hotel_rate_visualisation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualisation of hotel trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# graphing style\n",
    "plt.style.use(\"seaborn\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Year Trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Yearly Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006</td>\n",
       "      <td>46488.121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>79795.282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>55845.469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>43366.102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>19596.943</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Yearly Rate\n",
       "0  2006    46488.121\n",
       "1  2014    79795.282\n",
       "2  2011    55845.469\n",
       "3  2009    43366.102\n",
       "4  2004    19596.943"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load csv file into a dataframe\n",
    "year_df = pd.read_csv(\"Data/hotel_yrrate.csv\")\n",
    "year_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort the dataframe by year\n",
    "year_df.sort_values(\"Year\", inplace = True)\n",
    "year_df = year_df.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a subset of complete years (incomplete: 2004 and 2018)\n",
    "inc = [2004, 2018]\n",
    "\n",
    "year_subdf = year_df[~year_df[\"Year\"].isin(inc)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the difference per year\n",
    "values = pd.Series(year_subdf[\"Yearly Rate\"].pct_change() * 100)\n",
    "year_subdf.insert(loc = 2, column = \"% Year Diff\", value = values.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar graph to show trends in hotel yearly rates\n",
    "plt.bar(\"Year\", \"Yearly Rate\", data = year_subdf)\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Annual Hotel Rate (USD)\")\n",
    "plt.ylim(0, max(year_subdf[\"Yearly Rate\"] + 20000))\n",
    "\n",
    "# Save figure\n",
    "plt.savefig(\"Images/hotel_yr_rates.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "year = np.arange(1,len(year_subdf[\"Year\"])+1)\n",
    "diff = year_subdf[\"% Year Diff\"]\n",
    "\n",
    "\n",
    "plt.bar(year, diff, color = [\"green\" if diff[i] > 0 else \"red\" for i in year])\n",
    "plt.xticks(year, year_subdf[\"Year\"])\n",
    "plt.axhline(y = 0, color = \"black\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"% Difference\")\n",
    "\n",
    "# add label to each bar\n",
    "count = 1\n",
    "\n",
    "for i in diff:\n",
    "    if i < 0:\n",
    "        y = -2\n",
    "    else:\n",
    "        y = 0.7\n",
    "    plt.text(count, y, str(round(i, 1)) , color = \"white\", ha = \"center\")\n",
    "    count += 1  \n",
    "\n",
    "# Save figure\n",
    "plt.savefig(\"Images/hotel_yr_diff.svg\")\n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Monthly Trends\n",
    "- Find trends in monthly rates (Which month is slowest? with highest demand? any changes after peer-to-peer accommodations came up?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Average Daily Rate</th>\n",
       "      <th>Hotel Occupancy</th>\n",
       "      <th>Date</th>\n",
       "      <th>Monthly Rate</th>\n",
       "      <th>Corrected Monthly Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>157.8543</td>\n",
       "      <td>0.740657</td>\n",
       "      <td>2005-04</td>\n",
       "      <td>4735.629</td>\n",
       "      <td>3507.477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>169.9458</td>\n",
       "      <td>0.770442</td>\n",
       "      <td>2006-04</td>\n",
       "      <td>5098.374</td>\n",
       "      <td>3928.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>183.4274</td>\n",
       "      <td>0.764485</td>\n",
       "      <td>2007-04</td>\n",
       "      <td>5502.822</td>\n",
       "      <td>4206.826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>183.7442</td>\n",
       "      <td>0.768456</td>\n",
