Global-superstore-sales-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Global Superstore sales EDA & Visualization\n",
    "\n",
    "This is my first data analysis project. In this project I will try to dive into a dataset called global-superstore. It is well known dataset for beginner projects. I found this dataset on kaggle. I will be using python libraries such as opendatasets ,Numpy, Pandas, Matplotlib and seaborn for data importing, data wrangling, numerical computation and data visualization. I will try to use the skills I have developed over last few weeks to try to analyze this dataset and draw some inferences. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downloading the Dataset\n",
    "\n",
    "We will use the python library opendatasets for downloading the dataset file from kaggle into our directory. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install jovian opendatasets --upgrade --quiet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's begin by downloading the data, and listing the files within the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# storing the URL of the dataset into dataset_url variable\n",
    "dataset_url = 'https://www.kaggle.com/datasets/shekpaul/global-superstore' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping, found downloaded files in \".\\global-superstore\" (use force=True to force download)\n"
     ]
    }
   ],
   "source": [
    "import opendatasets as od\n",
    "od.download(dataset_url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has been downloaded and extracted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# storing the location of data in data_dir variable\n",
    "data_dir = './global-superstore'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Global Superstore.xls']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.listdir(data_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us save and upload our work to Jovian before continuing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "project_name = \"global-superstore-sales-analysis-visualization\" "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation and Cleaning\n",
    "\n",
    "importing the libraries such as numpy and pandas, provided by python for numerical computation and data manipulation.\n",
    "using these libraries we will deal with missing values, inconsistent data, duplicated values, wrong datatypes. We will parse date columns using pandas. \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using the read_excel function of pandas to read the data into a pandas dataframe. \n",
    "#using Parse_dates argument to tell pandas which columns contain dates\n",
    "data = pd.read_excel(data_dir + '/Global Superstore.xls', sheet_name='Orders', parse_dates=['Order Date', 'Ship Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>...</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Shipping Cost</th>\n",
       "      <th>Order Priority</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32298</td>\n",
       "      <td>CA-2012-124891</td>\n",
       "      <td>2012-07-31</td>\n",
       "      <td>2012-07-31</td>\n",
       "      <td>Same Day</td>\n",
       "      <td>RH-19495</td>\n",
       "      <td>Rick Hansen</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>New York City</td>\n",
       "      <td>New York</td>\n",
       "      <td>...</td>\n",
       "      <td>TEC-AC-10003033</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Plantronics CS510 - Over-the-Head monaural Wir...</td>\n",
       "      <td>2309.650</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>762.1845</td>\n",
       "      <td>933.57</td>\n",
       "      <td>Critical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26341</td>\n",
       "      <td>IN-2013-77878</td>\n",
       "      <td>2013-02-05</td>\n",
       "      <td>2013-02-07</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>JR-16210</td>\n",
       "      <td>Justin Ritter</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Wollongong</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>...</td>\n",
       "      <td>FUR-CH-10003950</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Novimex Executive Leather Armchair, Black</td>\n",
       "      <td>3709.395</td>\n",
       "      <td>9</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-288.7650</td>\n",
       "      <td>923.63</td>\n",
       "      <td>Critical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25330</td>\n",
       "      <td>IN-2013-71249</td>\n",
       "      <td>2013-10-17</td>\n",
       "      <td>2013-10-18</td>\n",
       "      <td>First Class</td>\n",
       "      <td>CR-12730</td>\n",
       "      <td>Craig Reiter</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Brisbane</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>...</td>\n",
       "      <td>TEC-PH-10004664</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>Nokia Smart Phone, with Caller ID</td>\n",
       "      <td>5175.171</td>\n",
       "      <td>9</td>\n",
       "      <td>0.1</td>\n",
       "      <td>919.9710</td>\n",
       "      <td>915.49</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13524</td>\n",
       "      <td>ES-2013-1579342</td>\n",
       "      <td>2013-01-28</td>\n",
       "      <td>2013-01-30</td>\n",
       "      <td>First Class</td>\n",
       "      <td>KM-16375</td>\n",
       "      <td>Katherine Murray</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>...</td>\n",
       "      <td>TEC-PH-10004583</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>Motorola Smart Phone, Cordless</td>\n",
       "      <td>2892.510</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-96.5400</td>\n",
       "      <td>910.16</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47221</td>\n",
       "      <td>SG-2013-4320</td>\n",
       "      <td>2013-11-05</td>\n",
       "      <td>2013-11-06</td>\n",
       "      <td>Same Day</td>\n",
       "      <td>RH-9495</td>\n",
       "      <td>Rick Hansen</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Dakar</td>\n",
       "      <td>Dakar</td>\n",
       "      <td>...</td>\n",
       "      <td>TEC-SHA-10000501</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Copiers</td>\n",
       "      <td>Sharp Wireless Fax, High-Speed</td>\n",
       "      <td>2832.960</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>311.5200</td>\n",
       "      <td>903.04</td>\n",
       "      <td>Critical</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Row ID         Order ID Order Date  Ship Date     Ship Mode Customer ID  \\\n",
       "0   32298   CA-2012-124891 2012-07-31 2012-07-31      Same Day    RH-19495   \n",
       "1   26341    IN-2013-77878 2013-02-05 2013-02-07  Second Class    JR-16210   \n",
       "2   25330    IN-2013-71249 2013-10-17 2013-10-18   First Class    CR-12730   \n",
       "3   13524  ES-2013-1579342 2013-01-28 2013-01-30   First Class    KM-16375   \n",
       "4   47221     SG-2013-4320 2013-11-05 2013-11-06      Same Day     RH-9495   \n",
       "\n",
       "      Customer Name      Segment           City            State  ...  \\\n",
       "0       Rick Hansen     Consumer  New York City         New York  ...   \n",
       "1     Justin Ritter    Corporate     Wollongong  New South Wales  ...   \n",
       "2      Craig Reiter     Consumer       Brisbane       Queensland  ...   \n",
       "3  Katherine Murray  Home Office         Berlin           Berlin  ...   \n",
       "4       Rick Hansen     Consumer          Dakar            Dakar  ...   \n",
       "\n",
       "         Product ID    Category Sub-Category  \\\n",
       "0   TEC-AC-10003033  Technology  Accessories   \n",
       "1   FUR-CH-10003950   Furniture       Chairs   \n",
       "2   TEC-PH-10004664  Technology       Phones   \n",
       "3   TEC-PH-10004583  Technology       Phones   \n",
       "4  TEC-SHA-10000501  Technology      Copiers   \n",
       "\n",
       "                                        Product Name     Sales Quantity  \\\n",
       "0  Plantronics CS510 - Over-the-Head monaural Wir...  2309.650        7   \n",
       "1          Novimex Executive Leather Armchair, Black  3709.395        9   \n",
       "2                  Nokia Smart Phone, with Caller ID  5175.171        9   \n",
       "3                     Motorola Smart Phone, Cordless  2892.510        5   \n",
       "4                     Sharp Wireless Fax, High-Speed  2832.960        8   \n",
       "\n",
       "  Discount    Profit  Shipping Cost  Order Priority  \n",
       "0      0.0  762.1845         933.57        Critical  \n",
       "1      0.1 -288.7650         923.63        Critical  \n",
       "2      0.1  919.9710         915.49          Medium  \n",
       "3      0.1  -96.5400         910.16          Medium  \n",
       "4      0.0  311.5200         903.04        Critical  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this will show first few rows of the dataframe\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has been succefully imported into the dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51290, 24)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this shows number of rows and columns in dataframe\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see our dataset has 24 columns and 51290 rows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Row ID', 'Order ID', 'Order Date', 'Ship Date', 'Ship Mode',\n",
       "       'Customer ID', 'Customer Name', 'Segment', 'City', 'State', 'Country',\n",
       "       'Postal Code', 'Market', 'Region', 'Product ID', 'Category',\n",
       "       'Sub-Category', 'Product Name', 'Sales', 'Quantity', 'Discount',\n",
       "       'Profit', 'Shipping Cost', 'Order Priority'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this shows various different columns in dataframe\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the various columns from the dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " .info method of a dataframe returns the value counts of the various dataframe columns as well as their datatypes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 51290 entries, 0 to 51289\n",
      "Data columns (total 24 columns):\n",
      " #   Column          Non-Null Count  Dtype         \n",
      "---  ------          --------------  -----         \n",
      " 0   Row ID          51290 non-null  int64         \n",
      " 1   Order ID        51290 non-null  object        \n",
      " 2   Order Date      51290 non-null  datetime64[ns]\n",
      " 3   Ship Date       51290 non-null  datetime64[ns]\n",
      " 4   Ship Mode       51290 non-null  object        \n",
      " 5   Customer ID     51290 non-null  object        \n",
      " 6   Customer Name   51290 non-null  object        \n",
      " 7   Segment         51290 non-null  object        \n",
      " 8   City            51290 non-null  object        \n",
      " 9   State           51290 non-null  object        \n",
      " 10  Country         51290 non-null  object        \n",
      " 11  Postal Code     9994 non-null   float64       \n",
      " 12  Market          51290 non-null  object        \n",
      " 13  Region          51290 non-null  object        \n",
      " 14  Product ID      51290 non-null  object        \n",
      " 15  Category        51290 non-null  object        \n",
      " 16  Sub-Category    51290 non-null  object        \n",
      " 17  Product Name    51290 non-null  object        \n",
      " 18  Sales           51290 non-null  float64       \n",
      " 19  Quantity        51290 non-null  int64         \n",
      " 20  Discount        51290 non-null  float64       \n",
      " 21  Profit          51290 non-null  float64       \n",
      " 22  Shipping Cost   51290 non-null  float64       \n",
      " 23  Order Priority  51290 non-null  object        \n",
      "dtypes: datetime64[ns](2), float64(5), int64(2), object(15)\n",
      "memory usage: 9.4+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row ID            False\n",
       "Order ID          False\n",
       "Order Date        False\n",
       "Ship Date         False\n",
       "Ship Mode         False\n",
       "Customer ID       False\n",
       "Customer Name     False\n",
       "Segment           False\n",
       "City              False\n",
       "State             False\n",
       "Country           False\n",
       "Postal Code        True\n",
       "Market            False\n",
       "Region            False\n",
       "Product ID        False\n",
       "Category          False\n",
       "Sub-Category      False\n",
       "Product Name      False\n",
       "Sales             False\n",
       "Quantity          False\n",
       "Discount          False\n",
       "Profit            False\n",
       "Shipping Cost     False\n",
       "Order Priority    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking to see if there are any missing values in the dataset. \n",
    "data.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row ID                0\n",
       "Order ID              0\n",
       "Order Date            0\n",
       "Ship Date             0\n",
       "Ship Mode             0\n",
       "Customer ID           0\n",
       "Customer Name         0\n",
       "Segment               0\n",
       "City                  0\n",
       "State                 0\n",
       "Country               0\n",
       "Postal Code       41296\n",
       "Market                0\n",
       "Region                0\n",
       "Product ID            0\n",
       "Category              0\n",
       "Sub-Category          0\n",
       "Product Name          0\n",
       "Sales                 0\n",
       "Quantity              0\n",
       "Discount              0\n",
       "Profit                0\n",
       "Shipping Cost         0\n",
       "Order Priority        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting count of missing values in each column\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .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>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>...</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Shipping Cost</th>\n",
       "      <th>Order Priority</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Row ID, Order ID, Order Date, Ship Date, Ship Mode, Customer ID, Customer Name, Segment, City, State, Country, Postal Code, Market, Region, Product ID, Category, Sub-Category, Product Name, Sales, Quantity, Discount, Profit, Shipping Cost, Order Priority]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 24 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking to see if there are any duplicated rows in dataset\n",
    "data[data.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset does not have any duplicated rows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>...</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Shipping Cost</th>\n",
       "      <th>Order Priority</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32298</td>\n",
       "      <td>CA-2012-124891</td>\n",
       "      <td>2012-07-31</td>\n",
       "      <td>2012-07-31</td>\n",
       "      <td>Same Day</td>\n",
       "      <td>RH-19495</td>\n",
       "      <td>Rick Hansen</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>New York City</td>\n",
       "      <td>New York</td>\n",
       "      <td>...</td>\n",
       "      <td>TEC-AC-10003033</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Plantronics CS510 - Over-the-Head monaural Wir...</td>\n",
       "      <td>2309.650</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>762.1845</td>\n",
       "      <td>933.570</td>\n",
       "      <td>Critical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26341</td>\n",
       "      <td>IN-2013-77878</td>\n",
       "      <td>2013-02-05</td>\n",
       "      <td>2013-02-07</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>JR-16210</td>\n",
       "      <td>Justin Ritter</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Wollongong</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>...</td>\n",
       "      <td>FUR-CH-10003950</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Novimex Executive Leather Armchair, Black</td>\n",
       "      <td>3709.395</td>\n",
       "      <td>9</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-288.7650</td>\n",
       "      <td>923.630</td>\n",
       "      <td>Critical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25330</td>\n",
       "      <td>IN-2013-71249</td>\n",
       "      <td>2013-10-17</td>\n",
       "      <td>2013-10-18</td>\n",
       "      <td>First Class</td>\n",
       "      <td>CR-12730</td>\n",
       "      <td>Craig Reiter</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Brisbane</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>...</td>\n",
       "      <td>TEC-PH-10004664</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>Nokia Smart Phone, with Caller ID</td>\n",
       "      <td>5175.171</td>\n",
       "      <td>9</td>\n",
       "      <td>0.1</td>\n",
       "      <td>919.9710</td>\n",
       "      <td>915.490</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13524</td>\n",
       "      <td>ES-2013-1579342</td>\n",
       "      <td>2013-01-28</td>\n",
       "      <td>2013-01-30</td>\n",
       "      <td>First Class</td>\n",
       "      <td>KM-16375</td>\n",
