{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6fc0b75e",
   "metadata": {},
   "source": [
    "# Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5111862a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import datetime as dt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b30f5e37",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "      InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0  12/1/2010 8:26       2.55     17850.0  United Kingdom  \n",
       "1  12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "2  12/1/2010 8:26       2.75     17850.0  United Kingdom  \n",
       "3  12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "4  12/1/2010 8:26       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import data\n",
    "data = pd.read_csv('data ecommerce.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5d840f84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "row number = 541909, column number = 8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "InvoiceNo       object\n",
       "StockCode       object\n",
       "Description     object\n",
       "Quantity         int64\n",
       "InvoiceDate     object\n",
       "UnitPrice      float64\n",
       "CustomerID     float64\n",
       "Country         object\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Print data size and type\n",
    "print('row number = {}, column number = {}'.format(data.shape[0], data.shape[1]))\n",
    "display(data.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a6429ea",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo           0\n",
       "StockCode           0\n",
       "Description      1454\n",
       "Quantity            0\n",
       "InvoiceDate         0\n",
       "UnitPrice           0\n",
       "CustomerID     135080\n",
       "Country             0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check if there is any missing value\n",
    "display(data.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9eb32a6d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of rows =  406829\n"
     ]
    }
   ],
   "source": [
    "#Dropping missing values\n",
    "data = data.dropna()\n",
    "print('number of rows = ',data.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "119e8287",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final number of rows =  397924\n"
     ]
    }
   ],
   "source": [
    "# Removing data with quantity number less than 0 \n",
    "data = data[data.Quantity >= 0]\n",
    "print('final number of rows = ',data.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8405c419",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo              object\n",
       "StockCode              object\n",
       "Description            object\n",
       "Quantity                int64\n",
       "InvoiceDate    datetime64[ns]\n",
       "UnitPrice             float64\n",
       "CustomerID              int32\n",
       "Country                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Changing the columns type\n",
    "data.CustomerID = data.CustomerID.astype('int')\n",
    "data.InvoiceDate = pd.to_datetime(data.InvoiceDate)\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eb9cea3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>invoice</th>\n",
       "      <th>product</th>\n",
       "      <th>description</th>\n",
       "      <th>quantity</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>country</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>15.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  invoice product                          description  quantity  \\\n",
       "0  536365  85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1  536365   71053                  WHITE METAL LANTERN         6   \n",
       "2  536365  84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3  536365  84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4  536365  84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "                 date  price  customer_id         country  total  \n",
       "0 2010-12-01 08:26:00   2.55        17850  United Kingdom  15.30  \n",
       "1 2010-12-01 08:26:00   3.39        17850  United Kingdom  20.34  \n",
       "2 2010-12-01 08:26:00   2.75        17850  United Kingdom  22.00  \n",
       "3 2010-12-01 08:26:00   3.39        17850  United Kingdom  20.34  \n",
       "4 2010-12-01 08:26:00   3.39        17850  United Kingdom  20.34  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Renaming columns to ease the typing process\n",
    "data = data.rename(columns = {'InvoiceNo': 'invoice', 'StockCode': 'product', \n",
    "                              'Description': 'description', 'Quantity': 'quantity',\n",
    "                             'InvoiceDate': 'date', 'UnitPrice': 'price', 'CustomerID': 'customer_id',\n",
    "                             'Country': 'country'})\n",
    "\n",
    "# Add 'total' column as the total revenue for each product order\n",
    "data['total'] = data.quantity * data.price\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc7c99dc",
   "metadata": {},
   "source": [
    "# Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac759810",
   "metadata": {},
   "source": [
    "## Revenue per month, AOA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0b75dbd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Declaring color codes for visualization\n",
    "green = '#134f5cff'\n",
    "orange = '#ff9900ff'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "19defe20",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>invoice</th>\n",
       "      <th>date</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>country</th>\n",
       "      <th>product</th>\n",
       "      <th>quantity</th>\n",
       "      <th>total</th>\n",
       "      <th>average_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>139.12</td>\n",
       "      <td>3.478000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536366</td>\n",
       "      <td>2010-12-01 08:28:00</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>22.20</td>\n",
       "      <td>1.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536367</td>\n",
       "      <td>2010-12-01 08:34:00</td>\n",
       "      <td>13047</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>12</td>\n",
       "      <td>83</td>\n",
       "      <td>278.73</td>\n",
       "      <td>3.358193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536368</td>\n",
       "      <td>2010-12-01 08:34:00</td>\n",
       "      <td>13047</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>70.05</td>\n",
       "      <td>4.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536369</td>\n",
       "      <td>2010-12-01 08:35:00</td>\n",
       "      <td>13047</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>17.85</td>\n",
       "      <td>5.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>18561</th>\n",
       "      <td>581583</td>\n",
       "      <td>2011-12-09 12:23:00</td>\n",
       "      <td>13777</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2</td>\n",
       "      <td>76</td>\n",
       "      <td>124.60</td>\n",
       "      <td>1.639474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18562</th>\n",
       "      <td>581584</td>\n",
       "      <td>2011-12-09 12:25:00</td>\n",
       "      <td>13777</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>140.64</td>\n",
       "      <td>1.172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18563</th>\n",
       "      <td>581585</td>\n",
       "      <td>2011-12-09 12:31:00</td>\n",
       "      <td>15804</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>21</td>\n",
       "      <td>278</td>\n",
       "      <td>329.05</td>\n",
       "      <td>1.183633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18564</th>\n",
       "      <td>581586</td>\n",
       "      <td>2011-12-09 12:49:00</td>\n",
       "      <td>13113</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>4</td>\n",
       "      <td>66</td>\n",
       "      <td>339.20</td>\n",
       "      <td>5.139394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18565</th>\n",
       "      <td>581587</td>\n",
       "      <td>2011-12-09 12:50:00</td>\n",
       "      <td>12680</td>\n",
       "      <td>France</td>\n",
       "      <td>15</td>\n",
       "      <td>105</td>\n",
       "      <td>249.45</td>\n",
       "      <td>2.375714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18566 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      invoice                date  customer_id         country  product  \\\n",
       "0      536365 2010-12-01 08:26:00        17850  United Kingdom        7   \n",
       "1      536366 2010-12-01 08:28:00        17850  United Kingdom        2   \n",
       "2      536367 2010-12-01 08:34:00        13047  United Kingdom       12   \n",
       "3      536368 2010-12-01 08:34:00        13047  United Kingdom        4   \n",
       "4      536369 2010-12-01 08:35:00        13047  United Kingdom        1   \n",
       "...       ...                 ...          ...             ...      ...   \n",
       "18561  581583 2011-12-09 12:23:00        13777  United Kingdom        2   \n",
       "18562  581584 2011-12-09 12:25:00        13777  United Kingdom        2   \n",
       "18563  581585 2011-12-09 12:31:00        15804  United Kingdom       21   \n",
       "18564  581586 2011-12-09 12:49:00        13113  United Kingdom        4   \n",
       "18565  581587 2011-12-09 12:50:00        12680          France       15   \n",
       "\n",
       "       quantity   total  average_price  \n",
       "0            40  139.12       3.478000  \n",
       "1            12   22.20       1.850000  \n",
       "2            83  278.73       3.358193  \n",
       "3            15   70.05       4.670000  \n",
       "4             3   17.85       5.950000  \n",
       "...         ...     ...            ...  \n",
       "18561        76  124.60       1.639474  \n",
       "18562       120  140.64       1.172000  \n",
       "18563       278  329.05       1.183633  \n",
       "18564        66  339.20       5.139394  \n",
       "18565       105  249.45       2.375714  \n",
       "\n",
       "[18566 rows x 8 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Order dataset for each order characteristic\n",
    "order = data.groupby(['invoice', 'date', 'customer_id','country']).agg({'product': 'count','quantity': 'sum','total':'sum'}).reset_index()\n",
    "order['average_price'] = order.total/order.quantity\n",
    "order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f3ff0332",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29e63af64c0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Comparing total revenue and total order per month\n",
    "\n",
    "# Total Revenue per Month\n",
    "permonth = data.groupby(pd.Grouper(key = 'date',freq = 'M'))['total'].sum().reset_index()\n",
    "permonth.date = permonth.date.dt.strftime('%B %Y')\n",
    "\n",
    "plt.clf()\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(permonth.date, permonth.total, color = '#134f5cff', label = 'Revenue per Month')\n",
    "ax.set_ylabel('Revenue (in Million)')\n",
    "ax.set_xlabel('Month')\n",
    "plt.xticks(rotation = 90)\n",
    "ax.legend()\n",
    "\n",
    "#Total order per month\n",
    "permonth_order= order.groupby(pd.Grouper(key = 'date', freq = 'M'))['invoice'].count().reset_index()\n",
    "permonth_order.date = permonth_order.date.dt.strftime('%B %Y')\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(permonth_order.date, permonth_order.invoice, color = '#ff9900ff', label = 'Order count per Month')\n",
    "ax2.set_ylabel('Order numbers')\n",
    "\n",
    "lines_1, labels_1 = ax.get_legend_handles_labels()\n",
    "lines_2, labels_2 = ax2.get_legend_handles_labels()\n",
    "lines = lines_1 + lines_2\n",
    "labels = labels_1 + labels_2\n",
    "\n",
    "ax.legend(lines, labels, loc=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1428456a",
   "metadata": {},
   "source": [
    "The total revenue is decreasing significantly, despite its increase throughout the year. It can be seen that it is positively related to the also-decreasing number of orders at the end of the year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e5438978",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Average Spend per Order\n",
    "aod = order.groupby(pd.Grouper(key = 'date', freq = 'M'))['total'].mean().reset_index()\n",
    "aod.date = aod.date.dt.strftime('%B %Y')\n",
    "\n",
    "plt.plot(aod.date, aod.total, color = green, label = 'Average Spend per Order')\n",
    "plt.ylabel('Average Spend')\n",
    "plt.title('Average Spend per Order by Month')\n",
    "plt.xticks(rotation = 90)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3612bda5",
   "metadata": {},
   "source": [
    "It is interesting to see that the revenue and order number are both decreasing significantly while the average spend per order is increasing in December 2011"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac9e23db",
   "metadata": {},
   "source": [
    "## Order Percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aaa47726",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>country</th>\n",
       "      <th>invoice</th>\n",
       "      <th>quantity</th>\n",
       "      <th>total</th>\n",
       "      <th>average_per_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1</td>\n",
       "      <td>74215</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>77183.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>7</td>\n",
       "      <td>2458</td>\n",
       "      <td>4310.00</td>\n",
       "      <td>615.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348</td>\n",
       "      <td>Finland</td>\n",
       "      <td>4</td>\n",
       "      <td>2341</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>449.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349</td>\n",
       "      <td>Italy</td>\n",
       "      <td>1</td>\n",
       "      <td>631</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>1757.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350</td>\n",
       "      <td>Norway</td>\n",
       "      <td>1</td>\n",
       "      <td>197</td>\n",
       "      <td>334.40</td>\n",
       "      <td>334.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4342</th>\n",
       "      <td>18280</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>180.60</td>\n",
       "      <td>180.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4343</th>\n",
       "      <td>18281</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>80.82</td>\n",
       "      <td>80.820000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4344</th>\n",
       "      <td>18282</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2</td>\n",
       "      <td>103</td>\n",
       "      <td>178.05</td>\n",
       "      <td>89.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4345</th>\n",
       "      <td>18283</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>16</td>\n",
       "      <td>1397</td>\n",
       "      <td>2094.88</td>\n",
       "      <td>130.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4346</th>\n",
       "      <td>18287</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>3</td>\n",
       "      <td>1586</td>\n",
       "      <td>1837.28</td>\n",
       "      <td>612.426667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4347 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customer_id         country  invoice  quantity     total  \\\n",
       "0           12346  United Kingdom        1     74215  77183.60   \n",
       "1           12347         Iceland        7      2458   4310.00   \n",
       "2           12348         Finland        4      2341   1797.24   \n",
