gistfile1.txt

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%storage read --object \"gs://126275408/Query Results.csv\" --variable text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "import pandas as pd\n",
    "from cStringIO import StringIO\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3659721 entries, 0 to 3659720\n",
      "Data columns (total 6 columns):\n",
      "booking_id      int64\n",
      "arrival_date    datetime64[ns]\n",
      "hotel_id        int64\n",
      "destination     category\n",
      "ttv             float64\n",
      "commission      float64\n",
      "dtypes: category(1), datetime64[ns](1), float64(2), int64(2)\n",
      "memory usage: 143.1 MB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>arrival_date</th>\n",
       "      <th>hotel_id</th>\n",
       "      <th>destination</th>\n",
       "      <th>ttv</th>\n",
       "      <th>commission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7920580</td>\n",
       "      <td>2010-01-23</td>\n",
       "      <td>2269</td>\n",
       "      <td>London</td>\n",
       "      <td>139.83</td>\n",
       "      <td>27.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8738147</td>\n",
       "      <td>2010-03-09</td>\n",
       "      <td>68127</td>\n",
       "      <td>Edinburgh</td>\n",
       "      <td>59.00</td>\n",
       "      <td>8.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8467294</td>\n",
       "      <td>2010-02-09</td>\n",
       "      <td>1055</td>\n",
       "      <td>Manchester</td>\n",
       "      <td>53.00</td>\n",
       "      <td>7.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8634797</td>\n",
       "      <td>2010-02-23</td>\n",
       "      <td>93669</td>\n",
       "      <td>London</td>\n",
       "      <td>158.00</td>\n",
       "      <td>22.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8642582</td>\n",
       "      <td>2010-02-23</td>\n",
       "      <td>3189</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>55.00</td>\n",
       "      <td>9.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   booking_id arrival_date  hotel_id destination     ttv  commission\n",
       "0     7920580   2010-01-23      2269      London  139.83       27.96\n",
       "1     8738147   2010-03-09     68127   Edinburgh   59.00        8.85\n",
       "2     8467294   2010-02-09      1055  Manchester   53.00        7.95\n",
       "3     8634797   2010-02-23     93669      London  158.00       22.12\n",
       "4     8642582   2010-02-23      3189     Glasgow   55.00        9.35"
      ]
     },
     "execution_count": 520,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(StringIO(text))\n",
    "\n",
    "df = df.rename(columns={'Booking_BKey': 'booking_id',\n",
    "                'ArrivalDate': 'arrival_date',\n",
    "                'HotelID': 'hotel_id',\n",
    "                'Destination': 'destination',\n",
    "                'TTV': 'ttv',\n",
    "                'Commission': 'commission'\n",
    "               })\n",
    "\n",
    "df['arrival_date'] = pd.to_datetime(df['arrival_date'])\n",
    "df['destination'] = df['destination'].astype('category')\n",
    "print df.info()\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 385 entries, 15 to 151362\n",
      "Data columns (total 6 columns):\n",
      "booking_id      385 non-null int64\n",
      "arrival_date    385 non-null datetime64[ns]\n",
      "hotel_id        385 non-null int64\n",
      "destination     385 non-null category\n",
      "ttv             385 non-null float64\n",
      "commission      385 non-null float64\n",
      "dtypes: category(1), datetime64[ns](1), float64(2), int64(2)\n",
      "memory usage: 18.5 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>arrival_date</th>\n",
       "      <th>hotel_id</th>\n",
       "      <th>destination</th>\n",
       "      <th>ttv</th>\n",
       "      <th>commission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8245766</td>\n",
       "      <td>2010-01-23</td>\n",
       "      <td>85655</td>\n",
       "      <td>Manchester</td>\n",
       "      <td>117.5</td>\n",
       "      <td>16.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>8313674</td>\n",
       "      <td>2010-01-23</td>\n",
       "      <td>156213</td>\n",
       "      <td>Manchester</td>\n",
       "      <td>125.0</td>\n",
       "      <td>18.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>8245488</td>\n",
       "      <td>2010-01-23</td>\n",
       "      <td>158302</td>\n",
       "      <td>Manchester</td>\n",
       "      <td>84.0</td>\n",
       "      <td>11.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1161</th>\n",
       "      <td>8214410</td>\n",
       "      <td>2010-01-23</td>\n",
       "      <td>66042</td>\n",
       "      <td>Manchester</td>\n",
       "      <td>39.0</td>\n",
       "      <td>5.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1171</th>\n",
       "      <td>8343584</td>\n",
       "      <td>2010-01-23</td>\n",
       "      <td>197629</td>\n",
       "      <td>Manchester</td>\n",
       "      <td>109.0</td>\n",
       "      <td>16.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      booking_id arrival_date  hotel_id destination    ttv  commission\n",
       "15       8245766   2010-01-23     85655  Manchester  117.5       16.45\n",
       "84       8313674   2010-01-23    156213  Manchester  125.0       18.75\n",
       "1126     8245488   2010-01-23    158302  Manchester   84.0       11.76\n",
       "1161     8214410   2010-01-23     66042  Manchester   39.0        5.85\n",
       "1171     8343584   2010-01-23    197629  Manchester  109.0       16.35"
      ]
     },
     "execution_count": 521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df = df[(df['arrival_date'] == '2010-01-23') & \n",
    "   (df['destination'] == 'Manchester')]\n",
    "\n",
    "print temp_df.info()\n",
    "\n",
    "temp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 680468 entries, 2 to 3659717\n",
      "Data columns (total 6 columns):\n",
      "booking_id      680468 non-null int64\n",
      "arrival_date    680468 non-null datetime64[ns]\n",
      "hotel_id        680468 non-null int64\n",
      "destination     680468 non-null category\n",
      "ttv             680468 non-null float64\n",
      "commission      680468 non-null float64\n",
      "dtypes: category(1), datetime64[ns](1), float64(2), int64(2)\n",
      "memory usage: 31.8 MB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>arrival_date</th>\n",
       "      <th>hotel_id</th>\n",
       "      <th>destination</th>\n",
       "      <th>ttv</th>\n",
       "      <th>commission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8467294</td>\n",
       "      <td>2010-02-09</td>\n",
       "      <td>1055</td>\n",
       "      <td>Manchester</td>\n",
       "      <td>53.0</td>\n",
       "      <td>7.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8245766</td>\n",
       "      <td>2010-01-23</td>\n",
       "      <td>85655</td>\n",
       "      <td>Manchester</td>\n",
       "      <td>117.5</td>\n",
       "      <td>16.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>8463345</td>\n",
       "      <td>2010-02-09</td>\n",
       "      <td>84333</td>\n",
       "      <td>Manchester</td>\n",
       "      <td>186.3</td>\n",
       "      <td>37.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8145805</td>\n",
       "      <td>2010-01-09</td>\n",
       "      <td>89035</td>\n",
       "      <td>Manchester</td>\n",
       "      <td>140.0</td>\n",
       "      <td>19.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>7837878</td>\n",
       "      <td>2010-05-09</td>\n",
       "      <td>145565</td>\n",
       "      <td>Manchester</td>\n",
       "      <td>108.0</td>\n",
       "      <td>16.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    booking_id arrival_date  hotel_id destination    ttv  commission\n",
       "2      8467294   2010-02-09      1055  Manchester   53.0        7.95\n",
       "15     8245766   2010-01-23     85655  Manchester  117.5       16.45\n",
       "25     8463345   2010-02-09     84333  Manchester  186.3       37.26\n",
       "27     8145805   2010-01-09     89035  Manchester  140.0       19.60\n",
       "36     7837878   2010-05-09    145565  Manchester  108.0       16.20"
      ]
     },
     "execution_count": 522,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df = df[(df['destination'] == 'Manchester')].copy()\n",
    "\n",
    "print temp_df.info()\n",
    "\n",
    "temp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def purchase_classifier(array):\n",
    "    upper_lim = array.max()\n",
    "    lower_lim = array.min()\n",
    "    diff = upper_lim - lower_lim\n",
    "    cheap = lower_lim + (diff / 4)\n",
    "    lower_medium = lower_lim + (2 * (diff / 4))\n",
    "    upper_medium = lower_lim + (3 * (diff / 4))\n",
    "    \n",
    "    def classifier(value):\n",
    "        if value <= cheap:\n",
    "            return 'cheap'\n",
    "        elif value > cheap and value <= lower_medium:\n",
    "            return 'lower_medium'\n",
    "        elif value > lower_medium and value <= upper_medium:\n",
    "            return 'higher_medium'\n",
    "        elif value > upper_medium:\n",
    "            return 'expensive'\n",
    "    \n",
    "    def do_classification(array):\n",
    "        classification_array = array.apply(lambda x: classifier(x))\n",
    "        return classification_array\n",
    "    \n",
    "    return do_classification(array)      \n",
    "\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['classification'] = df.groupby(['arrival_date', 'destination'])['ttv'].apply(purchase_classifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>arrival_date</th>\n",
       "      <th>hotel_id</th>\n",
       "      <th>destination</th>\n",
       "      <th>ttv</th>\n",
       "      <th>commission</th>\n",
       "      <th>classification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7920580</td>\n",
       "      <td>2010-01-23</td>\n",
       "      <td>2269</td>\n",
       "      <td>London</td>\n",
       "      <td>139.83</td>\n",
       "      <td>27.96</td>\n",
       "      <td>cheap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8738147</td>\n",
       "      <td>2010-03-09</td>\n",
       "      <td>68127</td>\n",
       "      <td>Edinburgh</td>\n",
       "      <td>59.00</td>\n",
       "      <td>8.85</td>\n",
       "      <td>cheap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8467294</td>\n",
       "      <td>2010-02-09</td>\n",
       "      <td>1055</td>\n",
       "      <td>Manchester</td>\n",
       "      <td>53.00</td>\n",
       "      <td>7.95</td>\n",
       "      <td>cheap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8634797</td>\n",
       "      <td>2010-02-23</td>\n",
       "      <td>93669</td>\n",
       "      <td>London</td>\n",
       "      <td>158.00</td>\n",
       "      <td>22.12</td>\n",
       "      <td>cheap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8642582</td>\n",
       "      <td>2010-02-23</td>\n",
       "      <td>3189</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>55.00</td>\n",
       "      <td>9.35</td>\n",
       "      <td>cheap</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   booking_id arrival_date  hotel_id destination     ttv  commission  \\\n",
       "0     7920580   2010-01-23      2269      London  139.83       27.96   \n",
       "1     8738147   2010-03-09     68127   Edinburgh   59.00        8.85   \n",
       "2     8467294   2010-02-09      1055  Manchester   53.00        7.95   \n",
       "3     8634797   2010-02-23     93669      London  158.00       22.12   \n",
       "4     8642582   2010-02-23      3189     Glasgow   55.00        9.35   \n",
       "\n",
       "  classification  \n",
       "0          cheap  \n",
       "1          cheap  \n",
       "2          cheap  \n",
       "3          cheap  \n",
       "4          cheap  "
      ]
     },
     "execution_count": 525,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb270e14710>"
      ]
     },
     "execution_count": 526,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UVJhMJphMJqSlpTmOp6SkwGRSaCsRZ6Yq2//leh67Cy4u86CppMYj77Iv6F0T\nUctYrUSo2UD4+Ymuwo83Nr+4Gtl5FYpo8nY/ATnx7gki1BH9JVZXV2PRokVYunQpYmJieAc+d02U\nZVkwDMOpoQYiTrcoc7HXU1x/tHJo4//94ay0RvHVpKAgEFuUqaQGP7lNOAKXZEXL0xRl277neIHk\nKvy9Q6C+0YLn39+HDzefQlFZrUBb/NoUgiCaEaVpNzU1YdGiRZg+fTpuvvlmAEC7du1QVFSEpKQk\nmM1mJCYmArBp0AUFLQNQQUEBkpOTkZqain379rkcv/766wXrNhrjBM9hGMbjPKMxDtFR4YiJifJa\nXlx8K5cBx/338HBbDOpWrSJgNMahjmM7TZhCpsmICPHxrvlo2zYGRmMc8iVkwPrv1rP47aR+jr9f\nWnMAa16chLi4KC9XScdZW/v+YK6PA73ns5aKwaBAGQKaZ1xsy70TU9f735zErTf1cjmW0Caa99rD\nZwpxLNu2DTEiIgyJibEuv8vtHwDc+fy3jn8L7Vtv27a16HrDwg2KtC/Y6KEPYqG+qgdRQnvp0qXo\n2bMn7r//fsex8ePH44svvsD8+fOxceNGZGRkAAAyMjKwdu1aTJkyBUePHkV8fDySkpIwZswYvPHG\nG6isrITVasXu3buxZMkSwbrNZmFnNYbjvOLiKkRFhKG62jVgift5lRV1LhrfrkM56NO5reNvuwdv\nfX0TzOZK1DV4Cm2ltIwGjrKlUlpaDXOkAWUCmpE7zveltLIeuVfLUFHpY+pLHpytFJ//6Lt1Qsw7\n4Y2K6gZs+kmedcQiEHimqqrlvRPbXvfzSktrYI6N5Dx3xdqW8KtNjVYUF7s6rcm5R/tOmsCCdUk6\nI+TJXlpaA3RsI6pei8Uq+xkGG6MxTvN9EAv1NTjt4ENQaB86dAhff/01evfujRkzZoBhGCxevBgP\nPvggHn/8cWzYsAHt27fHW2+9BQAYN24ctm/fjgkTJiA6OhqvvvoqACAhIQGPPPIIZs+eDYZhsHDh\nQsTHxyvURX6kCtTXPjnCGULSrktzlednHyDfkNjx7Lxyl7+tLKvrRfYPvpWXK1vP5uBVX53wOGb1\nc3A8giDEISi0hw0b5rLX2pk1a9ZwHn/xxRc5j8+aNQuzZs0S37pAwACstwEpgIOzIhHRfCzjzx+7\nJs7w5knvK+Q1rCDOD1pEfnXZ1SlYPr0GBOE7uoiIxikMeMYYU6lnpCtRA5KXkUYphzolHYtkJ0qx\nKi8GlBoFYSsIAAAgAElEQVSsy6sCH6NdKh/5kGP7vJu1Q+z7wAB+n1zq2bJAEFpCF0LbG+5jzXOr\n9rr8LSRI3K/nGryqahulNktUXcEsi8tLXj7yxXaTxYrFK39RoC3y8Ifn9l8+9kwTylu/wO/HL/LE\nyvcRoffhXG4ZcgtFrgXq2OTyy6/52HbEe3IhgpCDToS2l0FAYHBlHf/hu5x1q8F/KoealBl/mMcJ\naXh7dV2s4xyxfV7/7JiibRHanvfJj+fw8Gs/iSpLvyLb5ivxsQ9WFoIQiy6ENndQL/FCRzXiSaGG\nsCyLi1crZJXhD01bTwqWat6ZZvzdHjKPE4Q60IzQLveSOauxycprrhQaa6TKES2MXb9eKMYXOy7I\nKkPbgU8CgJoiogUAJZcD9DR5I4hAoxmhvVggc9a6bdkufzvGGKXGmuaRxp+yzN0RyVcu5cvfZ2ix\nsqrMya0WAnJnmu//mZxSAec7xu+qME3iCEIdaEZoC/H9/hyfr9WTcLKnGpULmccFCMA7wwIoqajD\na58cwXOrXR0ond9ZMfe1uq5R1nvu733aZVX12HYkz08OkAShHyQlDNEigkOAgJLiSH+oVINCGD0l\nigiEaGEBVNbYdibUNfBHJGME2lNUVoun39uDkdekYMGt/X1riwSB/93ey+jRIYH3d64tkis+PYq8\nompER4bh+v6pHFcRBAHoSNMGgBOXSjyOKaVFa0lLVKvhQEv3UC2Idaj0pqFeKrAtl+w76ZpVr6au\nEUfPFYn6RsQqwCUVdVj3czaWrz0s7oJm8oqqAQClPMsA5rJa7qQqBBFi6ErTXvHpUZ+u85qa0/1v\ntUrEZiwWFicve05efEHdPQ0udg3Yr7DiJmCNFiuef3+f8Ilu/GPDrzhzpQyPzhyIYX2MXs8Va7Zu\nFIjJDgBVNQ2wsiwMHLM4PmvM0tV7YbGy6GCMQecUdSd0IAh/oitN22ckCGK1C7Kvd19Cdp687V5A\n8y1RPCQaqdpS8KZlO7+yZVX8Oyts5XBz5koZAO4oge6I9pMQcVpFTSNWc8Q394Y9boBzEhPCv7As\ni8sFlYLJYojAoluhbR9j5KfTtv26/1Qhjpw1y26XvznJsUSgFkhkS0SsnBQKICTwOwNbpLkVnx3F\ngdOFIhvHU5fI8/afKkRF8zZOKY6TqkzO04zarXBSOXGxBH9acwArPpG21EH4F90KbTtcn1HmoVzH\nv4Wco5yvX/X1CdWr2k0W9TaQFG1psOCfdCr9lM9eKcOJiyV4d9NxWeVIEVyLV+7Cudwy/P61bY5j\nQu+IUnH+/YHOZDYuNvtC/HLsapBbQjijY6HN/wWt/cE1l7LYj41hGLXLbEJn8JvIJSzpiIgwFAyB\nw7LAq/911eKERDLXOrha8HemtUDg4rugt1mITtCx0LYj/cU7fbmU83L1DhfK448BiPbgSsPdr4Av\nMQ2vNi7JV0Mbz0bFMlsxfjhwBTuzAq/drtt2Hr//6zav0SeJ4BMCQlsY98Htr/87wnkewyCkZp9K\n99RUWqtwiXrHVZQuemtnyy8SfDWEBDIDRj2vtYBUDgXz+P8yz+Hfm08rU5gEvttnC1CVbY/MqOJ7\nHcroVmhL+X64zt2ZdRX1DRYUljkLmtAxj7PeFlSJgCDaYZvnxOVrD6O2vonzBS+tdN0PrdSj9vcr\nQ3LE/zQ0WpB5KBe15KmvSnS1T9sdlmXFDSIc5/x782kcOOXqSRtK44XFwmL3CQpmEUxYx394fhPg\nfG459p00ISoyzOO3Z1ftcfzbJgiVkbZyS9HymrZe+HbPZUewG0J96FbT3rjjAua9tg01ArNFi9XK\nm6jj+EXX7VM19U3IM4fGy7z14BVF9nsTMmC9mLbF+gvxyLjGJtcgKKoxqgh6jwemGb4gdA/3nzIh\nv1j944e5jJax1IxuNe2fDucBAM7llnk9738/npMUsGHFZ75FXdMaV2mmHXRYiLQUeUGsEq2YzPaz\n9Ffzmra3u1haWY/3vrQFlPnw2fGBapBPhIcZ0NDk5wwxhM/oVtO2kyugGVOEJW70FihCz3hVtBlG\n0BGtscmKTzPP+b0tYhA2j8usIEjU1AUg7K1ChIfrXixoGt1q2oQ88ouFQ1sSfoblnzy5CmKhiGje\nq8k8nItygVCoYnlNYqIQMXziFFdBzZq2t/uspd2O4WHqvcdECGjaBKFLRK5pixl+qxRMfiLXcsUl\nlH90jmDogzyprmtEvZfUpkrhTS7zxShQo0WLnP3UjSaEdl0DmbAJgg9vubbFSG0Vyg1FeezNnVj4\n5o6gtoErvvp3ey9j3mvbPLbfBRs1TiSIFlQvtK0si0deD+4HRxDBQm7scTGBU6Qk7NAqlkDYp72a\nxz1/XPdzNgDg+MViUcVXVDcERKBqyZQfiqhfaNMbRIQwvJHHRcfL106IUjFw9ZtlWVVMPLzdZ2/j\nmFDSIgA4dakEj/9jF9Zvz/apbYR+UL3QJlMNEdIo8f5r6BPyZTn1pTUH8cf39ynfGAXxKrRF9Dnr\ngk0bd85QqCQ5pkrHv9UwASL4Ub33OL0/RKjS0GTF2h/Pehy3Wlk0WcTto9WaT5Evzb3sJHCCiT+9\nx+1li9HKfeH//n3Aoy5Cnahf0w52AwhCAcqqpDsb7cq6ipIKz+v+KSHnNaO1ePkCs4xg9eXwWbOL\nNioVb9qrpIlVACZhZN1UN6rXtLU14hAEN0+s/EXyNbU8XuGHz5pFlyFWI1cr5QKTnUCYcq1WFiu/\n+BUAdzSz+kYLlrzzC24Y1J63DNZN1V7383nHv4W054v5Fdh64Erzuf6HZLa60YCmTW8QEZoo4fH8\n7+9O49cLrt7Jv/yaL7tcf+EulBa7T3bcJUoAhgehiUFuYRWq65rw/f4c0WV8t9fpXAFJ/PJ/Drac\nSpp2yKN+oU3vD6FxPuFYlxaDxaLMy3/ojKtm/sG3pxQpNxj8ctw185waJvWiEgmKDIAjLDD9L7Up\ntLO6IaFNEH7mx4O+efxarNo2bfuEgEz6fl8OCkpUFmKXZ4y6XFCJ1z87iorqBpy4VMJ9EuDSZy0N\nd7nmqoBEmiNcUf2athpm0gQRDAISEERliNEja+tbNEE1TOq5xqgj58z4xwbbOvjfPz2KXHMV7/WM\nBKmtls0ABSU1ePGD/eiWFocX7h8R7OaEFKRpE4RK4drbe/R8URBaoi4CvY/Yl+rsAhuAV4ENuK5T\nm0q9WxHUsoXP1GztuJivju12oYSg0F66dClGjx6NadOmOY6tXLkSY8eOxcyZMzFz5kzs2NESZnTV\nqlWYOHEiJk+ejF27djmO79ixA7fccgsmTZqE1atXi24gOUUQoQqXpv32+ixZZao9RaSYLF5sc/az\nL3ZcwBYvzl+2c5UYP+RlURNLfaMFz3MEiQnTaj5Swi8ImsdnzZqFe++9F08//bTL8Tlz5mDOnDku\nx7Kzs/Hdd99h8+bNKCgowJw5c7B161awLIuXX34Za9asQXJyMm677TZkZGSgR48egg0kkU2EKv5I\nJLHwzZ2KlxlwWOBKYRW+2X1J+FQNDSB19Z4OYE0Wq8vkTS2pSTV0W3WHoKY9fPhwxMfHexznmsFm\nZmZiypQpCA8PR8eOHdGlSxdkZWUhKysLXbp0QYcOHRAREYGpU6ciMzNTVAO19NERBOF/WLAu69pC\n58quz6mIPScKOH6XV0dNXRPe//oEp4Pdv745KatsQn/4vKa9du1aTJ8+Hc8//zwqK23rGiaTCWlp\naY5zUlJSYDKZOI8XFhaKq4ikNkEQTrAs0NgkzrOea/ioqG6Q5BvgXMT7XysvRL/efQl7TpgcAVyc\n2X9K5DhJhAw+eY/ffffdePTRR8EwDN544w0sX74cf/7znzlnnAzDwCpj60rbxBifryUIQlvEx7WC\n0Rjn/ZyEaFTXeq7Nc13X2GTx+P3FDzORW1iFvz12A/p2TeSs49yVUmzZexkLZg6CxS2qnL0c+/8L\nyqUtY7i3s6F5AlLjZj3g6k9VbSOqGq3o1j5BUp1ycW9LQmE1729aR+398UloJya2vOh33HEHHnro\nIQBAamoq8vNboi0VFBQgOTkZLMvi6tWrjuMmkwnJycmi6ioq8u55SRCEfqisqoPZ7N0j+XJuGVZ9\ndcLjONd1zkLb/ntuoW1MOZltRruYCM46nnjT5lybGBuJkxdd91ibzZUwGuMc5ZWVSds37t5OR4hT\n1vt5dpat3oMVj6ZLqlMu7m0pL6/l/U3LOD/XYLeDD1HmcXcN2mxuibD0ww8/oHfv3gCA8ePHY/Pm\nzWhoaMCVK1eQk5ODQYMGYeDAgcjJyUFeXh4aGhrw7bffIiMjw5e+EAShY8RksdolIQyrt9U1MYFB\nNu28gGPZxV7PkbuAZ/ctE1tOZU2DzBrlQ/Ezgoegpv3kk09i3759KCsrw4033ojHHnsM+/btw6lT\np2AwGNChQwe89NJLAICePXti8uTJmDp1KsLDw7Fs2TIwDIOwsDC88MILmDt3LliWxW233SbKcxyg\n3K4EEarsOe7p9AV4z03tjleh3SgstBsahZf2/LUttYpjCQAAmhQKb0toE0GhvWLFCo9js2fP5j1/\nwYIFWLBggcfxsWPHYuzYsRKbB9pbQBAhhF3rtFpZvM/jOS1FSGpZI1z0lg625xGKo/qIaCEYfZkg\nQh5vFjYp0V3lKsFitkUHa1pw/GIxMg/5FtfeVxqbrDhxsUSStYNQFtXHHqctXwQRenj77KUsmbmf\nWiMxg5UtmIlAfUEaol7/7BgAIGNYRwBAXlE11m49gwem9ENym2i/1Llheza2HriCHh08Y3cQgUH1\nmjbJbIIIHSxWW3hSc1kt7zmsJC3P9dy/fnJYUnvExB9Tiwl+zeZTOJ1Thv/94FsqWDGculwKAMjO\nq/BbHYR3VK9pkyMaQYQO247k4XJBJfZyRB6zI8k87vZ3TqG0LaSiwoaqZIiy3xd/ZoezD8ci7A+E\nn1C9pk0QROhQUW3bzlRUXsd7jpSJPJ8HtljUKrMLnbKB2R3zDM2juX8VHSepTQQF1Qtt8ncgiNBB\njMDh8x5f/3O2y985pko8t2ovbzlitGi1JOhw51mOfhma2+pPJzF7yWL20xP+QdVCu66+CS/8yzNV\nHUEQ+sQujw3ehCWPTNq897KLo9m53HLZ7RG1pi3bQ12eALRXb0/h6VdFx65ok8wOGqoW2icueo9E\nRBCEvrBr0d6Eglih1LqVfJcdb+2orW/CDwevoKY+yDnKHYKUW9O2sqzsZQK3qoggompHNPJBI4jQ\nwi5wvDtT8f/GMEBJRR0+3HwKfTq1kd0eb1rw+u3Z2HY4D/ExkZLK9FcENYND03Yt/91Nx3HojBl/\nfWgUkty2gplKaxAdJV4M+KvthHhULbQJgggtxITo9HZGbX0T1v+cjZOXSnHyUqlyDeOgoNjmDGZ3\nnhPLOxuPK9oO25YzhndN+9AZW66IK+YqD6Htbc3fndLKeuQXS0uOQiiPqs3jtN2LIEILi5g0vl6G\nhSX/3I2CEm7BkmOSnr3Jm2bpq9Z5+KxZ+CQfaFa0Ud9o4WybVz8BEfz1f0dkXU8og6qFNi2gEERo\nIUbTds877c6lAm7h/H//PiC5PUJ1qQGH816z1M4vrsHqrz3jtst1HjM5TYbIES14qFpo63395Jm7\nhwS8TruHKUFoldLK+oDV5W0IUtvwZHD6tvedNHn8ruz2NRpHgoW6hXawG+BngrkHNFA1h4ep+hUj\nCJ9RanxSyrNb6JtWcrhpslAqp2Ch6hFV75q23DUmWQSsan0/Q0K7yP4EVDI+iW2Gu5Kg9/FVr6hc\naAe7Bf4lGDJbVPAKgiAE0drw5D7YK9X+rOwiHL9AMTUChbqFdrAb4GeCYR4PdEYivU+8iNBFPa+2\nuDBlHuONhA548yN4c10WXv/8mPjCCFmoWmir6KvwC0G1jpOmTRDyUMn45Jx5yxurvj7hsodbygT+\nmfd2+9Aywh+oWmirJU+tvxArNxfOGqhcpRQ7mCBsyPwG1DY+CX3T5VUNOJdb5vhbihVMzFY8IjCo\nW2jr3EFRbKacXh0TMLhHO0XqzBjWEQBwz4TeipRHECGLSuSYlGY0WVhYWRZrt57Ford2+q1NhP9Q\nt9BWy1fhJ8Rqu0qasjunxOGDZ27C0N5GxcokiFBEi6OTxWrFxfwKZB7ORV2DJdjNIXxA3UJbi1+F\nBIK1rswwDG33IAiZqOUTqq1vwhGO0Kj/2JDlccxiZVFWKS1WOqEuVJ0wRCXfhN+QIrP1fi8IItCI\nXZ7iQy0T339uPI7zeeUe48mRc0Ue57onEyG0h8o1bX2/YMHQszsYYwAEbhKg80dI6JD6Ru9m48xD\nuTh1sYQ3xnmgOZ9XDkDct2axsvRNahx1a9o6f7nEmseVsqKPGZSGbmnxyhSmMaKjwlGrgeQPRHCx\nsiweXrHd6zlrfziLtT+cDVCLlMViYWEIp60jWkbVmrY3fTA6KiyA7fAPgV7S7mSMDWyFqkLnM0BC\nEUw8aT21yqadF1z+rm1owpYDOUFqDaEEmtW09a6FO+MX2R6g+6eWHQCh9L4QvvP8+/uC3QRF+eqX\nSy5/f7P7EsqqyBFNy6ha0/Y20OohOIj4+N+MZoUOZfnSN+kDU4PdBEICJLC1j6pH1Jp6/pR1WhVi\nLgRx4hGo25fcNhpTR3UJUG1EoJHrgU0QhDRULbTf33Q82E3wK6L1bA2PiwyA2eN6BLsZKjHS6w8t\nv5sEoUVUvabtDS0PFnff3AvRUeEhkrQjFPoYuoTEK0xIhmXZEBnfAo+qNW1vaNk8fvPwTkgfmCZJ\nnCnu0BWwG6iSB+WlGeFhNLj4jnbvHckU//DDgSuY99o2mMtqg90UXaJdoR3sBigAzUQDh1q82PWG\ngV5hwo3/ZZ4DwB2RjZCPZoW2FriuX7LX38UnDFGgMW4ESoR1SY0LUE3ekeIw1a9LWz+2RGfoaOKp\n9wiMAcfpfmZlF+EwR3x0QjqCQnvp0qUYPXo0pk2b5jhWXl6OuXPnYtKkSZg3bx4qK1vC+b3yyiuY\nOHEipk+fjlOnTjmOb9y4EZMmTcKkSZOwadMmhbuhTubf2h/RUfLdBhgwiktZQwBUpKiIMNWkAJWi\naf/htkF+bIm+0JHM1vSSmxpxvp1vrsvCyi9+DVpb9ISg0J41axY++OADl2OrV6/GqFGjsGXLFowc\nORKrVq0CAGzfvh05OTnYunUrXnrpJSxbtgyATci/8847WL9+PdatW4eVK1e6CHq9YmAYtImN5P09\nmObx+NaR+M3orn6tY/TAVLSKVImvo4QBOTJC+9H2lIIvloA9hn1Ca/73W2vQEoo8ispqceB0oeNv\nmgT5B0GhPXz4cMTHu8arzszMxMyZMwEAM2fORGZmpuP4jBkzAACDBw9GZWUlioqKsGvXLqSnpyMu\nLg7x8fFIT0/Hzp0yE7Br5IVQJECMn2T7rLHd/VNwMwaNOCmFGWiVSCov3j8CKx5NV8SSpBZIyMjj\nudV78a7Ot+mqAZ9Gq5KSEiQlJQEAjEYjSkpKAACFhYVITW2JkJSamgqTyQSTyYS0tDTH8ZSUFJhM\nJjntVg1ylGUpmrYmxxMVyWy++3fn+J6IjY4IaFu0BN8rGhFuQNu4KFU9YzmUVdXj4JlC4RMJXixu\naT/JcuEfFFUx3B057Hv1uBw8ZJuGVTJYyIkIpbSi3VUlTl9aYuJ1naHUlGhQj3aKlEMElryiajyx\n8hes/upksJuiK9Zty8ax8+RBrjQ+2bbatWuHoqIiJCUlwWw2IzExEYBNgy4oKHCcV1BQgOTkZKSm\npmLfvn0ux6+//npZDVeJzLY1hGfMNxrjEMaxB9hotAnXyOh6UVUkGeNErbNGCJwTGxvlqDsQxLSO\nDGh9vmA0xsHgFh/d1zbfOakvordnY9+JAuGTNY79HsXFtgpyS3zH/j3MXf5TsJuiW97ekIWv/j7d\n8bfaxwNA/W0UJbTdNeXx48fjiy++wPz587Fx40ZkZGQAADIyMrB27VpMmTIFR48eRXx8PJKSkjBm\nzBi88cYbqKyshNVqxe7du7FkyRJZDbeqZQHKSzPM5ko0WVxPeOH+4TCbbU54VbX8sdWdKS6qREOj\nRfC8pibv51RV1TvqDgTtE6MDWp83+F4Xs7kSVovV45gvVJTXIqWNdoWYFBzvcFVdkFviO5WVgf0e\nQpGIcIPLPea634fOmNEpOQbJbVsHsmmcGI1xqngnvE0cBIX2k08+iX379qGsrAw33ngjHnvsMcyf\nPx9/+MMfsGHDBrRv3x5vvfUWAGDcuHHYvn07JkyYgOjoaLz66qsAgISEBDzyyCOYPXs2GIbBwoUL\nPZzbQoH4mEh0S/Ox32qZpIjkN6O7YkRf7/vUAwv//VPqzupp+xNBKEFkuHfrX0lFHd7ZaNsKdldG\nLyTGt8KwPsZANE2zCArtFStWcB5fs2YN5/EXX3yR8/isWbMwa9Ys8S0TQiUyjPFiHgfgVdiKH+QZ\ntXTXhYhwAxqbrJy/dUuLC7mIb6GS8Wre1H6Of4faMyakERHu3W2qpq7J8W97JLUPnx3v1zZpHdrr\nIhM5CrDYQV6pcXFIryThupSpyoOMYR39VLI4vD0n5996dOC3hIRRzE4AQPrANOGTNEBhaU2wm6B7\nhL4ZNSojake7Qlsj46fzS+ne5EApKZERBnzwzE1IahMtq5wZY7qJr9PNLDZhRCdZdfuT+OYAIf27\nJWLJnUN8Lic5Ud791SJaVrR/Pno12E3QPULvB4WOlY52hbYOnnWgBrx3Fo9VxIx5q5vQHt6He836\nhkFp6NfVNX63msf2h2cOwI3XtsdD0/sjysdoaG//4QbEt44UNAc6c21PT8uH1oSgxppLBBghayLJ\nbOloV2jrAClroHJebl8jfoWH8V83ZmAaxg/twPnbXTf38gh/qebBPblNNO67pS9iWnEHWRna2+YY\n4y35SUwrm3vI+KEd0Tk5VlS9D8/o73Es0Ovi3p6xGGhNm5DCz0fzBM/ZmXUVi1fuEr27JtQgoS0T\n5zHrgcl9PU/wah+XXofAmWJPlE1ifJS0CzQ8tj80vT9e+f1I9GifwHuOXXhFR4XjwWnXCJaZktga\nEQKetVqAZDbhFcbVBP7R92dw4lIJln24HyUVdZxR0/69+TTKqxqQlU2BWbjQrNBWi1Wla5pN+0pr\n1xpjB7eXdK0W/JrcQ3zaNcqoyDDeZ8Cl2QdKg1y+gDtojxxLRXiYAe2TYhwDjBI9sZfx+O3ayijW\nyc2KQJo24Q0Gnt/eG58dw5XCKmw9cMXrd/ntnsuobxCOTxFqaFZoq4HfTeyNtrESNU4XRHqP+9HL\nfPSAVNcDbmW88Vi6y9/P3D3UZhof0pFz5vT47YM513UDNbbzBWiIibZNNuJbt0xCVj4+lrecP829\njr8Sob5I6OygHsIe/VJ47aFREq+QNpt54f7hLn+TzCa8wjAegbDsfxsYxqvQzi+uwea9l/3ZOk2i\nWaEdSAcGPueiiHCDIy8137qxs/mnW6rrdiIpA56Y/Ne+jJ+//w2/KZdhPPvVMTkWc6f2a9a0PR+C\nWuNvj+qfihljuuG5e4c5jrVuxR+mwF2j5OOpO6/FHK5lkSBhbBONa9ycAJXEfQ1c7ITSVwc/f1PX\n0CR8EuEzNk2be7BmGGHv8fJqcaGeQwnNCu1AwvtescDtN/bENV3bYsGt3MKvRwfbOmhau9b4/W/6\nufwmJTXn7yb2Fj7NT2rPY7MHcppx28XbQnaKEXBKtM05qIdUpozqglvHdEOKAqESnQVVv66JuEHi\nsgjgXw310ZkDBc+5d1IfAPL3XIvtx+Ce6pzMfb37UrCboGsKSmpQVcs9MWIY4aBRfPncQxnNCm1f\nn+XvJvZ2CBvBOkTU1S6hFZbcOQQdjNyC696JfbDg1v7409zr0NrNO1m02RtAStvWgvmvlVgjbxXZ\non3aixvSy8hpxk2Mb4VX51+P5520Vz7kfnutIsN8FjCLZg9y7MWWRfMII9SXYA8z0VHh6NWR32kO\nAEb1T8EHz9yEtER5kxgxg+ryh0ahfbsYWfX4i6Iy7cZO1wrLPtzPedxgENa0GS04/gQYzQptX83j\n6QPS8LdHRmP2uBYBOLB7OyycNRB33NTTsb0HgGPg692pDXdhXt4nu2YcHRWOkdekcG6tkSrIuLr8\n9h9ucAQ96cXRzgHdEwXLdTZpS9VmUxJbi8pAFtRPT6HKY5vXw9slyE8Kwmd58HF3ngdCnwcDRpSm\nI4TQO/zH+4YjuU20ahxH3amtF28e799N+FsiPOHbusXA+5o2AISRpu2BZoW2z3C8A4vvGIyhvY24\nZWRnLJzVYlqcOrornvjtYEmRwOyMHyoctlOsydh+Xv+unoNGbHQEbh3TDR88cxOnNvnEHdcKlv/4\n7YMd/05r1xq3XNdZVLskIaKvd2X0Ur5eBZk0ojMmj+yMRbOV9fi+/aYe6GiMweSRnT289f2GUmOh\nQDmdU2wWKCGNyj5pDjQWq/jpBJlqlUWMn46Yc0KN0BPazYwekIbY6Ag8NN0zwIWdcAODAd3acebE\nBhDwfWfd28e7CFhnGIaRZYJ++q4hmHJ9F6T6aC59+w834M1FY3h/F9M0MaFO33hsjHfPbgUZOzgN\nvZ3MzFGRYbj9pp6CmraY5+B8yuSRXfDSvJG4XUmhJfBu2uu/YVCaYxufOwaGwQAB7VIpQRZmYIKi\nyUoJoykl2p0WCfScRIwjGgltT0Tl01YnvklM+yvQNi4Kb//hBuWaEyD4BlhA3gvet0tb9O3iu9ex\noIbI0zQDx5YQbyTERCIhJhLP3zcMx84Xo7HJgi37r0iq+snfXitKo31gsu+Ob2qkW1o8LuZXAGgZ\noFu3isDTdw/1WHfs2TEBz94zFB98c9JrmULWIrGCgGGC7wsgRDjf5F0npCa2Rn5x4JKoCG35sp9D\nuKLvqaO/CcL75O0dV9x7XMHi5Bbl3rUe7RMwa2x3tI1r0XrvutlmXp87xbYlbVR/2x70bu1dt9r1\n78ZDVO8AACAASURBVJboNSSpIH60sPgzk9gf73N2GGyph0vbWfq7YaIGTLGtFbZCy7MUBYIImSFf\n1c6YQWlIH5gqfKJC7Dh2FT8dzvV6Dmnanmj2LfTVEc2XgYGvrjglPJIF+NfTN7nW6UVDVGrQ49p/\nLRdfJxRp7WzmevvWOY9ynf49YbjNvD5mUBrefWIcHpx2Df71NPdavxwUuT88t2PhLGXWy29N7+pZ\npdMzcH4crSJ930Mt7Ekv4bkHQWpLGUfCg2geD4TGaWAYjOib4vd67BSV1+HgGbPXc/hkNsuykpwI\n9YRmhTYfq5aMcwzevmI3ncZ7iXZ21829MJgjkMjscd1l7Sd2x32mmZLYmncfrtIftpKhR/maJiQA\nxw5ujwenXYMFt/L7HnhDrTN1vlZ1SY3zqa+//00/PDxjgOPvAd3bYdrorqKuTW7bGg9M7ouX53n6\nCgh6oYve/9YcApbn9GBp2WeulIk+V25yFTn4YyLtDtNck5rg+34/zTyPR9/YgVxzVYBbFHw0u6bN\n95FHhIcJDADCo8NL865DjqkSHZL495byTQymjuoqWL5Y+EJSDutj5Dyu1Ljnj2hzfG1jwHAOSG3j\nolBaWY82sVEYeU3gZv9i8Hc0Pt/C0coLkmKPm39relfU1DlpMEIObSLb2jqqebtcfCsUlXPvjVbn\n9KoFfy5dqAW1pcrkU0R+OGjzYzl1uRQdeWJkAEBeUTV+ycrHrHHdgzrpUhLN9sKfL1eb2CjFY0L7\ngrFNtKTzldIq7S93VISSrwd32/gG/efuGYo7buqJEX25c3YHk8jm+8LnaS9uKUDcs1pyp/CWPT6c\nI9V1FbmGP+OG7rh7gnD0PTuCjmjN/x8/tAMmXdcJS+68Ftd0bYvpbtsoGQRO2/7gmZuET+IgqE5R\nARKmXdPihU8KIPaJ0r6TJp+06j9/dBDf78/BvpMmpZsWNDSrafuK2p1dxHL9NSkcGZdcz/nrw1KT\nR9i4ZWRn5BdX49Z06fvT+eA1j7sNRmMGpiHXXIWkNtG4ZaTwfvH0gWk4cLoQt47pKr+RIgkzGLDy\n8RtEBZXxBWdBeA3H3nyxDOtjxDN3D0HrVhFITbRNAB+bNRBXCqsU0zqEPid7XyIjwvDb8TZHwSV3\nDgEAfLnroqg6UhJbw1SinFezr/4VISCzkRATiXefGIeHX98eoBq9wzAMqmobseqrEwCAD58dL+n6\nuuYsYTU6Wv/WpdBW+uMKhMlocK8kHDsnPn/sfI51T/fwrEkJ0jR1O7HREXhM4QAifLibxudK9Ado\n3SocS0WEUVUa95C0SqJY3BOGQZ/Ortv4hvQ2Ykhv7uUVLrq1j8deL1qKnB0Lby4ag8ff3tVckLhr\nxgxKw66sfJ/rlINa/SOURk39DDMwXtNzymnpDweu4FJBBe67pa9qE9pwoVnzeLB447ExWPFouvCJ\nQaB3pza4U6VRxfjG9hsGtUd4GIN7JJhk1Y4o47iM0WZYHyM6GmMDEkEsY2hHXNePf4lCzvge3zoS\nGcNskQO7pPCb7+3hhKeP6YbeHXlCCnuBy5PeF4KpaQekbhWaIQ0GRjAAi6gAORyn/C/zHPacMOHh\nFeqwKohFF0J7pkAiDSVJiIlE2zg5ObS5cffU9mX9jGEYVa4BA9ye6EkJrXBnRk+sfuomx+AdKnh7\nukIOT11T4/DSvOswcUQnTBzRCX+8b7jX8+VgMDAY05yoJZnDx0JubIB7JvTG+0/f6HX75D0398aj\nMwdg6qguPnlRz7hB/viw+I7Biu6mkErX1PiA7aFWU+Q3g8F7fPyrRdWY99o27D1Z4LUclfnXyUIX\n5vFpo7ti444Ljr+9fVwqnEzacGrXkjuvRWcvmocW4brv08d0c8kqRtgY2KMdru2ZhBuHdPB6nsHA\nBMSy0r9bIh6cdg36cUTMU+J74stFDwBzpvRFVGQYhvUJ7mQ0zMAE1WxsYIB5U6/BL796F058MBCT\nRKaFIb2ScETCcp2/MAiEOt1+9CoA4OMtZ3H9Nb5Paqwsq5noa+qZUklkqNu63Nt/uKHFbK30mnYA\n52kR4QZc0zUxcIkjAoRGvgdl8NJXRsQ54WEGLLptkCP72tLfDQtYvHUuGIbBqP6paMMRt8Dfj/WG\nQW65yn38FMV6z3tDzDvs7bt99p6hsttgR6plSqpFRPHoijLwFk3P/pPgfErAhN7UZJXUpmCiSaE9\nZ0pfzGpOrTm52cM4NjrCYbZO9GK+DqaJSwzqbp0c9NszSTD2/4m/Hz07JnjsFFALSg7u/hQUU67v\nIrsMMe3zljSHN8WvqMpt/xs7uD3uurmXZB+Q+BhhJSA+pmWJQi2+aCzErVnbn82B04XIK6qWXE+j\nRTtCW5O2yVaR4Uhp2xqrltzIuf5y45AOsLI2k9baH87Krq+jMRato8IxUUQWKl9RzmNYoYIURq3t\nCjQLbu2Pb/dcxgOT+wa7KYoQSs9VjCDzt4nV+b3p27kNTucIR3SbPqYbSirqsNOL1336gFQXfxjV\naNqsmLj1tqWin4/m4aPvzwAQ3hpmKnXdQqglTVuTQtsOn8NEeJgBE0d0wsHThZ4/+vAuRkWEYeXi\nsdIvlIBSH4naIhqFInxP8pEZAzC8bzKu6+dbhLd7JvTG2h/OBn1915lAD+7eXu/endrgLE9YUiU+\nC7Wted6Z0Qv/9+8DHscfnHYNjG2ikWeugrFNNK7pmohPfvSuvEwY0cllzV4tXf1oyxk8JSLAkMVi\ndQhsALhUUIGuqS2BYuzPn2VZvP75MZy4WOJyfXFFPRK8hK1WE5oU2ip5n5RFl51qQS2DQDAZyBGr\nXgoZwzpi/NAO6tGCoK7nevfNvVBW1QADA7z++TGX36TkzebCFrHN/52Nigzzui/ZGT7HOHt2u55O\nSXZuTe+Gnw7lcabB7dE+Hu3dQjaraYLy5S+XBM+xuKnjL605iFd+P9LlWG19E5b8czdnopHqukZZ\nbQwkmlzT7ihyfY/rM1XPq+gfoqNsQQLkZG7yB2r3JQgEStwBNQlsgL89T/72WslLAFJ79uRvXTWw\niHADBvVohwHd2+H+W/q4/KZE8IxA3PrXeWJAcH0/XFsD+ayPsdER+PujowF47lt//r7hHhHy1PSa\nlVa2xKr/7KdznOdwjfV//Nc+x79/+TUfxy+W8GYGkzupCySaE9p/mnsdb8xnd7T0IOy5euXukWwV\nGY6X512Hvz48WolmKYaaBgG/E0Kd5etq/26JjiQkSuL8Tffv5hri1XkC4R71bWD3drhlZGcse2CE\nz3UrqX324XBKe/n3IxEdFY7uTvnfve3Z59K0X7iff89+m9go/OuZm0TtW1eTpm0uaxHaW/Zf8amM\nXHM13t10nPd3MevmakFzQju5rfjQnPbg96MHBC6xu6/MvLEnBvdo54jLLIcOxljVbRlTm4YYDPR4\nCxQd3GUW5Xx5q2bN2r6jxGBgcMdNPdFFxtYvqV3t0YE/+cb0Md3QwehqkrZnFXz+3mGIaraUOTy6\nOeq2ukma5LbRXjNeAeKfl9a+V7kKGtf13+65hC93XURJRR3W/5ytmvzdmlrTfvquIZLMXMltorHy\n8bGIjgrD7uO2oARqfRnjWkfgD7cPDnYzCCJoiPky7ZHT4lpzTEqdCoiMCMPfHxmNGKUmrwwjeexI\nH5iGrqnxyDyUy/l7OE9QGYZh8Or863GlsArf78tBaWU953nuFkcprXvzsTF4/B+7+E9Q5zDJi1yj\nKtf1G7bbAnaduFSC87nlYBhg9rge8ipSAE1p2n05IjIJ0bpVOBiGQfrAVLSJ5Q+VGGzUOpnwB+0S\nbIlNkhJaCZypTULnSQbejHptryTcOb6nI3TrU3e1WKbcW5IY30qRtexBPdqhd6cEwYho0VGuOpCB\nYVycwZxhYYshz0eb2CgM7N7OEXPCPRkQYBszfLWoOe/J5kJr77B8TZv/N3NZLQDg2z2X8cK/9gVd\n49aUpi2HeVOvCXYTiGZeW3gDjpws8MhARWgPLpntvCYrhYQYm4Dq16UtTl0u5TzHwDCYeF1LylaX\n0Kp+mkAsnDUQYQaDYPFvPuYaWEWoNVNGdUGb2Ch8uPkU7zm/zeiFtvGtMGG4iAhoiga6UayogNBk\nkSe0LVYrtuzPwcRR3TyeG+u0DJFXVI3tR6+KShvsL2QJ7fHjxyM2NhYGgwHh4eFYv349ysvLsXjx\nYuTl5aFjx4548803ERdnW0d65ZVXsGPHDkRHR2P58uXo109aGkZCHyTERnrVMgjtwGUh8jVValRk\nGN59YhwiIww4dMaMypoGSdf7a8eEvYtCVgW7E+nA7u3w64VidEqJRYFbHvB28a1QXFGHtnFRMDAM\n0pK8O9XGRkdglsiESFLl7LU9k5BTWKlQaYGjpKJO+CSJ7D9ViMNnzfjpSB5eWzDK5Td3J7VgOzjL\nEtoMw+Djjz9GQkKLCWj16tUYNWoUHnzwQaxevRqrVq3CkiVLsH37duTk5GDr1q04duwYli1bhs8/\n/1x2B/SC1ma2ctD7UgBf77wlxtAqXI9Sjsnc7oA1XEK2uuUPjUJBcTXivWQKk4N9u5XYbi2cNRDm\nslq0T4px5F1Pb3aGXTZnBPKLq0XvgBHCWYBIve2LbhvEK4DU/Ik+9e5uxcssq7L5DZhLaz1+q6p1\n3cMd7PFL1ijCsiysVtfwb5mZmZg5cyYAYObMmcjMzHQcnzFjBgBg8ODBqKysRFGR+CwyT4qIiqNl\ngv0iBJLQ6WkLKx5ND2qWKH+hhh4lt4nGoB5J/qtApKZtJyLc4AhWYneGnTvVZlWMjY5ALx9ygvsL\nvnHHW0+DvR3MH4quuye+NxgGeOeLXwXTgfoLWUKbYRjMmzcPs2fPxrp16wAAxcXFSEqyfUBGoxEl\nJbZwcYWFhUhNbdl6lZKSApPJJLqu/l0ThU8iCDXAMab5Iwe7GnAf9K/rp54Qq0ph76GvssruDMtF\nWqJNuPuaK9tvllovnR3a248TpCDBFSmOD1NpLQ6dNWP1Vyc5