueaaPP300xXz33PPPTPylk2ZMoWHH3641fn1MOCVJEmSpHnQCSecwDnnnDOjGzFk985+8YtfZM8996RLly7suuuurL766lx//fV0796d7bffnr/85S8AvPDCCzz77LOttvAWDR06lM8//5xBgwYBMHnyZHr2bH5fc8+ePfnwww9b5L399tsZOXIkJ5xwQsVl77fffqy55pp897vfrXrdq2XAK0mSJEnzoK985Stss802nHrqqTOmvfnmm6y44orN0q244oq88cYbQNbVuCngvfzyy9lhhx3o3r07J598Mj169KBHjx7st99+zfKfe+65XHrppdx444107doVgB49ejBp0qRm6SZNmtSsxRfgwQcfZLfdduOqq67iS1/6Uot1OOywwxg9ejR//etfiYgObonWGfBKkiRJ0jzq+OOP54ILLpgR0C6zzDK88sorzdK8+uqrLLvssgBsscUWjB8/nscff5y//OUvM7ozH3300UyePJnJkydz/vnnz8h78cUXc+qpp3LHHXew3HLLzZjev39/XnrppWYtuk888USzbsmPPfYY2223HRdffDGbbrppi7ofd9xx3Hzzzdx2220suuiinbA1WjLglSRJkqR51Kqrrsouu+zC2WefDcD3vvc9nn/+eS6//HKmTZvGlVdeyTPPPMM222wDQJcuXdh555057LDDeP/999l8881bXfZll13G0Ucfze23387KK6/cbN6XvvQl1lprLY4//ng++eQT/vGPf/Dkk0+y0047ATB69Gi23HJLzjnnHLbddtsWyz7llFO4/PLLuf322+nVq1dnbY4WfA6vJEmSJLWnhufkzm6/+tWv+NOf/gRAr169uOGGGzj44IPZf//9WXXVVbnhhhvo3bv3jPS77bYbG2+8MQcccABdurQeEh577LG89957rLfeejOm7bHHHjNagK+44goGDhzI4osvzgorrMBVV13FEkssAcAZZ5zBu+++yz777MM+++wDZF2rmwa1Ovroo1lwwQX54he/OGPZRx99NEcffXQnbZVMpJQ6dYGa7dyBkiRJalffI2+sOc/YU7eeBTWZN4wZM4Z+/frN6WqooI190urNv3ZpliRJkiQ1JANeSZIkSVJDMuCVJEmSJDUkA15JkiRJUkMy4JUkSZIkNSQDXkmSJElSQzLglSRJkiQ1JANeSZIkSVJDMuCVJEmSJDWkLnO6ApIkSZI0t1tj5BqzdPlP7f1U1Wn79u3LuHHj6NKlC/PPPz9f/vKX2WuvvfjJT37CfPN1XpvmjTfeyCmnnMLo0aNZaKGF2HbbbTnzzDNZZJFFAJg6dSr7778/V111Fd27d+fwww/n0EMPBeD555/nsMMO4/7772f69Omst956nH322ay22moAjBw5krPPPpsXXniBRRddlN12242TTz6ZLl06N0S1hVeSJEmS5jHXX389H374Ia+88gpHHnkkp512Gvvss0+nljFx4kSOPfZY3nzzTcaMGcPrr7/OYYcdNmP+0KFDeeGFF3jllVe46667+M1vfsMtt9wCwIQJE9huu+147rnnGDduHOuvvz7bb7/9jLwff/wxZ511FuPHj+ehhx7ijjvuYNiwYZ1afzDglSRJkqR5Vs+ePdluu+248sorGTlyJKNHj2bq1KkMGTKEFVZYgaWWWor99tuPKVOmzMhz7bXXstZaa7HooouyyiqrzAhSy3bbbTe23HJLunfvzuKLL86+++7LfffdN2P+pZdeyi9/+UsWX3xx+vXrx7777ssll1wCwPrrr88+++zDF77wBRZYYAF+/vOf89xzz/Hee+8BsP/++7PRRhux4IILsuyyy7L77rs3W3ZnMeCVJEmSpHnc+uuvz3LLLce9997LEUccwfPPP8/jjz/Of//7X9544w1OOOEEAP7973+z1157cfrppzNhwgTuuece+vbtW1UZ99xzD/379wfggw8+4M0332TNNdecMX/NNdfk6aefbjVvnz596NWrV7vL7kzewytJkiRJDWCZZZbh/fff54ILLuDJJ5/kC1/4AgBHH300u+22G6eccgoXXXQR//d//8fmm28OwLLLLlvVsm+//XZGjhzJQw89BMDkyZOBrIW5Sc+ePfnwww9b5H399dcZPHgwZ555ZsVljxgxgv/85z9ceOGF1a9slWzhlSRJkqQG8MYbbzBt2jQ+/vhjvva1r7HYYoux2GKLseWWW/Luu+8C8Nprr7HKKqu0yHvvvffSo0cPevTo0aKl9cEHH2S33Xbjqquu4ktf+hIAPXr0AGDSpEkz0k2aNGnGgFZN3n33XbbYYgsOOOAAdt111xblXnPNNRx55JHcfPPN9O7du74NUIEBryRJkiTN4x5++GHeeOMNdthhB7p168bTTz/NhAkTmDBhAhMnTpzRIrv88svz4osvtsi/0UYbMXnyZCZPntysW/Jjjz3Gdtttx8UXX8ymm246Y/riiy/O0ksvzRNPPDFj2hNPPNEsWP7ggw/YYost2G677TjmmGNalHnLLbew7777cv3117PGGrNmFGwDXkmSJEmaR02aNIkbbriBH/3oR+yxxx6sueaa7Lvvvvz85z/nnXfeAbKW31tvvRWAffbZhxEjRnDHHXfw+eef88Ybb/Dss89WXPbo0aPZcsstOeecc9h2221bzN9rr7349a9/zQcffMCzzz7LBRdcwMCBA2fU67vf/S7f/OY3OfXUU1vkvfPOO9l99925+uqrWX/99Ttpa7QUKaVZtnDNFu5ASZIktavvkTfWnGfsqVvPgprMG8aMGUO/fv3mdDUqKj6Hd7755uPLX/4ye+yxB/vttx/zzz8/n3zyCSeccAJXXHEF48ePZ9lll2X//ffnoIMOAuAf//gHxx13HC+//DJLLbUUw4cP57vf/W6LcgYNGsTIkSPp3r37jGkrrrjijBbg4nN4u3XrxhFHHDHjObwjR45k4MCBdO/enYiYkf+ZZ55hhRVWYJNNNuHee+9loYUWmjFvo4024uabb251vdvYJ1FpIhjwNgJ3oCRJktplwFubuTng/V/VkYDXLs2SJEmSpIZkwCtJkiRJakgGvJIkSZKkhmTAK0mSJElqSAa8kiRJkqSGZMArSZIkSWpIBrySJEmSpIZkwCtJkiRJakgGvJIkSZKkhtRlTldAkiRJ876+R95Yc56xp249C2oizRpjVu83S5ff79kxVacdMGAAe+yxBz/+8Y87tQ5Dhgzh2muv5e2332bZZZfl6KOPZq+99pox//HHH2efffZhzJgx9OvXj4suuoi11loLgJEjR3L22WfzwgsvsOiii7Lbbrtx8skn06VLFnLuscce3HHHHXz00Uf06dOHww8/vNPrX4ktvJIkSZIkFl54Ya6//nomTpzIyJEjOfjgg7n//vsB+PTTT9l+++3ZY489+OCDD9h7773Zfvvt+fTTTwH4+OOPOeussxg/fjwPPfQQd9xxB8OGDZux7KOOOoqxY8cyadIkrrvuOo499lgeeeSRWb5OBrySJEmSNA/64IMP2GabbVhiiSVYfPHF2WabbXj99ddnzB8wYAC//OUv+eY3v8kiiyzCFltswfjx41td3vHHH8/qq6/OfPPNxwYbbMBGG23EAw88AMCoUaOYNm0ahxxyCF27duWggw4ipcSdd94JwP77789GG23EggsuyLLLLsvuu+/OfffdN2PZ/fv3p2vXrgBEBBHBiy++OCs2SzMGvJIkSZI0D/r8888ZNGgQr7zyCq+++irdunXjwAMPbJbm8ssvZ8SIEbzzzjt8+umnzVpd2zJlyhQefvhh+vfvD8DTTz/NV7/6VSJiRpqvfvWrPP300xXz33PPPTPyNjnggAPo3r07q6++OksvvTTf+973alndDjHglSRJkqR5UK9evdhpp53o3r07iyyyCMcccwx33313szSDBg3iS1/6Et26deOHP/whjz/+eFXL3m+//VhzzTX57ne/C8DkyZPp2bNnszQ9e/bkww8/bJF3xIgR/Oc//2HIkCHNpp933nl8+OGH3Hvvvey4444zWnxnpbkq4I2IjSPiuoh4IyJSRAysIe8XI+LDiJhcYd63I+KRiPgkIl6KiP0qpNkpIp6JiKn53+9XUeYKEXF9RHwUEeMj4uyIWLCUZo2IuDsipuTr9asoXhapsn6SJEmSVPTxxx/z05/+lBVXXJFFF12UjTfemAkTJjB9+vQZafr06TPj/+7duzN5chYu7bfffvTo0YMePXpw8sknN1vuYYcdxujRo/nrX/86o0W3R48eTJo0qVm6SZMmscgiizSbds0113DkkUdy880307t37xZ1nn/++fnWt77F66+/zu9///v6NkAV5qqAF+gBjAYOBqZUmykPMq8A7qkwbyXgJuB+YG3gFOCciNipkGZD4ErgMmCt/O/fImKDNsqcH7gRWATYCNgV2Bk4o5BmUeB2YBywHnAQcBhwaC31kyRJkqSyM844g+eee46HHnqISZMmcc89WTiUUmo37/nnn8/kyZOZPHkyRx999Izpxx13HDfffDO33XYbiy666Izp/fv358knn2y27CeffLJZt+VbbrmFfffdl+uvv5411lijzfKnTZv2v3cPb0rpppTS0Smlq4DPa8h6GvAk8LcK8/YD3kwp/SylNCaldAEwEii2rx8C3JVSOilPcxIwKp/emi2A/sCeKaVHU0q3A4cD++aBLsDuQHdg75TS6JTS1XldDy208lZTP0mSJElq5sMPP6Rbt24stthivP/++xx//PF1Le+UU07h8ssv5/bbb6dXr17N5g0YMID555+fs88+m6lTp3LuuecC8J3vfAeAO++8k913352rr76a9ddfv1ned955hyuuuILJkyczffp0br31Vv7yl7/MyDsrzfPP4Y2IrYFtgHWASq2iGwK3labdCuwdEQuklD7L05xTIc2BtG5DYExK6bVSnq7A14C78jT3ppSmlNKcCPQFXq6yfpIkSZLmoFqekzs7RASHHHIIu+22G71792aZZZbhF7/4Bddcc02Hl3n00Uez4IIL8sUvfrHZtKbp11xzDT/+8Y858sgj6devH9dccw0LLpjd0XniiScyceLEZgNRbbTRRtx8881EBL///e/Zb7/9+Pzzz1lxxRU566yz2H777Ttc12rN0wFvRCwNXADsmFL6sHRrbJM+wD9L08aRrXtv4K08zbgKafrQukp5xgPTC/n6AK+X0owrzHu5yvpJkiRJEpDdO9urVy+WWWYZRo0a1WzeT3/60xn/l+cNHDiQgQMHtrrc9rpCr7322q0+O/euu+5qNd8SSyzRYjCt2WWu6tLcAX8Gfp9SerCddOU9FxWmV0rTXuf31ua3t9yOpJEkSZL0P+7pp59mzJgxrL322nO6KvOEeT3g/Q5wXERMi4hpwEXAwvn7n+Rp3qZlS+2SwDTgvXbSlFtwiyrl6Q3MX8jX2nKpIk2xfpIkSZL+xx1xxBFsscUWnHbaaay44opzujrzhHk94F2DbFTlptevyEZ3XouZA1g9AGxWyrc58J/C/bEP5NPKae5vo+wHgH4RsVwpz1TgkUKajSJioVKaN4GxNdRPkiRJ0v+40047jTfeeIODDjpoTldlnjFXBbwR0SMi1oqItcjqtkL+foVK6fORj2e8gDeAz/P3H+TJzgeWi4izIqJfRPwYGAgMKyzqd8B3IuKoiFg9Io4CNgHOaqO6twFPA5dGxNoRsRlwOnBBSqnpAVWXAx8Dl0TEVyJiR+BI4Mw0s4N8NfWTJEmSJNVorgp4gXWBx/JXN+D4/P8TACJiaETUdF9rSull4HvAxsDjwDHAQfkjgprS3A/8CNib7PFGewG7pJQeakoTEQMjIkVE3zzPdGBrsoD2PrLn+P6dwuOEUkoTyVprlwH+Awwne07vmbXUT5IkSdLsV83zbDV7fP55LU+tnWmuGqU5pTSKmQM2VbIScHsb+S8BLqkw/W6yxxa1VfZVwFXtlP0MhVGXU0qvkj0Sqa3lPkUWzLaVpt36SZIkSZp9FlpoId577z169epFK0+D0WyQUuKzzz5j3LhxLLzwwjXnn6sC3rZEdpR9B9h0DlXhe8CBKaVpc6h8SZIkSbPJcsstx+uvv8677747p6vyP69Lly707NmT3r171553FtRnlsjveV1+Dpa/3pwqW5IkSZojhvbsQJ6JnV+POWCBBRZgpZVWmtPVUJ3mtnt4JUmSJEnqFAa8kiRJkqSGZMArSZIkSWpIBrySJEmSpIZkwCtJkiRJakgGvJIkSZKkhmTAK0mSJElqSAa8kiRJkqSG1GVOV0CSJElS41hj5Bo153lq76dmQU0kW3glSZIkSQ3KgFeSJEmS1JAMeCVJkiRJDcmAV5IkSZLUkAx4JUmSJEkNyYBXkiRJktSQDHglSZIkSQ3JgFeSJEmS1JAMeCVJkiRJDcmAV5IkSZLUkAx4JUmSJEkNyYBXkiRJktSQDHglSZIkSQ3JgFeSJEmS1JAMeCVJkiRJDcmAV5IkSZLUkAx4JUmSJEkNyYBXkiRJktSQuszpCmje0PfIG2vOM/bUrWdBTSRJkiSpOrbwSpIkSZIakgGvJEmSJKkhGfBKkiRJkhqSAa8kSZIkqSEZ8EqSJEmSGpIBryRJkiSpIRnwSpIkSZIakgGvJEmSJKkhGfBKkiRJkhqSAa8kSZIkqSEZ8EqSJEmSGpIBryRJkiSpIRnwSpIkSZIakgGvJEmSJKkhGfBKkiRJkhqSAa8kSZIkqSEZ8EqSJEmSGpIBryRJkiSpIRnwSpIkSZIakgGvJEmSJKkhGfBKkiRJkhqSAa8kSZIkqSEZ8EqSJEmSGpIBryRJkiSpIRnwSpIkSZIakgGvJEmSJKkhGfBKkiRJkhqSAa8kSZIkqSEZ8EqSJEmSGlKXjmaMiC8BmwEbAysAvYEpwDvA48BdwJ0ppU/qr6YkSZIkSbWpOeCNiB8BBwDfbJpUIdmmwKHAhIi4BDgnpTS2g3WUJEmSJKlmVXdpjohNIuIx4HLgK8BI4CfAWkAfYEGgJ7Ay8D3gROA54OfAmIg4LSIW7dTaS5IkSZLUilpaeO8AHgV2Aa5NKX1aIc2H+WsscAswNCK+COwHHAhMJguEJUmSJEmapWoJeHdOKf291gJSSi8Av4iI04G+teaXJEmSJKkjqg54OxLslvK/DbxdzzIkSZIkSaqWjyWSJEmSJDWkDge8EbFUROwfESMi4oaIuD4iLo6In0bEkh1c5sYRcV1EvBERKSIGtpN+QERcGxFvRcTHEfFkRPxfhXTfjohHIuKTiHgpIvarkGaniHgmIqbmf79fRX1XyNf7o4gYHxFnR8SCpTRrRMTdETElX69fRUSU0rRbP0mSJElSbToU8EbE4cBLwLnA3mSjMm8NDATOA16KiEM7sOgewGjgYLJn+rbnG8BTwM5kI0f/HvhjROxWqOtKwE3A/cDawCnAORGxUyHNhsCVwGVko05fBvwtIjZoreCImB+4EVgE2AjYNa/HGYU0iwK3A+OA9YCDgMPIHtlUdf0kSZIkSbXryHN4jyALygD+BYwC3iB7Hu8ywCZkz+g9PSIipXRGpeVUklK6iSz4I39+b3vpTy5N+n1EbALsRPb4JMhGiH4zpfSz/P2YPJAdAlydTzsEuCuldFL+/qR8OYeQBbKVbAH0B1ZMKb2W1/lw4MKIOCalNAnYHegO7J1SmgKMjoh+wKERcWZKKVVZP0mSJElSjWpq4Y2IZYChwHvAd1JKG6eUfpVS+kNK6fz8/42AzYAJwIkRsXRnV7odiwIfFN5vCNxWSnMrsG5ELNBOmm+0Uc6GwJimYLeQpyvwtUKae/Ngt5hmGWaOWF1N/SRJkiRJNaq1S/MewILAT1JKo1pLlFK6E9gXWIislXO2iIhtgE2BPxYm9yHrUlw0jqx1u3c7afq0UVylPOOB6YV8rS2XKtIU6ydJkiRJqlGtAe+3gbEppX+0lzB/jNHLwIAO1KtmEfFNsm7MB6WU/l2uTjl5hemV0pSnlbU2v73ldiSNJEmSJKkGtQa8/YH7akh/X55nloqIbwE3A79KKf2+NPttWrbULglMI+ua3Vaacstre8vtDcxfyNfacqkiTbF+kiRJkqQa1RrwLg68VUP6N4Ev1FhGTSJiY7Jg9/iU0lkVkjxAdk9x0ebAf1JKnxXSbF4hzf1tFP0A0C8ilivlmQo8UkizUUQsVErzJjC2hvpJkiRJkmpUa8DbA/iohvRTgIWrTRwRPSJirYhYK6/bCvn7FVpJP4As2D0fuCwi+uSvJQrJzgeWi4izIqJfRPyY7PFJwwppfgd8JyKOiojVI+IostGmz2qjurcBTwOXRsTaEbEZcDpwQT5CM2RdrD8GLomIr0TEjsCRQNMIzdXWT5IkSZJUo1oD3mg/SV151gUey1/dgOPz/08AiIihEVG8r3Ug2WN/hpC1PDe9Hm5KkFJ6mew5wRsDjwPHkN3ne3Uhzf3Aj8ieKfwksBewS0rpoRkrETEwIlJE9M3zTCd79vDHZF23rwT+ntelabkTyVprlwH+Awwne07vmbXUT5IkSZJUu5qfwwsMiKg6hh1Qy4LzkZ/bWvhKwO2F9APJgt72lns3sE47aa4Crmqn7GeA1wt5XgW2aWe5T5EFs3XVT5IkSZJUmw4FvFQXyCaqG+m4KpFF2d8he+zQnPA94MCU0rQ5VL4kSZIkqQa1BrzHz5JaVCG/53X5OVj+enOqbEmSJElS7WoKeFNKcyzglSRJkiSpFrUOWlWziFhzVpchSZIkSVLZLAl4I6J7ROwTEQ8x85m0kiRJkiTNNh0ZtKpVEbEO8BNgV7Jn9gbwfGeWIUmSJElSNeoOeCOiB7AbWaC7dj75E+Ay4MKU0j31liFJkiRJUq06HPBGxHpkQe4uwMJkrbmPAF8DLksp/aRTaihJkiRJUgfUFPBGxKLAHsC+wFfJgtxxwB+BESmlpyPi806vpSRJkiRJNaq1hfdNoBvwGfAP4BLg5pTS9E6ulyRJkiRJdak14O0OfA6cC/w2pfRG51dJkiRJkqT61fpYoouAj4GfA2Mj4uaI+GFELNj5VZMkSZIkqeNqCnhTSvsCywAHAE8A3wX+ArwdEcMjYv3Or6IkSZIkSbWrtYWXlNLklNIfUkrrAusAF+TL2R94AEjAKhGxVKfWVJIkSZKkGtQc8BallB5PKe1H1uq7L/AfspGbNwFei4h/RMTW9VdTkiRJkqTa1BXwNkkpfZxSuiiltAGwJnAeMBnYHriuM8qQJEmSJKkWnRLwFqWUnkopHUjW6vt/ZN2cJUmSJEmarTo94G2SUvokpXRJSulbs6oMSZIkSZJaU1PAGxHzRcRfI+LKiFigjXQL5mmuqL+KkiRJkiTVrtYW3p3y17Uppc9aS5RS+hS4BvhBROzU8epJkiRJktQxtQa8PwTeJHv2bnuuAN4Adqu1UpIkSZIk1avWgHc94M6UUmovYZ7mTmDdjlRMkiRJkqR61Brw9gFeryH9G8CSNZYhSZIkSVLdag14PwW61pC+K9Dqvb6SJEmSJM0qtQa8bwJr1pB+zTyPJEmSJEmzVa0B733AtyNi1fYS5mkGAPd2oF6SJEmSJNWl1oD3fKAL8LeIaPXe3IhYAvhrvvw/drx6kiRJkiR1TJdaEqeUHo6IPwA/BZ7O/7+TbCCrBCwHbAr8BOgFnJ9SerhzqyxJkiRJUvtqCnhzPwPmB34MHJW/ygK4ADio41WTJEmSJKnjau3STEppWkrpJ8A3gT8DLwOf5K+XgT8B30op/TSlNL0zKytJkiRJUrU60sILQErpAeCBTqyLGs3Qnh3IM7Hz6yFJkiTpf1LNLbySJEmSJM0Lqg54I6JbvYV1xjIkSZIkSapGLS28L0fEwRHRtdZCImLNiLgWGFJrXkmSJEmSOqKWgPc24EzgrYj4fURs0laLbUSsHBH7R8QDwKPAmsBd9VVXkiRJkqTqVD1oVUppr4g4GziZ7Dm7PwGmR8QY4C3gA2Ahsufvrgb0Jns80TjgGOC3KaWpnVt9SZIkSZIqq2mU5pTSf4AtIuKLwD7ApsBawBqlpO8CfweuBq5OKX1Wf1UlSZIkSapehx5LlFJ6ATgSICK6A8uStexOAd5JKb3VaTWUJEmSJKkDOvwc3iYppY+BF/KXJEmSJElzBZ/DK0mSJElqSAa8kiRJkqSGZMArSZIkSWpIBrySJEmSpIZkwCtJkiRJakgGvJIkSZKkhmTAK0mSJElqSB1+Dm9EzA90zZ/DW5z+HWB74GPgjymll+uroiRJkiRJtaunhXcY8H5E9GyaEBE/Am4HfgYcAfw7Ipavr4qSJEmSJNWunoB3Y+CulNLEwrTjgAnAXsDhwGLAoXWUIUmSJElSh9QT8C4P/LfpTUSsDKwGnJNS+nNKaRhwM7BlfVWUJEmSJKl29QS8iwKTCu+/CSTglsK0p4Hl6ihDkiRJkqQO6fCgVcBbwEqF95sBU4BHCtN6ANPqKEP/Y9YYuUbNeZ7a+6lZUBNJkiRJ87p6At4Hge0iYhvgE2Bn4I6U0meFNCsDb9RRhiRJkiRJHVJPl+aT8/zXArcCCwInNc2MiEWBAcBDdZQhSZIkSVKHdLiFN6X0VERsAOydT7oypfRwIclXgduAv9RRP0mSJEmSOqSeLs2klJ4ChrQy71/Av+pZviRJkiRJHVVPl+ZmImLxiFi+s5YnSZIkSVI96mrhjYgewPHA7sASZI8l6pLP2wA4Djg2pfRonfWUJEmS1KDGrN6v5jz9nh0zC2qiRtPhFt6I6Ak8APwceBMYA0QhyVPARsCu9VRQkiRJkqSOqKdL8zFAf2BgSmkd4G/FmSmlj4G7gU3rKEOSJEmSpA6pJ+DdEbg1pXRpG2leAZatowxJkiRJkjqknoB3OeDJdtJMBnrWUYYkSZIkSR1ST8D7IbBkO2lWAsbXUYYkSZIkSR1ST8D7MLBNRCxSaWZELA18D5/FK0mSJEmaA+oJeH8H9AJuiohm44jn7/8GLAScXUcZkiRJkiR1SIefw5tSujUihgJDgdHAZwARMR5YnOwRRUeklO6vv5qSJEmSJNWmnhZeUkonkD126DrgA2A6kICbgM1SSqfXXUNJkiRJkjqgroAXIKV0V0rp+ymlpVNKC6aUlkgpbZtSurPWZUXExhFxXUS8EREpIgZWkWeNiLg7Iqbk+X4VEVFK8+2IeCQiPomIlyJivwrL2SkinomIqfnf71dR9goRcX1EfBQR4yPi7IhYcFbUT5IkSZJUm7oD3k7Wg6x79MHAlPYSR8SiwO3AOGA94CDgMODQQpqVyFqc7wfWBk4BzomInQppNgSuBC4D1sr//i0iNmij7PmBG4FFgI2AXYGdgTM6u36SJEmSpNp1OOCNiE0j4uKIWKaV+cvk8wdUu8yU0k0ppaNTSlcBn1eRZXegO7B3Sml0Sulq4DTg0EIr6n7Amymln6WUxqSULgBGAkMKyzkEuCuldFKe5iRgVD69NVsA/YE9U0qPppRuBw4H9s0D3c6snyRJkiSpRvW08P4M+EZK6c1KM/PpG+bpZpUNgXtTSsXW4FuBZYC+hTS3lfLdCqwbEQu0k+Yb7ZQ9JqX0WilPV+BrnVw/SZIkSVKN6gl41yHrhtuWfwHr1lFGe/qQdRcuGleY11aaLkDvdtL0oXWV8ownG7irvbJrrZ8kSZIkqUb1BLxLAhVbdwvG5elmpVR6HxWmdzRNeVp7ZVea3ln1kyRJkiTVoJ6AdyKwfDtplgc+qqOM9rxNy1bYpgB7XDtppgHvtZOm3PLaXtm9gfmrKLvW+kmSJEmSalRPwPtvYIeIqNjtNx/Maoc83azyALBRRCxUmLY5Wcvz2EKazUr5Ngf+k1L6rJBm8wpp2uqy/QDQLyKWK+WZCjzSyfWTJEmSJNWonoD3HLJH8twbEdtFRFeAiOgaEdsD95A9ZujsahcYET0iYq2IWCuv2wr5+xVayXI58DFwSUR8JSJ2BI4EzkwpNXUHPh9YLiLOioh+EfFjYCAwrLCc3wHfiYijImL1iDgK2AQ4q43q3gY8DVwaEWtHxGbA6cAFKaVJnVw/SZIkSVKNOhzwppRuA04EVgH+AXwUEe+SdWH+O7AycGJK6ZYaFrsu8Fj+6gYcn/9/AkBEDI2IGfe1ppQmkrWGLgP8BxhO9hzcMwtpXga+B2wMPA4cAxyUPyKoKc39wI+AvYEngb2AXVJKDzWliYiBEZEiom+eZzqwNVlAex/Zc3z/TuFxQp1VP0mSJElS7brUkzmldFxE3Ef26KENgMWA94EHgXPyZ9PWsrxRzBywqZKVgGbLTCk9RRYstrXcu8lGlW4rzVXAVe2U/QzweiHPq8A27Sy3U+onSZIkSapNXQEvzGjpLT9HttNFRADfATad1WW14nvAgSmlaXOofEmSJElSDeoOeGeX/J7X9kaFnpXlrzenypYkSZIk1a6eQaskSZIkSZprVd3CGxGfA58DX04pPZ+/T+1kg6xxdp5pSZYkSdJsMrRnB/JM7Px6SGpYtQSi95AFuB+X3kuSJEmSNNepOuBNKQ1o670kSZIkSXOTDt/DGxEbR8RanVgXSZIkSZI6TT2DVt0F/KSzKiJJkiRJUmeqZzCp8cCUzqqIJEmS1J41Rq5Rc56n9n5qFtRE0rygnhbeUcA3OqkekiRJkiR1qnoC3mOB1SLixIhYoLMqJEmSJElSZ6inS/NRwGjgaGCfiHgCeJuWjypKKaV96ihHkiRJkqSa1RPwDiz83yd/VZIAA15JkiRJ0mxVT8C7UqfVQpIkSZKkTtbhgDel9EpnVkSSJEmSpM5Uc8AbEYsBPwPWJ+uu/CAwPKU0sXOrJkmSJElSx9UU8ObB7r+BVYDIJ28N7B0RG6SUJnRq7SRJkiRJ6qBaH0t0BLAqMCb//0jguXzaEZ1bNUmSJEmSOq7WLs3bAG8A66eUPgaIiPOAZ4FtyR5VJEmSJEnSHFdrC+9KwPVNwS5ASmkycB3QtxPrJUmSJElSXWoNeLsDb1eYPg7oVn91JEmSJEnqHLUGvJIkSZIkzRM68hzetSJir/I0gIjYk5mjN8+QUrq0A+VIkiRJktRhHQl4t89fZQFc0koeA15JkiRJ0mxVa8A7cpbUQpIkSZKkTlZTwJtSGjSrKiJJkiRJUmdy0CpJkiRJUkMy4JUkSZIkNSQDXkmSJElSQzLglSRJkiQ1JANeSZIkSVJDMuCVJEmSJDUkA15JkiRJUkMy4JUkSZIkNaS6A96I2DYiroiIJyLiv4Xp/SLi8IhYtt4yJEmSJEmqVZeOZoyIAC4B9sgnTQG6FZJ8AJwMBHBaR8uRJEmSJKkj6mnhPQDYExgBfAEYVpyZUnobuA/Yuo4yJEmSJEnqkHoC3n2AJ4B9U0oTgVQhzQvASnWUIUmSJElSh9QT8K4G3JVSqhToNnkHWKKOMiRJkiRJ6pB6At5pwELtpFkWmFxHGZIkSZIkdUg9Ae8zwIB88KoWImIh4DvAY3WUIUmSJElSh9QT8P4JWB34bUQ0W05EzA+cCSxDNpKzJEmSJEmzVYcfSwT8AdgOOAj4AfAhQERcBXydLNi9NqV0Wb2VlCRJkiSpVh1u4U0pTQe2AU4AFgS+RPbM3R2B7sCJZIGwJEmSJEmzXT0tvKSUpgFDI+J4soC3FzAReDYPiCVJkiRJmiPqCnib5I8meq4zliVJkiRJUmeoZ9AqSZIkSZLmWlW38EbEnR0sI6WUNu1gXkmSJEmSOqSWLs0DOlhG6mA+SZIkSZI6rOqAN6Vk92dJkiRJ0jzDIFaSJEmS1JA6ZZRmSZIkaW41ZvV+Nefp9+yYWVATSbNbpwS8EbEcsCzQtdL8lNI9nVGOJEmSJEnVqivgjYgtgN8Cq7eTdP56ypEkSZIkqVYdvoc3IjYAbgAWA84FArgHuAB4Nn9/PXBC3bWUJEmSJKlG9QxadTTwCbBeSungfNpdKaX9gK8AJwKbAVfVV0VJkiRJkmpXT8C7IXBdSunN8vJS5jhgDHB8HWVIkiRJktQh9QS8PYFXC+8/BRYupbkP2LiOMiRJkiRJ6pB6At53gMVL71cppVkA6FZHGZIkSZIkdUg9Ae/zNA9wHwQ2j4gvAUREH2An4IU6ypAkSZIkqUPqCXhvAb4dEV/I3/+OrDX3sYh4mGyk5iWAs+qqoSRJkiRJHVBPwPsHsvtzPwNIKd0H/AB4mWyU5reA/VNKl9ZbSUmSJEmSatWloxlTSpOAh0rT/gH8o95KSZIkSZJUr3paeCVJkiRJmmt1uIW3kojYDvgOEMA9KaWrO3P5kiRJkiRVq6YW3ojYNiLuiYhvV5g3gqw780HAz4C/RoQBryRJkiRpjqi1S/N2wDqU7t2NiG2AvYGPgV8DRwAvATtExK6dUE9JkiRJkmpSa8C7PvBASumT0vT/AxIwKKX0q5TS6cBGwCfA7vVXs3URMX9EnBgRL0fEJ/nfX0dEl0KaiIihEfFmREyJiFER0b+0nK4RcU5EjI+IjyLiuohYroryd4qIZyJiav73+xXSHFCo3yMRsVFpfrv1kyRJkiTVptaAtw/wYoXpGwMTgBldmFNKbwM3Amt3tHJVOgIYTNaVenXg4Pz9UYU0hwO/IOtqvR7wDnB7RCxSSHMWsBOwK1mwvihwQ0TM31rBEbEhcCVwGbBW/vdvEbFBIc0uZM8oPplsW9wP3BwRK9RYP0mSJElSDWoNeBcH3i9OyAO3LwD/SimlUvqXgV4dr15VvgFcn1K6PqU0NqV0HXAdsEFevwAOAU5NKV2dUhpN1v16EWC3PE1PYB/gsJTS7SmlR4E9ga8Cm7VR9iHAXSmlk1JKY1JKJwGj8ulNDgUuSSldkKf5GfkziqutnyRJkiSpdrUGvB8C5W6+X8v/PtZKnnL35872L2CTiFgdICK+TDZS9E35/JXIWqZva8qQUpoC3EMWLEO2DguU0rwGjCmkqWTDYp7crU15ImLBfNnlNLcVlltN/SRJkiRJNar1sURPAVtHRI+U0uR82vfJ7t/9V4X0K5G1Zs5Kp5G1hj4TEdPJ1umklNJ5+fw++d9xpXzjgGULaaYD4yuk6UPr+rSy3KY8vYH5W0nT1HJcTf0kSZIkSTWqtYX3MrJuzXdHxEERcS7ZoFRvA3cVE+Zddb8FPNMZFW3DLsBeZN1/18n/PyAi9imlK3e3jgrTyqpJU81yOyuNJEmSJKlKtbbwXgTsCHyXbJCmAD4DDk4pTS+l3ZSs9fKfddaxPacDw1JKV+Tvn4qIFckGrbqILBgnr8trhXxLMrNV9W2yltjewLulNPe0UfbbtGwBLi53PFnLcVtpqqmfJEmSJKlGNbXwppQ+B7YmG9DpfLJn7m6QUrqqQvLeZKMTX1dvJdvRnSyoLJrOzHV7mSyo3LxpZkQsRDYS8/35pEfIAvdimuWAfoU0lTxQzJPbvClPSunTfNmtpqmyfpIkSZKkGtXawtsU9F6Wv9pKdwVwRVtpOsn1wJER8TLwNNmjfw4FLs3rkSLiLOCYiHgWeB44FpgMXJ6nmRgRFwGnR8Q7wHvAmcCTtN1C/Tvgnog4CvgH2f3Mm5B15W5yJvCniPg3cB+wH7AM2QWDquonSZIkSapdzQHvXOhnwInAeWTdgN8CLgBOKKT5DdANGE52D/JDwBYppQ8LaX4OTCN7rm434A5gr2JX7YgYBZBSGpD/vT8ifkTW0n082TOKd0kpPdSUJ6V0ZUT0IgtilwZGA99LKb1SY/0kSZIkSTWY5wPePCg8hObPvi2nScDQ/NVamk/IgueftVHcSuQts4V8VwGVunQX05xHFpB3uH6SJEmSpNrUOkrz/6yI6A9MBc6Y03WRJEmSJLVvnm/hnV1SSk8DX5rT9ZAkSZIkVccWXkmSJElSQzLglSRJkiQ1JANeSZIkSVJDqvse3ohYANgU6Af0SCmdmE9fCFgUGJ8/u1eSJEmSpNmmrhbeiNgSGAvcSDZ68dDC7LXInom7Sz1lSJIkSZLUER0OeCNiXeAaIAE/By4vzk8pPQi8DHy/jvpJkiRJktQh9bTw/hL4GFg3pXQ28EKFNA8Da9ZRhiRJkiRJHVJPwPtN4JqU0tttpHkNWLqOMiRJkiRJ6pB6At4ewPh20nSvswxJkiRJkjqknmD0DaB/O2nWAl6qowxJkiRJkjqknoD3ZuC7EfGtSjMjYivgG8ANdZQhSZIkSVKH1BPwngJMAG6LiNOALwNExNb5+7+RPZbozHorKUmSJElSrbp0NGNK6Y2I2AL4K3BYYdZ1QAAvAjumlNq7z1eSJEmSpE7X4YAXIKX0aESsBmwNbAj0AiYCDwLXppSm1V9FSZIkSZJqV1fAC5BSmk7Wqntd/dWRJEmSJKlz+MggSZIkSVJD6nALb0TsVUWyz4FJwJiU0gsdLUuSJEmSpFrV06X5EiBVmzgingYGp5TuraNMSZIkSZKqUk/AOwjYAdge+CfwL2AcsBSwEbApcA1wH7AO8EPg1ojYMKX0RB3lSpIkSZLUrnoC3neBrYCtUkq3lmdGxJZkAe8FKaUzIuJi4DbgCGC3OsqVJEmSJKld9QxadQzw90rBLkBK6Rbg78Cx+fs7gNuBb9dRpiRJkiRJVakn4F0TeKmdNC8BXy28fxroXUeZkiRJkiRVpZ6A91NgjXbSfBX4rPC+C/BxHWVKkiRJklSVegLeu4BtIuInlWZGxH7ANsCdhcmrA6/XUaYkSZIkSVWpZ9CqI4EBwO8j4hfAA8wcpXlDYFVgAnA0QEQsBWwC/L6OMiVJkiRJqkqHA96U0gsR8Q3gPLJA9oulJKPInrv7fP7+HWARmndxliRJkiRplqinhZeU0nPAphGxHLAW0BOYBDyWUnq9lDYBU+spT5IkSZKkatUV8DbJg1vvzZUkSZIkzTXqGbRKkiRJkqS5Vt0tvBGxHvBdYFmga4UkKaW0T73lSJIkSZJUiw4HvBERwCXAHkAAKf/bJBWmG/BKkiRJkmarero0HwjsCfwJWJcsuD0L+AbZo4g+BK4AVq6vipIkSZIk1a6eLs17A8+llAYCZA2+TEgpPQg8GBG3Ag8CtwMj6qynJEmSJEk1qaeFdzXgztK0GQF0Sukx4AbggDrKkCRJkiSpQ+oJeAOYWHj/EfCFUpoXgNXrKEOSJEmSpA6pJ+B9g2xk5iYvAV8rpfkiWSAsSZIkSdJsVU/A+2+aB7g3A+tHxC8jon9EDAa2J7uPV5IkSZKk2aqegPdqYP6IWCl//xvgFeB44EngHGACcGQ9FZQkSZIkqSM6PEpzSuka4JrC+/cjYm1gX2AVYCxwaUrprfqqKEmSJElS7ep5LFELKaWJwLDOXKYkSZIkSR3R4S7NETE9Ii7vzMpIkiRJktRZ6rmH90Oye3YlSZIkSZrr1NOl+THgy51VEamjxqzer+Y8/Z4dMwtqIkmSJGluUk8L72nA9yJi886qjCRJkiRJnaWeFt4lgVuAmyPiGuBh4G0glROmlC6toxxJkiRJkmpWT8B7CVlwG8CO+QuaB7yRvzfglSRJkiTNVvUEvIM6rRaSJEmSJHWyDge8KaWRnVkRSZIkSZI6Uz2DVkmSJEmSNNeqp0szABGxBLAT0A9YOKX048L0lYCnUkpT6i1HkiRJkqRa1BXwRsQ+wNnAQswcoOrH+eylgAeAnwAX1VOOJEmSJEm16nCX5vz5u38Enge+D/y+OD+lNBp4GtihjvpJkiRJktQh9bTwHgG8BXw7pTQpItaukOZJYMM6ypAkSZIkqUPqGbRqXeCGlNKkNtK8DvSpowxJkiRJkjqknoB3QeCjdtIsBkyvowxJkiRJkjqknoB3LPC1dtJsADxXRxmSJEmSJHVIPQHvtcBGEfGDSjMjYhDwVeDqOsqQJEmSJKlD6hm06jfAj4C/RMTOQE+AiDgQ2AjYEXgBOKfeSkqSJEmSVKsOB7wppQ8i4tvApUCxlffs/O+9wG4ppfbu85UkSZIkqdPV08JLSulVYEBEfJXs8UO9gInAgymlRzqhfpIkSZIkdUhdAW+TlNKTZM/clSRJkiRprtDhQasi4rSI6NeZlZEkSZIkqbPUM0rzYcDoiPh3RAyOiC90VqUkSZIkSapXPQHvbsCtwNpkA1W9GRFXRcS2ETF/p9ROkiRJkqQO6nDAm1K6IqX0PWA54AiyRxDtCFxDFvyeGRFrdUYl2xMRS0fEyIh4NyI+iYhn8hGkm+ZHRAyNiDcjYkpEjIqI/qVldI2IcyJifER8FBHXRcRyVZS9U17e1Pzv9yukOSAiXs7r9khEbFSa3279JEmSJEm1qaeFF4CU0riU0rCU0hrA14BzgQAOAR6JiMfrLaMtEbEYcF9e5tZAP+BnwDuFZIcDv8inr5fPuz0iFimkOQvYCdiV7DnCiwI3tNVaHREbAlcClwFr5X//FhEbFNLsAvwOOJmsNfx+4OaIWKHG+kmSJEmSalB3wFuUUnospXQwsAzZPb7TgDU6s4wKDgfeSintlVL6d0rp5ZTSHSmlMZC1npIF36emlK5OKY0G9gYWIeuWTUT0BPYBDksp3Z5SehTYE/gqsFkbZR8C3JVSOimlNCaldBIwKp/e5FDgkpTSBXmanwFvAftXWz9JkiRJUu06NeCNiJ4R8RPgbuA3wALApM4so4IdgIci4sqIeCciHo+IA/NAEmAloA9wW1OGlNIU4B7gG/mkr+V1LaZ5DRhTSFPJhsU8uVub8kTEgvmyy2luKyy3mvpJkiRJkmpUd8AbEfNFxFYRcQVZy+XvgQ2AO8haSZept4x2rAwcALwEfJes+/CpwOB8fp/877hSvnGFeX2A6cD4NtJU0qed5fYG5q+i7PbqJ0mSJEmqUZeOZoyINYC9gN2BpcjuoX0euBS4NKX0eqfUsH3zAf9JKR2Vv38sIr5IFvCeW0iXSvmiwrSyatJUs9zOSiNJkiRJqlKHA17gifzvROBCsvtUH6i/SjV7C3imNG0McHD+/9v53z7Aa4U0SzKzVfVtspbY3sC7pTT3tFH227RshS0udzxZy3FbaaqpnyRJkiSpRvV0ab6dbFClPimln1YKdvPuztvXUUY17gNWK037EvBK/v/LZEHl5oV6LUQ2EvP9+aRHgM9KaZYjG/G5KU0lDxTz5DZvypNS+jRfdqtpqqyfJEmSJKlGHW7hTSl9t7V5EbEi8GNgELA0WevprPJb4P6IOIbsEUFrAwcBR+f1TBFxFnBMRDxL1u36WGAycHmeZmJEXAScHhHvAO8BZwJPAv9so+zfAfdExFHAP4DvA5sA3yqkORP4U0T8myw434/svubzq62fJEmSJKl29XRpbiZ/Xu32wE/IHuUzH9k9qG0FjHVLKT0cETuQPef2l8Cr+d/zCsl+A3QDhgOLAw8BW6SUPiyk+TnZY5SuzNPeAeyVUprelCAiRuVlDsj/3h8RPwJ+DRwPvAjsklJ6qFC/KyOiF1kQuzQwGvheSqmpBbra+kmSJEmSalB3wBsRK5O15g4kG7wKsntX/wBcVArsZomU0o3AjW3MT8DQ/NVamk+An+Wv1qxE3jJbyHcVcFU79TuP5gF4zfWTJEmSJNWmQwFvRHQh6777E7IuvPMBnwJ/B3YCrk0p/aqzKjk3iIj+wFTgjDldF0mSJElS+2oKePPH/ewL7E02onEAjwKXAJenlN6PiM87u5Jzg5TS02SDYUmSJEmS5gG1tvA+R3Zf7jtkg0WNyANBSZIkSZLmKh15LFECbgKuMtiVJEmSJM2tag14f0n2fNtBwH0R8UxEHB4RS3d+1SRJkiRJ6riaujSnlE4CToqI75Ldy7stcGo+7TZgZOdXUdIsM7RnB/JM7Px6SJIkSbNAR7o0k1K6NaW0M7A8cDRZq+9WwF/IujyvFRFf67RaSpIkSZJUow4FvE1SSu+klE5NKa0KbE72PNrPgHWBf0fEYxExuBPqKUmSJElSTTr0HN5KUkp3AHdERG9gILAPsCZwNjC8s8qRNGetMXKNmvM8tfdTs6AmkiRJUtvqauGtJKU0PqU0LKXUD/gOWTdnSZIkSZJmq05r4a0kpTQKGDUry5AkSZIkqZJOb+GVJEmSJGluMEtbeCXNPn2PvLHmPGMXmgUVkSRJkuYStvBKkiRJkhqSAa8kSZIkqSEZ8EqSJEmSGpIBryRJkiSpIRnwSpIkSZIakgGvJEmSJKkhGfBKkiRJkhqSAa8kSZIkqSEZ8EqSJEmSGpIBryRJkiSpIRnwSpIkSZIakgGvJEmSJKkhGfBKkiRJkhqSAa8kSZIkqSEZ8EqSJEmSGpIBryRJkiSpIRnwSpIkSZIakgGvJEmSJKkhGfBKkiRJkhqSAa8kSZIkqSEZ8EqSJEmSGpIBryRJkiSpIXWZ0xWQ1PjGrN6v5jz9nh0zC2oiSZKk/yW28EqSJEmSGpIBryRJkiSpIRnwSpIkSZIakgGvJEmSJKkhGfBKkiRJkhqSAa8kSZIkqSEZ8EqSJEmSGpIBryRJkiSpIRnwSpIkSZIakgGvJEmSJKkhGfBKkiRJkhqSAa8kSZIkqSEZ8EqSJEmSGpIBryRJkiSpIRnwSpIkSZIaUpc5XQFJUiuG9uxAnomdXw9JkqR5lC28kiRJkqSGZAuvJDWQNUauUXOep/Z+ahbURJIkac6zhVeSJEmS1JAMeCVJkiRJDcmAV5IkSZLUkAx4JUmSJEkNyYBXkiRJktSQDHglSZIkSQ3JgFeSJEmS1JAMeCVJkiRJDcmAV5IkSZLUkAx4JUmSJEkNyYBXkiRJktSQDHglSZIkSQ3JgFeSJEmS1JC6zOkKdLaIOBo4CRieUjownxbAccBPgMWBh4DBKaWnC/m6AsOAXYFuwB3AASml19spbyfgRGAV4EXgmJTSP0ppDgAOA5YGngYOSSndW5jfbv0kSdLsscbINWrO89TeT82CmkiS6tVQLbwR8XVgX+DJ0qzDgV8APwPWA94Bbo+IRQppzgJ2Igt4NwIWBW6IiPnbKG9D4ErgMmCt/O/fImKDQppdgN8BJwNrA/cDN0fECjXWT5IkSZJUg4Zp4Y2InmQB5z7ArwrTAzgEODWldHU+bW+yoHI34A953n2AQSml2/M0ewKvAJsBt7ZS7CHAXSmlk/L3J0XEJvn0XfNphwKXpJQuyN//LCK2BPYHjqqmfh3bIpLmJn2PvLHmPGMXmgUVkSRJ+h/SSC28fwSuSindWZq+EtAHuK1pQkppCnAP8I180teABUppXgPGFNJUsmExT+7WpjwRsWC+7HKa2wrLraZ+kiRJkqQaNUTAGxH7AqsCv6wwu0/+d1xp+rjCvD7AdGB8G2kq6dPOcnsD81dRdnv1kyRJkiTVaJ7v0hwRq5HdH7tRSunTNpKmctYK01osvoo01Sy3s9JIkiRJkqrUCC28G5K1pI6OiGkRMQ34NnBA/v97ebpya+mSzGxVfZusJbZ3G2kqebud5Y4nazlur+z26idJkiRJqlEjBLzXAGuQjZLc9PoPcEX+//NkQeXmTRkiYiGykZjvzyc9AnxWSrMc0K+QppIHinlymzflyVucH2krDfByFfWTJEmSJNVonu/SnFKaAEwoTouIj4D3U0qj8/dnAcdExLNkAfCxwGTg8nwZEyPiIuD0iHiHrFX4TLLHG/2zjeJ/B9wTEUcB/wC+D2wCfKuQ5kzgTxHxb+A+YD9gGeD8vOzUXv0kSZIkSbWb5wPeKv0G6AYMBxYH/r+9+w6TraryPv79IVEGjANiQDGRJKgjji9JQNHRGUQdsw5izqMoimkEs4IooKijgCjqGAYdFROYlSBgAgGReAVEwIACgoDr/WOf5vbt2zcpt3d11ffzPPeprn1OdS9su+qss/de6yRg16r607RzXgbcQOuruxbwDeA/qurGqROSfBugqh48PB6f5InAm4H9gHOBJ1TVSVOvqapPJbkdLYndADgdeERVXbiC8UmSNLH+ltZeABe8/ZE3cySSpPlkLBPeqYR02vMC9h3+Lek11wIvHv4tyUYMM7PTXvdZ4LPLiOdQ4NClHF9mfJIkSZKkFTMOe3jnRJLNgeuAd/WORZIkSZK0bGM5w7syVNXPgXv3jkOSJEmStHyc4ZUkSZIkjSVneCVJ0vja91Yr/pqNNrz545AkdeEMryRJkiRpLDnDK0mSJEmd/S3t12y9tmwmvJIkSX+nMzfZdIVfs+lZZ66ESCRNlL9l2wbAvlfevHGMMJc0S5IkSZLGkgmvJEmSJGksmfBKkiRJksaSe3glSZIkaYJsceQWK/ya0/Y4bSVEsvI5wytJkiRJGksmvJIkSZKkseSSZkmacH9LOxWwpYokSRp9zvBKkiRJksaSCa8kSZIkaSyZ8EqSJEmSxpIJryRJkiRpLJnwSpIkSZLGklWaJUmSJElL9bd0dRiFjg7O8EqSJEmSxpIJryRJkiRpLJnwSpIkSZLGkgmvJEmSJGksmfBKkiRJksaSCa8kSZIkaSzZlkiSJOBu+xyzwq+54O2PXAmRSJKkm4szvJIkSZKkseQMryRJI+7MTTZd4ddsetaZKyESSZLmF2d4JUmSJEljyYRXkiRJkjSWXNIsSdLfat9b/Q2vufLmj0OSJM3KGV5JkiRJ0lgy4ZUkSZIkjSUTXkmSJEnSWDLhlSRJkiSNJRNeSZIkSdJYMuGVJEmSJI0lE15JkiRJ0lgy4ZUkSZIkjSUTXkmSJEnSWDLhlSRJkiSNJRNeSZIkSdJYMuGVJEmSJI2lVXsHIEnSJNniyC1W+DWfXglxSJI0CZzhlSRJkiSNJRNeSZIkSdJYMuGVJEmSJI0lE15JkiRJ0lgy4ZUkSZIkjSUTXkmSJEnSWDLhlSRJkiSNJRNeSZIkSdJYMuGVJEmSJI0lE15JkiRJ0lgy4ZUkSZIkjSUTXkmSJEnSWDLhlSRJkiSNJRNeSZIkSdJYMuGVJEmSJI0lE15JkiRJ0lgy4ZUkSZIkjSUTXkmSJEnSWDLhlSRJkiSNJRNeSZIkSdJYMuGVJEmSJI0lE15JkiRJ0lia9wlvklcnOTnJH5NcnuSLSe4z45wk2TfJJUn+nOTbSTafcc4aSQ5JckWSq5N8Icmdl+PnPzbJGUmuGx4fPcs5L0hyfpJrk5yaZPsVjU+SJEmStGLmfcILPBg4FPh/wM7ADcBxSW477ZxXAi8HXgw8ALgMODbJOtPOeQ/wWOBJwPbAusCXktxiST84yYOATwEfB7YeHj+T5IHTznkCcBDwVuC+wPHAV5JsuILxSZIkSZJWwLxPeKvqYVV1RFWdXlWnAU8D/hHYFtrsKfBS4O1V9b9VdTqwB7AO8OThnFsBzwT2rqpjq+pHw/fZEnjIUn78S4FvVdVbqurMqnoL8O1hfMpewEeq6kPDOS8Gfg08f3njkyRJkiStuFRV7xhuVkk2AC4Btq+q7ye5O3AusE1VnTztvGOAK6pqjyQ7A98A1quqy6ed83Pgs1X1hiX8rAXAIVW1/7SxvYEXVdVdk6wOXAM8qao+M+2c9wH3qaodlye+Zfz3fhW4/XL+zyNJkiRJ4+aKqnr4bAdWnetI5sBBwE+AE4bndxgefzPjvN8Ad5p2zo3AFbOccweW7A5L+L5Tr7k9cIslnDM1c7w88S3Rkn6xkiRJkjTpxirhTXIgsB2wXVXdOOPwzKnszDK22LdcjnOW5/veXOdIkiRJkpbTvN/DOyXJu2kFp3auqvOmHbp0eJw5U7seC2dVL6XNxM5cGjz9nNlcuozvewVt5nhZP3tZ8UmSJEmSVtBYJLxJDqIVeNq5qs6acfh8WlL50Gnnr0mrxHz8MHQqcP2Mc+4MbDrtnNmcMP01g4dOvaaq/jJ87yWes5zxSZIkSZJW0Lxf0jwUgHoasDvw+yRTM6VXVdVVVVVJ3gO8NslZwNnA64CrgE8AVNWVSQ4D9k9yGfBb4EDgZ8BxS/nxBwHfTfJq4HPAo4GdaMuqpxwIfCzJD4EfAM8D7gh8YPjZy4xPkiRJkrTi5n2V5iRL+g/Yr6r2Hc4J8AbgucBtgJOAFw4tgKa+z5rA/rSZ4rVoVZtfUFW/mnbOtwGq6sHTxv4deDMwVW35tVV19IwYX0DrtbsBcDrwsqr67rTjy4xPkiRJkrRi5n3CO5eSXAh8oKre1jsWSZIkSdLSjcUe3rmQZHPgOuBdvWORJEmSJC2bM7ySJEmSpLHkDK+kpUqye5Jb9I5DkjSZkmzdOwbNLslqSU5KsnHvWKQlcYZX3SXZuqp+0jsOzS7J1cCfgCOBw6vqF51DkqSbXZIHArsA6zFjQqCqXtIlKAGQ5K/Aj4EPA5+oqis7h6Rphg4n21XV2b1j0eyS/NcSDhVwLXAO8NWq+vPcRTV3THjVnR9koy3JOrTq5XsCD6D1nz4M+HRVXd0zNi2U5JbA1sx+sX70bK/R3EmyJ/AkYENg9enHquruXYLSTZK8Angn7aLvEtpF4JSqqp27BCYAktwLeAatDeVtgaOBw6rqW10DEwBJ9geoqr17x6LZJTmN9vmzNu09Dlqb1KuBy4G7AJcBO1bVeV2CXIlMeNWdH2TzR5LNgGcCTwFuCXyK9rs6sWtgEy7JQ4BPAreb5XBVlUvSO0qyN/Bq4IPAy4BDgXsCOwAHVNWbO4YnIMmvgHdU1Xt7x6IlS7IK8C+0G7D/BlwEHA4cWVUX9YxtkiU5lHZdcD5wKi2JuokrJPpL8nTgqcDTp/5WktyZ9vdzFHAM8Gngqqp6VK84VxYTXo0MP8jmh+EN8jm03tJ/ofWt/hHw7Kr6Wc/YJlWSnwMnA6+pqkuWdb7mVpKzab+bzyb5E7BVVZ2X5PXAhlX17M4hTrwkVwL3HceZjXGUZE3g+cDbaCsmbqDdLH95VV3cM7ZJlGRpExSukBgBSc4HHjXzOm3YH//5qrpbkn8G/q+q1u8R48pkwquR4wfZ6EmyGvBo2kz8LsBJtCXonwJuA7wD+Keq2rRbkBNs2Ge9ZVWd2zsWLS7JNcAmVbVg2Ou2a1X9JMk9gR9W1W07hzjxknwA+FlVHdo7Fi1Zkm1on0NPAP4IHEG7Mb4B8CbgNlX1gH4RSqNp+Bx6cFX9cMb4A4FvVdUtk2xEex9cp0uQK9GqvQOQpszyQfZ2Fv0g+zxtD6nmUJJDaHsPC/gYsFdVnTHtlD8neS1wQYfw1PwA2Bgw4R1NlwK3BxYAFwIPAn5CW9bsXefR8CtgvyTbAj8Drp9+sKoO7BKVAEiyF+364F60pZdPoRXY+etwyoIkLwDO6hSigCS3B+4B/KSqrusdjxZxHPDBJM+hLTsHuD/wfuDY4fkWtGXpY8cZXnU3ywfZh1n0g4xhJuSsqvImzRxL8g3gQ8DRVfWXJZyzKrBtVX1nToMTAEkeA7wZOBA4jcUv1n/UIy41ST4MXFRV+yZ5HvBu4ETgfrTiby5p7mxY7rckZWGxvpL8klYs8Yiq+s0SzlkdeFJVHTmnwWmquOXhwGNpN/HuNWzb+ABwaVXt2zM+QZL1gI8CuwI3DsOrAF8H9qiqy5LsBKxWVV/vFOZKY8Kr7vwgG13DUuajaPsPnT0cUUOl8yWxaFVnQ32CVarqhuH5E4BtgbOBD1bV9Ut7vSSNsqFo1VbAC4Hv07bYnJfkX4G3VNVWXQPUTYZ+yRsDAc6clFZSJrySlirJ74H7W8xldCW569KOV9WFcxWLNN8l+QfajSLbro2QJFsAz6UtmX1GVf06ye7AhVX1467BTbgkFwGPrqqTZxTmm1rePHZ7QuezJOsDl09fSTnuXB6qkZBkA1qhqs2GoTOB91txdiQcDTwGOKB3IJqdCe3os0/y6EvyQuBVwJ2G5xfRWhVZyKqzJLsCXwC+AuxM6w4ALfl9OrB7l8A05TbAb2cZX4eFy2fV0bBi7y20a+21gHsD5yV5B+2m0Vi/z5nwqrskDwX+j1Y05KRh+HHAy5PsPo57CeaZBcDrkmwPnMLi/fUs5jICkmwJvIJ206iAM2g9Xk/rGpiW2ScZcMl5Z0leQ+uVfABtSSbA9sDbk6xbVW/vFpygFa7cq6oOHWYQp3wbeHmfkDTNycBuwHuG51PLR58LHN8jIC3mDbSWn08FPjFt/Ie0G31jnfC6pFndJTmTViHuP2va/yGTHERr32Grm44s5jL6kuxGm4n/Hgsv1rcb/j2mqr7YKzbZJ3k+SLIAeFVVfXLG+FOAt1bVUrcNaOVKchVwn6q6YMaS2Y1o+xDX7BziREvy/4CvAf9DS6g+DGwObAPsYOHE/pKcS9sK8J0Zf0MbAydV1a37RrhyOcOrUXA34L21+N2X9wFWL+2sqjbqHYOW6c20wiBvmD6Y5I3DMRPevu4G7GayO9LWo92UmOmHwPpzHIsW93vaUvMLZozfD7hozqPRIqrq+CHpfQWtPd4uwI+AB7nKaGTckdYWb6ZVmYB8cJVlnyKtdKfQen/NtAVgIQpp2e5N65E808do1RjV11SfZI2us4EnzzL+ZOAXcxyLFvcJYP8kd6Ytl101yY60Jegf7RqZAKiq06pqj6q6T1VtVlVPNdkdKT8Hdphl/PEs7Ms7tsY+o9doSnK/aU8PBd6d5F603pQA/0zbWL/PXMemxSW5N/DvwIbA6tOPVdUzugSl6S6jNZA/Z8b4/YFZW31pTn0AOCDJHbFP8qjaF/h0kh1oNyiKtiVgR1pNCfX1OuAjtBmq0GoUhJYIv6VfWAJI8mPaDdZPVNWlvePRrPYDjkpyF1rdiMcl2YR2U++RXSObA+7hVRdD39CifWAtjT1EO0vySOB/abPt96ct+7sHsAbwvararWN4ApK8nla4ZX9agZCpi/VXAPtXlReEHdkneX5Icn/gZcCmLEyq3mXLm76GPtab0AoorkdbxrwK8OOq+mXP2NQkeSvwROAutEJiHwOOrqqresalRSV5GPAa2rXcKrRl52+chOKwJrzqYll9Q6ez5UpfSU4FPltVb5sqdABcQvtAO8Eqzf0lCfBSWtJ7x2H4EloCfPAs++M1h+yTLP3thve364DNqmrmKhaNkCTb0WYMHwfcktZK6mNV9eWugWnimfBKWqqhOuaWQzW/39EqLp6eZAvgmKrasHOImibJOgBV9adlnStpoSRrAE9hYWuvnwOfrKrrugYmkpwGPKeqTugdi5YtyarAw2ntpLZ0FcvoSLIz7T0O4Iyq+mbPeOaKe3g1EoY3x22YfY+oBSn6+hMw1fLh18A9gdNp7x+36RWUFkryTVr7oT9MT3STrAt8vqp27hfdZEryGOCLVXX98PUSVdXRcxSWliDJZsBXgXVp+6yhdQnYL8nDq+rMbsEJ4JW0olUvAn7qqpXRNewRfTLt5tHmLGyVp46GFl5H0wrCTnUMuONwM+mxVXVet+DmgDO86m7YNP9FYCPavqkbacnU9cB1VbVux/AmXpLPA1+uqv9O8k7gsbSqmI8GLquqXXvGp5v2iN6hqi6bMb4ecHFVrdYnssk1/XfiHt7Rl+RY4BrgaVX1x2FsXeAoYI2qeljP+CbdsJ1mTdq+wxtoS5xv4nVCX0luQ1vG/BRgW1pl848DR1XVgp6xqRlujN+C9h63YBjbEDiS9jk01jfGneHVKHgPrST61sClw+OtgPfTKjOqr72Afxi+3hdYh5b0nj0cUyczqp1vOSw5n3IL4GHAxXMblQCqapXZvtbI2hZ4wFSyC1BVf0zyWhZ2D1A/L+odgJbqUuAK4FPAy6w8P5IeBPzz9BsQVbUgycuAsd8qYMKrUfAAYMequnqYCVm1qn6U5JXAIcCWfcObbNOXuVTVNbR2URoNp9D2GhYwW5XFPwMvntOIpPnpWuDWs4zfajimTpKsBjwCeE1Vnds7Hs3q34Djqmppq1nU1wJgrVnG1wR+NcexzDkTXo2C0JaSAVwO3Im2HOYi2n5RSbOb2gZwHm0P/OXTjv2FtuT8xh6BaaEk/7GEQ0VLps6x9U13XwQ+lOTZLJzRfRDwQVqlWXUy7IPfFXh171g0u0loazMGXg4cnOQltPaS0Cac3jMcG2vu4VV3Sb4LvLuqPpfkE8DtgLfSCoZsWVXO8HY0rWfybK4FzgEOq6qD5y4qaf4Y9h+uDqwGTM2ArEKrU8Aw/mPg4VV1+eLfQStbklvT9rL9G62OBLTf0ReAPavqD30iE0CSw4Azq+qA3rFocUPhoyUmFF7H9Td8Dq1B2+40/XPoRmasYhnHPfHO8GoUvAVYe/j6dcCXgG/R9oM8vldQusmLaHt3PwecNIw9ENgdeAet0fzbk1RVHdIjQN1UGXN7YD3ah9hN7JXc3eOBNwAvY9E76+8C3kzbZ30EcCDwtB4BTrohoX1UknsCm9JWTpxh39eRsQB4XZLtaVs5rp5+0Pe47j474/lqtHos2wLvm/NoNJuJ3gfvDK9GUpLbAr+39UB/Sf4P+EJVHTZj/JnAblX1qCTPA15cVZt3CXLCJXkKcDiteunlLHqnvarq7l0CEwBJzgSeXlUnzRj/Z+CIqto0yU7Ax6rqzl2CnHBJ/gs4YKhTMH18LWDvqnpjn8gEkOT8pRz2PW5EJdkbuGtVTXSypf5MeCUtVZKrgK1nznQMMyE/raq1k9wDOK2qbtklyAmX5FxadczXu2d39CT5M7BNVZ02Y3xL4KSqWivJ3YCfV9Xas30PrVxJbgQ2mKW11+1oe+FtHSWtoOHa4JSquk3vWCZRkttW1e+mvl7auVPnjSuXNKu7JGsC/wnswuzLMd370ddvacuXZ+6d2p227Bxa26Ir5y4kzbA+8GGT3ZH1Q+DAJE+rqksBktyB9jc1Net7L1qhPvURZt+DeF9grC8EpZVoBxYWJdXcuzzJ1I28K5j9PW7qvW+sb+qZ8GoUHAo8GvgMcDxLKXygLvajVS/dmXbhXrSKwLvSCosBPBT4Tp/wBHyZtq/6vGWdqC6eBXweWJDkEtrf0J1ovax3H85Zm7afV3NoKOQy1drrvCTTP39uQWvZ8YEesWmhJEstilhVL5mrWLS4JDMrmQfYgHbDaL+5j0iDnVl4w26nnoH05pJmdZfkd8Djq+q43rFodkkeROvnugntg+xM4OCqOnGpL9ScGFqpvB74KHAaC6v/AlBVR/eISwslCe0m0cYs/Bs61joFfSXZg/b7OBx4KYuuVPkLcEFVndAhNE2T5FszhlajfR6tCvyoqnae+6g0JckRM4b+Sqsn8U1bFmkUmPCquyQXAbtU1S96xyLNR0PrqCUp9x9KS5dkR+D4qrp+mSdrJAzboQ4DvldVzsJLyyHJHZl9++CP+kQ0N0x41d3QBHtz4PlVtbQLd82RZRU3mG7cCx1IN4ckLwBeCGwE3KeqzkuyD3BeVX26b3Sabthfvfr0sapa0CkcLUWSzYCvVdVdesciSHJ3YDPaFoEzq8ptNiMiyX2Bo1i4Um+6sb8x7h5ejYKH0vqHPjzJGSy+HHO3LlFNtiUVN5huIgodSH+vJC8FXknrW/32aYcupvVGNOHtLMm6wCG0nsmrz3KK73Oj6R9pRRPV0fD3cxjwWNpy5mE4/ws8s6r+1C04Tflv4Fe02itTtSQmhgmvRsEVwOd6B6FFTHRxg/koySOBV7Hw7voZwDuq6stdAxPA84BnV9UxSaYXpvoRbXWL+nsXsBWtiNjRwDNohcX+E3h5v7AEkGSvmUO0okhPoRXtU18HAVvSrh2OH8a2pRV8ew/wzD5haZrNgPtW1dm9A+nBJc3qKsmqwHOAz1fVJb3jkeajJM+iVTv/OPD9YXh74Em0rQKH94pNN/Xh3aSqLhyqAm81LGm+N/AT+1f3N9SSeFJVfS/JH4H7VdU5SZ4EPKOqHto5xImW5PwZQzcVRQLe5gxiX0l+C+xeVd+bMb4D8Lmqul2fyDQlyYnAK6vqu71j6cEZXnVVVTck2R84pncsWjb3to2sVwF7VdV7p40dluRUYB9aBVr1cx5wP+DCGeOPoFVrVn+3ZuHv50rgdsA5wAnAhzvFpEFVbTT9eZLVgDWq6qpOIWlRawG/nWX8d7TWXurvNcA7k7yO2bs5jHU9llWWfYq00p0I3L93EJpdklslOXKYpboYOH/GP/W3IfDVWca/Atx1jmPR4g4A3pvkKbSlmA9K8gbgrcA7u0amKecCdx++PhN44tBK6jEs7GOpOZZklySPnzG2D/An4A9Jvprk1l2C03Q/AN6U5KbVKknWpvXgPX6Jr9JcOg7YBvg6bQ/v5cO/K4bHseYMr0bBh4ADkmwInApcPf3guJdKnwcOwL1to24BrfjbOTPGd2XxWUXNsao6Yti+8VbglsDHWFiwyovB0fAR2h7Eb9MKi32J9vtZhfZepz72od24AyDJNrS/o8NoNyb2Bl47PKqfl9Fuul6c5Ge0OhJb0a7nHtYzMN1komuzuIdX3dlDdLS5t230JXkurcLskbQEqoDtgKcBL66q/+4YnqZJcntaEnUL4HW0v6G1+kalmYYbsP8E/LKqTusdz6RKcinwyKo6dXi+P/CgqtpueP444M1VtXHHMAUkWQt4Kgvb3pwBfLyq/tw1MAlneDUaNlr2Kero1ri3baRV1QeTXEabcX/MMHwm8Piq+r9+kU22Yanl+2gz7dfTZg4PAV5P23d9Bm3FhDpL8q9V9aWp50NtggXDsddU1Vu7BTfZbg1cNu35tixalflk2oojdTYkth/qHYeWbFhyvjWwHjO2tVbV0T1imismvOquqlxyOdqm9rYtYOHeth/i3raRUlWfw/Zeo+atwA60mfeHA++mLT1fG3hEVX2nY2xa1MeTPKKqfjB9MMlraTeSTHj7+DVwD+BXSdYA7ku7YTRlHeC6HoFpoSRvAX5VVR+YMf484E5V9frZX6m5kuQhwCdpkxYzFWPea9yEV90leczSjo/7Xad54CPMvrftFsBLukWlWSVZk8Xv3F7TKZxJ90hgz6o6LsmhtJUR51bVS/uGpVm8EPhCkp2q6mcAQzXTvXAPYk9foVWW3QfYjbYndHrrmy1ZvHaB5t7TgMfNMn4q8GoWvUmhPg6idUR5zSS2AXUPr7pbyh7eAnAP72iZtrft7Ko6vXc8giR3BQ6mFaVYe+Zx/4b6SHI9cNepi4sk1wAPqKqf941Ms0nyEtrF+XbAk2nJ7q5VdXLXwCbYsOf9aNrv5Cpgj2E1y9TxbwAnVNXrOoUoIMm1wGZVdd6M8bsDZ1SVrYk6S3I1sGVVnds7lh6c4VV3VbXIbNRQzfS+wP606ovqIMm/AO8HtqqqK6fGq2pBkiuBnyZ5TlV9vVuQmnIUrdfhi4HfMNwsUnersGivwxsBZ9tHVFUdPCRYJ9P+hh4yVSxJfVTVFcAOSW4FXFVVN8445XG0RFh9LQC2p/Ucn24H4KK5D0ez+AGwMW2b2sQx4dXIqaobgJOTvIYh4eoc0qR6EbD/9GR3SlVdmeQdtHYdJrz93Zc2c3hm70C0iABHJZnaY7gm8KFhpvcmVbXbnEcmkuw1y/AfaAnU94Adk+wIUFUHzmFommG2z6Fh3DoSo+GDwLuTrA58cxjbBXgb8I5uUWm6D9BagN4ROI1Fb8aOfQtQlzRrZCXZDPhhVf1D71gmUZJf0WY4frGE4xsD36iqO89tZJopyQ+AV1fVd3vHooWSHLE851XVnis7Fi0uyfnLeWpV1d1XajDSPJfkbcBLgdWHob8AB1XVPt2C0k0mvQWoCa+6S3K/mUPABrS2HVTV9nMelKb25GxRVb9cwvF7Az+1h2h/STan7eE9GDidxe/cLugRlyRpciRZG9iMoQ9vVbncfEQMtT6WaNw7prikWaPgFNp+qcwYPxFw5qOfi2gVMGdNeIdjF89dOFqKVWh99T7Hovt3wwS0G5BWhiT3BC6qqmt7xyLNB1V1NW0PvEbMuCe0y2LCq1Gw0YznfwUu9yKju2OANyX58tBQ/iZD8/I3DueovyOBy4F/w6JV0gpL8lbgF1V1ZJIAxwI7A1cm+ZeqOrFvhNLoGtrh/Sdt3+56LN4ab8secU26oe3nF6vq+klvAeqSZnWXZI2qum74+k7Ac4C1aH+k31vqi7XSJFkP+DEteToEOGs4tCmtoFWA+1XVb/pEqClDEaStq+rs3rFI81GSC4EnVNWJSR5Bu4n0SOAptFYeO3UNUBphSQ4HHg18BriEGTddq2q/HnFNumHf7h2q6jL38JrwqpOh6NHRwCbAz2gXFscC69JmedcG/r2qPt8rxkk37Pl4P/AwFi45L+BrwAuq6oJOoWmaJN8G3lZVX+sdizQfDTUL7llVFyV5L+366IXDsuZTqurWfSOURleS3wGPr6rjeseiRSV5MHB8Vf2lcyhduaRZPR0A/Bp4JfBE4Mu0ROpZw/FDgH2Az/cITjft+XhEktsA96Qlvb+sqt/3jUwzvB94T5J3MYHtBqSbwW+Bu9JqF+wKvHoYX5XF60tIWtQ1wK96B6FZfRO4NskJw9ffpHVAmdnTeqw5w6tuklwOPLSqfpJkHeBKYJuqOmU4vglwonfWpaWb9KVK0t8rycHAo4CzaX2t71pVVyd5IrB3Vd2/a4DSCEvyEmBz4PlVtbTPI82xYZXKTsCDh38bAFcD36clv98CTq0xTwhNeNXN9L0Fw/M/AVtV1XnD8/WBS7xYl5Zu0tsNSH+vJKvSiu5sCHykqn48jL8M+FNVfbhnfNIoS/JFYHvaxMUZLL7KaLcecWlxw2TSTsCOtAT4H4Erq+q2PeNa2VzSrN5m3nHxDoy0gkxopb9PVd0AvGuW8Xd3CEeab66gtcXTiKuqs4Y917+j3aB4IvAPfaNa+ZzhVTfDDO+xwHXD0L8A36HtBQFYA3iIM7zS4pL8x/KeW1UfXZmxSOMiyR1ps7yrTx+vqu/2iUiS/j5Jbkebzd2J1m7t7sCptGvu7wDfH3oojy0TXnWT5IjlOa+q9lzZsUjzzbAFYLrVgdVoFc6h9UG8Hriuqtady9ik+WZIdD9JW5ZZtEJVN10geeNV0nyU5KfAvYFTWJjg/qCqrlnqC8eMS5rVjYms9LerqnWmvk7ySGBf4KXAScPwA4EDgTfNdWzSPPQe4AZgM+Bk4OHA+sAbgZf1C0uaH5LsCTyJ2VdI3L1LUAK4F/B74DzgXOCcSUt2oc0ASJLmtwOAl1TVD6rqhuHfD2gJ8GL7EiUtZkfgVVV1Fm1m9/KqOhp4Fd40kpYqyd60z5pTgbvR2kmeDtwWOLxbYAK4FfB44BzgacDPk1yY5MgkeybZqG94c8OEV5Lmv7vR2gzMdA3tbrukpVuLVngHWjGX9YavzwC27BKRNH88G3hOVb2atpXmvUNl5nfR+lurk6q6vqq+X1VvqqqdgdsAewDnA08HzkhyQccQ54QJryTNfycBBye509TA8PW7gRO7RSXNH2cBmwxf/wR43tDu64XAxb2CkuaJOwM/HL7+MzBVN+KTwGO7RKQl+eu0f1P1Cu7SNaI54B5eSZr/nklbQnZBkqmL8zsBvwAe3SsoaR45CLjD8PUbga8CT6Z1EVjuiujShLoUuD2wALgQeBDtxtE9sd1kV0OP8W1oFZp3ov1u1qT9rr4FHDY8jjWrNEvSGEgS4CHAprQ7tmcAx5Vv8tIKS7IusDVwRlVdsYzTpYmUZJeq+kaSDwMXVdW+SZ7HwtVF9wM+XVXP7hroBEtyFW3Lxq9pie23gG9V1fldA5tjJrySNE8lOR54RFX9YXj+NmD/qvrd8Pz2wI+qyn280iyS7ALcrqo+PW1sH1rV81WB44AnTv2NSVooyV+BC4AjgMOr6uJh/AnAtsDZwAer6vpuQU64JM8FvllVv+wdS08mvJI0Tw0XG3eoqsuG538Etq6q84bn6wOX2ENUml2SY4GvVNWBw/NtaDNThwFnAnsDR1XV3v2ilEZTkk1pW2qeCtwO+DrwIeCLVXVjz9ik6SxaJUnjI70DkOaZLYDvTHv+OOD4qnr2kAS/BNitS2TSiKuqM6vqFbSiVU+g7df9DHBxknck2bhrgNLAhFeSJE2qWwOXTXu+La1g1ZSTaQXgJC3B0Pv96Kr6V1obooOBx9Ba3ny3b3SSCa8kzWfF4hUw3aciLb9fA/cASLIGcF/ghGnH16FVapa0HKrqEuBQWtL7B9pNJKkr2xJJ0vwV4KgkUxfkawIfSnLN8HyNPmFJ88ZXgHcOhap2A64Gvjft+JbAOT0Ck+abJA8BngHsDlxL68P74Z4xSWDRKkmat5IcsTznVdWeKzsWaT4aKpkfDWwHXAXsUVWfm3b8G8AJVfW6TiFKIy3JhsCewNNpy5m/S0tyP1tV13YMTbqJCa8kSZpoSW4FXDWzsmyS2w7jf+kTmTS6hirnO9H2wR8JHFZVrojQyHFJsyRJmmhVdeUSxn8317FI88ifacWpjrENkUaZM7ySJEmSpLFklWZJkiRJ0lgy4ZUkSZIkjSUTXkmStFyS3C1JJflI71gkSVoeJrySJE24JJskOSTJ6UmuTPKXJJckOSbJM5OsuYzXf2RIhO82RyFLkrRcrNIsSdIES/JfwBtoN8FPpLUXuQpYH3gwrafm84F/Ai4GNgVmrWosSdKoMeGVJGlCJXkNsB/wK+BxVXXSLOf8K/BygKq6HjhrToOUJOnv4JJmSZIm0LD8eF/geuARsyW7AFX1JeDhU6+ZuYc3SQF7DE/PH45XkguG4ycmuXFJy52TvGI4/+U3y3+YJEnTOMMrSdJk2hNYDfifqjp9aSdW1XVLObwfsDuwFXAQ8IdhfOrxUNoy6WcDr53l9c8CrhvOkSTpZmXCK0nSZNpuePzG3/NNqmrfYfZ2K+A9VXXBjFM+BRwIPCPJvsOyaACSPBjYGPhEVV3x98QhSdJsXNIsSdJk2mB4vGhl/pBhdvgI4A7AbjMOP3d4/ODKjEGSNLlMeCVJmkwZHmsOftb7h58zleCS5PbAo4Ezq+q7cxCDJGkCmfBKkjSZLhke77yyf1BVnQd8DXhIknsMw08H1sDZXUnSSmTCK0nSZPr+8LjLHP2899NmlZ89PH8WcC3w0Tn6+ZKkCWTCK0nSZDqC1pLosUk2W9qJSdZYxve6cXi8xVLO+RKwANgzya60YlWfrqrfL2e8kiStMBNeSZIm0FBNeV9gdeCYJP8023lJHg58ZRnf7rfD44ZL+Xl/Bf4bWA84fBj+wPJHLEnSikvVXNSqkCRJoyjJfwFvoN0EPx44BbgKWB/YAbgXcEpVPWBoP3Q+cGRVPX3a93gY8FXgXOCzw+v/UFXvnfGz1qfN8q4OnFZVW67U/zhJ0sQz4ZUkacIl2RR4AbATbZZ2Tdqs7U9oCexRVXXdkhLe4XvsRdufe3daQnthVd1tlp/1OWB34EVV9b6V8h8kSdLAhFeSJM2JJKsA59Bmjzeoqj92DkmSNObcwytJkubKvwMbAR812ZUkzQVneCVJ0kqVZB/gtsBzgFWBzavqwr5RSZImgQmvJElaqZIUrQXSGcDeVXVs55AkSRPChFeSJEmSNJbcwytJkiRJGksmvJIkSZKksWTCK0mSJEkaSya8kiRJkqSxZMIrSZIkSRpL/x+RajF97d396QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size':20})\n",
    "ax=grouped_avg.plot.bar(figsize=(15,10), ylim=(200000,1600000),xlabel = 'City', ylabel = 'Average Sale Prices (CAD)', fontsize=14)\n",
    "ax.legend(fontsize=12, loc=0)\n",
    "pos = ['left','right','top']\n",
    "for char in pos:\n",
    "    ax.spines[char].set_visible(False)\n",
    "    ax.tick_params(left=False, bottom=False)\n",
    "    \n",
    "ax.text(-1.5, 1750000,'Average Prices of Houses Sold in Each City (October 2022 - January 2023)',size=22, weight='bold')\n",
    "ax.text(-1.5, 1700000,'Source: honestdoor.ca' + ' '*170,color='#8B6A9E',size=12)\n",
    "\n",
    "\n",
    "plt.savefig('avg_prices_per_city.png', bbox_inches='tight',  facecolor='w')\n",
    "\n",
    "ax.get_yaxis().set_major_formatter(\n",
    "    matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e68911eb",
   "metadata": {},
   "source": [
    "**Observation**\n",
    "\n",
    "- In the Cities in British Columbia, the Average prices reduced each month, with at least 34% reduction from oct to jan\n",
    " - Average prices in Winnipeg also redcued month to month and approximately 10.3% reduction over the past 4 months\n",
    "- In The two major cities in Alberta, Calgary & Edmonton, the average price increased from october to november but continued to decrease until january, with up to 14% reduction in Edmonton\n",
    "- Overall the average prices of houses in the cities reduced between October, 2022 and January 2023. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1baf65c",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "From the analysis of this data, There is evidence that the increase in mortgage rates led to to a reduction in prices and demand for houses."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aa597ed",
   "metadata": {},
   "source": [
    "### 2. What Cities have the highest demand?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a25bf9ce",
   "metadata": {},
   "source": [
    "To have an idea of the city/cities with the highest demand for houses, I'll check the no of houses sold in each city during the period (oct to jan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1178db26",
   "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>closeMonth</th>\n",
       "      <th>2022-10-01</th>\n",
       "      <th>2022-11-01</th>\n",
       "      <th>2022-12-01</th>\n",
       "      <th>2023-01-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cityName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Calgary</th>\n",
       "      <td>2146</td>\n",
       "      <td>3698</td>\n",
       "      <td>3764</td>\n",
       "      <td>376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edmonton</th>\n",
       "      <td>2297</td>\n",
       "      <td>1858</td>\n",
       "      <td>2163</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Winnipeg</th>\n",
       "      <td>542</td>\n",
       "      <td>418</td>\n",
       "      <td>247</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vancouver</th>\n",
       "      <td>507</td>\n",
       "      <td>296</td>\n",