       "3           12349           Italy        1       631   1757.55   \n",
       "4           12350          Norway        1       197    334.40   \n",
       "...           ...             ...      ...       ...       ...   \n",
       "4342        18280  United Kingdom        1        45    180.60   \n",
       "4343        18281  United Kingdom        1        54     80.82   \n",
       "4344        18282  United Kingdom        2       103    178.05   \n",
       "4345        18283  United Kingdom       16      1397   2094.88   \n",
       "4346        18287  United Kingdom        3      1586   1837.28   \n",
       "\n",
       "      average_per_order  \n",
       "0          77183.600000  \n",
       "1            615.714286  \n",
       "2            449.310000  \n",
       "3           1757.550000  \n",
       "4            334.400000  \n",
       "...                 ...  \n",
       "4342         180.600000  \n",
       "4343          80.820000  \n",
       "4344          89.025000  \n",
       "4345         130.930000  \n",
       "4346         612.426667  \n",
       "\n",
       "[4347 rows x 6 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Customer table to see the characteristics for each customer\n",
    "\n",
    "customer = order.groupby(['customer_id', 'country']).agg({'invoice':'count','quantity':'sum', 'total': 'sum'}).reset_index()\n",
    "customer['average_per_order'] = customer.total/customer.invoice\n",
    "customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f7471d9f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>country</th>\n",
       "      <th>invoice</th>\n",
       "      <th>quantity</th>\n",
       "      <th>total</th>\n",
       "      <th>average_per_order</th>\n",
       "      <th>order_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>74215</td>\n",
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       "      <td>77183.600000</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>12347</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>7</td>\n",
       "      <td>2458</td>\n",
       "      <td>4310.00</td>\n",
       "      <td>615.714286</td>\n",
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       "      <th>2</th>\n",
       "      <td>12348</td>\n",
       "      <td>Finland</td>\n",
       "      <td>4</td>\n",
       "      <td>2341</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>449.310000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349</td>\n",
       "      <td>Italy</td>\n",
       "      <td>1</td>\n",
       "      <td>631</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>1757.550000</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>12350</td>\n",
       "      <td>Norway</td>\n",
       "      <td>1</td>\n",
       "      <td>197</td>\n",
       "      <td>334.40</td>\n",
       "      <td>334.400000</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>180.60</td>\n",
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       "      <td>18281</td>\n",
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       "      <td>80.82</td>\n",
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       "    <tr>\n",
       "      <th>4344</th>\n",
       "      <td>18282</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2</td>\n",
       "      <td>103</td>\n",
       "      <td>178.05</td>\n",
       "      <td>89.025000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4345</th>\n",
       "      <td>18283</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>16</td>\n",
       "      <td>1397</td>\n",
       "      <td>2094.88</td>\n",
       "      <td>130.930000</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4346</th>\n",
       "      <td>18287</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>3</td>\n",
       "      <td>1586</td>\n",
       "      <td>1837.28</td>\n",
       "      <td>612.426667</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4347 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customer_id         country  invoice  quantity     total  \\\n",
       "0           12346  United Kingdom        1     74215  77183.60   \n",
       "1           12347         Iceland        7      2458   4310.00   \n",
       "2           12348         Finland        4      2341   1797.24   \n",
       "3           12349           Italy        1       631   1757.55   \n",
       "4           12350          Norway        1       197    334.40   \n",
       "...           ...             ...      ...       ...       ...   \n",
       "4342        18280  United Kingdom        1        45    180.60   \n",
       "4343        18281  United Kingdom        1        54     80.82   \n",
       "4344        18282  United Kingdom        2       103    178.05   \n",
       "4345        18283  United Kingdom       16      1397   2094.88   \n",
       "4346        18287  United Kingdom        3      1586   1837.28   \n",
       "\n",
       "      average_per_order order_category  \n",
       "0          77183.600000              1  \n",
       "1            615.714286              7  \n",
       "2            449.310000              4  \n",
       "3           1757.550000              1  \n",
       "4            334.400000              1  \n",
       "...                 ...            ...  \n",
       "4342         180.600000              1  \n",
       "4343          80.820000              1  \n",
       "4344          89.025000              2  \n",
       "4345         130.930000             16  \n",
       "4346         612.426667              3  \n",
       "\n",
       "[4347 rows x 7 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grouping categories to ease the visualization process\n",
    "customer['order_category'] = np.where(customer.invoice <=50, customer.invoice, '>50')\n",
    "customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c31cca87",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
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\n",
      "text/plain": [
       "<Figure size 1080x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Pareto chart for order numbers\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "\n",
    "plt.clf()\n",
    "fig = plt.figure(figsize = (15,3))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "color1 = green\n",
    "color2 = orange\n",
    "line_size = 4\n",
    "\n",
    "ax.bar(customer.order_category.value_counts().index, customer.order_category.value_counts().values, color=color1)\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(customer.order_category.value_counts().index, \n",
    "         customer.order_category.value_counts().values.cumsum()/customer.order_category.value_counts().values.sum()*100, \n",
    "         color=color2, marker=\"D\", ms=line_size)\n",
    "ax2.yaxis.set_major_formatter(PercentFormatter())\n",
    "ax2.hlines(y = 50, xmin = 0, xmax = 50, color= 'red', linestyle = 'dashed', alpha = 1)\n",
    "\n",
    "ax.tick_params(axis='y', colors=color1)\n",
    "ax2.tick_params(axis='y', colors=color2)\n",
    "\n",
    "plt.title('Order Pareto Chart')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f3e6da1",
   "metadata": {},
   "source": [
    "It  be seen that the first time orders only take up 50% of the whole order, which means that half of the orders are from new customer and the other half are from recurring customer. This percentage of recurring customer is really nice to have, since acquisition costs to gain new customers are tend to me higher that retention cost to keep the existing customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7c8d9172",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29e66856220>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total Revenue\n",
    "percentage=((customer.groupby('order_category')['total'].sum()/customer.total.sum())*100).reset_index()\n",
    "percentage.order_category = np.where(percentage.order_category == '>50', '51', percentage.order_category)\n",
    "percentage.order_category = percentage.order_category.astype('int')\n",
    "percentage = percentage.sort_values(by = 'order_category')\n",
    "\n",
    "plt.xticks(rotation = 90)\n",
    "\n",
    "plt.clf()\n",
    "\n",
    "fig = plt.figure(figsize = (10,8))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.bar(percentage['order_category'], percentage['total'], label = 'Revenue Number', color = green)\n",
    "ax.set_ylabel('Revenue', color = green)\n",
    "ax.set_xlabel('Order Count')\n",
    "plt.xticks(rotation = 90)\n",
    "ax.legend()\n",
    "\n",
    "#Average spend per order\n",
    "cus_ao = customer.groupby('order_category')['average_per_order'].mean().reset_index()\n",
    "cus_ao.order_category = np.where(cus_ao.order_category == '>50', '51', percentage.order_category)\n",
    "cus_ao.order_category = cus_ao.order_category.astype('int')\n",
    "cus_ao = cus_ao.sort_values(by = 'order_category')\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "plt.plot(cus_ao['order_category'], cus_ao['average_per_order'], marker = 'o', linestyle = 'dashed', color = orange, label = 'Average Spend per Order')\n",
    "ax2.set_ylabel('Average Spending', color = orange)\n",
    "\n",
    "lines_1, labels_1 = ax.get_legend_handles_labels()\n",
    "lines_2, labels_2 = ax2.get_legend_handles_labels()\n",
    "lines = lines_1 + lines_2\n",
    "labels = labels_1 + labels_2\n",
    "\n",
    "ax.legend(lines, labels, loc=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb64297",
   "metadata": {},
   "source": [
    "There are two interesting peaks for each revenue and average spending graph. This can add more interesting insight if the used data is expanded to customer demographic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "98bb0701",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Customer type comparison\n",
    "customer['customer_type'] = np.where(customer.invoice ==1, 'new','recurring')\n",
    "cus_order = order.merge(customer, on = 'customer_id')\n",
    "df2 = cus_order.groupby([pd.Grouper(key = 'date', freq = 'M'),'customer_type'])['customer_id'].nunique().reset_index()\n",
    "df2.date = pd.to_datetime(df2.date)\n",
    "df2.date = df2.date.dt.strftime('%B %Y')\n",
    "sns.lineplot(data = df2, x = 'date', y = 'customer_id', hue = 'customer_type')\n",
    "plt.title('Customer type count')\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Month')\n",
    "plt.xticks(rotation = 90)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69adb237",
   "metadata": {},
   "source": [
    "The pattern is different in the middle of the year, but both customer type decreasing significantly at the end of the year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56a2d7a4",
   "metadata": {},
   "source": [
    "## Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9d3edcd7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "      <th>invoice</th>\n",
       "      <th>average_per_order</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>138521.310</td>\n",
       "      <td>57</td>\n",
       "      <td>2430.198421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>10198.680</td>\n",
       "      <td>17</td>\n",
       "      <td>599.922353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahrain</th>\n",
       "      <td>548.400</td>\n",
       "      <td>2</td>\n",
       "      <td>274.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>41196.340</td>\n",
       "      <td>98</td>\n",
       "      <td>420.370816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>1143.600</td>\n",
       "      <td>1</td>\n",
       "      <td>1143.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>3666.380</td>\n",
       "      <td>6</td>\n",
       "      <td>611.063333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Channel Islands</th>\n",
       "      <td>20450.440</td>\n",
       "      <td>26</td>\n",
       "      <td>786.555385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cyprus</th>\n",
       "      <td>13590.380</td>\n",
       "      <td>16</td>\n",
       "      <td>849.398750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Republic</th>\n",
       "      <td>826.740</td>\n",
       "      <td>2</td>\n",
       "      <td>413.370000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>18955.340</td>\n",
       "      <td>18</td>\n",
       "      <td>1053.074444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EIRE</th>\n",
       "      <td>265545.900</td>\n",
       "      <td>261</td>\n",
       "      <td>1017.417241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>European Community</th>\n",
       "      <td>1300.250</td>\n",
       "      <td>4</td>\n",
       "      <td>325.062500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>22546.080</td>\n",
       "      <td>41</td>\n",
       "      <td>549.904390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>209024.050</td>\n",
       "      <td>390</td>\n",
       "      <td>535.959103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>228867.140</td>\n",
       "      <td>457</td>\n",
       "      <td>500.803370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>4760.520</td>\n",
       "      <td>5</td>\n",
       "      <td>952.104000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>4310.000</td>\n",
       "      <td>7</td>\n",
       "      <td>615.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>7221.690</td>\n",
       "      <td>5</td>\n",
       "      <td>1444.338000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>17483.240</td>\n",
       "      <td>38</td>\n",
       "      <td>460.085263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>37416.370</td>\n",
       "      <td>20</td>\n",
       "      <td>1870.818500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lebanon</th>\n",
       "      <td>1693.880</td>\n",
       "      <td>1</td>\n",
       "      <td>1693.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lithuania</th>\n",
       "      <td>1661.060</td>\n",
       "      <td>4</td>\n",
       "      <td>415.265000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malta</th>\n",
       "      <td>2725.590</td>\n",
       "      <td>5</td>\n",
       "      <td>545.118000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>285446.340</td>\n",
       "      <td>95</td>\n",
       "      <td>3004.698316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>36165.440</td>\n",
       "      <td>36</td>\n",
       "      <td>1004.595556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>7334.650</td>\n",
       "      <td>19</td>\n",
       "      <td>386.034211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>33439.890</td>\n",
       "      <td>57</td>\n",
       "      <td>586.664737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RSA</th>\n",
       "      <td>1002.310</td>\n",
       "      <td>1</td>\n",
       "      <td>1002.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saudi Arabia</th>\n",
       "      <td>145.920</td>\n",
       "      <td>1</td>\n",
       "      <td>145.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>21279.290</td>\n",
       "      <td>7</td>\n",
       "      <td>3039.898571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>61577.110</td>\n",
       "      <td>90</td>\n",
       "      <td>684.190111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>38378.330</td>\n",
       "      <td>36</td>\n",
       "      <td>1066.064722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>56443.950</td>\n",
       "      <td>51</td>\n",
       "      <td>1106.744118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>3580.390</td>\n",
       "      <td>5</td>\n",
       "      <td>716.078000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>1902.280</td>\n",
       "      <td>3</td>\n",
       "      <td>634.093333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>7308391.554</td>\n",