       "      <td>Katherine Murray</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>...</td>\n",
       "      <td>TEC-PH-10004583</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>Motorola Smart Phone, Cordless</td>\n",
       "      <td>2892.510</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-96.5400</td>\n",
       "      <td>910.160</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47221</td>\n",
       "      <td>SG-2013-4320</td>\n",
       "      <td>2013-11-05</td>\n",
       "      <td>2013-11-06</td>\n",
       "      <td>Same Day</td>\n",
       "      <td>RH-9495</td>\n",
       "      <td>Rick Hansen</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Dakar</td>\n",
       "      <td>Dakar</td>\n",
       "      <td>...</td>\n",
       "      <td>TEC-SHA-10000501</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Copiers</td>\n",
       "      <td>Sharp Wireless Fax, High-Speed</td>\n",
       "      <td>2832.960</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>311.5200</td>\n",
       "      <td>903.040</td>\n",
       "      <td>Critical</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",
       "      <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>51285</th>\n",
       "      <td>29002</td>\n",
       "      <td>IN-2014-62366</td>\n",
       "      <td>2014-06-19</td>\n",
       "      <td>2014-06-19</td>\n",
       "      <td>Same Day</td>\n",
       "      <td>KE-16420</td>\n",
       "      <td>Katrina Edelman</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Kure</td>\n",
       "      <td>Hiroshima</td>\n",
       "      <td>...</td>\n",
       "      <td>OFF-FA-10000746</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Fasteners</td>\n",
       "      <td>Advantus Thumb Tacks, 12 Pack</td>\n",
       "      <td>65.100</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5000</td>\n",
       "      <td>0.010</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51286</th>\n",
       "      <td>35398</td>\n",
       "      <td>US-2014-102288</td>\n",
       "      <td>2014-06-20</td>\n",
       "      <td>2014-06-24</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>ZC-21910</td>\n",
       "      <td>Zuschuss Carroll</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "      <td>...</td>\n",
       "      <td>OFF-AP-10002906</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>Hoover Replacement Belt for Commercial Guardsm...</td>\n",
       "      <td>0.444</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>-1.1100</td>\n",
       "      <td>0.010</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51287</th>\n",
       "      <td>40470</td>\n",
       "      <td>US-2013-155768</td>\n",
       "      <td>2013-12-02</td>\n",
       "      <td>2013-12-02</td>\n",
       "      <td>Same Day</td>\n",
       "      <td>LB-16795</td>\n",
       "      <td>Laurel Beltran</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>Oxnard</td>\n",
       "      <td>California</td>\n",
       "      <td>...</td>\n",
       "      <td>OFF-EN-10001219</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Envelopes</td>\n",
       "      <td>#10- 4 1/8\" x 9 1/2\" Security-Tint Envelopes</td>\n",
       "      <td>22.920</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.2308</td>\n",
       "      <td>0.010</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51288</th>\n",
       "      <td>9596</td>\n",
       "      <td>MX-2012-140767</td>\n",
       "      <td>2012-02-18</td>\n",
       "      <td>2012-02-22</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>RB-19795</td>\n",
       "      <td>Ross Baird</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>Valinhos</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>...</td>\n",
       "      <td>OFF-BI-10000806</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Acco Index Tab, Economy</td>\n",
       "      <td>13.440</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.4000</td>\n",
       "      <td>0.003</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51289</th>\n",
       "      <td>6147</td>\n",
       "      <td>MX-2012-134460</td>\n",
       "      <td>2012-05-22</td>\n",
       "      <td>2012-05-26</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>MC-18100</td>\n",
       "      <td>Mick Crebagga</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Tipitapa</td>\n",
       "      <td>Managua</td>\n",
       "      <td>...</td>\n",
       "      <td>OFF-PA-10004155</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Eaton Computer Printout Paper, 8.5 x 11</td>\n",
       "      <td>61.380</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51290 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Row ID         Order ID Order Date  Ship Date       Ship Mode  \\\n",
       "0       32298   CA-2012-124891 2012-07-31 2012-07-31        Same Day   \n",
       "1       26341    IN-2013-77878 2013-02-05 2013-02-07    Second Class   \n",
       "2       25330    IN-2013-71249 2013-10-17 2013-10-18     First Class   \n",
       "3       13524  ES-2013-1579342 2013-01-28 2013-01-30     First Class   \n",
       "4       47221     SG-2013-4320 2013-11-05 2013-11-06        Same Day   \n",
       "...       ...              ...        ...        ...             ...   \n",
       "51285   29002    IN-2014-62366 2014-06-19 2014-06-19        Same Day   \n",
       "51286   35398   US-2014-102288 2014-06-20 2014-06-24  Standard Class   \n",
       "51287   40470   US-2013-155768 2013-12-02 2013-12-02        Same Day   \n",
       "51288    9596   MX-2012-140767 2012-02-18 2012-02-22  Standard Class   \n",
       "51289    6147   MX-2012-134460 2012-05-22 2012-05-26    Second Class   \n",
       "\n",
       "      Customer ID     Customer Name      Segment           City  \\\n",
       "0        RH-19495       Rick Hansen     Consumer  New York City   \n",
       "1        JR-16210     Justin Ritter    Corporate     Wollongong   \n",
       "2        CR-12730      Craig Reiter     Consumer       Brisbane   \n",
       "3        KM-16375  Katherine Murray  Home Office         Berlin   \n",
       "4         RH-9495       Rick Hansen     Consumer          Dakar   \n",
       "...           ...               ...          ...            ...   \n",
       "51285    KE-16420   Katrina Edelman    Corporate           Kure   \n",
       "51286    ZC-21910  Zuschuss Carroll     Consumer        Houston   \n",
       "51287    LB-16795    Laurel Beltran  Home Office         Oxnard   \n",
       "51288    RB-19795        Ross Baird  Home Office       Valinhos   \n",
       "51289    MC-18100     Mick Crebagga     Consumer       Tipitapa   \n",
       "\n",
       "                 State  ...        Product ID         Category Sub-Category  \\\n",
       "0             New York  ...   TEC-AC-10003033       Technology  Accessories   \n",
       "1      New South Wales  ...   FUR-CH-10003950        Furniture       Chairs   \n",
       "2           Queensland  ...   TEC-PH-10004664       Technology       Phones   \n",
       "3               Berlin  ...   TEC-PH-10004583       Technology       Phones   \n",
       "4                Dakar  ...  TEC-SHA-10000501       Technology      Copiers   \n",
       "...                ...  ...               ...              ...          ...   \n",
       "51285        Hiroshima  ...   OFF-FA-10000746  Office Supplies    Fasteners   \n",
       "51286            Texas  ...   OFF-AP-10002906  Office Supplies   Appliances   \n",
       "51287       California  ...   OFF-EN-10001219  Office Supplies    Envelopes   \n",
       "51288        São Paulo  ...   OFF-BI-10000806  Office Supplies      Binders   \n",
       "51289          Managua  ...   OFF-PA-10004155  Office Supplies        Paper   \n",
       "\n",
       "                                            Product Name     Sales Quantity  \\\n",
       "0      Plantronics CS510 - Over-the-Head monaural Wir...  2309.650        7   \n",
       "1              Novimex Executive Leather Armchair, Black  3709.395        9   \n",
       "2                      Nokia Smart Phone, with Caller ID  5175.171        9   \n",
       "3                         Motorola Smart Phone, Cordless  2892.510        5   \n",
       "4                         Sharp Wireless Fax, High-Speed  2832.960        8   \n",
       "...                                                  ...       ...      ...   \n",
       "51285                      Advantus Thumb Tacks, 12 Pack    65.100        5   \n",
       "51286  Hoover Replacement Belt for Commercial Guardsm...     0.444        1   \n",
       "51287       #10- 4 1/8\" x 9 1/2\" Security-Tint Envelopes    22.920        3   \n",
       "51288                            Acco Index Tab, Economy    13.440        2   \n",
       "51289            Eaton Computer Printout Paper, 8.5 x 11    61.380        3   \n",
       "\n",
       "      Discount    Profit  Shipping Cost  Order Priority  \n",
       "0          0.0  762.1845        933.570        Critical  \n",
       "1          0.1 -288.7650        923.630        Critical  \n",
       "2          0.1  919.9710        915.490          Medium  \n",
       "3          0.1  -96.5400        910.160          Medium  \n",
       "4          0.0  311.5200        903.040        Critical  \n",
       "...        ...       ...            ...             ...  \n",
       "51285      0.0    4.5000          0.010          Medium  \n",
       "51286      0.8   -1.1100          0.010          Medium  \n",
       "51287      0.0   11.2308          0.010            High  \n",
       "51288      0.0    2.4000          0.003          Medium  \n",
       "51289      0.0    1.8000          0.002            High  \n",
       "\n",
       "[51290 rows x 23 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping the postal code column\n",
    "data.drop('Postal Code', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2012\n",
       "1        2013\n",
       "2        2013\n",
       "3        2013\n",
       "4        2013\n",
       "         ... \n",
       "51285    2014\n",
       "51286    2014\n",
       "51287    2013\n",
       "51288    2012\n",
       "51289    2012\n",
       "Name: Order Year, Length: 51290, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#extracting the year of every order and adding it to the new column in dataframe as \"Order Year\"\n",
    "data['Order Year'] = data['Order Date'].dt.year\n",
    "data['Order Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Row ID', 'Order ID', 'Order Date', 'Ship Date', 'Ship Mode',\n",
       "       'Customer ID', 'Customer Name', 'Segment', 'City', 'State', 'Country',\n",
       "       'Postal Code', 'Market', 'Region', 'Product ID', 'Category',\n",
       "       'Sub-Category', 'Product Name', 'Sales', 'Quantity', 'Discount',\n",
       "       'Profit', 'Shipping Cost', 'Order Priority', 'Order Year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extracting the year in which every order was placed from the Order Date column and adding it to the new column \"Order year\"\n",
    "this will later help us when grouping the various columns on the year basis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0\n",
       "1        2\n",
       "2        1\n",
       "3        2\n",
       "4        1\n",
       "        ..\n",
       "51285    0\n",
       "51286    4\n",
       "51287    0\n",
       "51288    4\n",
       "51289    4\n",
       "Name: Shipping duration, Length: 51290, dtype: int64"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#subtracting ship date from order date so we can see how much time it takes for order to ship. \n",
    "data['Shipping duration'] = (data['Ship Date'] - data['Order Date']).dt.days\n",
    "data['Shipping duration']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have the order date and ship data. so using this we computed how many days it took for the order to get shipped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "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>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "      <th>...</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Shipping Cost</th>\n",
       "      <th>Order Priority</th>\n",
       "      <th>Order Year</th>\n",
       "      <th>Shipping duration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</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",
       "      <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>2011-01-01</th>\n",
       "      <td>48883</td>\n",
       "      <td>HU-2011-1220</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>AT-735</td>\n",
       "      <td>Annie Thurman</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Budapest</td>\n",
       "      <td>Budapest</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>...</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Tenex Box, Single Width</td>\n",
       "      <td>66.1200</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.6400</td>\n",
       "      <td>8.170</td>\n",
       "      <td>High</td>\n",
       "      <td>2011</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>22253</td>\n",
       "      <td>IN-2011-47883</td>\n",
       "      <td>2011-01-08</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>JH-15985</td>\n",
       "      <td>Joseph Holt</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Wagga Wagga</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>...</td>\n",
       "      <td>Supplies</td>\n",
       "      <td>Acme Trimmer, High Speed</td>\n",
       "      <td>120.3660</td>\n",
       "      <td>3</td>\n",
       "      <td>0.10</td>\n",
       "      <td>36.0360</td>\n",
       "      <td>9.720</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2011</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>42433</td>\n",
       "      <td>AG-2011-2040</td>\n",
       "      <td>2011-01-06</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>TB-11280</td>\n",
       "      <td>Toby Braunhardt</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Constantine</td>\n",
       "      <td>Constantine</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>...</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Tenex Lockers, Blue</td>\n",
       "      <td>408.3000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>106.1400</td>\n",
       "      <td>35.460</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2011</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>22254</td>\n",
       "      <td>IN-2011-47883</td>\n",
       "      <td>2011-01-08</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>JH-15985</td>\n",
       "      <td>Joseph Holt</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Wagga Wagga</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>...</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Eaton Computer Printout Paper, 8.5 x 11</td>\n",
       "      <td>55.2420</td>\n",
       "      <td>2</td>\n",
       "      <td>0.10</td>\n",
       "      <td>15.3420</td>\n",
       "      <td>1.800</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2011</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>11731</td>\n",
       "      <td>IT-2011-3647632</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>EM-14140</td>\n",
       "      <td>Eugene Moren</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>...</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Enermax Note Cards, Premium</td>\n",
       "      <td>44.8650</td>\n",
       "      <td>3</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-26.0550</td>\n",
       "      <td>4.820</td>\n",
       "      <td>High</td>\n",
       "      <td>2011</td>\n",
       "      <td>4</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",
       "      <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>2014-12-31</th>\n",
       "      <td>8857</td>\n",
       "      <td>MX-2014-110527</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CM-12190</td>\n",
       "      <td>Charlotte Melton</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Managua</td>\n",
       "      <td>Managua</td>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>...</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Hon Color Coded Labels, 5000 Label Set</td>\n",
       "      <td>26.4000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.3600</td>\n",
       "      <td>0.350</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2014</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>26333</td>\n",
       "      <td>IN-2014-30390</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>First Class</td>\n",
       "      <td>JD-16150</td>\n",
       "      <td>Justin Deggeller</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>...</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Lesro Training Table, Adjustable Height</td>\n",
       "      <td>1048.7313</td>\n",
       "      <td>9</td>\n",
       "      <td>0.57</td>\n",
       "      <td>-1195.2387</td>\n",
       "      <td>86.860</td>\n",
       "      <td>High</td>\n",
       "      <td>2014</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>50446</td>\n",
       "      <td>TZ-2014-8350</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>First Class</td>\n",
       "      <td>JA-5970</td>\n",
       "      <td>Joseph Airdo</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Dar es Salaam</td>\n",