       "      <td>217</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Surrey</th>\n",
       "      <td>489</td>\n",
       "      <td>219</td>\n",
       "      <td>169</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saskatoon</th>\n",
       "      <td>220</td>\n",
       "      <td>212</td>\n",
       "      <td>158</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Regina</th>\n",
       "      <td>196</td>\n",
       "      <td>162</td>\n",
       "      <td>113</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burnaby</th>\n",
       "      <td>246</td>\n",
       "      <td>108</td>\n",
       "      <td>94</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "closeMonth  2022-10-01  2022-11-01  2022-12-01  2023-01-01\n",
       "cityName                                                  \n",
       "Calgary           2146        3698        3764         376\n",
       "Edmonton          2297        1858        2163         200\n",
       "Winnipeg           542         418         247         118\n",
       "Vancouver          507         296         217          13\n",
       "Surrey             489         219         169          14\n",
       "Saskatoon          220         212         158          58\n",
       "Regina             196         162         113          27\n",
       "Burnaby            246         108          94           4"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tables = pd.crosstab(house_from_oct['cityName'], house_from_oct['closeMonth'])\n",
    "tables.sort_values(by = datetime.date(2022, 11, 1), inplace=True, ascending=False)\n",
    "tables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d884641",
   "metadata": {},
   "source": [
    "- From the table above, it's clear that the number of houses sold in calgary in the past 4 months, is significantly higher than the other cities.\n",
    "\n",
    "- So, it seems like Calgary has the highest demand "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09dbdfd1",
   "metadata": {},
   "source": [
    "### Why is there a considerably higher demand in Calgary?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9aad7db",
   "metadata": {},
   "source": [
    "One of the things that coul contribute to the demand in calgary, is the population. \n",
    "\n",
    "So to confirm, lets compare the population in Calgary to the population of people in the other cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7cfd70a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2) = plt.subplots(ncols =2, nrows=1,figsize = (14,5))\n",
    "sorted_population = population.sort_values(by='Population',ascending=True)\n",
    "\n",
    "ax1.barh(sorted_population['City'], sorted_population['Population'], height=0.4, color='#af0b1e')\n",
    "ax1.set(xlabel='Population', ylabel ='')\n",
    "ax1.spines['left'].set_visible(False)\n",
    "ax1.spines['right'].set_visible(False)\n",
    "ax1.spines['top'].set_visible(False)\n",
    "ax1.spines['bottom'].set_visible(False)\n",
    "ax1.xaxis.tick_top() \n",
    "ax1.tick_params(left=False, top=False) \n",
    "ax1.tick_params(axis='x',colors='grey')\n",
    "ax1.set_xticks([0,700000,1400000])\n",
    "ax1.set_xticklabels(['0', '700,000', '1,400,000'], fontdict = {'size':10} )\n",
    "ax1.yaxis.set_tick_params(labelsize=10)\n",
    "ax1.axvline(x=700000, ymin=0.01,c='grey', alpha=0.5)\n",
    "ax1.text(x=-350000,y=9,s='The Higher Population, The Higher The Demand',size=18,weight='bold')\n",
    "\n",
    "#ax1.text(x=-350000,y=10, s='Population of Major Cities in the Prairie & West Coast Region',size=14)\n",
    "ax1.xaxis.label.set_fontsize(16)\n",
    "\n",
    "sorted_counts = pd.crosstab(house_from_oct['cityName'], house_from_oct['closeMonth']).sort_values(by = datetime.date(2022, 11, 1), ascending=True)\n",
    "sorted_counts.columns = ['Oct-2022', 'Nov-2022', 'Dec-2022', 'Jan-2023']\n",
    "\n",
    "ax2.yaxis.tick_right()\n",
    "ax2.invert_xaxis()\n",
    "\n",
    "ax2.barh(sorted_counts.index, sorted_counts['Oct-2022'], height=0.4)\n",
    "ax2.barh(sorted_counts.index, sorted_counts['Nov-2022'], left = sorted_counts['Oct-2022'], height=0.4)\n",
    "ax2.barh(sorted_counts.index, sorted_counts['Dec-2022'], left = sorted_counts['Nov-2022'] + sorted_counts['Oct-2022'], height=0.4)\n",
    "ax2.barh(sorted_counts.index, sorted_counts['Jan-2023'], left = sorted_counts['Dec-2022'] + sorted_counts['Nov-2022'] + sorted_counts['Oct-2022'], height=0.4)\n",
    "ax2.yaxis.set_tick_params(labelsize=10)\n",
    "\n",
    "ax2.spines['left'].set_visible(False)\n",
    "ax2.spines['right'].set_visible(False)\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax2.spines['bottom'].set_visible(False)\n",
    "\n",
    "ax2.tick_params(right=False, top=False) \n",
    "ax2.xaxis.tick_top() \n",
    "ax2.tick_params(axis='x',colors='grey')\n",
    "ax2.set_xticks([0,10000])\n",
    "ax2.set_xticklabels(['0', '10K'], fontdict = {'size':10} )\n",
    "ax2.set(xlabel='No of Houses Sold', ylabel ='')\n",
    "ax2.xaxis.label.set_fontsize(16)\n",
    "\n",
    "\n",
    "#ax2.text(x=-350000,y=11,s='Calgary has the Highest Population, with Edmonton Following Behind',size=18,weight='bold')\n",
    "#ax1.text(x=-350000,y=10, s='Population of Major Cities in the Prairie & West Coast Region',size=14)\n",
    "\n",
    "\n",
    "plt.savefig('population.png',bbox_inches='tight',  facecolor='w')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90848302",
   "metadata": {},
   "source": [
    "The plot above shows that there is a direct relationship between population and the number of houses sold.\n",
    "So the high population in Calgary, may just be the reason why the demand for houses in that city if higher than demand in other cities.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dee348b3",
   "metadata": {},
   "source": [
    "**Let's take a closer look at the type of houses being sold in Calgary**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a4b07192",
   "metadata": {},
   "outputs": [],
   "source": [
    "calgary_properties = house_from_oct[house_from_oct['cityName']=='Calgary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "794c6bc3",
   "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>Oct-2022</th>\n",
       "      <th>Nov-2022</th>\n",
       "      <th>Dec-2022</th>\n",
       "      <th>Jan-2023</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Residential</th>\n",
       "      <td>1703</td>\n",
       "      <td>2818</td>\n",
       "      <td>2798</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condo</th>\n",
       "      <td>372</td>\n",
       "      <td>703</td>\n",
       "      <td>768</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Commercial</th>\n",
       "      <td>55</td>\n",
       "      <td>148</td>\n",
       "      <td>171</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parking Lot</th>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Oct-2022  Nov-2022  Dec-2022  Jan-2023\n",
       "                                                   \n",
       "Residential      1703      2818      2798       224\n",
       "Condo             372       703       768       115\n",
       "Commercial         55       148       171        37\n",
       "Parking Lot        16        29        26         0\n",
       "Other               0         0         1         0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "property_type = pd.crosstab(calgary_properties['assessmentClass'], calgary_properties['closeMonth'],rownames = [''] )\n",
    "property_type.columns = ['Oct-2022', 'Nov-2022', 'Dec-2022', 'Jan-2023']\n",
    "property_type.sort_values(by = 'Oct-2022', ascending=False, inplace=True)\n",
    "property_type"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6befcacb",
   "metadata": {},
   "source": [
    "From the data, it seems like most people are interested in residential buildings "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3446bc6",
   "metadata": {},
   "source": [
    "**How many bedrooms do people prefer?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "08bc585a",
   "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>Oct-2022</th>\n",
       "      <th>Nov-2022</th>\n",
       "      <th>Dec-2022</th>\n",
       "      <th>Jan-2023</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3-bedroom</th>\n",
       "      <td>588</td>\n",
       "      <td>1003</td>\n",
       "      <td>975</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4-bedroom</th>\n",
       "      <td>496</td>\n",
       "      <td>884</td>\n",
       "      <td>892</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-bedroom</th>\n",
       "      <td>371</td>\n",
       "      <td>634</td>\n",
       "      <td>715</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-bedroom</th>\n",
       "      <td>168</td>\n",
       "      <td>321</td>\n",
       "      <td>335</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 - bedroom</th>\n",
       "      <td>92</td>\n",
       "      <td>169</td>\n",
       "      <td>182</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6-bedroom</th>\n",
       "      <td>39</td>\n",
       "      <td>66</td>\n",
       "      <td>71</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7-bedroom</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8-bedroom</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9-bedroom</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Oct-2022  Nov-2022  Dec-2022  Jan-2023\n",
       "3-bedroom         588      1003       975        96\n",
       "4-bedroom         496       884       892        76\n",
       "2-bedroom         371       634       715        86\n",
       "5-bedroom         168       321       335        32\n",
       "1 - bedroom        92       169       182        33\n",
       "6-bedroom          39        66        71        11\n",
       "7-bedroom           1         5        10         3\n",
       "8-bedroom           1         4         3         0\n",
       "9-bedroom           0         1         1         0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calgary_houses = calgary_properties[(calgary_properties['assessmentClass'] == 'Residential') | (calgary_properties['assessmentClass'] == 'Condo')]\n",
    "calgary_houses['bedroomsTotal'].value_counts()\n",
    "no_of_rooms = pd.crosstab(calgary_houses['bedroomsTotal'], calgary_houses['closeMonth'],rownames = [''] )\n",
    "no_of_rooms.columns = ['Oct-2022', 'Nov-2022', 'Dec-2022', 'Jan-2023']\n",
    "no_of_rooms.index = ['1 - bedroom', '2-bedroom', '3-bedroom', '4-bedroom', '5-bedroom', '6-bedroom', '7-bedroom', '8-bedroom', '9-bedroom']\n",
    "no_of_rooms.sort_values(by = ['Oct-2022', 'Nov-2022', 'Dec-2022', 'Jan-2023'], ascending=False, inplace=True)\n",
    "no_of_rooms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5cd20d7",
   "metadata": {},
   "source": [
    "Most of the houses being sold in Calgary are 3 bedrooms & 4 bedrooms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1887f46d",
   "metadata": {},
   "source": [
    "**Now let's see if more people are buying newer houses or older houses**\n",
    "\n",
    "We will create a new column called 'yearCat' and based on the year the house was built, would have any value in  2015 - 2023, 2000 - 2014, 1980 - 1999, 1960 - 1979 or Earlier than 1960 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2eae4787",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_6656\\3468428569.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  calgary_houses['yearCat'] = calgary_houses['yearBuiltActual'].apply(cat_year)\n"
     ]
    }
   ],
   "source": [
    "#create a function to get 'yearCat' value based on the value 'yearBuiltActual'\n",
    "\n",
    "def cat_year(year_built):\n",
    "    \n",
    "    \"\"\"\n",
    "    Inputs:\n",
    "    year_built:the year the house was built \n",
    "    Output: \n",
    "    Any value in [2015 - 2023, 2000 - 2014, 1980 - 1999, 1960 - 1979, Earlier than 1960] indicating when the house was built\n",
    "    \"\"\"\n",
    "    \n",
    "    if year_built >= 2015:\n",
    "        return '2015 - 2023'\n",
    "    elif year_built >= 2000:\n",
    "        return '2000 - 2014'\n",
    "    elif year_built >= 1980:\n",
    "        return '1980 - 1999'\n",
    "    elif year_built >= 1960:\n",
    "        return '1960 - 1979'\n",
    "    else:\n",
    "        return 'Earlier than 1960'\n",
    "    \n",
    "#create the new column with the 'cat_year function'\n",
    "calgary_houses['yearCat'] = calgary_houses['yearBuiltActual'].apply(cat_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6052ab22",
   "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>Oct-2022</th>\n",
       "      <th>Nov-2022</th>\n",