fz90phAffHPSb2Z0WebxTz/91CGY\n586di27duvG+nFwdEKtd3nit8kEa1IYaNJZAoXejQrA1kUDi3NVPXp6M2irl1xuDjX2c8sdzbd0q\nHKufutEjIplYpo7qgnU/ZwNQOOMaz/ERfZMxYXgnHDxjVqwuNSDFkU3IYPbORlsQl8nXd3EJD2tl\nWZRXNciewMsS2kajzZkoMTERN998M7KystCuXTsUFRUhKSkJZrMZiYk2DTklJQUFBS3mhIKCAiQn\ni5uVP3mv71GMNAMDGJPk5/zVBgyMRv321RDp+Vnptb+NTsN7XOtIxz5qPWF/dgkJ5R6/pSS2Rs+O\nbXD9gNSgPOP7pg1Adn4lDp8pRHi4QbE2tOLZStazc1u0aaPMeryauOqUztNojENDI3/897YJLQG+\nqhqt6Naee1tfQpvWjuexO+sqXv2PLbnLG4vHoaeMJRKfhXZtbS2sVitiYmJQU1ODXbt2YeHChRg/\nfjy++OILzJ8/Hxs3bkRGRgYAICMjA2vXrsWUKVNw9OhRxMfHO8zoQpjNfB6O+oEBExL9BGzamZ77\nWl7t6fWs1/6WuKU41GM/7X2qrPS0IrAsi3lT+rqcF2j6dErA4TOFuKZLW8XaUMeTQKNjYjRKy6Tn\nq9YS2w9cxopPj/L+XlzW4qx2/lIJYiO4rSQlJdWICbdNau0CGwAO/HoVCVHedzp4m3z5LLSLioqw\ncOFCMAwDi8WCadOmYcyYMRgwYAAef/xxbNiwAe3bt8dbb70FABg3bhy2b9+OCRMmIDo6Gq+++qqv\nVesShoEy+QM1gN6NxyFkHQ8pB0ou87gaep8xtCO6pcaja5pymj5Xvx6Y3Bf9uiaisFloxcdEooJj\ngqp1/u5FYAPAz0fyHP9mwaK2vgmtIsPAMIzLMvD53DJ04khuJTfOuc9Cu1OnTvjyyy89jrdp0wZr\n1qzhvObFF1/0tTpCT+h8oA+lNW39bWKzMaRXEo6cc93dotYJisHAoGdHbhOtr3BlFUtuE+34/zN3\nD0FaUgwef9tLKFSVY2AYSQ5oXKz66gQaGq0Y0D0RT9xxrUs+94+3nsVNQz2D4jh7qm/eexkWK4tp\no7uKb7esFhME4YGzo8rrj4/FqwuuD15j/IxaBZlcuCZenA5IOu0/l6rt3NU+ndty7oufPqabYk24\nb1If4ZOCTEOjbcvz8QsluFpUjeo64RCnrJNJdf3P2di444KkOlUttB+aORAvzb0u2M0ICHod/LjQ\ne0+dn2VauxiktNWf444dvb62XP3i+kZ12n2f+9U5JRZ/fWiU8IkC3HFTT7Tx8zZJpd/dkso6UVvw\npOwJ50LVQnvqmO7oyLEmoEf0+vFzodeB3o6Llqbzzup1ssnVL66Adjrtvmiu7ZnkGv8dUGQwC8R9\nVSwDXDO25DTCAtmunfuKqoU2oU/0OtDbcR7c9d1T/QotsZq2buHoKpfmu+i2QXjqriG4/5Y+SEpo\nhb6d27qsh/fr0hbjh3ZQonpF+dfTNyFM4QiFYpfHv959CVnZ4qOBukNCWy2E0Higd5wHd72P83oV\nZJymcH12lRN3R7SHpvf3uswz7toO+OvDoxEdFe5yn566awh+N1F9a9MGA4M+nZQNJ8vlSX/qciln\nYLHtR6/6HDGNhLZK4PLWJLSJwUVo6/u56rV3XAMql3PazBvEZeDSGu5d9TCBS0SpBClKct8tfTDj\nBuUc5/656biHtv23/x3BvNe2cZ5v8XFtm4S2StD52B5SuCxpB68ZAUGv7y2XEuQ+Abt5WEdJ2ci0\njBRTMtdE9U9zr0NCjIRoeQq9WJHh/CKuVWQ4bk3v1rwWrQw/H80TPgm296uxqWVt23mrmBAktAlC\nYZgQktp6tSRw6UDOcuu6a1Lxm/SugWpOwHF/rHIz1EWEGyQJbQZQJNiUGGW23kvIUqmcvFQq6jwr\ny2L99mzH3xeuVoiug4S2StDp2Bfy6FWo2dFhtlEAwgmOXpg30m/5u9WA+3Kd0k5bIhqgEJ7PcaaC\nJnFfycouxrbDLVr559vOi76WhDZB+BGdyjQHup2UcGhooRTpzv3FlaJpx7W2baUa0st7bonE+BZv\n9Ltv7oUJwzuhf1fb2nlHp+xYcujT2XMtflq6q9A2tmmlSF2BQlaWL0I5dDv4hTj0XLVJBMdaaCg9\nSveuSpmwhIcZ8P7TNwpe069LW/zyqy3z483DOwEA6hssyCmsRK+ObXD0nLhtUb+b2Bv/3XrW4/id\n43vCYGBw4mKJ1+v7dGoLc1m+qLr8yb6TJvTr2hZZ54sxM8MPCUMIZQmh8SCk0PtAr1fts01cFO6b\n1Afd0uJbDuqzq4Jc28soefIZxhGJ5paRnbH665O4Z0JvGNu0Qp/ObdEmNsoll3hUZBh6SUhb2Toq\nHOOHdoTFwiK5bTQOnzVjZ5ZNAPfp3BZnc10dvLgSeIixIgQin9Oqr06gdVQ4auqbMDOjN+95JLQJ\nwo/ofZzXqcwGANw4RHpQEN3Q/FzDwxi8/NBoRVJ+Xt8/FcP7JrsI6dnjeoi+/t0nx+HhFdtdjv3x\n/uEAgAkjbJr64J5JmHFDd1zMr0CX1DhczHd18Fo2Z4RHuWFhwi/xgO7tUF3XKMlhzBdq6oVjl9Oa\ntlrQ8eAX0uhZqiG0zP+hFEvB3leZSbA8cBbYUnhoen+PrVmzxnbn3P/dNi4KQ3sbAQCjB6Sio7FF\nu+ayDIWJeIcjwg34433D8ew9Q6U2XXFIaBOEH9H7MK9XmR1KCb24UEVfnbfY9Utx+SkqMkxUeNTI\niDA8eee1AID41tyxxqU42fVWOIqaL5B5XC34e8GECAqqGPz8iG77J5CaMlRQWtOWQv+uic2xyz1z\nUi++fTBatxKX8CMhJhLLHhiBdgncXuJizOPOvHD/cLz8n4OSrlESEtoqgWS2PtG7+Viv/eMyhYeU\neby5q2wQR6aIcAOeumuIImV1SeX3xpa6B93FOTEIkHmcIAifCR0xFlqatr/WtJVCyWfBtwNCTLx0\nJUOgioWEtkrwNeMLQQQT3Wra+uyWaOSGLfUXj8wYgME92qF7e+W03TAe57in727R8vnuxrP3DEXP\nDgkY1KOdYu0RgszjBEEQItDrBIULtfZ0eN9kxZO08JnH28RGoYMxBnnmao/fnrhjMArLatElNQ5L\n7x0GAJi7/CfeOmJahaO6Tng7lxhI0yYIghBBCMnskOorl3n8n0+MBQD8bkJvxEZH4P/bu/fwpso8\ngePf9H4vbdOWAqWl5VLohSKIjggiF6tAoaWgrsvCrjzrZVcRdVYRh2fWYZRnQOBxB3cYGdFnHtkB\ndKg41FEERpBRLgVBxlqwFyhtaem9SZs0SfPuH5XIpS0tTdPE/j5/tScnb36/nJzzy3lzzvumXzc5\nTFJc2A0XyK364Z7x9oyJDW13+XMPjWVuNyeekaLtJKR3XAjHeTorudvP6Ud1zGm7x3tDe1eP+3i1\ndUKPGhrC/zwzmaGRHV/IdkVYUPfHME8aFkbG5DhCAr1vvvIPpGgLIfqdcSPCu/+kfnT62Z9+CrDX\nDGYe3bx1bN2Td7W7XNvBrWlXSNF2Ev1oHxHC6bW3P/anXbQ/HY/s1avQ3mhvi9NGkRIfxujYa2cb\niwzxvea+8asvRL4ndVDnr9PDOEUPLV+YQv6FesKCfe0yvq8QovuGhPtT2s4FR1frT4WsP92Tbr8z\n7RuL9vhR4UwdN5jD3/w4i9ir/37HDd3hi9MSeDP7DMsXjmVM7I3TiV7zOnaJVtyylHgtKfGdzzsr\nhOhd119Tkjr8FrrPf0L60U/aN3xBGTkk+JbaufqM/eV/Gc+FSh2Bfl4ApI7QMiDAi6x74okKu3Gu\n8NQRWra8cG+XXkeKthCiX5qUPNA2n7P1h6o9ISGCpbNHtztoRleHzfwp6E+/aV9t12/SqavV97id\n+MHBxA/+sfgH+Hqy4am7e9wuSNEWDvTKoxNpMbf2dRgOsXzhWBqbTH0dhkOs/89JeHq43uUxt40I\nv6poty1z03Q8ylWwvxfPPjiWqC6MlOXq+tPV41d/P/H0cOvRF5bX/+MuvL16d5Q019vThMuKjghg\n+OBb63pyNSnxYdydEtXXYThESKA3Ab6udxZ6dY/4+B+mckzs4H7aK5LjwtAO8O3FqJzDuBFtP9k9\nMmNEH0fS++x5u21okA/+vdwjI2faQoh+6eqD9fwpcdwxJpIh4Tf+3tgfhQb58PaL9/bbbnJnJkVb\nCNFP/Vi13dw0REcE9GEszqe/FGxXS1O6x4UQ/ZKMQijA9T4HUrSFEEIIFyFFWwjRL119gvXB9x+x\np2hvn8Ui+o50jwshhAu4eujIv108zF/P7+twXbPVPtMqCtFTDi/ahw4d4v777yctLY233nrL0S8v\nhBAADPphZKqR0QM6Xa/OWM/yz1ey6/s9jghLOFh7U3M6M4cWbavVyurVq3n77bfZs2cPOTk5FBYW\n9rhdo8VIq9X5B+1oMjejN3U+vnFXKaWuOVNwVkopKpur+joMAM5U52GwGPo6DIfQmfSU6yv6OgwA\nms3N/KP6ux63U22o4f1zuzG3mjt4ne5t20HhfqxYnIQ2Jc+27OTlbzC1mrEqq21ZQX0xAPsvHrqF\nqJ1fjaGOakNNt59nsBiueZ9c1fhR4aTEh/H8w6kAtFpbMViMfRxVxzTKgUf+U6dOsWnTJv7whz8A\n2M60H3vssXbXt1qtrPj0N5xvLCHEewDpcWmYrCa+LD/OQP8IZgy9B4vVwtrc35Iansx9MVPRm5s5\nWPp3GloamTjwNowWI8bWFr6tyeeeIZO40HgRN40bQwOHUNRwgWTtaM7VFaD1DePAxS9oNOlYc/cq\nvq8rpLypksYWHcHegRQ3lGCymkiLmUaQVyB+nr7kFH9GlH8kRytOEhM4BH9PP2qN9cQHx1Df0oi7\nmztebl58W/MdGcNns/Hk7wBYmrSIcv0lqgw1aNAwLDiGSy3lxPnHEeGnJb+2gDPVeVisFu4Zchfb\n8j+wvScPxE4HNNd05UX5RzI9egrbz+4iNSKZ3MpTAPh5+NJsMZARPwt/T38aTY2MChlBiE8wfy8/\nRoSvlr9dPEx50yVigqIZ6B9JjaGW72rPEeDpj97c9gXjvph7CffVUqYvJ1k7Bh8Pb1osJgobiqkx\n1hEbFM2+CwepNtYCMFabiI+HD/dGT+Z3p7fSYGpEg4YnUv6VCD8thYYCahralt01aCLv5v2JwQGD\nGB8xloqmSnKK9xLmG0a4bxijQoZzrq6QQK8AvN292VP8KZ5unpitZpLCEhgfmcq5ukLc3dw531BC\nqb4cgGnRk7l78J0cLjvCgYtfXJPPbREpzBh6D6X6cqIDBxMdMBhjq5F9Fw5yW+RYTlSeZnzkWMr1\nFVxqqmRwwEBCfEJ459v/w13jhs7UhLHVyMOj5rP97C6GDxhGbNBQ9pUctG2T1PAkpg65m2M1uTQ0\n6fm2Jp/bIlIYGjiEZouBKP9I9CY9Fmsru4v+Soo2kW+qv2Vp0iI83TyoMtSQGp5EXs1ZChvOc7m5\nGlOrifKmCuKCY6lvaSDEO5iU8EROVJ6ixlDHzwbdzrToyZyrK+TdvD8BsChhIf+oyefe6Lv5qPCv\nzBo2E4vVgsFiJDZoKBd0FynTX2Lvhb+RrB3DbREpGC0txAZHs/PsbsZFJFNnrKfKUMOYsFEcLjtC\nqE8Ij6csocZQx/HKk3i5e7G/5BDDtbH826h/plVZaWzRsa/kICariSOXcgGYEJlKoFcAYT6hNJp0\nlDSW4qZxY8qQn3GurpADF78AID44ln9N/Ce+LD/Ol+VHeXrcYwR7BfJfX/w3AD7uPhhbjSxNWsTl\n5iq+rTlLk7mJyuYqEsMSMFiMtLS2MCt2Rts+ptGQXZDD3Lj7SQ1P4uPz+2z7SHeNC0/m66ozLEld\nwJiAROpbGjhx+TQ6k55ArwBaWluobKoiwMufIQGDmDToDi4bqihuKOGD7z8CIMJPy4SIVKqNG8oH\n0gAADB5JREFUtfh5+DIzZiq1xjrWn/hf/D38WDByLn4evsQERfPbU1so07dNNjE37n4GB0QR5R/J\n5eZqLuhKSQxLwMvdk5yivYwIieer8uMMDRrChMhUSnSl1BsbGBQwkLN1BZytLSA9/n4aWxr5qOgT\nAB4amcmOc9kAts8zQHTgYEaFDMdsNTMoJJxR/gmYrWYuNVWQok2kydLMy39/1XacSY9L40jFCe6L\nuZfLzVVo0NDSaiLEJ5jU8GRqjLWYWy1E+GlRKFqtrRytOEGp/hLBXoGU6EoxWy3cHjmO4QOGofUN\nw8fdm6MVJxkbnsj39UXoTXqOXDrBhMhURoTEUaa/xADvYOKDYylvqkBvaiLEJ5iGFh3+nn5c1Jej\nlCI2KBoAL3cv8mvP4efpR2xQNAGe/uTVnCUlPBGDxcjWf2xj6vA72Hz8PdvnNSF0JOPCk2k06ahv\naaCltYUdZz9kypCfcVFXxv2x0wnxDubdvO1UNFWiMzcxK3YGU6PvJr/2HJu/eZdFox/EU+NOsHcw\nNcZahg+II9RnALXGetw0GkJ9QqhsriLIKxB3jTvuGjcGRnbc++PQov3pp59y+PBhVq9eDcDu3bs5\nc+YMv/jFL9pd/8EdTzoqNCGEEMIp7Hzodx0+5tDucVfozhVCCCGclUNHRBs4cCDl5eW2/ysrK4mI\niOhw/c6+bQghhBD9jUPPtJOTkykpKaGsrAyTyUROTg7Tp093ZAhCCCGEy3Lomba7uzurVq3i0Ucf\nRSnFggULiI+Pd2QIQgghhMty6IVoQgghhLh1MiKaEEII4SKkaAshhBAuQoq2EEII4SIcXrQrKipY\nvHgxs2bNIj09nT/+8Y8ANDQ08Oijj5KWlsbSpUvR6XQAFBUV8fDDD5OcnMw777xzTVvOPI65PfNc\nuXIld911F+np6Q7PoyvslWtH7TgTe+VqMplYuHAhGRkZpKens2nTpj7JpzP2/AxD2wiHmZmZPPHE\nEw7Noyvsmeu0adOYO3cuGRkZLFiwwOG53Iw9c9XpdCxbtowHHniA2bNnc/r0aYfn0xl75VpcXExG\nRgaZmZlkZGQwfvz4vjs+KQe7fPmyysvLU0oppdfr1X333acKCgrU2rVr1VtvvaWUUur3v/+9Wrdu\nnVJKqZqaGnXmzBm1ceNGtXXrVls7ra2tasaMGaq0tFSZTCY1d+5cVVBQ4Oh0OmSvPJVS6vjx4yov\nL0/NmTPHsUl0kb1y7agdZ2LP7drc3KyUUspisaiFCxeq06dPOzCTm7Nnrkop9c4776jnn39ePf74\n445Loovsmeu0adNUfX29YxPoBnvm+uKLL6oPPvhAKaWU2WxWOp3OgZncnL0/w0q11Z5Jkyap8vJy\nxyRxHYefaYeHhzN69GgA/P39iY+Pp7Kykv3795OZmQlAZmYm+/a1ja0dGhpKUlISHh7X3p32zTff\nEBMTw+DBg/H09GT27Nns37/fscl0wl55AkyYMIGgoCDHBd9N9sq1vXYuX77swExuzp7b1dfXF2g7\n67ZYnG/qR3vmWlFRwcGDB1m4cKHjEugGe+aqlMJqdd6JNOyVq16vJzc3l6ysLAA8PDwICAhwYCY3\nZ8/tesWXX37J0KFDiYqK6v0E2tGnv2mXlpaSn5/P2LFjqampQavVAm1vdF1dXafPraysvOZNi4yM\ndLoD/BU9ydPV2CvXK+2kpKT0Vqg91tNcrVYrGRkZTJo0iUmTJv2kc33ttdd44YUX0LjANIg9zVWj\n0bB06VKysrLYuXNnb4fbIz3JtbS0lJCQEF566SUyMzNZtWoVRqPzzo5lr2PTxx9/zOzZs3srzJvq\ns6Ld1NTEsmXLWLlyJf7+/t3emZWL3F7e0zxdib1yvb4dZ2SPXN3c3Pjwww85dOgQp0+fpqCgoBci\n7bme5vr555+j1WoZPXq00++39tiu27dvZ9euXWzZsoVt27aRm5vbC5H2XE9ztVgs5OXl8cgjj5Cd\nnY2Pj4/TXVt0hb2OTWazmQMHDvDAAw/YOcKu65OibbFYWLZsGfPmzWPGjBkAhIWFUV1dDUBVVRWh\noaGdttHdccz7gj3ydBX2yrW9dpyNvbdrQEAAEydO5IsvvuiVeHvCHrmePHmSAwcOMH36dJ5//nmO\nHj3KCy+80Ouxd5e9tmt4eDjQ1tU6c+ZMzpw503tB3yJ7HYMHDhxIcnIyAGlpaeTl5XX6nL5gz/31\n0KFDJCYm9ulxu0+K9sqVKxk+fDhLliyxLZs2bRq7drXN5Zqdnd3umORXf0t3hXHM7ZFnZ8ucib1y\nba8dZ2OPXGtra21XrBqNRr766ivi4uJ6OfLus0euzz33HJ9//jn79+9nw4YN3HHHHaxdu7b3g+8m\ne+RqMBhoamqbt725uZnDhw8zYsSIXo68++yRq1arJSoqiuLiYgCOHDnilMNS2/M4nJOTw5w5c3ov\n2C5w+DCmJ06cYNGiRYwcORKNRoNGo+HZZ58lJSWF5cuXc+nSJQYNGsQbb7xBUFAQ1dXVZGVl0dTU\nhJubG35+fuTk5ODv78+hQ4d49dVXbeOYP/bYY45MpVP2zPPK2Ul9fT1arZann37advGHM7BXrvn5\n+e22M2XKlL5O0cZeuZaWlrJixQqsVitWq5VZs2bx5JPONX+8PT/DVxw7doytW7eyefPmPszsRvbK\ntba2lqeeegqNRkNrayvp6elOdVwC+27X/Px8Xn75ZSwWC9HR0axZs4bAwMC+TtHGnrkajUamTp3K\nvn37+vSCOxl7XAghhHARMiKaEEII4SKkaAshhBAuQoq2EEII4SKkaAshhBAuQoq2EEII4SKkaAsh\nhBAuQoq2EEII4SKkaAvxE5SZmYnJZLrl57/00kts27atW89JSEjAYDB0uk5ZWZnTT6IhhDOToi2E\nC7t+CsjW1lagbWhGLy8vh8bSlUkYSktL2bFjhwOiEeKnqeNJQ4UQfernP/8558+fx2QyERMTw2uv\nvcZ3333Hr3/9axITE8nPz2f58uV88sknuLu7U1xcTHNzM9nZ2SQkJPD111+zd+9ePvvsMzZt2gS0\nFfWpU6eyY8cO9Ho9r7zyCgaDAZPJxIMPPsjixYu7HN/evXvZuHEjPj4+zJw586axBwYGsnr1asrK\nysjMzGTo0KG88cYbFBUVsWbNGurr6zGbzSxZssQ217EQ4jpKCOGU6urqbH9v3LhRvf766+ro0aNq\nzJgx6vTp07bHVqxYobKyspTRaLQtS0hIUM3NzcpgMKg777zT1taBAwfUkiVLlFJKNTU1KZPJZPt7\n1qxZqrCw0Nbme++912FsNTU1auLEier8+fNKKaW2bNlie832Yl+/fr1SSqmjR4+qrKws22MWi0Vl\nZmaqoqIipZRSer1epaWl2f4XQlxLzrSFcFLZ2dn85S9/wWw2YzQaiY2NZfLkycTExJCSknLNumlp\naXh7e9v+Vz9MKeDj48P06dPZs2cPixYtIjs7m/nz5wNtM1L98pe/JD8/Hzc3N6qqqsjPz+/SbGOn\nTp0iKSmJmJgYAB566CHWr1/faeztOX/+PEVFRTz33HO2mM1mM4WFhQwbNqzrb5YQ/YQUbSGcUG5u\nLtu3b2fHjh0MGDCAPXv22C7g8vPzu2H965dd/ftyRkYGa9asYc6cORw7dox169YBsGHDBsLDw1m7\ndi0ajYalS5d2+eI1dd08Q1f/31ns7bUTGhpKdnZ2l15XiP5OLkQTwgnpdDoCAwMJDg7GZDLx5z//\n2fbY9QWzPVevM2HCBPR6PRs2bGDmzJm2M3KdTkdUVBQajYZz586Rm5vb5fjGjRtHXl4eJSUlALz/\n/vtdij0gIMA2jzjAsGHD8PHxYffu3bZlRUVFtjmphRDXkqIthBOaMmUK0dHRpKWlsXjxYhITEwFs\ncwLfzPXrZGRk8P7779u6xgGefPJJdu7cybx583jzzTe5/fbbuxxfaGgoq1ev5vHHH2f+/PmYzeab\nxg4watQohg0bRnp6Os888wzu7u5s3ryZjz/+mHnz5jFnzhx+9atfXdOeEOJHMp+2EEII4SLkTFsI\nIYRwEXIhmhCiQ2+++SafffaZrbtdKYVGo+Htt98mNDS0j6MTov+R7nEhhBDCRUj3uBBCCOEipGgL\nIYQQLkKKthBCCOEipGgLIYQQLkKKthBCCOEi/h8Oaaxh0yNvYQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb27072b1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[(df['classification'] == 'cheap')].groupby('arrival_date').agg('size').plot()\n",
    "df[(df['classification'] == 'expensive')].groupby('arrival_date').agg('size').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb2904f27d0>"
      ]
     },
     "execution_count": 527,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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et7ZemhOkL+Kq4EzeQFWt/zp5EoQ347QI0Gq1mD59OlJTUzF58mQsXboUAJCbm4sHHngA\nKSkpmD9/PnQ6nXn/559/HuPHj8eDDz6IW7dumctavnw5xo8fjwkTJiAzM9O8fe/evbjnnnuQkpKC\nFStWmLdz1UFw42h9vVxjT2e7KjExACievu+z7+QtzF2yD/vdYNUhCMIap0VASEgIVq1ahYyMDGRk\nZGDv3r04efIkPv74YzzxxBPYunUroqKisHbtWgDA2rVrERMTg23btuGxxx7DRx99BAC4fPkyNm/e\njE2bNuHLL7/Em2++CYZhYDAY8Pbbb2PlypXYsGEDNm7ciCtXrgAAZx0EN0JGk84G4nF2wCqmX9e7\ncXTsTgM92z3wV0OAaUrngIPYFwRBuAZZpgPCwsIAGEf5Op0OKpUKhw8fRkpKCgAgLS0N27dvBwDs\n2LEDaWlpAICUlBQcOnQIALBz505MnDgRQUFBaNOmDdq3b4+srCxkZWWhffv2aN26NYKDgzFp0iTs\n2LEDAHDo0CGrOn777Tc5TsevcdzBeoctQMzoniwBBEEQ0pFFBBgMBqSmpiI5ORnJyclo27YtoqOj\nERBgLD4pKQkajQYAUFBQgKSkJABAYGAgoqKiUFZWBo1Gg5YtW5rLTExMhEajYd1eUFCA0tJSxMTE\nWNVRUFAgx+l4LQzD4H+Z2bieXym9DAedprc4pIvp1t0SXtdLhuD+uyDSiL+HgiYIb0SW1QEBAQHI\nyMhAVVUVnnvuObO53hLTkie2F12lUnFuNxjYl4AxDGN3jL8vq7pyswIZmdnIyORIpiMAYdMBkouX\n5Xgx6PXK6Tj8tY/077eWILwbWZcIRkZGYtCgQTh58iQqKipgMBgQEBCA/Px8JCQkADCO5PPz85GY\nmAi9Xo/KykrExMQgKSkJeXlNjkGmYxiGsXIe1Gg0SEhIQFxcHGcdfKjVUXKettvILRGfGMj2XGtY\nOk3TPsFBgQCA0NAgxMdHcpYRHh7isM6YmDDJ11itjhJlkVBZLKWzrdNgYGRZatcszHi+AQEBdnW4\n6lliGu+FJbGx4S6v15aC0hqoY8NcKrCDQ4yfoZCQINbz8tX3VQp0rv6JN5+r0yKgpKQEwcHBiIqK\nQl1dHQ4ePIhZs2ZhyJAh2LJlCyZOnIj09HSMGzcOADB27Fikp6ejb9++2LJlC4YOHWre/sILL+Dx\nxx+HRqNBTk4O+vTpA4PBgJycHNy8eRNqtRobN27E4sWLAQBDhw5lrYOPwkLp5nRXoTcYEBjQNDvD\nMAxuFVUjO68SI/oYp0PKy8WLANtzLSmu5txHp9MDAPKKqlBgcZxtGTU1Wod1lpXXSr7GhYWVoszC\n2ga91bEmfth+Cb8dvYF/z7sTEc2CJbXFRF2t8XwNDGN3Xq56lkrK7UMil5bWuLxeS/afysPKjecw\nfUwnTBjS3mX1NGhNK4d0duelVkd55fvqCuhc/RNvOVcuIeK0CCgsLMQrr7wCg8EAg8GAiRMnYtSo\nUbjtttswf/58LFmyBN27d8e0adMAANOnT8eLL76I8ePHIzY21tyhd+7cGRMmTMCkSZMQFBSEhQsX\nQqVSITAwEP/4xz8wY8YMMAyDadOmoVOnTgCABQsWsNbha1y5WY5F3x7DY/d0xah+rQEA760+jsu5\n5QCAzm1ikBQX7qgIwTicDmgc7OVoqswhfL0drvHpb43hkG9oqtCtfXPB5V28UYbE5mGIiQx1um3l\nVfUor9aiXaL4UQDb/L+7fQKOXzQGlDp0RuNSEUAQhOdwWgR07doV6enpdtvbtm2LNWvW2G0PCQnB\nkiVLWMuaPXs2Zs+ebbd95MiRGDlypOA6fI0DZ4xLo37Zc9UsAkwCAACqZcyWJzTgzJ4Tt/h34sIL\nfQrqtDqEBAciwIFZu6Jai/dXH0dwUACWvzDa6TqfX7ofALDixdEICpTBB9dDPgH+6otAEARFDPQK\nTB0T13I303Y5RoIcfpYAAJXFuJrLJM8wDApKxU9LeAoGxqiCzy7ei8U/nXC4ryn6YIPO+iI5e9Ul\nOS96Qcfb5AfgnsaczymTVfASBMEPiQCJVNZosXLjWRSUOd8hBjY6r3GN0uVcC+9wvt1ikMxV5ZYj\nOfj9vOOlmO40W/P6qzEMSirrAQBnr5U63DWQx4mQ7VdB/gsy+dS5e0TuNglgcROXrTvl6toIgrCA\nRIBE1u29iv2n8vHl/844XZYQS4CBYXD8YpHTdQn9oHMJkt/PeVcsBr6OkRGyUyNCVxKcu1ZiXT4P\nUjQAW7keixPgxmrP55S5rzKCIEgESKWmzmg6rq5zPl+BqfPhCnxjYIDDZzTY/cdNp+tyhGVnxTXC\nFbRSzFmfABH78rXn4x9PoJDF054NPksAYLzvH/1oMa0gxBAgQQWwigCeuk5eLsKqrRdkC7pjarcX\nzEwQfoKBYWSxnhLyQSLASSw/8JsOXcd32y6ILsMkArgsAXoDg7ySGtbfXIU/OYPtO+mEk6MN9RZL\nEgFxo3OGYaApqXFZZLwla7Ow+4+buFUs07PiIMAXQUhh9W8X8coXB3H8gndZFJUMiQCJsH0W1+6+\ngp3HxY/WTSNQrk+twcDAHenlLQUN94efvyHe1mXonIwq6KgPFNI/mvbZcSwXr644hN+O5kqqVGhn\nLJcPCUXyI+Rmb+Oqo9NXnJ/aJOSBRIAEyqvqcZTHOU4MfB38zaIq/G//Nafq0JTW4Jst580e8Hw4\nM/grqRBmfncXOr2DJREW8J4yy30Sc51M6+5dPQqSezrA23M0kaXCd6Bb5X2QCJDA5+uddwa0hM8h\n7eAZjdN1LP3lFPacuIWNB6872KupHVyjSSHz299vv2TlPMeH3Udc5g+FXqgIkFSvgFwMPL/nl9Tg\nTHYJSivr8dEPfyBHU+nUJfD2D+3hsxock0kI7TiWi5kf7PI64UkQvgKJAAnkWYTedRR8Rih8IkCO\ntdMVjaF+BVsCOLYLPdtzOY6X48mFkJj2UjINXrxh6aXOfbyUDtf2kNdWHMInP53Az7su49z1UixL\nZ18mJ/Q0hAaE4sN8bWVWFcv/dwbL0k831WPz+7ELhYLLWv3bRQBGp0iCIMRDIqCRzKw8fCvQqc/q\nmyjDxKmKp5DyKsex+oXVYcRhmAAZJ4HF9BuuHrhK8Ql4f/VxQfsJKrlxJ3MmTY7dKqobcxQYOHYS\neFHl6rPdGyqoCS4R5BA/zyDqqxgMDOt0nL9nfPUlSAQ08tWmc9h1/KasgXm8CnMHxH1+gl5LV7y7\nTlxyIfPBOo4wiXnF1fgs47S585XCpoPXxc9Jc+xe02il4fo+Cq1F6hz5mt2X8eJn+6E3XS9PqQAY\nz+Ho+QLB94a6FO/kxc8P4NnFez3dDMIBJAIkYPmR9ZWPj7mdjj7oMp6MI5O0SWiVV2sb57+b9i2v\n1totw3OEkBEFlyVg6bpTOHq+AL/uv4br+ZWsvgOW95ph0NRBNvLrgWu4WWifmdGqjMbzMzX18s1y\nXL5ZbrefZbhi9mBBwpBqCdh8KAfFFfWorDFOP5murFzTC2I4cbkIn2WcxsIvD7q9bkI+SivrBTvm\nEp7B6QRC/obxg+3ert0tljEBgV/4piWE7uOoorW7r2DToev4dE6yOcHO8F5J5t9f/vyAsPJN7REU\nvKipMUfPF6B5dCg6tYoxjzJ3n7iJHcftl+1tP3oD32+/hJ4d4wAYzfUvfW7fKakErt+03Ou9b49h\n5StjWfcrKq9DdS2LH4ibfQIsW1xaWY/IsGAEB8k/bmC7h6b8FJdvCIwg6CtqXOF4LOolwQlZAmwQ\ns+4bEDe3dTq7GEXlnomW1eQT4ORL6JwGwKZDxtUJlyyyJB44nW/+W6sTOWoQcr8s/v4s4zQWrToG\nAKjTGi0OXI6D32+/BAA4k+14pYOWx3LBdsnZp/ybti769hjLMUJ9AuRdIlhd14AFy/Zj0aqjspRr\ni7evZiAIf4ZEgJMIlQAVNVos/ukk+0hS3iaxYhIrDl0e5GyIkJj+cuBEm4WuGuCr4u1vjuJSroAR\nK49g5O0MBV40uR0Da+uNIienoEqegl0AGQJ8A8GWRMJtkAiQgJRvbJ3ApXmuxl2BVfhGrW6Naudk\nVcHB/K8JX4ZCwL6tV1j8AuSA7R7rDQbB13z/qTxjGW76XrNpI7IO+Cc0HeB9kAiwQdjHx3I+QGDB\nHl4SIyQ1vKyGAJ7rKNe8tZBSnK0rOJD/NZFy7WxN/jIZAlj3m7skEws+2y/o+F/2XMX566U+NWqj\nJWcEIQ0SAXYYP6EXb5Th6Y93s47WbPuUqtoG3hG20E/UB6uPY82uywL3Fo4jT+/1mdlgGGEaXeh5\n8Kb49aGhXpAAEeCIOq0eN4uq+S8ezzUResnY7nFtvU5UvInyGi3Z2AlCAZAIsMH0/Vyz6zK0OgMy\n9l2138fi7xxNFeYu2Yfvf7skvVKLUcyFG2XYfDhHelnclQBg70jWZ2YjT2DmOcEioPEq/bD9Ema8\nv9PuOl69VSGwJHna41QdQipRGac4jpzToMYmwuO73x7FP/5zGCUV9XaH/byzSfDJpYvkKCdA5Ut2\nAMLXoGfLeyARYAMDSJoPZVtiVlReix93XEJtvc7jD31TR8beQ8i9lvfQGQ3qG/T47egNALBLgCRU\ndMiB0wsiBKgAFYD9p/PwxfozWP6/s1a/FTd2/mzx7bccaRJ8/LYYcasDispqJU+FBKhUnp7BEoUP\nNZUgvAqKE2DD1iM52H40F0GB1p+VS7lliIkMRUJsmKBvsbZBb14JEBCgwtg7WruiuaJx2h9PYM9Q\nVduAtbuucLfDrZEZnaurtNJ+BG+HSoXCMuPyz9PZxZJawT+Fwt8MwHiPT14uwpK1WRg/qC0eGne7\nsAMtMAofz3WtP7tgSowgCHvIEmBDxr5sVNU2oKxx/tRkGXjvu+N45YuDjdv4v8Y/7GiaHjAFPvEk\nTblg2NsuZLRrYBjcELFM7Goet8lf70afALaa6rXCoxIKJTgo0Fgf16m5yU/iX2uzsOuPmwCAfVm3\nJJUREOBxX1Zx+FJblYzvuAIpBrIE8MAwLM+tgAf51NWm0SBf5j73BAx0nLzGURt+2XMFNwur0bVd\nrOAshIDj0b47LQFsfev73wtLECQUFcAbTY9PPMp5RbKusFsjhKIUnwDTdIkc2UAJwhchESABIR9r\nbYP1HLvHP6mN1XN5iFfUaHE5l33d+saDxih/YmL6A/Zx9q1/kz+0rRiu51fKVH8TlqsINCXifR74\nhJGUKybVuBAQoHKLKaCiWovzOQJDAztA6vu1YOl+NAsNwnuzhjrdBoLwRUgECEFskjiGgVZn3WF6\neqDBV/3HP57gLUOsuTrXQWIdxo2WAEFz+k5ie39fXXHIfh+eu1DGt4RPwiWTepXd9bz+tFPaqppT\nV4sRFR5s/l9qe8urtSh3IoskIREyvHgNJAIEYDsC5nXgAtBgYQk4d70Upx3Fn/eadW6O4crGJxRL\nESGXT4CnxZUYfClamqvM45bWofPXS3HwjEZSOZ/+fJJ3n70nbyGvuBoPjrV2jGQYBvuy8tC9fXNJ\ndROEP0EigAeGYfDcp7b5sPlVgO0e3227aPX/+sxsZGbl4b3Z7jFDyvFJF+MPwMbrK4+Y/5Y7vr0l\ndVr3h2hWqVSSpgDE4E4R4YoIfAYDg6c+3G3+/8Mf/pC9Dku+3nweAOxEwKXccny9+TxCQwJdWj9B\n+AIkAnhgy2onpQOzNaWvz8wGABSzrB13Bd4wYr5Z1DQ9IJcnPFsp85cKC48rJ+eul/JmG3S6D5d4\nPNeSRUcIzI4sCm/JK19ZYwzm5IoVIoRjfMcWphxoiSAPUiLbsTm9WQZtYaz+ltYuT2DZiTuLay0B\n7v+4n+UTADIg5ZLpdAYs/onfdG6Lr8XiF9NcHzs1gnApJAJEknWliHefuUv22W2z7PQ+WH3cYjvj\n1lTC3oJ8Oe+947wEnY0Hmip1FUaAC0wBrtS7YlYHeMcTQxDeAYkAkfxzTRbvKJZvKd1Fi6V47rAE\n5BZW4ZaMo3g5kM0S4EtfdCfPWco1k3p55LqstfU67Mu6hQad3tUqgPAhPL5kmjBDIkACcmbAu3ij\nDN9vdyL5kABW2zglegNypRL2FkuAEHxo5oeXCzmlmL80EzcLuSNINugMeOnzA/jvpvN4+YuDqKpt\n4NzXWUQLT6RnAAAgAElEQVQ9Bb7zyEBvMGDxzydw8HS+p5tC+CkkAjzMqq0XXF7HhRvOB2PxVnxI\nA3jE/4OryltF1Xj7m6Pcfh481/WrTedQVqU1B5JiI2PfVVTXGVdqlFVp8VnGaQEtlgavU6aL2XPi\nJra4IPvn9fwqnL5agi83nOXfmSAkQCJAAv40ovMUReXyrIrwJRHgseUBLKzaegHZeRX4TqQINYXD\nrmns3MObcS8wumQTgTLbQS4JZ9l/Oh+Xb7JHvLTFFabob7ZccEnSI29ZUUH4LyQCCJ/Gl+YWnbUE\nyGlJMF01sUWagvSYVmA0C+EWAXJOmwnBlMWRF995ZEgEEC6HRACAglL35bYnlIs3LQc1W1CcbJQj\nS4xbs0XD/aLDHTgbpZMg+FC8CGjQGfDKcvs474Rv4EuheOVyhpQT7qySzg+X3d0pO8hXZYUPGQL8\n1hLgW9N4/o3iRYC/vmSKwfv6VZch16laZu7jKrOqVou9J25xt0VAY1yhAWrqGjizLXKJDl+2EMjx\nfWrQ6fHxj3/gxCX+GCfuwodvid+h+LDB/p5HvMRNYYk9hZK+JVyd2bq9V0RFtvxpp4UDG8cF/GL9\nGcGWC4Zh8FnGaXRr1xzjBrThba9U6rV6zPnnPnRsGcX6u9D2+tIrL4f16MTlYpy9Voqz10rx1Stj\nZWgV4U8oXgR4o4lWTt5ZddTTTXAptrfven6lZxriQTYc4F6mx0ZNXdN6fa7pFDGhl+u0ehy7UIhj\nFwqR3DsJ9Vo9YiJDZfcJqKwxpvzNzmO/x5bPglVobthOAfiQCpABRxYShnFNdEgA2HjwGudvviTE\n/B3FiwD7DIH+BW+Oej/jza9/93QTfAsnO2rbj/m8f2VCqzPgq1fGyu6vwVeaZWf3f/85LGvdcqNt\n0GN9ZjZG9WuFhObhLq2LTQP8c81JZF0xLvdM7p2EJyZ0l10M/LLnqqzlEa5B0T4BXHOLhC9B91As\nDMffksuzKMQq66bMt4avOMvXOa/YYsWPFz4iO4/fxObDOZKSO4mFTYyZBAAA7D+Vj6MXClzbBj+3\nuPoyihYBeqHuxAThBXjTd1TIKF/2qTae8oR2NN5giq5unJJxSypxAZdFaRZDogmnRUB+fj4effRR\nTJw4EZMnT8aqVasAAOXl5ZgxYwZSUlIwc+ZMVFY2zeO98847GD9+PO6//36cO3fOvD09PR0pKSlI\nSUlBRkaGefuZM2cwefJkpKSkYNGiRebtjuoQAq3B9X28qWP0RZy/ftw9quwagO93jh1sBQufBjAw\nDE5cLkJtvU5w28RQU6fDueulwnaW4RoKKaJB59r02/Saei9Oi4DAwEC8+uqr2LRpE3788UesXr0a\nV65cwYoVKzBs2DBs3boVQ4YMwfLlywEAe/bsQU5ODrZt24a33noLCxcuBGDs0JctW4a1a9dizZo1\nWLp0qblTf+ONN7Bo0SJs3boV165dw759xlS9XHUIRWqaVcJ7UNIdlGuO3bqzlFimgDJkNwHzFLfp\n0HWUV9Xzl8OjAo6eL8C/1mbh8/X2uQ5KKurw8Y9/ILeAO3ESH/9ce1LUag6nEXAbGnQutooq6UX1\nMZwWAWq1Gt27dwcAREREoFOnTtBoNNixYwfS0tIAAGlpadixYwcAYMeOHUhNTQUA9O3bF5WVlSgq\nKkJmZiaSk5MRFRWF6OhoJCcnY9++fSgsLER1dTX69OkDAEhNTcX27dvNZVnWYdouFBIBfoCSbqFM\n52opJuTop7leI3dbAsqrjUmKft2fLbkdOr0BO47lAgBOX7VPSrRm9xWcvVaKFb9KT+hzOVdYjgO5\nECIeXS0CxFpjCPch6+qA3NxcnD9/Hn379kVxcTHi4+MBGIVCSYnxhSooKEBSUpL5mKSkJGg0Gmg0\nGrRs2dK8PTEx0bzdcn/TdgB2dZSWCjSxNaKnQEE+jy9FDHQW2c6UYf1THCrjwdoGPTYfZl+iKLdP\ngBDLwtVbFXaJi8Twv/3ZDo+vb1w66aJVdS5BUGAnju3X8ytxIacU4we3c3kbCM8gmwiorq7G3Llz\n8dprryEiIoIzz7vti8wwDFQqFesL7mi7M6jVxmAj+gBF+0X6BUFBgea/TffVX4mOamb+W+q5qtVR\nCA5uumaBgQGiy7Lcf9vvN1j3qWeAgEB53i9TfXUCNDubdS8+PgrBQU1tiS2xTzRkqiM7v4p1u5nG\nb09EeAjrdZNyXyyPsT0+Krqc8zc2SirqUKfVoVV8JACjSNv5x03eMsLD2M9nxvs7AQB3DmiLdknR\nvPVzoVZHIcjmefD399USbz5XWUSATqfD3Llzcf/99+Ouu+4CALRo0QJFRUWIj49HYWEh4uLiABhH\n8vn5+eZj8/PzkZCQgKSkJBw+fNhq+9ChQ5GUlIS8vDzzdo1Gg4SEBABAfHw8ax18FBYafQ0KSyhx\nkK+ja2hyaDLdV3+lwsKTXOq5FhZWQqttcnjTNehFl1VYWMlrQnjmg51oER0qpYns9QEoLa2WdHxR\nUaVVB1Rebi8CTHXobRzkbK9NVW2jFz3DsF43KffFdIxaHWV3fGVFrd1+jjB12qbIgBsPXkO2hf8B\nVxk1tVqH5d/Kr0BYoPTBV2FhpZ0I8Pf31QTbffVUO9iQRaq/9tpr6Ny5Mx577DHztrFjx2LdunUA\njF7/48aNAwCMGzfO7Pl/4sQJREdHIz4+HiNGjMCBAwdQWVmJ8vJyHDhwACNGjIBarUZkZCSysrLA\nMAwyMjLMZXHVIRTyCfB96A6KR444AUKOk/v1kmpStjvOQV8WyGPn1zXOndt2aJ6gpk6HNbsvo7SS\n2xmyuEKAo6QAxH4r7S2+sjSDcAFOWwKOHTuGX3/9FV26dEFqaipUKhWef/55PPXUU5g3bx5++eUX\ntGrVCkuWLAEAjBo1Cnv27MHdd9+NsLAwvPfeewCAmJgYPPvss5g6dSpUKhXmzJmD6Gij+WnhwoV4\n9dVXUV9fj5EjR2LkyJEAwFmHYOjJ9HnoForHOryu+OO/YPGaZ0Nunxu5brWjbl7FIwJMbeCakTRN\nb8qFo3PO2HcV24/l4np+JV54qL9T9fAumxQrAmz+1+kNigzp7Qs4LQIGDBhgtdbfkq+//pp1++uv\nv866fcqUKZgyZYrd9l69euHXX3+12x4bG8tZhxCo/yB8CTYnSE8kiDpyTlh0uYqaBv6dxCBZ8Qk/\nLtDJDtw+T4HrKGtcDunIEiAXerHX3mb3NbsuY7dlVkqW61yv1eP4pUIM7KpGsIWvD+FaPG/T8iSk\nAvwA5dxEtu/wC58dcLZUJ493H3K1dLeFo5wtTsfPd+PlNA3O+aYw5IBxcm7nDwFpjH/adRlf/noW\n6zOvOVUXIQ5FiwDf+fwRXFjew++2XfBYO3yFmroGXMtvchQzXT+NLzjJyuATcD2/EkcvFAraV1Jd\nAhopV/pyk4neVF5VrcyWFwt0oqcD7FeB8ZGdZ3wucwulB2IixKNsEUATyr6PxS3ceZx7hOePaBvE\nh3p98+vfUVtvcVzj9Xt1xSGZWuU65Hhbq+u4O8pNh67jxGX+Easti386Yf5byCdFDg1w9HyBua0m\nP4a5S/Y5XzAHon0CbHYnH2zvRbGphEsq6vDGfyntLOE72H5YpeS+KCyz9yHwFTEstZ2WRzkKYLR2\n9xXesvQW17ygrBZllfU4nd0UWVCuS8kwDDSltZzK57OMJudMh9MBQhvEI0xsRYCmpAY6vQGt1ZF2\n++ZoKqGODbPaRiuxvBfFigArJxXCZ1HSp8Xe1CxTLgFZSvFinFwNYeLbrResTNWvfHHQcWVOsPuP\nm/h220W0UUfw7itmekFqFEfb40yWI1M8AhNnr5Xg4x9PoF/nePH1+v2D6J0oVgREhwd7ugmEDPjK\nKFYODp3RWP0vy5mrlHUNHZ2rSuVYJOxy4FBoQq4Br8mRLreQP0CSQw1g8+PHP/xhPR1k2k2mNQ3X\nGpcB2k6rmMItczQLQJPI9aGIzH6BYkUAQfgatuln5ei7bxZWIzvPN9ZvSw4WZCGXuDppvcHAWX5e\nsYhIhUIGvDIPisWsDjifUyaiZM8gZ5wFgh/FigDljH0IwjHvfnvM001wG1yWgO+2XeQ85u9fHub8\nza582VI9Ci+HrdOsqNbiwo0yt0fTou7b91CsCCD8AwVZsu1QkhkfkN7BComQuP9UHvsPTtTFuY+A\n82DbY31mNqLDg3Fbqxir7WyxDT74/jjyimsQH9PM7jc29p68hfGD2yI6PETQ/pw4owKU9Th7DSQC\nCJ+Gvhv+T229DpsP56BjkvRMbDmaSiTFhXMKJ7F6KutKsfRyJE4ZrM/MZt1XxeLXkVdsjPtQLDCi\nZFVtAz5bdwo6A4Ou7WIxfXRnQccRvo9iRUBtnY5/J4LwYpQigH49cA1bDudIPv5aXgU++vEEurdv\njjH9W7PuI9Vr3h5x5egNDM5cK0HPDsIyoLKhgoqzVuNvwtp05VYF9AYGV29VkAhQEIoMFrTzeC4y\nOFQ14WMozCRuhUJOvczJ2Pi5RUbHvnPXS7k7e7mm8iXs88mPJ+z3EflcHzqTz7rdl3zs+JIzEa5B\nkZYApUWW82dcGSrV21GKT4CzZ2nZp3CZ1LnqOHZBWLIkczkOGnuzsIp1eZ4c/GcDexI3MUF6jA6G\nynimiCYUKQLckG+DcBPVCp3WKauqx/yl+z3dDLfgrNix9J43zZULZVm6sLTJJhy19R8rj4goR3id\nco2c5ShHrngDhPtQ5HSAXAk8CMJT2MYM8GecNXgcPS9uNO8MnvEskAd3fhXZ6jLdZ4YBftlzxZxQ\niHAtihQBKjIFED6OO9LHegvOdogXbrgvQI58/oW+YZaXa2mlEeM5X8otw8aD1/H2N0dlLJvgQpEi\ngCwBhK+jlGfYOE3tGx0iANna6pEzdvBIfbH+DC7fLLfbvnLjObuQwM5SJ3N5hGMUKgI83QKCcA6l\nWAIYBrjoxpG8s/j2dIDjZ+qbzedRz5O+Wqo2LS6vM+dJEJu2mHAORYoAmg5wDUoZnXoDbFHi/JWK\nGt9ZAeKhcAPywPNI3SyqxjOf7HFJ1R98f9z8t1ynXlpZj3n/2off3egT4osoUgRQZ0X4OoGB9Ax7\nI1yrA86LdOQUEyKZK3qhWIQ+UUvWnJSlPkuKyoVFNhTDgdN5qKhpwOcZ4lZ4KA2FigBPt4AgnCOQ\nhKxP8bvIeAPevFz/pI3okJTTwQ2Pr5KsZc6gTBFADwchA0EeHI3TM+ydyBV+2COzARIfqaalfYxX\n+XCSxVcYigwWRI+Ga1BewDHPnfAve656pF6CB/dnEpYR576ML3x2AKUSQjw36FyzGoAtxTJhj+JE\nwO5jN3DmmnICrbgTbxoF+Dtsy7UIzyPfK+D+l6m23jr6pphIjUfOaSQJgEu5ZXjvu+P8O0qAjGXC\nUNx0wCffu+aBIwiC8KdYQR98/4eg/RgG2Cwxy6NQAVBZo0VhWa2osmnKTBiKEwEEQRCugm30XFuv\nwy6RScs8rQE2H74uOD7DjYJKXM+vdGl7/vavTLz8xUFRx5BPgDBIBCiUFtHNPN0EgvA/WHrv347e\nkKUcd7Jm1xXB+wq1GFjijkRDZAkQBokAhfLBM8M83QSfhwYahC1sfXd1rfhMl5KW3RFW0PspDMU5\nBhJGXGEqCw4OkD2OOEH4EqbpgENn89E8MhRd2zWHzmAQfPz566U4dFYDEYcQHNB0gDDIEkA4zXNp\nvdE2IRJvPjHIo2vnCcLTmFwCVvzvrNlMrtcL79E//OEP7D15C7mFVa5oniJo0Blw8HQ+tDpSUkIg\nSwDhNAO6qjGgqxoA8Mz9vfDvdac83CKC8AxsRny9nkz7csEwDO/6/02HrmN9ZrZikmw5C1kC/ABP\nPurPpfWy+p8SgBFKhnVtPfVFsmF5dW8UVKGmzt7fwmRF0dPHSBAkAginGNA1wdNNIAjvgaXfOXha\n4/52+CkmkVVSUYeFXx3BW1//zrKTmxvl45AIIGSG3kBCubA9/XLlE/A3LuSIj9xqupQljdEJC1gC\nCNHVFgeJAEJW6HtHKBkxoXaVjpT4ApbJigh5IBHgD/jQnGPXtrFOl+EtS3+8oxWENyG1b6pvUNbS\nWjGvsKUlxdT5GxzM93MJhNyCKhw8nS+8YoVAIsADJMaFc/42uLtzc+wPjOns1PHOwvcNjAoPdks7\nfA0v0TWEk9iap7PzKgQd98wne7DzqLT4+/7Oi58dMP/dZAkQX87rXx3BlxvOoqJaK1PL/AMSAR7g\njccHoWfHONbfnr6/FyKaSV+56elQme4w0/llh0nWTb/gq03nrP6/kCMs/j4AbNyfLXdz/ALL7ISm\nSIqOvjNsP22xSHCkExG3QQmQCPAEKsem5IVPDDL/LWSt63NpvQEAaSNv83gHGR0e4tkGeAEDuqg9\n3QTCQ2i1epqvlhHbJEbP/3s/aut1ENuN/7zrsvlvTw+UvA0SAR6A7xGMjwnD+EFtAQDd2jd3uO9f\nUrriji5qrHx5DCYP7+Dx+fKu7WIxbXQn7h0Ets/RXp1aRYtrlBtpHhWK1JG3iT/Qi79LFHRFBCrp\nRh13JNXxNd5fbZ1quL5Bjws5ZVZCq6q2QVSZfMGGlAaJABcSEsR+eYU8gw+M6Yx3nhyCQd2sfQSa\nhQRal2Uu0/iXp7/XKpUKI/q0lKEg9s0fPTPcoU+FWBJiw6QfzNLGt2cOQev4CPxtWh/p5XoZn/51\nhKeb4FtYqIDKWpp/lpu6Bp2VyX/ukn0oKK3BmWslgo4nDWANiQAXwu3Ayv8UBgSo0Co+wm77krl3\n4u6BbTmLUnlaBfCQ2Fxgp8tx7QICVJg6upNsL7JT5bC0MbzRn6Nv53i0bCGfWPEkkWHkzCkGywyA\nmw+Rsx8bzlgsVVDZrQ54ZfkhfPLjCWgbBEzH0GyNFbKIgNdeew3Dhw/H5MmTzdvKy8sxY8YMpKSk\nYObMmaisrDT/9s4772D8+PG4//77ce5ckyNNeno6UlJSkJKSgoyMDPP2M2fOYPLkyUhJScGiRYsE\n1eENcD2MKhUkm3+DgwLw8F23c/7u6ekAPiKaBePp+3vy7+jgNKLDQ/DEhO6ytMeVpkHTveh/ezyG\n9UxyWT3+RufWMZ5ugmQYxolYGd796sqKs68d1zXWGxje5ZakAayRRQRMmTIFK1eutNq2YsUKDBs2\nDFu3bsWQIUOwfPlyAMCePXuQk5ODbdu24a233sLChQsBGDv0ZcuWYe3atVizZg2WLl1q7tTfeOMN\nLFq0CFu3bsW1a9ewb98+h3V4C0kcI0GVCi57Et2hAUb3a4W7BrQRtOZ/ysjbMLpfK6tt6kYTvEoF\nLHpqCOtxXPOjpvOTK9+6K52EmoUap25CggPRi2M1CGFPuBOrY7yBOonptC9cFx9Bz1dxRnyrVNwD\nrP9sOIvzfCsynHTcPHahEL/9fsOpMrwJWUTAwIEDER1t7ay1Y8cOpKWlAQDS0tKwY8cO8/bU1FQA\nQN++fVFZWYmioiJkZmYiOTkZUVFRiI6ORnJyMvbt24fCwkJUV1ejTx/jHGtqaiq2b9/OWodpu6cw\nOVCN6d8aD4zpjFf/fAeG9ky0208OByCTv0F4qPUH09WWgOiIEPwlpSv+dHcXvPznO1j3sXQku3d4\nBzx6Tzer3zu2jMZj93TFu7OGomUL+ykPR5hLlklEOXO5+Jrw5L09cEcXtcdjNxDupbbePqkNYY2z\nFjiuqdY/LhXxHuvsp2NZ+in8sOOSk6V4Dy6T3CUlJYiPjwcAqNVqlJQYnTYKCgqQlNRkGk1KSoJG\no4FGo0HLlk0OZYmJiebtlvubtgNAcXGxVR2lpZ5V0qbnOiw0CPcMaQcAmDGxOw6dsUkgIkM//frj\ng3DwTD4G2iTwcbUICAsN4n2BI5oFI23kbbitJbcX/6h+raU1QObzc+X1SmwejjlTerusfF/AOGrz\ndCvcC61D58f56QDpD5XSnkc+3O4YaHvzTPmh2W6qo+2+gm1TFz01RHLHM//Bvua/W8VHYOqoTnbm\nbJWXuHpOHt6BMyASF0Kuimkfoe8x3/I2Z0SAMx+TZ1J78e/kI/z90QFOlzFzUpOPh++83ezo9NTL\n8OG0CHDmWI4Xd9/JW1i394oTJQM1dTqfSxjlMktAixYtUFRUhPj4eBQWFiIuztghJCYmIj+/KX5z\nfn4+EhISkJSUhMOHD1ttHzp0KJKSkpCXl2fertFokJBgHP3Gx8ez1iEVuUYt4eEhUKujAAB6m1FB\nn25Gq0ZIoxk/IMDa09V0nInIyGbmv8cM7sBbd2y0sDCltvUIJSgwQPKxABAZGcp9vIWvBJffRIsW\nkYiNCrW6Lo5Q8ThghNgsubQlolkQqllylnPBdW5R0dbzlBPv7ITD5wpw/EKBuZ22H6d7kztigxdE\nkeO734N6tQJwjPU3Ie4v8bFhSB3bBSs3Gp2EQ0N9ezXCwq+OeLoJXo8z4luvUmGdRfAfscTFRULN\nskrpv5vPAwBmT+0nqBzb92Ltzkv4ZuNZBAUGIP3DyQ739SZkEwG2H7CxY8di3bp1mDVrFtLT0zFu\n3DgAwLhx47B69WpMnDgRJ06cQHR0NOLj4zFixAh8+umnqKyshMFgwIEDB/DCCy8gOjoakZGRyMrK\nQu/evZGRkYG//OUvDuuQfhJOHt54fE2NFoWFRqdG26Uspu1a07yhTZ2m301UVdVx/saG5f6OEFIW\nG3q9QfKxAFBVVc99vIDrX1JShYY6reDzDAgA4MBPy1ak2eJY1dv/xnVulZV1dvs9l9oTMz8o4CoK\nU+70DhHAd7+Liqs4fxPyShkM1s+UVktz6gQ3X2acdur44uIqQMf9jO3+/TrWZ2Zj3rQ+CG/GLUht\n34tvNp4FYJwOsvxNrY5y6pspF1xCRBbj8YIFC/DQQw8hOzsbo0ePxi+//IJZs2bhwIEDSElJwcGD\nBzFr1iwAwKhRo9CmTRvcfffdeP31182rA2JiYvDss89i6tSpeOCBBzBnzhyzs+HChQvx97//HSkp\nKWjfvj1GjhwJAHjqqadY6/AUrOLWzbZNH5opsUdA28VOBf11quOgPXwjEgfJysxIveSumtbiClLl\nDG/PHMz5my8/coTy4FtZ9MmPJ3A5txyZWXkO9/OX8NCyWAI++eQT1u1ff/016/bXX3+ddfuUKVMw\nZcoUu+29evXCr7/+arc9NjaWsw6lIqejW3REiFsybr3z5BCUVtZj8c8nzNssT+PZ1F74TIL6H9Ij\nER2SHJvheFcIOnzPXbjWUyIj+7bCqH6t8PY3R2Utt7U6Ep1aRePKLfvpJmfFDIkI5eHJe+4oDbEV\nPM81w/j4oKsRL3Ej818sn5FHxndxfX0yPZUrXx6DxXOSZSmLj1bxEejZMc5qxYBluOSB3ezTKwtR\n4YIcDfledJtOvls7Y2yEB8Z0Rmiw8NdHrJOkFKaP6YTHJ3RDsAssAQDw8p/vQEyk6xJEtUuIRL/O\n8X7xYSUcs/OY59bZv7L8EG4WVfPuZ/kYnskuQfreq1bfHcupwmMXCuVsolvx7agcPoBKpcKsyT2g\njg1DJ8tIaI1PWFCgClqdfKPJAJm+/yoVezQDV36gH7m7C+4Z0g6BKhUqarQ4aLu00qJ+oVeMr7l8\nwYJstcZLf7rDvKJl48FrAlthjHK4eE4y5i/dz/p7YKAKBovnoDVLyGg+WkQLc5aUSlBgAB4Y3Rlf\nbjhr3maKgTB3ah98u+2CVdpX4RjvwRszjFMO//4ly+m2EvyEBAVAq/PMcsaLIlIsu4L3vzuGf88b\nybtfaWU9YiND8MlPRivlgK5NGUJN34a84mosSz/lkna6A7IEuIGhPZOsBYAFrdUR5oyBcuDq5ZOu\nnAYLCFAhITYMLWK4OzNho/umv/maO6QHSzAni+N7drAfwdte44RGT+P2PFMPbLz2yAD0vz0eyb2s\nwwq/OYN7Dp4PVz4BlqOfrm1jzfEw+t0ej0+eS8azqb0QFup4xQXhHXjXRJZ7EbLi5+y1EixYth9r\ndjUtGyy3mB69UWB0iGWbMuULXexNkAiQgVn39cBzab3NoyLbzH+OUeGhcdy5AMTC5hMg1k/gFY5I\ngJ7ksXu6YkiPRISZIiQ6+IIJjcg4YWg7DLfofJfMHYF//e1OBDaaU0b1a4VOrbkDHpn6w163tcBf\np/TGggcdLy0KCrR/3Tq3icFfp/ZBSLB1x2myUMyb7l3ZCPksJwO7JeCtGU2hoKVkafSlOCC+jJ/4\ntUlm8c8nkKPh9to/nW0McLflSFMSKEt/gndWHbXbZmLJmpNo0BlwKbfM6x0IaTpABob2aOpIxtzR\n2tyJeAI5vp9dHOQEkDq3/eS93bFq6wUM7i5MINm+N6P6tbbyGXD0WoWFBpqVvqP3LyY8xEogRYUb\n57sDA1TQ6YHQ4ECH9zIyLBg19To0CwlE/y5qzv0s93/y3u5oo450uF/anR3Nf/fpFM9brgl3dJ4D\nuqjxJc8+LWKa4bVHBuDd747hT3d3Qc+OcXjyg12c+7dWi5/6sCRApfK5AC2E5zl9tQT5xTX48Jnh\nrL+zPVKFZbVW/+sNBlzMLbfb73xOGVZtPY/9p/KxgFGhZ1vvTYpFlgBLOL6hJmcwIcgtAMSqSCmr\nA/ii6lkiNQ7+8F4t8cWC0Yhz8bz1y3/qjwgRqW/ZLtedfY3hq7u2jcWofq0wgKODnzutD5J7JWFC\no0lcCMN7tUS7RO5pg9DgQExO7mi1bXR/cSGWXdkdhgQHmkWio0etc5sYrHx5DHrf1sLqmbwvuYPd\nvpbRAgFaLUC4D0dme7alhN9vt84ZsD7zGtZnssfyOH7R6Cx46QZ7OPvaeh2WpZ9Cdp6wAG+ugkSA\nAF58uL/L6+jWLlYWz2spWfGE6oz2iVEu8zwXDUeju7ZrbtUL8okottHzQ2Nvx3uzhqLf7fEICw3C\ncxzx/1vFR2DmvT0cBhQRDcvte2R8F0wYyi80vK3ztLy2Hz87HP+edydS77zNap87+7Q0W2Ck18P9\nW59+r0gAACAASURBVNsEx1YXOTCG8L6Nf0evg6wnlTUN3PEABFyeU1eKOX8zzRJwTU/uOXELxy4U\n4t1v2aNtugsv+aJ7N+4ws774cH8sfs75JXlsTY2PlT76HtazaapDZ3CfJzFfQA+hYseylDu6qNEi\nOpR1P8v5+oAAFRLjwq3u+9szB7vUV2LsgDaICg/G7Pt62v0WoFKJ8vyX42lt5iCccpdG02bnNsIt\nZHHRzRAhVCyJPAFHOs+VqaJNJMSGWb0nhG+x6dB11u1CJNJ1Bz4FViHQWdA3fk/1AuIWNOgM2HI4\nB+VVUlbfOIZEAA+P3dMVAASNxJxBpVLJIjbYpgNG9G7JOhdvmt/vfzv3vPMTE7uZzb96L0qMMlTo\nR9eiyXOm9Gad//vkuWQsmTvCYTGt1ZEOfSWcJSE2DEvm3ol+ndnvhZgno1mI864+XdvGYtroTqyR\nAu9L7oj5D/RlNe3LgVwBr1758x2CprqcXc2gUvmmMyO5URgRHDxIJFqdcaqB69kQ88zsOp6Ln3dd\nxtJ18i9FJBHggNceGWB2Rps+2nouvDPHkj+hmD7UjkZcUmB7sAIDVLizbyvz/6aP3vPT++Kffx2B\nmfd2R5yDEbJp9OxNKVJDgwMxY2J3xzup7D3yVSoVkuLCAQCxUcbzah4V2rTqwIcx3foWMc3w1OQe\n6N6+uWQfjqCgAEwc2h6tWZwYgwID0Ou2FqyrHcTC9vnl67htBRvXt5RLtP11am/8/dEBeGvmYDyT\n2svKQuE1012E23CVU6mpWK7HWYxuLGmMv5ErIMiRWHz/y+ciEmLD0LmNdUcfEhwAbYMBs+7rgYFd\nEzDro92Sy3943O0IClBhCs9c4m2tjG0YyrKenQ1Wv0Sbh+2TxmmHgAAVoiOM87Fj72iDtbvZ02ia\nnB2FmK3kIjSYXxwJ8cEMbxaEZ1J7oVWLcPO2Fx/ujxOXi1gjEXotgr4YTfsM65lkNlHHxzQTHXb5\nobHyLVsVC98ISYwPAdv3vf/tTY6ebdSR+HmnuIx04we1xbbfrSPeSZ118JbA0306tUCWg/ltf8ad\n3zUrJFRr+ZjV1OlQW69zGFdFCCR7RfD6Y4MwaVh7DOqW4PRcY/OoUMy6ryfiYxyvo26bEIlPnkvG\nk5N7CCqXc428xQMn1lzct9FEndzbffOereMjMG10J7z++EDOfYSa0wZ1S7Aa0TaPCsWY/q1lzbPg\napxp6cBuCejVOPUzZSS/A9sLD/Vz+sPiDEJM+G88MQgDuqjRvX1z/LkxHHfPjnF48eH+ePvJIXh3\n1lARNTa9HLai4VOb0NkDuqgxmWUaROp0gLdMIzxzfy+XlPvVK2Odtpq6GgPDuHSJKVtfseJ/Z7CG\nY9DFhq3oBIB5/96HFz8/4PR0BlkCOOjQ0n4Zl9ELuJP5/5TBbdEuwfV5optHsZvq2XCFI9SArmp8\n+PQwt3YMKpUKE4e2d7gPVyfO51TorzjqT/46tTcKyurQOj4CYaFBWP3bRQDA0/f3RH5xDQIDVdh7\n8hYWPTVUFjO/Mwh5htslRplXbTAMg7ioUNzeJlbStI6jpyUm0vrdUwWoEMwyxSRVpQUEAAYPBZcz\n9XsdW0YhVOZpSat6vPx9LK/SYtWWCy4rn03oHTprHxKdi7zipikAy6J0jT5asz/ejS9fGiO5fSQC\nLGgWEojaej3CQoPw2D3dePd/0IMmUy5cNbCIlxD5zdV4ySDKPTh5rsFBgeZ8BOEWHeXg7k3TTJOG\ndXCuEplwZAmwnaIDjB9Z7qBK/B0Q3yBwYLcEHD1fAAAIDlQhJDgQrz8+EIt/Oomq2gaoIN2Z0Xic\nZzrJps7Z2PbhvZJw4HS+R9riafaevOWysoU+Gis3nEWdVm+3JPk/G85ZlmZ3nN7AIL+kxuzrJBaa\nDmik/+3xePWRARjQVY33Zg/1WUcxro9RTOPcP9d5xTeO8vlS73oTvmTOdwe+cjUSmzsWlFyWgOiI\nELwqcpmmqYN3dG0sQzY/PM7ekXL2fT3w/AN90a9zPKY1Ogh3SIq2iush9VFUuWEJoyVDe3Lnynjy\nXmFTjlzYirdFTzWGj/ZuQ4DLMU3RGgwMvlh/mnVJ4oHTedh/Oh/HLtpnI2wQkOTptRWHJKd9JxHQ\nyF+n9kEbdSSeS+uNaCeDl3gSro9RmwTjubEt+QKMo52n7u2BeQ/0dWHr5IVrPrVL4/p1d0zVuAs5\nuwpPm2dffWQAet1m9FFgW1/PJe7emjFY9Bx6ZGP0yGAHaZ/npPXCHV3U+OfcEWb/F0sCAwLQ+7YW\nmDutj/XUnMVlFJqvwhZXC1nbaJOzJjfFoTAtEe5zWwuHZfTqGIcHx4pfZdKyhdHypHANYHYa3Xvy\nFo6cK2B1wLYc7b+y/KAkH4UyiTEESAT4GY7mUwd0VXOG7Q1QqTCsV5JPCSCu1QGPjO+K59J64a6B\nbdzbIBciqPMT2J80jzI+A/Eecv6LjgjB/Af6YfkLo9G9fXO739nua1hooHklixhmTupuHMFb+PLY\n0lodiTlTjOJfkn+YSrolwBlDwODuCby5PB5N6cr52/hBbfH64wNx7/AO5m3LnrdPrzuiT0vO0NmE\nABofjlVbhfkdFJTWYu/JW9hy2JS4SNhDKdW3kUSAn+Et3sbugOtcQ0MCMaBrgscd3LyVbu1i8fT9\nPfHaXwZ4tB1ca/LZR8fSnuuYyFDMndZH8HypGCuJ5Z6SpwOcfF8dZa40ZcAcw5F7QqVSoUNStNXA\nwXa6cPLwDhjULQHxsWG4d3gHvPAQd32Thjl25FUqUoTeqi0X8PMu49JVxsriJD+K/0r+47GBePlP\nrs8N4C7cPMXoUSw7CyXnsE9tzDp4u8AwviqVCoO7JyI2UviqE3fiihUuQufexSyfNeWlUEF6Z+6O\nsMZ/vrsL63aumi0789bqCPO5TRl5G3p0aLI8mCJGfvj0MCx/YRTGDWC3vCk+MqGTt1jo1ICBYcAw\nDNL3XsWVm/aZDblQvAjo2DLamHTGT1CSs5yl2Xj1WxPw2Xx7U6a/4Oiu3pfcEStfHmOe//Z1XGHN\nChfo6BsZFiw+YZhKqkeAMNEuLuaBBRI736kOpk4sSb3zNnz50mjEx4YhOCjQQXXKVgHSnw4jluHa\nVSpg3d6rmPfvTLv9GMaYy+DXA9ewSERSIt90gSc4sfyAzpneF0vXnMSgrj4UGU8ElucaHBQoS8x8\nb0XHERDk740mfX+aBnLF4DhGhD8Bm58CH660BAidylCprEfdXF3vc2m9sOfELUG5MNjOa+Hjg1Bb\nrwMgLHW60i0B324+h+zcMsnH6y0St1XX6bDhwDXW/RgwqNeKD+2ueEuAv2H5UUkZ2gErXx7jlWv8\n5UBJVg+udKedvDwamxTYOka27IpiiItuhj/ddbtZNMlBXONKgZjwECccA6U/w3cPbGv1/8qXx7Lv\naFPFgK4JmP9gP8k+M+2TotCNRShFhQVjYFd7B8Kpo4VZFgDXLnNVO5FN1VkyT3GkK+ahpk6HBoGJ\n2xhGmuDy36GTQrH9pvjTCNEWJfk/lFbWeboJbsO2Y7yjixp9OjlexiaEu2w6TWeZeW8P/Pb7DUwa\n1l6SCBjWM0nU3K0lj6Z0NQvAwd0TwGYoakpgo8LzD/Q1ixZXoVKp8Gxab1TUaK1iBvTsEIevXhmL\nGe/vdGn9fCx6aiheX3kE+SU1Hm2HGL7efE7w+n+GYbBkbZb5/5OXi1BcUYfRPCHSFS0CQhysHfZV\nlDQ69meBY8vArgnYfizX081wC7aWAG+9y7GRoZjemKVR7LruD54ehhbRzfD3Lw9Jq9ziojzNEfff\nclqjN08sAAHVCMZblxkHqFQujTKaEBuGgrJaWcvMuio8qRPDAPUNTTGoTYIgMizYKjKoLf7XCwrk\nubTeeH/2ME83Q3aUNDpWkuAZ0aelp5vgNixva7ukKN5Mm66gZYtwUdYHsU+iOjYMAQEqyasDAjme\n/TaNibJm39fTvGrEWxneiyUhmStfaRd/Lt6bLdGB0wHaBuFz/P+ysAJYUlTu2IqoSEtA+6QoDGCZ\nu/ImtHottPoGRIZEiDpOSaNjlYIkLNt9/dff7vRAS1yPpTf1shfHorCw0u1teOfJIaLeJcmOgRzH\nhQQFQKsz2FkrX/5Tf2w9cgODOVKL/9+jA1BerYXaST+gbu1icT6nDC3jxX1/xPDkvT3schWk3nkb\n0vdedUl9rv4yevrbW9PorGmLjifssCJFgC90k69mvo06fT2Wjf1Q1HFKGh0r6VzZTtVflgR6I+76\noHPVMzm5A7QNBgyx6ey7tmvucElzSHCg0wIAAJ5/oB+KK+okJ6URy4sP90e3drG4mleB9L2uqUOl\nUnm8o/YEDXoSAXbc6QOm1Tq9xDjQihode7oF7kNJHy8FnSrr+9qlbSzGD2pnFVFxzpTeqKyRliBG\nCsFBAW4RAC881A+Hz2rQtV2sW55xoTUEBQZAx9N5+gq/nyvA9fxKvDeH3XKoOBHw3Zv3QFvrvpfJ\n3Siqs/AJm448KMnXQ0nPMJs1a+wdre1CKt/hp7H7e3SIs4pCyMbLf+qPD77/Q54KBT5aYh9B0QGm\n3EhBWa1Dh0UFjRuNxHhpqFQuDIw4Nap0E7m/YntfO7aM9lBLXI+CbiurY2AUTfNY0bVdc7NjbESz\nIPTp1AIvubjTHdWvleAok4C0AFPeguJEgK/BiFx6pKSOUUlY3teV/3c3XvnzHZ5rjIvx12d4ZF9j\nRxYU2HSCtuc6eXgH1kA8/kBosPT8Hp0bYyIM6KrGvOl9ea/Rq4/cgQlD2mHu1D5W24U+Wklx4Xjl\nEfZ3LNBGuElJs+xNkAjwcvQiLQFKMqUq9VwTmodzZuDzB/z1vppiwFtG6rO18Iwb2MZvz19ID9wu\nIRLq2GZWHevXZ35EfrMjeOnh/lbJkOZO7YNXWTrq958ehtvbxGL6mM7od3u8+EYAGNm3FdqoI/HQ\nuNvxxhODzNtn39cTzUKaxExi8zCkDG5n/j/1zo6IjfTOOAlcKM4nwNcQOx1A+Cf+2i8oCb2BXwSE\n+XH+CyGPcHBQID54ejgAYFC3BOgNDN44vhAA8MDYVKt97Tt44PEJ3ZDgYHWEkPdozB2tzfdo/CDr\nKJNDeiTiu20XzP/bTufcl9wR9yV3FBQd8eU/9UfbhCj8338OoazKc35q/juc8BMY+J4IuFWVjwqt\n69d2e1u/WFZfjv9d2YI6nfwhfpXkBOmvgsfkbW5pxTF1Ip1aReOnRRP92sIjlrjoZrIsd3z/6WF4\n/fGBAJq+GWzRYmdO6o7YyBC7jt8RXFabli34V1ZEhAUjvFkQHp/QXXB9rsB/ZaefIDYcqacxMAYs\nOrIYAETHOBCNl3UW35z5ERfLrsDAGJDaeaKnm+Oz+IU5XGVAcMdT0Be2gaHSGHkwrNHRzDKGv+VA\nMrxZMKr9OEeEq27rO08OwZbDOSgorcHg7vYZU42WAWsx0a1dc0SHh2BYryR89INx5UFy75ZI7s2+\nfPzzBaPM/lmWn2Quq4NeSNKfxl3aqF0XkEkIJDs9TF61BmeLL+Bq+XVszP7NzhGQbzogv7oAdTpp\nMQVcgc7AHrXKxIarW/HJsc9EOzyy4W1dRWGtMc63K6wgvtYv1unqsfjY5zhVdBaAOAdXHztVVgJi\nihAUn4fQ7r/D9LW/f0RHjO7f2iojoqpRBXBkinY5NQ01yK8ucFNtTXc2RsZ581bxEZgxqTteeWQA\nbzpxk0Nhp9YxmDGpu2Cv/tDgplTl8THGbISBASo8PrEb6/71Oj3rdjbiopvhw6ftQ9gn924KqxzG\nslKhRTR3VsSE5sItKGQJkIjeoEd+TQFaRSQJGrlUaCtxoeQyBiVZL2155/AnVv/3U/dC68gmNepI\nBJTXV+Dtwx8jKSIR/xiywOq3EAdmxZqGGoQFhfG2O79ag6OaE5jY8W4ECIzRaysCDIwBV8uvo310\nWwQHBGHztR0AgFpdLcKD+U1mG7N/Q0V9BR7uNtW8rU5Xj3JtBaByTzQzNhiGQZ2+DmFB3pmm2cAY\ncLLwDLrF3Y6wIPenUM0qOoMr5dm4kpWNwUl34EpZNt4a/qqwg92sAvQGPU4UnkKPFt2srlWtrhbv\nHF6MlPZjMbKN4zwj6y5tQGCLUjDaMIR0OgldQZNJObjDGTRc64WIsGA8mtIVgHHKbN3lDdAH9gDg\nOYvfGwc/RLWuBp+Oegchgfwdc01DLU4VncXAxH4IDOD29s+vLkBsaAyaBdkvyR7cPQEvPjoIB68c\nR35NAe5qN8qpcxDD1FGd0LNjnFXn/9Ezw0WJ7DlTe2PXHzdx3/COCA1hvwZClmrHWXTiURZJl+ZN\n7wtNaQ3G9G+N/aeMYZVtVyQ4YvkLoxAYEIAnP9wlaH8SARL56WIG9t86jKf7PI4ecV1hYAwIDmxa\n31teX4HdufvRProtWkYkYkXWN8ivKUBYUDP0ijfOAZ0uOmdXbr3e2kHkVNFZjGjNnpiirN6YhjS/\nWoM6Xb35hVs6byRu1dxEjdY+QERetQbvHP4EveN74Ok+jzs8xw+O/htavRaFtcWo1dUhKiQSnWI6\nYnirQVb76Q16XCy9gmZBoYgOiTJvP6Y5idL6MqRf3oj+6t54svdfLNpegWZBzZBfXYCkiAQ7kaE3\n6FGvr8em7N8AAA93m4pvzv6IiOBwXCi5jFvV+fhbd2vh4wzXKnIQExKN5s1iBe2/7+ZB/HQxAyNa\nDcGDXdPAMAxK68sAiJu/v1R6BScKTyMyOAITOt4FrV6LnMqb6BTTwSmz+MG83/H9+V/QV90Ls3o/\nKrkcoRTVFqN5aKy5Y7C8n0fyjwMAtPoGAAxvZ2M8awYB0cWoaZA3K5sleoMeGVc2YeeNfQCAvvE9\nMavPY+bfL5ZeQVl9OX66mO5QBOgNeuy4sRchnQBGGwJViBbBbS6bfw9KyEXDtV5WT8XXZ3/Azao8\nRIfrAHQB4wJTgM6gw/u/L8GI1kMxuk2y3e81DTWo1hnT6tbq6gSJgNXn1+BE4WnU6Goxpu0I1n1s\nBye1ujqEBTUznn9gA+rDcxESPASfZ/0XADC27Z0IUAWAYRiUaysQGxpjLksOi6ElwUEBdhkVW8SI\nE8nxMWGYPtrxskC2Plsd2wx39mmFsqp6TB/d2UpAWL7qxsRVxjYmNA9DQWmtYJEyd1ofBAfZC5Mn\n7+X2OyARwMKtqnz85/R3eLzHQ2gX3QbnSi6iVURLxIQ2dXCH848BAC6XZWNz9g5cr7yB+GZxaBvd\nBk/2egSLDi82v2CWbL2+C51iO+J6xQ3zS2BJ+uUNVh3pDxfWISkiEfFhcYgNjcGai+uRGK7GiNZD\nrTqbBXv/gUe6TceAxH6o0Jfi0xPL0PNWFzzb60mr8q+UZQMwioufLmSgXFuBR7pNR3iwcURbXl+B\ner0WCeHx0DYKkqOaE+bjD+UdRUFNISZ2vAt51Rq0jEjE83v+j/U6fnVmtfnvPwpPoby+wvx/WX05\nLpZdwZqL6zEwsR8e/f/27jw+qupu/PhnJivZ941AyEJIWEVWAaMGIbInImitlSqtS5+KitYq1voo\nVlpQqBWeH4roT19qRVHkkQCyVZAtGJawhBCSELLv6yQZZiZznz8GhoSswBAS5vv+J5k79557vrPc\n+d5zzz0n+kFs1DYcLTnBz/kHOFOZ0aK8Sz8ml9QaOp6LXVGUdn9Mqy/UsPncdvYWJAHw/j1/R61S\nU6+vZ/3ZH8wtG3MiZ+Fqb5qhbXfefr5O/x6AvQVJBLv2btYCcrAomd8MnNtiXxXaSnrZOvL56fXc\nFTyOE2Wp5h8ggHv6TOCLtPUcKTnOQK8BzAy/j42ZW5gRFocrvqhdy7H1z0FnmNJhzEmFptcqvTKz\nxfPHSk/yTfpGnhr6GP9O+5aZ4fcR5dWf785uQmfU89CABAxGA1rDhWYTWO3K2YO7gzsj/IcBoDca\nOFNxliCXAF4/8A8Geg3gv26bT9WFaj459WWL/S7at5gGg5aXRj5DH9febbYuqVQq1O5lOAw4zIr9\n9Twx8LftxnutPk39isMlKebHKWWnSKs4i7+TL56OHhwuvvzcl2nrGeoziINFh/ntwIf44MSn5NcW\n8uqYhRwtuTx7m8q+7V7elz6GqeVnyNcUXlxq+pFrrSWgQFNEUX0Jt/sNbfFcZ6w6tpbCumK+Sd/Y\nIgko0BSZ++4A6I168//5mkJc7V1ws3flZNlpjpWeJNy9H6MChpNx8fhxuf4mBqOBwroSgl0Czclw\nUV0xR0qOs/bk5zwa/SAqFdhHHCPdppz9Of2abWtvY8/+wkN8mfYt8wY+xOiA2y++Ll3XMfp8TS7f\nZ27htwN/1exY3xmKopBRdY4w9xBs1DatHnP+9vuxze4MacrezoZ7RwYTdsUgYIP6eVFSmd/i8zEk\nzJvC8jrz42VPj2uRzDw2NYpPNqcB4OfRdqupJAFAUuFh/Jx8CXELZm9+EoeKDlNcX8K/jn1IQ5Oe\n3oO8oyjQFGFvY2c+6O/I2W1+vkxbQZm2gpL6slYTAICs6mxe3PNXZoW3fiDPqj7fYtmKI/+vxbJ1\n6d/zu8G/abbs87RvSCo6zJ0XWw5OlaS32K7pmdWe/P0A+PXyIT5iKpXaKv6y/+1W69XU9pyf2J7z\nU4frXWnRvrfM/+8rOMSx0hOAKclILj6Gp4OH+QBypeJWrlva2zb/op0qP0M/tz442znxn9y9rD/7\nvwD0dgnkQqOOhPCp3OY3hHp9A1uydzCl30Re27+ERuXy9btn/vMyD/SfSVZ1NkeaHNwPl6QwLXQS\niRdbJpo6UPgL52tymy37Of8AHg7uqFU2DPIeQFFdCYuT3jE/fyn2plalrDW//6kVZ0itMN2KdLoi\nnReGLLx4fRne3b+G30Wbzu4vfQ4L64pxs3ejXFvBmYoMMqtNB+sGQ0OLRGjNic8AWH54FTqjnveP\nreG54U+yM9c0c8v9EdP57PQ6849bQsQ0hvsO4duMTQD0ce3N2apMvkz7tln9L9X3h6wfW8Rmqovp\nu7Q0+X0SIqZxb9+7OF56ir0FScyNjMenl2n4WBWgdjYleClFqTDwchkZVecIdgkitzYPJzunZpfO\nDEYDKaWn+Dr9e14e9Sw1ulqWJr/P5JB7zN+3E2WpeDh4sPr4J+aWtKbeP7YGgJjedzRLEPYVHGJf\nwSEA9I16UstNsf7zyGoK6opalHMlx9t3crTUkzCPvqxKWWterrHPRe3hSXnIcf646ScejpxDf0/T\n1MmXfqQHxryJYyuXdM5WZvJ1+kZ+N/gRvkr/nin9JhLpGW5+Pr3qcgK4Lfs/bMzawuSQe/Bz8qX2\nQvN+K5f6Fekb9bx9aAVg6th76UTlQOEvpJSdRKOvMz/W6Osob6jgldHPse7M9+wvPEQvW8dmx8xt\n2aZb5T47vQ6V3URs3E39Zkrqyszr6C8mAZcS130FSTc0CThdkU6oW0iLSxVLk98H4MfzO5kbGU9K\n6SnKtRWEuoUQ6t63taLMDhUd4bPT67iz9x08NCChxZm7jU8eySVHGBs4stXtG42NPHxvJEmFh/nq\nzAEejIy/OOERoGrEOGgbnrXheNYO57k5w3DpZceX29PZcTiP306JarU1486hQYyI9CW7qJaIYPeW\nO71IpVi6vaUHKC2txWA0sP38bkYHDOevB/4OwIywuDYPYD3V+/f8ne8zNzPCbxhHSo43S1qa6u8R\nxtmqGzOF5410l3ccu8t/JCZkDHvOm87mIz3Cmx0AmxoTMMLcitNVnO2cGBsw0vwjawmPDfwVVboa\nNmQkdrjug5HxRHsNMPdheP3i5/1GeHb4k7x39INr3PYJvBw9MV5w4L8PLUFlq0elUrHynn8AcKYi\ng38d+7DZNm+NW8TBwmTSq7JIb6X1qKnZ/Wfw7dkfrqluXWlV7FIajY0s+MnUh+Kpob+lsK6YjZlb\nmB4aR2b1OU5XXE7wVahQuHwYV6vUPH/7U7x7+H86vc8+LkFMC5vMx6e+NLcADvaO5mR5y0uWVxob\nOJKDhckd70TjBS4VAHg4ulGlNbUMvjbmRZYm/8t8KdRObceC4b+/eGbdz3wStOKut6g3NODh4I7e\naKDR2Ej1hWrcm/Q9KK4rYXP2DgZ4RlChrWRvfhIvj37WfIkhMWsbm7N3MMg7iruCx+Nq70xf1+Bm\nr/dA7wEYGg3NjiGvjXmBAGfTjI4XGnW8k7ySe/pMYFzQaLac28mW7B00Ko042zqxNOa/eXn1AUpq\nalE5NKA0uNJr9Fag+R1TW87tJLUizZz4vzTyGXMi8u7FxG/9Txns0n6Kyt6UpC2Z8Brbsv9DlFd/\nai5o8FUiCO/t0ak+CL6+rbduWF0SsCb5S7Zn/tzxikKIbmFV7FL+dfTDVi8R3YpW3PUWH5/6ghOt\n9BkSpoQlV1NgfjwhaAwJEdOpvlDNm01a267U9Ee2qQBnf0rry5q1CLbm/ojpGBUjR0tPmFv+EiKm\ntUjE7dR22KdPpjZoN2onDd4XBlPucBKAPwybT9DFZOLKVte7gsezO29fs2VRHgNIqzpDWwZ6DyC1\n/AyeDh4sGP57GgxasqrPE+jsj4eDOxlVWYzwv43MqnPcEz261TJuiSRgz549vP322yiKwuzZs3ni\niSfaXHfuuqe7sGZCCCGsjbrBC2OviptdjWa+frDlZWW4BcYJMBqNLF68mLVr17Jp0yYSExPJzGy9\nKfhGGRfYeoZ1pZlh9/FI9FwGeEbwm+i5PDv8SQAm9o1ptt5zw59ibuTlITJH+A3rsGx3+6vryNIR\nZ9vWO5LMCp/C1NBJrT73jztf592YxebHfx65gFlhzfs+RHtFsip2KeODRtPHJajN/b9xx8vXUOtr\nc7W30M0Kn8Kq2KVEe0W2eO6dmDd5ccQfr6o8Pycf3hq36Kq26SoBTi0HX7nSYwN/1eZrGNzOe3wz\nRXp2btKXELc+nfpuLbit9ROPhkNxV1UvcWvobglAe3p8S8CxY8dYuXIlH330EQAffmi6ZthWrVLg\nhwAAFPVJREFUa0BrLQF+vXxwsXfmsUEP8/qBf5g7owz2jkZv1DM+aDT9PcPJqDrH2pOfAxAfPpXS\nhnJiet9BsGsQe/IOcKQkhazq89wdPJ5B3lEM8DIdaDKqzhHk7N/uffENhga+O5vItLBJ5utXSYWH\ncXdwI8qrP1nV2RRoilh/9gcUFP4w9HH8nX1Zffz/MztiBv09w6i+UGPufHeb72COlZ40l9/HtTe5\ntfkM9BqAdy8vNDoNowKGM9A7Cju1Lf+16yUAxgWOYpjvYAb7RFN9oYYt2TuJ6X0HQS4Bzepbqa3C\nVm2L88WYmvb0rtBWYlSM+PS6fCtOrU6Do60jNip1s3UVReGjk5+TXpnB3yf8tdm9x1nV53G0cSDI\nJYDc2ny+Sd+Im4Mb00InUdZQTrm2km/SN7b6eoa69WV2/xm8c3hVs+WDvaOY2DeGtIoMpl1MZmzU\nNqSUnsRWbYeiGInwCON42Sl+zj+IoijMH/xr8jQFuNu7cbT0BNNCJ2GrvtyntrS+nJKGMvx6+eDr\ndDnmprc81enr2Zq9k/v6TaSwrpgAZz8KNaa7Ky71wP932rdkVZ+nj2tvXOydsVfbU1BXhKONAwWa\nQsq0FQQ5B5BZnW3eRy9bRxxsHPBy9CCr+jyj/IeTqylgpN8wjIqRqgvV5GkKyKnNB0zJ3T19JjDM\ndzBpFenmzn5TQycR0/sO9EY956pzOF52ikl978bf2Y9fio4wOuB2qi/Usvr4Jzwc9QAl9aXY2dg1\n67neYNBSoa3kYGEyu3J/Jj58KpNC7gagpL6MxHPb0OjqKK4vZXzQGEYFDMfN3pV9BUmsP/u/F6/T\njuN/Uj5u9T29Uh+XIMb3HoObvSsaXR1qtQ2RHuF8l7GJY6UnGB1wO+eqz5sHcQJTAhfo7M8g7yi0\nBi0Nhgt8mvoVYwNHACq+PfsD8wf/mp05e7gjcCS3+w+jwaDlvaMfMD10MsfLUimsK2aE/zDGBIyg\nXFuBu70bLnbOPLvrdfTlPjRWBhDh70t6hgEa7Xjrd2Oosynmn0dXE+XZn0ei56DR17E3/6D5rpTO\n8O3l3SyWQGd/CuuKzY/7e4QR5RXJD1mm688j/W8jyrM/x8tSqdHVkhAxzdz50s3eFbVKjVExkl2T\ny+68fZTWl2OrtuHxwb8mq/o8oW59cbJzorS+jHpDPUEugTjZ9qJAU8QPWT9yu99QPju9DoB7gidw\nZ/AdvHlwGQC3+w1Fo6vjD8Mep1pXe839UN684xV25+9jZ47l+tNcaWbYfZwqT2v2vVKh4q7gceRr\nClv0lQpx7cP52lx6mrZaAnp8EvDjjz+yd+9eFi82nYFu3LiREydO8Je/tH7b2sniM2g1jTjZOuHm\n4IpDK/fGGowG9EZ9qwPB1OvrUanUN2UAlks6uu3tEl9fV0pKalBQOhzsp7iuhAptFdHeLc9uewJv\nb2d+TN1HuHs/3Oxd2x3IpKdz83Qgv7jcfMtiR4yKEaNibJa4dGdag5bM6mzs1Lac0aRz4PxhZoZP\nYWzgSIyKkcyqc0R4hLX7HajX1+No60i9voFfio9yV/C4Tg94da0UReH1j38hr1TD8P4+nMiqwNBo\n5J/PTMDNufV78HNq8tiZu4cIj1ACvb0pLq+kn3tfApz8aFSM2DcZewTgVHkaLnbOBLsEYaO2Ia+2\ngMMlKYwNGIG/c8etNpamNVxArVKZxxhoMDSga9Tj7uDWYl2Nrg6dUce/074z30lyybPDn6DRaCTE\nrQ8qlYpGYyO2altzhz+jYqRCW4WXowe5tfn0dQ2mQlvFztzdxIdPbTbGQaW2Chc7Z2p0Grwujvuh\nbbyArdoWO7UtBZoibNU2FNaV0Ne1t3lskAptJZuythEfMbXZbdp1+nqMipFduT8TFxJrrtOlAcNq\ndXV4O3pevMvBjtza/Fb7HgB4O3oyLmg01RdqzH/d7F3JrsllR85u/Jx8GOwTTXFdCff2vZtPU/9N\nsGtvvBw9MCpGNmZu4fnbn+ZUedrFJLQSraGBMPd+nChLZXzQGL5O/569BUlEekaQUZWFl6Mnfxm9\nkKAAr1br1OOTgK1bt7Jv375OJwFgujvAGvj6ukqstyCJtft67aMk8svqGBHpy8OTIimprGdA384N\nTdvTYr0el2LVNeqxU9veGvNFNFFRX8vG1N3cEzGM0KAAGmqM2KhtUKO6aScobd0d0DNODdoREBBA\nQcHlnqLFxcX4+bWfEbf1YtyKJNZbk8TaPQX5uZBfVkfvAFciw3y42na1nhTr9bqVY/XFlZdCfnV5\nwc2dI6hdPT4JGDJkCDk5OeTn5+Pr60tiYiLLly9vdxtry7atgcR6a+ppsf56Yn96ezkxaUTwVde7\np8V6PSTWm1OP1vT4JMDGxobXXnuNxx9/HEVReOCBBwgPD+94QyGEsDA3Z3tmTgi92dUQotN6fBIA\nEBMTQ0xMTMcrCiGEEMKsx48TIIQQQohrI0mAEEIIYaUkCRBCCCGslCQBQgghhJWSJEAIIYSwUpIE\nCCGEEFZKkgAhhBDCSkkSIIQQQlgpSQKEEEIIKyVJgBBCCGGlJAkQQgghrJQkAUIIIYSVkiRACCGE\nsFKSBAghhBBWSpIAIYQQwkpJEiCEEEJYKUkChBBCCCslSYAQQghhpSQJEEIIIayUJAFCCCGElZIk\nQAghhLBSkgQIIYQQVkqSACGEEMJKSRIghBBCWClJAoQQQggrJUmAEEIIYaUkCRBCCCGslCQBQggh\nhJWSJEAIIYSwUpIECCGEEFZKkgAhhBDCSkkSIIQQQlgpSQKEEEIIKyVJgBBCCGGlJAkQQgghrJQk\nAUIIIYSVkiRACCGEsFKSBAghhBBWSpIAIYQQwkpJEiCEEEJYKUkChBBCCCslSYAQQghhpa4rCdi6\ndSvTp08nOjqaU6dONXvugw8+YPLkyUyZMoW9e/eal+/Zs4f77ruPuLg4PvzwQ/PyvLw85s6dS1xc\nHAsXLsRgMACg0+l4/vnnmTx5Mg8++CAFBQUd7kMIIYQQHbuuJCAyMpKVK1cyatSoZsszMzPZsmUL\nmzdvZs2aNbzxxhsoioLRaGTx4sWsXbuWTZs2kZiYSGZmJgDvvPMOjz32GD/++COurq6sX78egPXr\n1+Pu7s62bduYN28ey5YtAyAjI6PVfQghhBCic64rCQgLC6Nfv34tfnx37tzJ1KlTsbW1JTg4mJCQ\nEI4fP87x48cJCQmhd+/e2NnZMW3aNHbu3AnAwYMHiYuLAyAhIYEdO3aYy0pISAAgLi6OgwcPArBr\n165W9yGEEEKIzrkhfQKKi4sJDAw0P/b396e4uLjV5SUlJVRWVuLu7o5abapOQEAAxcXFAJSUlBAQ\nEACAjY0Nrq6uVFVVtbkPIYQQQnSObUcrPPbYY5SVlbVY/vzzzxMbG9vqNq01y6tUKoxGY5vrX7mN\nSqVqt6y2lgshhBCiczpMAj755JOrLjQgIIDCwkLz46KiIvz8/FAUpVnHvuLiYvz8/PDy8qKmpgaj\n0YharTavD6Yz/KKiIvz9/WlsbKS2thZ3d/c299EZvr6uVx1TTyWx3pok1luTxHpr6s6xWuxyQNMz\n89jYWDZv3oxOpyM3N5ecnByGDh3KkCFDyMnJIT8/H51OR2JiIhMnTgRg7NixbN26FYANGzaYl8fG\nxrJhwwbAdDfC2LFj292HEEIIITpHpVxHl/odO3awePFiKisrcXNzIyoqio8++ggw3b63fv16bG1t\nefXVV5kwYQJgukXwb3/7G4qi8MADD/DEE08AkJuby8KFC6mpqSE6Opply5ZhZ2eHTqfjT3/6E6dP\nn8bDw4Ply5cTHBzc7j6EEEII0bHrSgKEEEII0XPJiIFCCCGElZIkQAghhLBSkgQIIYQQVqrHJwFF\nRUU8+uijTJ06lRkzZvDZZ58BUF1dzeOPP05cXBzz58+ntrYWgKysLB566CGGDBnS4vbHtuY16C4s\nGeuiRYsYN24cM2bM6PI4OsNSsbZVTndiqVh1Oh1z5swhPj6eGTNmsHLlypsST3ss+RkGMBqNJCQk\n8NRTT3VpHJ1hyVhjY2OZOXMm8fHxPPDAA10eS0csGWttbS0LFixgypQpTJs2jZSUlC6Ppz2WivXc\nuXPEx8eTkJBAfHw8I0aMuDnHJ6WHKykpUVJTUxVFURSNRqNMnjxZycjIUJYuXap8+OGHiqIoygcf\nfKAsW7ZMURRFKS8vV06cOKGsWLFC+fjjj83lNDY2Kvfee6+Sl5en6HQ6ZebMmUpGRkbXB9QOS8Wq\nKIryyy+/KKmpqcr06dO7NohOslSsbZXTnVjyfa2vr1cURVEMBoMyZ84cJSUlpQsj6ZglY1UURfnk\nk0+UF154QXnyySe7LohOsmSssbGxSlVVVdcGcBUsGeuf//xnZf369YqiKIper1dqa2u7MJKOWfoz\nrCim35/x48crBQUFXRNEEz2+JcDX15fo6GgAnJ2dCQ8Pp7i4uNmcA03nIvDy8mLw4MHY2jYfJ6m9\neQ26C0vFCjBy5Ejc3Ny6rvJXyVKxtlZOSUlJF0bSMUu+r7169QJMrQKXZuLsTiwZa1FREbt372bO\nnDldF8BVsGSsysUJ2LorS8Wq0WhITk5m9uzZANja2uLi4tKFkXTMku/rJfv376dv377NhsLvKj0+\nCWgqLy+PtLQ0hg0bRnl5OT4+PoDpTausrGx327bmNeiurifWnsZSsV4qpzsPKnW9sRqNRuLj4xk/\nfjzjx4+/pWN9++23eemll3rEcOHXG6tKpWL+/PnMnj2br7/++kZX97pcT6x5eXl4enryyiuvkJCQ\nwGuvvYZWq+2Kal8TSx2bNm/ezLRp025UNdt1yyQBdXV1LFiwgEWLFuHs7HzVBwalBw2XcL2x9iSW\nivXKcrojS8SqVqv5/vvv2bNnDykpKWRkZNyAml6/6431p59+wsfHh+jo6G7/3bXE+/rVV1/x3Xff\nsWbNGr744guSk5NvQE2v3/XGajAYSE1N5eGHH2bDhg04Ojp2y/5ZYLljk16vZ9euXUyZMsXCNeyc\nWyIJMBgMLFiwgFmzZnHvvfcC4O3tbZ74qLS0FC8vr3bLCAgIaHVeg+7GErH2FJaKtbVyuhtLv68u\nLi6MHj2an3/++YbU93pYItYjR46wa9cuJk6cyAsvvEBSUhIvvfTSDa/71bLU++rr6wuYmpYnTZrE\niRMnblylr5GljsMBAQEMGTIEME0fn5qaemMrfg0s+X3ds2cPgwYNumnH7VsiCVi0aBERERHMmzfP\nvCw2NpbvvvsOaD4XQVNNzyDam9egO7FErO0t604sFWtr5XQ3loi1oqLC3CNZq9Vy4MABwsLCbnDN\nr54lYl24cCE//fQTO3fuZPny5YwZM4alS5fe+MpfJUvE2tDQQF1dHQD19fXs3buX/v373+CaXz1L\nxOrj40NgYCDnzp0D4ODBg4SHh9/gml89Sx6HExMTmT59+o2rbAd6/LDBhw8f5pFHHiEyMhKVSoVK\npeL5559n6NChPPfccxQWFhIUFMR7772Hm5sbZWVlzJ49m7q6OtRqNU5OTiQmJuLs7NzmvAbdhSVj\nvXT2VFVVhY+PD88884y5M053YKlY09LSWi0nJibmZodoZqlY8/LyePnllzEajRiNRqZOncrTTz99\ns8NrxpKf4UsOHTrExx9/zOrVq29iZC1ZKtaKigr++Mc/olKpaGxsZMaMGbf0sSktLY1XX30Vg8FA\nnz59WLJkCa6u3WcWPkvGqtVqufvuu9mxY8dN6wDZ45MAIYQQQlybW+JygBBCCCGuniQBQgghhJWS\nJEAIIYSwUpIECCGEEFZKkgAhhBDCSkkSIIQQQlgpSQKEEEIIKyVJgBCiUxISEtDpdNe8/SuvvMIX\nX3xxVdtERUXR0NDQ7jr5+fndflIdIborSQKEEM1cOWVtY2MjYBoK1d7evkvr0plJWfLy8li3bl0X\n1EaIW0/bExwLIW45L774ItnZ2eh0OkJCQnj77bc5ffo0b731FoMGDSItLY3nnnuOrVu3YmNjw7lz\n56ivr2fDhg1ERUVx9OhRtm3bxvbt21m5ciVgShLuvvtu1q1bh0aj4Y033qChoQGdTsfcuXN59NFH\nO12/bdu2sWLFChwdHZk0aVKHdXd1dWXx4sXk5+eTkJBA3759ee+998jKymLJkiVUVVWh1+uZN2+e\nea53IUQTihDCalRWVpr/X7FihfLOO+8oSUlJysCBA5WUlBTzcy+//LIye/ZsRavVmpdFRUUp9fX1\nSkNDgzJ27FhzWbt27VLmzZunKIqi1NXVKTqdzvz/1KlTlczMTHOZn3/+eZt1Ky8vV0aPHq1kZ2cr\niqIoa9asMe+ztbq/++67iqIoSlJSkjJ79mzzcwaDQUlISFCysrIURVEUjUajxMXFmR8LIS6TlgAh\nrMiGDRv44Ycf0Ov1aLVa+vXrx5133klISAhDhw5ttm5cXBwODg7mx8rFaUYcHR2ZOHEimzZt4pFH\nHmHDhg3cf//9gGnGu9dff520tDTUajWlpaWkpaV1ajbDY8eOMXjwYEJCQgB48MEHeffdd9ute2uy\ns7PJyspi4cKF5jrr9XoyMzMJDQ3t/IslhBWQJEAIK5GcnMxXX33FunXr8PDwYNOmTeYOdU5OTi3W\nv3JZ0+vz8fHxLFmyhOnTp3Po0CGWLVsGwPLly/H19WXp0qWoVCrmz5/f6c6EyhVzmTV93F7dWyvH\ny8uLDRs2dGq/Qlgz6RgohJWora3F1dUVd3d3dDod3377rfm5K3+AW9N0nZEjR6LRaFi+fDmTJk0y\ntxjU1tYSGBiISqUiPT2d5OTkTtdv+PDhpKamkpOTA8A333zTqbq7uLhQW1trfhwaGoqjoyMbN240\nL8vKyqKurq7TdRHCWkgSIISViImJoU+fPsTFxfHoo48yaNAgAPOc6B25cp34+Hi++eYb86UAgKef\nfpqvv/6aWbNmsWrVKkaNGtXp+nl5ebF48WKefPJJ7r//fvR6fYd1BxgwYAChoaHMmDGDZ599Fhsb\nG1avXs3mzZuZNWsW06dP580332xWnhDCRKV05hRACCGEELccaQkQQgghrJR0DBRCdKlVq1axfft2\n8+UFRVFQqVSsXbsWLy+vm1w7IayLXA4QQgghrJRcDhBCCCGslCQBQgghhJWSJEAIIYSwUpIECCGE\nEFZKkgAhhBDCSv0fy2GlOqBd9EYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb27072b290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[(df['classification'] == 'cheap')].groupby('arrival_date')['ttv'].agg('sum').plot()\n",