       "      <td>Dar Es Salaam</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>...</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Fellowes Folders, Wire Frame</td>\n",
       "      <td>22.9230</td>\n",
       "      <td>1</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4.0530</td>\n",
       "      <td>6.110</td>\n",
       "      <td>High</td>\n",
       "      <td>2014</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>8856</td>\n",
       "      <td>MX-2014-110527</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CM-12190</td>\n",
       "      <td>Charlotte Melton</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Managua</td>\n",
       "      <td>Managua</td>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>...</td>\n",
       "      <td>Supplies</td>\n",
       "      <td>Kleencut Ruler, High Speed</td>\n",
       "      <td>72.3200</td>\n",
       "      <td>8</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.4000</td>\n",
       "      <td>5.639</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2014</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>41743</td>\n",
       "      <td>TU-2014-5170</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>VD-11670</td>\n",
       "      <td>Valerie Dominguez</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Konya</td>\n",
       "      <td>Konya</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>...</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>SanDisk Keyboard, Bluetooth</td>\n",
       "      <td>32.5920</td>\n",
       "      <td>1</td>\n",
       "      <td>0.60</td>\n",
       "      <td>-25.2780</td>\n",
       "      <td>3.400</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2014</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51290 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Row ID         Order ID  Ship Date       Ship Mode Customer ID  \\\n",
       "Order Date                                                                   \n",
       "2011-01-01   48883     HU-2011-1220 2011-01-05    Second Class      AT-735   \n",
       "2011-01-01   22253    IN-2011-47883 2011-01-08  Standard Class    JH-15985   \n",
       "2011-01-01   42433     AG-2011-2040 2011-01-06  Standard Class    TB-11280   \n",
       "2011-01-01   22254    IN-2011-47883 2011-01-08  Standard Class    JH-15985   \n",
       "2011-01-01   11731  IT-2011-3647632 2011-01-05    Second Class    EM-14140   \n",
       "...            ...              ...        ...             ...         ...   \n",
       "2014-12-31    8857   MX-2014-110527 2015-01-02    Second Class    CM-12190   \n",
       "2014-12-31   26333    IN-2014-30390 2015-01-03     First Class    JD-16150   \n",
       "2014-12-31   50446     TZ-2014-8350 2015-01-03     First Class     JA-5970   \n",
       "2014-12-31    8856   MX-2014-110527 2015-01-02    Second Class    CM-12190   \n",
       "2014-12-31   41743     TU-2014-5170 2015-01-04    Second Class    VD-11670   \n",
       "\n",
       "                Customer Name      Segment           City            State  \\\n",
       "Order Date                                                                   \n",
       "2011-01-01      Annie Thurman     Consumer       Budapest         Budapest   \n",
       "2011-01-01        Joseph Holt     Consumer    Wagga Wagga  New South Wales   \n",
       "2011-01-01    Toby Braunhardt     Consumer    Constantine      Constantine   \n",
       "2011-01-01        Joseph Holt     Consumer    Wagga Wagga  New South Wales   \n",
       "2011-01-01       Eugene Moren  Home Office      Stockholm        Stockholm   \n",
       "...                       ...          ...            ...              ...   \n",
       "2014-12-31   Charlotte Melton     Consumer        Managua          Managua   \n",
       "2014-12-31   Justin Deggeller    Corporate        Bangkok          Bangkok   \n",
       "2014-12-31       Joseph Airdo     Consumer  Dar es Salaam    Dar Es Salaam   \n",
       "2014-12-31   Charlotte Melton     Consumer        Managua          Managua   \n",
       "2014-12-31  Valerie Dominguez     Consumer          Konya            Konya   \n",
       "\n",
       "              Country  ...  Sub-Category  \\\n",
       "Order Date             ...                 \n",
       "2011-01-01    Hungary  ...       Storage   \n",
       "2011-01-01  Australia  ...      Supplies   \n",
       "2011-01-01    Algeria  ...       Storage   \n",
       "2011-01-01  Australia  ...         Paper   \n",
       "2011-01-01     Sweden  ...         Paper   \n",
       "...               ...  ...           ...   \n",
       "2014-12-31  Nicaragua  ...        Labels   \n",
       "2014-12-31   Thailand  ...        Tables   \n",
       "2014-12-31   Tanzania  ...       Storage   \n",
       "2014-12-31  Nicaragua  ...      Supplies   \n",
       "2014-12-31     Turkey  ...   Accessories   \n",
       "\n",
       "                                       Product Name      Sales Quantity  \\\n",
       "Order Date                                                                \n",
       "2011-01-01                  Tenex Box, Single Width    66.1200        4   \n",
       "2011-01-01                 Acme Trimmer, High Speed   120.3660        3   \n",
       "2011-01-01                      Tenex Lockers, Blue   408.3000        2   \n",
       "2011-01-01  Eaton Computer Printout Paper, 8.5 x 11    55.2420        2   \n",
       "2011-01-01              Enermax Note Cards, Premium    44.8650        3   \n",
       "...                                             ...        ...      ...   \n",
       "2014-12-31   Hon Color Coded Labels, 5000 Label Set    26.4000        3   \n",
       "2014-12-31  Lesro Training Table, Adjustable Height  1048.7313        9   \n",
       "2014-12-31             Fellowes Folders, Wire Frame    22.9230        1   \n",
       "2014-12-31               Kleencut Ruler, High Speed    72.3200        8   \n",
       "2014-12-31              SanDisk Keyboard, Bluetooth    32.5920        1   \n",
       "\n",
       "           Discount     Profit Shipping Cost  Order Priority  Order Year  \\\n",
       "Order Date                                                                 \n",
       "2011-01-01     0.00    29.6400         8.170            High        2011   \n",
       "2011-01-01     0.10    36.0360         9.720          Medium        2011   \n",
       "2011-01-01     0.00   106.1400        35.460          Medium        2011   \n",
       "2011-01-01     0.10    15.3420         1.800          Medium        2011   \n",
       "2011-01-01     0.50   -26.0550         4.820            High        2011   \n",
       "...             ...        ...           ...             ...         ...   \n",
       "2014-12-31     0.00    12.3600         0.350          Medium        2014   \n",
       "2014-12-31     0.57 -1195.2387        86.860            High        2014   \n",
       "2014-12-31     0.10     4.0530         6.110            High        2014   \n",
       "2014-12-31     0.00     6.4000         5.639          Medium        2014   \n",
       "2014-12-31     0.60   -25.2780         3.400          Medium        2014   \n",
       "\n",
       "            Shipping duration  \n",
       "Order Date                     \n",
       "2011-01-01                  4  \n",
       "2011-01-01                  7  \n",
       "2011-01-01                  5  \n",
       "2011-01-01                  7  \n",
       "2011-01-01                  4  \n",
       "...                       ...  \n",
       "2014-12-31                  2  \n",
       "2014-12-31                  3  \n",
       "2014-12-31                  3  \n",
       "2014-12-31                  2  \n",
       "2014-12-31                  4  \n",
       "\n",
       "[51290 rows x 25 columns]"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#setting date as the index of the dataframe with set_index() method and sorting the dates with sort_index() method\n",
    "data_sorted = data.set_index('Order Date').sort_index()\n",
    "data_sorted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the set_index method I set Order Date column as the index the dataframe. \n",
    "Then using the sort_index method sorted the Order Dates From earliest to latest. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset if cleaned, validated and ready to be explored. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Analysis and Visualization\n",
    "\n",
    "Now we will start exploring the data. We will do statistical as well as visual exploration of the data and try to find some pattern, trends and insights. I will be using the python graphing libaries matplotlib and seaborn for creating some visuals from the data. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's begin by importing`matplotlib.pyplot` and `seaborn`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "sns.set_style('darkgrid')\n",
    "matplotlib.rcParams['font.size'] = 14\n",
    "matplotlib.rcParams['figure.figsize'] = (9, 5)\n",
    "matplotlib.rcParams['figure.facecolor'] = '#00000000'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's determine the time range of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2011-01-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "#using min function to get earliest date\n",
    "print(data_sorted.index.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2014-12-31 00:00:00\n"
     ]
    }
   ],
   "source": [
    "#using max function to get latest entry in data\n",
    "print(data_sorted.index.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So as we can see the data ranges for exactly four years from 1 January 2011 to 31 december 2014. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's explore some of the columns to see how many unique fields they contain. \n",
    "we will define a function unique_values which will take a list of column names as a positional argument and print the \n",
    "unique values and the no. of unique values in each column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unique_values(column_names_list):\n",
    "    for column in column_names_list:\n",
    "        print(data_sorted[column].unique())\n",
    "        print(\"The number of unique\", column , \"in column:\" , data_sorted[column].nunique())\n",
    "        print('\\n')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#defining a list of columns that we are interested in\n",
    "list_of_columns = ['Country','Region', 'Category', 'Sub-Category', 'Segment', 'Market']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Hungary' 'Australia' 'Algeria' 'Sweden' 'Canada' 'United Kingdom'\n",
      " 'New Zealand' 'Iran' 'Guatemala' 'Tanzania' 'Iraq' 'Philippines'\n",
      " 'Thailand' 'Malaysia' 'Brazil' 'Cuba' 'Japan' 'Mexico' 'Sudan'\n",
      " 'United States' 'France' 'Taiwan' 'Indonesia' 'Vietnam' 'China' 'Angola'\n",
      " 'Lebanon' 'Mozambique' 'Singapore' 'Netherlands' 'Nigeria' 'South Africa'\n",
      " 'Egypt' 'Venezuela' 'Austria' 'India' 'Spain' 'Turkey' 'Italy' 'Germany'\n",
      " 'Nicaragua' 'Dominican Republic' 'Denmark' 'El Salvador' 'Saudi Arabia'\n",
      " 'Zambia' 'Myanmar (Burma)' 'Russia' 'Mongolia' 'Kenya' 'Belgium'\n",
      " 'Colombia' 'Estonia' 'Madagascar' 'Morocco' 'Norway' 'Portugal'\n",
      " 'Sierra Leone' 'Czech Republic' 'Central African Republic' 'Benin'\n",
      " 'Bangladesh' 'Panama' 'Chile' 'South Korea' 'Moldova' 'Switzerland'\n",
      " 'Uganda' 'Zimbabwe' 'Senegal' 'Hong Kong' 'Niger'\n",
      " 'Democratic Republic of the Congo' 'Poland' 'Ireland' 'Pakistan'\n",
      " 'Azerbaijan' 'Ukraine' 'Romania' 'Honduras' 'Israel' 'Albania' 'Cameroon'\n",
      " 'Cambodia' 'Georgia' 'Argentina' 'Finland' 'Lithuania' 'Haiti' 'Peru'\n",
      " 'Somalia' \"Cote d'Ivoire\" 'Afghanistan' 'Guinea' 'Liberia' 'South Sudan'\n",
      " 'Turkmenistan' 'Kazakhstan' 'Lesotho' 'Burundi' 'Qatar' 'Bulgaria'\n",
      " 'Martinique' 'Croatia' 'Ghana' 'Rwanda' 'Ecuador' 'Paraguay' 'Ethiopia'\n",
      " 'Syria' 'Tajikistan' 'Slovakia' 'Belarus' 'Papua New Guinea' 'Togo'\n",
      " 'Libya' 'Djibouti' 'Yemen' 'United Arab Emirates' 'Uzbekistan' 'Barbados'\n",
      " 'Jamaica' 'Bolivia' 'Uruguay' 'Republic of the Congo' 'Swaziland'\n",
      " 'Kyrgyzstan' 'Guinea-Bissau' 'Bosnia and Herzegovina' 'Tunisia' 'Armenia'\n",
      " 'Mali' 'Jordan' 'Trinidad and Tobago' 'Namibia' 'Gabon' 'Macedonia'\n",
      " 'Nepal' 'Mauritania' 'Guadeloupe' 'Sri Lanka' 'Chad' 'Eritrea' 'Bahrain'\n",
      " 'Equatorial Guinea' 'Slovenia' 'Montenegro']\n",
      "The number of unique Country in column: 147\n",
      "\n",
      "\n",
      "['EMEA' 'Oceania' 'Africa' 'North' 'Canada' 'Central' 'Southeast Asia'\n",
      " 'South' 'Caribbean' 'North Asia' 'East' 'West' 'Central Asia']\n",
      "The number of unique Region in column: 13\n",
      "\n",
      "\n",
      "['Office Supplies' 'Furniture' 'Technology']\n",
      "The number of unique Category in column: 3\n",
      "\n",
      "\n",
      "['Storage' 'Supplies' 'Paper' 'Furnishings' 'Machines' 'Bookcases'\n",
      " 'Copiers' 'Art' 'Phones' 'Appliances' 'Fasteners' 'Chairs' 'Envelopes'\n",
      " 'Accessories' 'Tables' 'Labels' 'Binders']\n",
      "The number of unique Sub-Category in column: 17\n",
      "\n",
      "\n",
      "['Consumer' 'Home Office' 'Corporate']\n",
      "The number of unique Segment in column: 3\n",
      "\n",
      "\n",
      "['EMEA' 'APAC' 'Africa' 'EU' 'Canada' 'LATAM' 'US']\n",
      "The number of unique Market in column: 7\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "unique_values(list_of_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- we can see there's quite a lot of location diversity in our dataset. \n",
    "- The Product orders span across 147 different  countries. \n",
    "- The countries are divided into 13 regions.\n",
    "- The products are classified into 3 main categories which are further classified into 17 Sub-Categories. \n",
    "- The customers are divided into 3 segments. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now let's compute some summary statistics of the numerical variables in the data**\n",
    "\n",
    "The .describe() method returns the various summary statistics of the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Row ID</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Shipping Cost</th>\n",
       "      <th>Order Year</th>\n",
       "      <th>Shipping duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>51290.00000</td>\n",
       "      <td>9994.000000</td>\n",
       "      <td>51290.000000</td>\n",
       "      <td>51290.000000</td>\n",
       "      <td>51290.000000</td>\n",
       "      <td>51290.000000</td>\n",
       "      <td>51290.000000</td>\n",
       "      <td>51290.000000</td>\n",
       "      <td>51290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>25645.50000</td>\n",
       "      <td>55190.379428</td>\n",
       "      <td>246.490581</td>\n",
       "      <td>3.476545</td>\n",
       "      <td>0.142908</td>\n",
       "      <td>28.610982</td>\n",
       "      <td>26.375818</td>\n",
       "      <td>2012.777208</td>\n",
       "      <td>3 days 23:15:53.589393644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14806.29199</td>\n",
       "      <td>32063.693350</td>\n",
       "      <td>487.565361</td>\n",
       "      <td>2.278766</td>\n",
       "      <td>0.212280</td>\n",
       "      <td>174.340972</td>\n",
       "      <td>57.296810</td>\n",
       "      <td>1.098931</td>\n",
       "      <td>1 days 17:30:23.320880732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1040.000000</td>\n",
       "      <td>0.444000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6599.978000</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>0 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12823.25000</td>\n",
       "      <td>23223.000000</td>\n",
       "      <td>30.758625</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.610000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>3 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>25645.50000</td>\n",
       "      <td>56430.500000</td>\n",
       "      <td>85.053000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.240000</td>\n",
       "      <td>7.790000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>4 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>38467.75000</td>\n",
       "      <td>90008.000000</td>\n",
       "      <td>251.053200</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>36.810000</td>\n",
       "      <td>24.450000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>5 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>51290.00000</td>\n",
       "      <td>99301.000000</td>\n",
       "      <td>22638.480000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>8399.976000</td>\n",
       "      <td>933.570000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>7 days 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Row ID   Postal Code         Sales      Quantity      Discount  \\\n",
       "count  51290.00000   9994.000000  51290.000000  51290.000000  51290.000000   \n",
       "mean   25645.50000  55190.379428    246.490581      3.476545      0.142908   \n",
       "std    14806.29199  32063.693350    487.565361      2.278766      0.212280   \n",
       "min        1.00000   1040.000000      0.444000      1.000000      0.000000   \n",