       "      <td>2008-04</td>\n",
       "      <td>5512.326</td>\n",
       "      <td>4235.983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>157.9462</td>\n",
       "      <td>0.743636</td>\n",
       "      <td>2009-04</td>\n",
       "      <td>4738.386</td>\n",
       "      <td>3523.632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Average Daily Rate  Hotel Occupancy     Date  Monthly Rate  \\\n",
       "0            157.8543         0.740657  2005-04      4735.629   \n",
       "1            169.9458         0.770442  2006-04      5098.374   \n",
       "2            183.4274         0.764485  2007-04      5502.822   \n",
       "3            183.7442         0.768456  2008-04      5512.326   \n",
       "4            157.9462         0.743636  2009-04      4738.386   \n",
       "\n",
       "   Corrected Monthly Rate  \n",
       "0                3507.477  \n",
       "1                3928.002  \n",
       "2                4206.826  \n",
       "3                4235.983  \n",
       "4                3523.632  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load csv file\n",
    "mon_rate_df = pd.read_csv(\"Data/hotel_monthlyrate.csv\")\n",
    "mon_rate_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average Daily Rate</th>\n",
       "      <th>Hotel Occupancy</th>\n",
       "      <th>Date</th>\n",
       "      <th>Monthly Rate</th>\n",
       "      <th>Corrected Monthly Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>141.6038</td>\n",
       "      <td>0.738484</td>\n",
       "      <td>2004-07</td>\n",
       "      <td>4389.7178</td>\n",
       "      <td>3241.735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>143.1187</td>\n",
       "      <td>0.719571</td>\n",
       "      <td>2004-08</td>\n",
       "      <td>4436.6797</td>\n",
       "      <td>3192.504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>137.4212</td>\n",
       "      <td>0.750757</td>\n",
       "      <td>2004-09</td>\n",
       "      <td>4122.6360</td>\n",
       "      <td>3095.097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>141.5987</td>\n",
       "      <td>0.778466</td>\n",
       "      <td>2004-10</td>\n",
       "      <td>4389.5597</td>\n",
       "      <td>3417.122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>148.4632</td>\n",
       "      <td>0.682347</td>\n",
       "      <td>2004-11</td>\n",
       "      <td>4453.8960</td>\n",
       "      <td>3039.104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Average Daily Rate  Hotel Occupancy     Date  Monthly Rate  \\\n",
       "0            141.6038         0.738484  2004-07     4389.7178   \n",
       "1            143.1187         0.719571  2004-08     4436.6797   \n",
       "2            137.4212         0.750757  2004-09     4122.6360   \n",
       "3            141.5987         0.778466  2004-10     4389.5597   \n",
       "4            148.4632         0.682347  2004-11     4453.8960   \n",
       "\n",
       "   Corrected Monthly Rate  \n",
       "0                3241.735  \n",
       "1                3192.504  \n",
       "2                3095.097  \n",
       "3                3417.122  \n",
       "4                3039.104  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort values by date\n",
    "mon_rate_df.sort_values(\"Date\", inplace = True)\n",
    "mon_rate_df = mon_rate_df.reset_index(drop = True)\n",
    "mon_rate_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Average Daily Rate</th>\n",
       "      <th>Hotel Occupancy</th>\n",
       "      <th>Date</th>\n",
       "      <th>Monthly Rate</th>\n",
       "      <th>Corrected Monthly Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>148.8121</td>\n",
       "      <td>0.729100</td>\n",
       "      <td>2005-01</td>\n",
       "      <td>4613.1751</td>\n",
       "      <td>3363.466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>151.1379</td>\n",
       "      <td>0.740805</td>\n",