    "df[(df['classification'] == 'expensive')].groupby('arrival_date')['ttv'].agg('sum').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11074 entries, 0 to 11073\n",
      "Data columns (total 3 columns):\n",
      "arrival_date      11074 non-null datetime64[ns]\n",
      "classification    11074 non-null object\n",
      "ttv               11074 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(1), object(1)\n",
      "memory usage: 259.6+ KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>arrival_date</th>\n",
       "      <th>classification</th>\n",
       "      <th>ttv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>cheap</td>\n",
       "      <td>74397.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>expensive</td>\n",
       "      <td>7723.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>higher_medium</td>\n",
       "      <td>6706.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>lower_medium</td>\n",
       "      <td>29631.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>cheap</td>\n",
       "      <td>100902.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  arrival_date classification        ttv\n",
       "0   2010-01-01          cheap   74397.13\n",
       "1   2010-01-01      expensive    7723.86\n",
       "2   2010-01-01  higher_medium    6706.06\n",
       "3   2010-01-01   lower_medium   29631.85\n",
       "4   2010-01-02          cheap  100902.75"
      ]
     },
     "execution_count": 528,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily = df.groupby(['arrival_date', 'classification'])['ttv'].agg('sum').reset_index()\n",
    "\n",
    "print daily.info()\n",
    "\n",
    "daily.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb289322850>"
      ]
     },
     "execution_count": 529,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8Ho/hdp/PF/J1w72GmYKCPEuPQ/xxbtzLzedmzYdbQ66n1rJlMw05tVMCSxYf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\n4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dD5h1WbFBNIxCg1YuNKgcum5CqOaq8pASq1o29wPEf3LffasR0ONgEUtM+QDmF7gigh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\ntDXX4sG714Y6Hi6K4c1qa67F8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f5hlCsO61uzpUsPHj2N6VQa5dEINqxdVpIJP1s9OWws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\nIgU4XBERERERESnA4YqIiIiIiEiBfwBoIRsOduflXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb27072b510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(14, 10))\n",
    "ax = fig.add_subplot(111)\n",
    "plot_df = daily[(daily['classification'] == 'cheap')].copy()\n",
    "ax.plot_date(x=plot_df['arrival_date'], y=plot_df['ttv'], label='Cheap')\n",
    "plot_df = daily[(daily['classification'] == 'expensive')].copy()\n",
    "ax.plot_date(x=plot_df['arrival_date'], y=plot_df['ttv'], label='Expensive')\n",
    "ax.set_ylim(0, 500000)\n",
    "ax.set_ylabel('Daily TTV (Pounds)')\n",
    "ax.set_title('Daily TTV by Purchase Type')\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb27053f750>"
      ]
     },
     "execution_count": 530,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CjRs3TosWLdKgQYMkSYMGDdL8+fM1dOhQffLJJ0pJSVFqaqp69+6txx9/XAcO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\nz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HSthx9+GN/+9rfzf/ft2ze8my8y+vbti5tuuikf/cvlcvjFL36BGTNm4JxzzsGjjz6a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\neC56nmep+AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb27cfa1250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(14, 10))\n",
    "ax = fig.add_subplot(111)\n",
    "plot_df = daily[(daily['classification'] == 'cheap') & \n",
    "                (daily['arrival_date'] > '2016-07-01')].copy()\n",
    "ax.plot_date(x=plot_df['arrival_date'], y=plot_df['ttv'], label='Cheap')\n",
    "plot_df = daily[(daily['classification'] == 'expensive') & \n",
    "                 (daily['arrival_date'] > '2016-07-01')].copy()\n",
    "ax.plot_date(x=plot_df['arrival_date'], y=plot_df['ttv'], label='Expensive')\n",
    "ax.set_ylim(0, 120000)\n",
    "ax.set_ylabel('Daily TTV (Pounds)')\n",
    "ax.set_title('Daily TTV by Purchase Type')\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IOError",
     "evalue": "File C:\\stacked_bar.csv does not exist",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mIOError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-542-21d3ae630737>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#Read in data & create total column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mstacked_bar_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"C:\\stacked_bar.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mstacked_bar_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"total\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstacked_bar_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstacked_bar_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#Set general plot properties\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    643\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[1;32m    644\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 645\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    646\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    647\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    386\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    387\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    389\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mchunksize\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    727\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 729\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    730\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    731\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m    920\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    921\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 922\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    923\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    924\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'python'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1387\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'allow_leading_cols'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex_col\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1389\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_parser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1391\u001b[0m         \u001b[0;31m# XXX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/parser.pyx\u001b[0m in \u001b[0;36mpandas.parser.TextReader.__cinit__ (pandas/parser.c:4175)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/parser.pyx\u001b[0m in \u001b[0;36mpandas.parser.TextReader._setup_parser_source (pandas/parser.c:8440)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mIOError\u001b[0m: File C:\\stacked_bar.csv does not exist"
     ]
    }
   ],
   "source": [
    "#Read in data & create total column\n",
    "stacked_bar_data = pd.read_csv(\"C:\\stacked_bar.csv\")\n",
    "stacked_bar_data[\"total\"] = stacked_bar_data.Series1 + stacked_bar_data.Series2\n",
    "\n",
    "#Set general plot properties\n",
    "sns.set_style(\"white\")\n",
    "sns.set_context({\"figure.figsize\": (24, 10)})\n",
    "\n",
    "#Plot 1 - background - \"total\" (top) series\n",
    "sns.barplot(x = stacked_bar_data.Group, y = stacked_bar_data.total, color = \"red\")\n",
    "\n",
    "#Plot 2 - overlay - \"bottom\" series\n",
    "bottom_plot = sns.barplot(x = stacked_bar_data.Group, y = stacked_bar_data.Series1, color = \"#0000A3\")\n",
    "\n",
    "\n",
    "topbar = plt.Rectangle((0,0),1,1,fc=\"red\", edgecolor = 'none')\n",
    "bottombar = plt.Rectangle((0,0),1,1,fc='#0000A3',  edgecolor = 'none')\n",
    "l = plt.legend([bottombar, topbar], ['Bottom Bar', 'Top Bar'], loc=1, ncol = 2, prop={'size':16})\n",
    "l.draw_frame(False)\n",
    "\n",
    "#Optional code - Make plot look nicer\n",
    "sns.despine(left=True)\n",
    "bottom_plot.set_ylabel(\"Y-axis label\")\n",
    "bottom_plot.set_xlabel(\"X-axis label\")\n",
    "\n",
    "#Set fonts to consistent 16pt size\n",
    "for item in ([bottom_plot.xaxis.label, bottom_plot.yaxis.label] +\n",
    "             bottom_plot.get_xticklabels() + bottom_plot.get_yticklabels()):\n",
    "    item.set_fontsize(16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>classification</th>\n",
       "      <th>ttv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>cheap</td>\n",
       "      <td>161860.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>expensive</td>\n",
       "      <td>34885.244880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>cheap</td>\n",
       "      <td>231361.489805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>expensive</td>\n",
       "      <td>31639.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>cheap</td>\n",
       "      <td>235815.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   week classification            ttv\n",
       "0     1          cheap  161860.810000\n",
       "1     1      expensive   34885.244880\n",
       "2     2          cheap  231361.489805\n",
       "3     2      expensive   31639.120000\n",
       "4     3          cheap  235815.600000"
      ]
     },
     "execution_count": 531,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily['week'] = daily.arrival_date.apply(lambda x: x.weekofyear)\n",
    "\n",
    "plot_df = daily[(daily['classification'].isin(['expensive', 'cheap'])) & \n",
    "                 (daily['arrival_date'] > '2016-07-01')] \\\n",
    "  .groupby(['week', 'classification']) \\\n",
    "  .agg('sum') \\\n",
    "  .reset_index()\n",
    "  \n",
    "plot_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "daily['week'] = daily.arrival_date.apply(lambda x: x.weekofyear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>arrival_date</th>\n",
       "      <th>classification</th>\n",
       "      <th>ttv</th>\n",
       "      <th>week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>cheap</td>\n",
       "      <td>74397.13</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>expensive</td>\n",
       "      <td>7723.86</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>higher_medium</td>\n",
       "      <td>6706.06</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>lower_medium</td>\n",
       "      <td>29631.85</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>cheap</td>\n",
       "      <td>100902.75</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  arrival_date classification        ttv  week\n",
       "0   2010-01-01          cheap   74397.13    53\n",
       "1   2010-01-01      expensive    7723.86    53\n",
       "2   2010-01-01  higher_medium    6706.06    53\n",
       "3   2010-01-01   lower_medium   29631.85    53\n",
       "4   2010-01-02          cheap  100902.75    53"
      ]
     },
     "execution_count": 533,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sns.barplot(x=plot_df.week, y=plot_df.ttv, color='red')\n",
    "bottom_plot = sns.barplot(x = plot_df.week, y = plot_df.tt, color = \"#0000A3\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jX+KFF15hzZpVnD17hjNnTufLPf/11/vx8/NX7nkXorXQnZu9pvnpZsC+FOjt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\nMwzDMIwFw4meYRiGYSyY/wEaBVRnkQKuOAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb2900e0310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df = daily[ \n",
    "                 (daily['arrival_date'] > '2016-07-01')].copy() \\\n",
    "        .groupby(['week', 'classification'])['ttv'] \\\n",
    "        .mean() \\\n",
    "        .unstack('classification').plot(kind='bar', stacked=True)\n",
    "\n",
    "# plot_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>arrival_date</th>\n",
       "      <th>classification</th>\n",
       "      <th>ttv</th>\n",
       "      <th>week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9495</th>\n",
       "      <td>2016-07-02</td>\n",
       "      <td>cheap</td>\n",
       "      <td>116697.56</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9496</th>\n",
       "      <td>2016-07-02</td>\n",
       "      <td>expensive</td>\n",
       "      <td>8210.70</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9497</th>\n",
       "      <td>2016-07-02</td>\n",
       "      <td>higher_medium</td>\n",
       "      <td>3538.92</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9498</th>\n",
       "      <td>2016-07-02</td>\n",
       "      <td>lower_medium</td>\n",
       "      <td>14971.49</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9499</th>\n",
       "      <td>2016-07-03</td>\n",