       "      <td>16676</td>\n",
       "      <td>438.258069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unspecified</th>\n",
       "      <td>2667.070</td>\n",
       "      <td>8</td>\n",
       "      <td>333.383750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            total  invoice  average_per_order\n",
       "country                                                      \n",
       "Australia              138521.310       57        2430.198421\n",
       "Austria                 10198.680       17         599.922353\n",
       "Bahrain                   548.400        2         274.200000\n",
       "Belgium                 41196.340       98         420.370816\n",
       "Brazil                   1143.600        1        1143.600000\n",
       "Canada                   3666.380        6         611.063333\n",
       "Channel Islands         20450.440       26         786.555385\n",
       "Cyprus                  13590.380       16         849.398750\n",
       "Czech Republic            826.740        2         413.370000\n",
       "Denmark                 18955.340       18        1053.074444\n",
       "EIRE                   265545.900      261        1017.417241\n",
       "European Community       1300.250        4         325.062500\n",
       "Finland                 22546.080       41         549.904390\n",
       "France                 209024.050      390         535.959103\n",
       "Germany                228867.140      457         500.803370\n",
       "Greece                   4760.520        5         952.104000\n",
       "Iceland                  4310.000        7         615.714286\n",
       "Israel                   7221.690        5        1444.338000\n",
       "Italy                   17483.240       38         460.085263\n",
       "Japan                   37416.370       20        1870.818500\n",
       "Lebanon                  1693.880        1        1693.880000\n",
       "Lithuania                1661.060        4         415.265000\n",
       "Malta                    2725.590        5         545.118000\n",
       "Netherlands            285446.340       95        3004.698316\n",
       "Norway                  36165.440       36        1004.595556\n",
       "Poland                   7334.650       19         386.034211\n",
       "Portugal                33439.890       57         586.664737\n",
       "RSA                      1002.310        1        1002.310000\n",
       "Saudi Arabia              145.920        1         145.920000\n",
       "Singapore               21279.290        7        3039.898571\n",
       "Spain                   61577.110       90         684.190111\n",
       "Sweden                  38378.330       36        1066.064722\n",
       "Switzerland             56443.950       51        1106.744118\n",
       "USA                      3580.390        5         716.078000\n",
       "United Arab Emirates     1902.280        3         634.093333\n",
       "United Kingdom        7308391.554    16676         438.258069\n",
       "Unspecified              2667.070        8         333.383750"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Country table to see orders characteristics for each country\n",
    "countries = order.groupby('country').agg({'total':'sum', 'invoice':'count'})\n",
    "countries['average_per_order'] = countries.total/countries.invoice\n",
    "countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "caeefbec",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29e66cfa760>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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x45g/fz6HDx8G4O233+bqq6+moKCAxx57jK+//polS5Zwyimn8PTTTx9tKy4ujtmzZzNp0iQuu+wyFi5cyPLly+nfvz+vvPIKAHfffTd33303CxcupFOnTkeP/eqrr8jJyWHBggUsW7aMxYsX8/333x8T65AhQ2jfvj09evTgpptuYvr06ce8f/jwYebOncsLL7zAzTffDMDjjz/OOeecw8KFC/n222+5//77j/67li1bxttvv83KlSt5++232b59O7t27eKPf/wjc+bMYcaMGaxZs6bW792KFSv49NNPmTdvHn/605/YuXNnnf+OdevWcf3117N06VK6det2tJ0NGzbQtWtXkpKSjmn/lFNOYfXq1SccW1BQwFtvvcXSpUv54IMPWLhw4dFjMjIy+Ne//sXixYt58sknuf3224++t379er7++mueeuqpWv9N4c7W+axa62KgjZ3nCCsxraDLBGjWCRK7mym2Qgjhi9bHdjoTUkwSMiGEEA03feyJ23peBQNvhwUPQUXxse9VFMPcu00OjtICmHHFse9fNKveU06bNo177rkHgEmTJjFt2jSGDx/OhAkTmD59OldccQWffvopf//73/nuu+9Ys2YNp59+OmDWIY4ePfpoW1dfffXRr1etWsXvf/97Dhw4QFFREePHjwdg3rx5ZGdnA3Dttddy3333AabT+dVXXzFsmCn9WFRURE5ODmPGjDnaZmRkJF988QULFy5k5syZ/PrXv2bx4sU8+uijAFxzzTUAjBkzhkOHDnHgwAG++uorPv7446NrHktLS9m2bRsA5557Li1atABgwIABbN26lYKCAsaOHUvbtm2P/pvWr1/v83t3ySWXEB8fT3x8PGeffTYLFixg9uzZPv8dXbt2pVu3bowaNeqEdrTWKKXq3F7z2B9++IFLL72UZs2aAXDxxRcfPdfcuXO58sorj7ZRVlZ29Osrr7zy6EhqU9XEF1G6TMp55gFmpLNsHxwphJjmzsYlhHCfylJo2Rda9DGvEzrDlmzTGfXxASqEEKKRaitHVba30U3u3buXb775hlWrVqGUorKyEqUUf//737n66qt5/vnnad26NSNGjKB58+ZorRk3bhzTpvlesZaQkHD06xtvvJHs7GyGDBnClClTmDVrVp2xaK156KGH+OUvf1nnfkopRo4cyciRIxk3bhw33XTT0U7n8R03pRRaa95//3369u17zHs//vgjsbGxR19HRkYeXevoqwNYWyy+zufr37Fly5Zjvj819e7dm61bt1JYWEjz5tXX20uWLDk6rfn4Y33FWFVVRcuWLVm2bJnP89R2/qYkWCVTREMleob+pVanEMKXqHi4fCkM8Hy4JqSYjmjZPmfjEkKIUHTRrBMfAz3TIxO7+j7Ge60Wl3zisfV47733uP7669m6dStbtmxh+/bt9OjRg9mzZzN27FiWLFnCyy+/fHQEc9SoUcyZM4cNGzYAZi1ibaOAhYWFdOzYkfLycqZOrZ4CPGrUKN5//32AYxIXjR8/nldfffXoGtEdO3awe/fuY9rcuXMnS5ZUJ7hctmzZMdNU3377bQBmz55NixYtaNGiBePHj+df//rX0YQ8S5curfN7cuqppzJr1iz27t1LeXk5777rs/AFAB999BGlpaXs3buXWbNmMWLECL/+HcdLSEjghhtu4N577z2ahOl///sfxcXFnHPOOSfsP2bMGD788ENKSkooLCw8Os04KSmJHj16HI1Za83y5cvrPHdTIyOdbvLVZVBVDhOmQ+dz4aLvoHkPp6MSQoSChM7muXgnxMmqBiGEsMyIx80azppTbKOame2NNG3aNB588MFjtl1++eW8+eabnHnmmUycOJEpU6bw+uuvA9C2bVumTJnCNddcc3Ta5mOPPUafPn1OaPvPf/4zp556Kt26deOkk06isLAQgGeffZbrrruOp556igsvvPDo9Nbzzz+ftWvXHp2um5iYyBtvvEG7du2OtlleXs59993Hzp07iYuLo23btvznP/85+n6rVq047bTTOHToEK+++ioAjzzyCPfccw+DBw9Ga0337t355JNPav2edOzYkUcffZTRo0fTsWNHhg8fXms23pEjR3LhhReybds2HnnkETp16kSnTp18/jvqm9b617/+lfvuu48+ffoQERFBv379jpayOd7w4cO5+uqrGTp0KN26dePMM888+t7UqVO57bbbeOyxxygvL2fSpEkMGTKkznM3Jer4dMBOSkhI0N4Fxm6SnJYe0PEF2Vn+7fj+cEjoBBNq/4UUQggAtk6HJY/BuPchMQUqSoEqcyEkhBCiTmvXrqV///7+H5AzFRY+DEXbzMjniMfNes4QUlxcTHx8PEop3nrrLaZNm2ZJrc2xY8fy5JNPcsopp1gQZf0effRREhMTj65JDXW+fhaVUsVa67CakysjnW5SkgfJw6tfb/4AYttAp7Oci0kI4U4Hc2DPAoj2fCZJjU4hhLBP6uSQ62Qeb/Hixdx5551orWnZsuXREUkhgkE6nW5RVQklu02NTq8fH4Dkk6XTKYQ4UfEuiIyFmJbmtdbw4/3Q7lToeWWdhwohhGh6zjzzTFvWGdaXqMhq3uRFIrRIIiG3KNsLurK6/AGYDLaSSEgI4Yu3XIp3zYlSZvrX9i+djUsIIYQQ4jjS6XQLraHfz6HN0OptidLpFELUomaNTq+EzlC8w5l4hBAixLgpr4lomprSz6BMr3WLZu1hzMvHbkvsZi4sK0plvZYQ4lgt+1ev5/RKSIFDG52JRwghQkhcXBx79+6lTZs2fteGFMJKWmv27t1LXFzTuMaXTqdbVB6BiChQNQafm3vqHx3eDi1SnYlLCOFOZ/z7xG0JnSHv++DHIoQQISYlJYXc3Fz27NnjdCiiCYuLiyMlJcXpMIJCOp1usfIZWPQI3HjQFH0H6HYJTNpUe1FiIYSoKSEFUDI7Qggh6hEdHU2PHlILXYhgkTWdblGcB5Fx1R1OgNiWkNQDIuouaiuEaGIKt8IbnWHLcfXVhj4IN+yVDqcQQgghXEU6nW5RkndsuRSvFc/Atk+DH48Qwr2Kd5pHRMyx22VdkhBCCCFcSDqdblGcB/E+Op0rn4GN7wQ/HiGEexXvMs/H36gqLYCvr5KyKUIIIYRwFel0ukVtI53Nu0HRlqCHI4RwsaOdzuNKpkTGw6Z3Ye/S4MckhBBCCFELSSTkFn1v9p0wKLEb5M0OfjxCCPcq3mUyXce1PXZ7dALEtITDUqtTCCGEEO4hnU63GHK/7+3Nu8PGt6CqwpRUEUKIlv0g9XrfScYSOsPh3ODHJIQQQghRC5le6waVZWZNZ1Xlie8ldgNdad4XQgiA1Otg7Gu+30tIkZFOIYQQQriKdDrdoGApvNERcr868b3U6+CWUkhsGoVjhRB+0FW1v9dqIMS2Cl4sQgghhBD1kPmablBbJko4tm6nEEIATO0Cva6G0U+f+N7op4IfjxBCCCFEHWSk0w1KPFNnfXU6tYa595iMlEIIUVVhblRFJzodiRBCCCGEX6TT6QbFeYA6MRMlmGLvG6f5nnorhGh6SnYD+sRyKV4FSyB7tHkWQgghhHAB6XS6QXEexLetPTttYnco3BrUkIQQLlVbjU4vFQm758OhTcGLSQghhBCiDrKm0w16XgHJw2t/v3k32Ls8ePEIIdzr6HT8WjqdCZ6kY5LBVgghhBAuIZ1ON0gZV/f7id1g63SzvlOp4MQkhHCnZp1hwB3QvIfv92NbQ2Ss1OoUQgghhGvI9Fo32LscSgtqf795d4hpCWX7gxWREMKtkofCGf+G+Ha+31dKanUKIYQQwlVs7XQqpVoqpd5TSv2klFqrlBpt5/lCktbw4UhY/mTt+wy4HdJ3QVzr4MUlhHCnIweh8kjd+3Q6GxK7BiceIYQQQgSNUqqLUupbT99qtVLqbs/2R5VSO5RSyzyPn9U45iGl1Aal1Dql1Pga209WSq30vPecUvZNqbR7eu0/gS+01lcopWKAZjafL/SU7YeqI77LpXjJlFohhNesG+FgDly5qvZ9xrwctHCEEEIIEVQVwG+01kuUUs2BxUqpGZ73ntFaHzOSpZQaAEwCBgKdgK+VUn201pXAi0AGMB/4DJgAfG5H0LaNdCqlkoAxwCsAWusjWusDdp0vZHmTgsTX0emsKoevLoOcN4ITkxDCvYp31Z5ESAghhBBhTWu9S2u9xPN1IbAW6FzHIZcAb2mty7TWm4ENwEilVEcgSWs9T2utgf8BaXbFbef02p7AHuA1pdRSpdR/lVIJNp4vNBV7M1HW0emMiIad30L+/ODEJIRwL386nRvfhqldoGRPcGISQgghhJWilFKLajwyfO2klOoODAN+9Gy6Uym1Qin1qlKqlWdbZ2B7jcNyPds6e74+frst7Ox0RgHDgRe11sOAw8CDx++klMrwfkMrKipsDMelSvzodIIpm1IktTqFaNK0Njeq6ut0RkSb7LWSTEgIIYQIRRVa61NqPDKP30EplQi8D9yjtT6EmSrbCxgK7AKe8u7qo31dx3Zb2NnpzAVytdbenvd7mE7oMbTWmd5vaFRUE6zg0m40nPVa/Uk/ErtB4ZaghCSEcKmyfZ414PV0OhM8NyqlbIoQQggRdpRS0ZgO51St9QcAWut8rXWl1roKeBkY6dk9F+hS4/AUYKdne4qP7bawrdOptc4Dtiul+no2nQusset8ISupB/S9EaLqybGU6Bnp1LbdgAgvOVPhze6QGWGec6Y6HZEQgYuIgpF/g45j6t4vwfMZIiOdQgghRFjxZJh9BVirtX66xvaad6QvBbwZBz8GJimlYpVSPYBUYIHWehdQqJQa5WnzeuAju+K2e2jxV8BUT+baTcBNNp8v9BQsASJM7b26tB4ELftBZUn9HdSmLmcq/JABFcXmddFW8xogdbJzcQkRqJgWMPSB+veLbw8qAoql0ymEEEKEmdOBdGClUmqZZ9vvgGuUUkMxU2S3AL8E0FqvVkq9gxn8qwDu8GSuBbgNmALEY7LW2pK5FkBpF42cJSQk6MOHDzsdxgmS09IDOr4gO6v2Nz8dB+VFkDYvoHOIGt7s7nv9a2I3uHZLsKMRwjqlBebvRWJX06msy6wbodO50Cewv19CCCGECC6lVLHWOqwSsNq5plP4oziv/iRComGKtjVsuxCh4qdXYVoPKPfj5tzYKdLhFEIIIYQrSKfTaSV5ddfo9Covgg9HwE+v2B9TqKstKVN9yZqEcLviXRCVADHN/dtfV9kbjxBCCCGEH6TT6aSqcjNdzp9C71EJsG817F9tf1yhbsTjJ657jWpmtgsRyvyp0em18Pfwv7b2xiOEEEII4QfpdDqpON88+zO9VilTq7NQanXWK3UynJlpahWCWct5ZqYkERKhr6QBnc7o5qbEypFCe2MSQgghhKhHEyyM6SJxbeBnM6Bl3/r3BWje3XeCHHGi1MnmsXsBoKHdqU5HJETgindBm2H+7estm1K8A2L62ReTEEIIIUQ9pNPppKh4SDnP//0Tu8HuhfbFE47m3m2m1k6c6XQkQgRuxOMQ28a/fRM6m+fDO0y5JSGEEEIIh0in00n718D+tdDtIoiMqX//9qOhZA9UVZgi8aJ2exbB8idNWYlDG52ORghr9LzS/329I52Hc+2JRQghhBDCT7Km00mbP4Cvr8DUcPVDnxvg/Pelw+mPvctg09vQItWUSqksczoiIQJz5BDkzTbP/kjoDANug6Te9sYlhBBCCFEP6XQ6qTgPYltBZGzDjtN+dlKbssItoCKh41hAm9dChLK9y+DjM2H3fP/2j4qHM16ADqfbGpYQQgghRH2k0+mk4l3+1ej0Ki2ArA6w9iX7YgoXhZtNXU5vkiaZYitCXXGeefY3ey2Yqfil++yJRwghhBDCT9LpdFJJXsMuIGNbmxIIMmpXv8LNJttv68Ew8VsZ7RGhr3iXeW7I34wvL4HPxtkTjxBCCCGEn6TT6aTiPP9qdHqpCEjoImVT/BGVAK1PgugE6DQWYlo4HZEQgSneZWrP+pu9Fsy6zsM77ItJCCGEEMIPkpHGSRd8DhGRDTumeTfpdPrjwhnVX++YCaV7oddVzsUjRKC80/GV8v+YhM5Qkg+VR/zLkC2EEEIIYQMZ6XRSyz6Q1KthxyR2k+m1DbXmRVj0iNNRCBGYwffCmJcbdoy3bIp3aq4QQgghhAOk0+mUkj2w8p8N70B2mQA9r5YMtnXZOQs+Oh0OrDevk3qbNZ5VlY6GJURA2gyBLuMbdkxCZ/MstTqFEEII4SDpdDrlwE8w7x44uKFhx/W6Gk57pmFT7Jqa/Wsgf65ZzwnQojdUlcPh7c7GJUQgNr0PB9Y17JjWJ8GIv1R3PoUQQgghHCCdTqcczUTZgERCXpVlUFFqbTzhpHCzqX3qzfLpncLc0A6+EG5ReQS+vgI2vtWw4xI6w7CHTCZnIYQQQgiHSKfTKSXemnsN7HQWbYNX4mHDm9bHFC4Kt5i1r8rz453U2zxLrU4RqkoaUaPTq2g7FEryMSGEEEI4R7LXOqU4D1SUqb3ZEM06mqm1ksG2doWboXmP6tcJnWHSRkjs6lxMQgSiOIBO56fnQpthcN7b1sYkhBBCCOEn6XQ6pTgPmrWvHo3zV0Q0NOsknc66tOwPLftWv1YRkNTTuXiECNTR6fiN6HQmpEitTiGEEEI4SjqdTjn9X1C2r3HHStmUup2TdeK2LR/BvlUw/OHgxyNEoALpdDbrDHk/WBuPEEIIIUQDyJpOp0QnQGKXxh3bvLuMdDbUzlmw7K9SakaEpp5XwMU/QHwjEo8lpkDxTtBV1sclhBBCCOEH6XQ6ZfH/Qe5XjTu21yQYdLe18YSLrZ/Am91MSZqaknpBxWEoyXcmLiECEZcMHc6AiMiGH9ussykZVLLH+riEEEIIIfwgnU4nVFXCkj/BrkZOees2EU66x9KQwsahjSbDb1zysdtbeDPYStkUEYI2fwjbPm/csSnnw9lZENXM2piEEEIIIfzk15rO5LT0CGAI0AkoAVYXZGfJkFFjlRaYqW6NqdEJUFXh6Vi1gZgW1sYW6go3Q3QixLY5dnvNWp0dzgh+XEIEYunjEN8Wul7Q8GNb9jEPIYQQQgiH1NnpTE5L7wU8AJwH5AB7gDigT3JaejHwEvB6QXaWLBZqCG/NvcaszwIzWvdOfzj7DUidbF1c4aBwMyR2N2VlakrsZjL/yvRaEYqKd0HrwY07tqoSdv9osmV7b74IIYQQQgRRfSOdjwEvAr8syM46JgNLclp6O+BaIB143Z7wwtTRmnuN7HR6601KBtsTHV+j0ysyBm4qhMjY4MckRCCqKs3NksZkrvWafhYM+S2MfNy6uIQQQggh/FRnp7MgO+uaOt7bDTxb1/FKqS1AIVAJVGitT2l4iGGo1JPQo7GdzqhmENdWMtj6knI+JPX2/Z50OEUoKtsLurLxnc6ISHPs4Vxr4xJCCCGE8JPfdTqT09JPA7rXPKYgO+t/fhx6tta6oOGhhbHU66DHZRAZ1/g2pGyKb6OerP29Te/D5vfg3GnBi0eIQAVSo9MrMQWKd1gTjxBCCCGankw13MfWg8BWMnRFfYf7m0goC+gFLMOMWgJowJ9Op/Al0EySid1g30prYgkXVeVARO1lJYq2wMa34IwXILZVMCMTovFaDYRrNp+YHKshmnWG/ausi0kIIYQQTc0LwHBgBaCAQZ6v25CpbiVD11kL0t+RzlOAAcev6/SDBr5SSmngJa11ZgOPD0+rnoPywzDsoca3MfB2KNtvXUzhYEs2zLwWrlgOrQac+L532u2hjdBWZnqLEBERZWY2BCIhBXK/sCQcIYQQQjRJW4BbyNCrAchUA4D7gT8DHwB1djr9rdO5CmjMAsTTtdbDgQuAO5RSY47fQSmVoZRapJRaVFFR78hseNj8AWxvZM09r05nmym6otqhzaArzAW2LzXLpggRKnJnwLK/gW7oPb8a+v8Cxk8PrA0hhBBCNGX9jnY4ATL0GmAYGXqTPwf7O9KZDKxJTktfAJR5NxZkZ11c10Fa652e591KqQ+BkcD3x+2TCWQCJCQkNI0rouI8aNPI8gde5YdhzyIzohff1pq4Ql3RFohtDTFJvt9P6mmeD20MWkhCBGzrx5CTBUMfbHwbrQb4Hv0XQgghhPDPOjLVi8BbntdXA+vJVLFAeX0H+zvS+SiQBvwFeKrGo1ZKqQSlVHPv18D5mBFTUZLX+BqdXoc2wCdjYdcsKyIKD4Wb656GGNUM2gzFzPoWIkQU7wosiRDAkUOw8W0psySEEEKEOKVUF6XUt0qptUqp1Uqpuz3bWyulZiilcjzPrWoc85BSaoNSap1SanyN7ScrpVZ63ntOqeML3R/jRmADcA/wa2CTZ1s5cHZ9cfs10lmQnfVdclp6e2CEZ9MCT8mUurQHPvTEHgW8qbWWRUUVJXDkYOPLpXgldjPPhZLB9qhDm6H1oLr3uXxpcGIRwipWdDpLC2DmJDjrVeh7kzVxCSGEEMIJFcBvtNZLPAN8i5VSMzAdwJla678ppR4EHgQeUEoNACYBA4FOwNdKqT5a60rgRSADmA98BkwAfK8BzNAl1D7wWFRf0P5mr70K+AcwC5Ot6F/Jaen3F2RnvVfbMVrrTcAQf9pvUkr3miyUgV5ExraEmBZSNqWm/r+o7owLES6Kd0H70YG10ayTeZZanUIIIURI01rvAnZ5vi5USq0FOgOXAGM9u72O6bc94Nn+lta6DNislNoAjFRKbQGStNbzAJRS/8PMbPXd6cxUp2Nmv3ajZh8yQ/f0J25/13Q+DIzwjm4mp6W3Bb4Gau10ilokpsANBdYk9EjsJtPlahr8m/r32fwBLHkMLv4BohPsj0mIQGgNJfmB36SKioO4ZDgstTqFEEKIcKGU6g4MA34E2ns6pGitdyml2nl264wZyfTK9Wwr93x9/PbavIKZVruY6hKafvO30xlx3HTavfi/HlT4UueUaT8ldjPJc4RZs3bkECR0AlXHj2ZVOexdCoWboPVJwYtPiMZQCm7YD1VHAm8rIUVGOoUQQojQEKWUWlTjdebxpSeVUonA+8A9WutDdSzH9PWGrmN7bQ6SoRtdfsPfTucXyWnpXwLTPK+vxsz7FQ215SPIecOsrYppHlhbJ/8RdJU1cYW6rdPh2+vgyjXQqn/t+9UsmyKdThEKImPMI1AJnWWkUwghhAgNFVrrWovKK6WiMR3OqVrrDzyb85VSHT2jnB0B74BhLtClxuEpwE7P9hQf22vzLZnqH5ianEermZChl/jzD/JrtLIgO+t+TFmTwZh1mpkF2VkP+HOsOE7BEtj8PkTFB95W25Oh3Yj692sKvNOM68peC9WdTimbIkLBvlUw+05rEoaNfgbGfVD/fkIIIYRwLU+G2VeAtVrrp2u89TFwg+frG4CPamyfpJSKVUr1AFKBBZ6puIVKqVGeNq+vcYwvpwKncGw1kyf9jdvfkU4KsrPex/SoRSCK80xdzQi/v/W1K9kDuV9C53MDX/MV6go3Q3z7+jvzsa1MLc9DG4ITlxCB2LcC1jwPA+8IvK0WqYG3IYQQQginnQ6kAyuVUss8234H/A14Ryl1C7ANuBJAa71aKfUOsAaT+fYOT+ZagNuAKUA8JoFQ7dNnM3S9ZVHqUmfPJzktfXZBdtYZyWnphRw7x1cBuiA7KymQkzdJJXnWdRALt8C36XB+NnS/xJo2Q1XhZmjew799u10ECV3q308IpxXvMs9W/M04tBm2fACp15sbX0IIIYQIOVrr2fhejwlwbi3HPA487mP7IqDueoOZ6joy9Btkqnt9vp9xzGhrrersdBZkZ53heQ5w8aE4qngXxAdYo9Oruac8iJRNMZ3OdqP823fsFFtDEcIyxbsgMs6URwrUoQ0w/z5oO1I6nUIIIYTwl7fcQ0D9wfpGOlvX9X5Bdta+QE7eJMW1gRZ9LWqrLUTGW7PeK9SNeNxMrxUinBTvMqOcVmS7TvBkQZdkQkIIIYTwV4Z+yfP8f4E0U9/CwsXUnVLXr2KgooYLGp1p+ERKQWJXGekE6H2N//tu+xRm3QgXz4GWfWwLSYiAVZSaUidWONrplLIpQgghhPBTpnquzvcz9F3+NFPf9Fo/F8kJxzTvLp3O4nxTd7PNMIiKq3//mJZQWmAy2Eqn0xk5U2Hhw1C0zdw4GfE4pE52Oir3Of990HWVzGqA6CSITpSRTiGEEEI0xGLP8+nAAOBtz+sra7xXr/qm1w6v6/2C7Cy/6rIIj4M5MOsGGPkEdDzTmjbPeN6s+WrKtn8G390MkzZUl0Spi5RNcVbOVPghAyqKzeuireY1SMfTFyum1nrbadZZRjqFEEII4b8M/ToAmepG4GwydLnn9X+Ar/xtpr7ptU/V8Z4GzvH3RAIzqpM/D45mKbaAP52scFe4BVD+Z6SNbw9RCVI2xSkLH67ucHpVFJvt0umsVlEC31wL/TKg6wXWtHnRtxDTypq2hBBCCNGUdMIkE/Lm9En0bPNLfdNrA6rHIo5TnGeeraypWbgFNkyDPtdXr9lqago3m3VvkTH+7a+U6azLSKczirY1bHtTVZwHW7Kh60XWtdnU6/kKIYQQorH+BiwlU33reX0W8Ki/B9c3vfacguysb5LT0i/z9X5BdtYH/p5IYGp0AjSzqGQKmPVZC38HbYY27U5nUgOXH/e+FlSkPfGIutWW/Cqxa/BjcTMra3R65c2GTe/DqCchQn7+hRBCCOGnDP0amepz4FTPlgfJ0Hn+Hh5Rz/tneZ4v8vGY2MBQRXGeWX8ZnWRdm4lSq5PCLZDYvWHHDH0AhtxnRzSiPiMe54SE2FHNPNvFUXZ0OveugFXPQulu69oUQgghRFMRCewB9gN9yFRj/D2wvum1f/Q83xRQeMJo1gE6n2ddYhDw1PCLatqdznOmNq4jX1Fsvnf+TssV1mh/GqDNutqKwxDbBk77p6znPJ4dnU5v+ZWiXJlqK4QQQgj/ZaongKuB1UCVZ6sGvvfn8Pqm195b1/sF2VlP+3MS4TH4N+ZhpYhISOwChU2409nR75ss1XZ9D9PPggtnQmfJhxVU26ab58uXwsdnQqdzpMPpi/Ikx4pLtq7NRE+ns3gHMMK6doUQQggR7tKAvmTossYcXN/02ieB64A2mAxFzY97CDdI7NZ0k7Ac2gyb3oUjhQ07zjstWZIJBd/W6dCyH7RIhZTzIfcrqLIwo3O4GHgHTN5m7drLZp5130VSNkUIIYQQDbIJiG7swfWVTBkOTAIuxBT/nAbMLMjOsqhaeRPz/lDoNQmGPmhtu+PehxgL14mGkh0z4IdfwrVbIaYB90ESUiAiWsqmBJvWZuSug6dOba9JZnptRXHD/v9E48S3hYgYKNvrdCRCCCGECC3FwDIy1UygerQzQ9/lz8H1relcBiwDHkxOSz8NuAb4V3Ja+gMF2VkfNzLgpqnyCOxdDt19JgIOTFxr69sMFYWbTeexWQMz90ZEQvOeMtIZbErBudOqX3f9mXmIE30zGVr0hZP/YF2bKgJuKpR1zEIIIYRoqI89j0apb6QTgOS09LbAMOAkIBeQ1IcNVeL5lllZLsVr30pY8yIMe7jplU05tNmU2mjMFMQWvWWkM9jKDkBsy2O3VVXA/tXQZogTEbnXjq9NVl+rSYdTCCGEEA2VoV8nU8UDXcnQ6xp6eJ1rOpPT0m9KTkv/AngXU+PgqoLsrHEF2VnzGxdtE+at0RlvQ6eztMB0Og80+P8/9BVtgebdG3dsn5ug/21WRiPqUlUJb/WGBQ8du33p4/DBcCjb70xcblRVASV77Mkwu/51mH2n9e0KIYQQInxlqoswM2C/8LweSqbye+SzvkRCrwAdgUJgPPDf5LT0j72PRgXcVBV7Op12jHQ25VqdhZuheY/GHdvzchjwS2vjEbXbPc+sJWwz7Njtnc8FXQU7ZjoTlxuV7Aa0PZ3OvStg/Wtmfa0QQgghhH8eBUYCBwDI0MsAvy/C65tee3bjYhIniG0J3S4x5U2slpACqKZZNuWSuaAamd2zqsJ0WuOSIbaVtXGJE22dbuqidhl/7PZ2oyCmBeR+CT2vcCY2t7GjRqdXQmeTuOnIAfm5F0IIIYS/KsjQB8lUNbf5fQe7vkRC3zU2KnGcDmeYhx0iYyChk5lq2tQk9Wr8sYc2wjv9YOz/oE+6dTEJ37ZOh05nmQ5mTRFR0Olc2P6FGX1TyvfxTYqGdqdCYnfrm07w1Oo8vEM6nUIId8qZCgsfNuXgErvCiMelnrMQzltFproWiCRTpQJ3AXP9Pbi+6bXCKnZPZWve04xeNCX7VsGKp6F0X+OOb97dZPOUZEL2O7gBDqyFrhf5fr/LBDica/YR0PYUSJsPyUOtb9ubbOyw1OoUQrhQzlT4IcOzZEib5x8yzHYhhJN+BQzElEuZBhwC7vH3YL+y1wZCKRUJLAJ2aK0n2n0+15o5CUry4aJZ9rR/0SzTgWpKds6C+b+B3tc27vjIWEjoIp3OYIhvC2e9Cp3H+X6/e5rJJpzUO6hhNUkJKRDfDipKnI5ECCFOtPDhE2+iVxSb7TLaKYRzMnQx8DCZ6glAk6ELG3K47Z1O4G5gLZAUhHO51+FciIyzr/2m1uEEsx4zMg7i2ze+jRa9pVZnMMS0gL431f5+fFuIlyXkRy34Hez+ESbakFypeTdIz7e+XSGEsELRtoZtF0IER6YaAbwKNPe8PgjcTIZe7M/h/tbp7APcD3SreUxBdtY5dR2nlEoBLgQeB+7151xhqzgP2o+yr/28ObDsCRjzkj3JR9yocItnimwAawCb94LN71sVkfDlyEHIeQN6XmlG2GpzYD2sexVO/oM99SlDyf411bV9hRCiKUns6jsbf2LX4McihKjpFeB2MvQPAGSqM4DXgMH+HOzv8Ni7wBLg95jOp/dRn2eB3wJVfp4nPGlt6nTaUaPTq7wQtk2HQ5vsO4fbBFIuxavfLTAmU8pH2Gn7FzDnTjiYU/d+hZth+ROwU/KXUbzL3ptH838Lc39tX/tCCNFYIx4HFX3stqhmZrsQwkmFRzucABl6Nqaspl/8nV5bUZCd9WJDolJKTQR2a60XK6XG1rFfBpABEBMT05BThI7yIrMewY4anV7eWp2FW6DD6fadx02KtkL70YG10W4kpuSQsM3W6aYsTbt6Rvo7jjHTpXO/hK4XBCc2tyrJg5Z97Wv/4DrTyRdCCLfxrtucf5/JhSHZa4VwiwVkqpcwSYQ0cDUwi0w1HIAMvaSug/3tdE5PTku/HfgQk7EIgILsrLrShp4OXKyU+hkQByQppd7QWl9XcyetdSaQCZCQkBCew026AgbcDm1t7Nx4p534mpISribvgMrSwNqoPAJ5s806t0DKrwjfqipg+2fQ7WKIqKeealQ8dDzLjIw2ZVqb6fh2jnQmdDY/90II4Ta6ynQwpZMphNsM9Tz/8bjtp2E6oXUuu/S303mD57nmlFoN9KztAK31Q8BDAJ6RzvuO73A2GbGt4Izn7T1HdALEtYXCJtTpjIozj0BUlcOn55q7qMN+Z01colr+XCjbX3uplON1mQDzfl29XrcpqiyFbheZsil2SUiBsn0mg21UvH3nEUKIhlr5LKz+F1z0A2zJhk5jofUgh4MSQpChA8r46FensyA7K8CFc01cRakZ5YmIrn/fQLQb2XQSsOTPh41vwbCHTebTxopOMGttJYOtPfYuM1NmU873b/+U8SbT7cH1TbfTGRUP496z9xxHa3XuMBmchRDCLfLmAMpcz8z9FYx8QjqdQjgpU10ErCBDb/W8/gNwObAVuJsM7dd6nTo7nclp6ecUZGd9k5yWfpmv9wuysz7w5yRa61nALH/2DUs/ZcLce+D6PRDXxr7zTPjEvrbdJn8urPonDP9D4G216C21Ou0y6C7ocwPENPdv/5b94PoCiAhGNacmLKkXJJ8c+PR0IYSwktbm873zuRDX2iwd2rvU6aiEaOoeB0xijkw1EbgOuAYYBvwHGO9PI/Vd2Z0FfAP4mhunAb86nU1ecR6oSDPNVlijcAtEJ1nzPU3qBTu+Drwd4VtMC//3VQqU58+S1oGVwwlVOW+YKcaXLjJrje3Q4Qy4bJE9bQshRGMVbjGJ1LwJEdsMMzNmhBBO0mToYs/XlwGveGpzLiZT3e5vI3V2Oguys/7oea6jqruoV3EeNGsPyt8KNY209RNY9Hv42YzAppyGgsLNgdfo9ErqDetfl/VtVlv1b9j2CZyf3bC1t/tWwtdXwZmZ0PFM28JzrcM7oLTA3lkRQgjhRvlzzHP708xzm6Gw9WMoP2yWwwghnKDIVIlAMXAu8EKN9/y+wKuzF5Scln5dclp6rfskp6X3Sk5LP8PfkzVZJbvsrdHppSth73JzpzDcWVGj0ys1HdJ+tH/NbVOz+T0o3tnwZE+JXU1Nz9wv7YnL7Yp3QXSiedjp0/Ng0fEJ6IQQwkFJvWHAHdDKs4YzeZh5PrjOuZiEEM8Cy4BFwFoytJkqlamGAbv8baS+6bVtgKXJaelmCBX2YHq0vTFTbwuABxsWdxNUnGeyRdrNm3ilaCu0G2H/+ZxUUWJdp7N5N/umMTZVZftNSY4hDzT82JgWpv7q9i9gxGPWx+Z2xbvsLZdS8zz7Vtl/HiGE8Ff7UebhlTIebjzof14AIYT1MvSrZKovgXbA8hrv5AF+z4atb3rtP5PT0v+NqbtyOjAYKAHWAukF2VnbGhp3k9T3luBMlUv0dJyaQq3OazaaWl5W0FWQk2XusHrXkYjAbP/cjLx387NUyvFSJpip4iW7Ib6dtbG5XYnNNTq9mnWGw7n2n0cIIfxRUQyHNkGrAdXLkaLiaMDsPSGEXTL0DmDHcdv8HuUEP0qmFGRnVQIzPA/RGIPuDM55Ylua5DpNpVanVWtkVYTJLtz7Gul0WmXrdNNZbDeyccd3GW86nbkzml6B8JTxwSl9lJACB9bYfx4hhPBH3mz4bLzJS5FyXvX2da+ZpUOnPetYaEK4jVLqVWAisFtrPciz7VHgF5iZqQC/01p/5nnvIeAWoBK4S2v9pWf7ycAUIB74DLhba63tiNnmzDYiWlWaTmBVeXBO2O0iSOwSnHM5JXcGfHU5FOdb12aL3nBQyqZYpv1pcNKvG39jIHm4mSGQ2NXauELBsN/BSffYf56EzmaKbVWF/ecSQoj65M0xnxnH36zctxLWviR/q4Q41hRggo/tz2ith3oe3g7nAGASMNBzzAtKqUjP/i8CGUCq5+GrTUtIp9NmfRIPwbTusOXj4JzwnDdgyP3BOZdTCpbAlg+sHQ1q3gsObbSuvaZu0K9gaADLvVUEnPXfppe9VldBRZBqZyYPh64XmSltQgjhtPy50PokiEk6dnubYaam8AFJJiSEl9b6e2Cfn7tfAryltS7TWm8GNgAjlVIdgSSt9TzP6Ob/gLQ6W8pUZ5CpbvJ83ZZM5XeCFel02qxdbIn5IhhrtJqKws0Q28baxAItepu1sMEakQ5n+1ZCeZE1bR3cYBJxNRWHNsKr8bDhTfvP1eNSGJ994gWeEEIEW1UF7J4P7X0scfFmsJV6nUL4406l1Aql1KtKKW8x+87A9hr75Hq2dfZ8ffx23zLVH4EHgIc8W6KBN/wNzK9OZ3JaevvktPRXktPSP/e8HpCcln6Lvydpyqo7nUEomQKw/n/wejKUHQjO+ZxQuBmSLMpc65XUyyS+aSrrYe2iNXx1GcycFHhbJXvg7VSznqepKPasyY8L8zq7QghR075V5maltz5nTS37QmQs7F0a/LiEcE6UUmpRjUeGH8e8CPQChmJKmTzl2e6rqL2uY3ttLgUuBg4DkKF3An6PAPk70jkF+BLo5Hm9HrjH35M0Ze29nc749sE5YVQ8lO0N7wy2hVusK5fi1eNyuG6n6XyKxju4Dg5tgC4/C7yt+LZmWlXuF4G3FSq8nc5gzIwoOwBZHWHVv+0/lxBC1KVFb5jwCaScf+J7EdHQ4UxzU1OIpqNCa31KjUdmfQdorfO11pVa6yrgZcC7QDoXqJnwJQXY6dme4mN7bY6QoTXejmmmSvD7X4P/nc7kguysd4AqgILsrApM9iNRj3axpRDdHKIb9P/SeN6yKeE6Yqe1GQVqfZK17cYkmQt95eumj/Db1unmudtEa9rrMgHy5sKRQ9a053bB7HTGtICyfVAkla+atJyp8GZ3yIwwzzlTnY5INEXRidD1QnOz0ZcLZ8Dop3y/J4QAwLNG0+tSwFuM+2NgklIqVinVA5MwaIHWehdQqJQapZRSwPXAR3Wc4h0y1UtASzLVL4CvMZ1bv/jb6TycnJbeBk/PNjktfRRw0N+TNGWf5nWBUU8G74ThXqtTKbhkNgx/xPq2VzwFG6ZZ325TsnU6tBlqXdbZlPGgK2DnN9a053bFuyAiBmJb238upUzZlOId9e8rwlPOVPghw/N5oc3zDxnS8RTBt+o5M8VWCOEXpdQ0YB7QVymVq5S6Bfi7UmqlUmoFcDbwawCt9WrgHWAN8AVwh9baO3h4G/BfTHKhjcDntZ40Qz8JvAe8D/QF/kCG/pe/Mddbp9PjXkwvuVdyWvocoC1whb8nacrm7W8P/f2Zhm2R+HYQGRe+nU47rX8dErubep2i4Ur3Qf4cGPawdW22H21mCmz/ArqnWdeuW3UcC1EJwRtxT+gMh3Pr30+Ep4UPn5i9uKLYbG9q9XGFcw7vhLl3w6inofUg3/sc2ggzroARf4GuFwQ3PiFcSGvt62L1lTr2fxx43Mf2RUAtv3g+ZOgZwAy/96/Br05nQXbWkuS09LMwvVoFrCvIzpI0n34YnLTXZN8MViIhpaDfL6D1kOCcL9g2fwDLnjBZN62egpjUCw6ut7bNpiS2FVy+DGJaWtdmZAxM+BRa9beuTTfrekFwL6gSUmD3j8E7n3CX2qZWy5RrEUz5c81zBx+Za73i28Pe5bBnoXQ6hXBKpirkxERDB4FFwG/I0JvqOtyvTmdyWvr1x20anpyWTkF21v/8DrSJyj71a1j6Fzj9ueCdNJjnCrb9q2HPAohpVf++DZXUG7Z/bmolKqkm1GBKWb/WFppWrc6i7RCXbBKCBUOXCZDQqf79RHhK7Op7VoxV0+OF8EfeHIiMry6N4kt0IrRIlbIpQjjraUyioTcxg5CTgA7AOuBVYGxdB/t7ZT2ixuNM4FFMylxRh/iICpKiy52p0VlREvxzBsOhzeb7GRVnfdtJvaCyzEz1EQ1TeQS+/yXsWWx927oKVjwNW+pa2x4m3h8G8+4N3vn6XB/cNefCXUY8bpZj1BQRY7YLESz5c6HdCJOlti5thkrZFCGcNYEM/RIZupAMfYgMnQn8jAz9NlDvaJC/02t/VfN1clp6CyCrUeE2IW1jS80XwZpa67XiaZj/G7j5MEQ1C+657Va42fpyKV5Jvc2HXvEOSEypf39RLe8H+CnTZB9se7K1basIWPuSuSnQ/RJr23aTyiOm3FGwb1JVHjGj1PVd8InwkzoZcrIg9yvzOiIKWvaT9ZwieCqPwIG1MPDO+vdNHgab3oGy/WY5hxAi2KrIVFdhkgnBsfl96q1p1Ng5hMWYdLuiDu2O1ugMcqfTWxM0HMumFG2B5t3tabvTWLi5BNqdak/74WzrdDNiknKePe13mQA7Z0FFqT3tu0FJnnkOZqdzzyJ4JRZyG5UTQISDsn3QcQxkVMGkTXCZDbMVhKhNZAxcvweG/Lb+fTucAb0mQXmR/XEJIXyZDKQDu4F8z9fXkanigXrvHPm7pnM61T3YCGAAJvWuqMPRTmewRzqb1yibEk4JWLSG5FOgfR3JBgIR4W8yZ3EMrU2ns/O59o2sp4w3KfXzfoCUcfacw2nBrNHp5b1BJRlsm6aKYihYCkPuN6+9MzzKD5ubSBGRzsUmmo7IWPOoT4czzEMI4QyTKOiiWt6dXd/h/l5l11z0UwFsLcjOkquUeiw92IaMpaeTeWPv4J7YW6sz3EY6lYJx79p7jkWPmgstO+qAhqsDa6Fwk393qhur41nmomT7F9LptFKzDmb68mGp1dkk7V5o6uDWzBp6YD18fAac9hz0nuRcbKJpmP9baNYeBv/G/2PKi0xiISFEcGWqOOAWYCBQnRAgQ9/sz+H+run8rjGxNXW7ShP4YFcPMmOaB/fEzTqBipJanY2xZ4EpcSOdTv8d3mFudHSbaN85ohOg0zlmKmC4ajXI1KlL6hW8c0ZEm9FOGelsmg6uAxUJ7UZXb2vRG+LbwtLHoNdVkslb2EdXwbpXGlaDeea1Jov9FcttC0sIUass4CdgPPAnzHTbtf4e7O/02suAJ4B2mBS5CtAF2VlJDY22KRnaYi+Rqir4J/aO1LUbGfxz22ndFFj4O7h0kX1lHpJ6Qd5sM2VUKXvOEW5SxsE1m+3/fk34JLwvgFv0hsG/Dv55EzrLSGdT1T8Del977KiRioBhv4dvroXNH0LPy52LT4S3g+vNjcSGLJlp3gM2vWsyzfszJVcIYaXeZOgryVSXkKFfJ1O9CXzp78H+Tq/9O3BRQXaW371ZAff1XkFK/GFnTn7yH5w5r50ObYSSfHMX3i5JvaG8EEoL7D1PuKg8YtbCBqMz6D1HuN4QOLDe3DAK5kgnQL8MWc/clPmaptjzKlj8f7DkT9Dj0vC+2SOckzfHPHc4zf9jkoeaKeH7V0PycFvCEkLUqtzzfIBMNQjIA7r7e7C/nyT50uFsuHaxpeSXBanI+/EqSk0nLZwUboaELvaWdmjhWX8bbt87u2ycBm90hKJtwTnfzGvhmzAt5zD/XpjhwKhS/19A35uCf17hrAPr4NPzoWDZie9FRMLw38O+FbDr+6CHJpqI/LkQ2xpa9PX/mDbDzLOvn1shhN0yyVStgN8DHwNrMDNh/eLv7e1FyWnpbwPZQJl3Y0F21gf+x9n0tIstYV1RC2dOvuyvsOTPcEupSUkeDgq32Fej0ysp1ZRkKS+09zzhYut0syYsIUh1TaOaweb3oKoi/EbnivOCX6MToKrcTK9N6Cy1OpuSvB9gx4zaM073mmT+Fkq2UGGXqATodlHDZq4k9TSj83uX2heXEOJEmSoCOESG3g98D/RsaBP+jnQmYWpzno9JlXsRYGPWkHCgaRdbym6nRjoTuwE6vBKEFG62v9PZso9ZnxiuGVKtVFkGuV9C14nBm37XZTwcOQi7FwTnfMFUvAviHeh0bnwbpvWAQ5uCf27hnLw5EJcMLWopuR0RVd3h1A7kJhDh7/TnYOyUhh2jImDE4+ZzRwgRPBm6Cj9qcdbF3+y1DZ57pZSKw/SEYz3neU9r/ceGthOqWkYfISaiit1lcfXvbIfm3c1z0VZzZzDUaQ09r4T2o+vfVwTHru9M6vputZVsskHn88xFR+4XDVsH5HZVlWa9shMjnQmdzfPhHdCyAdPcRGjLnwvtT6t/lGnpX83v28RZ4bmWWjhDVzX+ZuWgu6yNRQjhrxlkqvuAt4HqpDUZ2q/SAn79xienpfdJTkufmZyWvsrzenByWvrv6zmsDDhHaz0EGApMUEqN8ud84eBwRRQT553P9LyuzgTQPMxqdSoFpz0Lva62/1wLfgczr7H/PKFu6yemgHznc4N3zthW0O5U2O53srTQUFoAutKhTqdnanQ4zYoQdSvZYzKH+pM1NC7ZrOvM/cr+uETTsfBheHeQueHWUJVHoGAJlO23Pi4hRF1uBu7ADCou9jwW+Xuwv4uiXgbuB14CKMjOWpGclv4m8FhtB2itNVDkeRnteWh/Awt15TqS+fvbORdAQhdAmXWQ4aCixKwdDMb61NIC2DHT/vOEup5XQqsBta8Js8uAO6AkL7yy2EYnwnnvVCfJCKaaI52iaSjbB53Oho5j6t+3zw0mP8CS/4OU88Pnd044K2+OWdMZEdnwY/ethA9PMX8ze15pfWxCCN8ydEBr3Pyd29CsIDvr+EVUFfUdpJSKVEotA3YDM7TWPzYwvpCVmnCQyzttJj6i3m+TPSJj4IwXoFuYrHtY/zq8EgeHd9p/rqReULoHjhyy/1yhrOOZMODW4J83dTIM/k14XfxGJ5iLJ2/25GCKamZGkGWks+lo2RcmfgPt/Zh8FBkDwx6C/Hmw8xv7YxPhr/II7FkIHRpQn7Om1oNARUGBJBMSIqgyVTMy1e/JVJme16lkKr87Gv52OguS09J74RmpTE5LvwLYVd9BWutKrfVQIAUYqZQadPw+SqkMpdQipdSiigqHOmg2GNduBy8NnUNUhIMJGAbcCm1Pce78VircbDJrNutg/7mSpGxKvXZ+B7sdvIdUui+8kgkd2mhG16vK69/XDqf+w9RmFE1DZVn9+9TU92YzIr7kz/bEI5qWvUuhstSsKW6MyFgzy2bvMkvDEkLU6zXgCOD95c2ljlmvx/O303kHZmptv+S09B3APYDfQxxa6wPALGCCj/cytdanaK1PiYoKnxII7WJLKK6MpLDCwRIExbsgb7Zz57dS4WazTjUYWVKTepnnQxvsP1eoWvAgzL7DufPPuQO+vMRMsQ0HG96CT88zpWCc0O8W6HSWM+cWwVVZBq+3gRVP+39MZCyc9Rqc+ZJ9cYmmI2+ueW5spxOgzVAZ6RQi+HqRof8OmDvkGboE8Hvamb9X8FsLsrPOA9oC/Qqys84oyM6qM0ONUqqtUqql5+t44DzgJ38DC3XV5VIcnAK46jn45JzwSHcfjHIpXkm9oMOZwV+rGCpKdptRzm4XOxdDlwlmXee+Fc7FYKXiXRDTAqIcKrFUsttMnxThr2AJVBxu+N/TlHGS3VhYo81gOOleSOjU+DaSh5nPgOI86+ISQtTnCJkqHm+OnkzVC5M41i/+djo3J6elZwKjqE4OVJ+OwLdKqRXAQsyazk/8DSzUtYstca5cildiNzNdr7jemdDudyiInc6Y5nDx99D1wuCcL9Rs+xTQwS2VcryU883z9i+ci8FKJXnOZK71Wv08fHS6c9N7RfDkBzDKdHgnfHVZ+MygEc7ofC6MfiqwNrpfCj/7CmJaWhKSEMIvjwJfAF3IVFOBmcBv/T3Y305nX+BrzDTbzclp6f9OTks/o64DtNYrtNbDtNaDtdaDtNZ/8jeocFA90umgRG/ZlC2OhhEwXQVDH4AelzkdiQDYOt2U2Wgz1LkYmnWE1oMhN0xKpxTvcrbTmZAC6PC4QSXqljfHzOZo1r7hx8a0MB1OWdspGqvsABxYF/jSiObdzOh7lMM394VoSjL0V8BlwI3ANOAUMvQsfw/3q9NZkJ1VUpCd9U5BdtZlwDAgCfiuwcE2IZMXjeWPa4c7G4S3VmdRiNfqVBEw5P7q0a1gWPAwvNM/eOcLFVWVZhpm14nOZ4/tMsFcAJf7O/nCxRzvdErZlCZBa8if4199Tl+iE2DwfaZmZ/58a2MTTcO2T+GdftYsjdg5CzZ/EHg7bpIzFd7sDpkR5jlnqtMRCVEtU30MnA/MIkN/QoYuaMjhfmfuSU5LPwu4GrgAM11WUh3WYXtJotMh1BjpDPFOZ+leU74kWImEwCTOOLAOKkrlTmpNEZFw7RYoL3Q6Ehh4h8mqGZXgdCSBO/ct59ZzgmekEymbEu50BQx9CFoNbHwbA2+H5X83o50XfGpdbKJpyJ8D0c2h1QnFDBpu1XOwf3X4zILKmQo/ZEBFsXldtNW8BlMqTAjnPYXpC/6NTLUAeBv4hAxd6s/Bfl3BJ6elb8ZkrP0BGFSQnXVVQXbW+42LN/y1jC7jVz1X0yvB4TqP0Qlw/ofQ62pn4wjUhqnwVk/T+QyWFr0BbRIYiWNFxkJcstNRQGJXk9jE6RFXK7QbCa1Pcu78RzudMtIZ1iKi4aR7zLTExopOhMH3wvbPYM8iy0ITTUT+XGg3ytzADFSboXAwJzxmuwAsfLi6w+lVUWy2C+EGGfo7MvTtQE8gEzMAudvfw/0d6RxSkJ3lcA8qdHRvVsQf+y1lfVELNh5OcjiYNGfPb4VDm00m2WB2dGqWTWll4zTbnKnmA6Vom+lEjXjcvXc0tYYvfgap6dD7WqejMXbOgi3ZcNqzDgcSgJI9sP1z0xFwaoptbCs4Zxq0G+HM+UVwFCyB+A6BZQ0FGHinqbPYvLslYYkm4sgh2LcShj1iTXvJwwANe1dAhwDKr7hF0baGbRfCCSZ77UWYEc/hwOv+HurvXMUOyWnpM5PT0lcBJKelD05OS/99gwNtItrFlgA4n70WzB/4UF/z4C2XEswRraTe5vnQRvvO4Z1KU7QV0NVTady6hmPfCpMttqLE6Uiq7V8Dq/4JB0O4puq+lTDrBjjgYEUppaD3pOqbLSI8fXsDfH9L4O3EJMEpf3LHjAcROnb/aBIDdmjkmuLjeZPZ7Q2Tep2JXWvZ3iW4cQhRm0z1NrAWOAd4HlO381f+Hu5vp/Nl4CE8xUALsrNWAJMaFmnT0d7T6cx3OnstwLrX4Nv0wDPFOSmYNTq9Ylub9YItUu07R6hNpdk63Ty7qZRMl/HmOZRLp3gzxjqZSAjMaEHuDGdjEPYpO2DWvzWmVEptcr+ChRaNWonw13aEWfLTfrQ17SWkQGwb2LvcmvacNuJxiDyuPnhEDIz4i7mGO7TJmbiEqPYapqN5Kxn6G2A0mep5fw/2t9PZrCA7a8Fx2yr8PUlT4x3pLDjigpHOxG6mI1PaoART7qG1KfkS7E6nUnDWK/Z2sEJtKs3W6dB2JDTr4HQk1ZJ6mVHpUC6d4pZO5/K/VyetEOFn93xANz5zrS95c2DpY7BvlXVtivAV29Is+Ym2KNGiUnDZYjjjBWvac1rqZBiT6UkCqczzWa+a7Runwdt9Yd5v4MhBpyMVTVWG/gI4iUz1BJlqC/AY4Pc0LX/XdBYkp6X3AjRAclr6FYAUdKtFu9hS9h2J4UiVBQvlA+Vdc1O0FeLbOhpK42g48yVI6unM6Y8cNLXp7JDYxXcHs7YpNk7JmQoLHjSZTWNamNduWneaMh7WvwaVZSbJUagpyYPIeIh2eP13Qmc4vNNMfwtWlmgRPHlzQEWapFVWGXQ3rHwaljwG571lXbsi/FRVmqUQ3S72JOqziLc0XDhY+zIcXA/XbDrxb3DncWb21cpnYMMbZvSz703yt1oER6bqg5nheg2wF5O1VpGhz25IM/7+tN4BvAT0S05L34HJZHtrQ07UlDyy9mTO/GGi02EYoV42RUWYtWZWXij5a9Gj8HoyVNk0qN/hzBO3RTUzU2zcwrvu1FtK48hB96077TLBTLNy6whxfbw1Op3OwpuQAlVH7JkVIbXnnJc/F9oMsW6UCSCuNQz8FWx6B/avta5dEX72r4L5vzHrOq10aCPMvjO01/V7rXsFdn3vuyMZ3xbGvASXLYKkVPj+5/C1VC4UQfMTcC5wERn6DDL0v4DKhjbiV6ezIDtrU0F21nlAW6AfMBY4o6EnayqOVEWSX9as/h2DwXsXsChEO52FW2HXD1B5JPjnTuxq6trZ0ZkpLTBFslsNqh7ZjGkJZ2a6axQxFNaddr0Qrl5n7/pbO416GiZMdzoK+8qmhFrCrHA15mU44z/Wtzv4XnOzbKmLbpYJ98mfa56tSiLkVVkGa56H/HnWthtsxfmwewF0q2fAInk4XPwDnDPVjHyCSe53eKf9MYqm7HIgD/iWTPUymepcoMF3yuvsdCanpSclp6U/lJyW/u/ktPRxQDFwA7ABU5tF+HB/7xVMaLfd6TCMmJaQNt9MwwhFm96F6WNO7PgEg3cK0CEb7qCWFkCLPnDe23DtVjN1Jra1e0qReIXCulPvCGFVg2+6uUOz9tBqgNNRmOm1UD2qbZVQuHHRFCT1tKckTlwynPyo75kbQnjlzTEzOhItng7bog9ExsHeZda2G2zbPwc0dPVjlpxS5lqh68/M6xVPwdt9YOlfTSdcCKtl6A/J0FdjBh5nAb8G2pOpXiRTne9vM/WNdGYBfYGVwC+Ar4ArgbSC7KxLGhN3+NPc2XM1o1v7XSvVXhvehK+vhtfbhOa0tsLNpuMc2zL45z5aq9OGsikt+5mbAd7ORupkKNxk/dSjQNWawt1l6063fARZbUPzbu/yf0D+fKejgFYD4aLvre88hMKNi3CXOwPWvGTfjZkh98GAX9rTtggP+XNNEiurlxFEREHrwaFfNmXbJ9CsU3UZmIbofQ10Pg8W/g7eHQhbPg7tigXCvTL0YTL0VDL0RCAFWAY86O/h9XU6exZkZ91YkJ31Embx6CnAxILsrGWNDDfsJUaVkxBVyW43lEsJh2ltTpRL8WrW0SR4sXKtiNaw6l9QuvfYD9/ul5okOBtc9n8z4nFOmEHhtnWnYOp1lu2HqZ1D6+ZKRQn8+FvY+Y3TkUB0AnQ809obPFpDVC1/C9124yKc/fSKmf4aYWNyu8oy07G1s7axCE2le6Fou7XlempqMxQKloZ2Ryu2DfSe3LhOeVIvGJ8NP/vSlFj56hL48QHznqynD1tKqVeVUruVUqtqbGutlJqhlMrxPLeq8d5DSqkNSql1SqnxNbafrJRa6XnvOaX8/CHM0PvI0C+Roc/xN+b6Op3l3i8KsrMqgc0F2VmF/jbeFLWPLQVgd5kLyqWEw7Q2JzudKgJG/NkkqrHKtk9g7l2wYdqx22OSoOtFsPFt+xIXNUbrkwANsa04msLdbetOc6aasg1eoXRzxS3lUry2fAzbPreuPaWg+2UQEX3sdjfeuAhn+XPtu+D3KtsH8+42U/yEqCmuDdx0CPr93J72k4dCdPPQLQ0HJknQqL8H1kbK+XDFchj9DPS6ynwGfv+L0B54EHWZAhx/gfogMFNrnQrM9LxGKTUAk312oOeYF5RS3ruQLwIZQKrnYeFF77HqK5kyJDkt/ZDnawXEe14rQBdkZzmc49992sWYGp35bhjpDPVpbd4anV1+5lwMg39jXVuVR0z2vpb9fU9F659hStxUFJtOqBusf910GK5eb9ZuuVFdN1fc1Dn2xW2dzmV/MaVbul4QeFvF+Wa96jlZ5sbNwofN357ErqbD6fb/m3BRtB0Ob7c+gcvxmnWEfhmw5kUY/vvqcl1CQO0zHqzQ/1YYcJt97dutbL/nxq4FIqLhpHvM1zOugMqSY98Plc9GUS+t9fdKqe7Hbb4Ek+wV4HXM+ssHPNvf0lqXAZuVUhuAkUqpLUCS1noegFLqf0AaYOHd52p1djoLsrNcUGgytLSKKaNKu6TTmdjVd9bakJnWpuHCr53t7FQUw8Ecs2Yk0LUoq/9l2rrg8xNHfgBSxpmHW+gqUwqh60Xu7XBCaN9ccVuns1lnOGBB6Yu1L5sbLBfPhjaDzQVO6mRzcTXzGvOzJYLDmzXU7pFOgCG/hbUvwbK/wZk2ZMoVoWn27Sbrql0jnU6XmwrUhyOh41lw1n+tbTeUPxtFY7XXWu8C0FrvUkq182zvDNRMHpHr2Vbu+fr47baQqrIW+yy/K52+uJb1RS2cDsWMJkQdV7olMjZ0prWpCHN3vmVf52JY/zq8P7S6c9BYJbth8Z+gywV1T9etqoTcr53J1ns8FQGXL4VR/3A6krqFSrIjX4rzzLNbOp2JKYFnr938Icy+FTqcAa36H/teTEtzI2zN84GdQ/jv4HpTm7PNEPvPlZgC/W6Bda+aEVYhKkrgp//av9Z33r2mcxtqDqw3GfKTh1vfdih/NgqAKKXUohqPjADa8nVnRtex3RbS6bRBhY5AN7x8jfVSJ5v1d4ndAGWyvPX7ZehMq9i32mTfrSipf1+7HM1gG2Ayoapy6DIeRj9d9355P8Bn42DrJ4GdzypxyabUgpv5urkSKmsGB9wKk3e4ZyS5WWcoL4Qjh+rf15dd38M310DbkXDeuyeO6CtlpsLt/tEk/hD2G/4IXLfL/P0PhqEPQkJX+PBUSV4iYM9C8/ln90h7cZ6pfR1qtnk+67teaH3bPgceQuSzUQBUaK1PqfHI9OOYfKVURwDPs7eURi7QpcZ+KcBOz/YUH9ttIZ1Oi93cbR0Ppi5zOoxqqZPh2i2QUQW3HIHT/+l0RP7b+hF8M9nZqXhWlU1J6AznvWNKpdSlw5kmbfqGNwM7X6BKC2D62e4o5VGf42+uNOsMSb3NtE63i4iChE5mVNkNEj2fPYd3NPzYgznw5cUm8deET0w2XF/6XG+yQq+R6ZdBE50YvHPt+gFKdpmHJC8RR6d3j7b3PG2GmmmjpfvsPY/Vtn0CrQZBc4vrl8KJn41RCTDqqdAZeBCN8TFwg+frG4CPamyfpJSKVUr1wCQMWuCZiluolBrlyVp7fY1jLOeSK53wcX7bHZzb1qW1ApUymVHz5jodiX8KN0N8u9ovXoMhsRuoqMaXTdEaFj4CB37yb/+ISOg1CbZ/Zta/OWXDNNg168S7pG5V8+bKFctN4pS597g/hf6qf5tyFm7R9UK4ZjO0SG34sc27Q99bTNr+uDa17xfbyvyMb5ja+BFV4Z+8OfDFRDi0KXjnDIes6cI6eXOgRV/7Z3MkDzPPe5fZex4rHTlobtJ0m2jfOWp+Nt5cBANvte9cIqiUUtOAeUBfpVSuUuoW4G/AOKVUDjDO8xqt9WrgHWAN8AVwh9baW7j5NuC/wAZgIzYlEQLpdFquXWyJO2p01mbp4zB9jMkK63ZOlkvxiogydyAbO9K59SNTzmPHTP+P6X2tmY606b3GndMK66eYNSahMFp4vLg2cPKfTO3LrbbdsLPGuv/Clmyno6gW08J0HhsyFbNkj8lUGxENo5/yb73QoF+ZaZ/CXju/hW2fQWzr4J1TkpeImmJaQNcgZKD3rlkOpU5nRAycnQWp1wfvnHuXQ6GPBJMi5Gitr9Fad9RaR2utU7TWr2it92qtz9Vap3qe99XY/3GtdS+tdV+t9ec1ti/SWg/yvHen1vbdrZdOp8Xau73T2fcWM5VvxVNOR1K/wi2Q2N3pKMx0lEF3Nfy4yjKYfx+0Ggj9fZRIqU3ycHNneLttN5vqtncFFCyBPjc6c34rDLjVfN/n3QsVpU5HU7viXe5JIgRmZHjZ32H7l/7tf6QQPr/ArEOuqqx/f6/kYTD0AfeUBgpX+XPM70Fsy+CdU5KXiJrOeaP+XAZWiG9nZmpYVXokGKLiofekExOu2aXsAGSfCstdnhxQhC3pdFoogiqSY8vIL4tzOpTaJaZA7+vMlL6SPU5HU7uqSnNnPMnhkU6A7pdAh0YkQVj5TzNCOvqZho0cKQU/+8IkYnGCtzZn72ucOb8VIqLgtH+a0XK3ZkqtqjC/g27qdCoFy//u3+hr5RGYcZkZWRj5VzM1vCEqj5h1fgXLGhGoqJeugvx5wSmVUpOv5CVEwCl/Dm4cwnnBzscw4RPoe1Nwz9lYVZXmGiGYMwBiW0KPKyHnf1BeFLzzCuEhnU4LJUWXs6csjl2lLl8HN+R+qCw1dSPdSkXApA2NG2G0WmmBySbbkD/SxflmWm3XixpXe7N594ZfxFsleRgMvt89GVUbq/O5MPZ1U7DejUryAe2uTif4VzZFV8GsG2DH13DWK43LvFh1xJQ4WPFk4+IUddu/xqwZ63B6cM97fPKS2GSgSqbXNkU//BKmnxXcc1ZVhkYd4D0LYd49wc+xMfB2k6Hc6WSFokmSTqeFDpTHMuiby3l9ex+nQ6lbq/7QPc1cMLo10YpSZjqWGy7I8+fBlxfB/tX+HxMVZ6bUjgrggnr1CyYJSLClXgcjwySlep/rIaa5Oy9CSgtARbrjZ7ymZp3rz1677G+w8S0Y+QT0uaHufWsTnQip6bDpXfO9ENaqOGxqpQa70wnHJi+5YY9ZotD3xuDHIZyVNxuigziFfsc3MCUJ9iwO3jkba9sn5u9/l/HBPW+7UWb965oX3Hv9J8KWdDqbqjH/hYtnm86dG+XPh2VPQPlhpyMx5TegYcmEYlrAqH9AywBuQFSVm7pj+9c0vo2Gyv3KjI6EkwM/wbuD3Ff+pc0Q+PkRe+qzBSLBj5HOfr+A054zsyYCMeA2M+K57rXA2rFCzlRTUzJcaku2OxUu/qG67JOTBt9rykbpqhMz24rwVLrX/O0N5vTu5t3Mz1coJBPa9om5IRTsNahKQf/bzPWMzD4QQWZbp1Mp1UUp9a1Saq1SarVS6m67zuUW49rmknXyLFpHuzhxiVdcazOFtbzIrK1ym+1fwIKHglfQvC7NewDKv7IpWsP3v4SdswI/b6+rzP/RhmmBt+WP0gIzsrrkseCcL1gSUuDIAZh7t/tGPFWEO37Ga0roDGV7ff9d2DHTbI9vazLQBnrTqvVAU5t27UvO/t/kTDW1JIu2Eja1JSvLnI7gWFrDFxfBrJtkhKUpyJ9nnoM50t68hxlZdXunsyjXZJHt6sBMJjAzgCbvtKc2qBB1sHOkswL4jda6PzAKuEMpNcDG8zmuf/MDXNA+l9Iql11E1ubQZnizK+S84XQkJyrcbC5+I2OdjsRMlU3o7N9I5+YP4KdM/+ty1qVZR+h0jll7EYyLtA3TzOhqnyCmbw+G6EQzDXTPAnf9rG94y9ygcNsF+JD74OYSiIw5dvuWbPjsfFN2yUoDboPIOJPJ1ynhVluyOB9eaw7rs5yOpJpS0HEMbHrHHSPbwl75c0yN67anBO+cKsLMINm7NHjnbIyCRWZqrVOdzqh4z7ITbT7zhQgS2zqdWutdWuslnq8LgbVAZ7vO5wbtYkspLI+muDJEOp3Nu5uSJMv/3rByB8FQtMXE5xZJveFQPSOdFaXw433Q+iTo93Nrztt7MhRuMh0mu62fAsknm/jDTepkM93wxwdMmQ832PUdbH7ffVPco5qd2OHc9T3MnATJpwQ+pfZ4va6GK1aaGztOCbfakvlzzcVki95OR3KsIfebG2lzfwUH1jkdjbBTu9Ew7Hc+MhnbLHmYKfvltmuamrqnwfUF0LKfczGUF8H7Q2Dls87FIJqcoKzpVEp1B4YBPwbjfE5pH1vC7iMuLpdyPKVg6INwcB1s/cjpaI51aLNnWqtLjH7GZOmsy8pnTG3R0c9aN2Wyx6Um2UpUgjXt1SYcanPWRUWYNYglefDTy05HY7itRqdX6V6Y8yuTBATMz8aXF5sbVBd8akaOraQizN+i8iLn1hOHW23J/DlmlkjycKcjOZaKgLP/B5Hx8M217lzaIazR/WI45f+Cf95ul5ibG1Uum15+vNiWzt5wjE6EmJbOL20QTYrtnU6lVCLwPnCP1vqQj/czlFKLlFKLKioq7A7HVu1iS8gvjXc6jIbpcblJNLHsb+6Z5ldVbjoHbup0Jg+t+65kcR4s+4v5wOt8jnXnjWlhLtJaD7KuTV92zISImNCuzVmfdiNh4jcwyCXLy93a6VSRsPrfsPtHM1owc5K5QLnwK/vK6JQdgKkpzt1191lbEhj6UPBjsUL+XDMq7YblCcdL6Gxu4JXs8ayhFWGnZA8c2uTMNUXnc+DkPwZ/hNVf27+A6WPdMYtiwO1m2VDuDKcjEU2ErZ1OpVQ0psM5VWv9ga99tNaZWutTtNanREWFyLTUWuw5EseawpZOh9EwEZEw5LemZtSeRU5HY0REw82HTVxuUZwHa140CQB8iUuGkX8LrERKXfatsmadaG0G/xqu2Qxxbew7hxt0Otv8zLuhMLZbO53bPgUUzL8P3uplRtov+MLeUb/YltB+tBmFrgryzUetzc99zdqSCZ1h0D0w4JfBjcUKFaWmZIQTpVL81f0SuHodtEh1OhJhh5w3zN+Okjxnzl+238yWcqMtH5lrrfj2TkcCPS6D+HamfIoQQWBn9loFvAKs1Vo/bdd53OTnS8fw4JqRTofRcKnXwyVzod0IpyOpFhEN0TZPKW2I4p2mkH1taysjomDgHfasoao8Ah+fAUv/an3bUH03OqGTPe27TcFSUxLDybu7WptRKLdlD/RmccXzM1G0FZY+ZjIt2q3/raY+6Nbp9p+rpvWvw+cXQHTz6tqSk3PhtGfM+9u/MD8zoaKqHEY8Zi4o3Swq3vxtW/x/ULLb6WiElfLnmpwMTt1U++QcmH2bM+eui9amVErKOHfMQoiMgb63mJjcMPIqwp6dI52nA+nAOUqpZZ7Hz2w8n2isqDgzygDumGK7/UuYfac7RqO8vLXuji+bojXMuMLesiaRMdDjCtjyAVSUWN/+N5PNGr6motUAM7I27x7nMvcpBZNy4JQ/OXP+2jiZxbXrhaa8zdr/2H8ur9ICM6Lb/nTo5iOTZFW5+d34dJyZbRAKYpqbNW3tTnU6kvod2gjL/grf3eyOzx4ROK3NmuJg1uc8Xpuh7iybsm+lqYHsVNZaXwbcCmNfh7h2TkcimgA7s9fO1lorrfVgrfVQz+Mzu87ntM5xh5l5+meclexg2v9Azf8tfOuCchm7vjOL2yNdlJQppoWZQnt8BttN75oMpHZ3kHtfa85h9ShQyR7zb4iIqX/fcBEZC6Oegv1rYE0QOzihwMksrhFR0O8XkPtV8O66z/+tSV505n9MkpsTYoqGCz43PzOfnmvvFHer7P7R/F6Hglb94dR/mCndq//tdDTCCkVbzdKB9g5O704eBiX5zpZh8mXbJ+a5q4vGXxK7Qup1ZvBBCJsFJXttU9AxrpghLfYRSQjfrY2Igg1T4WCOs3EUboHELtZlgLVKUu9ja3VWlMCP95u6YH1vtvfcHc8yU5U2vGltuxunga6APjdY267bdbsYOp8Hi/5gRruCLX8+fP4z53/Xjud0FtcBt8JlS4Jzvp3fwfrXTF3SuhJ1tegNE2earz8598TZDm6iNXwxEX500Xr4+gy8E7r8zPwt3bvC6WhEoPLmmGdHRzqHmWe3TYtP6mWWEbhtLX9lGSz/h1lKIISNpNNpkXaxZtrj7rIQvls06G4zlXP5P5yNo9Bl5VK8knod20lY8ZQZkRn9rElOY6eISOh1DeyaZe0U23VTTFmFNoOtazMUKGX+38oLzUhvsB1cB9s/B1xWo9NXFteoZmZ7MMS3M6MUwVBVBh3OgOGP1L9vy35w4Uxzcbb+dftja6yDOeYmipMX/A2lFIx9zZRv+O4mmWYb6lLOh/Pecbbec5sh5tltU2x7XQ1nvuh0FCdSUSaZ0LInnI5EhDnpdFqkfWwpALvLQqxkSk3N2psRu/Wvw+GdzsXh1k7nqCfhypXm69ICsxap+2XQaWxwzj/0Qbh2u0nAYYW9y2Hv0vCtzVmf1gPhyjUwwIGEE8WerI5uu+OdOvnYLK6J3czr1MnBi+HIIfj2Btj4jr3nSTkfLvre/9IKrQfB5Uvctw63pvy55tnNmWt9iW9nOipnveZs7cJA5Uw1ScoyI8xzzlSnIwq++LbQ80r7b8TWJSYJzn7D5EJwi8KtJquuG0VEQv9fmpva+9c6HY0IY9LptEi72BIqtaLgiAsykgVi8P2gK2HlM86cv/KIWUflTdzjJs06mLWdALFt4Lx37SuR4kt8W5MkxCpxyTD0d+Fdm7M+LfuY58KtwR1hKd5lsqW6KUOzV+rk6iyu124JbocTTE3Q/Lmw6jl72j+4wWSCrjzS8A5OYldzzMENJrmQ29aM5c+B2FZ11xR2q45jqmdcuO376g9v5ueirYA2zz9kNK2O55FCWPlPKNrudCTm75b377sb/PgAvDfYvSP5fW82117BTOQmmhzpdFpkd1k83+zpSFWof0uTepiRjQG3OnP+yBiYvN2M6rnN6v/Aay3MXexpPcxdy6Qgj8jmz4cPR9ReL7QhEjrDyMdN57Mpy58Hb6fCZp+lhO3h1hqdbqAizN+f/Dkm26OVtDalj5b9NbC1vKV7zM/Np+e5K2lP3hxoN9p3UqRQsfQv8O5JpnxOKFnwoHOZn91i948mK/gBF4yWFeebHAhuyIJfVQ65X0DKePeO5Me3MyPU61+H8sNORyPCVAh/MrnLa9v6cM2ic5wOwxr9bnZ+pNFtf5hzpsL8e6H8EI7exY5LNoWlN74VWDu7F8C2z6Cq0pq4QlnbEWZk6Mf77ClJ40tcMiSfHJxzhaI+N5qMsVZnF974FuyYASP+Elhd2vajYcInpgD9p+dB6T7rYgzE+R/AyL84HUVgelwOlSUmk7qucjqa+hUsg1k3mVIYvjSl+of5c8wNj3ajnI4EChaZcmBuWNeZN8dkyfZVlslNBtxuSi2VuuhGmggr0ukUvu1bCd+kB+8i3GvjO/DlJe64O1nTwofNhVBNTtzFbtEb2o4MPIvtsr+ZpB2EwEWd3SKi4LR/mqzJK54KzjnPeB7OtTgTcTiJawM9r4KcLOv+FpTtN6MwbUdYs46301gY/5FJCvXZ+VB2IPA2A9WyX3USlVDVsi+c9hzs/Mb5pHZ12f4lTD8LPhgGm96BqETf+0UnQkVpcGNzSt4ck0AoJsnpSEytTnBHBtttn5iyZJ3PczqSunU4HX72JTTv7nQkIkxJp9MiM0//jAdTlzkdhnVK98GGN2Dda8E9756FkPul/8k9gsXJ+oXH632tSQDU2AX/JXtMvc/e15k1HAI6nW1GWJb91ZqpyyJwA+80dTsry6xpb8FDZkrtmS9Zl+QkZRyMe990LJyenbH5Q1j/P2djsErfm81Uv4W/h90LnY6mWtkBM1USzN/gwq2mzujkXFPr9fjPLRVlMmRnj4R9q4IeblBVVcLu+e7JnNysE8S1dcdI57ZPzU2q6FpuTLhNUa65CSuExaTTaQlN/+YHiIpw6QLxxug4xkwhW/EPqKoI3nkLN0Nid/etSXK6fmFNva4y35/GjnZ6a3P2vdHSsELeqf8w39dd39l7niOFJqGE3dlZQ127kTD6KTPqaYW+t8DoZ6wvydL1Qpj4rUkyVlHi3HqoVf+E1f925txWU8rcHGg1AErynY4GDvxk1gJP7Qyb3jPbBt0DkzaYOq+xrXxnfh47BSZ8CiW73VeT12qHNkDFYWjvkszJSpnRTjeMdJ7/EYz8m9NR+KeyDN47CRY/6nQkIgxFOR1AOGgVfYSYiKrQLpdyPKVgyIPw1SVm6lDva4Nz3sLNwU/O448Rj5s1nDUTRQSzfmFNzTqaLMONvXhe/7pZT+hkHTU3SuoB124zF5B2Kt5lpq9bNYIXznQV7PjGJL1q1T+wttqNMA87KGWSFH15CVAF46dbV9rIH1XlZp12/4zgndNusa3g8qXO3YDU2sy6WfmseY6IMZ+D3unLUT5qcqdO9p3t+eqc6szjm943N3QDWVPsNjlTzVITrc2MAgh+1mtf2gyFVc+aTNWRMc7F4aYsuvWJjIVek2D9FBj1NMS1djoiEUZcNpwUmtrFmrV+u8t8fAiFsm4TzZ3mZX8LXppvt9bodEP9wppO/Rv0uKzhx5XuNdNrm2ptzvp4O5z58+1LYuItByHZa+tXfhhmXArL/974NpY/Cd//onpapF2UgtR000mecXlwbyoULDNrzkOtPmd9VIT57Pn2evhf2+DUv6yZXG3+b0w941P+ZLKqj33NfCY2lLfDeeQQfP9zM9Nh84fWxOu040vFHN7unlIxJ90NkzY6u4xk6V9h2+fOnb8xBtwGlaWm4ymEhaTTaYHqTmcYjXSC+cA/+f/MWreqI/afr7IMWvR17wic0/ULj1ecD7u+b9gxcW1M7P1/YUtIYWHnd/DRaDMibIeSPPMsnc76xTQ3a483vtW4DLGFW2DRH8yNlmBcePZJhzGZsP1z+Ppq+zu6XvlzzLNb1tNZKecN04EpLcDSzOE5U00H1tuRXfEszPsNvNXTTIFXCsZ/DNduheGPmJISgYpJgrT5JlHLjMvgu5+7L2leQy182L2lYhI6Q2IX59ZbHymExX+End86c/7GajPYTJNe+5/QyCAtQoZMr7VAYUUM2bu6sq3YhYXeA9XzCuCK4JwrMhbS5gXnXOFgzp2Q9z1M3mEysNanqhKoMhffkbG2hxeyvOuZFzxkbrhYnYlRRjobZsCt5uIn539w0j3+H6c1zL7D3Dw7/V+2hXeCfj83N9Dm3AmfTYBDG03CscSuZjq+HTerDm4wsy8SOlvfttMWPcIJWba9nZrUybDlI0CZv2veR2KKKfulNexfY/4+1nx/22cw91fVnaWirTD/16adnleZ0lgxze0pHdayL1wy16yZW/Y3yPsBLl8W3OnYVnJTkj1fVr9g/oanXhf8c++YYW48db0w+OcO1IDbYNYNZilIqGfEFq4hnU4LLDvYhp8vHeN0GPbRVbAl20x7tToJh2i83tfC5vdMaYGU8+vff9un8MMvYOKswNfHhTOlTMmGD0fA1C4m+6SVHYa4tiZbrt1rR8NFmyHmJsCa/8Cgu/0ftdj8Pmz/DEY9ZUY7gmngHbBnsUnaVekpl+EdoQPrO55n/BvKQyRRSUPV16n5+soTR5QH3mluNFQdgfcGnXhsdNKJo3NgMp6eF2ANZH9Exph6ql0ugILF1R1OrZ3PgtxQ0c099auP40SSPV9yssxNVic6nVs/gZiW0CEEZyD0vMJk3A3HG1nCMdLptIQGQuyDoiEqSuD7DDN1a8LH9p1n7cuw5kW4+PvQSS3upC4XmIyZOVP963SunwIoU+tT1O3AOlCR1RdTVnYYaks2ImrX/zZY8IDpaDTvVv/+usqMhLUZCoPusj08n3Z+U93h9Ko5Qme1cP2bmdjVs17Qx3aASxeZTmfNhzdJj4qEc9822bprvj/7Dt/nKt5pz7+hNh3PNA+AHTNh0R/h7P9BUs/gxtFYm941fyNVlPkeezmVZM+X5GEm03uwO/S6ytz06jIhNEuTRcZWdzhD8WaIcCVZ02mB5wfP5ZvTP3U6DPtEJ5gLt23T7a01tn8VHMqBqDCcpmyHqDjocQVs+cDcGKiLtzZnqtTm9MvCh0FXHrutothMycudAUXbZa1LMPWeZNbW+dPhBDOl9sIZcPYb/k09t0Owph1u+Qi+TPOseQxDIx4/sf5lzU5Nm8HQ9mRoP8p04DqfAy37mfciokyJqd7XQp8bzNTnAbe5qwSWV3mh+Qx8fwismxK85H2B2PapmYUw5r/uSbJ3vDZD4cjB4NedLN5lbgp3nRjc81qpqhw+PR8W/5/TkYgwIZ1OC3SIK6G0yqJi42418A7TGQwki2R9Dnky18odNf/1vtYkosibU/d+3tqcfW4ITlyhrraOQdl++Ox8eLMrvNbcUyLDY+e3sGeRyVBZm5yp8N84yFT2Z+EMJ961eFUVvqdF1lS6z1ywJ3aF1gODE58vtXVgmllcKiN3BuycaabxhSM7MofX15F1Qvc0uGKFKWf13U3w9VWw5qVjkx257e/FWa/BBV9A3xvclWSvpjZDzfPeZcE9b0JnuHod9L4muOe1UkS0uXHzU2bwkqKJsCadTgu0iy0Jv8y1x4trY2rAbXgTCn1MdQpUzlTI/cIsWnfjh6tbdTzLjAClnFf3fuumSG3Ohqi1w9AZJn4DZ7wI/X9ZfUED8N0tZh3olBaQ1RGmj4UVz1S/v/I5M029ylNKw6osnE3FkUMwrQeseLr2faoq4LNxMOvGoIVVK18dGzBlYPYut+48+XOg3SjnRnSDwerM4W4rgeWV2BUunAkjn4DNH8Dcu6pLkbjl70VVBcy5y9wkVsr6RGtWa32SmSp6eHtwz+stveNUnVmrDLjNjNpusXFplWgyQvy3wR3axZaGf6cT4KR7oUWq9X+8vXW+vHfS3PLhGgoiIuufEqY1jH4KRv41ODGFg9pGQk59wiQBGnArjH4aTqkx7WjCpzDuA/N97jLBJDHxrhGrqoR5d0OlS0sLhIKYJJP5c23msbUUa1r9byhY4o5skb46NiP/CrEtrJtie6QQ9q0Iz1IpdnNbCSyviEgY+lto1uHEUmVu+Hux4EFY/S+TOT0URMXDjYeCu7b78A7IaufJrBziuvzMXGOsfdHpSEQYCONbo8ERrSppE1NGflmc06HYLzEFrlxj3fTXilJTTuDH+2uv8+WWCwE3O3IIvrnWrO/se+OJ7ytlOkrCf96fu4UP+1/uolX/2rMCH78+tCa3lBYIBf1vg6+vMAk6ul107HtF2015jS4XQM8rnYnveL6SRg26x6zHBvP3L5CyHLt/NGuLO5ze+DaEO3lLKx3Pyb8XOVNhxVMw4I7QWqoRGRPc8237DMr22VNyJ9giIs2snoUPmwR7Lfs6HZHwUEptAQqBSqBCa32KUqo18DbQHdgCXKW13u/Z/yHgFs/+d2mtvwx2zDLSGaCYiCpe3NyPBfstKBwdCpSCda+Z6YP+rDOpPALFedWvFzwEn54Hb3aDV5uZdPZu/HANJdHN4WCOqWN4vKpy8z0/mBP8uEKdlSMhkTGeES8f3FJaIBR0v9jUN13znxPfm3uXGQE9/Xl3rwv3djjzZsPbfWHpXwJIGlNlpta2G2VZeMIlgrUm2F97FsP3Pzd1jE97pv793WTnLHPdEaxkW9s+NX/vWzm4ptxKfW8xpafi2zsdiTjR2VrroVrrUzyvHwRmaq1TgZme1yilBgCTgIHABOAFpVTQk9FIpzNAhyujeWTtKfywt4PToQRHzlT4/hdQkofPdSYb3zHrPT6/AKb1glfj4YsaU932rjBTwjqcCSf/Ec6Zai4ifZGLcf8oZRIK7ZxlpvXUtO1zU4D8wE+OhCZqcGPyklATEW0ykG7//NhslMX5sHsBnPwHSOrhWHgN0nYk9JpkRhBm327WyjVUyvmQNs/96+pEwwVrTbC/Fv+fqTF83ruhlwG9qsKUpClYZv+5KkphxwyTtdbNN78aoll7GHwvxLZ0OhJRv0uA1z1fvw6k1dj+lta6TGu9GdgAjAx2cDK9NkBxEeZCobSqiXwraysl4Z0K6y3InpQKbUeYzlDN5DUX+Cgto7XpuNacYisX4w3T+1pY/ChsfAsG/6Z6+/opEN/OrDEUzmrMlF1xov63Qoczjr0p1aw9XLUWIkNomUNkjKnJmNjF3Bg6vAPOnWZKVPlDV5mL6WBPHRTB4evvRf9fwprnYfULMOal4MZz7jQ4nGs+T0JN8lDzvHdp/Un3ArVrlrmWccO6citVVUJOFsS3Db9/W+jSwFdKKQ28pLXOBNprrXcBaK13KaW8v7Cdgfk1js31bAuqJtJTss+VnTfzzEk/MvibS9lZ2gTqS9ZXe27saxAZ37A7fHIxHrgWnk7+hjerO53e2pyD7gq9O9PhytcaP9EwCZ3Mw2vHTDNzIhRH+1SESS6U0MXUgN34NvS72b9j962E7FFw/odyUylc+fp70fdGiG1jvi4vguhEe2NYNwV6XmHOE6rr+eKSISElOGVTmveAwfdDp7H2nyuYVASseNIkZpJOZ7BEKaUW1Xid6elYep2utd7p6VjOUErVNaXN10V50IsBS6czQO1iSwAoOBJCd9gDkdjVk8Ldx3bwPR3IH3IxHriT7jFp7KsqzeJ/qc0pwtVPr8K8X0O5pyZqtzQY/6GjIQVk4O0mA22bIea193e4LvlzobIUWoRoR0A0jnc5Suk++Gg09LoaTv4/e6ZyrnsNvrsZSvfAkPutbz+Y2gwLTqezZV8YZWM9c6coZWaZzP2VqUfd9pT6jxGBqqixVvMEWuudnufdSqkPMdNl85VSHT2jnB2B3Z7dc4EuNQ5PAXbaFHetZE1nAC7ruJk7e65Ba/hxzMdc1nGz0yHZT9aluVfva2H4w9UXq0cKTdKHNoOdjUsIK+VMNRc+3g4nQO6XoV9iKXmoubDbvxbeHQD58+veP28OxHeA5t2DEZ1wm5jmZpr5kj/DrBtM0j4r7f4RfrgVOp8HJ/3a2rad0GmsSe6jq+w7R1Guya3gLf8Wbvqkm+u9NVI+xWlKqQSlVHPv18D5wCrgY8A70nAD4K3b8zEwSSkVq5TqAaQCC4IbtXQ6G+2yjpt59qT5NI+qQCno0uwwz540P/w7nm4tqi2MihJTxFlr0wGdOMvpiISw1sKHTyyxVFnifP1Cq0REm3Xzn5xTd52//LmmVEq4JCsRDRMRDWP+C6f8yay1+/wCOHLQmraLd8FXl0FCZzj3LYgIg0lxg++FCz4z00TtsmEqfHI2lOyuf99QFNMCel8HG6ZB2X6no2nq2gOzlVLLMZ3HT7XWXwB/A8YppXKAcZ7XaK1XA+8Aa4AvgDu0rquWmz1s++1TSr2qlNqtlFpl1zmc9EjfZTSLOvb/q1lUJY/0XeZMQMHk1qLaAubeDV9dAi9HmrI0G950OiIhrFXfuvJQ16I3XDIXWg+CGZf5HlUo3gWFm6G91Ods0pSC4Y/A2Ndh1/cmc7wVfrgVjhyA87Mhro01bTotZ6op8eZPqbfG2vapmcabEPT8LMEz4DZo2ccklRKO0Vpv0loP8TwGaq0f92zfq7U+V2ud6nneV+OYx7XWvbTWfbXWnzsRt50jnVMwtWDCUuf4ww3aLoTtcqZCzhueF9pchNcsZyNEOKitlFI4lViKbwcTv4UuPzPlVDZMO/Z9FQmnPCYJhITR53q48Gs41aK1hKc9B+M+CJ+lGTlTzWdh0VZ8lnqzQuk+yJ8T/kl2koeaRElfXGRvB16EJds6nVrr74F99e4YonaU+M5UW9t2IWy38GEzzbAmbzkbIcJFU1lXHp1gMtOOegq6X3rse/HtzPT5Vv2diU24T6ezTOmgqnL4+irY/mXD29izyKx5bN4Nuoy3Pkan+JqSb/VnY+4X5nvXbaJ1bbpRMDrwImw5vqZTKZWhlFqklFpUUdGI4tgO+fO6oRRXHJtdsLgikj+vG+pMQEKE+7RDIaBprSuPiDJr0aLizBqqT86FqV3NCMPULnKhJ0505CAcXA9fXAg/veL/cXmz4aPTYNkT9sXmlGB8NuZ+DXFtTemycBaMDrwIW46vDvfUnMkESEhICHrNmMb6YFcPwKzt7Bx/mB0lCfx53dCj24UIuvrK2QgRLppiiaWlf4Wd31S/PpxrRhig6X0vRO3ikuGiH+DrK+H7n0PhFpNsqK6EU0W5MOMKkwl5wG3BijR4avtsjG9vnot3QVSiyQjcWGNegkOb7E1U5AZyc1sEIMx/O+z1wa4eDJt1Ke0+v45hsy6VDqdwVlOZdihEU7TpnRO3yQiD8CWmOUyYDv1+Dksfg/n31b5vRSnMuNT8LJ3/EcS2DFqYQePrszEyHkY9ab5e8DC80QG+vR52zGxcWZWIaFOjM9zVehNbw64fghqKCD3S6RQiXDSlaYdCNDUywiAaIiLa/P0f+be6PwPm3GnWcp6dFb5rhH19No55ufr7MuBWSE2HrR/Dp+eZ5DjLn/S//ZXPwoKHTKmycFdbB77XNaaEE8CexeZmhhDHUdqmXxKl1DRgLJAM5AN/1FrXucAgISFBHz7svuyvyWnpAR1fkJ1lUSRCCCGapDe71zJ9vpspXSVEfVY8YzIfr3za3KxI7Go6W7GtYfCvnY7OeRUlpuO5/nVo2R9GP2VGPXOyoNsltY8CvzsQmnU0GYSbgpypZoaF92doxOPVHfgjhTCtG8S0NCPJ3S+VWsKNpJQq1lqHVXZS2zqdjSGdTiGEEMIHb9bImkk8oprJbAbhn8M74K3UEzOcy8+Qb1qbzlL+PJNgKTLWdKD63ACdx0FEpPmd/PG3ULwTYlrB6f+S7yOYpErz7oH9q6HjWDjtWWgzxOGgQk84djpleq0QQgjhdjJ9XgQiobPvkTpZF+ybd3Su3Si4dCH0/TnkfgWfXwBvdoFl/zA3gYp3mv2O7JfSIV4p58Hly+D052HfSvhgOBxY73RUwgVkpNMPMtIphBBCiJCWGQH4uuZTkNGI5DlNTWUZbPsUNr9vSsz4Wk8t092PVbYfNn8A/W4xr3fOgvanQWSMo2GFAhnpFEIIIYQQoae2zKNSVss/kbHQ4zI4ZyoUbfe9jyT2OlZsq+oOZ9F2+HQcvDcItn7SNBIviWNIp1MIIYQQItxJWS3rSAe+4RK7wPiPgAj48iL4fALsX2Pey5lqkqVlRphnmaYclqTTKYQQQggR7mRdsHWkA984XX8GV66E0c/A7h/hg1NgzUtmPWzRVkCbZ1kfG5ZkTacfZE2nEEIIIYQ4qq7SIaJ+pQWw63uYd6+Ug/IhHNd0RjkdgBBCCCGEECEldbJ0MgMRl2zWyM64wvf7sj427Mj0WiGEEEIIIUTw1bYONq6tJBsKM9LpFEIIIYQQQgSfr/WxKCg/DOVFjoQk7CGdTiGEEEIIIUTw+UpwddZrcMlsiGkOVZUw5y7Yt8rpSEWAZE2nEEIIIYQQwhl1rY89sBbWvw6r/w2p18HJ/wdJPYIbn7CEjHQKIYQQQggh3Kf1ILhmMwy5Hza9C+/0hTm/kqm3IUg6nUIIIYQQQgh3imsNpz4BkzZA35th57cQGWfe01XOxib8Jp1OIYQQQgghhLsldIYz/wOXLYGIKDPa+e4gWP4PqChxOjpRD+l0CiGEEEIIIUJDZIx5LjsAzbvBj7+Ft1NhbSZUlUPOVHizO2RGmOecqQ4GK7wkkZAQQgghhBAitCSmwAWfw87vYOFD8MMvYdEf4MghqPSMfBZthR8yzNe1JSsSQSEjnUIIIYQQQojQ1OksuHgOnP+Rqe9ZedxU24piWPiwM7GJo6TTKYQQQgghhAhdSkH3i6HisO/3i7YFNx5xAul0CiGEEEIIIUJfYteGbRdBI51OIYQQQgghROgb8ThENTt2W1Qzs104ShIJOSA5LT3gNgqys2xvUwghhBBCiJDhTRa08GEzpTaxq+lwShIhx0mnUwghhBBCCBEeUidLJ9OFpNMpahXo6KmMxgohhBBCCCFkTacQQgghhBBCCNtIp1MIIYQQQgghhG1keq0IaTJlVwghhBBCCHezdaRTKTVBKbVOKbVBKfWgnecSQgghhBBCCOE+tnU6lVKRwPPABcAA4Bql1AC7zieEEEIIIYQQwn3snF47Etigtd4EoJR6C7gEWGPjOYUIWChk7XVjm5KtWAghhBBC+GJnp7MzsL3G61zgVBvPJ4QIc021s+3GNkMhRjvalJsrQgghRMMprbU9DSt1JTBea/1zz+t0YKTW+lfH7ZcBZHheDgdKbAnIXlFAhcvbDIUY7WgzFGK0o81QiNGONkMhRjvaDIUY7WgzFGK0o81QiNGONkMhRjvaDIUYQ6XNUIjRjjZDIUY72rQjxmCJ11qHVZURO0c6c4EuNV6nADuP30lrnQlk2hiH7ZRSi7TWp7i5zVCI0Y42QyFGO9oMhRjtaDMUYrSjzVCI0Y42QyFGO9oMhRjtaDMUYrSjzVCIMVTaDIUY7WgzFGK0o007YhSNZ2cPeiGQqpTqoZSKASYBH9t4PiGEEEIIIYQQLmPbSKfWukIpdSfwJRAJvKq1Xm3X+YQQQgghhBBCuI+d02vRWn8GfGbnOVzCjunBVrcZCjHa0WYoxGhHm6EQox1thkKMdrQZCjHa0WYoxGhHm6EQox1thkKMdrQZCjGGSpuhEKMdbYZCjHa0GdLL98KNbYmEhBBCCCGEEEKIsMqKJIQQQgghhBDCXaTTKYQQQgghhBDCNrau6QxXSqlI4EKgOzW+h1rrp52KSQghAqGUuht4DSgE/gsMAx7UWn/laGBCCFdSSiUAJVrrKqVUH6Af8LnWutzh0GyllHoSeK2pJces+f/teR0BxGmti52NrJpSKg64BRgIxHm3a61vdiwocZR0OhtnOlAKrASqrGpUKXU6sExrfVgpdR0wHPin1nprA9v5rdb6756vr9Rav1vjvb9orX/XyPjuBKZqrfc35vg62m0G/AboqrX+hVIqFeirtf7EyvO4gVLqsrre11p/EGD7gznxZkij21RKxQKX+2jzT41o63TgUaCbpy1lmtI9Gxufp92JwGfeD8IA27q3rvcbc2NJKdW6njb3NbRNT7tW/97crLX+p1JqPNAWuAnTCQ2o06mUukBr/flx227VWv+nke0N0lqvCiSmetpvBXTRWq8IoA3LLnxs+pm0vE1Pu32AF4H2WutBnr9HF2utH2tEW7b+rfScox3H/v9sC7TNGm3HARfV/PwNQ98DZ3p+Z2YCi4CrgcmORmW/n4BMpVQU5m/kNK31wcY2ZtdnxHHn6AVcA0zSWg9qZDMzgfOAIs/rZpjPh9MCjc9CWZj/n/HAnzA/i2sdjUgcJZ3OxknRWg+2od0XgSFKqSHAb4FXgP8BZzWwnUnA3z1fPwTU/NCbADSq0wl0ABYqpZYArwJfamsyUb0GLAZGe17nYmJu8MWzXXe5PBdATwDtMJ0lb4cpqYFNXeR5bof5Q/2N5/XZwCwgkA7iq8BgYDXVN0N0IG0CHwEHMf8/ZQG0A+bn+deetioDbKumScA/lVLvY+4+B/IB09yimGpajPl/UEBXYL/n65bANqBHI9u17PfGQ3mef4b5Pi5XSqm6DvDTI0qpMq31NwBKqQeAsUCjOp3Afzy1n6cAb2qtDwQaoFJqFnAx5jNxGbBHKfWd1rrOzlkdrLzw8f5M9gVGUF3v+iLMRX8gbVrtZeB+4CUArfUKpdSbQIM7ndj7t/Ji4CmgE7AbcyNsLeZzo9E8s6DOx1zcjwd+4NjP34a01RZ4ABjAsZ9l5zSirZWYv0E+BXA9o7TWxUqpW4B/aa3/rpRa2si2qhs1Ny/up/oGpTfOBv/bPe1Zel2gtf4v8F+lVF/MzbkVSqk5wMta628b0WTNz4gTTgc06sasUqoj5ibAtZhrg79ifjYbK05r7e1worUu8tz8DIiF11cAvbXWVyqlLtFav+75+/NloDEKi2it5dHAB+aX43wb2l3ief4DcEvNbQ1sZ6mvr329bkTbCvNh+hawAfgL0CvANhf5iHt5I9t6F/gzsBG4AXMX7p8W/N9sAPpb+H/9CdCxxuuOwAcBtrnGqvhqtLnKwrZ+tDq+Gm0nAb8E5gPzgAyguV3na2SM/wF+VuP1BcBTAbRn2e+N51jvqGYO5g52c2CxBf/uZM//y5nA48D7QHSAbaZiLqA2AG8C4wJsb6nn+efA/3m+XmFBeys8z9HANwHG+FXNn2nP/88Xgf7/WPkAFtb893u+XhZgm3b8rVwOtKnx/3Q2kBlAe2M8v9/bPT/feUAzC/6/b8F0hs/C3Oh9opFtdfM8/u55nOR5/A34QwAxLsXc9JoPDPRsW2nBz9Fy4DZgJHCy9xFAe5ZfF2Dqz18CZGM6jQ9gZsG9Fei/34Lv3y8wN2nWY274DAY2W9DuHGB4jdcnA/MsaNey6ytggef5e2CQ5/Nnk9P/J/IwDxnpbJz5wIee+ezlBHZXpqZCpdRDwHXAGM9d0+hGtKNr+drX64Y1rLVWSuVhPlQrgFbAe0qpGVrr3zay2SNKqXhvbJ5pII0dVbPrLle+DmwE7Xjdtda7arYP9AmwzXlKqQFa6zUBtlPTXKXUSVrrlRa09a1S6h+YEYqj/79a6yWBNqy1PuQZ6YwH7gEuBe5XSj2ntf5XQ9uzacR8hNb61hptfa6U+nMA7Vn5ewPm3zsU8wFdrJRqg7mLHxCtdYFnZOlrzMXZFdpzVRBAmzlKqd9jpvM9BwzzjMr+Tjdu2mWUZ1TgKuDhQGLz8K5pO6CUGoT5e9k9wDa7AkdqvD4SaJtWTof1KPD8HHp/Jq8AdtV9SL3s+FtZrrXeq5SKUEpFaK2/VUo90ZiGlFK5mBkLLwL3a60LlVKbdeDr3NporV9RSt2ttf4O+E4p9V1jGtKeJTpKqdO11qfXeOtBzwhdg5dLeNyDmU31odZ6tVKqJ9CYkb7jVWitX7SgHS9LrwuUUk9jRuK/Af6itV7geesJpdS6RrTXT2v9k1JquK/3G/EZ+Tzm5uu1WutFnnNYMSvtHuBdpdROz2vvSGqgrLy+yvRM934EMyskETOQI1xAOp2N8xTm7t7KQC+ejuOdBnGL1jpPKdUV+Ecj2hmilDqE6QzHe77G8zqu9sPqppS6C3OXsACTaOR+rXW5p/Odg5kS3Bh/BL4AuiilpgKnAzc2si07LvYAFiml3sbc1azZYWrsFK9ZSqkvgWmYC7RJBP5h/Tqm45nnidF7MySQqeBnADcqpTZb0OapnudTamzTQKOmTHkppS4Cbv7/9s47TLKySuO/d0aHNIKiiMsqoKACkiQnEVBRUFQUBAQBZRWFJRrWtIKgrouiorLkRZIgiEgSSSJ5iBIFAyCiriBBQKIM7/5xvpqu7qnu6br3Vlf1zPk9Tz9ddavr1Onq6nu/8J73AMsQssa1bD9QZD93AF1POulNXciDZaJ0IvF77wA8VCNeI/83HQY6r2lCVSvpcYYvck0jZGJbSaq8SFcmRh8mzNwuJOrmbpS0BDHQqvI/eQAxCL3C9nVl8Py7KvkVejHwOQG4VtIZxPu6JVF+UYcm5bAAuxPN2JeT9GfgHuJzXodenCv/Lmk6sRtykqQHiEXUKpwOvJe4fs+UdCY1F3cLrWvZ/0l6J/AX4JU1Yy4kaQPbVwBIWg9YqGqwtsnwQuX+3cCeNXMEOFvSbsAZDL/eVq1tbHpccBvwxVEWFtaqEG9fQp1zcIfHqlwjlwC2Br4laXHgVKptYAxPJM6NyxFSfwF3uhnTqMbGVw7pM8ClVJQlJ71Dzc6Z5g3KBXAzN2BcMpmQdABwjDsYG0lavs5KVdlVWYc4kc2w/WDFOP9GDAJWJuSC0wn5UNX6sVbcYzscdp2dr1LH8KZy9zLbZ1SNVeL9nrh4DTO46vT36iLmUp2O14nZNJKOB462PVt9m6S32L64Qsxf2X6jpFtsryzphUQNc+UJssIsYj9Cigcx4P1ylYFUWejZijB2qPV/I6k1gJ+fkEvdUuKtTEiiN+g2ZltsEaY8TRq0XEYsep1m+6kRj33I9glNvdagIWl1YiEI4pxRq35O0nW212x93suxm2yvWjPuQsAU24/XidMWr+lz5UKEGaCIBaVFCJO8SotA5XO+MVEvtzkh99+FMDj7x1jPHSPmu4ia0FcRC2cLA/vbPrtKvBJzdUKmuwgxmXmUMBCrpDaRtC5Rqz/d9pIKP4pdbe9WNccS954Oh+2KpnNNjwskXWz7LXM6NghIeiWxULMdUTZxhrs0k5S0ie1faBRzrxqL7634tcdXknawfaJGMUlzdpcYCHLSWQFJPyBWUM5j+KpMVce/kTsCw2hAttt6nRcDu9v+ao0YU4HFGV7cX2tAKWlLot7p0bY8N7L90zpx5zUk/aLOpGiMuBsAr7V9rMLcYrrtToOC8cR6J7NLVqtKu3qGpGttr1UmOLsRK+PXVh309AJJl9necM4/Oe54pwBfbUmpy47Ap2zvXDPuDbZXbyDFnlEGPbOdg7tdVOr1wKfp86+k84B/JybwqxU57C62N6sY72vAQS7mTmW395O2v1g1x8lGWaB6BzHI39T2yyrGWd/2lXM6VjH2wsT4r7LjaolzDbH4dVbbosVtru6OOtAoyi4WJHbaN2LI+GdholXM8jXj3wV8o30yLOkc2++qE7ct1uuA7Wx/ucvnfdn2fr1YfG8KSbvaPkLSfp0e7/Z3TnpDymurcU/5mla+amH7RTBrJ/GvhIyqtQLbtcugpFcRsq4lCLnCD4ki+h3L7UooWqbsT9TUtLuj1nXy3a995dr238uJ46dd5NaTwZ5K+xlJ36PzoLSSlEjNurW1uLNI486mGQkw5e+wBiGnOZaQ6JxISDm7jXU4ccHemNip2gq4dswnjS/uOsROwPLE/+NU4Ima72Xj8shywf8Us7efqbpQcKGkTwE/Ap5oi1dVgrac22p3bd8madWKsdqZIWlN29fVCaLRHTibkJG3O/7OT0hX/zLKz45FS6rYuDuspD2InfL7CfdnUf/827QcdrP2XRTbj0jaHKg86WzyXDkRC7yOkpMriM9U5XIW4pw2Uvre6di4KVLLrwFL2N5M0grAuraPqRrT9n0aLsev7UxeJu6fYEgV8kvgiG6lnD0YF+xK1DUuAbTvDj9G1FHW5Z/AxpLWJnaMnwX+tdsgRaH097aF/I0JCfi9hAFbV9jer3yvXePfTpPjK9utEoGcXA4wOemsQOtDLelFcbeafKYDb7e9dtv9w8pK4kGjPWEUjif07KcTK64ziDYaK9n+a4389ib6ANapQ+vElA7Huv1s9mqw15IMX99w3IOIWrQmzYkWICabm7YdM/VapmwJvJFygbX9l/K5r8J6Rap6i+0vSzq4Zm4tvk/Ih04jJsg7AsvWCeje1IWcRjhcHk0zLWNaq8u7tx0z1fO9Q9LRDK85beLzuTGwq6R7iclx1UliI6v9nbB9evt9SScTxkfdxjmi7EY+ZvvbTeVX2IuGz7+OGry3NiiHnSppPtvPACiMruarGbOxc2XTC7wl1peAUx1GMPMRddarEDWiH6TLz1GRrK4HLDZisrQwsaBWhx8Qi4cts6zfEotWVSed9ynqQq1oY7QnzZwzDiMWOP+n3P9QOfZvXcZpdFxg+xCiPdcermBQNw6etL2NpM8Al0v6ANXqg08lrt2PloXD04jJ5irE5Pij3QQbbdLeooaCo/HxlaIe/xCi7MREnf8+5VyX9JmcdFagyM5OABYt9x8EdrR9e83QMyVtT7QjMSHPqTI4XdT2/uX2+ZLuJ5wz6/ZZvI+oAWma6xVucIcSv/cehMvluOnVKpdL/Yzt45qMS/NuuI2vQhaetW0V57syOK1Kq/7uSYXpy0NU71E5DNu/lzTV9kzgWElX1Yk3ykX2UaKFyE0VwzbqyGi7kfeujQ8Tuwt7lfuXEQO9ulSSao7EE1tH/FrCLbZrbM9UuPU2Pensyfm3XfLe2rGqIXk/Ebi4Ta78EcLgrA6NnytpboEXwkCo5UK9U/m+GOGwexzdL15MI5QVL2D4ZOkxQh1Sh5fZPlXhko/t5yTVWQD7ODHA/1eiT/AFRDlCXda0vUrb/V9IurnbIE2PC1q1jcCfO9U31lEVtV6ixDlI0g2EudmiFeIsYLul1NgB+F/bByu8AG6qEK8nfX17NL76ITGW3LLc35YwIVt71GckE0ZOOqtxJLCvSxNgSRsRLoDr1Yz7QeIEfghxwb6yHOuaIg1saV7+CiyoIYe5qvK7uwknwXNpoJa1jT0IGeOPiJwvYPjuzRyR9N2xHq8qg22L31ij7kLTbriN1aWN4FRJRwAvlvRRYhB5VMVY5yjqdb9B7Jy6Rqx2niyr7DdJOoho0VBncgyxY7oGIVWGcEq9Dvi4pNNsVxmcNurI2JQErS2Pp4mJUqOTJQ+1a3g59eSGlDiNy6k7yC7/Svy/V+UqSd9ndulznfZAjZ9/m5a8l8HyLcBbiXP5gbbrtqxq/FxJcwu8UBbmyu23E30aZxLKga7HWB5yhP1BDxZanlCY9rUWEdeh3kLG621v335A0vrE2KUOMyUtY/uuEvM11FCHlOv3R5m9tKHba+ObiTYpW3R4rK6qCNpKOGxfLGlTqjn5t+udNyHa2mD7eVVwJu+1ZLXh8ZU83EzuxFIalgwAaSRUAUk3j1iF63isX0j6A1Fz2ensYld3gBvYAm1JO431eN2VNEkXEAPITxGruzsBf7NdaWCq3rjhvr/t7qy6tAYm3G8jJLsiHFwvrBOvxJwPmN81jSxKrKWIOrdpwD6EM+P/2P59jZjnA+9vSecV7RV+TLynN9heoULMph0ZjyYkaK3P9oeAmba7laC14q1P1GwvxfCBWS15cdn1O5iog3qgxL/D9hsqxrueDnJq25X6a0o9cdjt1NLDNRapenL+1ZA7c+v7dOAntjed45NHj7kUYTx2kaJt0dQ6st0enSuXJhZ312dogXdv23+oEGsGIfu8H/gNsLqL0ZqkO20v12W8sxm77vTd3ebYFns1YsFmRaLtx2JE39xbKsa70fZqczpWIe5bCBnw3cR1Zyngw62F/grxriKcgG+gbfI6UlY/CJQNg9cyfPI1mzP7HGIcQvTQ/D/g3cDrHLXG/wKcbXuNMQOMHrcn0tUmxlcKd3iI1n1/Z2hBaRtgPtt1emInDZGTzgoo+qTdSEhsIeQLa9h+b824Ta3GTSrUvMFK46g4cLYGZ+XYpbbf3O/cRqNIaS4alPdR4fy3G9HywcAVwGFlh61O3E2IdiF1m7G3x7wDWMVh5NCaJN9ke3m1tZfoJ00vfkm6k5i0jxyY1aohLLK4TYjP4hsVphbb2f5YxXjX215jxP/iVbYrK03UsMOupNeMHIh1OtZvNOTSPAN4HyF5v832ayvG+yjRb3BR28tIei1wuAewlURTKExfjiMmcN+2/ZVyfHPgQ7a36zLemNeUshNambL72uqz+JsqyggN1Z3uzXBlxMLAlk0swJdzbns/yMrlQWqgDVCH3N7P7GOWWk7sitYuexH9WG8iJndXd3sNLwtp2xATz1Nt/7kcfyPw8qrqg3KeOJSQq0Is/u3h4VL1KnFrj6/Koq5peLMlaZaU11bjI8CXCSmFiNqnJurpziRW4y6inpRkzFXGqhKvMin+DLO3vKg7qWnMYKUHMtgWjTbqLhOwXZj9vWxygaFyXVoHueEwKkoZjwceJ1baISRtJxBNrOuwM3C4pIeI/5/LgStsP1Ij5g8J19Uzy/0tgJMVEvVfVw2qqAcf+dk8vmK4RiVowKO2z6vx/NH4p+2HJE2RNMX2JZL+u0a8XsipG3HYbePHzO4yehrRB7USPTr/nq1mJe+7A2sB15TcfqeQVVemF+fKhmOuz9B7Zkn7AA8S56CuJpwlh1qTyk5o9D6Lr5NURarcy7rTFqszNKlbpeRZ9Vx5jqTNbf+sodzOpNT40yb5boC9gDWJRdSNJS1HjDW7wrGbdEqHh24hJopV6ZV0tfb4ys17HCQ9ICedFSiD2VqSxVFYsKpccwQHj/GYiV2HKpxESCDeRZsEomKsdpo0WGnl+E6azfErkhYBPslQo+59asQ7AbiTqAE6gHBPrGWW0TZRbLVSqFyX5h64PBL1P+0r4JeogjnESGzvCKAwJ9qKWIldghrnN9sHSvoZsSsr4OO2Ww5724/+zNEp8siNiEnnzwiDnSuIyXgVPk28h8MkaBVjUWJ9g1hMa6+dq1OHCPD3Itu8DDhJ0gOEs2dVPkQ4Xv878T/4KmKXrg6NOOyWQeIbgEVGDPAXpn49a6Pn36KEuNjRU/N0SedQX/L+jO1nVerGyq5aXTlV4+fKhmN2Oh8uDXxB0v62Ow3+50jZJf4vZl+kqrJjsyEN1iK6t3WnSDoBWIbY7WstpJnq58q9gM9LeoaY4NRtUfZK2++o+NyxeNr205JQuEDfKen13QZR9GHdnTB4Ogu4kDhffop4T0/qMl5LunqJpM8yXLp6brf5daDR8VXDi7tJg6S8tgt6WWtR4n8FuKrB1bhG6ZXEVNL+RK1XbYOVySKDbUk0NVRL9UKiXnIgpLAtJF0zUjrT6dg4Y/2AkNrNKPfXBnayXcvtUNIOwJuAlSg7DMDltq+uEbPjDrFr1P0p+kyuAvzK9iqKnnlH2+40EBxvzCYlaI3XIZa4CwFPM7RosQhwUlXZrqS9HK0LxjzWZcylOh3vdkAt6T1EP7x3E4O9Fo8TBjOVXZV7cW6TdLXtdas+v0O8g4h6qh0Jg7jdgF+7Yr1tidn4uXIizr9loH6RK9Y3Knp97kfIV7cgFpTk0jOxy1h72T5E0ga2r6iSzyhxe6J+UpQ3rOABHaBKOhL4ntv6GjcU9wzi77w3sTnwCPBC25t3GefM8tyrgbcALyF2p/dyBff1XkpXFS2m9nRDLaZGW9y13dQOfFKD3Onsjm+W7+8DXkHYw0PIBP/QQPxGVuNUGu6W21vbPq3tsa+5rXl3lzQqMW2jZQL06bZjVfsN9iRHDRXQr0uYNNUtoG/l+feyKvdXYnW8Sm7LlRXRjoObmjtVTbo8rg3sKKk1cVuScHi8lWp9G1t8B7iLkGhf4gpmIB04l6EFpgWI1i6/IQZXVXnK4R74XFmJfoAaPUA1e43s5ZIOd8UaWdsbV81lDnGfgFmr72fP4cfHw07E/2I7O3c4Nm5s31v+f1rv5ZVV/m9snwmcKWndOoseo9CLc9sFCgOynzQ0wP8sIVu9FdiVGPQdPeYz5kxj58oexxyG7YelClahQyzgcDBVWfzYX9LlxES0Wz5M/H98l9ll33XolfrpNmKM9X8NxAKaMehpYwNg5zIZe4aKyoiR2G61+di/LAIuQvR97ZbX2F4JQGE49yCwpCsaevVSuurmW0xtxdDi7odbi7sNxU5qkpPOLiiSEiQdaHvDtofOllT15NUev6leSNsy1G/sc0QtUYt3AFUnnU1LTIHGT2g9yZHmez8dWS6C/0nsiEynzS69S/YlzDs6yarryKmhwTY+xGevcWy/TNIbCAnZV4ss7Te2P1Qj5krt98uEZNd6mXK9on7uKKIW6B/UaE9BD2pk1dazsXXM9c0xdiUkjE8x5Krd9aKSpO2Iz96rJbXvIr6IMMCpk+OXiPetJTM8VtEa5ysVQ24p6Xbid/45MQja2/aJYz9tTHpxbtuXqId9TlJrN7qy7LAsqpwIXGb7NzVza9HkubKXMYehMDirU1f+tEIC/TtF3dyfgar1sXcoXO0XU7S0mZUm9SZLL7V9TNlJbUlum6hJfRnwa0nXMlz9VElNplEMeqh+bWyk93A75W99i+0VoXZt7yxzqDKpu6fqhHNEjjt2Ot6AdLXJFlONLu4mzZLy2goU6cc7W7tckl4N/Mz28g3EbsIu+1cu7poa4bQ58v6goAY0+E3LNEbE7iQznWF7naZfa25HI/o11pGslngLE2YebyZkti8jjBjGbKNT4XVqtwJoi7U0sLArtiooMZp2r+3Ys9H2LlVzLHF/B6xr+8GacZYidpz/i9hRa/E4MVirXCdazulvbO0SS1oAuLHqOV3FLVPSloTcdh9iF34g2mr1irJj8Q1gmu1XS1oVOKDqZGEy0FJqjDi8KLETvaPtOyvGXZOoM30xcCCx63WQS3lChXivAM4npN/D6FZG3hZzhu11FC2mvkv8zj+2vUyVeG1xO0rGq07Eyt+oZdCzqopBj+1tKsZrvPyixD0J+FwDcWYyNHkTodZ5kpqLSpK+13Z3fkK6e2Nd6aoaLO2Q9D/Exsq2xALdPwjn+SbMPpOa5E5nNfYhmnS3pJVLU38XpMnVOI9yu9P9bvJ7NVGnszTDbcLr1rI2YrDSA5lGOx0L6FUK7D3O+lNJ+471uOs1eu9kpvIocKvtByrGfB1wGLC47RUlrQy8u8oOkEbp10g9ySrEZ6X19X3bf6oZb+TfaQohSaskGxtN9tx6rIb8+VeS1vHwGtk6TdnX81DPxi9LOpj6zc4hpM+129mUgfG9hMS9af5ADKJa0uT5iLyr8sLyfXPg5CK1rBSoDPTG8hKobGqn6M16k+0nFLXRqwHfqTHo3Y9wr/1lye2mssBSJbdeniubbHnxrpGpAQ+1ZOVV8ZCT8j9owB3f9l+JHfcm6ZX6qWkH30YMetpolV+IOG80UX4B0eLk9rLD277j19UYy/bUmnmMFneP9vvlb3/CKD/eTdxGSjuKnP2/HOZoh0v6OTUXd5NmyUlnBWz/vEj4Wk2faxl4tNGIXTZhL/4YZYWr3IahE2RVfgocQ9RlPV8jzkia1OA3KdNop7Ui2uot2BpBfoTupIJNSag7sQsxIG+tGm4EzCBs8Q/wcKvz8XIUUWt7BIDtWyT9EKgiOzyQWEgZ1q+xQpxZlN3tC2x/qk6cDrT/nZ4jBhlVG4n3yk266RrZp8r3JxVOwA8Rg6m6fI74v7yG4VK5riZLGr2NT10nSkpet0u6sLzG24ArJH23Sq5EycWdxHu6m8JwpWo/2uvn/COVOYy4XqxCGMIcQwwiq5oTPWf70aoT7BH08lzZWMuLqruEozFCOt7p9bpe5JV0qu0PdNiVrSyvLefe19o+h3gvG6sJl7QOMYldnjDAmQo8UeN//E+K0oafAhdKeoTYla1Ej8ovoNp4r588SSjzatHUIpBtS/oppTWVm/F3SBokJ50V6LCjtIykWjtKhUZW43q1ykXk990exG1Sg99qEN9+sqo8sC8Sp/tc6k4l7UScHP8A7D/eHc5ZicQOUq9kwM8Dy9u+v+S6ODGoXJtoV1Fl0rmg7WtHDCKryhib7tfY2t3uhVz8124z4IIw5WJ4ffS4aGoVtwNN18ieo9l7NjZhwHAE0a7hVmosVrm5mvdOnFG+WvyyTjDbny2f7cfKZ/QJ4D0VYx3Xfl/Si+Kw/1Enx8JzZaD2HuAQR31eHVn6bZI+CEwtC7N7ApUce233cgDeq5YXTbAucB/hGXANnR1Du2Wv8n3krmxleqws+j4hjzwNWINwQ648uXFzBj2jxb+xjBXqxpm1wyvpZcSO+cDUwGl4B4epxKLAqQ2EbrLvadM9l5MGyUlnNVo7Sr8gLggbUX9HCRpejesBhxQp7AU028evSYOVXTzCUVbhPFuVI4C3ljgbEvVkewCrAkdSoRF2Dy/WS7cmnIUHgNcVad8/R3vSHHhQ0jKUC42krajuKPh3Rb/Gy2mmX2OLm8ruwGkM392uIw0dacA12rGukLQes6/mVjJhaO2wqKEaWdsHlptN9Wxs8ZztMaWSA8BDRF1+IwoOtRlujFiwqWy4oah7P4GoF5SkvxE1g7dXjQk8LulzwA7AhmVB7IVzeM5Y7AF8gbg+/JCoIaxqxgQ0K/Fv4ypJK7nhlhcN8Qpip71lnHUuIdGu/He23TpnTwH+z8NrlxevkWuvlEXY/r2kqbZnEsZeddoNtddg3lO+vwKodK5ssvyixFsH+DrwMKEIOoHwJpgiaUfbjU2Qa/LNttvPAfc2Uc5Cs4tAjfRcTnpDGglVoKz2/FuHHaV/I1z7VmzgNd5MWY2z/WzdeE0g6b+Ixux3MbRjYdfrlybihHNfub80NTT46mD2otLfrmK8WcYskg4F/mZ7/3L/JturVoz7VeLv29jFWlFAvyRDE6P3A38i5LHnVNlxKxP2I4kd5EeIC/b2VSRlkhZkqF/jDkT9z0nd7hZ3iHtsh8O2/ZEKsTYj6vA+QPxtWixM9I1bq1qWoFEanleQbrbidayRtd1VXVEH5cYwak7eW5/1ewlZfq0+vL1C4bi6LiGhPtb2HTXjNW64UQbdX7B9Sbm/EfA12+uN9bw5xHwFMbG5zvblZXC+UZWFkDJhPd/2W6vmM0rcSykSfw8Z5N1W5zor6dfAssT5rLGWF01TZIfbEeqDA2x/bw5PmVO864na7WfL/WlEe6BKu3TqXW/fy4jF3qOJdjb/B+zs6iZpLVnxsBrMbs+VbfHa29Y8RyifTnfFdlXl7/J5YkxwJLCZ7RmKEquTPUDmj+WcsRbxfl7nqBWuG7OxvqdqqOdy0hty0lkBSbe2a/rLxOnWsgr7q25PECpmNKMxKIMzRY3Syk1PgutMCttiLEcU8R/E8H6fCwOfrnFxuQ1Y1fZz5ff/mIubcJ2BTy8u1uVz+H7CyVWEsc7pTchzJC0ETHEF23V1rsVrbf88TSxifMH2xfWyrI+itm1VQp7d3kLhccJ9tHILBDXc8FzSzYRsfFiNrO2PzeGpI+N0mrS3qDR5HxH/ng6H7RoNxXuBQtq/HWHaYuBYYsDXRKuBRYATXMN0TQ27FfeCojj4UEM75K2Y19leU8Nd2Ssv+JXnD/TAtEw230l8Hpcm2rr8r+0/14w72/s2aJ8hmPX3uZ+o59yHmIz9j+3fNxR/NWBX203UYdam/e8i6Q63uWZXGVP2CoXZ5ZcYUvm9mVgM+d+K8VqLAS8g5NN30+AiUBm3vBf4oO131omVNEPKa6txeZGfte8oXVY+4H+vEO8GhlbhWrTud93ProfcTNi316lb7UQTGvzXE/UqLwa2aDv+OPDRGnFPJnqPPUiYglwOIGlZogahElV2HccR08CPy1cjSLqLkI5fTtSF/rpCXqPW4pXdkRWJJuNVJ/CvJEwn1if+X64A9qoi+7F9c1lo2NQjaukaoOmG543UyLrHVvLuYWPxJrH9mKTTifYCexM9eT8t6bt1d5gIw43X1Yxxt6T/ZKg2eweGpIJdMcpCENQ3ZXoauFVhyNSu4KjssEuzEv9WPh2l6YOApOOIc+F5RFuP2xoM/zdJ77Z9Vnmt9wBdtzKStIPtEzWKw7BrOAuX599bdmGXJhy0f9PkYrdr1mBKWoOQkS/F8FKJqpOkdln/UyMeG6SdoU8TraUeApD0UqJmu9KkkwZrjFuUz83mhILjHYR65fCmXyepRk46q7E7w3eUjmdoR6nryYSjn5mAV7lmf6Yeszhwp6TraKBhcxsbAx9XNK+upMG3fSZwpqR1bV9dM5/2uF+VdDFhZX5B2y7VFKJ+qRJFkv01YAnbm0lagehleEyFWL109VyBMCJ6E/DNsqN8s4eMGWrhqNe5eYQcsVuOJerHti73dyjH3lY1J0kvlTSt4V39RhueM1QjexkN1MhK2ot43x4n6qtXAz5r+4KqMdtiN1bL2gskbUE4US9DTOrWsv1AkYTfQSxqdBOvF4YbHyHcLVty58uo2EpjrIWgmpxbvmDo969rhLM7ITlcTtKfiYn2DnUCjiZNp37Liyb4EHEdfB2wp4Zqgps4n3+cOFccSvx9/kSY9HTLQuV7Tz5Hkt5JTBTuIn7vV0va1fZ5FeM1WoNJLJJ+mprmaG30quNA0/yJuD60eJwwvarESGVBnUUgSa066LcTDv6t83j25xwgUl47QDQhM+0lar5h85K2/9ik1EnSQYRxxVOEO90qwN62T6ySY6+QdB4xwP+C7VUkvYBoGbPSHJ46oZS81iRkNBsALwVuGRRZEowqGasrvzuCGJicxfAdmzq9AZv+/1mIoRrZ7QkJ2kmtVegK8W4un8W3EwP9/yTqG0ftMzrOuI3WsjZJUSy8gmiFdHSbdP5NwF9s3yXpLd1Kv0f8rZ8j/kbb2d69Qo7zE5OFZYlB7v/armoM1hPKjtkrbR9a7l8LLEZMbP7DI5ygK75GZYl/h1iNSNMnK2WxSk28l71AUcryrpactux0n2t7ubGfOWq8pmswr7C9QZXnTmYkHQ+sRLjNmnDkvhb4LVS/Po62COQuyqIkPU8osna2fU85dveglXHM6+ROZwUUxhv/DbycGEw0sQIJA2z1LGkKcKgbMElq46fAakVKc7rt9zcQc1Pbn5G0JbEqtzWx6jVQk07gZbZPVThH4qgZnTmnJ80JSRsQvdOOVViuv6h1Aq7IY8RA91vAUVUnND3mQUVj+5PL/e0IN9I6/KV8TaGh1fyqk8sx4rU3n29CCtzaUtmcmGzeLDXScHENGqxlbZjvAJ+3PXK356ny2BbdTjgh/taSViUkXh8gdueq9nk9DvgnMaDajNg13btirF7xGaLFRYtpRK+86cTiWuVJp6SvAQc5Gr4j6SXAJ21/sXK2PWjfNBloUmFT4r2aUPwszXAVQ1310wMeXr95NzXKetx8+539JB0NXMxw1Uot07VJwF3lq8WZ5Xvda2QTPbxXJ85BF0m6GziFUJkkA0ROOqtxEDEYqeVw2IHaMtNe4eijeXNrd7KhsO0D2qZWo1p2/5sTJiAPNzNubpwnSj1Eq05pHWrUiJYY+xED/NcTA71pxGR7/RphtyN2OHcD/k3hoHlZlYF4D/kI0dft28T7eVU5VpnWIEUN9ERsrYp3kEHXWqwaRVb9KHA9MSi/e/ZnjckNki4gnB0/V373JqRjTdeyNsnS7uCUbft6hZN2Vyjae2zL0MLHj4gdpTo13Cu0FBCSjqF6O6leMs3FgbxwhcMA7+GyQ1mHzWx/vnXH9iOSNgfqTDoblaZPIn5AUdiU+78lPqOVJp3EwvExhDN17XOFhpy0b5f0M0KSbmLxuPJifPm//BSzT46rGvd9GFiOGG/McvJnSPo+V9J2XVxoxKJnXWovAtn+FfAr4D8krU+cg6cVVdkZto9sMN+kIjnprMb9PZhwQqxiDzL/QlwMrmW45LDqqqZHuV2Hs4s05ylgN0mLERLEQeOThHRzGUlXElK0rcd+yhzZEngjcCOA7b+UiUNl2mpllyM+n3sTuxoL1Eu1OcoiSN2V9WFoeE9EFEZSVXsibg89qaP7FrEb+0NiArstMbn7DWHssFGX8XYhnHvvtv1kWRRpoh6m6VrWJhmrfqjKZ/xOYkdyizZp4D5VEmtjlpS2KCJqhusJL2m/Y/vf2+4uVjP2VEnz2X4GQNFbcr4qgYqcenFCFvgU4Yy6PSHnq1yjP4loWmHztO3vNpQbDDcBvJ8o64Cov3zJ7D8+bk4jakSPZkjiX4dVBq0UZiKQtC6xyDAdWFLh9r6r7d1qhm50Ecj2lcCVkvYkvB22JerCkz6TNZ0VkHQIMbj7KQ1LK0bIIxcDpteURzZGD2rSZjK0o7sA4fAI9XeAXgI85jCEWZDo+1m7l1TTlHrJ1xO/72/q1mlJutb2Wiq9SssOw9V1dsoVbp6rAr9nyMH22qq1ME0i6UtjPGzbB9aI3VhPRLX1jm1QRo6ka2yvPeLYDNvrqIs2CIr2AaPimo3emz5vNImkk4Ff2D5qxPFdCKn+Nl3G25IY4KxH1JSfQtSKVnbwbTtPwvBzZVNlHbWRdBLwyw7v465E389upXLtMT5DLCodSyxOfgQ4y/ZBFWKdQ8ipbxlxfA1gP9tbdH7m3IGkXxImiBeWa8Q6wH/b7vg/Oo54HyRaXVzA8LFQrXNG06hhvwxJRwHftt21m/tkRtI1wFbE/18jPXNLjIWIRaApNOBPkAwuudNZjYWJi/6mbcdqSys6yCNfSH15ZGOUOqWliEnxRWVCV1kzb7txvb2kHdtutz80ME6ZQKsVyTdsH9527BzbdSzET1UY4LxY0keJwdlRc3jOaPmtSbjSfZ3YOd2BGKy8nJBL9n3SSdtuexsLETt2LyXqRKqyUGvCCWD7lzVkgr2QkQM8L+kDDLXI2artsW5WEw8e4zEThiuVafq80TB7A2dI2p5oXQVxDp5GKAe6wvYZJV6rP9w+wOKSDiMkXl07AffiPNkD9gF+WiYhrQnH6sSO5HvrBLZ9kKKf31uI/6UDbZ9fMVyjcupJyL7MrrDZauynjMlKhNvuJgyXmdY6ZxQ57GHA4o7+5ysD77b9lS7jtHqgny1pN+AMhk+Oq/ZA3wDYSdGDuLG+kpMB2/eNGFvV3jluk+o+L+lc4CHnjthcSe50DhCSbqLII9tWkW4ZlBNZmch8DFjU9jKSXgscbvstfU5tFhreemN+YqByo+06F9bGKRLgm4nFi11tP6sGmkArbMM3JS6C59u+sGKcG4G3lprYDYkdmz2IXc/lB/D9fBGwFzHhPBU42HZl4wlJZxCD5/aeiGvYfm+FWO07nbNu10XSa4BDgHWJgd4MYvD/Z2B121c08Tp1mSTnjY0Z6hN7u+1fNBh7UUI6v02NGrJJgaRNGGo70uj72ASSfm972W4fm5toUmFTrmMru9nWUki6lGhJckSdHbUyKRzZA72FXdHZVA067k8mJP2YKOv4PmH8sydxXdx2zCeOHm8dYmH7YWKR+ASiHGMKUc7y8ybyTgaH3OnsAkmfKauu36PDToLrtwB41rYltcxl6howNM3uwFrANQC2f6foqzQw2B5WlyNpEYYmDoPEk7a3KdKxy8uOVe0VoDLJvFDhXFtHmjK1bRV4G+BI26cDp5fFkYGgDOj3JSQ5xxFuyI80ELqxnoiM3YOtsjzSYRQ0mhxwICachclw3riEcLnuReyHgSPK11xNmWQ2MtFUb/oPXyfpo6PIqW8Y5TlzDYr2O7sRO3Umrj2H1yiXuBl4MTWcZUdhQdvXjthR67rGryVrlzT/yN+xvBeVcDjuvwR4FcPH0XP1pJNo3XQI8K9Ed4ALiM9TVb4PfJ6Q0/6CMA2bofCQOJkoURgXkha2/Vjb7vYwauxqJw2Sk87uaJkHXd+j+I3JI3vEM2VHDpi1YjroW+VPEk22Bw3BLOnYDcD5FNOargONsVooqepq4VRJL7D9HLFb3N6/biDOG5K+AbyPMAhYyTUcZttijuyJ+Mk6OwHQO3mkoub7o8zuyFjLubcHTMbzRtJn3LzxFjQsp56EHA88DrQUQdsR14uqJnaLA3dKuo5mTcIeVPTmbC3Ab0U99+uriL7Lczo2LiQdCOxMtA9pnctqy4oHHdsPUozxYJZ/xm7AVyuGfEGr5EDSAbZnlNe5U90bpv0QeBfxfz1yd9s0W9qSVGQgBo+TiD8C2J6tJ56kT9QNbvubRR75GCF/+VJVeWSPuFTS54ndmrcRJ5uz+5zTMCSdzdBFYCrR0+7U/mU0KrNMcGxfLOntwE4VYzW2WtjGycTf+0GiwP9ymOX+WKu1S4N8khjofBH4QttFqs5OyGToidjiTCLPi2jGkbFXDPx5I5k3sH0/sN4IOfW5gyYD7iGv93CDsUsk3Vwj3n51ExqF3YnFxOUk/Znoc7v92E+ZHUmvIHblFpD0RoYmIgsDC9bI7wPAMk3LigcVSa8C/hNYgqiLPRk4ANiRof7YVWhvs/PUiMe6Wphs+WG4hmlb0nuyprMLFA1nt7Z9w4jjXyYs8hup1RpUJE0hauZaBkrn2z66jynNhoY7ZT5HXGS2s717n1LqiKSLR9a0dTo2zlg32V613L7D9vJtj1WuEy07qP8CXNAq9C8GD9M9YO6ETSHpVg/1RHwB4dQ7kP/X7X/3mnF67V7bft4Qsat/dBpFJMnEIukHRD31jHJ/bWAnd9nyQtJytu8st2e1syn312nFbyDfhYApth+XtLft73T5/J2IHck1GK5Qexz4gSt2HFC4un+ijm/AZELSJcClwNXAOwj10+3APq7RGUBjdzCY3/YLR3tuh1g9vY4lzZCTzi6QtDrR72l721crtlYOI+Sb77X92JgB5hz/fcB/Ew6hYkAs8SW9B3il7UPL/WsJ1zsDn7H947GeP9FIWhX4ILEaeQ9wuu3v9zWpQpFvLkjUj23E8JXX89onjF3EHNWopknjmnmByfT+SfoKcJXtn9WMM1Yto5swvylSYGz/rW6sJEmqIekOQkX1x3JoSaJs6Hm6cF/txzVH0h9tL1nxue8vngRN5bIGoTS5jcHrPdw4GtGCS9L9wJLtiw39pu06Nj+xyHAzMb5aGbjG9gb9yi0ZIuW1XWD7BknvJWpCdifqqQDe0ZDM4iBix/SOOf7kxPIZovdci2mEHf50orVL3yedZQduW6JG5SHgR8SiysZ9TWx2diXkmksw1FoAQlJ9aMWYYxnVVDZLmEdZZcT7t0Dbe9v3BaAR7AV8XtIzhCS4Uo69+h8pi3L7Af9eclNZ2f6e7QN68ZpJkozJOxqKo1Fud7rfFF3HlbSD7ROBpSXtO/Jx29+qmMtxxAbBrQyXiM61lPrN1t/gr8CCZSd6IEx6WtcxSacAH7N9a7m/IvCpfuaWDJGTzi4orlh/ImrvfkrUUv07MF1SE/949w/ghBNgmu372u5fUX7XhzU4Drt3EvVtW9j+PYCkffqb0uzYPgQ4RNIetr83xyeML+Zk6OM3KZhM72XTRiuK/pn7EivYH1O0Nnm97XMqhtyb6DG8pu17ymu8BjhM0j62v91E3kmSjJsXAH+y/YykjYhdoONt/73LOB7ldqf7TVElbmt8Mr2heC0etP3dGs+fbCxCGPS0T/xbi+aDZtKzXGvCCWD7tqJ+SwaAlNd2gYZ6PsHQP1/LJcuu3vPpfeXmm4FXEBPadslGpbqDptDYvc3usr3MROfUIY8tiZ3O9QjjnFOIurGBKiqXtIntX7T9zYfR7791Mvi06qlGq2GpWrsi6UfEwGJHR0P2BYCrq9aNSvoV8LbieNh+fDGiTrhWT9okSbpD0e5qDcLx+nzgLGJhafMu4zxAXGNFtNQ6pfUQ8AHbi1fMb6w2OQvYbmyjpEqNaNtzv0WM0c5i+Fgt6wb7jKSTiTrRE4nP0g6ED8V2fU0sAXLSORBIOnaMh+0+t0CQdBLwS8/e22xXYKNB+mcuO6/vJWS2mxAymDNcbLn7jaQv295vlL953//WyeAj6ciyE9leiznrRF61BlPS9bbXaDefGlnL02W8UZu5j/VYkiS9oVVvqegP/ZTt71UxmysGPaPiDg7/g0bNGtFOdfCN1L8n9Si+GZ8ANiyHLgMOc/VetEmD5KRzgJC0vu0r53RsolE0cv8psaLXWslbHZiPMFC6v0+pjUmRQ28NbJMXg2RuQdJawB9broFlAPh+4A/A/lVl/pKuIlwJrywD02WAk22vVTHeqIYig2zQlCRzK5KuAb4DfIEoRblnXl0AknSf7Vf1O48kmZfISecA0WkgNkiDM0mbAG8od2/3vNPbrHEk3QXMIOpQL7P96z6nlEwSJN0IvNX2w5I2JKRtewCrAsvb3qpi3LcRPU9XAC4g6jF3tv3LivFadvizPUSXdvhJktRH0grAxwnZ/MmSXk0