       "      <th>Dec-2022</th>\n",
       "      <th>Jan-2023</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000 - 2014</th>\n",
       "      <td>0.349880</td>\n",
       "      <td>0.366089</td>\n",
       "      <td>0.357543</td>\n",
       "      <td>0.315634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980 - 1999</th>\n",
       "      <td>0.217349</td>\n",
       "      <td>0.228912</td>\n",
       "      <td>0.244812</td>\n",
       "      <td>0.212389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960 - 1979</th>\n",
       "      <td>0.212530</td>\n",
       "      <td>0.208179</td>\n",
       "      <td>0.204992</td>\n",
       "      <td>0.203540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 - 2023</th>\n",
       "      <td>0.115181</td>\n",
       "      <td>0.117012</td>\n",
       "      <td>0.108805</td>\n",
       "      <td>0.171091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earlier than 1960</th>\n",
       "      <td>0.105060</td>\n",
       "      <td>0.079807</td>\n",
       "      <td>0.083847</td>\n",
       "      <td>0.097345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Oct-2022  Nov-2022  Dec-2022  Jan-2023\n",
       "                                                         \n",
       "2000 - 2014        0.349880  0.366089  0.357543  0.315634\n",
       "1980 - 1999        0.217349  0.228912  0.244812  0.212389\n",
       "1960 - 1979        0.212530  0.208179  0.204992  0.203540\n",
       "2015 - 2023        0.115181  0.117012  0.108805  0.171091\n",
       "Earlier than 1960  0.105060  0.079807  0.083847  0.097345"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now lets see the demand of the houses by the year category\n",
    "\n",
    "demand_by_year = pd.crosstab(calgary_houses['yearCat'], calgary_houses['closeMonth'],rownames = [''], normalize='columns')\n",
    "demand_by_year.columns = ['Oct-2022', 'Nov-2022', 'Dec-2022', 'Jan-2023']\n",
    "demand_by_year.sort_values(by = ['Oct-2022', 'Nov-2022', 'Dec-2022', 'Jan-2023'], ascending=False, inplace=True)\n",
    "demand_by_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "23f66cb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cranston          2.03\n",
       "Mckenzie Towne    1.96\n",
       "Beltline          1.92\n",
       "Evergreen         1.85\n",
       "Auburn Bay        1.69\n",
       "Mahogany          1.59\n",
       "Evanston          1.57\n",
       "Copperfield       1.53\n",
       "Coventry Hills    1.51\n",
       "Panorama Hills    1.47\n",
       "Name: neighbourhoodName, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighbourhood_demand_perc = calgary_houses['neighbourhoodName'].value_counts(normalize=True)*100\n",
    "neighbourhood_demand_perc = neighbourhood_demand_perc.apply(round, args = (2,))\n",
    "neighbourhood_demand_perc[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9fd86605",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "neighbourhood_demand = calgary_houses['neighbourhoodName'].value_counts()\n",
    "\n",
    "ax = neighbourhood_demand[:10].sort_values(ascending=True).plot.barh(width=0.4, color='#af0b1e', fontsize=12, figsize=(6,6))\n",
    "pos = ['left','right','top', 'bottom']\n",
    "for char in pos:\n",
    "    ax.spines[char].set_visible(False)\n",
    "    ax.tick_params(left=False, bottom=False)\n",
    "\n",
    "ax.text(-75, 10.5,'Top 10 Neighbourhoods by No of Houses Sold ',size=18, weight='bold')\n",
    "ax.set_xticks([])\n",
    "ax.bar_label(ax.containers[0], size=12)\n",
    "\n",
    "plt.savefig('top_calgary_neigbourhoods.png',bbox_inches='tight',  facecolor='w');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8588fb1a",
   "metadata": {},
   "source": [
    "### 3. Where would you advise someone with limited funds to buy a house?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff00ad98",
   "metadata": {},
   "source": [
    "My answer to this question will be based on why the person wants to buy the house.\n",
    "\n",
    "Does the person want the house for residential or investment purposes?\n",
    "\n",
    "If the person wants to buy the house for residential purposes, we would consider the prices of the houses in each of the cities\n",
    "However, if the person wants to  invest, we would consider prices as well as demand."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06249656",
   "metadata": {},
   "source": [
    "**Where would you advise someone to buy a house for residential properties only?**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "399803f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Oct-2022</th>\n",
       "      <th>Nov-2022</th>\n",
       "      <th>Dec-2022</th>\n",
       "      <th>Jan-2023</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cityName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Burnaby</th>\n",
       "      <td>1305339.0</td>\n",
       "      <td>1105072.0</td>\n",
       "      <td>1049663.0</td>\n",
       "      <td>667100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calgary</th>\n",
       "      <td>550025.0</td>\n",
       "      <td>568786.0</td>\n",
       "      <td>561302.0</td>\n",
       "      <td>491524.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edmonton</th>\n",
       "      <td>404685.0</td>\n",
       "      <td>449809.0</td>\n",
       "      <td>444928.0</td>\n",
       "      <td>346480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Regina</th>\n",
       "      <td>313344.0</td>\n",
       "      <td>301715.0</td>\n",
       "      <td>266673.0</td>\n",
       "      <td>309581.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saskatoon</th>\n",
       "      <td>385900.0</td>\n",
       "      <td>372042.0</td>\n",
       "      <td>408113.0</td>\n",
       "      <td>365531.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Surrey</th>\n",
       "      <td>1421484.0</td>\n",
       "      <td>971818.0</td>\n",
       "      <td>958983.0</td>\n",
       "      <td>849857.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vancouver</th>\n",
       "      <td>1572062.0</td>\n",
       "      <td>1375918.0</td>\n",
       "      <td>1353510.0</td>\n",
       "      <td>1035015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Winnipeg</th>\n",
       "      <td>368699.0</td>\n",
       "      <td>365617.0</td>\n",
       "      <td>355540.0</td>\n",
       "      <td>330470.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Oct-2022   Nov-2022   Dec-2022   Jan-2023\n",
       "cityName                                             \n",
       "Burnaby    1305339.0  1105072.0  1049663.0   667100.0\n",
       "Calgary     550025.0   568786.0   561302.0   491524.0\n",
       "Edmonton    404685.0   449809.0   444928.0   346480.0\n",
       "Regina      313344.0   301715.0   266673.0   309581.0\n",
       "Saskatoon   385900.0   372042.0   408113.0   365531.0\n",
       "Surrey     1421484.0   971818.0   958983.0   849857.0\n",
       "Vancouver  1572062.0  1375918.0  1353510.0  1035015.0\n",
       "Winnipeg    368699.0   365617.0   355540.0   330470.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_avg = pd.crosstab(house_from_oct['cityName'], house_from_oct['closeMonth'], aggfunc ='mean', values = house_from_oct['closePrice'])\n",
    "grouped_avg=grouped_avg.round(0)\n",
    "grouped_avg.columns = ['Oct-2022', 'Nov-2022', 'Dec-2022', 'Jan-2023']\n",
    "grouped_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1a3c08ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "house_from_oct['catYear'] = house_from_oct['yearBuiltActual'].apply(cat_year)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9822554",
   "metadata": {},
   "source": [
    "The two cities that have the lowest monthly average prices are Winnipeg & Regina but to decie on which city to buy a houes in, we will look at:\n",
    "   - The value for money in each of the city (i.e are 3 bedroom houses in Winnipeg more expensive than 4 bedroom houses in Regina)\n",
    "   - What city has newer houses for a lesser price?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f9e624fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_28012\\861122986.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  winnipeg_regina['bedroomsTotal']=winnipeg_regina['bedroomsTotal'].replace({1.0:'1-bedroom', 2.0:'2-bedroom', 3.0:'3-bedroom', 4.0:'4-bedroom', 5.0:'5-bedroom', 6.0:'6-bedroom', 7.0:'7-bedroom', 8.0:'8-bedroom', 9.0:'9-bedroom'})\n"
     ]
    }
   ],
   "source": [
    "winnipeg_regina = house_from_oct[((house_from_oct['cityName']=='Regina') | (house_from_oct['cityName']=='Winnipeg')) & ((house_from_oct['assessmentClass']=='Residential') | (house_from_oct['assessmentClass']=='Condo'))]\n",
    "winnipeg_regina['bedroomsTotal']=winnipeg_regina['bedroomsTotal'].replace({1.0:'1-bedroom', 2.0:'2-bedroom', 3.0:'3-bedroom', 4.0:'4-bedroom', 5.0:'5-bedroom', 6.0:'6-bedroom', 7.0:'7-bedroom', 8.0:'8-bedroom', 9.0:'9-bedroom'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbae20cd",
   "metadata": {},
   "source": [
    "**In which city can a home buyer get a good value for their money?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a5c26a91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cityName</th>\n",
       "      <th>Regina</th>\n",
       "      <th>Winnipeg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedroomsTotal</th>\n",
       "      <th>catYear</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1-bedroom</th>\n",
       "      <th>1960 - 1979</th>\n",
       "      <td>103425.0</td>\n",
       "      <td>146515.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980 - 1999</th>\n",
       "      <td>128686.0</td>\n",
       "      <td>156180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000 - 2014</th>\n",
       "      <td>154750.0</td>\n",
       "      <td>209500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 - 2023</th>\n",
       "      <td>NaN</td>\n",
       "      <td>226817.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earlier than 1960</th>\n",
       "      <td>100729.0</td>\n",
       "      <td>146294.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2-bedroom</th>\n",
       "      <th>1960 - 1979</th>\n",
       "      <td>152350.0</td>\n",
       "      <td>227679.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980 - 1999</th>\n",
       "      <td>234700.0</td>\n",
       "      <td>287632.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000 - 2014</th>\n",
       "      <td>267670.0</td>\n",
       "      <td>299976.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 - 2023</th>\n",
       "      <td>277967.0</td>\n",
       "      <td>292641.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earlier than 1960</th>\n",
       "      <td>165783.0</td>\n",
       "      <td>219538.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">3-bedroom</th>\n",
       "      <th>1960 - 1979</th>\n",
       "      <td>263784.0</td>\n",
       "      <td>329760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980 - 1999</th>\n",
       "      <td>368317.0</td>\n",
       "      <td>375198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000 - 2014</th>\n",
       "      <td>396440.0</td>\n",
       "      <td>463117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 - 2023</th>\n",
       "      <td>380894.0</td>\n",
       "      <td>498217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earlier than 1960</th>\n",
       "      <td>250752.0</td>\n",
       "      <td>296504.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">4-bedroom</th>\n",
       "      <th>1960 - 1979</th>\n",
       "      <td>285807.0</td>\n",
       "      <td>390162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980 - 1999</th>\n",
       "      <td>412759.0</td>\n",
       "      <td>499018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000 - 2014</th>\n",
       "      <td>502308.0</td>\n",
       "      <td>604020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 - 2023</th>\n",
       "      <td>535350.0</td>\n",
       "      <td>569225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earlier than 1960</th>\n",
       "      <td>261585.0</td>\n",
       "      <td>343982.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">5-bedroom</th>\n",
       "      <th>1960 - 1979</th>\n",
       "      <td>340350.0</td>\n",
       "      <td>423017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980 - 1999</th>\n",
       "      <td>463014.0</td>\n",
       "      <td>701947.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000 - 2014</th>\n",
       "      <td>478367.0</td>\n",
       "      <td>680512.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 - 2023</th>\n",
       "      <td>587000.0</td>\n",