       "      <td>cheap</td>\n",
       "      <td>50443.57</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     arrival_date classification        ttv  week\n",
       "9495   2016-07-02          cheap  116697.56    26\n",
       "9496   2016-07-02      expensive    8210.70    26\n",
       "9497   2016-07-02  higher_medium    3538.92    26\n",
       "9498   2016-07-02   lower_medium   14971.49    26\n",
       "9499   2016-07-03          cheap   50443.57    26"
      ]
     },
     "execution_count": 535,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily[ \n",
    "                 (daily['arrival_date'] > '2016-07-01')].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb26b0b8f10>"
      ]
     },
     "execution_count": 536,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jU4+DZ2y3S/7ru7M4fkFtSjxEiIQvW6ACCEH22b/99tt46aWXoNEYHIPq6uoQ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\npm91AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb28bf57e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[(df['classification'] == 'cheap')].groupby('arrival_date')['commission'].agg('sum').plot()\n",
    "df[(df['classification'] == 'expensive')].groupby('arrival_date')['commission'].agg('sum').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb29811fa90>"
      ]
     },
     "execution_count": 537,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lELNssKahXfLKgZ4eM7p7zDhW5Lpc8v++u4izV/S2jYfU5F+fnsY/17nfFyCo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\nWJch8dHe1o3IqHC0NHYgUijFKgf2TyhpYDSuVTZb3KAi5i1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nzZmb4/McSYL6zz9Fkl0H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dmzZ7N8+XJaW1v585//THt7Ozqdjquuuorrr7/e7/atX7+e5557jsjISObOneuz7XFxcSxdupTy\n8nIWL15MdnY2zz//PMXFxTz55JM0NTWh1+u54YYbrPnGBQKBHbJAIAg5GhsbrX8/99xz8jPPPCPv\n3LlTHjNmjLx//37rsUceeUResmSJrNVqrWWjRo2SNRqN3N7eLk+bNs1a16ZNm+QbbrhBlmVZbmtr\nk3U6nfXv+fPny0VFRdY633vvPY9tq6+vl88++2y5tLRUlmVZfu2116zf6a7tzz77rCzLsrxz5055\nyZIl1mMGg0FevHixXFxcLMuyLLe2tsrz5s2zfhYIBDbEyl4gCEFWrVrF559/jl6vR6vVkpuby/nn\nn09OTg75+fkO586bN4+IiAjrZ7kzXUZkZCRz5sxh9erVXHfddaxatYrLL78cMGdpe/zxxzl69CgK\nhYLa2lqOHj3qV/a9ffv2MW7cOHJycgC4+uqrefbZZ7223R2lpaUUFxfzwAMPWNus1+spKioiLy/P\n/x9LIDgDEMJeIAgx9uzZwwcffMDy5ctJTExk9erVVsO26Ohol/Ody+z3zxctWsSTTz7JggUL2LVr\nF08//TQAy5YtIy0tjaeeegpJkrj55pv9NuqTnXJv2X/21nZ39SQnJ7Nq1Sq/vlcgOJMRBnoCQYih\nVquJi4sjISEBnU7Hxx9/bD3mLGjdYX/OlClTaG1tZdmyZcydO9eqAVCr1WRlZSFJEseOHWPPnj1+\nt2/SpEkcPnyYkydPAvDRRx/51fbY2FjUarX1c15eHpGRkXz66afWsuLiYmteeIFAYEMIe4EgxJg5\ncyaDBw9m3rx5XH/99YwdOxbAmpfbF87nLFq0iI8++siqwge48847+fDDD7nssst48cUXmTp1qt/t\nS05OZunSpdx+++1cfvnl6PV6n20HGDlyJHl5eSxcuJBf//rXKJVKXn75ZdauXctll13GggUL+Mtf\n/uJQn0AgMCPy2QsEAoFAEOKIlb1AIBAIBCGOMNATCAQ9wosvvshXX31l3RaQZRlJknjjjTdITk4+\nza0TCM4shBpfIBAIBIIQR6jxBQKBQCAIcYSwFwgEAoEgxBHCXiAQCASCEEcIe4FAIBAIQhwh7AUC\ngUAgCHH+H5tmLO0Ky7XcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb271b6a7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[(df['classification'] == 'cheap')].groupby('arrival_date')['commission'].agg('sum').plot()\n",
    "df[(df['classification'] == 'expensive')].groupby('arrival_date')['commission'].agg('sum').plot()\n",
    "df[(df['classification'] == 'higher_medium')].groupby('arrival_date')['commission'].agg('sum').plot()\n",
    "df[(df['classification'] == 'lower_medium')].groupby('arrival_date')['commission'].agg('sum').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "metadata": {
    "codeCollapsed": false,
    "collapsed": false,
    "hiddenCell": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>arrival_date</th>\n",
       "      <th>hotel_id</th>\n",
       "      <th>destination</th>\n",
       "      <th>ttv</th>\n",
       "      <th>commission</th>\n",
       "      <th>classification</th>\n",
       "      <th>commission_percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>8838147</td>\n",
       "      <td>2010-03-15</td>\n",
       "      <td>149391</td>\n",
       "      <td>London</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>cheap</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>8276762</td>\n",
       "      <td>2010-01-15</td>\n",
       "      <td>146125</td>\n",
       "      <td>London</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>cheap</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>8833138</td>\n",
       "      <td>2010-03-17</td>\n",
       "      <td>88026</td>\n",
       "      <td>London</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>cheap</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1778</th>\n",
       "      <td>8702909</td>\n",
       "      <td>2010-03-12</td>\n",
       "      <td>71872</td>\n",
       "      <td>Manchester</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>cheap</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2319</th>\n",
       "      <td>8401532</td>\n",
       "      <td>2010-04-09</td>\n",
       "      <td>83877</td>\n",
       "      <td>Edinburgh</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>cheap</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      booking_id arrival_date  hotel_id destination  ttv  commission  \\\n",
       "148      8838147   2010-03-15    149391      London  0.0         0.0   \n",
       "151      8276762   2010-01-15    146125      London  0.0         0.0   \n",
       "328      8833138   2010-03-17     88026      London  0.0         0.0   \n",
       "1778     8702909   2010-03-12     71872  Manchester  0.0         0.0   \n",
       "2319     8401532   2010-04-09     83877   Edinburgh  0.0         0.0   \n",
       "\n",
       "     classification  commission_percentage  \n",
       "148           cheap                    NaN  \n",
       "151           cheap                    NaN  \n",
       "328           cheap                    NaN  \n",
       "1778          cheap                    NaN  \n",
       "2319          cheap                    NaN  "
      ]
     },
     "execution_count": 538,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['commission_percentage'] = (df['commission'] / df['ttv'])\n",
    "df.head()\n",
    "# df['commission_percentage'].mean() 0.1635927163751118\n",
    "# df['commission_percentage'].min() 0.0\n",
    "# df['commission_percentage'].max() 0.5\n",
    "# Lower Quartile is 15%\n",
    "# Upper Quartile is 17%\n",
    "\n",
    "# 130 not cheap, 10460 cheap at 0 comission\n",
    "zero_hotels = df.loc[df['commission'] == 0]\n",
    "zero_hotels.loc[df['classification'] == 'cheap'] .head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_id</th>\n",
       "      <th>hotel_id</th>\n",
       "      <th>ttv</th>\n",
       "      <th>commission</th>\n",
       "      <th>commission_percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.649131e+06</td>\n",
       "      <td>3.649131e+06</td>\n",
       "      <td>3.649131e+06</td>\n",
       "      <td>3.649131e+06</td>\n",
       "      <td>3.649131e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.089936e+07</td>\n",
       "      <td>1.219365e+05</td>\n",
       "      <td>1.436040e+02</td>\n",
       "      <td>2.346520e+01</td>\n",
       "      <td>1.637604e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.408950e+06</td>\n",
       "      <td>9.001559e+04</td>\n",
       "      <td>1.519729e+02</td>\n",
       "      <td>2.513223e+01</td>\n",
       "      <td>2.302607e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.434710e+06</td>\n",
       "      <td>1.050000e+02</td>\n",
       "      <td>-1.775000e+03</td>\n",
       "      <td>-3.195000e+02</td>\n",
       "      <td>1.494118e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.453825e+07</td>\n",
       "      <td>6.942200e+04</td>\n",
       "      <td>6.500000e+01</td>\n",
       "      <td>1.050000e+01</td>\n",
       "      <td>1.500000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.098133e+07</td>\n",
       "      <td>9.595700e+04</td>\n",
       "      <td>1.010000e+02</td>\n",
       "      <td>1.680000e+01</td>\n",
       "      <td>1.500000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.734968e+07</td>\n",
       "      <td>1.947680e+05</td>\n",
       "      <td>1.690000e+02</td>\n",
       "      <td>2.760000e+01</td>\n",
       "      <td>1.700000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.382197e+07</td>\n",
       "      <td>5.044170e+05</td>\n",
       "      <td>3.172560e+04</td>\n",
       "      <td>4.051350e+03</td>\n",
       "      <td>5.000000e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         booking_id      hotel_id           ttv    commission  \\\n",
       "count  3.649131e+06  3.649131e+06  3.649131e+06  3.649131e+06   \n",
       "mean   2.089936e+07  1.219365e+05  1.436040e+02  2.346520e+01   \n",
       "std    7.408950e+06  9.001559e+04  1.519729e+02  2.513223e+01   \n",
       "min    5.434710e+06  1.050000e+02 -1.775000e+03 -3.195000e+02   \n",
       "25%    1.453825e+07  6.942200e+04  6.500000e+01  1.050000e+01   \n",
       "50%    2.098133e+07  9.595700e+04  1.010000e+02  1.680000e+01   \n",
       "75%    2.734968e+07  1.947680e+05  1.690000e+02  2.760000e+01   \n",
       "max    3.382197e+07  5.044170e+05  3.172560e+04  4.051350e+03   \n",
       "\n",
       "       commission_percentage  \n",
       "count           3.649131e+06  \n",
       "mean            1.637604e-01  \n",
       "std             2.302607e-02  \n",
       "min             1.494118e-02  \n",
       "25%             1.500000e-01  \n",
       "50%             1.500000e-01  \n",
       "75%             1.700000e-01  \n",
       "max             5.000000e-01  "
      ]
     },
     "execution_count": 539,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_no_zero_commission = df.drop(df[df['commission'] == 0].index)\n",
    "# df['commission_percentage'].mean() 0.1.635946\n",
    "# df['commission_percentage'].min() 0.0\n",
    "# df['commission_percentage'].max() 0.5\n",
    "# Lower Quartile is 15%\n",
    "# Upper Quartile is 17%\n",
    "\n",
    "df_no_zero_commission.describe()\n",
    "\n",
    "#so the 0 commission hotels hardly affect these stats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_no_zero_commission.head()\n",
    "\n",
    "#classify based on commission\n",
    "def commission_classifier(array):\n",
    "    high = 0.17\n",
    "    low = 0.15\n",
    "\n",
    "    def classifier(value):\n",
    "        if value <= low:\n",
    "            return 'low'\n",
    "        elif value >= high:\n",
    "            return 'high'\n",
    "        else:\n",
    "            return 'average'\n",
    "    \n",
    "    def do_classification(array):\n",
    "        classification_array = array.apply(lambda x: classifier(x))\n",
    "\n",
    "        return classification_array\n",
    "    \n",
    "    return do_classification(array) \n",
    "  \n",
    "df['commission_classifier'] = df.groupby(['arrival_date', 'destination'])['commission_percentage'].apply(commission_classifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'figure'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-541-bd9f8b7e2a15>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m111\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'commission_classifier'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'low'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'arrival_date'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'commission'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sum'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'commission_classifier'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'high'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'arrival_date'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'commission'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sum'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Daily TTV (Pounds)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'figure'"
     ]
    }
   ],
   "source": [
    "fig = plot.figure(figsize=(14, 10))\n",
    "ax = fig.add_subplot(111)\n",
    "df[(df['commission_classifier'] == 'low')].groupby('arrival_date')['commission'].agg('sum').plot()\n",
    "df[(df['commission_classifier'] == 'high')].groupby('arrival_date')['commission'].agg('sum').plot()\n",
    "ax.set_ylabel('Daily TTV (Pounds)')\n",
    "ax.set_title('Daily TTV by Purchase Type')\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles, labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(25, 10))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "plot_df = df[(df['commission_classifier'] == 'high')].groupby('arrival_date')['commission'].agg('sum').plot(label='High', color='purple')\n",
    "plot_df=df[(df['commission_classifier'] == 'low')].groupby('arrival_date')['commission'].agg('sum').plot(label='Low', color='pink')\n",
    "\n",
    "ax.set_ylim(0, 60000)\n",
    "ax.set_ylabel('Commission (Pounds)')\n",
    "ax.set_xlabel('Arrival Date')\n",
    "ax.set_title('Daily Commission by commission type')\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(25, 10))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "plot_df = df[(df['commission_classifier'] == 'high')].groupby('arrival_date')['commission'].agg('sum').plot(label='High', color='purple')\n",
    "plot_df=df[(df['commission_classifier'] == 'average')].groupby('arrival_date')['commission'].agg('sum').plot(label='Average', color='plum')\n",
    "\n",
    "ax.set_ylim(0, 60000)\n",
    "ax.set_ylabel('Commission (Pounds)')\n",
    "ax.set_xlabel('Arrival Date')\n",
    "ax.set_title('Daily Commission by commission type')\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(25, 10))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "plot_df = df[(df['commission_classifier'] == 'low')].groupby('arrival_date')['commission'].agg('sum').plot(label='Low', color='pink')\n",
    "plot_df=df[(df['commission_classifier'] == 'average')].groupby('arrival_date')['commission'].agg('sum').plot(label='Average', color='plum')\n",
    "\n",
    "ax.set_ylim(0, 60000)\n",
    "ax.set_ylabel('Commission (Pounds)')\n",
    "ax.set_xlabel('Arrival Date')\n",
    "ax.set_title('Daily Commission by commission type')\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(25, 10))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "plot_df = df[(df['commission_classifier'] == 'high')].groupby('arrival_date')['commission'].agg('sum').plot(label='High', color='purple')\n",
    "plot_df=df[(df['commission_classifier'] == 'average')].groupby('arrival_date')['commission'].agg('sum').plot(label='Average', color='plum')\n",
    "plot_df=df[(df['commission_classifier'] == 'low')].groupby('arrival_date')['commission'].agg('sum').plot(label='Low', color='pink')\n",
    "\n",
    "ax.set_ylim(0, 60000)\n",
    "ax.set_ylabel('Commission (Pounds)')\n",
    "ax.set_xlabel('Arrival Date')\n",
    "ax.set_title('Daily Commission by commission type')\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(14, 10))\n",