syn69z6lNODV3OhchTNJau2mXAgfYHpQe1vMsI8rgZlG1/C1plpx0DgCS1iVqEfcG2s01Fga2rCpvSwYXSfMBawNvIgb3ywE3296yr4klA0+75FXSocDfbO9f7tfq3SnppcA6xMRwxsh6zCRJkslCr2pEFX06byPKdwA+BKxiu6NXw9yEpKnA4rQZkdr+4+jPmFjKNazF/ITabVHbX+pTSkkb6V7bBUWuOSo13GunEe5qLwDaHSkfAyrtWiQDz0yizcVMwnL9fmCeaDSd1GaqpBfYfo6Qw36s7bG65/T5gUdKnBWKK/dlNWMmSTIASFof2B9Yivgfr2WCOOg07fDdxjK23992/8vFpGmuRtIexA7v/Qy1ijHhgjwQ2H5oxKHvSLoCyEnnAJCTzu64gSG32iWJwZmAFxPNlis5pdq+FLhU0g9s3ytpIdudZGnJ3MNjRI+vbwFHdThRJslonEycLx4EniIk2khaFqgs75L034QT5e0MH1DkpDNJ5g6OAfYhxjIz6wYrLZAOAdYlzhlXA/vYvrtu7AHnKUkb2L4CZk3mn+pzThPBXoTb8cCOV0a4uk8h3Jp7tfiQdEnKaysg6XDgLNs/K/c3I2qsPlkz7rrERWG67SUlrQLsanu32kknA4Wk9wAbAGsBzwJXEbWdF/c1sWRSIGkd4F+I1iNPlGOvI84dVVum/AZY2fYzc/zhJEkmHZKusb12g/FmAIcSC2EQbcv2aPI1BpEyNjue6F8JsQGxs+2b+5dV7ymuvW8rKpuBZISz8HOEwd43bf+mPxkl7eSkswKSbrC9+ohj19teo2bcawg57VltLQvmSWe5eQVJywGbEfW8L7e9QH8zSuZVJJ0HbG37H/3OJUmS5pH0dWAq8BMa6C/ZaRIraYbtdWolOkmQtDCA7cf6nUsvkbRvufkG4PXAuQz//HyrH3klk4+U11bjQUlfZHjz2UbkBrbvk9R+qLYEJhk8ihHBqsDvCXnkh4Br+5lTMs/zJHCTpIsZPqDYs38pJUnSIK0JYmvRXMQYpquWYm3+FpdI+izhoG1Cnn9uA3kOJGXy9ajtY2BosllqHafa/k4f0+slLXnqH8vXtPI1kEh6JzFBnr91zPYB/csoaZE7nRUoJ9yWXXar5umAGkZCrbg/Jmr8vk84SO4JrGF723oZJ4OCpDWB+4BXET1Pd6CBHotJUpfRGr5PhkbvSZKMTttOVWtF28DfgCts31MhXqsthTo8PNcaE0m6DVjN9rMjjs8HXGd7YAx15lVK+duCwMbA0YR68Frbu/Q1sQTISWctJE1vUoom6WVEUf5biZP5BcBeg1y0nXRHr3osJkmSJEknJO3X4fCiwNuJxc5TJjilSYmkW22v1O1jcwuSLiRKMP5e7r8EOMX22/uaWBuSbrG9ctv36cBPbG/a79ySlNdWQtJ6xArKdKAxw5/SE2/7BlJMBpepbbuZ2wBH2j4dOH1esFxPBpd5rZ1Ckswr2P5yp+NFtXURsfhZCUkrAiswXMp4fNV4g46kxW3fP/JYv/KZYBZrTTgBbD8i6eV9zKcTLRfhJyUtQZS+VeoskTRPTjqr8W1ihfAsANs3l12rSkgaq3+QbR9YNXYycPSyx2KS1KHRdgpJkgw2RXHTSSI7LsoO6kbEpPNnhCneFYSz69zIN4BzJX2SKI+BqI89CPhm37KaOGZKWtL2HwEkLUXIrAeJcyS9mPhb3Ujkd1RfM0pmkYPcijRs+NOpJ+dCwC7AS4GcdM499KTHYpI0wKO2z+t3EkmSTAySNiHafVRlK2AV4Fe2P1x2/I5uJLkBxPbxkv4GHACsSExobgf2m0fOnV8ArpB0abm/IcMXzvtO2ybN6ZLOAea3nWOrASEnndW4r0hsLWkaYfhzR9Vgtg9u3Zb0IqIB74cJycvBoz0vmXzY/mpxB231WGytEk4hajuTpF9cIukbNNROIUmSwUDSrcy+I7Uo8Bdgxxqhn7L9vKTnSvuQB4C5Wo5fJpfzwgRzNmz/XNJqhNGlgH1KWVjfaZk02v5rub8jYdJ4r6Q0aRwQctJZjY8Thj//CvyJMPypVc9Zaiv2JWo6jyMc0uqsQCYDiu0ZHY79th+5JEkbrXYK7f2Gu26nkCTJwPGuEfcNPGS7k8qqG64vUsajCFn+P8jWX3MtRYr9DuA1tg+QtKSktWwPwt/8CMKEk1Lu9nWGTBqPJHblkz6T7rUVkLS+7SvndKyLeN8A3kf8YxyazdmTJEmSJBlUygTklbbvK/eXBha2fUtfE0t6hqTDgOeBTWwvX9xrL7C9Zp9TQ9LNtlcptw8F/mZ7/3L/Jtur9jG9pJCTzgpIutH2anM61kW85wk523MMl8C03CMXrpxskiTJOMmm2kmSjBdJN9hevd95JBNDa5wr6Ve231iOzZrs9Tm324BVbT8n6U7gY7Yvaz1me8X+ZphAymu7QtK6wHrAYm3NlgEWBqZWjWt7St3ckiRJ6jBaU+2+JpUkySAzQ9Katq/rdyITiaT5iHrBpWkbR88DC3T/lDSVsjkiaTFi53MQSJPGSUBOOrtjGtGb8wXAi9qOP0bqxZMkmdys19ZU+8uSDiZMhZIkSTqxMbCrpHsJF/6WOmvl/qbVc84kJjI30Ga6Ng/wXeAM4OWSvkqMe7/Y35SCNGmcHKS8tgKSlrJ9b7/zSJIkaQpJ19heW9IMosb8IeA226/tc2pJkgwgpU/jbMzt46N5Wa4paTmix7iAi21X7tyQzHvkTmcXSPqO7b2B70uabbZu+90Tn1WSJEkjdGqqPdf23EuSpB7tk0tJCwHvBT4IvLNfOU0QV0layfat/U5kIpF0DPA924e2Hdu/ZdiTJHMidzq7QNLqtm+Q9OZOj9u+tNPxJEmSyUSpWcqm2kmSjErpU745MdF8B3A68BPbZ/c1sR4j6dfAssA9hLx2npAVS/oT8CDwLdvHl2OVTTSTeY+cdCZJkiQASFqP2c0xju9bQkmSDByS3gZsB7wduAT4EbEDtnQ/85oo5mFZ8Y3ARsBJwB+BvYDrWk62STInUl5bAUnrA/sDSxHvYWuV6zX9zCtJkqQqkk4AlgFuAmaWwwZy0pkkSTvnE+6gG9i+B0DSIf1NaeJoTS4lvZy29lLzALL9GLCFpP2BS4FF+ptSMpnISWc1jgH2IZzLZs7hZ5MkSSYDawArOOUvSZKMzerAtsBFku4GTqFG27jJhqR3AwcDSwAPEBsQdxA9judmzmrdsL2/pOuBfcf4+SQZRvaHrMajts+z/YDth1pf/U4qSZKkBrcBr+h3EkmSDDa2f2X7P2wvQ6i+3ghMk3SepI/1N7sJ4UBgHeC3tl9NuLle2d+UeoekZSWtb3u/EQ89Bny0Hzklk5Os6ayApK8Tq3o/oa1Hk+0b+5ZUkiRJBSSdTchoXwSsClzL8PNaunInSTImkqYAbwO2tf3hfufTSyRdb3sNSTcDb7T9vKRrba/V79x6gaRzgM/bvmXE8TWA/Wxv0Z/MkslGymursXb5vkbbMQOb9CGXJEmSOnyz3wkkSTK5sf08Uet5fr9zmQD+Lmk6Udd6kqQHgOf6nFMvWXrkhBPA9vWSlu5DPskkJXc6kyRJ5mEkLQssbvvKEcc3BP5s+67+ZJYkSTJ4lJ6kTxElatsTZjonza1lVpJ+b3vZbh9LkpHkTmcXSBpZMG2iZ9EVLQe3JEmSScZ3gM93OP5keSylU0mSJAXbT5S2Ka+1fZykBZm7jZSuk/RR20e1H5S0C2GomSTjInc6u0DSyCJqgEWJXlX72z5lglNKkiSphaTbbK84ymO32l5ponNKkmRwkbToWI/bfniicukHkj4KfAxY1PYykl4LHG77LX1OrSdIWhw4A3iWoUnmGsA0YEvbf+1XbsnkIiedDVBOwBfZXq3fuSRJknRDSqeSJOkGSfcQSi8BSwKPlNsvBv5YHF3nWiTdBKwFXGP7jeXYXL9AJ2ljoLVAebvtX/Qzn2TykfLaBrD9sCT1O48kSZIKpHQqSZJx05pUSjocOMv2z8r9zYC39jO3CeIZ28+2hn2SXkBMwudqbF8CXNLvPJLJS046G0DSJsRKX5IkyWRjb+AMSdvTQTrVr6SSJBl41rT98dYd2+dJOrCfCU0Ql0r6PLCApLcBuwFn9zmnJBl4Ul7bBZJuZfbVrEWBvwA72r5z4rNKkiSpT0qnkiTpBknnE21DTiTGRjsAG9p+e18T6zGlJ+kuwKaErPh84GjngDpJxiQnnV1Q3MraMfCQ7Sf6kU+SJEmSJEk/KH4W+wEbEuOhy4AD5nYjIQBJ04DliN/7N7af7XNKSTLw5KQzSZIkSZIkqYSk6bb/0e88JgpJ7wQOB+4idjpfDexq+7y+JpYkA05OOpMkSZIkSZKukLQecDQw3faSklYhJl+79Tm1niLpTuBdtn9f7i8DnGt7uf5mliSDzZR+J5AkSZIkSZJMOr5N9Cl/CMD2zYTUdm7ngdaEs3A38EC/kkmSyUK61yZJkiRJkiRdY/u+ER3jZvYrlwnkdkk/A04lajq3JlpPvQ/A9k/6mVySDCo56UySJEmSJEm65b4isXUx1tkTuKPPOU0E8wP3A28u9/9GdDLYgpiE5qQzSTqQNZ1JkiRJkiRJV0h6GXAI8FbCUOcCYM95wb02SZLuyZrOJEmSJEmSpFteb3t724vbfrntHYDl+51Ur5H0SklnSHpA0v2STpf0yn7nlSSDTk46kyRJkiRJkm753jiPzW0cC5wFLAH8K3B2OZYkyRhkTWeSJEmSJEkyLiStC6wHLCZp37aHFgam9ierCWUx2+2TzB9I2rtfySTJZCF3OpMkSZIkSZLxMg2YTmxcvKjt6zFgqz7mNVE8KGkHSVPL1w6UtjFJkoxOGgklSZIkSZIkXSFpKdv39juPiUbSksD3gXUJt9qrgL3mxfciSbohJ51JkiRJkiTJuJD0Hdt7SzqbmHQNw/a7+5DWhCBpKnBcMU1KkqQLsqYzSZIkSZIkGS8nlO/f7GsWfcD2TEmLSZpm+9l+55Mkk4nc6UySJEmSJEmScSDpCGA1wsH2idZx29/qW1JJMgnInc4kSZIkSZKkKyStD+wPLEWMJwXY9mv6mdcE8JfyNYUwUEqSZBzkTmeSJEmSJEnSFZLuBPYBbgBmto7bTifXJElmI3c6kyRJkiRJkm551PZ5/U5iopF0CZ0NlDbpQzpJMmnISWeSJEmSJEnSLZdI+gbwE+CZ1kHbN/YvpQnhU2235wfeDzzXp1ySZNKQ8tokSZIkSZKkK8qO30g8L+74SbrU9pv7nUeSDDK505kkSZIkSZJ0he2N+51DP5C0aNvdKcDqwCv6lE6STBpy0pkkSZIkSZKMC0n7jjhk4EHgCtv39CGlieYG4ncWIau9B9ilrxklySQgJ51JkiRJkiTJeOnUJmRp4AuS9rd9ygTnM6HYfnW/c0iSyUjWdCZJkiRJkiS1KLLTi2yv1u9ceoGkz9g+qNze2vZpbY99zfbn+5ddkgw+U/qdQJIkSZIkSTK5sf0wITmdW9m27fbnRjz2jolMJEkmIznpTJIkSZIkSWohaRPgkX7n0UM0yu1O95MkGUHWdCZJkiRJkiTjQtKthJFOO4sCfwF2nPiMJgyPcrvT/SRJRpA1nUmSJEmSJMm4kLTUiEMGHrL9RD/ymSgkzQSeIHY1FwCebD0EzG/7hf3KLUkmAznpTJIkSZIkSZIkSXpG1nQmSZIkSZIkSZIkPSMnnUmSJEmSJEmSJEnPyElnkiRJkiRJkiRJ0jNy0pkkSZIMJJJeKelMSb+TdJekQyRNG+dzfylpjZqvv6Ok2yTdLunXkj5VJ94or5EN5ZMkSZK5npx0JkmSJAOHJAE/AX5q+7XA64DpwFc7/Gzt9l+Spo64vxmwN7Cp7TcAqwGP1n2dDuSkM0mSJJnryUlnkiRJMohsAjxt+1gA2zOBfYCPSFpQ0s6STpN0NnCBpAUknSLpFkk/IloaACBpU0lXS7qxPGd6Of4HSV+SdAWw9YjX/xzwKdt/Ka//tO2jyvNWlTSjvNYZkl5Sjs/aXZX0Mkl/KLd3lvQTST8vu7YHleNfBxaQdJOkk3rzNiZJkiRJ/8lJZ5IkSTKIvAG4of2A7ceAPwLLlkPrAjvZ3gT4BPCk7ZWJ3dDVISZ/wBeBt9peDbge2Lct7NO2N7B9yojXX3Hk67dxPPAf5bVuBfYbx++zKrANsBKwjaRX2f4s8JTtVW1vP44YSZIkSTIpqS1JSpIkSZIeIKLp/FjHL7T9cLm9IfBdANu3SLqlHF8HWAG4MhS7TAOubov3o66SkhYBXmz70nLoOOC0cTz1YtuPlhi/BpYC7uvmtZMkSZJkspKTziRJkmQQuR14f/sBSQsDrwLuInYynxjxnNEmqRfa3m6U1xkZo/31Vwd+Md6EgecYUhDNP+KxZ9puzySvv0mSJMk8RMprkyRJkkHkYmBBSTvCLKOfg4Ef2H6yw89fBmxffnZFYOVyfAawvqRly2MLSnrdOF7/v4CDJL2iPG8+SXuW3cpHJL2p/NyHgNau5x8osl5gq3H+nv+U9MJx/mySJEmSTEpy0pkkSZIMHLYNbAlsLel3wG+Bpxnd7fUwYHqR1X4GuLbE+RuwM3ByeWwGsNw4Xv9nwKHARZJuJ+o7W7uTOwHfKPFWBQ4ox78JfELSVcDLxvmrHgnckkZCSZIkydyM4rqeJEmSJEmSJEmSJM2TO51JkiRJkiRJkiRJz8hJZ5IkSZIkSZIkSdIzctKZJEmSJEmSJEmS9IycdCZJkiRJkiRJkiQ9IyedSZIkSZIkSZIkSc/ISWeSJEmSJEmSJEnSM3LSmSRJkiRJkiRJkvSMnHQmSZIkSZIkSZIkPeP/AeqCplt201n4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot for country\n",
    "plotting = countries.sort_values(by = 'total', ascending = False)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.clf()\n",
    "\n",
    "fig = plt.figure(figsize = (15,5))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.bar(plotting.index, plotting['total'], label = 'Revenue Number', color = green)\n",
    "ax.set_ylabel('Revenue (in Million)', color = green)\n",
    "ax.set_xlabel('Order Count')\n",
    "plt.xticks(rotation = 90)\n",
    "ax.legend()\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "plt.plot(plotting.index, plotting['average_per_order'], marker = 'o', linestyle = 'dashed', color = orange, label = 'Average Spend per Order')\n",
    "ax2.set_ylabel('Average Spending', color = orange)\n",
    "\n",
    "lines_1, labels_1 = ax.get_legend_handles_labels()\n",
    "lines_2, labels_2 = ax2.get_legend_handles_labels()\n",
    "lines = lines_1 + lines_2\n",
    "labels = labels_1 + labels_2\n",
    "\n",
    "ax.legend(lines, labels, loc=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef1bba59",
   "metadata": {},
   "source": [
    "United Kingdom contributes the highest to the total revenue, while Singapore and Netherlands has the highest average apend order"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25d32b4c",
   "metadata": {},
   "source": [
    "# Customer Value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c6a4709",
   "metadata": {},
   "source": [
    "Customer value will be determined by the recency, frequency, and monetary value.\n",
    "1. Recency : How recent the last purchase from recent date\n",
    "2. Frequency: Purchase frequency\n",
    "3. Monetary: Total money spent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8613bf5c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>date</th>\n",
       "      <th>recency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346</td>\n",
       "      <td>2011-01-18 10:01:00</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347</td>\n",
       "      <td>2011-12-07 15:52:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348</td>\n",
       "      <td>2011-09-25 13:13:00</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349</td>\n",
       "      <td>2011-11-21 09:51:00</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350</td>\n",
       "      <td>2011-02-02 16:01:00</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4334</th>\n",
       "      <td>18280</td>\n",
       "      <td>2011-03-07 09:52:00</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4335</th>\n",
       "      <td>18281</td>\n",
       "      <td>2011-06-12 10:53:00</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4336</th>\n",
       "      <td>18282</td>\n",
       "      <td>2011-12-02 11:43:00</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4337</th>\n",
       "      <td>18283</td>\n",
       "      <td>2011-12-06 12:02:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4338</th>\n",
       "      <td>18287</td>\n",
       "      <td>2011-10-28 09:29:00</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4339 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customer_id                date  recency\n",
       "0           12346 2011-01-18 10:01:00      325\n",
       "1           12347 2011-12-07 15:52:00        1\n",
       "2           12348 2011-09-25 13:13:00       74\n",
       "3           12349 2011-11-21 09:51:00       18\n",
       "4           12350 2011-02-02 16:01:00      309\n",
       "...           ...                 ...      ...\n",
       "4334        18280 2011-03-07 09:52:00      277\n",
       "4335        18281 2011-06-12 10:53:00      180\n",
       "4336        18282 2011-12-02 11:43:00        7\n",
       "4337        18283 2011-12-06 12:02:00        3\n",
       "4338        18287 2011-10-28 09:29:00       42\n",
       "\n",
       "[4339 rows x 3 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Making recency data\n",
    "recent_date = data.date.max()\n",
    "recency_data = data.groupby('customer_id')['date'].max().reset_index()\n",
    "recency_data['recency'] = recency_data['date'].apply(lambda x: (recent_date - x).days)\n",
    "recency_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "51f8aa9a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>recency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346</th>\n",
       "      <td>1</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347</th>\n",
       "      <td>182</td>\n",
       "      <td>4310.00</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>12348</th>\n",
       "      <td>31</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>74</td>\n",
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       "    <tr>\n",
       "      <th>12349</th>\n",
       "      <td>73</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>18</td>\n",
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       "    <tr>\n",
       "      <th>12350</th>\n",
       "      <td>17</td>\n",
       "      <td>334.40</td>\n",
       "      <td>309</td>\n",
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       "    <tr>\n",
       "      <th>18281</th>\n",
       "      <td>7</td>\n",
       "      <td>80.82</td>\n",
       "      <td>180</td>\n",
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       "    <tr>\n",
       "      <th>18282</th>\n",
       "      <td>12</td>\n",
       "      <td>178.05</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18283</th>\n",
       "      <td>756</td>\n",
       "      <td>2094.88</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>18287</th>\n",
       "      <td>70</td>\n",
       "      <td>1837.28</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4339 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             frequency  monetary  recency\n",
       "customer_id                              \n",
       "12346                1  77183.60      325\n",
       "12347              182   4310.00        1\n",
       "12348               31   1797.24       74\n",
       "12349               73   1757.55       18\n",
       "12350               17    334.40      309\n",
       "...                ...       ...      ...\n",
       "18280               10    180.60      277\n",
       "18281                7     80.82      180\n",
       "18282               12    178.05        7\n",
       "18283              756   2094.88        3\n",
       "18287               70   1837.28       42\n",
       "\n",
       "[4339 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CLV table to store recency, frequency, and monetary value\n",
    "clv = data.groupby('customer_id').agg({'invoice': 'count', 'total': 'sum'}).reset_index()\n",
    "clv = clv.merge(recency_data, on = 'customer_id')\n",
    "clv.drop('date', axis =1, inplace = True)\n",
    "clv.set_index('customer_id', inplace = True)\n",
    "clv = clv.rename(columns = {'invoice':'frequency', 'total':'monetary'})\n",
    "clv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c435d063",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      "text/plain": [
       "             frequency  monetary  recency  rank_f  rank_m  rank_r    norm_f  \\\n",
       "customer_id                                                                   \n",
       "12346                1  77183.60      325    36.5  4330.0   161.0  0.000000   \n",
       "12347              182   4310.00        1  3823.0  4004.0  4203.5  0.880070   \n",
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       "\n",
       "               norm_m    norm_r  \n",
       "customer_id                      \n",
       "12346        0.997925  0.036605  \n",
       "12347        0.922775  0.979133  \n",
       "12348        0.768326  0.384472  \n",
       "12349        0.761872  0.740965  \n",
       "12350        0.283310  0.052810  \n",
       "...               ...       ...  \n",
       "18280        0.129783  0.081371  \n",
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       "18283        0.804057  0.935416  \n",
       "18287        0.773628  0.545465  \n",
       "\n",
       "[4339 rows x 9 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Normalizing ranks for each factor\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "clv['rank_f'] = clv.frequency.rank(ascending = True)\n",
    "clv['rank_m'] = clv.monetary.rank(ascending = True)\n",
    "clv['rank_r'] = clv.recency.rank(ascending = False)\n",
    "\n",
    "cols = ['rank_f', 'rank_m', 'rank_r']\n",
    "new_cols = ['norm_f', 'norm_m', 'norm_r']\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(clv[cols])\n",
    "clv[new_cols]= scaler.transform(clv[cols])\n",
    "clv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c2807ea3",
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "             frequency  monetary  recency  rank_f  rank_m  rank_r    norm_f  \\\n",
       "customer_id                                                                   \n",