       "25%    12823.25000  23223.000000     30.758625      2.000000      0.000000   \n",
       "50%    25645.50000  56430.500000     85.053000      3.000000      0.000000   \n",
       "75%    38467.75000  90008.000000    251.053200      5.000000      0.200000   \n",
       "max    51290.00000  99301.000000  22638.480000     14.000000      0.850000   \n",
       "\n",
       "             Profit  Shipping Cost    Order Year          Shipping duration  \n",
       "count  51290.000000   51290.000000  51290.000000                      51290  \n",
       "mean      28.610982      26.375818   2012.777208  3 days 23:15:53.589393644  \n",
       "std      174.340972      57.296810      1.098931  1 days 17:30:23.320880732  \n",
       "min    -6599.978000       0.002000   2011.000000            0 days 00:00:00  \n",
       "25%        0.000000       2.610000   2012.000000            3 days 00:00:00  \n",
       "50%        9.240000       7.790000   2013.000000            4 days 00:00:00  \n",
       "75%       36.810000      24.450000   2014.000000            5 days 00:00:00  \n",
       "max     8399.976000     933.570000   2014.000000            7 days 00:00:00  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sorted.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected it seems like there's quite a lot of skew in the data. The values of the Mean and median are huge differences for various columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 463.25x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#setting style of plot\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "\n",
    "#using seaborn's displot function to plot histogram of Sales variable\n",
    "sns.displot(data=data_sorted, x='Sales', hue='Category');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see the data for sales column is quite heavily tailed to the right. we will have to transform the data to look at the real distribution of sales variable. Let's use Log scale of the x-axis to observe the distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 463.25x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#defining scale to be logarithmic and hue parameter to be categories so we can see distribution of sales across product categories. \n",
    "sns.displot(data=data_sorted, x='Sales', log_scale=True, hue='Category', alpha=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So as we can see the variable sales has Log Normal distribution. Most of the data points seem to be situated between range of 10 and 100. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lets explore the Profit column\n",
    "#we will use seaborn library to plot a boxplot of profit column.\n",
    "sns.set_style('darkgrid')\n",
    "sns.boxplot(data=data_sorted, y='Profit', x='Category');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The values in profit column are highly concentraded close to zero. There are some outliers values both negative and positive. Lets try to look at the distribution by removing outliers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#by passing the sym keyword argument as empty string seaborn will understand that we want to omit the outliers from the plot\n",
    "sns.catplot(data=data_sorted, y='Profit', kind='box', x='Category', sym=\"\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that without outliers the distribution of profit is very much normal. The office supplies range looks like it is smaller compared to other two categories which might be due to the rates of the products. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explore the number of orders across various variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=data_sorted, x='Category');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows that substantial amount of orders are for office supplies. In fact we can see the orders for office supplies are more than ordersf or furniture and technology combined "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's further look at the distribution of orders among sub categories. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category         Sub-Category\n",
       "Furniture        Bookcases       2411\n",
       "                 Chairs          3434\n",
       "                 Furnishings     3170\n",
       "                 Tables           861\n",
       "Office Supplies  Appliances      1755\n",
       "                 Art             4883\n",
       "                 Binders         6152\n",
       "                 Envelopes       2435\n",
       "                 Fasteners       2420\n",
       "                 Labels          2606\n",
       "                 Paper           3538\n",
       "                 Storage         5059\n",
       "                 Supplies        2425\n",
       "Technology       Accessories     3075\n",
       "                 Copiers         2223\n",
       "                 Machines        1486\n",
       "                 Phones          3357\n",
       "Name: Sales, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sorted.groupby(['Category','Sub-Category'])['Sales'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see The count of orders by Categories further divided in sub-categories. But it's hard to look at any trend this way. SO let's visualize it using seaborns catplot function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x324 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "sns.set_theme(style=\"ticks\", font_scale=1.25)\n",
    "\n",
    "g = sns.catplot(data=data_sorted, x='Sub-Category', \n",
    "            kind='count', \n",
    "            order=data_sorted['Sub-Category'].value_counts().index,\n",
    "            palette='coolwarm')\n",
    "\n",
    "g.fig.set_size_inches(6.5, 4.5)\n",
    "\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected the Sub-categories belonging to the Office supplies categories have higher sales. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the sales by year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order Year\n",
       "2011    2.259451e+06\n",
       "2012    2.677439e+06\n",
       "2013    3.405746e+06\n",
       "2014    4.299866e+06\n",
       "Name: Sales, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sorted.groupby('Order Year')['Sales'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.barplot(data=data_sorted, x='Order Year', y='Sales', estimator=sum );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the sales have increased over the years. There seems to be a linear trend increase of the sales every year. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us save and upload our work to Jovian before continuing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Asking and Answering Questions\n",
    "\n",
    "We have done some basic exploration of the dataset. Now let's dive deep and try to answer some question.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q1: Which products categories are producting the most profit? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the groupby function of pandas to calculate profits acorss various categories of products. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "Profits = data_sorted.groupby([\"Category\", 'Sub-Category'], as_index=False) \\\n",
    "['Profit'].sum().sort_values('Profit', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Technology</td>\n",
       "      <td>Copiers</td>\n",
       "      <td>258567.54818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>216717.00580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>161924.41950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>141680.58940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>140396.26750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Technology</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>129626.30620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>108461.48980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>72449.84600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>59207.68270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Technology</td>\n",
       "      <td>Machines</td>\n",
       "      <td>58867.87300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Art</td>\n",
       "      <td>57953.91090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Furniture</td>\n",
       "      <td>Furnishings</td>\n",
       "      <td>46967.42550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Envelopes</td>\n",
       "      <td>29601.11630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Supplies</td>\n",
       "      <td>22583.26310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>15010.51200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Fasteners</td>\n",
       "      <td>11525.42410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>-64083.38870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Category Sub-Category        Profit\n",
       "14       Technology      Copiers  258567.54818\n",
       "16       Technology       Phones  216717.00580\n",
       "0         Furniture    Bookcases  161924.41950\n",
       "4   Office Supplies   Appliances  141680.58940\n",
       "1         Furniture       Chairs  140396.26750\n",
       "13       Technology  Accessories  129626.30620\n",
       "11  Office Supplies      Storage  108461.48980\n",
       "6   Office Supplies      Binders   72449.84600\n",
       "10  Office Supplies        Paper   59207.68270\n",
       "15       Technology     Machines   58867.87300\n",
       "5   Office Supplies          Art   57953.91090\n",
       "2         Furniture  Furnishings   46967.42550\n",
       "7   Office Supplies    Envelopes   29601.11630\n",
       "12  Office Supplies     Supplies   22583.26310\n",
       "9   Office Supplies       Labels   15010.51200\n",
       "8   Office Supplies    Fasteners   11525.42410\n",
       "3         Furniture       Tables  -64083.38870"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Profits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = sns.catplot(data=Profits, \n",
    "            y='Sub-Category', x='Profit',\n",
    "            kind='bar', hue='Category', dodge=False)\n",
    "\n",
    "f.fig.set_size_inches(15, 6)\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "plt.legend(loc='lower right');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting!! This plot shows us the profits by various subcategories of the products as well as which Major categories they belong to.\n",
    "- It looks like The products from technology category are generating the most profit. \n",
    "- The Furniture category looks ilke it's not doing very well in the deparment of Tables. The plot shows the store is loosing money in Tables. \n",
    "- office supplies are generating moderate profit with no losses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q2: Which are the top 10 countries in terms of sales and profits? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "Top_10 = data_sorted.groupby('Country', as_index=False)[['Sales','Profit']].sum().sort_values(by = \"Sales\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>Country</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>United States</td>\n",
       "      <td>2.297201e+06</td>\n",
       "      <td>286397.02170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Australia</td>\n",
       "      <td>9.252359e+05</td>\n",
       "      <td>103907.43300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>France</td>\n",
       "      <td>8.589311e+05</td>\n",
       "      <td>109029.00300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>China</td>\n",
       "      <td>7.005620e+05</td>\n",
       "      <td>150683.08500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Germany</td>\n",
       "      <td>6.288400e+05</td>\n",
       "      <td>107322.82050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>6.225906e+05</td>\n",
       "      <td>102818.09752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>India</td>\n",
       "      <td>5.896501e+05</td>\n",
       "      <td>129071.83500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>5.285763e+05</td>\n",
       "      <td>111900.15000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>4.048875e+05</td>\n",
       "      <td>15608.67790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>3.611064e+05</td>\n",
       "      <td>30090.49896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Country         Sales        Profit\n",
       "139   United States  2.297201e+06  286397.02170\n",
       "6         Australia  9.252359e+05  103907.43300\n",
       "44           France  8.589311e+05  109029.00300\n",
       "26            China  7.005620e+05  150683.08500\n",
       "47          Germany  6.288400e+05  107322.82050\n",
       "81           Mexico  6.225906e+05  102818.09752\n",
       "57            India  5.896501e+05  129071.83500\n",
       "138  United Kingdom  5.285763e+05  111900.15000\n",
       "58        Indonesia  4.048875e+05   15608.67790\n",
       "17           Brazil  3.611064e+05   30090.49896"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Top_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 748.8x514.8 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "plt.style.use('seaborn-poster')\n",
    "\n",
    "sns.pointplot(data=Top_10, x='Country', y=\"Sales\", kind='point', ax=ax, markers='x', color='r', join=False)\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "\n",
    "sns.pointplot(data=Top_10, x='Country', y='Profit', kind='point', ax=ax2, join=False, color='green')\n",
    "\n",
    "ax2.grid(False)\n",
    "\n",
    "ax.set_xticklabels(Top_10.Country, rotation=90);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this plot we can see the Top 10 countries sales wise and how the sales relate to their profits. \n",
    " \n",
    " - United states is by far the country with highest sales and profits both. \n",
    " - Among other countries china and india seem to doing good in terms of profits to sales ratio. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q3: Is there a correlation order priority and shipping time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order Date\n",
       "2011-01-01    4\n",
       "2011-01-01    7\n",
       "2011-01-01    5\n",
       "2011-01-01    7\n",
       "2011-01-01    4\n",
       "             ..\n",
       "2014-12-31    2\n",
       "2014-12-31    3\n",
       "2014-12-31    3\n",
       "2014-12-31    2\n",
       "2014-12-31    4\n",
       "Name: Shipping duration, Length: 51290, dtype: int64"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sorted['Shipping duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=data_sorted, \n",
    "            x='Order Priority', y='Shipping duration', \n",
    "            kind='bar', order=['Low', 'Medium', 'High', 'Critical'], \n",
    "            ci=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected the delivery time on average gets lower with higher priority of the order. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q4: Which products are in demand in which countries? \n",
    "\n",
    "for the simplicity we will consider only top 10 countries by sales "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['United States',\n",
       " 'Australia',\n",
       " 'France',\n",
       " 'China',\n",
       " 'Germany',\n",
       " 'Mexico',\n",
       " 'India',\n",
       " 'United Kingdom',\n",
       " 'Indonesia',\n",
       " 'Brazil']"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_10_country = list(Top_10.Country)\n",
    "top_10_country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "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>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "      <th>...</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Shipping Cost</th>\n",
       "      <th>Order Priority</th>\n",
       "      <th>Order Year</th>\n",
       "      <th>Shipping duration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</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",
       "      <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>2011-01-01</th>\n",
       "      <td>22253</td>\n",
       "      <td>IN-2011-47883</td>\n",
       "      <td>2011-01-08</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>JH-15985</td>\n",
       "      <td>Joseph Holt</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Wagga Wagga</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>...</td>\n",
       "      <td>Supplies</td>\n",
       "      <td>Acme Trimmer, High Speed</td>\n",
       "      <td>120.366</td>\n",
       "      <td>3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>36.036</td>\n",
       "      <td>9.720</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2011</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>22254</td>\n",
       "      <td>IN-2011-47883</td>\n",
       "      <td>2011-01-08</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>JH-15985</td>\n",
       "      <td>Joseph Holt</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Wagga Wagga</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>...</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Eaton Computer Printout Paper, 8.5 x 11</td>\n",
       "      <td>55.242</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>15.342</td>\n",