    "ax = fig.add_subplot(111)\n",
    "plot_df = daily[(daily['classification'] == 'cheap') & \n",
    "                (daily['arrival_date'] > '2016-07-01')].copy()\n",
    "ax.plot_date(x=plot_df['arrival_date'], y=plot_df['ttv'], label='Cheap')\n",
    "plot_df = daily[(daily['classification'] == 'expensive') & \n",
    "                 (daily['arrival_date'] > '2016-07-01')].copy()\n",
    "ax.plot_date(x=plot_df['arrival_date'], y=plot_df['ttv'], label='Expensive')\n",
    "ax.set_ylim(0, 120000)\n",
    "ax.set_ylabel('Daily TTV (Pounds)')\n",
    "ax.set_title('Daily TTV by Purchase Type')\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles, labels)\n",
    "\n",
    "df[(df['commission_classifier'] == 'high')].groupby('arrival_date')['commission'].agg('sum').copy()\n",
    "df[(df['commission_classifier'] == 'average')].groupby('arrival_date')['commission'].agg('sum').copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = df[(df['commission_classifier'] == 'high')].groupby('arrival_date')['commission'].agg('sum').copy().index\n",
    "y = df[(df['commission_classifier'] == 'high')].groupby('arrival_date')['commission'].agg('sum').copy()\n",
    "# y = df[(df['commission_classifier'] == 'average')].groupby('arrival_date')['commission'].agg('sum').copy()\n",
    "\n",
    "fig = plt.figure(figsize=(16, 12))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "highx = df[(df['commission_classifier'] == 'high')].groupby('arrival_date')['commission'].agg('sum').copy().index\n",
    "highy = df[(df['commission_classifier'] == 'high')].groupby('arrival_date')['commission'].agg('sum').copy()\n",
    "lowx = df[(df['commission_classifier'] == 'low')].groupby('arrival_date')['commission'].agg('sum').copy().index\n",
    "lowy = df[(df['commission_classifier'] == 'low')].groupby('arrival_date')['commission'].agg('sum').copy()\n",
    "avgx = df[(df['commission_classifier'] == 'average')].groupby('arrival_date')['commission'].agg('sum').copy().index\n",
    "avgy = df[(df['commission_classifier'] == 'average')].groupby('arrival_date')['commission'].agg('sum').copy()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "f, axarr = plt.subplots(2, 2, figsize=(25, 10))\n",
    "axarr[1, 0].plot(highx, highy, 'g')\n",
    "axarr[1, 0].plot(lowx, lowy, 'r')\n",
    "axarr[1, 0].set_title('High against low')\n",
    "axarr[0, 1].plot(avgx, avgy, 'b')\n",
    "axarr[0, 1].plot(lowx, lowy, 'r')\n",
    "axarr[0, 1].set_title('Avarage against low')\n",
    "axarr[0, 0].plot(avgx, avgy, 'b')\n",
    "axarr[0, 0].plot(highx, highy, 'g')\n",
    "axarr[0, 0].set_title('Avarage against high')\n",
    "axarr[1, 1].plot(highx, highy, 'g')\n",
    "axarr[1, 1].plot(lowx, lowy, 'r')\n",
    "axarr[1, 1].plot(avgx, avgy,'b')\n",
    "axarr[1, 1].set_title('All')\n",
    "# Fine-tune figure; hide x ticks for top plots and y ticks for right plots\n",
    "plt.setp([a.get_xticklabels() for a in axarr[0, :]], visible=False)\n",
    "plt.setp([a.get_yticklabels() for a in axarr[:, 1]], visible=False)\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "codeCollapsed": false,
    "collapsed": false,
    "hiddenCell": false
   },
   "outputs": [],
   "source": [
    "highx = df[(df['commission_classifier'] == 'high')].groupby('arrival_date')['commission'].agg('sum').copy().index\n",
    "highy = df[(df['commission_classifier'] == 'high')].groupby('arrival_date')['commission'].agg('sum').copy()\n",
    "lowx = df[(df['commission_classifier'] == 'low')].groupby('arrival_date')['commission'].agg('sum').copy().index\n",
    "lowy = df[(df['commission_classifier'] == 'low')].groupby('arrival_date')['commission'].agg('sum').copy()\n",
    "avgx = df[(df['commission_classifier'] == 'average')].groupby('arrival_date')['commission'].agg('sum').copy().index\n",
    "avgy = df[(df['commission_classifier'] == 'average')].groupby('arrival_date')['commission'].agg('sum').copy()\n",
    "ttvx = df.groupby('arrival_date')['commission'].agg('sum').copy().index\n",
    "ttvy = df.groupby('arrival_date')['commission'].agg('sum').copy()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "f, axarr = plt.subplots(2, 2, figsize=(25, 10))\n",
    "axarr[1, 0].plot(highx, highy, 'g')\n",
    "axarr[1, 0].plot(lowx, lowy, 'r')\n",
    "axarr[1, 0].plot(ttvx, ttvy, 'k')\n",
    "axarr[1, 0].set_title('High against low')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "axarr[0, 1].plot(avgx, avgy, 'b')\n",
    "axarr[0, 1].plot(lowx, lowy, 'r')\n",
    "axarr[0, 1].set_title('Average against low')\n",
    "axarr[0, 0].plot(avgx, avgy, 'b')\n",
    "axarr[0, 0].plot(highx, highy, 'g')\n",
    "axarr[0, 0].set_title('Avrage against high')\n",
    "axarr[1, 1].plot(highx, highy, 'g')\n",
    "axarr[1, 1].plot(lowx, lowy, 'r')\n",
    "axarr[1, 1].plot(avgx, avgy,'b')\n",
    "axarr[1, 1].set_title('All')\n",
    "# Fine-tune figure; hide x ticks for top plots and y ticks for right plots\n",
    "plt.setp([a.get_xticklabels() for a in axarr[0, :]], visible=False)\n",
    "plt.setp([a.get_yticklabels() for a in axarr[:, 1]], visible=False)\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "hiddenCell": true
   },
   "outputs": [],
   "source": [
    "sns.distplot(df.commission_percentage.dropna(), kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['year'] = df['arrival_date'].map(lambda x: x.year)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 2010    0.156540\n",
    "# 2011    0.159311\n",
    "# 2012    0.161064\n",
    "# 2013    0.160353\n",
    "# 2014    0.172321\n",
    "# 2015    0.172737\n",
    "# 2016    0.171695\n",
    "# 2017    0.170610                              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "avg_annual_com_plot = (df.groupby('year'))['commission_percentage'].agg('mean')\n",
    "type(avg_annual_com_plot)\n",
    "avg_annual_com_plot.index\n",
    "\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "axarr = fig.add_subplot(111)\n",
    "fig.suptitle('Year against average commission for that year', fontsize=20)\n",
    "plt.xlabel('Year', fontsize=18)\n",
    "plt.ylabel('Average Commission', fontsize=16)\n",
    "axarr.plot(avg_annual_com_plot.index, avg_annual_com_plot.values, 'black')\n",
    "axarr.get_xaxis().get_major_formatter().set_useOffset(False)\n",
    "\n",
    "highx = df[(df['commission_classifier'] == 'high')].groupby('year')['commission_percentage'].agg('mean').index\n",
    "highy = df[(df['commission_classifier'] == 'high')].groupby('year')['commission_percentage'].agg('mean')\n",
    "lowx = df[(df['commission_classifier'] == 'low')].groupby('year')['commission_percentage'].agg('mean').index\n",
    "lowy = df[(df['commission_classifier'] == 'low')].groupby('year')['commission_percentage'].agg('mean')\n",
    "avx = df[(df['commission_classifier'] == 'average')].groupby('year')['commission_percentage'].agg('mean').index\n",
    "avy = df[(df['commission_classifier'] == 'average')].groupby('year')['commission_percentage'].agg('mean')\n",
    "\n",
    "axarr.plot(highx, highy, 'b')\n",
    "axarr.plot(lowx, lowy, 'r')\n",
    "axarr.plot(avx, avy, 'g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "avg_annual_com_plot = (df.groupby('year'))['commission_percentage'].agg('mean')\n",
    "type(avg_annual_com_plot)\n",
    "avg_annual_com_plot.index\n",
    "\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "axarr = fig.add_subplot(111)\n",
    "fig.suptitle('Year against average commission for that year', fontsize=20)\n",
    "plt.xlabel('Year', fontsize=18)\n",
    "plt.ylabel('Average Commission', fontsize=16)\n",
    "axarr.plot(avg_annual_com_plot.index, avg_annual_com_plot.values, 'black')\n",
    "axarr.get_xaxis().get_major_formatter().set_useOffset(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Add year_month\n",
    "df['year_month'] = df['arrival_date'].map(lambda x: str(x.year) + '_' + str( x.month))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "highy = df[(df['commission_classifier'] == 'high')].groupby('year_month')['commission'].agg('sum').copy()\n",
    "lowy = df[(df['commission_classifier'] == 'low')].groupby('year_month')['commission'].agg('sum').copy()\n",
    "avgy = df[(df['commission_classifier'] == 'average')].groupby('year_month')['commission'].agg('sum').copy()\n",
    "ttvy = df.groupby('year_month')['commission'].agg('sum').copy()\n",
    "\n",
    "f, axarr = plt.subplots(2, 2, figsize=(27, 10))\n",
    "f.suptitle(\"Commission Value over time for different commission classes\", fontsize=14)\n",
    "\n",
    "axarr[0, 0].plot(range(0,len(avgy)), avgy, 'b')\n",
    "axarr[0, 0].plot(range(0,len(highy)), highy, 'g')\n",
    "axarr[0, 0].plot(range(0,len(ttvy)), ttvy, 'k')\n",
    "axarr[0, 0].set_xticks(range(0,len(ttvy),12))\n",
    "axarr[0, 0].set_xticklabels([2010,2011,2012,2013,2014,2015,2016,2017]) \n",
    "axarr[0, 0].set_title('Average against high')\n",
    "\n",
    "axarr[0, 1].plot(range(0,len(highy)), avgy, 'b')\n",
    "axarr[0, 1].plot(range(0,len(lowy)), lowy, 'r')\n",
    "axarr[0, 1].plot(range(0,len(ttvy)), ttvy, 'k')\n",
    "axarr[0, 1].set_xticks(range(0,len(ttvy),12))\n",
    "axarr[0, 1].set_xticklabels([2010,2011,2012,2013,2014,2015,2016,2017]) \n",
    "axarr[0, 1].set_title('Average against low')\n",
    "\n",
    "axarr[1, 0].plot(range(0,len(highy)), highy, 'g')\n",
    "axarr[1, 0].plot(range(0,len(lowy)), lowy, 'r')\n",
    "axarr[1, 0].plot(range(0,len(ttvy)), ttvy, 'k')\n",
    "axarr[1, 0].set_xticks(range(0,len(ttvy),12))\n",
    "axarr[1, 0].set_xticklabels([2010,2011,2012,2013,2014,2015,2016,2017]) \n",
    "axarr[1, 0].set_title('High against low')\n",
    "\n",
    "axarr[1, 1].plot(range(0,len(highy)), highy, 'g')\n",
    "axarr[1, 1].plot(range(0,len(lowy)), lowy, 'r')\n",
    "axarr[1, 1].plot(range(0,len(avgy)), avgy,'b')\n",
    "axarr[1, 1].plot(range(0,len(ttvy)), ttvy, 'k')\n",
    "axarr[1, 1].set_xticks(range(0,len(ttvy),12))\n",
    "axarr[1, 1].set_xticklabels([2010,2011,2012,2013,2014,2015,2016,2017]) \n",
    "axarr[1, 1].set_title('All')\n",
    "\n",
    "# Fine-tune figure; hide x ticks for top plots and y ticks for right plots\n",
    "plt.setp([a.get_xticklabels() for a in axarr[0, :]], visible=False)\n",
    "plt.setp([a.get_yticklabels() for a in axarr[:, 1]], visible=False)\n",
    "plt.legend(['High','Low','Average','Total'],bbox_to_anchor=(1.15, 2.25))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# High is above 17% Commission\n",
    "# Average is between 17% and 15% Commission\n",
    "# Low is below 15% Commission\n",
    "# Total is the cumulation of all commission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "highy = df[(df['commission_classifier'] == 'high')].groupby('year_month')['commission_percentage'].agg('mean').copy()\n",
    "lowy = df[(df['commission_classifier'] == 'low')].groupby('year_month')['commission_percentage'].agg('mean').copy()\n",
    "avgy = df[(df['commission_classifier'] == 'average')].groupby('year_month')['commission_percentage'].agg('mean').copy()\n",
    "ttvy = df.groupby('year_month')['commission_percentage'].agg('mean').copy()\n",
    "\n",
    "f, axarr = plt.subplots(2, 2, figsize=(27, 10))\n",
    "f.suptitle(\"Commission Percentage over time for different commission classes\", fontsize=14)\n",
    "\n",
    "axarr[0, 0].plot(range(0,len(avgy)), avgy, 'b')\n",
    "axarr[0, 0].plot(range(0,len(highy)), highy, 'g')\n",
    "axarr[0, 0].plot(range(0,len(ttvy)), ttvy, 'k')\n",
    "axarr[0, 0].set_xticks(range(0,len(ttvy),12))\n",
    "axarr[0, 0].set_xticklabels([2010,2011,2012,2013,2014,2015,2016,2017]) \n",
    "axarr[0, 0].set_title('Average against high')\n",
    "\n",
    "axarr[0, 1].plot(range(0,len(highy)), avgy, 'b')\n",
    "axarr[0, 1].plot(range(0,len(lowy)), lowy, 'r')\n",
    "axarr[0, 1].plot(range(0,len(ttvy)), ttvy, 'k')\n",
    "axarr[0, 1].set_xticks(range(0,len(ttvy),12))\n",
    "axarr[0, 1].set_xticklabels([2010,2011,2012,2013,2014,2015,2016,2017]) \n",
    "axarr[0, 1].set_title('Average against low')\n",
    "\n",
    "axarr[1, 0].plot(range(0,len(highy)), highy, 'g')\n",
    "axarr[1, 0].plot(range(0,len(lowy)), lowy, 'r')\n",
    "axarr[1, 0].plot(range(0,len(ttvy)), ttvy, 'k')\n",
    "axarr[1, 0].set_xticks(range(0,len(ttvy),12))\n",
    "axarr[1, 0].set_xticklabels([2010,2011,2012,2013,2014,2015,2016,2017]) \n",
    "axarr[1, 0].set_title('High against low')\n",
    "\n",
    "axarr[1, 1].plot(range(0,len(highy)), highy, 'g')\n",
    "axarr[1, 1].plot(range(0,len(lowy)), lowy, 'r')\n",
    "axarr[1, 1].plot(range(0,len(avgy)), avgy,'b')\n",
    "axarr[1, 1].plot(range(0,len(ttvy)), ttvy, 'k')\n",
    "axarr[1, 1].set_xticks(range(0,len(ttvy),12))\n",
    "axarr[1, 1].set_xticklabels([2010,2011,2012,2013,2014,2015,2016,2017]) \n",
    "axarr[1, 1].set_title('All')\n",
    "\n",
    "# Fine-tune figure; hide x ticks for top plots and y ticks for right plots\n",
    "plt.setp([a.get_xticklabels() for a in axarr[0, :]], visible=False)\n",
    "plt.setp([a.get_yticklabels() for a in axarr[:, 1]], visible=False)\n",
    "plt.legend(['High','Low','Average','Total'],bbox_to_anchor=(1.15, 2.25))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# High is above 17% Commission\n",
    "# Average is between 17% and 15% Commission\n",
    "# Low is below 15% Commission\n",
    "# Total is the cumulation of all commission\n",
    "\n",
    "## Observations\n",
    "Seems quite odd that since high is anything above 17% commission and average is anything up from 15% to 17% that there are no hotels with 17% - 18% commission whatsoever? This screams that I have mad a mistake somewhere in rounding perhaps, or do we really not have any hotels in that comission range ( perhaps an internal decision I am unaware of?)\n",
    "\n",
    "This was a mistake on my part, the above graphs are averages of the classes so while there are some low 17% comissions they are outweighed by everything else in the rather large segment of 17% - 100% if you don't believe me uncomment the below\n",
    "\n",
    "# plot = df.hist(column='commission_percentage', bins=100,range=[0.14, 0.19])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%storage read --object \"gs://adwords-costs/AdWords-Cost.csv\" --variable adwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "adwords_df = pd.read_csv(StringIO(adwords))\n",
    "adwords_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# convert times to the same format as above data source\n",
    "import datetime\n",
    "adwords_df['Date'] = adwords_df['Date'].apply(lambda x: datetime.datetime.strptime('20170809','%Y%m%d').strftime('%Y-%m-%d'))\n",
    "adwords_df.columns = ['arrival_date', 'cost']\n",
    "adwords_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Join DataFrames\n",
    "adwords_df.columns = ['arrival_date', 'cost']\n",
    "df = df.join(adwords_df, 'arrival_date')\n",
    "df.head()\n"
   ]
  }
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