       "      <td>2005-02</td>\n",
       "      <td>4231.8612</td>\n",
       "      <td>3134.983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>153.0208</td>\n",
       "      <td>0.744909</td>\n",
       "      <td>2005-03</td>\n",
       "      <td>4743.6448</td>\n",
       "      <td>3533.582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>157.8543</td>\n",
       "      <td>0.740657</td>\n",
       "      <td>2005-04</td>\n",
       "      <td>4735.6290</td>\n",
       "      <td>3507.477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>154.0527</td>\n",
       "      <td>0.756545</td>\n",
       "      <td>2005-05</td>\n",
       "      <td>4775.6337</td>\n",
       "      <td>3612.980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Average Daily Rate  Hotel Occupancy     Date  Monthly Rate  \\\n",
       "6             148.8121         0.729100  2005-01     4613.1751   \n",
       "7             151.1379         0.740805  2005-02     4231.8612   \n",
       "8             153.0208         0.744909  2005-03     4743.6448   \n",
       "9             157.8543         0.740657  2005-04     4735.6290   \n",
       "10            154.0527         0.756545  2005-05     4775.6337   \n",
       "\n",
       "    Corrected Monthly Rate  \n",
       "6                 3363.466  \n",
       "7                 3134.983  \n",
       "8                 3533.582  \n",
       "9                 3507.477  \n",
       "10                3612.980  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exclude data from 2004 and 2018\n",
    "inc_str = [str(year) for year in inc] # pass the years as string (because the date is in string form)\n",
    "\n",
    "mon_rate_df = mon_rate_df.loc[~mon_rate_df[\"Date\"].str.contains(\"|\".join(inc_str))] # get the data from 2005–2017\n",
    "mon_rate_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Corrected Monthly Rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-01</th>\n",
       "      <td>3363.466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-02</th>\n",
       "      <td>3134.983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03</th>\n",
       "      <td>3533.582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-04</th>\n",
       "      <td>3507.477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-05</th>\n",
       "      <td>3612.980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Corrected Monthly Rate\n",
       "Date                           \n",
       "2005-01                3363.466\n",
       "2005-02                3134.983\n",
       "2005-03                3533.582\n",
       "2005-04                3507.477\n",
       "2005-05                3612.980"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select Date and Corrected Monthly Rate for plotting\n",
    "mon_rate_df2 = mon_rate_df[[\"Date\", \"Corrected Monthly Rate\"]]\n",
    "mon_rate_df2 = mon_rate_df2.set_index(\"Date\")\n",
    "mon_rate_df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Data is in monthly periods so set period to monthly\n",
    "# Resource: http://earthpy.org/time_series_analysis_with_pandas_part_2.html\n",
    "\n",
    "mon_rate_df2[\"Corrected Monthly Rate\"].index = pd.to_datetime(mon_rate_df2[\"Corrected Monthly Rate\"].index)\n",
    "    # string converted to datetime format\n",
    "    \n",
    "mon_rate_df2.index = mon_rate_df2[\"Corrected Monthly Rate\"].to_period(freq = \"M\").index \n",
    "    # convert time stamps to time periods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot time series for corrected monthly rate\n",
    "time_series01 = mon_rate_df2.plot.line(figsize = (8,5), legend = False)\n",
    "time_series01.set_ylabel(\"Corrected Monthly Rate (USD)\")\n",
    "\n",
    "# Save image\n",
    "plt.savefig(\"Images/hotel_timeseries01.svg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Corrected Monthly Rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005Q1</th>\n",