       "      <td>1.800</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2011</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>22255</td>\n",
       "      <td>IN-2011-47883</td>\n",
       "      <td>2011-01-08</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>JH-15985</td>\n",
       "      <td>Joseph Holt</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Wagga Wagga</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>...</td>\n",
       "      <td>Furnishings</td>\n",
       "      <td>Eldon Light Bulb, Duo Pack</td>\n",
       "      <td>113.670</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>37.770</td>\n",
       "      <td>4.700</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2011</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-03</th>\n",
       "      <td>16385</td>\n",
       "      <td>ES-2011-4869686</td>\n",
       "      <td>2011-01-07</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>DD-13570</td>\n",
       "      <td>Dorothy Dickinson</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Southport</td>\n",
       "      <td>England</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>...</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Dania Corner Shelving, Traditional</td>\n",
       "      <td>854.490</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>290.430</td>\n",
       "      <td>12.560</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2011</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-03</th>\n",
       "      <td>20302</td>\n",
       "      <td>IN-2011-79397</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>Same Day</td>\n",
       "      <td>KN-16450</td>\n",
       "      <td>Kean Nguyen</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>...</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Avery Color Coded Labels, Laser Printer Compat...</td>\n",
       "      <td>35.883</td>\n",
       "      <td>3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4.743</td>\n",
       "      <td>7.460</td>\n",
       "      <td>Critical</td>\n",
       "      <td>2011</td>\n",
       "      <td>0</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",
       "      <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>2014-12-31</th>\n",
       "      <td>32594</td>\n",
       "      <td>CA-2014-115427</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>EB-13975</td>\n",
       "      <td>Erica Bern</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Fairfield</td>\n",
       "      <td>California</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>Binders</td>\n",
       "      <td>GBC Binding covers</td>\n",
       "      <td>20.720</td>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>6.475</td>\n",
       "      <td>2.060</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2014</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>1786</td>\n",
       "      <td>MX-2014-116267</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>EB-13975</td>\n",
       "      <td>Erica Bern</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>...</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Enermax Memory Card, Erganomic</td>\n",
       "      <td>74.800</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.420</td>\n",
       "      <td>7.312</td>\n",
       "      <td>High</td>\n",
       "      <td>2014</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>16469</td>\n",
       "      <td>ES-2014-3638865</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>First Class</td>\n",
       "      <td>SV-20935</td>\n",
       "      <td>Susan Vittorini</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Worcester</td>\n",
       "      <td>England</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>...</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Fellowes Shelving, Single Width</td>\n",
       "      <td>289.350</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.150</td>\n",
       "      <td>34.470</td>\n",
       "      <td>Critical</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>5029</td>\n",
       "      <td>MX-2014-168851</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>SC-20260</td>\n",
       "      <td>Scott Cohen</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Durango</td>\n",
       "      <td>Durango</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>...</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Avery Removable Labels, 5000 Label Set</td>\n",
       "      <td>22.260</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.960</td>\n",
       "      <td>2.274</td>\n",
       "      <td>High</td>\n",
       "      <td>2014</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>12929</td>\n",
       "      <td>ES-2014-3458802</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>JG-15805</td>\n",
       "      <td>John Grady</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Maidenhead</td>\n",
       "      <td>England</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>...</td>\n",
       "      <td>Phones</td>\n",
       "      <td>Motorola Audio Dock, VoIP</td>\n",
       "      <td>867.300</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>251.400</td>\n",
       "      <td>53.160</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28424 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Row ID         Order ID  Ship Date       Ship Mode Customer ID  \\\n",
       "Order Date                                                                   \n",
       "2011-01-01   22253    IN-2011-47883 2011-01-08  Standard Class    JH-15985   \n",
       "2011-01-01   22254    IN-2011-47883 2011-01-08  Standard Class    JH-15985   \n",
       "2011-01-01   22255    IN-2011-47883 2011-01-08  Standard Class    JH-15985   \n",
       "2011-01-03   16385  ES-2011-4869686 2011-01-07  Standard Class    DD-13570   \n",
       "2011-01-03   20302    IN-2011-79397 2011-01-03        Same Day    KN-16450   \n",
       "...            ...              ...        ...             ...         ...   \n",
       "2014-12-31   32594   CA-2014-115427 2015-01-04  Standard Class    EB-13975   \n",
       "2014-12-31    1786   MX-2014-116267 2015-01-03    Second Class    EB-13975   \n",
       "2014-12-31   16469  ES-2014-3638865 2015-01-01     First Class    SV-20935   \n",
       "2014-12-31    5029   MX-2014-168851 2015-01-02    Second Class    SC-20260   \n",
       "2014-12-31   12929  ES-2014-3458802 2015-01-05  Standard Class    JG-15805   \n",
       "\n",
       "                Customer Name    Segment         City            State  \\\n",
       "Order Date                                                               \n",
       "2011-01-01        Joseph Holt   Consumer  Wagga Wagga  New South Wales   \n",
       "2011-01-01        Joseph Holt   Consumer  Wagga Wagga  New South Wales   \n",
       "2011-01-01        Joseph Holt   Consumer  Wagga Wagga  New South Wales   \n",
       "2011-01-03  Dorothy Dickinson   Consumer    Southport          England   \n",
       "2011-01-03        Kean Nguyen  Corporate       Sydney  New South Wales   \n",
       "...                       ...        ...          ...              ...   \n",
       "2014-12-31         Erica Bern  Corporate    Fairfield       California   \n",
       "2014-12-31         Erica Bern  Corporate    São Paulo        São Paulo   \n",
       "2014-12-31    Susan Vittorini   Consumer    Worcester          England   \n",
       "2014-12-31        Scott Cohen  Corporate      Durango          Durango   \n",
       "2014-12-31         John Grady  Corporate   Maidenhead          England   \n",
       "\n",
       "                   Country  ...  Sub-Category  \\\n",
       "Order Date                  ...                 \n",
       "2011-01-01       Australia  ...      Supplies   \n",
       "2011-01-01       Australia  ...         Paper   \n",
       "2011-01-01       Australia  ...   Furnishings   \n",
       "2011-01-03  United Kingdom  ...     Bookcases   \n",
       "2011-01-03       Australia  ...        Labels   \n",
       "...                    ...  ...           ...   \n",
       "2014-12-31   United States  ...       Binders   \n",
       "2014-12-31          Brazil  ...   Accessories   \n",
       "2014-12-31  United Kingdom  ...       Storage   \n",
       "2014-12-31          Mexico  ...        Labels   \n",
       "2014-12-31  United Kingdom  ...        Phones   \n",
       "\n",
       "                                                 Product Name    Sales  \\\n",
       "Order Date                                                               \n",
       "2011-01-01                           Acme Trimmer, High Speed  120.366   \n",
       "2011-01-01            Eaton Computer Printout Paper, 8.5 x 11   55.242   \n",
       "2011-01-01                         Eldon Light Bulb, Duo Pack  113.670   \n",
       "2011-01-03                 Dania Corner Shelving, Traditional  854.490   \n",
       "2011-01-03  Avery Color Coded Labels, Laser Printer Compat...   35.883   \n",
       "...                                                       ...      ...   \n",
       "2014-12-31                                 GBC Binding covers   20.720   \n",
       "2014-12-31                     Enermax Memory Card, Erganomic   74.800   \n",
       "2014-12-31                    Fellowes Shelving, Single Width  289.350   \n",
       "2014-12-31             Avery Removable Labels, 5000 Label Set   22.260   \n",
       "2014-12-31                          Motorola Audio Dock, VoIP  867.300   \n",
       "\n",
       "           Quantity Discount   Profit Shipping Cost  Order Priority  \\\n",
       "Order Date                                                            \n",
       "2011-01-01        3      0.1   36.036         9.720          Medium   \n",
       "2011-01-01        2      0.1   15.342         1.800          Medium   \n",
       "2011-01-01        5      0.1   37.770         4.700          Medium   \n",
       "2011-01-03        7      0.0  290.430        12.560          Medium   \n",
       "2011-01-03        3      0.1    4.743         7.460        Critical   \n",
       "...             ...      ...      ...           ...             ...   \n",
       "2014-12-31        2      0.2    6.475         2.060          Medium   \n",
       "2014-12-31        1      0.0   28.420         7.312            High   \n",
       "2014-12-31        5      0.0   75.150        34.470        Critical   \n",
       "2014-12-31        3      0.0    3.960         2.274            High   \n",
       "2014-12-31        5      0.0  251.400        53.160          Medium   \n",
       "\n",
       "            Order Year  Shipping duration  \n",
       "Order Date                                 \n",
       "2011-01-01        2011                  7  \n",
       "2011-01-01        2011                  7  \n",
       "2011-01-01        2011                  7  \n",
       "2011-01-03        2011                  4  \n",
       "2011-01-03        2011                  0  \n",
       "...                ...                ...  \n",
       "2014-12-31        2014                  4  \n",
       "2014-12-31        2014                  3  \n",
       "2014-12-31        2014                  1  \n",
       "2014-12-31        2014                  2  \n",
       "2014-12-31        2014                  5  \n",
       "\n",
       "[28424 rows x 25 columns]"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_top_10 = data_sorted[data_sorted['Country'].isin(top_10_country)]\n",
    "data_top_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Accessories</th>\n",
       "      <th>Appliances</th>\n",
       "      <th>Art</th>\n",
       "      <th>Binders</th>\n",
       "      <th>Bookcases</th>\n",
       "      <th>Chairs</th>\n",
       "      <th>Copiers</th>\n",
       "      <th>Envelopes</th>\n",
       "      <th>Fasteners</th>\n",
       "      <th>Furnishings</th>\n",
       "      <th>Labels</th>\n",
       "      <th>Machines</th>\n",
       "      <th>Paper</th>\n",
       "      <th>Phones</th>\n",
       "      <th>Storage</th>\n",
       "      <th>Supplies</th>\n",
       "      <th>Tables</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</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",
       "      <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>Australia</th>\n",
       "      <td>55166.6880</td>\n",
       "      <td>94172.4990</td>\n",
       "      <td>15647.1390</td>\n",
       "      <td>16630.6320</td>\n",
       "      <td>127170.8370</td>\n",
       "      <td>142471.0140</td>\n",
       "      <td>138261.02700</td>\n",
       "      <td>12358.9080</td>\n",
       "      <td>7253.5920</td>\n",
       "      <td>24657.3780</td>\n",
       "      <td>6511.6740</td>\n",
       "      <td>45080.4300</td>\n",
       "      <td>17275.3950</td>\n",
       "      <td>101704.2990</td>\n",
       "      <td>55174.5540</td>\n",
       "      <td>20759.148</td>\n",
       "      <td>44940.6390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>20346.3880</td>\n",
       "      <td>28652.5040</td>\n",
       "      <td>5787.1240</td>\n",
       "      <td>5846.2760</td>\n",
       "      <td>55689.1640</td>\n",
       "      <td>55146.8760</td>\n",
       "      <td>54886.20296</td>\n",
       "      <td>6464.3000</td>\n",
       "      <td>2979.3800</td>\n",
       "      <td>12941.5280</td>\n",
       "      <td>2060.6520</td>\n",
       "      <td>8214.2120</td>\n",
       "      <td>5992.6120</td>\n",
       "      <td>44964.1600</td>\n",
       "      <td>18527.1400</td>\n",
       "      <td>6845.940</td>\n",
       "      <td>25761.9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>33029.3100</td>\n",
       "      <td>42119.2800</td>\n",
       "      <td>14069.3700</td>\n",
       "      <td>11430.7050</td>\n",
       "      <td>106034.2650</td>\n",
       "      <td>108831.4800</td>\n",
       "      <td>109251.15000</td>\n",
       "      <td>12166.5000</td>\n",
       "      <td>5535.9150</td>\n",
       "      <td>18132.6150</td>\n",
       "      <td>4468.1700</td>\n",
       "      <td>41903.9550</td>\n",
       "      <td>12585.7350</td>\n",
       "      <td>79330.5000</td>\n",
       "      <td>43739.1300</td>\n",
       "      <td>14320.725</td>\n",
       "      <td>43613.2200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>49149.2700</td>\n",
       "      <td>78103.7310</td>\n",
       "      <td>47016.0300</td>\n",
       "      <td>33894.1950</td>\n",
       "      <td>95310.9360</td>\n",
       "      <td>91514.5680</td>\n",
       "      <td>95134.28700</td>\n",
       "      <td>10129.9200</td>\n",
       "      <td>6155.2350</td>\n",
       "      <td>22553.1900</td>\n",
       "      <td>4602.1050</td>\n",
       "      <td>77802.7740</td>\n",
       "      <td>12539.8200</td>\n",
       "      <td>89964.0870</td>\n",
       "      <td>96580.4820</td>\n",
       "      <td>15742.020</td>\n",
       "      <td>32738.4330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>35566.7640</td>\n",
       "      <td>41873.1390</td>\n",
       "      <td>37413.6780</td>\n",
       "      <td>18917.7330</td>\n",
       "      <td>69342.9900</td>\n",
       "      <td>49819.6800</td>\n",
       "      <td>77091.33600</td>\n",
       "      <td>8500.7340</td>\n",
       "      <td>4709.3490</td>\n",
       "      <td>16705.8990</td>\n",
       "      <td>3457.4190</td>\n",
       "      <td>45006.7020</td>\n",
       "      <td>7685.5980</td>\n",
       "      <td>104591.1990</td>\n",
       "      <td>72657.2880</td>\n",
       "      <td>10735.644</td>\n",
       "      <td>24764.8785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>33645.4650</td>\n",
       "      <td>45209.1900</td>\n",
       "      <td>7878.3750</td>\n",
       "      <td>9794.0250</td>\n",
       "      <td>88983.0000</td>\n",
       "      <td>68874.4200</td>\n",
       "      <td>70386.06000</td>\n",
       "      <td>9136.2150</td>\n",
       "      <td>4746.1200</td>\n",
       "      <td>16215.3600</td>\n",
       "      <td>3572.5950</td>\n",
       "      <td>33600.2700</td>\n",
       "      <td>12734.6100</td>\n",
       "      <td>104627.2350</td>\n",
       "      <td>27638.3550</td>\n",
       "      <td>10625.340</td>\n",
       "      <td>41983.4700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>12366.8451</td>\n",
       "      <td>50057.7897</td>\n",
       "      <td>8742.3129</td>\n",
       "      <td>7298.1651</td>\n",
       "      <td>59808.5964</td>\n",
       "      <td>37654.5534</td>\n",
       "      <td>62119.65690</td>\n",
       "      <td>4376.2683</td>\n",
       "      <td>3036.8046</td>\n",
       "      <td>10071.7881</td>\n",
       "      <td>2046.2028</td>\n",
       "      <td>25854.7905</td>\n",
       "      <td>3906.9957</td>\n",
       "      <td>45730.5432</td>\n",
       "      <td>33787.9803</td>\n",
       "      <td>5525.250</td>\n",
       "      <td>32502.9549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>40153.6800</td>\n",
       "      <td>49545.5600</td>\n",
       "      <td>13187.6000</td>\n",
       "      <td>12690.5600</td>\n",
       "      <td>77313.4400</td>\n",
       "      <td>84392.0640</td>\n",
       "      <td>98952.93752</td>\n",
       "      <td>11673.8600</td>\n",
       "      <td>5627.7400</td>\n",
       "      <td>13228.5960</td>\n",
       "      <td>3875.7600</td>\n",
       "      <td>13829.4400</td>\n",