       "12346                1  77183.60      325    36.5  4330.0   161.0  0.000000   \n",
       "12347              182   4310.00        1  3823.0  4004.0  4203.5  0.880070   \n",
       "12348               31   1797.24       74  1827.5  3334.0  1653.0  0.416270   \n",
       "12349               73   1757.55       18  2888.5  3306.0  3182.0  0.662870   \n",
       "12350               17    334.40      309  1081.5  1230.0   230.5  0.242882   \n",
       "...                ...       ...      ...     ...     ...     ...       ...   \n",
       "18280               10    180.60      277   622.5   564.0   353.0  0.136200   \n",
       "18281                7     80.82      180   407.0   105.0   859.5  0.086113   \n",
       "18282               12    178.05        7   769.0   547.0  3795.5  0.170250   \n",
       "18283              756   2094.88        3  4310.0  3489.0  4016.0  0.993260   \n",
       "18287               70   1837.28       42  2842.0  3357.0  2343.5  0.652063   \n",
       "\n",
       "               norm_m    norm_r        rfm  \n",
       "customer_id                                 \n",
       "12346        0.997925  0.036605  60.241571  \n",
       "12347        0.922775  0.979133  91.559947  \n",
       "12348        0.768326  0.384472  62.432392  \n",
       "12349        0.761872  0.740965  73.008075  \n",
       "12350        0.283310  0.052810  24.813173  \n",
       "...               ...       ...        ...  \n",
       "18280        0.129783  0.081371  12.686705  \n",
       "18281        0.023974  0.199464   6.016470  \n",
       "18282        0.125864  0.884006  21.499419  \n",
       "18283        0.804057  0.935416  87.395384  \n",
       "18287        0.773628  0.545465  71.434238  \n",
       "\n",
       "[4339 rows x 10 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Determining the weight for each factor\n",
    "weight_r = 0.1\n",
    "weight_f = 0.3\n",
    "weight_m = 0.6\n",
    "\n",
    "#Counting customer value based on the determined weight\n",
    "clv['rfm'] = ((weight_r * clv.norm_r) + (weight_f * clv.norm_f) + (weight_m * clv.norm_m))*100\n",
    "clv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "de9f8c9d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>recency</th>\n",
       "      <th>rank_f</th>\n",
       "      <th>rank_m</th>\n",
       "      <th>rank_r</th>\n",
       "      <th>norm_f</th>\n",
       "      <th>norm_m</th>\n",
       "      <th>norm_r</th>\n",
       "      <th>rfm</th>\n",
       "      <th>segments</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346</th>\n",
       "      <td>1</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>325</td>\n",
       "      <td>36.5</td>\n",
       "      <td>4330.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.997925</td>\n",
       "      <td>0.036605</td>\n",
       "      <td>60.241571</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347</th>\n",
       "      <td>182</td>\n",
       "      <td>4310.00</td>\n",
       "      <td>1</td>\n",
       "      <td>3823.0</td>\n",
       "      <td>4004.0</td>\n",
       "      <td>4203.5</td>\n",
       "      <td>0.880070</td>\n",
       "      <td>0.922775</td>\n",
       "      <td>0.979133</td>\n",
       "      <td>91.559947</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348</th>\n",
       "      <td>31</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>74</td>\n",
       "      <td>1827.5</td>\n",
       "      <td>3334.0</td>\n",
       "      <td>1653.0</td>\n",
       "      <td>0.416270</td>\n",
       "      <td>0.768326</td>\n",
       "      <td>0.384472</td>\n",
       "      <td>62.432392</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349</th>\n",
       "      <td>73</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>18</td>\n",
       "      <td>2888.5</td>\n",
       "      <td>3306.0</td>\n",
       "      <td>3182.0</td>\n",
       "      <td>0.662870</td>\n",
       "      <td>0.761872</td>\n",
       "      <td>0.740965</td>\n",
       "      <td>73.008075</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350</th>\n",
       "      <td>17</td>\n",
       "      <td>334.40</td>\n",
       "      <td>309</td>\n",
       "      <td>1081.5</td>\n",
       "      <td>1230.0</td>\n",
       "      <td>230.5</td>\n",
       "      <td>0.242882</td>\n",
       "      <td>0.283310</td>\n",
       "      <td>0.052810</td>\n",
       "      <td>24.813173</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18280</th>\n",
       "      <td>10</td>\n",
       "      <td>180.60</td>\n",
       "      <td>277</td>\n",
       "      <td>622.5</td>\n",
       "      <td>564.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>0.136200</td>\n",
       "      <td>0.129783</td>\n",
       "      <td>0.081371</td>\n",
       "      <td>12.686705</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18281</th>\n",
       "      <td>7</td>\n",
       "      <td>80.82</td>\n",
       "      <td>180</td>\n",
       "      <td>407.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>859.5</td>\n",
       "      <td>0.086113</td>\n",
       "      <td>0.023974</td>\n",
       "      <td>0.199464</td>\n",
       "      <td>6.016470</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18282</th>\n",
       "      <td>12</td>\n",
       "      <td>178.05</td>\n",
       "      <td>7</td>\n",
       "      <td>769.0</td>\n",
       "      <td>547.0</td>\n",
       "      <td>3795.5</td>\n",
       "      <td>0.170250</td>\n",
       "      <td>0.125864</td>\n",
       "      <td>0.884006</td>\n",
       "      <td>21.499419</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18283</th>\n",
       "      <td>756</td>\n",
       "      <td>2094.88</td>\n",
       "      <td>3</td>\n",
       "      <td>4310.0</td>\n",
       "      <td>3489.0</td>\n",
       "      <td>4016.0</td>\n",
       "      <td>0.993260</td>\n",
       "      <td>0.804057</td>\n",
       "      <td>0.935416</td>\n",
       "      <td>87.395384</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18287</th>\n",
       "      <td>70</td>\n",
       "      <td>1837.28</td>\n",
       "      <td>42</td>\n",
       "      <td>2842.0</td>\n",
       "      <td>3357.0</td>\n",
       "      <td>2343.5</td>\n",
       "      <td>0.652063</td>\n",
       "      <td>0.773628</td>\n",
       "      <td>0.545465</td>\n",
       "      <td>71.434238</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4339 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             frequency  monetary  recency  rank_f  rank_m  rank_r    norm_f  \\\n",
       "customer_id                                                                   \n",
       "12346                1  77183.60      325    36.5  4330.0   161.0  0.000000   \n",
       "12347              182   4310.00        1  3823.0  4004.0  4203.5  0.880070   \n",
       "12348               31   1797.24       74  1827.5  3334.0  1653.0  0.416270   \n",
       "12349               73   1757.55       18  2888.5  3306.0  3182.0  0.662870   \n",
       "12350               17    334.40      309  1081.5  1230.0   230.5  0.242882   \n",
       "...                ...       ...      ...     ...     ...     ...       ...   \n",
       "18280               10    180.60      277   622.5   564.0   353.0  0.136200   \n",
       "18281                7     80.82      180   407.0   105.0   859.5  0.086113   \n",
       "18282               12    178.05        7   769.0   547.0  3795.5  0.170250   \n",
       "18283              756   2094.88        3  4310.0  3489.0  4016.0  0.993260   \n",
       "18287               70   1837.28       42  2842.0  3357.0  2343.5  0.652063   \n",
       "\n",
       "               norm_m    norm_r        rfm segments  \n",
       "customer_id                                          \n",
       "12346        0.997925  0.036605  60.241571   Medium  \n",
       "12347        0.922775  0.979133  91.559947     High  \n",
       "12348        0.768326  0.384472  62.432392   Medium  \n",
       "12349        0.761872  0.740965  73.008075   Medium  \n",
       "12350        0.283310  0.052810  24.813173      Low  \n",
       "...               ...       ...        ...      ...  \n",
       "18280        0.129783  0.081371  12.686705      Low  \n",
       "18281        0.023974  0.199464   6.016470      Low  \n",
       "18282        0.125864  0.884006  21.499419      Low  \n",
       "18283        0.804057  0.935416  87.395384     High  \n",
       "18287        0.773628  0.545465  71.434238   Medium  \n",
       "\n",
       "[4339 rows x 11 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Defining segment name\n",
    "clv['segments'] = np.where(clv['rfm']>75, 'High', np.where(clv['rfm'] <50, 'Low', 'Medium'))\n",
    "clv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "05129e0c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low       2189\n",
       "Medium    1185\n",
       "High       965\n",
       "Name: segments, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Counts for each segment\n",
    "clv.segments.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "abea174a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x29e6741be80>,\n",
       "  <matplotlib.patches.Wedge at 0x29e6741f5b0>,\n",
       "  <matplotlib.patches.Wedge at 0x29e6741fc40>],\n",
       " [Text(-0.023295126772800292, 1.6498355484922247, 'Low'),\n",
       "  Text(-1.043368714860772, -1.2782338302711993, 'Medium'),\n",
       "  Text(1.2633758824141488, -1.0613111606566057, 'High')],\n",
       " [Text(-0.012706432785163792, 0.8999102991775769, '50.4%'),\n",
       "  Text(-0.5691102081058755, -0.6972184528751996, '27.3%'),\n",
       "  Text(0.6891141176804446, -0.5788969967217849, '22.2%')])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting each segment proportion\n",
    "plt.pie(clv.segments.value_counts().values, labels = ['Low','Medium', 'High'],\n",
    "        autopct = '%1.1f%%', radius = 1.5, textprops = {'fontsize' : 15})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92f78425",
   "metadata": {},
   "source": [
    "Low value customer has the highest population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8e80a968",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the central tendency for each factor for each segment\n",
    "plt.figure(figsize = (12,5))\n",
    "\n",
    "column = ['recency', 'frequency', 'monetary']\n",
    "order_graph = ['Low', 'Medium', 'High']\n",
    "for i,ha in zip(column, range(1,4)):\n",
    "    plt.subplot(1,3,ha)\n",
    "    sns.boxplot(data = clv, x = 'segments', y = i, order = order_graph, showfliers = False)\n",
    "    plt.title(str(i).upper())\n",
    "    plt.ylabel('Average number')\n",
    "    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51bd15f1",
   "metadata": {},
   "source": [
    "The high value customer must be the most profitable segment since they have the most recent purchase, highest purchase frequency, and highest monetary value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cd5a0f32",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Revenue Percentage by CLV Segments')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Revenue percentage\n",
    "rev = clv.groupby('segments')['monetary'].sum()/clv.monetary.sum()*100\n",
    "sns.barplot(x= rev.values, y = rev.index, order = ['Low', 'Medium', 'High'])\n",
    "plt.xlabel('Contribution to Revenue (%)')\n",
    "plt.title('Revenue Percentage by CLV Segments')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e23a5e41",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Segments')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data = clv, x = 'segments', y = 'frequency', order = ['Low', 'Medium', 'High'], showfliers = False)\n",
    "plt.title('Purchase Frequency Comparison')\n",
    "plt.ylabel('Average Purchase Frequency')\n",
    "plt.xlabel('Segments')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e96ad731",
   "metadata": {},
   "source": [
    "The high value customer revenue percentage is significantly higher than the rest of the segments"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "025787b9",
   "metadata": {},
   "source": [
    "**Customer Value by Year**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5e10d0d2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>segments</th>\n",
       "      <th>customer_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-12-31</td>\n",
       "      <td>High</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-31</td>\n",
       "      <td>High</td>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-02-28</td>\n",
       "      <td>High</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-03-31</td>\n",
       "      <td>High</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-04-30</td>\n",
       "      <td>High</td>\n",
       "      <td>426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011-05-31</td>\n",
       "      <td>High</td>\n",
       "      <td>526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2011-06-30</td>\n",
       "      <td>High</td>\n",
       "      <td>503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2011-07-31</td>\n",
       "      <td>High</td>\n",
       "      <td>489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2011-08-31</td>\n",
       "      <td>High</td>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2011-09-30</td>\n",
       "      <td>High</td>\n",
       "      <td>586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2011-10-31</td>\n",
       "      <td>High</td>\n",
       "      <td>575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011-11-30</td>\n",
       "      <td>High</td>\n",
       "      <td>703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2011-12-31</td>\n",
       "      <td>High</td>\n",
       "      <td>363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date segments  customer_id\n",
       "0  2010-12-31     High          423\n",
       "1  2011-01-31     High          392\n",
       "2  2011-02-28     High          372\n",
       "3  2011-03-31     High          480\n",
       "4  2011-04-30     High          426\n",
       "5  2011-05-31     High          526\n",
       "6  2011-06-30     High          503\n",
       "7  2011-07-31     High          489\n",
       "8  2011-08-31     High          491\n",
       "9  2011-09-30     High          586\n",
       "10 2011-10-31     High          575\n",
       "11 2011-11-30     High          703\n",
       "12 2011-12-31     High          363"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Table for customer value by year\n",
    "clv_new = clv['segments'].reset_index()\n",
    "data_new = order.copy()\n",
    "cvy = data_new.merge(clv_new, on = 'customer_id', how = 'left')\n",
    "cvy_new = cvy.groupby([pd.Grouper(key = 'date', freq = 'M'), 'segments']).agg({'customer_id': pd.Series.nunique}).reset_index()\n",
    "cvy_high = cvy_new[cvy_new.segments == 'High'].reset_index(drop = True)\n",
    "cvy_high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8bbd0aef",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting high customer value per month\n",
    "cvy_new.date = pd.to_datetime(cvy_new.date)\n",
    "cvy_new.date = cvy_new.date.dt.strftime('%B %Y')\n",
    "sns.lineplot(data = cvy_new, x= 'date', y = cvy_new[cvy_new.segments == 'High']['customer_id'])\n",
    "plt.title('High Value Customers by Month')\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Month')\n",
    "plt.xticks(rotation = 90)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c04cde52",
   "metadata": {},
   "source": [
    "The number of high value customer is decreasing significantly at the end of the year"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "245.026px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
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}