       "      <td>3249.224500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005Q2</th>\n",
       "      <td>3551.346333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005Q3</th>\n",
       "      <td>3529.392333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005Q4</th>\n",
       "      <td>3669.394333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006Q1</th>\n",
       "      <td>3599.857000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Corrected Monthly Rate\n",
       "Date                          \n",
       "2005Q1             3249.224500\n",
       "2005Q2             3551.346333\n",
       "2005Q3             3529.392333\n",
       "2005Q4             3669.394333\n",
       "2006Q1             3599.857000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get quarterly data\n",
    "# start the quarter from November so that the data from Q1-Q3 of 2015 are included\n",
    "\n",
    "q_mean = mon_rate_df2.resample(\"Q-NOV\").mean() \n",
    "q_mean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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u4Fo6cfber5et+m4XFUSgnxcrt2Uyrk8MXp72uV+9tgMVm3769u3bGThwIABBQUF4e3tz/PhxDMNg48aNJCUl0atXL9avXw9AWloaCQkJte4rIiLnlFWaWfHTCUICvBnRU9faHc3by5MBXaIoLjez+/AZR5dj25H70aNHiYn5/z90L7/8Mk899RQWi4Xk5GS6d+9Ot27d2LRpE9OmTcMwDF577bVa9xURkXN+2HaCiioLNw9vi69Pw1+SlGs3uHs0K1OzWL8rh14JEQ6txabh/pvf/OaiP/fo0YPPPvvsom0eHh788Y9/vOS1l9tXRESgtMLMyp9OEBLow/BeLR1djvyiVWQQbVsEs+fIGQpLqggL9nVYLZrERkTEyfyw7TiV1RZu7NcaX2+N2huTwYnRGAZs2pPr0DoU7iIiTqS0wnzhDvmhPTVqb2z6dmqOj5cHG3bnYHXgYjIKdxERJ/LDtuNUVVu4sX+sRu2NUICfF306RpJfVMn+40UOq6POa+4nTpxg7dq1ZGZmYjKZiI2NZfjw4bRsqSNGERF7KvnlufbQIB+G9Yh2dDlSi8Hdo9m0N48Nu3PoFBvmkBpqHbmfOnWKJ554ghkzZpCdnU3r1q1p06YNOTk5PPHEEzzxxBPk5TW+BepFRFzV8n8btfto1N5otY8JpXl4AD9l5FNWaXZIDbWO3N9++21+97vfER8ff9mvZ2Rk8Pbbb/PWW2/ZrDgRETmnuLya1anZGrU7AZPJxJDEFixee5it6ScdMud/rSP3N998s9ZgB+jYsaOCXUTETn5IOU6V2cJN/WPxtsFU29KwBnaNwsNkcthiMle85n748GE+//xzjhw5gq+vL+3atWPy5MlER+uoUUTEXorLqlm1I4uwYF+GatTuFEKDfOnerik7D54mM6+E2Cj7zuNf68h9y5Yt3HHHHVRWVjJ06FAGDBhAQUEBkydPZtu2bfasUUTErS1POU612cqNGrU7lcGJ5w7E1jtg9F7ryP2dd97ho48+omvXrhdtv+2223jjjTdYsGCBzYsTEXF3Z8uqWf3LqH1Id43anUm3+HBCg3zYmn6SqcPb2fUmyFpH7qWlpZcEO0BiYiIVFRU2LUpERM5ZtjWT6hor4wfE4u2lqUmciaeHB8ndWlBRVUPq/ny7fnatPyleXjZf6l1ERK7gbGkVa3dmEx7iS3KiRu3OKDmxBYDdb6yrNcHLysr46aefMC4zfV55ecOvnS4iIhdblnKc6horNw1oo1G7k2oeFkDH1k3IOF7EycJymocF2OVzaw335s2b81//9V+X/VpkZKTNChIRESgqrWLNzmyahvgy+JfRnzinwYnRZBwvYuPuXCYNrf0R84ZUa7jPmzfPLgWIiMillm7NxFxj5aaBbfDy1KjdmfXuEMH8FV5s3JPLxMFt8fSw/ffzip+wZs0aTpw4AcDKlSt56KGHeOeddzCbHTOdnoiIOygsqWLtzhyahviR3E2jdmfn4+1J/y7NOVtazaGss3b5zFrD/aOPPuK9996jqqqKjIwMnnrqKUaOHElRURF/+tOf7FKciIg7Wro1kxqLlfEDYzVqdxHjB7RhWI9oWjQNtMvn1Xpa/uuvv2bRokX4+/vz5z//mREjRjBlyhQMw+DGG2+0S3EiIu6msKSKdWk5NAv1Y5BG7S4jLNiXe8Z1tNvn1XpIaDKZ8Pf3ByAlJYXBgwdf2C4iIraxdMv5Ubuutcv1q3Xk7unpSXFxMeXl5ezbt49BgwYBkJ2drWfgRURsoKC4knW7solo4sfArlGOLkecWK0p/cADDzBx4kRqamqYPHkykZGRLF26lL/85S888sgj9qxRRMQtfL81kxqLoVG71Fut4T5u3Dh69uxJYWEhHTueu04QGBjI7Nmz6devn90KFBFxBwXFlWzYlUNEEz8GdNGoXeqn1nDPyTk3VV5ISAg5OTl4eHjQp08fAgLsM7uOiIg7+W7LuVH7zQPbatQu9VZruN91112YTKaLpp8tLCwkISGBOXPm0LJlS7sUKCLi6s6cPTdqjwzzZ0DX5o4uR1xAreG+evXqy25fsmQJL7/8Mh9++KHNihIRcSffbzmGxWpw88A2dpm9TFzfNf8UTZw4kdzcXFvUIiLidk6frWDD7lyah/nTv4tG7dIwdIgoIuJA323OPDdqH6RRuzSca3pgvbS0lMWLFxMbG2urekRE3EZ+UQWb9uTSPDyAfp01apeGU2u4d+zY8aLZ6EwmEyEhIQwYMICXXnrJHrWJiLi089fab9GoXRpYreGekZFhzzpERNzKuVF7Hi2aBtCvk0bt0rBqPVScP38+Vqu11hdaLBat+S4icp2+3XzswrV2Dw+t2SENq9aRe3R0NHfccQd9+/YlKSmJqKgovLy8yM7OZuvWraSkpPDQQw/Zs1YREZeQV1DO5l9G7X07atQuDa/WcB8xYgTJycl8++23LFq0iMzMTEwmE7GxsQwbNozHH38cHx8fe9YqIuL0DMNg7vIMrIbBrYPjNGoXm7ji3fI+Pj5MmjSJSZMm2aseERGXtmF3LhnHi+jRrhm9O0Q4uhxxUbo9U0TETopKq1i0+hB+Pp7cNSbhoieSRBqSwl1ExE4+WXGAiqoapgyLJzzEz9HliAu75nAvLS21RR0iIi4tdX8+qfvzaR8TytCeWnhLbKvOcF+zZg1vvfUWZWVl3HDDDYwcOZIvv/zSHrWJiLiE8koz81fsx8vTxH03dMRDp+PFxuoM9/fee4+bb76ZpUuXkpiYyOrVq5k/f749ahMRcQmL1x7mbGk1Nw9sQ4umgY4uR9zAVZ2W79ixI2vXrmXEiBEEBgZiNpttXZeIiEvYf7yQdWk5xEQEckN/rcsh9lFnuDdr1oxXXnmFvXv3MnjwYN544w2io6PtUZuIiFOrNlv457IMTMB9N3TCy1P3MIt91PmT9vbbb9OtWzfmzp1LQEAArVq1Ys6cOfaoTUTEqX27+RgnCysYldSKuOgQR5cjbqTOJV/9/Pxo0qQJO3fuZOfOnQQGBrJixQomTpxY55t/8MEHrF69GrPZzPTp0+nbty/PPfccJpOJ9u3bM2vWLDw8PHjvvfdYu3YtXl5ezJw5k8TERDIzMy+7r4iIMzh+soRlW4/TLNSP24bEOboccTN1puWMGTP429/+dmE++fP/1CUlJYWdO3eycOFC5s2bR15eHq+//jpPPPEECxYswDAMVq1aRXp6Otu2bWPx4sXMmTOHl19+GeCy+4qIOAOL1crHy85NMXvP2A74+ng6uiRxM3WO3Pfv38+yZcuueSaljRs3kpCQwCOPPEJpaSnPPPMMn332GX379gVgyJAhbNq0ibZt25KcnIzJZCI6OhqLxUJBQQHp6emX7Dt69OjraFFExL5WbM8iM6+EAV2i6BrX1NHliBuqM9zj4+PJz88nMjLymt64sLCQnJwc3n//fbKysnj44YcxDOPCQUJgYCAlJSWUlpbSpEmTC687v/1y+15JWFgAXl4Nf3QcERHc4O/pDNy1b3Df3tV3w8g9XcaSjUcJDfLh0ak9CQlsvAts6XvuuuoM98rKSsaNG0dCQsJFq8DNnTv3iq9r0qQJcXFx+Pj4EBcXh6+vL3l5eRe+XlZWRkhICEFBQZSVlV20PTg4+KLr6+f3vZLCwvK6WrlmERHB5Odf+aDCFblr3+C+vavvhmEYBn/5NI1qs4X7buhAVXkV+eVVDfb+DUnfc9dQ24FKneH+4IMPXtcH9u7dm7lz5/KrX/2KU6dOUVFRwYABA0hJSaFfv36sX7+e/v3707p1a9566y3uv/9+8vLysFqthIeH07lz50v2FRFpzDbtyWNfZiGJ8U3p10nrtIvj1Bnuffv25eeff6a8vBzDMLBYLGRlZV24Hl6b4cOHs337diZPnoxhGLz44ovExMTwwgsvMGfOHOLi4hg7diyenp4kJSUxdepUrFYrL774IgDPPvvsJfuKiDRWZ8uqWbT6IL4+ntw9poNWfBOHMhmGYVxph+eff55t27Zx9uxZ4uLiyMjIoFevXnz00Uf2qvGq2OI0i6udvrla7to3uG/v6rv+/rFkL9szTnHn6ARG9o5pkPe0JX3PXUNtp+XrfBRu8+bNfP/994wdO5ZXXnmFuXPnUllZ2eAFiog4q50H89mecYr4liEM14pv0gjUGe6RkZF4e3sTHx/P/v376datW513rouIuIuKqhrm/3gATw8T993QCQ8PnY4Xx6vzmnvz5s354IMPGDBgAG+99RYA1dXVNi9MRMQZfL72MIUlVUxIbkvLZlrxTRqHOkfur776KjExMSQmJjJmzBi+++47XnrpJTuUJiLSuB04UcSandlENwvkRq34Jo1InSP3oKAgunfvztq1a5k+fTrDhg2jVatW9qhNRKTRMtdY+Nfy8yu+dcTbS2tfSONR50/j0qVLefjhh5k9ezZnz55l2rRpfP311/aoTUSk0fp2cya5Z8oZ0TuGdi1DHV2OyEXqDPf//u//ZuHChQQFBdG0aVO++uorPvzwQ3vUJiLSKGWdKmXZ1kzCQ3y14ps0SnWGu4eHB0FBQRf+HBkZqaVXRcRtWa0GHy/LwGI1uGdsR/x967y6KWJ3df5Utm/fnvnz51NTU8O+fftYsGABHTt2tEdtIiKNzsrULI7mFtO/c3MS47XimzROdQ7BX3zxRU6ePImvry8zZ84kKCiIWbNm2aM2EZFG5XRRBV+uP0yQvzfTRrV3dDkitapz5B4QEMCMGTOYMWOGPeoREWmUDMNg7g/7qTZbuXdsR0ICGu9SriK1hvuIESOuuPDBqlWrbFKQiEhjtCU9j71HC+jaNpz+XbTimzRutYZ7x44d2bdvH8OGDePGG28kOjrannWJiDQaxWXVLFx5EF9vT+4ZqxXfpPGrNdz//ve/U1paysqVK/noo48oKytj1KhRjBs3jubNddQqIu7j01UHKausYfrI9jRr4u/ockTqdMVr7kFBQUycOJGJEydSXFzMihUrePzxx/Hy8mL+/Pn2qlFExGF2HTrN1p9P0rZFiFMs5SoCV3G3PEBBQQFLly7l22+/pbS0lD59+ti6LhERh6uoqmHej/vx9DDxqxs6asU3cRq1jtxPnTrFihUrWL58OQUFBYwZM4bnnntOz7iLiNv4cv0RCoqruHlgG2Iig+p+gUgjUWu4Dx06lKioKMaMGUPHjh0xmUxkZGSQkZEBwMSJE+1WpIiIvR3KPsvq1CxaNA1g/MA2ji5H5JrUGu4TJkzAZDJRXFzMtm3bLvm6wl1EXJW5xsrHS/dhAPeO04pv4nxqDfc33njDnnWIiDQaS7eeW/FteK+WJLRq4uhyRK6ZDkdFRP5N9ukyvtt8jLBgXyYPjXd0OSLXReEuIvILq9Xgn8v2YbEa3D2mg1Z8E6dVZ7jv3r3bHnWIiDjcmp3ZHM4upm+nSHq0b+bockSuW52HpW+99RZFRUVMmDCBCRMmEBERYY+6RETs6szZSj5fd5hAPy+mj0pwdDki9VLnyH3evHm8//77VFdX8+tf/5oHH3yQ5cuXYzab7VGfiIjNGYbBvB/3U1VtYeqI9oQGasU3cW5Xdc29ZcuWTJw4kZtvvpmDBw8yb948xo8fz4oVK2xdn4iIzaXsO8nuw2fo3CaMQd2iHF2OSL3VeVp+8eLFfP311+Tn5zNx4kQWLFhAVFQUJ0+e5NZbb2X06NH2qFNExCZKyqtZsOIgPl4e3DOuo1Z8E5dQZ7hv376d3/3ud/Tr1++i7c2bN2fWrFk2K0xExB4+XXWI0goztw9vR6RWfBMXUWu4b9++HYApU6Zc9Ofz+vTpw9ixY21YmoiIbe3IOMWW9DzaRAUzuo9WfBPXUWu4v/POOwCYTCYMw7joayaTiblz59q2MhERG6qsruFvn6fhYTJx3w0d8fTQtB/iOmoN93nz5gGwcOFCpk+fbreCRERsyTAMUvfn88W6w5wqrOCmAbG0bh7s6LJEGlSd19w/+eQThbuIuISDWUV8tvoQh3OK8fQwMT65LTf3j3V0WSINrs5wj4qK4p577qF79+74+vpe2P7oo4/atDARkYaSe6aML9YdYceBfACSOkQwaWg8XTs0Jz+/xMHViTS8OsO9R48e9qhDRKTBnS2r5ptHYCmqAAAgAElEQVSNR1mXloPVMGgXE8rtw9vRrmWoo0sTsak6w/3RRx+lvLyc48ePk5CQQGVlJQEBAfaoTUTkulRVW/hh+3GWpRynqtpC8/AApgyLp2f7ZnqOXdxCneG+ZcsWXnzxRSwWC4sWLWL8+PG8/fbbJCcn26M+EZGrZrFa2bg7lyUbjnK2rJqQAG9uHxbP4O7ReHnqbnhxH3WG+5w5c1iwYAG//e1viYiI4JNPPuH3v/+9wl1EGg3DMNh1+Ayfrz1MzukyfLw9uHlgG8b1a61lW8Ut1flTb7VaL1oJrl27djYtSETkWhzNLeaz1YfYf6IIkwmGdI9mQnJbwoJ9636xiIu6qrvl16xZg8lkori4mE8++YTo6Gh71CYiUqtTRRV8ue4w2/adAqBHu2ZMGhZPy2aBDq5MxPHqDPc//vGPvPrqq+Tm5jJ69Gj69evHK6+8Yo/aREQuUVph5ttNx1i9IwuL1aBNVDC3D29Hx9gwR5cm0mjUGe4ZGRnMmTPnom0//vgjY8aMsVlRIiL/V7XZwsrULL7fkklFVQ3NQv2YPCyepI6ReOgOeJGL1BruS5cupbq6mnfeeYfHHnvswvaamho++OADhbuI2IXVMNiyN4+vNhyhoLiKQD8vpo1sz/CeLfH20h3wIpdTa7iXlZWxY8cOysrKSElJubDd09OTJ5988qrefOLEiQQHn5uzOSYmhqlTp/Lqq6/i6elJcnIyjz76KFarlZdeeon9+/fj4+PD7NmziY2NJS