       "      <td>746644.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earlier than 1960</th>\n",
       "      <td>359250.0</td>\n",
       "      <td>343261.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">6-bedroom</th>\n",
       "      <th>1960 - 1979</th>\n",
       "      <td>440650.0</td>\n",
       "      <td>515275.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980 - 1999</th>\n",
       "      <td>538650.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000 - 2014</th>\n",
       "      <td>627600.0</td>\n",
       "      <td>802317.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 - 2023</th>\n",
       "      <td>613900.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earlier than 1960</th>\n",
       "      <td>242350.0</td>\n",
       "      <td>294522.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">7-bedroom</th>\n",
       "      <th>1960 - 1979</th>\n",
       "      <td>388000.0</td>\n",
       "      <td>417033.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980 - 1999</th>\n",
       "      <td>NaN</td>\n",
       "      <td>524700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000 - 2014</th>\n",
       "      <td>NaN</td>\n",
       "      <td>624700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8-bedroom</th>\n",
       "      <th>2000 - 2014</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1199400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9-bedroom</th>\n",
       "      <th>1980 - 1999</th>\n",
       "      <td>NaN</td>\n",
       "      <td>683800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cityName                           Regina   Winnipeg\n",
       "bedroomsTotal catYear                               \n",
       "1-bedroom     1960 - 1979        103425.0   146515.0\n",
       "              1980 - 1999        128686.0   156180.0\n",
       "              2000 - 2014        154750.0   209500.0\n",
       "              2015 - 2023             NaN   226817.0\n",
       "              Earlier than 1960  100729.0   146294.0\n",
       "2-bedroom     1960 - 1979        152350.0   227679.0\n",
       "              1980 - 1999        234700.0   287632.0\n",
       "              2000 - 2014        267670.0   299976.0\n",
       "              2015 - 2023        277967.0   292641.0\n",
       "              Earlier than 1960  165783.0   219538.0\n",
       "3-bedroom     1960 - 1979        263784.0   329760.0\n",
       "              1980 - 1999        368317.0   375198.0\n",
       "              2000 - 2014        396440.0   463117.0\n",
       "              2015 - 2023        380894.0   498217.0\n",
       "              Earlier than 1960  250752.0   296504.0\n",
       "4-bedroom     1960 - 1979        285807.0   390162.0\n",
       "              1980 - 1999        412759.0   499018.0\n",
       "              2000 - 2014        502308.0   604020.0\n",
       "              2015 - 2023        535350.0   569225.0\n",
       "              Earlier than 1960  261585.0   343982.0\n",
       "5-bedroom     1960 - 1979        340350.0   423017.0\n",
       "              1980 - 1999        463014.0   701947.0\n",
       "              2000 - 2014        478367.0   680512.0\n",
       "              2015 - 2023        587000.0   746644.0\n",
       "              Earlier than 1960  359250.0   343261.0\n",
       "6-bedroom     1960 - 1979        440650.0   515275.0\n",
       "              1980 - 1999        538650.0        NaN\n",
       "              2000 - 2014        627600.0   802317.0\n",
       "              2015 - 2023        613900.0        NaN\n",
       "              Earlier than 1960  242350.0   294522.0\n",
       "7-bedroom     1960 - 1979        388000.0   417033.0\n",
       "              1980 - 1999             NaN   524700.0\n",
       "              2000 - 2014             NaN   624700.0\n",
       "8-bedroom     2000 - 2014             NaN  1199400.0\n",
       "9-bedroom     1980 - 1999             NaN   683800.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices_per_room = (pd.crosstab(index = [winnipeg_regina['bedroomsTotal'], winnipeg_regina['catYear']], columns =  winnipeg_regina['cityName'], aggfunc ='mean', values = winnipeg_regina['closePrice'])).round(0)\n",
    "prices_per_room"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17c9ef2f",
   "metadata": {},
   "source": [
    "From the above table, we see that houses witht he same no of bedrooms and which were built within the same period, have lower prices in Regina than in Winnipeg. \n",
    "So, Regina may be the best option."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f39749e",
   "metadata": {},
   "source": [
    "**Which city should a Real Estate Investor buy a House?**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba371e60",
   "metadata": {},
   "source": [
    "To advise an investor with this data. I'll look at:\n",
    " - The investor's budget - Investor A (with a budget of 500k CAD) & Investor B (with a budget of over 500k CAD)\n",
    " - Cities with low prices but high demand\n",
    " - what no of bedroom houses have a higher demand?\n",
    " - Houses in which year?\n",
    " - Value for money"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "714a05f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_28012\\3714732183.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  houses['bedroomsTotal']=houses['bedroomsTotal'].replace({1.0:'1-bedroom', 2.0:'2-bedroom', 3.0:'3-bedroom', 4.0:'4-bedroom', 5.0:'5-bedroom', 6.0:'6-bedroom'})\n",
      "C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_28012\\3714732183.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  houses['catYear'] = houses['yearBuiltActual'].apply(cat_year)\n"
     ]
    }
   ],
   "source": [
    "#remove other property types and leave only residential & condo & bedrooms between 1  & 6\n",
    "houses = house_from_oct[((house_from_oct['assessmentClass'] =='Residential') | (house_from_oct['assessmentClass'] =='Condo')) & ((house_from_oct['bedroomsTotal'] >= 1) & (house_from_oct['bedroomsTotal'] <= 6))]\n",
    "\n",
    "#replace the categories in the bedroomsTotal column\n",
    "houses['bedroomsTotal']=houses['bedroomsTotal'].replace({1.0:'1-bedroom', 2.0:'2-bedroom', 3.0:'3-bedroom', 4.0:'4-bedroom', 5.0:'5-bedroom', 6.0:'6-bedroom'})\n",
    "\n",
    "houses['catYear'] = houses['yearBuiltActual'].apply(cat_year)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef259781",
   "metadata": {},
   "source": [
    "**For Investor A with a budget less than 500k, we will consider cities like Winnipeg, Regina, Saskatoon and Edmonton.**\n",
    "\n",
    "These cities have had monthly average prices less than 500k in the past  months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "51a8c86d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">count</th>\n",
       "      <th colspan=\"4\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>cityName</th>\n",
       "      <th>Edmonton</th>\n",
       "      <th>Regina</th>\n",
       "      <th>Saskatoon</th>\n",
       "      <th>Winnipeg</th>\n",
       "      <th>Edmonton</th>\n",
       "      <th>Regina</th>\n",
       "      <th>Saskatoon</th>\n",
       "      <th>Winnipeg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedroomsTotal</th>\n",
       "      <th>catYear</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1-bedroom</th>\n",
       "      <th>1960 - 1979</th>\n",
       "      <td>102.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>115472.0</td>\n",
       "      <td>103425.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>146515.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980 - 1999</th>\n",
       "      <td>32.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>119309.0</td>\n",
       "      <td>128686.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000 - 2014</th>\n",
       "      <td>79.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>156980.0</td>\n",
       "      <td>154750.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>209500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 - 2023</th>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>212065.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>226817.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earlier than 1960</th>\n",
       "      <td>17.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>261386.0</td>\n",
       "      <td>100729.0</td>\n",
       "      <td>154443.0</td>\n",
       "      <td>146294.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2-bedroom</th>\n",
       "      <th>1960 - 1979</th>\n",
       "      <td>139.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>170278.0</td>\n",
       "      <td>152350.0</td>\n",
       "      <td>284100.0</td>\n",
       "      <td>227679.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980 - 1999</th>\n",
       "      <td>176.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>233053.0</td>\n",
       "      <td>234700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>287632.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000 - 2014</th>\n",
       "      <td>459.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>237465.0</td>\n",
       "      <td>267670.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>299976.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 - 2023</th>\n",
       "      <td>78.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>320131.0</td>\n",
       "      <td>277967.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>292641.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earlier than 1960</th>\n",
       "      <td>79.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>305575.0</td>\n",
       "      <td>165783.0</td>\n",
       "      <td>227342.0</td>\n",
       "      <td>219538.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">3-bedroom</th>\n",
       "      <th>1960 - 1979</th>\n",
       "      <td>317.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>279894.0</td>\n",
       "      <td>263784.0</td>\n",
       "      <td>349058.0</td>\n",
       "      <td>329760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980 - 1999</th>\n",
       "      <td>274.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>329529.0</td>\n",
       "      <td>368317.0</td>\n",
       "      <td>365938.0</td>\n",
       "      <td>375198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000 - 2014</th>\n",
       "      <td>765.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>408642.0</td>\n",
       "      <td>396440.0</td>\n",
       "      <td>458519.0</td>\n",
       "      <td>463117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 - 2023</th>\n",
       "      <td>480.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>463574.0</td>\n",
       "      <td>380894.0</td>\n",
       "      <td>501300.0</td>\n",
       "      <td>498217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earlier than 1960</th>\n",
       "      <td>203.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>243.0</td>\n",
       "      <td>360117.0</td>\n",
       "      <td>250752.0</td>\n",
       "      <td>333809.0</td>\n",
       "      <td>296504.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">4-bedroom</th>\n",
       "      <th>1960 - 1979</th>\n",
       "      <td>331.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>413183.0</td>\n",
       "      <td>285807.0</td>\n",
       "      <td>368151.0</td>\n",
       "      <td>390162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980 - 1999</th>\n",
       "      <td>238.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>464571.0</td>\n",
       "      <td>412759.0</td>\n",
       "      <td>429179.0</td>\n",
       "      <td>499018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000 - 2014</th>\n",
       "      <td>395.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>538044.0</td>\n",
       "      <td>502308.0</td>\n",
       "      <td>529206.0</td>\n",
       "      <td>604020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 - 2023</th>\n",
       "      <td>151.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>619150.0</td>\n",
       "      <td>535350.0</td>\n",
       "      <td>536950.0</td>\n",
       "      <td>569225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earlier than 1960</th>\n",
       "      <td>205.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>403194.0</td>\n",
       "      <td>261585.0</td>\n",
       "      <td>387630.0</td>\n",
       "      <td>343982.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">5-bedroom</th>\n",
       "      <th>1960 - 1979</th>\n",
       "      <td>149.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>443110.0</td>\n",