       "      <td>10028.8000</td>\n",
       "      <td>93231.2000</td>\n",
       "      <td>41959.0800</td>\n",
       "      <td>12108.140</td>\n",
       "      <td>40792.1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>23617.5690</td>\n",
       "      <td>47980.9680</td>\n",
       "      <td>24973.8210</td>\n",
       "      <td>15753.2670</td>\n",
       "      <td>69866.8530</td>\n",
       "      <td>26873.1000</td>\n",
       "      <td>68430.11100</td>\n",
       "      <td>9096.2670</td>\n",
       "      <td>3704.1120</td>\n",
       "      <td>7181.8530</td>\n",
       "      <td>2599.5900</td>\n",
       "      <td>47051.5650</td>\n",
       "      <td>6153.5880</td>\n",
       "      <td>77649.2340</td>\n",
       "      <td>60306.7110</td>\n",
       "      <td>8798.469</td>\n",
       "      <td>28539.2220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>167380.3180</td>\n",
       "      <td>107532.1610</td>\n",
       "      <td>27118.7920</td>\n",
       "      <td>203412.7330</td>\n",
       "      <td>114879.9963</td>\n",
       "      <td>328449.1030</td>\n",
       "      <td>149528.03000</td>\n",
       "      <td>16476.4020</td>\n",
       "      <td>3024.2800</td>\n",
       "      <td>91705.1640</td>\n",
       "      <td>12486.3120</td>\n",
       "      <td>189238.6310</td>\n",
       "      <td>78479.2060</td>\n",
       "      <td>330007.0540</td>\n",
       "      <td>223843.6080</td>\n",
       "      <td>46673.538</td>\n",
       "      <td>206965.5320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sub-Category    Accessories   Appliances         Art      Binders  \\\n",
       "Country                                                             \n",
       "Australia        55166.6880   94172.4990  15647.1390   16630.6320   \n",
       "Brazil           20346.3880   28652.5040   5787.1240    5846.2760   \n",
       "China            33029.3100   42119.2800  14069.3700   11430.7050   \n",
       "France           49149.2700   78103.7310  47016.0300   33894.1950   \n",
       "Germany          35566.7640   41873.1390  37413.6780   18917.7330   \n",
       "India            33645.4650   45209.1900   7878.3750    9794.0250   \n",
       "Indonesia        12366.8451   50057.7897   8742.3129    7298.1651   \n",
       "Mexico           40153.6800   49545.5600  13187.6000   12690.5600   \n",
       "United Kingdom   23617.5690   47980.9680  24973.8210   15753.2670   \n",
       "United States   167380.3180  107532.1610  27118.7920  203412.7330   \n",
       "\n",
       "Sub-Category      Bookcases       Chairs       Copiers   Envelopes  Fasteners  \\\n",
       "Country                                                                         \n",
       "Australia       127170.8370  142471.0140  138261.02700  12358.9080  7253.5920   \n",
       "Brazil           55689.1640   55146.8760   54886.20296   6464.3000  2979.3800   \n",
       "China           106034.2650  108831.4800  109251.15000  12166.5000  5535.9150   \n",
       "France           95310.9360   91514.5680   95134.28700  10129.9200  6155.2350   \n",
       "Germany          69342.9900   49819.6800   77091.33600   8500.7340  4709.3490   \n",
       "India            88983.0000   68874.4200   70386.06000   9136.2150  4746.1200   \n",
       "Indonesia        59808.5964   37654.5534   62119.65690   4376.2683  3036.8046   \n",
       "Mexico           77313.4400   84392.0640   98952.93752  11673.8600  5627.7400   \n",
       "United Kingdom   69866.8530   26873.1000   68430.11100   9096.2670  3704.1120   \n",
       "United States   114879.9963  328449.1030  149528.03000  16476.4020  3024.2800   \n",
       "\n",
       "Sub-Category    Furnishings      Labels     Machines       Paper       Phones  \\\n",
       "Country                                                                         \n",
       "Australia        24657.3780   6511.6740   45080.4300  17275.3950  101704.2990   \n",
       "Brazil           12941.5280   2060.6520    8214.2120   5992.6120   44964.1600   \n",
       "China            18132.6150   4468.1700   41903.9550  12585.7350   79330.5000   \n",
       "France           22553.1900   4602.1050   77802.7740  12539.8200   89964.0870   \n",
       "Germany          16705.8990   3457.4190   45006.7020   7685.5980  104591.1990   \n",
       "India            16215.3600   3572.5950   33600.2700  12734.6100  104627.2350   \n",
       "Indonesia        10071.7881   2046.2028   25854.7905   3906.9957   45730.5432   \n",
       "Mexico           13228.5960   3875.7600   13829.4400  10028.8000   93231.2000   \n",
       "United Kingdom    7181.8530   2599.5900   47051.5650   6153.5880   77649.2340   \n",
       "United States    91705.1640  12486.3120  189238.6310  78479.2060  330007.0540   \n",
       "\n",
       "Sub-Category        Storage   Supplies       Tables  \n",
       "Country                                              \n",
       "Australia        55174.5540  20759.148   44940.6390  \n",
       "Brazil           18527.1400   6845.940   25761.9600  \n",
       "China            43739.1300  14320.725   43613.2200  \n",
       "France           96580.4820  15742.020   32738.4330  \n",
       "Germany          72657.2880  10735.644   24764.8785  \n",
       "India            27638.3550  10625.340   41983.4700  \n",
       "Indonesia        33787.9803   5525.250   32502.9549  \n",
       "Mexico           41959.0800  12108.140   40792.1600  \n",
       "United Kingdom   60306.7110   8798.469   28539.2220  \n",
       "United States   223843.6080  46673.538  206965.5320  "
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_10_country_vs_products = data_top_10.pivot_table(values='Sales', index='Country', columns='Sub-Category', aggfunc='sum')\n",
    "top_10_country_vs_products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sub-Category', ylabel='Country'>"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 921.6x633.6 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(top_10_country_vs_products)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **Q5. Is there a seasonality in Sales figures?**\n",
    "\n",
    "- To answer this we will have to group the sales figures by yearly quarters\n",
    "- we will have to add a new column to dataset which will tell which order was placed in which quarter. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        3\n",
       "1        1\n",
       "2        4\n",
       "3        1\n",
       "4        4\n",
       "        ..\n",
       "51285    2\n",
       "51286    2\n",
       "51287    4\n",
       "51288    1\n",
       "51289    2\n",
       "Name: Quarter, Length: 51290, dtype: int64"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's use our original dataframe Data to extract quarters from order date column\n",
    "\n",
    "data['Quarter'] = data['Order Date'].dt.quarter\n",
    "data['Quarter']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have got the data we needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Order Year</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25084</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>66.1200</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>Office Supplies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22951</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>120.3660</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>Office Supplies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9352</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>408.3000</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>Office Supplies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42055</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>55.2420</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>Office Supplies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31579</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>44.8650</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>Office Supplies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50052</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>26.4000</td>\n",
       "      <td>2014</td>\n",
       "      <td>4</td>\n",
       "      <td>Office Supplies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3590</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>1048.7313</td>\n",
       "      <td>2014</td>\n",
       "      <td>4</td>\n",
       "      <td>Furniture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28699</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>22.9230</td>\n",
       "      <td>2014</td>\n",
       "      <td>4</td>\n",
       "      <td>Office Supplies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29674</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>72.3200</td>\n",
       "      <td>2014</td>\n",
       "      <td>4</td>\n",
       "      <td>Office Supplies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35641</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>32.5920</td>\n",
       "      <td>2014</td>\n",
       "      <td>4</td>\n",
       "      <td>Technology</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51290 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Order Date      Sales  Order Year  Quarter         Category\n",
       "25084 2011-01-01    66.1200        2011        1  Office Supplies\n",
       "22951 2011-01-01   120.3660        2011        1  Office Supplies\n",
       "9352  2011-01-01   408.3000        2011        1  Office Supplies\n",
       "42055 2011-01-01    55.2420        2011        1  Office Supplies\n",
       "31579 2011-01-01    44.8650        2011        1  Office Supplies\n",
       "...          ...        ...         ...      ...              ...\n",
       "50052 2014-12-31    26.4000        2014        4  Office Supplies\n",
       "3590  2014-12-31  1048.7313        2014        4        Furniture\n",
       "28699 2014-12-31    22.9230        2014        4  Office Supplies\n",
       "29674 2014-12-31    72.3200        2014        4  Office Supplies\n",
       "35641 2014-12-31    32.5920        2014        4       Technology\n",
       "\n",
       "[51290 rows x 5 columns]"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now let's select the necessary columns and sort them by dates and store in new dataframe. \n",
    "\n",
    "Seasonality = data[['Order Date','Sales','Order Year','Quarter', 'Category']].sort_values(by='Order Date')\n",
    "Seasonality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order Year</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>109885.09530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>90198.86040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>Technology</td>\n",
       "      <td>135696.05750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>154694.38550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>157863.44390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>Technology</td>\n",
       "      <td>166313.15110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2011</td>\n",
       "      <td>3</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>196398.77410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2011</td>\n",
       "      <td>3</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>200994.60280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2011</td>\n",
       "      <td>3</td>\n",
       "      <td>Technology</td>\n",
       "      <td>215912.98878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2011</td>\n",
       "      <td>4</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>295214.12910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2011</td>\n",
       "      <td>4</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>226549.54310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011</td>\n",
       "      <td>4</td>\n",
       "      <td>Technology</td>\n",
       "      <td>309729.86396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>135479.01990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>125282.80950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>Technology</td>\n",
       "      <td>138605.87898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>199143.93030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>180772.93490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>Technology</td>\n",
       "      <td>245675.99398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>222301.41520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>222450.35300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>Technology</td>\n",
       "      <td>293017.12494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2012</td>\n",
       "      <td>4</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>301978.19270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2012</td>\n",
       "      <td>4</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>266588.52960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2012</td>\n",
       "      <td>4</td>\n",
       "      <td>Technology</td>\n",
       "      <td>346142.51140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>206245.83780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>178544.41470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>Technology</td>\n",
       "      <td>180229.33540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2013</td>\n",
       "      <td>2</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>244283.74080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2013</td>\n",
       "      <td>2</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>241935.01460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2013</td>\n",
       "      <td>2</td>\n",
       "      <td>Technology</td>\n",
       "      <td>348620.73804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>311869.64150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>274183.40400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>Technology</td>\n",
       "      <td>346983.94154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2013</td>\n",
       "      <td>4</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>355324.33330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2013</td>\n",
       "      <td>4</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>316054.80860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2013</td>\n",
       "      <td>4</td>\n",
       "      <td>Technology</td>\n",
       "      <td>401471.23910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>217207.68220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>209414.20490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Technology</td>\n",
       "      <td>262584.79674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2014</td>\n",
       "      <td>2</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>305042.71530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2014</td>\n",
       "      <td>2</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>299630.06160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2014</td>\n",
       "      <td>2</td>\n",
       "      <td>Technology</td>\n",
       "      <td>328314.19364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2014</td>\n",
       "      <td>3</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>384428.67060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2014</td>\n",
       "      <td>3</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>366764.79990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2014</td>\n",
       "      <td>3</td>\n",
       "      <td>Technology</td>\n",
       "      <td>445289.39604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2014</td>\n",
       "      <td>4</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>471376.62270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2014</td>\n",
       "      <td>4</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>429842.44000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2014</td>\n",
       "      <td>4</td>\n",
       "      <td>Technology</td>\n",
       "      <td>579970.28694</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Order Year  Quarter         Category         Sales\n",
       "0         2011        1        Furniture  109885.09530\n",
       "1         2011        1  Office Supplies   90198.86040\n",
       "2         2011        1       Technology  135696.05750\n",
       "3         2011        2        Furniture  154694.38550\n",
       "4         2011        2  Office Supplies  157863.44390\n",
       "5         2011        2       Technology  166313.15110\n",
       "6         2011        3        Furniture  196398.77410\n",
       "7         2011        3  Office Supplies  200994.60280\n",
       "8         2011        3       Technology  215912.98878\n",
       "9         2011        4        Furniture  295214.12910\n",
       "10        2011        4  Office Supplies  226549.54310\n",
       "11        2011        4       Technology  309729.86396\n",
       "12        2012        1        Furniture  135479.01990\n",
       "13        2012        1  Office Supplies  125282.80950\n",
       "14        2012        1       Technology  138605.87898\n",
       "15        2012        2        Furniture  199143.93030\n",
       "16        2012        2  Office Supplies  180772.93490\n",
       "17        2012        2       Technology  245675.99398\n",