0t7ZJ9RcS9pB8t4LM1hzhxqhQvTw9u6NeamwbEEuDn7ejSRBq1WsN9ypQpTJkyhfnz53PXXXdd8xtXVVUB/38BGoAJEybw7rvv0qpVKx544AHS09PJzs6murqaRYsWkZaWxhtvvME//vEPZs2adcm+Xbp0uY4WRcTZHD9ZwuK1h0k/WoAJGNg1ilsHx9E01M/RpYk4hTqvuX/66afXFe4ZGRlUVFTw61//mpqaGn73u99RXV1N69atAUhOTmbLli3k5+czePBg4NxUt3v37qW0tPSy+yrcRVxbQXElX64/wpa9eRhAlzZhTBneTqu2iVwjmy0c4+fnx/3338+UKVM4duwYv/3tbwkJCbnw9cDAQE6cOEFpaSlBQUEXtnt6el6y7fy+VxIWFoCXl2dd7VyziAj3/KXirn2D+/buyL5LK8x8vuoA32w4grnGSpsWIfzq5i706hBp88921+83uG/v7tC3zRaOadu2LbGxsZhMJtq2bUtwcDBFRUUXvl5WVkZISAiVlZWUlZVd2G61WgkKCrpo2/l9r6SwsPy66rySiIhgt1wxyl37Bvft3VF911isrNmRzbebj1FaYSYs2JfbhsQxoEsUHh4mm9fkrt9vcN/eXa3v2g5UrmrhmH9nGAZZWVl1fuDnn3/OgQMHeOmllzh58iQVFRUEBARw/PhxWrVqxcaNG3n00UfJy8tjzZo13HjjjaSlpZGQkEBQUBDe3t6X7CsiriP3TBn/WJJOVn4p/r6eTB4Wz6jeMfh4N/wZOBF3U2e4L1q0iDfffJOKiooL22JiYlixYsUVXzd58mT+8Ic/MH36dEwmE6+99hoeHh489dRTWCwWkpOT6d69O926dWPTpk1MmzYNwzB47bXXAHj55Zcv2VdEXMOmPbnM+3E/1WYrQ7q3YNLQeIIDfBxdlojLMBmGYVxphxEjRvCvf/2Lv/71rzz55JOsW7eOHTt28Pbbb9urxqtii9Msrnb65mq5a9/gvr3bq+/K6hrm/3iAzXvz8Pf15L4bOtGno+2vq9fGXb/f4L69u1rf131avmnTprRq1YoOHTpw4MAB7rzzThYuXNjgBYqIaztxqpR/LNlLXkE5baKCeWhiVyKb+Du6LBGXVGe4+/v7s3XrVjp06MDKlSvp1q0blZWV9qhNRFyAYRisTcth4cqD1FisjOnTisnD4rW+uogN1fl/1/PPP8/q1asZPHgwRUVFjBs37rqeexcR91NeaeYfS/Yy74f9+Hp78NikRKaNbK9gF7GxOkfuCQkJzJw5E4B3333X5gWJiGs4mlvMP5bs5fTZStrHhPLgLV0ID9EMcyL2UGe4jxgxAtNlVlxatWqVTQoSEedmGAY/bj/B52sPY7UajB8Yy4Tktnh6aLQuYi91hvu/zw1fU1PDihUrqK6utmlRIuKcSsqr+ej7few+fIaQQB9+e3NnurQJd3RZIm6nznBv2bLlRX/+zW9+w2233cZ//Md/2KwoEXE+B04U8cE36RSWVNG5TRi/Hd+Z0CDful8oIg2uznDfvn37hf82DIODBw9eWPFNRMRqNfh+yzGWbDyKCRO3DYnjxgGxeFzmcp6I2Eed4f7OO+9c+G+TyURYWBhvvPGGTYsSEedwtrSKD7/9mX2ZhYQF+/LgLV1IaNXE0WWJuL1ruuZ+ntlstkkxIuI80o8W8N/fplNcbqZ7fFPuH9+ZIH9vR5clItQR7qmpqfz9739n165dWK1WunTpwiOPPMKGDRvo27cvQ4cOtVedItJIWKxWlmw4ytItmXh4mJg2sj2jk2Iu+1SNiDhGreGekpLC008/zUMPPcQf/vAHKisrSUtLY8aMGcTGxvL000/bs04RaQQKiit5/5t0DmWdJaKJHw9N6ErbFldejllE7K/WcH/vvff44IMP6NSp04VtXbt25bvvvtMRuogb2nkwn//9fh9llTX06RjJveM6EuBX55U9EXGAWv/PLCkpuSjYAQoKChg9ejRLliyxeWEi0jiYa6wsXnuIlT9l4e3lwT1jOzC0R7QO8kUasVqnjKqsrMRisVy0LTw8nHvvvVeT2Ii4iVOF5bw2P5WVP2XRomkAL9yTxLCeLRXsIo1creE+bNgwXn/99YsC3mKx8OabbzJkyBC7FCcijrNt30le+ng7mXklDOoWxYv39iEmMsjRZYnIVaj1tPzjjz/OI488wujRo+nUqRMmk4n09HTi4uL4+9//bs8aRcSOqs0WFq46yLq0HHy9PfnN+E4M7NrC0WWJyDWoNdz9/f353//9X1JTU9mzZw+GYXDfffeRlJRkz/pExI5yTpfxj6/3kp1fRkxEEA9P7EKLpoGOLktErlGdt7r27t2b3r1726MWEXEQwzDYuDuX+Sv2U222MrxXS6aNaIe3l6ejSxOR66DnWETcXEVVDXMW7mBtahb+vp78x8SuJHWMdHRZIlIPCncRN1ZjsfKnBTvJPFlC2xYhPDShCxFN/B1dlojUk8JdGo3K6hryiypppTuy7WbZ1kwyT5YwpGdL7hrVHi/PWh+gEREnonAXhzMMg5R9J/ls9SGKSquZNDSOmwa0cXRZLi+voJxvN2cSGujDw5O6U1Fa6eiSRKSBKNzFobJOlTJ/xQEOnCjC28uDkEAfvlh3BEABb0OGYTB3eQY1Fit3jE4gyN9b4S7iQhTu4hDllWaWbDzK6tRsrIZBz/bNmDayPQB/WrCTL9YdwWrAzQPbOLZQF7VpTx4Zx4voHt+UpA4Rji5HRBqYwl3symoYbNmbx+I1hyguNxMZ5s+doxPoFtf0wj7P3tGTNxfs5Kv1RzAMg1sGtXVgxa6nuLyaRasP4uvtyV1jOmgqWREXpHAXu8nMK2H+iv0czi7Gx9uDSUPjGNOnNd5eF9/E1ayJP8/e2ZM/LdjJkg1HwYBbkhXwDWXRqoOUVdYwbWR7mob6ObocEbEBhbvYXGmFma/WH2FtWjaGAUkdI5k6vN0Vg6VZqD/P3PFLwG88itUwmDg4zo5Vu6b0owVsST9JbFQwo3rHOLocEbERhbvYjNUw2LArhy/WHaG0wkyLpgHcOTqBzm3Cr+r1zUL9efaOXvxp4Q6+2XQMgAnJbXUa+TpVmS3M/SEDD5OJ+8Z1xMNDf48irkrhLjZxJKeYT1bs52huCb4+ntw+vB2jkmKu+TnqpqF+5wJ+wU6+2XQMqwG3DlbAX49vNx0jv6iSsX1bERsV7OhyRMSGFO7SoIrLq/ly3WE27MrFAPp3bs6U4e0IC/a97vcMD/G7cIr+u83HAINbB8cp4K/BiVOl/LDtOE1D/JiYrMsbIq5O4S4Nwmo1WLMzm6/WH6G8qoaYiEDuHJ1Ah9ZhDfL+4SF+PHtnL/60YAffbc7EMOC2IQr4q2G1GvxreQYWq8HdYzvg66PFYERcncJd6u1gVhGf/HiA46dK8ff1ZPqo9ozo1RJPj4adyjQs2Jdn7ujFnxbu5PstmVgNg8lD4xXwdVizM5sjOcX07RRJYnzTul8gIk5P4S7X7WxpFYvXHmbz3jwABnWLYvKwdoQG+tjsM8OCfXlmek/+tHAny7YeBwMmD1PA16awpIov1h0mwNeL6b9MEiQirk/hLtesxmJl9Y5svt54hIoqC7HNg7lzTALtWoba5fPDgn159pdr8MtSjmMYMGW4Av5yPllxgMpqC/eO60Bo0PXf9yAizkXhLtckI7OQT1YeIDu/jEA/L+4e24Gh3aPt/lhVk6BfAn7hTpZvO47VMJg6op0C/t/sOJDPjgP5JMSEMrh7tKPLERE7UrjLVSksqWLR6oNs23cKEzC0RzS3DYkjOMB2p+DrEhp07hr8Wwt38uP2ExgGTBupgAeoqKrhkxUH8PQwcc+4jnjo70TErSjc5YpqLFZWbD/BN5uOUWW20LZFCHeNSaBtixBHlwZAaKAPT0/vyVsLd7LipxMYGEwf2d7tA/7L9UcoLKnilkFtiG4W6OhyRMTOFO5Sq/SjBXyy4gB5BeUE+XszfVR7