       "      <td>340350.0</td>\n",
       "      <td>358029.0</td>\n",
       "      <td>423017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980 - 1999</th>\n",
       "      <td>115.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>505213.0</td>\n",
       "      <td>463014.0</td>\n",
       "      <td>428675.0</td>\n",
       "      <td>701947.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000 - 2014</th>\n",
       "      <td>136.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>631437.0</td>\n",
       "      <td>478367.0</td>\n",
       "      <td>578990.0</td>\n",
       "      <td>680512.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 - 2023</th>\n",
       "      <td>61.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>729886.0</td>\n",
       "      <td>587000.0</td>\n",
       "      <td>882150.0</td>\n",
       "      <td>746644.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earlier than 1960</th>\n",
       "      <td>82.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>443132.0</td>\n",
       "      <td>359250.0</td>\n",
       "      <td>451607.0</td>\n",
       "      <td>343261.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">6-bedroom</th>\n",
       "      <th>1960 - 1979</th>\n",
       "      <td>33.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>489176.0</td>\n",
       "      <td>440650.0</td>\n",
       "      <td>1199000.0</td>\n",
       "      <td>515275.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980 - 1999</th>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>556735.0</td>\n",
       "      <td>538650.0</td>\n",
       "      <td>224300.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000 - 2014</th>\n",
       "      <td>46.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>774725.0</td>\n",
       "      <td>627600.0</td>\n",
       "      <td>545050.0</td>\n",
       "      <td>802317.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 - 2023</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>835789.0</td>\n",
       "      <td>613900.0</td>\n",
       "      <td>654600.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earlier than 1960</th>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>467684.0</td>\n",
       "      <td>242350.0</td>\n",
       "      <td>376489.0</td>\n",
       "      <td>294522.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   count                                mean  \\\n",
       "cityName                        Edmonton Regina Saskatoon Winnipeg  Edmonton   \n",
       "bedroomsTotal catYear                                                          \n",
       "1-bedroom     1960 - 1979          102.0    4.0       NaN     13.0  115472.0   \n",
       "              1980 - 1999           32.0    7.0       NaN      5.0  119309.0   \n",
       "              2000 - 2014           79.0    2.0       NaN      1.0  156980.0   \n",
       "              2015 - 2023           17.0    NaN       NaN      6.0  212065.0   \n",
       "              Earlier than 1960     17.0    7.0       7.0     16.0  261386.0   \n",
       "2-bedroom     1960 - 1979          139.0   12.0       1.0     33.0  170278.0   \n",
       "              1980 - 1999          176.0    6.0       NaN     34.0  233053.0   \n",
       "              2000 - 2014          459.0   23.0       NaN     21.0  237465.0   \n",
       "              2015 - 2023           78.0    3.0       NaN     17.0  320131.0   \n",
       "              Earlier than 1960     79.0   58.0      95.0    146.0  305575.0   \n",
       "3-bedroom     1960 - 1979          317.0   51.0      40.0    174.0  279894.0   \n",
       "              1980 - 1999          274.0   12.0      21.0     96.0  329529.0   \n",
       "              2000 - 2014          765.0   35.0      21.0     41.0  408642.0   \n",
       "              2015 - 2023          480.0   17.0       3.0     52.0  463574.0   \n",
       "              Earlier than 1960    203.0   80.0     129.0    243.0  360117.0   \n",
       "4-bedroom     1960 - 1979          331.0   42.0      61.0     93.0  413183.0   \n",
       "              1980 - 1999          238.0   22.0      38.0     51.0  464571.0   \n",
       "              2000 - 2014          395.0   25.0      34.0     40.0  538044.0   \n",
       "              2015 - 2023          151.0    8.0       2.0     16.0  619150.0   \n",
       "              Earlier than 1960    205.0   33.0      69.0     93.0  403194.0   \n",
       "5-bedroom     1960 - 1979          149.0    6.0      28.0     18.0  443110.0   \n",
       "              1980 - 1999          115.0    7.0      12.0     17.0  505213.0   \n",
       "              2000 - 2014          136.0    9.0      30.0     25.0  631437.0   \n",
       "              2015 - 2023           61.0    2.0       2.0      9.0  729886.0   \n",
       "              Earlier than 1960     82.0   10.0      29.0     18.0  443132.0   \n",
       "6-bedroom     1960 - 1979           33.0    2.0       1.0      4.0  489176.0   \n",
       "              1980 - 1999           17.0    2.0       1.0      NaN  556735.0   \n",
       "              2000 - 2014           46.0    1.0       2.0      6.0  774725.0   \n",
       "              2015 - 2023            9.0    1.0       1.0      NaN  835789.0   \n",
       "              Earlier than 1960     13.0    2.0       9.0      9.0  467684.0   \n",
       "\n",
       "                                                                \n",
       "cityName                           Regina  Saskatoon  Winnipeg  \n",
       "bedroomsTotal catYear                                           \n",
       "1-bedroom     1960 - 1979        103425.0        NaN  146515.0  \n",
       "              1980 - 1999        128686.0        NaN  156180.0  \n",
       "              2000 - 2014        154750.0        NaN  209500.0  \n",
       "              2015 - 2023             NaN        NaN  226817.0  \n",
       "              Earlier than 1960  100729.0   154443.0  146294.0  \n",
       "2-bedroom     1960 - 1979        152350.0   284100.0  227679.0  \n",
       "              1980 - 1999        234700.0        NaN  287632.0  \n",
       "              2000 - 2014        267670.0        NaN  299976.0  \n",
       "              2015 - 2023        277967.0        NaN  292641.0  \n",
       "              Earlier than 1960  165783.0   227342.0  219538.0  \n",
       "3-bedroom     1960 - 1979        263784.0   349058.0  329760.0  \n",
       "              1980 - 1999        368317.0   365938.0  375198.0  \n",
       "              2000 - 2014        396440.0   458519.0  463117.0  \n",
       "              2015 - 2023        380894.0   501300.0  498217.0  \n",
       "              Earlier than 1960  250752.0   333809.0  296504.0  \n",
       "4-bedroom     1960 - 1979        285807.0   368151.0  390162.0  \n",
       "              1980 - 1999        412759.0   429179.0  499018.0  \n",
       "              2000 - 2014        502308.0   529206.0  604020.0  \n",
       "              2015 - 2023        535350.0   536950.0  569225.0  \n",
       "              Earlier than 1960  261585.0   387630.0  343982.0  \n",
       "5-bedroom     1960 - 1979        340350.0   358029.0  423017.0  \n",
       "              1980 - 1999        463014.0   428675.0  701947.0  \n",
       "              2000 - 2014        478367.0   578990.0  680512.0  \n",
       "              2015 - 2023        587000.0   882150.0  746644.0  \n",
       "              Earlier than 1960  359250.0   451607.0  343261.0  \n",
       "6-bedroom     1960 - 1979        440650.0  1199000.0  515275.0  \n",
       "              1980 - 1999        538650.0   224300.0       NaN  \n",
       "              2000 - 2014        627600.0   545050.0  802317.0  \n",
       "              2015 - 2023        613900.0   654600.0       NaN  \n",
       "              Earlier than 1960  242350.0   376489.0  294522.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houses_less_500 = houses[houses.cityName.isin(['Winnipeg', 'Regina', 'Saskatoon', 'Edmonton'])]\n",
    "cities_data_less_500 = (pd.crosstab(index = [houses_less_500['bedroomsTotal'], houses_less_500['catYear']], columns =  houses_less_500['cityName'], aggfunc =('count','mean'), values = houses_less_500['closePrice'])).round(0)\n",
    "cities_data_less_500"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a04268e",
   "metadata": {},
   "source": [
    "From the data in table above, I observed the following:\n",
    "    \n",
    "- Most people have bought 3 bedrooms & 4 bedrooms houses within the past 4 months, so we can say these are the no of bedrooms that are really in demand.\n",
    "- Edmonton has the overll highest demand across all the bedroom categories and year built categories\n",
    "- Regina has the lowest demand while in Saskatoon, people are buying older houses (earlier than 1960) more than newer houses.\n",
    "- The average prices for 3 bedroom and 4 bedroom houses in Edmonton is lower than in Winnipeg and Saskatoon.\n",
    "- **Since, there is very low demand in Regina, It's advisable for this investor to buy a 3 bedroom or 4 bedroom house in Edmonton.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b70dab9c",
   "metadata": {},
   "source": [
    "**For Investor B with a budget over 500k, we will consider cities like Calgary, Vancouver, Burnaby and Surrey.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ea5222b",
   "metadata": {},
   "source": [
    "These cities have had monthly average prices more than 500k in the past  months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "21d3c53e",
   "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 tr th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">count</th>\n",
       "      <th colspan=\"4\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>cityName</th>\n",
       "      <th>Burnaby</th>\n",
       "      <th>Calgary</th>\n",
       "      <th>Surrey</th>\n",
       "      <th>Vancouver</th>\n",
       "      <th>Burnaby</th>\n",
       "      <th>Calgary</th>\n",
       "      <th>Surrey</th>\n",
       "      <th>Vancouver</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedroomsTotal</th>\n",
       "      <th>catYear</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1-bedroom</th>\n",
       "      <th>1960 - 1979</th>\n",
       "      <td>8.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.0</td>\n",
       "      <td>399300.0</td>\n",
       "      <td>213950.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>541913.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980 - 1999</th>\n",
       "      <td>17.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>519994.0</td>\n",
       "      <td>209150.0</td>\n",
       "      <td>390906.0</td>\n",
       "      <td>623695.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000 - 2014</th>\n",
       "      <td>20.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>547235.0</td>\n",
       "      <td>234893.0</td>\n",
       "      <td>432944.0</td>\n",
       "      <td>676219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 - 2023</th>\n",
       "      <td>13.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>648654.0</td>\n",
       "      <td>274743.0</td>\n",
       "      <td>475217.0</td>\n",
       "      <td>637662.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earlier than 1960</th>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>415555.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>558233.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2-bedroom</th>\n",
       "      <th>1960 - 1979</th>\n",
       "      <td>17.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>600759.0</td>\n",
       "      <td>258359.0</td>\n",
       "      <td>544193.0</td>\n",
       "      <td>787356.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980 - 1999</th>\n",
       "      <td>67.0</td>\n",
       "      <td>357.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>653169.0</td>\n",
       "      <td>332060.0</td>\n",
       "      <td>601764.0</td>\n",
       "      <td>1062198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000 - 2014</th>\n",
       "      <td>71.0</td>\n",
       "      <td>759.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>755997.0</td>\n",
       "      <td>334673.0</td>\n",
       "      <td>625564.0</td>\n",
       "      <td>1134371.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 - 2023</th>\n",
       "      <td>24.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>867371.0</td>\n",
       "      <td>352460.0</td>\n",
       "      <td>614844.0</td>\n",
       "      <td>1022344.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earlier than 1960</th>\n",
       "      <td>3.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1282633.0</td>\n",
       "      <td>505221.0</td>\n",
       "      <td>2012183.0</td>\n",
       "      <td>1592523.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">3-bedroom</th>\n",
       "      <th>1960 - 1979</th>\n",
       "      <td>8.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1288238.0</td>\n",
       "      <td>403407.0</td>\n",
       "      <td>1262356.0</td>\n",
       "      <td>1488467.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980 - 1999</th>\n",
       "      <td>21.0</td>\n",
       "      <td>641.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1030348.0</td>\n",
       "      <td>498278.0</td>\n",
       "      <td>1025595.0</td>\n",
       "      <td>1893329.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000 - 2014</th>\n",
       "      <td>15.0</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>960373.0</td>\n",
       "      <td>569704.0</td>\n",
       "      <td>816497.0</td>\n",
       "      <td>1654276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 - 2023</th>\n",
       "      <td>6.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1261167.0</td>\n",
       "      <td>706005.0</td>\n",
       "      <td>943712.0</td>\n",
       "      <td>1681143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earlier than 1960</th>\n",
       "      <td>12.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1571450.0</td>\n",
       "      <td>619091.0</td>\n",
       "      <td>1129533.0</td>\n",
       "      <td>2044289.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">4-bedroom</th>\n",
       "      <th>1960 - 1979</th>\n",
       "      <td>10.0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1368020.0</td>\n",
       "      <td>574811.0</td>\n",
       "      <td>1267476.0</td>\n",
       "      <td>1941025.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980 - 1999</th>\n",
       "      <td>10.0</td>\n",
       "      <td>551.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1888290.0</td>\n",
       "      <td>600608.0</td>\n",
       "      <td>1458991.0</td>\n",
       "      <td>2185730.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000 - 2014</th>\n",
       "      <td>11.0</td>\n",
       "      <td>854.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1571927.0</td>\n",
       "      <td>743998.0</td>\n",
       "      <td>1136098.0</td>\n",
       "      <td>2592622.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 - 2023</th>\n",
       "      <td>1.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1149100.0</td>\n",
       "      <td>808351.0</td>\n",
       "      <td>1146526.0</td>\n",
       "      <td>2945560.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earlier than 1960</th>\n",
       "      <td>10.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1513190.0</td>\n",
       "      <td>682255.0</td>\n",
       "      <td>1649300.0</td>\n",
       "      <td>2234080.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">5-bedroom</th>\n",
       "      <th>1960 - 1979</th>\n",
       "      <td>11.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1722209.0</td>\n",
       "      <td>606835.0</td>\n",
       "      <td>1364855.0</td>\n",
       "      <td>1966285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980 - 1999</th>\n",
       "      <td>10.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2141660.0</td>\n",
       "      <td>671498.0</td>\n",
       "      <td>1551079.0</td>\n",
       "      <td>2173259.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000 - 2014</th>\n",
       "      <td>2.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2439200.0</td>\n",
       "      <td>914340.0</td>\n",
       "      <td>1566306.0</td>\n",
       "      <td>4027786.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 - 2023</th>\n",
       "      <td>2.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3614550.0</td>\n",
       "      <td>1028035.0</td>\n",
       "      <td>1838238.0</td>\n",
       "      <td>4019860.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earlier than 1960</th>\n",
       "      <td>7.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1641843.0</td>\n",
       "      <td>805537.0</td>\n",
       "      <td>1736650.0</td>\n",
       "      <td>2358003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">6-bedroom</th>\n",
       "      <th>1960 - 1979</th>\n",
       "      <td>2.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1719600.0</td>\n",
       "      <td>616993.0</td>\n",
       "      <td>1204050.0</td>\n",
       "      <td>3376167.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980 - 1999</th>\n",
       "      <td>4.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2112175.0</td>\n",
       "      <td>791810.0</td>\n",
       "      <td>1412300.0</td>\n",
       "      <td>3945736.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000 - 2014</th>\n",
       "      <td>5.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2777620.0</td>\n",
       "      <td>903898.0</td>\n",
       "      <td>1793053.0</td>\n",
       "      <td>2653240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 - 2023</th>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3664900.0</td>\n",
       "      <td>1302718.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5133850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earlier than 1960</th>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>626193.0</td>\n",
       "      <td>1058900.0</td>\n",
       "      <td>3374236.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  count                                mean  \\\n",
       "cityName                        Burnaby Calgary Surrey Vancouver    Burnaby   \n",
       "bedroomsTotal catYear                                                         \n",
       "1-bedroom     1960 - 1979           8.0    63.0    NaN      46.0   399300.0   \n",
       "              1980 - 1999          17.0    55.0   32.0      94.0   519994.0   \n",
       "              2000 - 2014          20.0   238.0   34.0     119.0   547235.0   \n",
       "              2015 - 2023          13.0    89.0   18.0      39.0   648654.0   \n",
       "              Earlier than 1960     NaN    31.0    NaN       3.0        NaN   \n",
       "2-bedroom     1960 - 1979          17.0   262.0   14.0      16.0   600759.0   \n",
       "              1980 - 1999          67.0   357.0  121.0     128.0   653169.0   \n",
       "              2000 - 2014          71.0   759.0   73.0     125.0   755997.0   \n",
       "              2015 - 2023          24.0   318.0   32.0      25.0   867371.0   \n",
       "              Earlier than 1960     3.0   110.0    6.0      13.0  1282633.0   \n",
       "3-bedroom     1960 - 1979           8.0   489.0   27.0       6.0  1288238.0   \n",
       "              1980 - 1999          21.0   641.0   65.0      38.0  1030348.0   \n",
       "              2000 - 2014          15.0  1010.0   75.0      21.0   960373.0   \n",
       "              2015 - 2023           6.0   345.0   25.0      34.0  1261167.0   \n",
       "              Earlier than 1960    12.0   177.0    9.0      28.0  1571450.0   \n",
       "4-bedroom     1960 - 1979          10.0   528.0   21.0       8.0  1368020.0   \n",
       "              1980 - 1999          10.0   551.0   44.0      10.0  1888290.0   \n",
       "              2000 - 2014          11.0   854.0   42.0       9.0  1571927.0   \n",
       "              2015 - 2023           1.0   211.0   19.0      15.0  1149100.0   \n",
       "              Earlier than 1960    10.0   204.0    6.0      41.0  1513190.0   \n",
       "5-bedroom     1960 - 1979          11.0   247.0   11.0      13.0  1722209.0   \n",
       "              1980 - 1999          10.0   256.0   19.0      15.0  2141660.0   \n",
       "              2000 - 2014           2.0   233.0   31.0       7.0  2439200.0   \n",
       "              2015 - 2023           2.0    50.0    5.0       5.0  3614550.0   \n",
       "              Earlier than 1960     7.0    70.0    2.0      34.0  1641843.0   \n",
       "6-bedroom     1960 - 1979           2.0    46.0    2.0       3.0  1719600.0   \n",
       "              1980 - 1999           4.0    40.0   11.0      11.0  2112175.0   \n",
       "              2000 - 2014           5.0    62.0   17.0       5.0  2777620.0   \n",
       "              2015 - 2023           2.0    25.0    NaN       4.0  3664900.0   \n",
       "              Earlier than 1960     NaN    14.0    1.0      14.0        NaN   \n",
       "\n",
       "                                                                  \n",
       "cityName                           Calgary     Surrey  Vancouver  \n",
       "bedroomsTotal catYear                                             \n",
       "1-bedroom     1960 - 1979         213950.0        NaN   541913.0  \n",
       "              1980 - 1999         209150.0   390906.0   623695.0  \n",
       "              2000 - 2014         234893.0   432944.0   676219.0  \n",
       "              2015 - 2023         274743.0   475217.0   637662.0  \n",
       "              Earlier than 1960   415555.0        NaN   558233.0  \n",
       "2-bedroom     1960 - 1979         258359.0   544193.0   787356.0  \n",
       "              1980 - 1999         332060.0   601764.0  1062198.0  \n",
       "              2000 - 2014         334673.0   625564.0  1134371.0  \n",
       "              2015 - 2023         352460.0   614844.0  1022344.0  \n",
       "              Earlier than 1960   505221.0  2012183.0  1592523.0  \n",
       "3-bedroom     1960 - 1979         403407.0  1262356.0  1488467.0  \n",
       "              1980 - 1999         498278.0  1025595.0  1893329.0  \n",
       "              2000 - 2014         569704.0   816497.0  1654276.0  \n",
       "              2015 - 2023         706005.0   943712.0  1681143.0  \n",
       "              Earlier than 1960   619091.0  1129533.0  2044289.0  \n",
       "4-bedroom     1960 - 1979         574811.0  1267476.0  1941025.0  \n",
       "              1980 - 1999         600608.0  1458991.0  2185730.0  \n",
       "              2000 - 2014         743998.0  1136098.0  2592622.0  \n",
       "              2015 - 2023         808351.0  1146526.0  2945560.0  \n",
       "              Earlier than 1960   682255.0  1649300.0  2234080.0  \n",
       "5-bedroom     1960 - 1979         606835.0  1364855.0  1966285.0  \n",
       "              1980 - 1999         671498.0  1551079.0  2173259.0  \n",
       "              2000 - 2014         914340.0  1566306.0  4027786.0  \n",
       "              2015 - 2023        1028035.0  1838238.0  4019860.0  \n",
       "              Earlier than 1960   805537.0  1736650.0  2358003.0  \n",
       "6-bedroom     1960 - 1979         616993.0  1204050.0  3376167.0  \n",
       "              1980 - 1999         791810.0  1412300.0  3945736.0  \n",
       "              2000 - 2014         903898.0  1793053.0  2653240.0  \n",
       "              2015 - 2023        1302718.0        NaN  5133850.0  \n",
       "              Earlier than 1960   626193.0  1058900.0  3374236.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houses_more_500 = houses[houses.cityName.isin(['Calgary', 'Vancouver', 'Burnaby', 'Surrey'])]\n",
    "cities_data_more_500 = (pd.crosstab(index = [houses_more_500['bedroomsTotal'], houses_more_500['catYear']], columns =  houses_more_500['cityName'], aggfunc =('count','mean'), values = houses_more_500['closePrice'])).round(0)\n",
    "cities_data_more_500"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5de7e882",
   "metadata": {},
   "source": [
    "From the data in table above, I observed the following:\n",
    "    \n",
    "- Most people have bought 3 bedrooms & 4 bedrooms houses within the past 4 months, so we can say these are the no of bedrooms that are really in demand.\n",
    "- Calgary has a significantly higher demand (across all the bedroom categories and year built categories) than in other cities\n",
    "- Burnaby has the lowest demand.\n",
    "- The average prices for houses in Calgary is as low as 30% of the avergae prices in the other cities\n",
    "- **With all these insights from the data, It's advisable for this investor to buy a house in Calgary.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bce89bd",
   "metadata": {},
   "source": [
    "**The last question on \"How well can we predict the price of a house?\" will be addressed in the \"House_Price_Prediction\n",
    ".ipynb\" notebook**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbb02365",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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