khNbNLpRYGigD8/8EvArf8rCMOCOUe4b8IdzzrI6NYuo8AAtmyviphTucolTheX8/as9pO7Px2SCkb1imDikLYF+3o4urVYhgT48fUdP/rxwJ6tSs8CAO0a7X8DXWKz8a9l+DODecR0uWZRHRNyDwl0usBoGP247wZKNR6k2W2gXE8pdoxNo3dw5pioNCfDhqek9+fPCNFbtyMKKwV2jE9wq4H/cfoKs/FIGJ7ZosAmERMT5KNwFOLdy20ff/cyuw2doEuzLPWMTGNAlyumCMSTAh6en9+DPn6axZkc2GHDnmIRGdynBFk4VlvPNxqOEBHgzZXg7R5cjIg5k03N2Z86cYejQoRw+fJjMzEymT5/OHXfcwaxZs7BarQC89957TJ48mWnTprF7926AWvcV2ziSU8zLH29n1+EzdGkTxntPDWdg1xZOF+znBQecu8mudWQQa3ZmM/+H/VgNw9Fl2ZRhGMz7YT/VNVamjWpPkH/jvYQiIrZns3A3m828+OKL+PmdW7P79ddf54knnmDBggUYhsGqVatIT09n27ZtLF68mDlz5vDyyy/Xuq80PMMwWJWaxevzUykormRCcluevL2HS0x2EuTvzVPTe9K6eRBr03KYu9y1A37rzydJP1ZI17bh9OvU3NHliIiD2Szc33zzTaZNm0ZkZCQA6enp9O3bF4AhQ4awefNmUlNTSU5OxmQyER0djcVioaCg4LL7SsOqqKrh/a/T+WTFAQL8vPj91B5MSG7rUmt8B/l789S0nsQ2D2b9rhzmLs9wyYAvrTDz6aqD+Hh5cPfYDk57xkVEGo5Nrrl/+eWXhIeHM3jwYD788EPg3Cjx/C+dwMBASkpKKC0tpUmTJhded3775fatS1hYAF5eDb/aVUSEc9xMdi2O5pzljXmp5Jwuo3PbcJ65O4mmof4X7eMqfUcAbzyazAsfbGb9rlx8fb15dEqPKx7EOFvvCz7dSUm5mV+N70zn9pHX/T7O1ndDcde+wX17d4e+bRLuX3zxBSaTiS1btrBv3z6effZZCgoKLny9rKyMkJAQgoKCKCsru2h7cHAwHv+2mtj5fetSWFjesE1w7gcgP7/uAwtnsmFXDvNXHMBcY2Vcv9bcNiQOa3XNRX26Yt+PT07k7U/TWLHtOBUVZu678fKztjlb7/syC1m5/TitI4MY2Dnyumt3tr4birv2De7bu6v1XduBik1Oy3/yySfMnz+fefPm0alTJ958802GDBlCSkoKAOvXrycpKYlevXqxceNGrFYrOTk5WK1WwsPD6dy58yX7Sv1UmS189P3PfLwsA29PD343qRu3D2+Hl6d7PAcd6OfNU9N60LZFMBv35PLx0n1Yrc59it5cY2Hu8gxMJrj3ho4NvsSuiDgvuz0K9+yzz/LCCy8wZ84c4uLiGDt2LJ6eniQlJTF16lSsVisvvvhirfvK9cs9U8Y/luwlK7+MNlHBPDyxKxFN/Ot+oYsJ8PNmxtSevL0ojU178jAM+PWNnZz2PoPvNmdysrCCUUkxjWY6YBFpHEyG4Rp3GNniNIsrnL7Ztu8kHy/LoKrawvBeLZk2on2ds5a5Qt9XUl5Zw5zP0jiSU8yALs25/6bOFwLeWXrPPl3GS/+7jZBAH2b/ph/+vvU7TneWvhuau/YN7tu7q/Vt19Py4njmGivzf9zP+1+nA/DgLV24e4ymIwUI8PNixtQexLcMYUv6Sf7n+5+d6hS91TD41/IMLFaDu8d0qHewi4jr0W8FF3S6qIJ/fL2Xo7kltIwI5D8mdqVFU60M9u/8fb34/e09+Mtnu9iafhIMuH98J0eXdVXWp+VwKOssvTtE0KN9M0eXIyKNkMLdxaQdPM3/fPcz5VU1DOoaxV1jO+Dr3fCPCLoCf18vnry9O39ZvIutP5/Eahg8d18/R5d1RUWlVSxeexh/X0/uGJXg6HJEpJHSOVoXYbFaWbzmEO98sRuzxcqvbujI/eM7K9jr4O/rxZNTutM+JpRt+07xyFur2bbvZKOd7GbhyoNUVNUweWg8YcHOP5OgiNiGRu4uoLCkig++3suBrLM0D/Pn4YldnWYlt8bg/Aj+i7VHWLcrm/e/Tic25TiTh8XTpU24o8u7YNeh02zPOEV8yxCG9mzp6HJEpBFTuDu59GMFfPhNOiXlZpI6RPCrGzvpBqvr4OfjxZ1jEpg6tiMffb2HlJ9P8vanaXRpE8bkYe2IjXLswVJldQ3zf9yPp4eJe8ddfgIeEZHzlAJOymo1+G7zMb7eeBQPDxN3jGrPyN4xmle8nlo0C+TBW7owrm9rvlh3mL1HC0j/53b6dork1iFxNA8LcEhdSzYc5UxxFTcNiCUmIsghNYiI81C4O6Hi8mr++9ufST9aQNMQXx6a2JX46FBHl+VSYqOC+f3UHuw7VsDitYfZtu8UqfvzGdIjmlsGtrHrynnH8opZ8dMJIsP8uXlgG7t9rog4L4W7kzmYVcT7X6dTWFJFYnxTfjO+s9butqFObcJ54d4wftqfz5frDrNmRzab9+Qxpk8rxvVrbfNLIBarlX8uy8Aw4J6xHfDRDZIichUU7k7CMAx+2HaCz9cexsBg0tA4bugfq2uvdmAymejTMZKe7ZuxcXcuX288yrebj7FmZzY3D2zDsJ4tbTY50Mqfsjh+spRBXaPo3Ihu7hORxk3h7gTKK8189P0+dh48TWigDw9N6EKH1mGOLsvteHl6MKxnSwZ0iWLFTydYlpLJwlUHWfHTCSYObkv/zlENOk/96aIKvtpwhCB/b24f0a7B3ldEXJ/CvZE7llfM37/ay+mzlXSKDeOBW7oQGujj6LLcmq+PJ+MHtmFoj2i+35LJ6h1Z/M93+1iecoLJw+LpFhde7xsbDcNg/ooDVJut3D2mA8EB+p6LyNVTuDdShmGwdmc2C1cdpMZiMH5gGyYmt3XaFcxcUXCAD9NGtmdUUgxfbzjK5r15/HXxLjq0asLk4fH1uslxe8Ypdh8+Q6fYMAZ2jWrAqkXEHSjcG6HK6hrmLt/P1p9PEuTvzW9v7ky3uKaOLktq0SzUn/vHd2bsL4/P7Tp8hlfnptI7IYLbhsZd87z+ZZVmFqw8iLeXB/eM66DHG0XkmincGxHDMEg7dJpFqw9xqrCC+JYhPDyhK+Ehfo4uTa5CTGQQj0/pzoETRSxee4jUA/nsPHia5MQWTEhue9XTxX6+9jDFZdVMGuq45+pFxLkp3BuJzLwSFq0+SMbxIjxMJsb1bc1tQ+Pw8tT0/84moVUTZt7Vm7SDp/l83WHW78phS3oeo5JiuLF/LIF+tT+6eOBEEevScmgZEcjYvq3tWLWIuBKFu4MVllTx5frDbN6ThwEkxjfl9uHtiG6mJVqdmclkomdCBIntmrJ5Tx5LNh5l2dbjrE/L4cYBsYzsFXPJM+vmGiv/Wp6BCbh3XEcd2InIdVO4O0hVtYVlKZks33acarOVmIggpo5s16gWKpH68/TwYHD3aPp1bs6qHVl8vzmTxWsOs/KnLCYmt2Vgtyg8Pc6F+LKUTHLPlDO8V0vatdSMgyJy/RTudmY1DDbvyePL9YcpKq0mNNCHO0bFkdythe6Ed2E+3p7c0C+WId2jWbo1k5U/ZfHxsgx+2H6CSUPiiGoawHebj9EkyIdJQ+IdXa6IODmFux3tO1bAotWHOH6qFB8vD24e2IYb+rfGz0ffBncR6OfNlGHtGNkrhm82HWXD7lze/XIPvt6e1FgM7hydQICffh5EpH70W8QOcs+UsXjNYdIOnQZgQJcoJg2N013wbiw8xI/7bujEmD6t+XL9EXYcyKdXQgS9EiIcXZqIuACFuw2VVpj5euNR1u7MxmI1SGjVhKkj2tG2RYijS5NGIrpZII/e1o38ogrCgn31TLuINAiFuw2Ya6ysSs3iu83HKK+qITLMn9uHt6Nn+2b65S2XFdHE39EliIgLUbg3IMMwSN2fz+K1h8gvqiTQz4tpI9szoldLPdYkIiJ2o3BvIEdyilm0+iAHs87i6WFidFIrbh7URmuti4iI3Snc6+nM2Uq+WHeYrT+fBKBXQgRThsXTPFzThoqIiGMo3K9TRVUNS7dm8uP2E5hrrMQ2D2bayHZaZ11ERBxO4X6NLFYrG3bnsmT9EYrLzYQF+zJpaBz9u0ThoZvlRESkEVC4X4O9R8+waPUhsvPL8PX25NbBbRnTtzW+/2eOcBEREUdSuF+F7PxSFq05xN4jBZiAwYktuHVIHE2Crm4JTxEREXtSuF9BUUkVc3/Yz7q0bAwDOsWGMXVEO1o3D3Z0aSIiIrVSuF+GYRj8sO0E324+RkVVDS2aBnD78HYkxjfVJDQiItLoKdwv40xxJZ+tOURIoA+ThiYwpHu0JqERERGnoXC/jGah/rx4XxKd20VSXlrp6HJERESuiYajtWgTFUKgZpcTEREnpHAXERFxMQp3ERERF6NwFxERcTEKdxERERejcBcREXExCncREREXo3AXERFxMTabxMZisfD8889z9OhRPD09ef311zEMg+eeew6TyUT79u2ZNWsWHh4evPfee6xduxYvLy9mzpxJYmIimZmZl91XRERErsxmablmzRoAPv30Ux577DFef/11Xn/9dZ544gkWLFiAYRisWrWK9PR0tm3bxuLFi5kzZw4vv/wywGX3FRERkbrZLNxHjRrFK6+8AkBOTg7NmjUjPT2dvn37AjBkyBA2b95MamoqycnJmEwmoqOjsVgsFBQUXHZfERERqZtN55b38vLi2WefZcWKFbzzzjusWbPmwqpqgYGBlJSUUFpaSpMmTS685vx2wzAu2fdKIiJsswyrrd63sXPXvsF9e1ff7sdde3eHvm1+EfvNN9/khx9+4IUXXqCqqurC9rKyMkJCQggKCqKsrOyi7cHBwRddXz+/r4iIiNTNZuG+ZMkSPvjgAwD8/f0xmUx07dqVlJQUANavX09SUhK9evVi48aNWK1WcnJysFqthIeH07lz50v2FRERkbqZDMMwbPHG5eXl/OEPf+D06dPU1NTw29/+lvj4eF544QXMZjNxcXHMnj0bT09P3n33XdavX4/VauUPf/gDSUlJHD169LL7ioiIyJXZLNxFRETEMfTguIiIiItRuIuIiLgYmz4K1xiZzWZmzpxJdnY21dXVPPzww7Rr1+6qZ85LT0/noYceok2bNgBMnz6dG2+80bFNXYX69v3kk09y+vRpALKzs+nevTt/+ctfHNzV1WmI7/msWbPw8fGhU6dO/Od//qdTzJZ4LX0DZGZm8sgjj/Ddd99d9D7//Oc/OX36NE899ZQj2rgu9e09Pz+fp556CrPZTEREBG+88Qb+/v6ObOmq1LfvoqIixo4dS0JCAnBuvpJ7773XYf1crfr2/eqrr5KRkQGc+96HhITw2WefOayfBmG4mc8//9yYPXu2YRiGUVBQYAwdOtR48MEHja1btxqGYRgvvPCC8eOPPxp79+417r77bsNqtRrZ2dnGbbfdZhiGYXz22WfGRx995LD6r1d9+z6vqKjIuOWWW4yTJ0/avYfrVd/eb731ViM1NdUwDMOYM2eOsWTJEsc0co2utm/DMIyvvvrKuPXWW42BAwdeeH1FRYUxY8YMY/To0cZbb71l/wbqob69z5492/jqq68MwzCMd955x/j444/t28B1qm/fmzZtMv74xz/av/B6qm/f51VXVxuTJ082MjIy7Fe8jTT+4UcDGzduHI8//viFP3t6el7TzHl79+5l7dq13HnnncycOZPS0lJHtXJN6tv3ee+++y533XUXkZGRdu/hetW395MnT9KrVy8AevXqRWpqqkP6uFZX2zdAaGgo8+fPv+j1VVVVTJw4kYceesh+RTeQ+vY+c+ZMbrnlFqxWK7m5uTRt2tR+xddDffveu3cv6enp3HXXXTz22GOcOnXKfsXXQ337Pm/+/PkMGjSIDh062L5oG3O7cA8MDCQoKIjS0lIee+wxnnjiicvOhldaWkpQUNBFryspKSExMZFnnnmGTz75hFatWvG3v/3NUa1ck/r2DXDmzBm2bNnCbbfd5pAerld9e2/VqhXbtm0Dzq2ZUFFR4ZA+rtXV9g0wfPhwAgICLnp9aGgoycnJdq+7IdS3d5PJhMViYfz48aSkpFw4uGvs6tt3XFwcjz32GPPnz2fUqFHMnj3b7j1cj/r2DVBdXc2nn37K/fffb9fabcXtwht8MTsAAAQWSURBVB0gNzeXe+65hwkTJnDzzTdfdja82mbOGz16NF27dgVg9OjR/Pzzz3av/3rVp2+A5cuXM378eKecb6A+vb/22mt88MEHPPDAAzRt2pSwsDBHtHBdrqZvV1Xf3r29vVm6dCmvvPIKzz77rK3LbTD16bt///7069cPcM3fb1eyZcsW+vTpc+H3nbNzu3A/ffo0v/71r3n66aeZPHkywGVnw6tt5rz777+f3bt3A+d+GLp06eKwXq5FffuGc/0OGTLEYT1cr/r2vm7dOl577TU+/PBDioqKGDRokCPbuWpX27crqm/vL730Elu3bgXOjfrOjwAbu/r2/fzzz/PDDz8Arvn77Uo2b97slL/fauN2k9jMnj2bZcuWERcXd2Hbf/7nfzJ79uyrmjkvPT2dV155BW9vb5o1a8Yrr7xy0ancxqq+fQPcdNNNLFy40OlGe/XtffXq1fzXf/0X/v7+9OvXjyeffNKB3Vy9a+n7vEGDBrFp06aL3ufLL7/kyJEjTnW3fH17P3z4MC+99BIAHh4evPjii8THx9u1h+tR375PnDjBzJkzgXPThs+ePdsp7q9piJ/1Bx54gCeffJJOnTrZtXZbcbtwFxERcXVud1peRETE1SncRUREXIzCXURExMUo3EVERFyMwl3+X3t379JYEIVh/AkSy9gErcQ6NoFb26XRKsEPrPQPCCqClqkt/AIJiI2VWIggphAEbSztki4gGCEpxMIUggRJLlvIyu6yWbLFbuDm+dV3YE71MjPccyRJETNwg2Mk9abRaDA9Pf31C1ir1SIIAjY2Nkgmk13XLS0tcXJy8r+2Kek3PLlL6mp0dJRSqUSpVOL6+ppkMsna2tof13xv1Supfwx3ST2JxWKsrq7y8PBAtVqlUCiwuLhIJpMhn8/TarW+epEvLCwAn53B5ufnyeVyrKys0Gw2+1mCNDAMd0k9Gx4eZmJigtvbW+LxOGdnZ9zc3PD29sbd3R2FQgGA8/NzXl9f2dvb4/j4mMvLS6amptjd3e1zBdJg8M1d0l+JxWJMTk4yPj7O6ekpj4+PPD098f7+/tN3lUrla5gHQBiGjIyM9GPL0sAx3CX17OPjg1qtRr1e5+DggOXlZWZnZ2k2m/zaybrT6RAEAUdHR8DnfPgfp+5J+ne8lpfUkzAMKRaLpNNp6vU6MzMzzM3NkUgkuL+/p9PpADA0NES73SadTlMul6nVagAcHh6yvb3dzxKkgeHJXVJXLy8vZLNZ4DPcU6kU+/v7PD8/s7m5ydXVFfF4nCAIaDQaAGQyGbLZLBcXF2xtbbG+vk4YhoyNjbGzs9PPcqSB4VQ4SZIixmt5SZIixnCXJCliDHdJkiLGcJckKWIMd0mSIsZwlyQpYgx3SZIixnCXJClivgGNa1WaG7eviQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot data by quarter\n",
    "time_series02 = q_mean[q_mean.index.quarter == 1].plot(figsize = (8,5), legend = False)\n",
    "time_series02.set_ylabel(\"Quarterly Means (USD)\")\n",
    "\n",
    "# Save image\n",
    "plt.savefig(\"Images/hotel_timeseries02.svg\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}