       "18        2012        3        Furniture  222301.41520\n",
       "19        2012        3  Office Supplies  222450.35300\n",
       "20        2012        3       Technology  293017.12494\n",
       "21        2012        4        Furniture  301978.19270\n",
       "22        2012        4  Office Supplies  266588.52960\n",
       "23        2012        4       Technology  346142.51140\n",
       "24        2013        1        Furniture  206245.83780\n",
       "25        2013        1  Office Supplies  178544.41470\n",
       "26        2013        1       Technology  180229.33540\n",
       "27        2013        2        Furniture  244283.74080\n",
       "28        2013        2  Office Supplies  241935.01460\n",
       "29        2013        2       Technology  348620.73804\n",
       "30        2013        3        Furniture  311869.64150\n",
       "31        2013        3  Office Supplies  274183.40400\n",
       "32        2013        3       Technology  346983.94154\n",
       "33        2013        4        Furniture  355324.33330\n",
       "34        2013        4  Office Supplies  316054.80860\n",
       "35        2013        4       Technology  401471.23910\n",
       "36        2014        1        Furniture  217207.68220\n",
       "37        2014        1  Office Supplies  209414.20490\n",
       "38        2014        1       Technology  262584.79674\n",
       "39        2014        2        Furniture  305042.71530\n",
       "40        2014        2  Office Supplies  299630.06160\n",
       "41        2014        2       Technology  328314.19364\n",
       "42        2014        3        Furniture  384428.67060\n",
       "43        2014        3  Office Supplies  366764.79990\n",
       "44        2014        3       Technology  445289.39604\n",
       "45        2014        4        Furniture  471376.62270\n",
       "46        2014        4  Office Supplies  429842.44000\n",
       "47        2014        4       Technology  579970.28694"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's group the data by years and quarters. \n",
    "\n",
    "seasonality_grouped = Seasonality.groupby([\"Order Year\", \"Quarter\", 'Category'], as_index=False)['Sales'].sum()\n",
    "\n",
    "seasonality_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NwYMHeeutt3jqqaeYNm0a4C25O3XqVJYvX84FF1zA2rVrWbduHa+88gpZWVkApKSkMGvWLLZt28bgwYN59913OXjwIMuXLyclJQWAkJAQHnroIW655RYSEhJYunQpLpeLxYsXExYWxqRJk3A6nSxZsoSZM2f6qmm1VXFx9fEbHSEmJhyAiorAllY+m+maB5aud2CdzPVOTIxsU7v23udAPweBpusdWLregRWI+5xIZxHQHrmysjI2btzIFVdc4bf97rvv5u9//zubN2/G4XAwefJk377o6GjGjh3LZ599BsDatWsB/NZfyczMpG/fvr42a9asIT4+3hfiAMaNG4fNZvO1Wb16NYMGDfKFOPCWCXa5XKxZs8bXZsKECYSFhfm1qaio8JUWFhERERERCbSA9sjt3LkTwzAIDw/n5ptv5n//+x82m42rr76aW2+9lf379wPQvXt3v9d169aNFStWALBv3z4SEhIIDw9v1qbx9fv27SMjI8Nvv9lsJj093ddm//79ZGZm+rWJjY3FZrP5tRk3bpxfm8Zz279/v2/Bz7ZqfErUVlar+YReJydO1zywdL0DKxDX+0SOrZ+DwNL1Dixd78DS9ZazSUCDXHl5OQD33HMP06dPZ9asWXzxxRcsXryYkJAQDMMgODiY4OBgv9dFRET4CpXU1NQQERHR7NgREREUFBQct03jcex2+wm1afz70YVTREREREREAiWgQa6hoQGAc889l3vvvReA8ePHU15ezuLFi5k7dy4mk6nF1zZuNwyjxTZHbjcMA7O5+ajRo7e3dpyWXnu0trQ5WnvHa2tcfeDpmgeWrndgBWLuyIkcWz8HgaXrHVi63oGlOXJyNgnoHLnG3qzzzjvPb/vEiRNxOBxERUXhdDp9ga9RTU0NkZHeXy6bzUZNTU2zYzscjja1sdlsJ9Wm8e+NbURERERERAItoEGucd7a0UHN5XIBYLVaMQyDnJwcv/05OTn07NkT8BY2KSkpoa6u7phtDh065Lff4/GQm5vr1+bo9ykvL8dutx+zTeNxe/Xq1Y5PLiIiIiIicuoENMj16dOH5ORkPvzwQ7/tq1atIikpiYsvvpiQkBCWLVvm21dZWcn69euZMGEC4F2uwO12+4qfgLfwyO7du/3aFBcXs2XLFl+bdevWYbfbfW3Gjx/P119/7ZtXB7Bs2TKCgoIYM2aMr83q1atxOBx+bWJiYhgwYMCpuiwiIiIiIiLtEtA5cmazmTvvvJN7772XX/3qV0ydOpXVq1fz5ptv8tBDD2Gz2bj22mt5+umnMZvNZGZm8txzz2Gz2bj88ssBb6/e1KlTeeCBB7Db7URFRbFw4UL69+/PlClTAG8Ay8rKYt68edxzzz24XC4ee+wxsrOzGTJkCADTp09n8eLF3Hjjjdx+++0UFRXxxBNPcMUVV5CYmAjA1VdfzT/+8Q/mzp3LnDlz2LFjB0uWLOGuu+5qVpBFREREREQkUEyGYRiBftP33nuP559/nv3795OamsqcOXP40Y9+BHiHWS5atIg333wTh8PBiBEjmD9/Pr179/a93uFwsGDBAj766CM8Hg8TJ05k/vz5JCcn+9qUlpbym9/8hlWrVhEcHMzkyZO5//77/ea2HThwgIcffpgNGzYQGRnJjBkzuPPOO/0W+t66dSuPPPII27ZtIyEhgauuuoq5c+ee0OfWguCdn655YOl6B5YWBBfQ9Q40Xe/AUrETOZucliB3tlKQ6/x0zQNL1zuwFOQEdL0DTdc7sBTk5GwS0DlyIiIiIiIicvIU5ERERERERLoYBTkREREREZEuRkFORERERESki1GQExERERER6WIU5ERERERERLoYBTkREREREZEuRkFORERERESki1GQExERERER6WIU5ERERERERLoYBTkREREREZEuRkFORERERESki1GQExERERER6WIU5ERERERERLoYBTkREREREZEuRkFORERERESki1GQExERERER6WIU5ERERERERLoYBTkREREREZEuRkFORERERESki1GQExERERER6WIU5ERERERERLoYBTkREREREZEuRkFORERERESki1GQExERERER6WIU5ERERERERLoYBTkREREREZEuRkFORERERESki1GQExERERER6WIU5ERERERERLoYBTkREREREZEuRkFORERERESki1GQExERERER6WIU5ERERERERLoYBTkREREREZEuRkFORERERESki1GQExERERER6WIU5ERERERERLoYBTkREREREZEuRkFORERERESki1GQExERERER6WIU5ERERERERLoYBTkREREREZEuRkFORERERESki1GQExERERER6WICHuTKy8vp379/sz8//elPATAMg8WLF5OdnU1WVhazZ89mz549fsdwOp08+uijnHPOOYwYMYKf/vSnFBYW+rWprKzkvvvuY9y4cYwZM4b58+djt9v92uTn53PrrbcyatQoJk6cyOOPP47T6fRrs2vXLq6//npGjBhBdnY2S5YswTCMDrgyIiIiIiIibWMN9Bvu2LEDgBdeeAGbzebbHhMTA8Czzz7LkiVLuPvuu0lPT2fx4sXMmjWLDz74gMjISAB+9atfsWLFCu69917Cw8NZuHAhc+fO5Y033sBisQBw2223kZOTw0MPPURdXR2PP/44JSUlPP/884A3DN5www2Ehoby+OOPk5+fz5NPPkldXR0PPvggAKWlpcyePZu+ffuyaNEitm3bxqJFi7BYLMyZMydQl0xERERERMRPwIPczp07SUhI4Nxzz222z26388ILLzBv3jxmzpwJwOjRozn//PN57bXXmD17NgcPHuStt97iqaeeYtq0aQAMGDCAqVOnsnz5ci644ALWrl3LunXreOWVV8jKygIgJSWFWbNmsW3bNgYPHsy7777LwYMHWb58OSkpKQCEhITw0EMPccstt5CQkMDSpUtxuVwsXryYsLAwJk2ahNPpZMmSJcycOZOgoKAAXTUREREREZEmAR9auXPnTvr379/ivs2bN+NwOJg8ebJvW3R0NGPHjuWzzz4DYO3atQBkZ2f72mRmZtK3b19fmzVr1hAfH+8LcQDjxo3DZrP52qxevZpBgwb5QhzAlClTcLlcrFmzxtdmwoQJhIWF+bWpqKhg69atJ3MZRERERERETthp6ZELCQnhyiuvZNu2bcTGxnLddddx4403sn//fgC6d+/u95pu3bqxYsUKAPbt20dCQgLh4eHN2jS+ft++fWRkZPjtN5vNpKen+9rs37+fzMxMvzaxsbHYbDa/NuPGjfNr03hu+/fvZ+TIke367DEx4cdvdASr1XxCr5MTp2seWLregRWI630ix9bPQWDpegeWrndg6XrL2SSgQc7j8bBnzx7CwsK49957SU1NZdWqVSxcuJD6+nqCgoIIDg4mODjY73URERG+QiU1NTVEREQ0O3ZERAQFBQXHbdN4HLvdfkJtGv9+dOEUERERERGRQAlokDMMg+eee460tDR69OgBwPjx43E4HPzlL3/h5ptvxmQytfjaxu2GYbTY5sjthmFgNjcfNXr09taO09Jrj9aWNkerqHC0q33j06T2vk5OnK55YOl6B9bJXO/ExMg2tTuRY+vnILB0vQNL1zuwAnGfE+ksAjpHzmKxMGHCBF+Ia3TeeedRW1tLWFgYTqeThoYGv/01NTW+ipU2m42amppmx3Y4HG1q01gp80TbNP79yIqbIiIiIiIigRTQIFdYWMjLL79MWVmZ3/b6+nrAW9jEMAxycnL89ufk5NCzZ0/AW9ikpKSEurq6Y7Y5dOiQ336Px0Nubq5fm6Pfp7y8HLvdfsw2jcft1atX+z68iIiIiIjIKRLQIOd0OnnwwQd55513/LZ/9NFHZGZm8r3vfY+QkBCWLVvm21dZWcn69euZMGECABMmTMDtdvuKn4C38Mju3bv92hQXF7NlyxZfm3Xr1mG3231txo8fz9dff+2bVwewbNkygoKCGDNmjK/N6tWrcTgcfm1iYmIYMGDAqbosIiIiIiIi7RLQOXLdu3dn+vTpPP3005hMJnr37s2HH37If//7X5599lkiIiK49tprefrppzGbzWRmZvLcc89hs9m4/PLLAcjIyGDq1Kk88MAD2O12oqKiWLhwIf3792fKlCmAN4BlZWUxb9487rnnHlwuF4899hjZ2dkMGTIEgOnTp7N48WJuvPFGbr/9doqKinjiiSe44oorSExMBODqq6/mH//4B3PnzmXOnDns2LGDJUuWcNdddzUryCIiIiIiIhIoJsMwjEC+YV1dHX/605947733KC4upnfv3tx6661873vfA8DlcrFo0SLefPNNHA4HI0aMYP78+fTu3dt3DIfDwYIFC/joo4/weDxMnDiR+fPnk5yc7GtTWlrKb37zG1atWkVwcDCTJ0/m/vvv95vbduDAAR5++GE2bNhAZGQkM2bM4M477/Rb6Hvr1q088sgjbNu2jYSEBK666irmzp17Qp+9uLi6Xe01QTrwdM0DS9c7sAJRBKC99znQz0Gg6XoHlq53YKnYiZxNAh7kzmYKcp2frnlg6XoHloKcgK53oOl6B5aCnJxNAjpHTkRERERERE6egpyIiIiIiEgXoyAnIiIiIiLSxSjIiYiIiIiIdDEKciIiIiIiIl2MgpyIiIiIiEgXoyAnIiIiIiLSxSjIiYiIiIiIdDEKciIiIiIiIl2MgpyIiIiIiEgXoyAnIiIiIiLSxSjIiYiIiIiIdDEKciIiIiIiIl2MgpyIiIiIiEgXoyAnIiIiIiLSxSjIiYiIiIiIdDEKciIiIiIiIl2MgpyIiIiIiEgXoyAnIiIiIiLSxSjIiYiIiIiIdDEKciIiIiIiIl2MgpyIiIiIiEgXoyAnIiIiIiLSxSjIiYiIiIiIdDEKciIiIiIiIl2MgpyIiIiIiEgXoyAnIiIiIiLSxSjIiYiIiIiIdDEKciIiIiIiIl2MgpyIiIiIiEgXoyAnIiIiIiLSxSjIiYiIiIiIdDEKciIiIiIiIl2MgpyIiIiIiEgXoyAnIiIiIiLSxSjIiYiIiIiIdDEKciIiIiIiIl2MgpyIiIiIiEgXoyAnIiIiIiLSxSjIiYiIiIiIdDEKciIiIiIiIl2MgpyIiIiIiEgXoyAnIiIiIiLSxSjIiYiIiIiIdDEKciIiIiIiIl3MaQtyTqeTiy66iPvuu8+3zTAMFi9eTHZ2NllZWcyePZs9e/Y0e92jjz7KOeecw4gRI/jpT39KYWGhX5vKykruu+8+xo0bx5gxY5g/fz52u92vTX5+PrfeeiujRo1i4sSJPP744zidTr82u3bt4vrrr2fEiBFkZ2ezZMkSDMM4xVdCRERERESkfU5bkPvjH//I3r17/bY9++yzLF68mBtuuIGFCxdSXV3NrFmzqK6u9rX51a9+xdtvv81dd93FggUL2LFjB3PnzsXtdvva3Hbbbaxfv56HHnqI+++/nxUrVnDXXXf59judTm644Qby8vJ4/PHHueWWW/jnP//J7373O1+b0tJSZs+ejclkYtGiRVxxxRUsWrSIv/71rx14VURERERERI7Pejre9JtvvuHvf/87sbGxvm12u50XXniBefPmMXPmTABGjx7N+eefz2uvvcbs2bM5ePAgb731Fk899RTTpk0DYMCAAUydOpXly5dzwQUXsHbtWtatW8crr7xCVlYWACkpKcyaNYtt27YxePBg3n33XQ4ePMjy5ctJSUkBICQkhIceeohbbrmFhIQEli5disvlYvHixYSFhTFp0iScTidLlixh5syZBAUFBfiqiYiIiIiIeAW8R87lcnH//fczZ84ckpOTfds3b96Mw+Fg8uTJvm3R0dGMHTuWzz77DIC1a9cCkJ2d7WuTmZlJ3759fW3WrFlDfHy8L8QBjBs3DpvN5muzevVqBg0a5AtxAFOmTMHlcrFmzRpfmwkTJhAWFubXpqKigq1bt56qyyEiIiIiItJuAe+R+/Of/0xDQwNz587l448/9m3fv38/AN27d/dr361bN1asWAHAvn37SEhIIDw8vFmbxtfv27ePjIwMv/1ms5n09HRfm/3795OZmenXJjY2FpvN5tdm3Lhxfm0az23//v2MHDmyXZ8bICYm/PiNjmC1mk/odXLidM0DS9c7sAJxvU/k2Po5CCxd78DS9Q4sXW85mwQ0yO3Zs4fnnnuOv/3tbwQHB/vts9vtBAcHN9seERHhK1RSU1NDREREs+NGRERQUFBw3DaNx7Hb7SfUpvHvRxdOERERERERCaSABTmPx8P8+fO57LLLGDFiRLP9hmFgMplafG3j9tbaHLndMAzM5uYjRo/e3tpxWnrt0drSpiUVFY52tW98mtTe18mJ0zUPLF3vwDqZ652YGNmmdidybP0cBJaud2DpegdWIO5zIp1FwObI/f3vfycvL4+f/vSnuFwuXC4X4A1PLpeLyMhInE4nDQ0Nfq+rqakhMtL7i2Wz2aipqWl2bIfD0aY2NpvtpNo0/r2xjYiIiIiIyOkQsCC3bNkyCgsLGTt2LIMHD2bw4MHs2LGDt956i8GDB2O1WjEMg5ycHL/X5eTk0LNnT8Bb2KSkpIS6urpjtjl06JDffo/HQ25url+bo9+nvLwcu91+zDaNx+3Vq9fJXAoREREREZGTErAg9+tf/5rXXnvN709mZqZveYGLL76YkJAQli1b5ntNZWUl69evZ8KECQBMmDABt9vtK34C3sIju3fv9mtTXFzMli1bfG3WrVuH3W73tRk/fjxff/21b14deINmUFAQY8aM8bVZvXo1DofDr01MTAwDBgzogCskIiIiIiLSNgGbI9dSL1ZoaCgxMTEMHToUgGuvvZann34as9lMZmYmzz33HDabjcsvvxyAjIwMpk6dygMPPIDdbicqKoqFCxfSv39/pkyZAngDWFZWFvPmzeOee+7B5XLx2GOPkZ2dzZAhQwCYPn06ixcv5sYbb+T222+nqKiIJ554giuuuILExEQArr76av7xj38wd+5c5syZw44dO1iyZAl33XVXs4IsIiIiIiIigWQyDMM4XW/+//7f/2PgwIH87ne/A7xrzC1atIg333wTh8PBiBEjmD9/Pr179/a9xuFwsGDBAj766CM8Hg8TJ05k/vz5fmvSlZaW8pvf/IZVq1YRHBzM5MmTuf/++/3mth04cICHH36YDRs2EBkZyYwZM7jzzjv9FvreunUrjzzyCNu2bSMhIYGrrrqKuXPnnvDnLS6ubld7TZAOPF3zwNL1DqxAFAFo730O9HMQaLregaXrHVgqdiJnk9Ma5M42CnKdn655YOl6B5aCnICud6DpegeWgpycTQI2R05ERERERERODQU5ERERERGRLkZBTkREREREpItRkBMREREREeliFORERERERES6GAU5ERERERGRLkZBTkREREREpItRkBMREREREeliFORERERERES6GAU5ERERERGRLkZBTkREREREpIs5qSC3Z88eVq1aRW1t7ak6HxERERERETmONgc5u93Oz372M5YsWQLAO++8w/Tp07n55puZNm0aOTk5HXaSIiIiIiIi0qTNQe73v/89y5cvJykpCYBnnnmGgQMH8swzzxAaGsqiRYs66hxFRERERETkCNa2NlyxYgV33nknl156KXv27OHQoUMsXLiQKVOm4HA4+N3vfteR5ykiIiIiIiKHtblHrqSkhAEDBgCwevVqTCYTEyZMACAxMZGampqOOUMRERERERHx0+Ygl5CQQEFBAQCffvop/fr1IzY2FoBt27b5hlyKiIiIiIhIx2rz0Mrx48fz+9//no0bN/LZZ59xxx13APC3v/2NZ599liuvvLKjzlFERERERESO0OYeufnz59OvXz/efvttpkyZwuzZswF46aWXGD16NLfeemuHnaSIiIiIiIg0aXOPnM1m489//nOz7a+++irx8fGn9KRERERERESkdW0Oco2Ki4tZvXo1hYWFfP/736e4uJiYmBgsFktHnJ+IiIiIiIgcpV1B7tlnn+W5556joaEBk8nEOeecw5NPPkllZSUvvvgikZGRHXWeIiIiIiIiclib58i98sorPPPMM1x55ZUsXboUwzAAuOqqq9i3bx/PPvtsh52kiIiIiEhrqqsa2LG1ijf/tZ8XF+9i59dVp/uURDpcm3vkXnrpJa666irmz5+P2+32bb/gggs4cOAA//73v7nvvvs65CRFRERERI5kr2og52AtuQccVJY3+O3Lz62l/5Co03RmIoHR5iB34MABfv7zn7e4b8iQIRQXF5+ykxIREREROZq9uoHcA7XktBDeGsUnhjB0VHSAz0wk8Noc5GJiYsjNzW1x36FDh4iO1i+MiIiIiJxa9moXuQccxwxv0bFBdOsRzsAhcUTFBFNR4QjwWYoEXpuD3Pnnn8+zzz7L0KFDGTRoEAAmk4nCwkL+/Oc/k52d3VHnKCIiIiJnkZpqFzkHHeQecFBRduzwlt4jDFtkEABRMcGBPE2R06rNQe6OO+5g7dq1/OhHPyI1NRWTycS9995LTk4OcXFx3HHHHR14miIiIiJyJquxN/W8HSu8pfcIp1tGGLaooACfoUjn0uYgFxcXx+uvv87f/vY3/ve//2G1WgkNDWXmzJnMmjWL2NjYjjxPERERETnDtCm8xXjDW3qPMCIV3kR82rWOXGRkJLfddhu33XZbR52PiIiIiJzBauwucg86yD1QS3mps8U2UTFBdOsRRnpGOJHRCm8iLTlmkNuyZUu7DjZs2LCTOhkREREROfM4Doe3nOOEt/SMMLr1UHgTaYtjBrkrrrgCk8l03IMYhoHJZGL79u2n7MREREREpOty1Li8SwUcdFBe0kp4i7YeHjYZTpTCm0i7HDPILViwIFDnISIiIiJdnKPGRe7hRbrLWglvkdFWb7XJjHCiYhTeRE7UMYPc97///UCdh4iIiIh0QY4aF3kHvYt0txreorw9b916KLyJnCrtKnbicrn49ttvcTqbfkk9Hg+1tbWsX7+e22+//ZSfoIiIiIh0LrUOb89bzgEHZcUthzdblNW3zltUdFCbpuuISNu1Ocht2bKF2267jaKiohb3m81mBTkRERGRM1RjeMs94KC0tfAW2djzFkZUjMKbSEdqc5B7+umncblc/PznP2flypWEhoYyadIkVqxYwZo1a/jXv/7VkecpIiIiIgFW63CTd9C7zpvCm0jn0q4euTvuuINrrrmG8PBwPvzwQ6655hquueYa5syZw0svvcSTTz7ZkecqIiIiIh2s1uEm75B3qYDSovoW20REWr3rvPUIJ1rhTeS0aHOQq62tpXfv3gD07NmTnTt3+vZddtllPPHEE6f+7ERERESkw9XVun2LdJccI7w1rvMWHavwJnK6tTnIxcfHU1ZWBkBGRgYVFRWUlZURFxdHTEwMpaWlHXaSIiIiInJqtSm82ayk91B4E+mM2hzkxo0bx1/+8hdGjBhBamoq8fHxvPvuu1x//fWsXLmSuLi4jjxPERERETlJdbVu8g55q02WFLYW3izeRbozwomJU3gT6azaHORuvfVWrrjiCn72s5/x73//m+uuu44FCxbw5z//mdLSUubMmdOR5ykiIiIiJ6Cuzk3e4WqTxUX1YDRvE26z0C0jnPQeCm8iXUWbg1yPHj348MMP2b59OwA//vGPsVgsfPHFF4wYMYIbb7yxw05SRERERNquvs7tXSrgoIPiwlbCW4TFt0i3wptI19OmILdnzx5CQ0NJT09n4sSJVFdX8+c//5ldu3YxYMAArrrqKqzWdq0tLiIiAWIYBjkHavlsdzERNitZY2OwWPSFTeRMU1/XNGzymOEtw7tId2x8sMKbSBd2zPRlGAYPPvggr732GnfeeSc33XQTLpeLmTNnsmPHDuLj41m9ejUff/wxr7zyCjabLVDnLSIibVBZ7mTzFxW+QgbFhdCrfwQxccGn+cxE5FRoDG+5h8Ob0UJ4C4toHDap8CZyJjEfa+dbb73Fq6++yjXXXMPUqVMBePXVV9m+fTvXXnstn3/+OR9//DG1tbX85S9/adMbOp1Ofv/733P++eczfPhwZs6cybZt23z7DcNg8eLFZGdnk5WVxezZs9mzZ0+zYzz66KOcc845jBgxgp/+9KcUFhb6tamsrOS+++5j3LhxjBkzhvnz52O32/3a5Ofnc+uttzJq1CgmTpzI448/jtPpv9jlrl27uP766xkxYgTZ2dksWbIEo6W7pIhIJ+Ks97D5i3JWfFDoV40us7eN6Nig03hmInKy6uvd7Ntt5/PlRXzweh6b1pVTVOAf4sLCLfQZaCN7ahJTL01l6KgY4hJCFOJEziDH7JF78803ueyyy/jlL3/p2/bxxx9jtVqZN28eAMnJyVx33XW88cYb3HHHHcd9wwULFvD2229z9913k5GRwd///ndmzpzJO++8Q3p6Os8++yxLlizh7rvvJj09ncWLFzNr1iw++OADIiMjAfjVr37FihUruPfeewkPD2fhwoXMnTuXN954A4vFAsBtt91GTk4ODz30EHV1dTz++OOUlJTw/PPPA94weMMNNxAaGsrjjz9Ofn4+Tz75JHV1dTz44IMAlJaWMnv2bPr27cuiRYvYtm0bixYtwmKxqLiLiHRKhmGw/9satn1VibPe49seHmFh/HeS6dHLRmVl7Wk8QxE5EfX1bvIP1ZJzoJbigrqWe97CLb513mIT1PMmcqY7ZpDbuXMnM2fO9P3d7XazadMmhgwZQnR0tG/7wIEDycnJOe6bVVdX8+qrr3LXXXdx9dVXAzB69GjGjRvH22+/zcyZM3nhhReYN2+e731Hjx7N+eefz2uvvcbs2bM5ePAgb731Fk899RTTpk0DYMCAAUydOpXly5dzwQUXsHbtWtatW8crr7xCVlYWACkpKcyaNYtt27YxePBg3n33XQ4ePMjy5ctJSUkBICQkhIceeohbbrmFhIQEli5disvlYvHixYSFhTFp0iScTidLlixh5syZBAXpqbaIdB5lJfVs/qKC8tKmkQVmM/QbHEW/wZEkJGj4u0hX4qxvnPN2/PCW3iOcOIU3XIV5uL7djtFrGKawiNN9OiId6phDKx0OBxERTb8EO3bsoLa2ltGjR/u1c7lcbbpxhIWF8corr/CDH/zAt81qtWIymXA6nWzevBmHw8HkyZN9+6Ojoxk7diyfffYZAGvXrgUgOzvb1yYzM5O+ffv62qxZs4b4+HhfiAPvOng2m83XZvXq1QwaNMgX4gCmTJmCy+VizZo1vjYTJkwgLCzMr01FRQVbt2497ucVEQmEujo3X64pY+WHRX4hLrV7GN+7JJVBWdFYrce83YtIJ+Gs97B/j53/rSjm/dfy2Li2nKJ8/xAXGm6h9wAbky5MYur3Uxk2Opb4xLN72KSnuICGvz5JyS2XUfHE/TQsefR0n5JIhztmj1xiYiJ5eXm+v69evRqTycSYMWP82n3zzTckJSUd/82sVgYNGgSAx+MhNzeXZ555BpPJxCWXXOILUN27d/d7Xbdu3VixYgUA+/btIyEhgfDw8GZt9u/f72uTkZHht99sNpOenu5rs3//fjIzM/3axMbGYrPZ/NqMGzfOr03jue3fv5+RI0ce9zMfKSYm/PiNjtD4xau9r5MTp2seWLreJ8fjMdi+tYJN60pwOpuGUUbFBDH+vGS69fB/Gh2I630ix9bPQWDpegdWW653fZ2bg/vs7Pu2mrxDNXg8zduER1jJ7G2jZ99IklLCzurQdiRXYR41r/0N54r3wO32bQ8OD9fPuJzxjhnkxo8fz9KlS7n44otxu9289tprhIeHM378eF+b8vJyli5dyqRJk9r1xn/605945plnAPjpT39Kr169+PjjjwkODiY42L+aWkREhK9QSU1NjV8v4ZFtCgoKjtum8Th2u/2E2jT+/ejCKSIigZSf42DNp4VUlDX1wFmDTIwYk8CgrFgtLyDSydXXuzm499jhLSzcQs8+kWT2iSQ5VeHtSO6ifOyv/Y3a5e/6BThTaBi2GT8i7Aczj/FqkTPDMYPczTffzA9/+EPOOecczGYz1dXV3HPPPYSEhACwZMkS/v3vf1NVVcUNN9zQrjeeMmUKY8eOZd26dfzpT3+ioaGB0NDQVm9SjdsNw2ixzZHbDcPAbG4+jOjo7a0dp6XXHq0tbY5WUeFoV/vGJ0ntfZ2cOF3zwNL1bj9HjYutGyvIPeBfsKR7ZjhDRsYQFm6hurp5MRPDWU/o9nWY4xKp7T6w3e+bmBjZpnYn8v+lfg4CS9c7sI683k6nh/wc71IBhfl1GC2Et5BQs2+R7vjEpjlvKlLkZZQW4np3Ke7P/gNuV9OOkFAsU35A/I+uxxwV4/35rm/fz3hb73MincUxg1xGRgb//ve/eeGFFygrK2PKlClcdtllvv0vvvgiYWFhLF68mJ49e7brjQcMGADA2LFjqamp4YUXXuDuu+/G6XTS0NDgV0ikpqbGV7HSZrNRU1PT7HgOh8OvTXFxcYttGte6O9ZxjtWm8e9aM09EAsntNtj9TTU7v67C7W6aLBMdG0TWmFgSkkJaf+3W9bheepr6Yu9Q+eAn/ok5MbXDz1lEvD1vh/bZ2bW9gqL8uhZ73kJCzaRnhNOtR5h3rptZPW9HM0oLcb33T9yfftA8wE3+PtapV2CKisEcpeGUcvY4ZpAD6N27N48+2vKE0TfeeIOkpKQ2d/UXFxfz6aefcuGFF/oFoYEDB+J0OomOjsYwDHJycvyC4ZF/z8zMpKSkhLq6OkJDQ/3ajBo1ytdm48aNfu/dOCdvxowZvjZHV9osLy/Hbrf7vdfRbQ4dOgRAr1692vSZRUROVn5OLVs2VFBjb/ryEhRsZlBWFL362lr90mdUlNHwr2fxrFvh22aKiMQUpMXARTpSdVUDBTm15OfWUVrU8iLdjeEtvUcYCQpvrTJKi3C9vxT3qqMCXHAolimXYp36I0xRMaft/EROp+MGuWNJTk5uV/uqqiruv/9+AH74wx/6tv/vf/8jPj6eKVOmEBISwrJly7jpppsA78Le69ev961bN2HCBNxuNytWrPAtP7B//352797t1+b5559ny5YtDBs2DIB169Zht9uZMGEC4J3/9+tf/5qCggJf5cply5YRFBTkK+Yyfvx4Xn75ZRwOh6+4yrJly4iJifH1KIqIdBR7VQObN1RQmFfnt71n3wgGZUUTEmpp8XWGx4N75Xu4Xl0CtU2jCoL6DyHqJ/dRExPfoectcrbxeAxKi+rJz6mlILcOe7WrxXYhoWbSMsLolhFOQpLC27EYZcW43lvq7YFzNTTtCA7FMvn/Yb3oR5iiYk/fCYp0AicV5Nqrd+/eXHjhhTz22GM0NDTQvXt3/vvf//L222/z6KOPYrPZuPbaa3n66acxm81kZmby3HPPYbPZuPzyywHvcM+pU6fywAMPYLfbiYqKYuHChfTv358pU6YA3gCWlZXFvHnzuOeee3C5XDz22GNkZ2czZMgQAKZPn87ixYu58cYbuf322ykqKuKJJ57giiuuIDExEYCrr76af/zjH8ydO5c5c+awY8cOlixZwl133dWsIIuIyKnicnnYsbWKb7dX+w3DiksIJmtMLLHxrd9/PIf20PC3hRh7vmnaGBaB9fK5xF16BSazGTQ3SuSk1de7KcytoyC3lsK8OhoaWuh2AyIirXTPtJGYHKTw1gZGWTGu9/+Je9X7RwW4ECzfvRTrNAU4kUYmw2ipw7/j1NbW8sc//pH//Oc/FBUV0adPH26++WamTp0KeNekW7RoEW+++SYOh4MRI0Ywf/58evfu7TuGw+FgwYIFfPTRR3g8HiZOnMj8+fP9eghLS0v5zW9+w6pVqwgODmby5Mncf//9fkM6Dxw4wMMPP8yGDRuIjIxkxowZ3HnnnX7z87Zu3cojjzzCtm3bSEhI4KqrrmLu3Lkn9NmLi6vb1V4T0gNP1zywdL39GYZBzoFatm6soM7RVIUtJNTMkJExZPQMb3Uou1Ffi+utF3F/9CpHpj/z+O8SdOWtmGLiTup6t7UIQHvvc6Cfg0DT9T4xhmFQXekiP7eWgpxaSkuc0Mo3qLiEYFK7hZGSHkpGZjQmk0nX+ziM8mJc7/0L96r3Wghwh3vgouOOe5xA3OdEOouAB7mzmYJc56drHli63k0qy51s/qKCkqJ63zaTCXoPsDFwaDRBwa1XynV/tYaGvz8NpYVNr01Mw3r9HViGNK37qSAnoOvdHm63QUlhPQW5teTn1uKwu1tsZw0ykZwaSkq3MFLSQv2GPet6H5tRXuLtgVvZQoA7/xKsF12JKeb4Aa6RgpycTQI6tFJERPw5nR62b65k7y67X0GEpJQQho2JJSo6qNXXGuXFNCz9I54NnzZttFixTLsS64xrMQW3XslSRFpWV+emILeWgpw6ivLrcLlaGTJps5DSLYzU9DASkkIwa+3GdjHKS3B98C/cn7zrH+CCgrF89xKsF13VrgAncjZSkBMROQ0Mw+DAnhq2baqkvr5pKGRYhIVho2JI69764r+Gx417+Vu4Xv8r1DU9dTb1G0rQ9XdiTs/s6NMXOWMYhkFlRVOVyfISZ4vtTCaITwwhJT2U1G5h2KKsWqD7BBgVpbje/xfule9CwxHXOijY2wM3TQFOpK0U5EREAqyspJ7NX1RQXtr0JcZshn6Do+g3OBKrtfVhlJ79u2h4cSHGvp1NGyOisF4xF8t5F3mLmYjIMbldHooLm6pM1jpaHjIZFGwiOS2M1PRQktNCCQ5puVKsHF+rAc4a5A1wF1+FSRV1RdpFQU5EJEDq6txs21TJgT01fttTu4UxbFQMEZGt35KNWgeuN/8P98dvgHFEMZNzLiDoRz/ROkoix1Hr8A6ZzM+ppbigHre75SGTkVFWUtLDSOkWSnxiCGZVmTwpRkXZ4SGU77QQ4GZ4e+BiE07fCYp0YQpyIiIdzOMx2LvLzvbNlX4lym2RVrLGxJCcFtbqaw3DwLPxcxqWPgNlxb7tppTuWGfegWXQyA49d5GuyjAMKsoaDve61VJR1tBiO5MJEpJDSD0c3myRrc9LlbYzKspw/edfuFe0EOCyp2O9+GoFOJGTpCAnItKBigvq2LyhgqqKpi+RVquJAUOj6DMg8pgFEozSQhr+/gc8X61u2mgNwjr9GizTrsKk9SxF/LhcHoryvVUmC3Jrqav1tNguOMRMSpq3ymRyaugxq8JK+xiVZbg++Le3B87ZVIUXaxCWSdOxTr8KU2zi6TtBkTOIgpyISAdw1LjYurGC3AO1ftu7Z4YzZGQMYeGtz7Ux3G7c/30d11v/B/V1vu3mgSOwzrwDc2pGh523SFfjqHH55roVF9QduYyin6iYIF+hkrj4YC3MfYoZVeXeALfi7RYC3MXeHrg4BTiRU0lBTkTkFHK7DXZvr2bn1iq/OTjRsUFkjYklIenYSwJ49m6n4W8LMQ5+27QxMpqgK2/BPPF7qpInZz3DY1Be6jy8MHcdlRUtD5k0myEhOZTUbqGkpIcRYdNXno7gDXAvHw5wTQ+esAZh+c40rNOvUYAT6SC6q4mInCL5ObVs2VBBjd3l2xYUbGZQVhQ9+9qOWTTBcNhxvfYX73CkIxaUs3xnGtYr5mKyRXfouYt0Zg1OD0UFdeTn1FKYW+e3ZMeRQkLN3kIl6aEkp4ZiDdKQyY5iVFXg+s+/cS8/KsBZrFgmTcN68TWY4pNO3wmKnAUU5ERETpK9uoEtGyooyK3z257ZJ4LBw6MJCT3GMErDwPPFShqW/hEqy3zbTWk9vGvC9R/WYect0pnVVLsO97rVUlxUf2SxVj/RsUGkdvOGt9j4YPVadzCjqgLXhy/jXvZW8wD3nWlYp1+NKT75tJ2fyNlEQU5E5AS5XB52bK3i2+3VfvNy4hKCyRoTS2z8sYuReIrzcb20CM/W9U0bg4KxXnIdlot+hMmq6nly9vB4DMpKnIcX5q6lutLVYjuzxURSSoiv5y08Ql9lAsGorsT1n5dxL3/Tb+4uFiuW8y7COuOa0xrgCu31bMytZGtxDQVVdfxgUDLnZmphcTmz6e4nItJOhmGQe6CWrRsr/BYSDgk1M2RkDBk9w4/ZK2C4XLg/fAXXOy/5FQUwDxntLWaSlN6h5y/SWTidHgrz6ijIqaUwrw6ns+Vut9Awi69QSWJKCFarhkwGilFdievDV3Ave+OoAGfxBrjp12BKSAn4edW53GwtqObL3Eo25laRU+U/IuI1j6EgJ2c8BTkRkXaoLHeyeUMFJYVNAcxkgt4DbAwcGn3cMuae3Vu9xUxy9zdtjIol6Op5mMedr2Fhcsarrmo43OtWR2lR/ZFTQv3Exgf7wlt0bJB+NwLMsDcGuDeh7ojqu4cDnOXiazAnBi7AGYbB/opaNuZW8mVuFdsKq2nwtPzDYwu28IPBgQ+XIoGmICci0gZOp4ftmyvZu8vu98UzMSWErNGxRMUcexikYa/C9eoS3Kveb9poMmHJnoH1spswRdg66MxFTi+Px6C0qN63RIC9uuUhkxaLiaS0UFLTvVUmQ8Nan1sqHcewVzX1wB0d4M6dimX6tQELcFV1Ljble3vcNuZVUupoZVF3oG9CBCPTopjUP5nBKZHYq+tabCtyJlGQExE5BsMwOLCnhm2bKv0q5YVFWBg2Koa07mHHHkZpGHjWLKPhX3+C6grfdlP3XgRdfxfmPoM68vRFTov6ejeFuXXk53qHTLoaWu45CYuwkJoeRkq3UBKTQ7FY1Ot2uhj2KlwfvYr74zegztG0w2LBcs5ULDOuwZyY2qHn4PYY7Cyx82VuFV/mVrK7pIZWOmyJDQtiVFoUI9OjGZEWRXSo92FaTEx4h56jSGeiICci0oqykno2f1FBeanTt81shn6Do+g3OPK483Q8BTm4/r4Iz7YvmzYGh2K99HosF1yGyapbsJwZDMOgurKpymRpiZPWvoHHJQT7qkxGxWjI5Olm1FTj+vBV3B+/7h/gzObDPXDXYE5K67D3L7LXszHPG9y+yquipsHdYjur2cSQ5EhGpkcxKi2azNhjP0QTORvoW4SIyFHq6txs21TJgT01fttTu4UxbFQMEZHHvnUaDU7cH/wb17v/AFfTUCBz1niCrrv9tBQGEDnV3G6DksJ6CnK9VSYd9la+gAeZSE4NJaVbGClpocdcjkMCx6ipbuqBqz3iXmc2YznnQiwzru2QAFfv8rC1oJqNeZV8mVvJocrWh0CmR4UyKj2KkWnRDEuJJDRIPzsiR1KQExE5zOMx2LvLzvbNlTQcMRTMFmll2JgYUtLCjn+MHV95i5kUHGraGJNA0DXzMI/+jp4gS5dWV+emILeWgpw6ivLrcLla7naLsFlI6RZGanoYCUkhmDVkstMwauy4/vsq7v++3kKAu+BwgDt1lXMNw+BgRR1f5lbyZV4lXxe0XqQkLMjM8FRvcBuVHk1KZMgpOw+RM5GCnIgIUFxQx+YNFVRVNPWgWawmBg6Nos+AyON+ETWqK3G9/Bzuzz9s2mgyYZl8KdYfzsEUFtFRpy7SYQzDoLKiqcpkeYmzxXYmE8QnhviqTNqirHpo0cl4A9xruP/7WvMAN/FwgEs+NQGuut7FprwqX6/bsYqU9IkPZ2S6N7gNSIzAatbSEiJtpSAnImc1R42LrzdWkHOg1m9798xwhoyMJiz8OMMoDQP35x/ievk5sFf5tpt69CVo1l2Ye/bvkPMW6Shul4fiwqYqk0eulXikoGATyWlhpKaHkpwWSnCIhr11RobDjvu/r+P66FX/AGcyY574PayXXHfSAc7tMdhVUuPtdcutZHdpDa10uhEbFsTIw0VKRh5RpERE2k9BTkTOSm63wbfbq9mxtQq3u+kbR3RsEFljYklIOv6QHk/eQRpeXIixc3PTxtAwrD+4Acvk72Oy6IutdA21Du+QyfycWooL6v1+J44UGWUl5XCVyfjEEMxm9bp1VobDjvvj13F99Bo47E07TGbME6dgnXEd5pRuJ3z84pp6vsytYmNuJV/lV2F3tl6kZFCSjVGHe916qkiJyCmjICciZ538nFq2fFlBzRHrWQUFmxmUFUXPvrbjfjk1nE5c7/0D9/v/AnfTMcyjziPomtswxSV22LmLnAqGYVBe6jzc61ZLRVkrQ99MkJAc4lsiwBap3pPOzhvg3vD2wB0d4CZM9vbApXRv93HrXR6+Lqzmy9xKNuZVcrCi9SIlaVEhjEqLZmS6t0hJmIqUiHQIBTkROWvYqxvYsqGCglz/LyCZfSIYPDy6TdX03Nu+xPXS7zEKc5s2xiURdN1PsYw451Sfssgp5XJ5+GJ1Md/uqGx1yGRwiJmUNG+VyeTUUIKCNWepKzBqa5oCXE110w6TGfP4yVgvuRZzakbbj2cYHKqs8w2X/LqwGmcrPbVhQWayUqIYlR7NyPQoUiNDT/bjiEgbKMiJyBnP5fKw8+sqdn9TjadpTW/iEoLJGhNLbHzwcY9hVJbR8O/FeNYsa9poNmO54DKsl87CFHr8ipYip1NpUT0b1pT59UQ3iooJ8hUqiYsPxqQhk12GN8C9ieujV44KcCZvgJtxHea0tgW46noXX+V713TbmFtJSStFSsBbpGRUejQj06IZmKQiJSKng4KciJyxDMMg90AtWzdW+PU+hISaGTIihoxe4cedq2F4PLg/fR/XK0v8himZeg0g6Pq7MPfo02HnL3IquF0evtlcxe7tTV/yzWZISA4ltVsoKelhRNj0daCrMWoduJe9gevDV6GmqdASJhPmcd/FesnM4wY4t8dgd0kNX+Z5g9vOkmMUKQm1MiI9mlFp0YxIiyImTMNsRU433blF5IxUWeFk8xcVlBTW+7aZTNC7v42Bw6LbNFzMk7OPhr89hfHttqaNYRFYL7sRy/kzMJk170M6t7KSer5cXUZ1VVMvXEJSKOdNScFkbnlopXRuRq0D9/I3cf3nlRYC3PmHA1yPVl9fUuNkY14lG3Ir+Srv+EVKvGu6RdEzLhyzipSIdCoKciJyRnE6PWzfUsnenXaMI54sJ6aEkDU6lqiY4z9FNurrcL39Eu6PXgF305cc89hsgq66FVNsQkecusgp43YbbN9Sya5vquHw74HJDAOHRjF2Ygpmi4mKCsfpPUlpF2+AewvXhy/7LXWCyYR57PneIibpmc1e52wsUpJXycbcKg5U1DZr0yg1MsRXXXJoSiThKlIi0qkpyInIGcEwDA7sqWHbpkrq65smwoVFWBg2Koa07m0ree3esg7XS4swSgp820yJqVivuwPLsLEdcu4ip1J5qZMv15T5LW4fHRvEqIlxxMQGH3dxe+lcjLpab4D7z7+bB7gx2Vj/30y/ANdYpGRjbiVf5lXxdUE19W5P8wMDYVYzWaneNd1GpUWRGqUiJSJdiYKciHR5ZSX1bP6igvJSp2+b2Qz9BkfRb3AkVuvxh1Ea5SU0/POPeL5Y1bTRYsEy9UdYL7kOU4i+4Ejn5nEb7Pi6ip1fV/l6o00m6D8kigFDohTguhijrhb3irdw/edlqK5s2mEyYR4z6XCA6wmA/XCRko153kIlxTXOVo4KvePCGZkexai0aAYm2QiyqEiJSFelICciXVZdnZttmyo5sKfGb3tqt1CGjYolIvL4tzjD48a94h1cr78AtU3HMfUZTNCsuzB363nKz1vkVKssd7JhdRmV5U29cFHRVkZNjG9TVVbpPIz6WtzL3/b2wB0Z4OBwgLseIy2TXaU1bPwqly/zqthZbG+1SEl0qNU3z21EWjSxKlIicsZQkBORLsfjMdi7y872zZU0NDR9e7FFWhk2JoaUtLYtBeA58C0NLz6FsXdH08ZwG9YrfozlO9MwqZy2dHIej8Gub6rZvqUSo3H0nAn6DYpk4LBoLOqF6zKM+lrvQ6UP/g3VFX77zKMnUTn1WjYZ0Wz8topNn22iur7lIiUW0+EiJenedd16qUiJyBlLQU5EupTiwjo2f1HhN//HYjUxYGgUfQZEtumLq1FXi+vN/8P98escubCcecIUgq78CabouA45d5FTqaqygS9Xl/kNKbZFWRk9IY64xJDTeGbSHkZ9nTfA/effUFXu2+40W9k55hI298/myyoTBz4vB8pbPEZKZAij0rzBbVhqlIqUiJwlFOREpEtw1Lj4emMFOQf8K651zwxnyMhowsLbdjtzb/ofDX//A5QV+baZktOxzvwZlsGjTuk5i3QEw2Owe0c133xV6bfAfZ8BNgYPj8bShjmhcvoZ9XW4PzncA1dVjgHkhifyVWw/vuo5nm3BydR7gIN1zV4b2lik5HB4S1OREpGzkoKciHRqbrfBt9ur2bG1Cre7aRhldEwQWWNjSUhqW8+DUVpEw9Jn8Gz8vGmjxYrl4quwTr8WU/CZN4/IYxgU2us5VFHHocpayurdDEyJ5Nz06NN9anKC7FUNbFhTRllxUy9chM3KqIlxbf5dkNPLqK/DvfJdXO//i5qaWrbG9mFTv/P5Kq4fxaFHjAY4qtBkr7hwRqV5K0wOUpESEUFBTkQ6sfycWrZ8WUFNddNixkHBJgZlRdOzrw2zuQ3DKN1u3MvexPXmX6GuqTfP1D+LoOvvxJyW0SHnHkgNbg/51fUcrKjlYGUdhypqOVRZR25lXbOy429uzee5S4eQEdO2eYTSORiGwZ6ddrZtqvR7oNGrn40hI6PbVJlVTi/DWU/DinfZueoTvgpOYVPvK9kVlYHH1PIwyKgQK6PSvcFtRGo0ceEqUiIi/hTkRKTTsVc3sGVDBQW5/kOKMvtEMHh4NCGhbZv/4dm3k4a/PYVxYHfTRlsU1h/9BMu5F7ZpXbnOpK7BzaFKb+/aoco6DlbUcqiijrzqulYr1h0tPTqUhPAzr/fxTFZT7eLLNWWUFNX7toVHWBg5IY6kFA2p68wMw6Aov4jPV6xh7a48Ntt6UN1/VottLSYTA5Ns3vCWFk3veBUpEZFjU5ATkU7D5fKw8+sqdn9T7Tf3Jy4hmKwxsW0uo27U1uB6/QXcy9+mqZQfWM6bivWKmzFFdu6hhVV1Lv+wdvi/x1obqiXJtmC6R4eRERNK9+gwBnWLoV9SJLX25nNupPMxDIN9u2vYurECt6spqWf2iWDoyBiCgtUL11kYNXaMwhyMwhyq8vPYWlLH5roQtgQlkhuWCMRAbEyz16XYQhh5OLgNT40iPFhFSkSk7RTkROS0MwyD3IO1bP2yglpHU0ntkFAzQ0bEkNErvE29Z4Zh4NnwKQ1L/wgVJb7tptQMgq7/GeYBwzvi9E+IYRiUOho4WFnrm8PW+N+KOtfxD3CYxWQiLSqE7jFhdI8OJSMmjIzoUNKjQwm1+n8pjIkJB6C2pQNJp+KocbFxTRlFBU29cKHhFkaNjyW5jctryKll1NdiFOT6ApunwPvfuqJCdlhi2BrThy2xfdgb2R9PsBlaeO4UYrgYlhDK6D5pjEyPJi0ypMuNDBCRzkNBTkROq/LSej5fUUxxYdMXVpMJeve3MXBYdJt7HTzFBbj+8TSezWubNlqDsF5yHZaLfoQp6PQMJ3R7Dhcc8Q2F9M5jy6msw9HQ8jpQLQmxmOkeE0r3aG/vWveYUDKiw0iNCsGq9e7OGIZhcGBPDVu+rMB1xBqJGb3CGTY6lmD1wnUow+nEKM7DOBzSmgJbru/hkNtkZo8tnS2xfdgSew47h/egwdzy/DWTYdDbVcaI0DqGD+nLkGEDVaRERE4ZBTkROS2qKxvYsaWQ7VsrMI6Y35WYEkLW6FiiYto2sd9wuXD/9zVcb70IzqYhg+bBo7BedwfmlG6n+tRb1OD2kFNV59e7drCyltzKOhraOoENsAVb6H64V83XyxYdRqItWPNlznC1Djcb15ZRmNf0cxwSambk+DhSu6kX7lQxXC6MkgJvUPMFtlw8BYe8y5IY/r+vBpATnsSW9HPYEtuHbTG9cFhb//8jPdjD8MQwsjKTyMpIoHtyFAAVFY6O/FgichZSkBORgDAMg7ISJ/mHask7VIu92n/4YFi4hWGjYkjLCGvzUCPPt9to+NtCjJy9TRujYgm68ieYJ0zpkCFLjga3rypk438PVtZSUF3f5oIjAPHhQd6etcPDIbsfDm4xoVYNtTrLGIbBoX0ONm8op8HZ9EPUPTOcYWNiCAnRvKn2MjxujNJiX6/akYHNKMkH97F7w4tDYtgS24etsX3YGtOH8pCoVtvGhweRlRrF8MN/EiJUTEhEAkNBTkQ6jNttUFxQR96hWvJzaqmv8zRrY7GY6Dsokn6DI9tcQt2oseN67c+4V77r9/Tckj0d6+VzMUVEnvS5V9Y1cPBw79rBI4JbiaOhzccwASmRIb6QdmRoiwjW7VegrtbNpvXl5B9qmrkYEmJm+LhY0jPCT+OZdX6GYUBFqW+uml9gK8oDV9t/V6ut4WxNHMDWlKFsjexBntnWatuIIAvDUiN9wa1bdKgevojIaaFvEiJySjU4PRTk1pKXU0thXp3fPJ8jxSUE06tfFL36RuFyt+0Ll2EYeNatoOGfz0JVuW+7qVtPbzGTvkPbda6GYVBc4/TvXTv836r6thccsZpNpEeF+uatNQa39KhQQrS+l7Qi54CDr9aX46xvesCRlhHG8LGxhLZxiY0znWEYUF3ZrMCIUZCDUZQL9e2swGoNwpSURn1yD7Yn9GdLWCpbXDb2OrxDKFsSbDExKOlwcEuLpHdcBJY2rGEpItLRFORE5KTVOlzk53h73ooL646s+O9jNkNiSihp3cNISQ8jLNziq6JYUXH8IOcpysX10iI8X29o2hgcgvX/XY/lwssxWVu/nbk9BvnVdb4etiODW52rhZNtRZjVTLfDIS3jcMGR7tFhpEaG6IudtFl9nZuv1peTe7CpFy442EzW2Fi69Wj70OIzyZHl+z1HDoMszAGHvX0HM5sxJaZiSu52+E867qRufBuSxOZaC1/l29lRbPc+ZGrh1mM2Qd/4CIanRZGVGsWgRBvBeiAjIp2QgpyItJthGFRXuXzz3cpLW17fzBpkIiU9jLTuYSSnhp7QuleGqwH3f17G9c7foaHpfczDxmG97nbMiam+bfUuD7lVdb7KkI1hLbeqDlc7JrBFhVj9etcyYryhLSE8+Kz8ki2nTt4hB5vWlfsNM05JD2XEuDjCws/sXrjWyvcbhblQXdG+g5lMEJeEOcUb1EzJ3b3/TemGKSEVLBb2V9SyOa+KTflVfL2lmlpXUauHy4gJZXiqN7gNTY7EFqKvRyLS+QX8TuV2u3nppZd45ZVXyM/PJy0tjauvvpprrrkGk8mEYRg899xzvPzyy5SXlzNy5Eh++ctf0rt3b98xnE4nTz75JO+//z4Oh4PzzjuP+fPnk5yc7GtTWVnJggUL+OSTT/B4PFxwwQX84he/wGZrGveen5/Pb3/7W9auXUtISAiXXnopd9xxB8HBTROVd+3axSOPPMKWLVuIjo7m6quv5qabbtKXOTnrHK9YSaPQMAup3UNJ6xZOYnIIZsuJ/654dm6h4cWFGHkHmjZGx+G88jZye4/yVog8cMg3HLKwur7V4VEtSYwI9ivn37h4dnRo2ypmirSVs97D5i/KObS/qXJhUJCJYWNiyejZtnUSuwKjwYlRlNdCRcgcv7Ud2ywmHlNyOubk7phS0r09bCndMCWmYwr2LypSWF3PpvwqNu88wOb8qmOux5gYEXy4QEkkWalRxIerQImIdD0BD3J/+tOfWLJkCbfccgvDhw9nw4YNPProo9TW1nLTTTfx7LPPsmTJEu6++27S09NZvHgxs2bN4oMPPiAy0lvA4Fe/+hUrVqzg3nvvJTw8nIULFzJ37lzeeOMNLBbvE83bbruNnJwcHnroIerq6nj88ccpKSnh+eefB7xh8IYbbiA0NJTHH3+c/Px8nnzySerq6njwwQcBKC0tZfbs2fTt25dFixaxbds2Fi1ahMViYc6cOYG+dCIB15ZiJQCR0VZSu3l73mLjT77XyrBX0vDKEkrXfkpOeDI5aRPIiUgiN30wOaEJlO10wc4dbTqW2eQtOJJxxNpr3WNC6RYdRnjQmd0DIp1Dfk4tm9aVUVfb9PuTlBrKyPGxhEd0vZ4fv/L9vsB2uKettLBZ+f7jioz2DYH0C2xJ6ZjCWi/4UlnXwFf5VWzOr+ar/CoKqutbbRsZYmFYyuHKkmlRWohbRM4IAf0XxOPx8H//93/MmTOHn/zkJwBMmDCBsrIy/vrXv3LVVVfxwgsvMG/ePGbOnAnA6NGjOf/883nttdeYPXs2Bw8e5K233uKpp55i2rRpAAwYMICpU6eyfPlyLrjgAtauXcu6det45ZVXyMrKAiAlJYVZs2axbds2Bg8ezLvvvsvBgwdZvnw5KSkpAISEhPDQQw9xyy23kJCQwNKlS3G5XCxevJiwsDAmTZqE0+lkyZIlzJw5k6AgPbWXM49fsZLcOlyu1ouVpHUPI7VbGJHRJ/674DEMCqvrOXi4OuTBXd9yMK+Y3JCx2CdmN39BK0/Zg8wm7/y1xsB2uDpkelSoFuCV06LB6WHLlxUc2FPj22YNMjF0ZAyZfSI6dZAwPB4oK/Kfr1ZwqM3l+5sJi/D2pCV7Q5q5sWctuVubq8zWNrj5utAb2jbnVbG3vLbVtiEWM4OTbQxP84a3XnHhWodRRM44AQ1y1dXVXHrppVxwwQV+23v27ElZWRlr167F4XAwefJk377o6GjGjh3LZ599xuzZs1m7di0A2dnZvjaZmZn07duXzz77jAsuuIA1a9YQHx/vC3EA48aNw2az8dlnnzF48GBWr17NoEGDfCEOYMqUKfzyl79kzZo1zJgxg9WrVzNhwgTCwsL82ixevJitW7cycuTIU32JRE6LEy1WciKcLg/biqrZlFfF18U17C+tOargSCjYurf6+vAgS7Ny/hnRoSTZVHBEOo/C/Do2rimj1tEUeBJTQhg1Po5wW+fohWss39+swEjBoXaX7wcgOMQX1BpDmvnwf4mMaXdwbXB72FlSw1d5VWzOr2JHcQ3uVnr7zCbon2g7vCRAJAMSbXqAIyJnvID+axIdHe0btnikTz75hJSUFAoLCwHo3t3/S1y3bt1YsWIFAPv27SMhIYHw8PBmbfbv3+9rk5GR4bffbDaTnp7ua7N//34yMzP92sTGxmKz2fzajBs3zq9N47nt37+/3UGusUJfWzWuqdXe18mJO1uuuWEYVJY7ObDXzoF9dkoKWy7hHRRspntmBD16RdItI+LEipUYBntKalh/sJz1B8r5KrcSp/v4lSJjjHp6psSQmRxLZly4709ChAqOnKhA/HyfyLHPpN+7BqeH9f8rYue2St82q9XEmHMSGTCk/WHmVGvY/y3219+hftsmXHmHMOpa79VqkTUIS0o61rTuWFK7Y03LwJLWHWtqd8xxiZjMJx6ePIbBt8U1bDhUzoaD5WzOq6S2ofV7Re+ECEZ3j2F091iGp0cT0UkLlJxJP99dga63nE1O+13v1VdfZfXq1fzyl7/EbrcTHBzsV2wEICIiArvdW364pqaGiIiIZseJiIigoKDguG0aj2O320+oTePfG9uIdBWG4Z3vdmCfnQN7q6lqpeR/eISVjF42evS0kZIejuUEipWU1NTzxcEK1h8oY8PBCkodLVe1BEhqqCK9Ko9ujiK6OYrIsDoZ+KOrSTr3glZfI9IZ5ec4+Gx5Afbqpt+t5LQwzpucQlT06SumYdTXUfv5Mmr/+xYNO7ce/wVmC5akVG9AS+uOJTXD+9+0DCwJyZgsp2ZuqWEY5FbWseFgORsOVbAxp4KK2tZ7AVOjQhndPYZR3WMZ3T2GuAgVKBGRs9tpDXLvvPMOv/rVr7jwwgu59tpref7551t9Wtm43TCMFtscud0wDMwtPBU8entrx2nptUdrS5ujVVQ4jt/oCE1rbLXvdXLizrRr3p5iJY3z3Y4sVlJd3ban9XUuN9sK7WzKq2RjXhX7jzF3JcbiYXhDEUNzN5F16EvinFXeHSYzlgt+gPXS2ZjCws+Y/w86k5P5+U5MbNs8phM5dlf/vXO5PGzbVMmenU0P+MwWE0OGR9N7gA2P4aKiou0LzJ8qnpx9uD95F/fq/0JtTfMG8cmYjxoK6S3fn4LJ6p336jn8xxevjlFQpC3KaxsLlFTxVV4VRTWtP+SJCrGSlRrpK1CSGhnatLPh9FzTE9HVf767mkDc50Q6i9MW5P72t7/xu9/9ju9+97s8+eSTmEwmIiMjcTqdNDQ0+BUSqamp8VWstNls1NQ0/wfJ4XD4tSkuLm6xTePyA8c6zrHaNP79yGUMRDqTNhcrSQwmrVsYqd3DiIxqX7ESj2Gwr8zBxrwqNuVVsa2wmoZW1mkLNhkMopKsom/I2r+WHjUFHP0IxdSzP0HX34k5s1+7zkPkdCstqmfDmjJqjliOIy4hmFET4k6qCNCJMpz1eL5YieuTdzG+3dZsvymlO7aLfkDY+dOo8nR8j5bD6WZrYTVf5VXxVX4VBypaf8gTajUzJDnSV6AkMzZMBUpERI7htAS5hQsX8vzzz3PppZfyyCOPYLV6T6NHjx4YhkFOTg49e/b0tT/y75mZmZSUlFBXV0doaKhfm1GjRvnabNy40e89PR4Pubm5zJgxw9cmJyfHr015eTl2u93vvY5uc+jQIQB69ep10tdB5FRpb7GS1G5hhIa1b3hUSY2TTXlVbMqvZFNeFZXHWKOpp7mWrIpvGbZ/HQMr9hLiaaGtxUJQvyGETZpK/djvYTJrKQDpOtwuD99srmL39mrfNrMZBmVF03dgJKYAF97x5O7DvfI93P/7LziOGvpvDcI8+jys2TMw9c8iIvbwlIEO6CFqcHvYXmT39brtLKmhlWc8WEwmBiRFHC5QEkW/hAgVKBERaYeAB7kXX3yR559/npkzZ3L//ff7DW8cMWIEISEhLFu2jJtuugnwLuy9fv165s2bB3iXK3C73axYscK3/MD+/fvZvXu3X5vnn3+eLVu2MGzYMADWrVuH3W5nwoQJAIwfP55f//rXFBQU+CpXLlu2jKCgIMaMGeNr8/LLL+NwOHzFVZYtW0ZMTAwDBgzo6Esl0irDMKiucvkW5y4vbXl4UlCQiZR0b69bclooQUFt/5JU1+B9kr4pr4qNeZUcrGi5IApAnNlFliOHYQe/IKt4OzENLc8hNWX0wTxopPdPv2HEpsQD4NSQI+lCykrq+XJ1GdVVTQ8oYuKCGD0xnqiYwPXCeXvfVuFa+S7G7q+b7TeldMcyaTqWcy/EFBndIefg9hjsLXP4gtu2Qjv1xyhm1Csu3LcI95DkSMK0lqOIyAkLaJArKiriySefpF+/flx88cVs3rzZb/+QIUO49tprefrppzGbzWRmZvLcc89hs9m4/PLLAcjIyGDq1Kk88MAD2O12oqKiWLhwIf3792fKlCmAN4BlZWUxb9487rnnHlwuF4899hjZ2dkMGTIEgOnTp7N48WJuvPFGbr/9doqKinjiiSe44oorSExMBODqq6/mH//4B3PnzmXOnDns2LGDJUuWcNdddzUryCLS0QzDoKzE6Qtv9uqWe8RCwyzeXrfuYSQmhWBuY7ESj2HwbamDTXneHrdviuy4Wh0u6WFIQwlZeV8xrHArGTWFzYZLApiS0pqC24ARmKJi2vhpRToft9tgx9ZKdm6rhsO/GiYzDBwaRb/BUZgD1AvnyTvQNPetptp/p8WKefR3sGTPwDwg65RXyTQMg9yqejbnV7Epv4ot+VXYna2vKZcaGcLw1CiyUqPISo0kOlTrr4qInComw2hlUZYO8MYbb/CLX/yi1f1r1qwhKiqKRYsW8eabb+JwOBgxYgTz58+nd+/evnYOh4MFCxbw0Ucf4fF4mDhxIvPnzyc5OdnXprS0lN/85jesWrWK4OBgJk+ezP333+83t+3AgQM8/PDDbNiwgcjISGbMmMGdd97pNz9v69atPPLII2zbto2EhASuuuoq5s6de0Kfv7i4+viNjqAJ0oHX2a75yRYrOZ4ie713uOTh+StV9a0Pl+xlVDG8+Buy8jYzoHI/QUYLX96iYjEPGoF54EjMg0ZhTkxp3uYIne16n+kCUQSgvfc56Bo/BxVlTjasLvOr9hodG8SoiXHExHb8gz3D6cSzYRWule9h7NrSbL8pOf1w79vU4z4wae/1LnU4fYtwf5VfRYmj9cqSMaFWsg4PlRyeGkVyZEib3uNM1hV+vs8kKnYiZ5OABrmznYJc59cZrrnT6aEw19vrVph3aouVOBrcbC2o9laXzK0ip6r14ZLx1JNVuZfhuRsZWv4t0Q0tVL0LDcfcP8vX62bq1rNdPQCd4XqfTRTk2s/jMdj5dRU7tlbR+K+lyQT9h0QxYEhUm3u8T/j98w7iXvmud+5bTZX/TosV86jzsGRPxzxgeJvXcDve9bbXu9hSUO2tLJlfxaHK1u8TYUFmhh5RoKRHTNhpXyuvs+nMP99nIgU5OZuc9nXkRMRbrKSx1624oJ6WHq+cSLESt8fg29IaX3XJ7UV23K08uwnFzeDaXIbnfkVW2S7SHUXNh0tarJj6DMbSGNx6DsBk1W1EzkyV5d5euMryph6oyGgroyfGExvfcb1whtOJ58tPvXPfdrbQ+5aUhiW7sfct9qTfz+ny8M3hAiVf5VfxbWnrBUqsZhMDE22+4NY3IRzrSSwCLiIiJ07fwEROA8MwqK50kZdTS/4pLlZSWF3PxsPz3L46xvwVEwZ9GsoYVrCV4SU76Fd1oPlwSZPpqAIlQzGFhLX784p0JR6Pwa5vqtm+pbKpAqwJ+g2KZOCwaCwd1AvnyT94uPLkR2A/uvfNgnnkuVjOn+Gdb3oS4cntMdhZVM1nu4rYnF99zOVDTEDv+HDfUMlByTZCrSpQIiLSGSjIiQRIY7GSvEPe8HaqipU4nG42F1Qd7nWrJK+q9QV7Ez0Oskp2MLx4G0PL9xDpaj70xJTcrSm4DRyOydYx1e5EOqOqyga+XF3m93DFFmll1MQ44hNP/Xwvo8GJ58vPvOu+7dzcbL8psbH37UJM0XEn/j6GwY7iGlbuLeXzg+WUH2OeW3pUqK+yZFZqFJEh+qogItIZ6e4s0oE6oliJ22Owq6TG1+u2o9je6jCoMMPFkKp9DC/8mqzy3aTWljQfLhkd5wtulkEjMcUnt3QokTOa4THYvaOab76qxHPEr2mfATYGD4/GYj21wwc9BYe8vW+ff9hy79uIw71vA0+u921/uYOVe8tYta+UQnvLPf9xYUFkpUYxIs1bWTIxQgVKRES6AgU5kVOsI4qV5FfV+XrcNudXU9PQ8nBJMwZ9agvIKtzG8PJd9K06iPXo1cHDIjAPOKJASVqmihPIWc1e1cCXa8ooLW4KOhE2C6MmxpOQdOpCjbf37XPcq97Ds31Ts/2mxNSmypMxJ977VlBdz6p9pazcW8aBitpm+61mE2N7xJKVZCMrNYru0aG6B4iIdEEKciKnwKkuVmKvd3mHS+Z612oqqG59uGRyQxVZxd8wvGwXQyr2YHMd9cXNGoS575DDSwKMxNSzPyaL5riIGIbBnp12tm2qxO1u+qXt1c/GkJHRWE9RL5ynIAf3qsO9b9WV/jstFswjzvFWnhw06oR738prG/hsfxkr95ayo7h5hVkTMDQlkuxe8UwblkZUaJCqKIqIdHEKciIn4FQXK3F5POwsrvH1uu0qab1qXLjHydCy3WSV7WR42S5S6sr8G5hMmDL7+YKbue8QTCGhJ/NxRc44NXYXX64po6Sw6SFJWISFUePjSEo9+d8Xw9WAZ+PnuD95t+Xet4QULJMuxnLeRZhi4k/oPWqcLv53oJxV+8rYnF/V4j2jb3w42b3iOS8zjoQIb6XNKC3KLSJyRlCQE2mjNhcrCbf4hky2VqzEMAxyq+rZdHie2+aCKmobWp4/ZzY89LPneIuUlO+iT3UOlqOGS5pSujcVKBkwHJMt6uQ/sMgZyDAM9u2uYevGCtxHDHvO7BPB0JExBAWfXC+cpzDX2/v22YdQXeG/02xu6n0bPPqEet/qXR6+yKlg5d5SvsipbLHaZLeoULJ7xTGpVzzpUXqIIyJyplKQEzmGU1mspLrexVf5VWzM9Ya3opqWe/EAUuvKyCrdwfCyXQyu2EuE+6gFeWMSMA8acXg9t1GY4hJP6nOKnA0cNS42ri2jKL+pFy403MLI8bGkpJ34shre3rf/4V75Lp5vNjZvEJ+MddJ0LN85sd43t8dgU14Vq/aVsvpgeYsPfRLCg5jUK57snnH0igvXnDcRkbOAgpzIUerr3RzaV3PSxUoa3B52FNt9i3HvLqmhldGSRLjrGFa6i6zy3WSV7yK5rty/QVgE5oEjDve6jcKU2l1f1ETayDAMDuypYcuXFbgamn4LM3qFM2x0LMEn2AvnKcrFvfJ979y3qqN+Z81mzMMnYsmegXnIKEzm9s1L9RgG24vsrNxXxuf7y6isaz4CICrEyrmZsWT3imdQkg2z7gkiImcVBTk5qxmGQW2Nm4pyJxVlDVRVlJKf6+DoQo9w/GIlhmFwqLKOTXlVbMyrZGtBNXWulnvwLIaH/pX7ySrfxfCy3fSqzsFyZMyzBmHuN7QpuGX2bfcXQRGBWoebjWvLKMxr6tUOCTUzYlwcad3b3wtnuFx4Nv0P98r38Gzb0LxBfDLWxrlvsQntO7ZhsK+8llV7S1m5r4ziFnrtQ61mJmTEkt0rjhFpUVhPYmkCERHp2hTk5KxheAzs1S4qyryhraLcSWVZA05ny2ELjl+spLKuga/ymhbjLjnWIruOIrLKdjG8fDeDK/YS5j6iEqXJjKlnf19wM/cZgik4+KQ/s8jZyjAMDu1zsHlDOQ3Opock3TLDyRoTQ0hIO3vIivJwf/o+7k//07z3zWTGPHyCd+7b0DHtfuiSX1XHyn3etd4OVtQ12281mxidHk12r3jGdo8m1KqHOiIioiAnZyiP26CqsqFZaDuyxHhrwiOspKSHtlispMHtYVuR3VekZE+po9XhkpENDoaV7yKrbDdZ5btJrK/w229K6+GtLDl4JOb+wzFF2E7iE4tIo7paN5vWl5N/qGkpjuAQMyPGxZKeEd7m4xguF56vVnvnvn3dQu9bXBLWSdOwfGcaptj2zVMtczj5dH8ZK/eWsauk5eUCslKjmNQzjok9YokM0T/XIiLiT/8ySJfnavBQWXFEaCtzUlXZ0OLwyKMFB5uJiQsiOi6YmNggumdGER0TTGWl9wugYRjsL3ccHi5ZxdcF1dS7Wz6w1eNmQOU+ssp3M7xsFz3teZiPjHlxiZgHjsQyeBTmgSPaPexKRI4v54CDr9aX46xv+j1Nywhj+NhYQkPb1pPlKS44XHnyP1B59PIeZsxZ4729b8PGtqv3rbrexerDywVsKWh5uYB+CRFk94rjO5lxxIWrV15ERFqnICddSn29m8rDYa2i3Ptfe1XLywAcLTTcQkxsEDFxwYf/BBEWbvErGhITE0JZjZNVe0rZeLjXray29eGS3WsKfD1ugyv2EOo5om1EJOYBwzEPHuVdiDu5mwqUiHSQ+jo3X31RTu6Bpl64oGAzw8fE0C3z+FUcDZcLz+Y13nXftm0A46iUFZeI9TsXe3vf2lElts7lZv2hSlbuLWVDbiWuFtJb9+hQsg9XnEzVcgEiItJGCnLSKRmGQV2t29fD1hjaamvcbXp9RKSVmLggYmKbQlvIMZ7G2+tdfPxtCSv3l7O72N5quyin3VdZMqtsN/HOqqadwSGY+472DpUcNBJTRh8VKBEJgLxDDjatK/dbHiQlPZQR4+IICz/276BRUoBr1fve3reKUv+dJjPmYeOwnD+jXb1vLo+HTXlVrNxbytqDFdS2UPQoMSKY7J7etd56xobpIY+IiLSbgpycdoZhUGN3NYW2sgYqy5zU1x9/bKTJBJHRQd7QdrinLTomqM2L+h6sqOXd7YUs31PaYoXJIE8DAyv3Hy5Ssose9oKm4ZJmM6beg44oUDIIU5CGQokEirPew+YN5Rza5/BtCwoyMWx0LBm9Wu+FM9xuPF+twb3qPTxb1zfvfYtNwPKdi7F+Zxqm+KQ2nYvHMNhWaGfVvlI+319OVX3zkQLRoVbOy4xjUs84Bmq5ABEROUkKchJQHo9BdWWDrwBJRVkDleVOv7WdWmO2mIiODfIbHhkVE4TF0r4vQ26PwfqcCt7dXsRX+VXN9mfY8xl+uMdtUOU+Qo4YLmlKz2wKbv2HYQpXgRKR06Egt5aNa8uoq216AJOUGsrI8bGER7T8T5tRUoDr0w+8lScrSvx3mkze3rfs6ZiHjcdkOX7vm2EY7C1zsHKvt+JkS1Vrw4LMTMzwrvU2PDUKi1nhTURETg0FOekwbldjEZLGnjYnVRUNeNpQhCQoyOQrQNIY2mxRVswn8SWout7FR7uLeX97IYU1/l+4gt1OvlO4iYtyV9OzJr9pR3wylkEjvMFt4EhMMXEn/P4icvIanB62fFnBgT1NlR6tVhNDR8WQ2SeiWS+c4Xbj2bLWO/etpd63mAQsk6Yd7n1LbtM55FbVedd621tGTlXz5QKCzCbGdI8hu2ccY7rFEGLVWm8iInLqKcjJKeF0eqg8PJet8nBoq65yNfvO1JKQULNvHltMXDAxscGE2yynbM7I3tIa3tm4j5V5NTgN/y9UiXVlXJS7hsn564l01WKyRRE88bu4+2Z5F+JOStPcFZFOojC/jo1ryqh1NM2VTUwJYdT4OMJt/v+cGaVFuD59H/enH0B5C71vQ8d6e9+yJrSp962kxrtcwKq9pewudTTbbzZ5lwvI7hnPxB4xRATrn1cREelY+pdG2s1bhKSpAEllmZMae9uKkITbLIcLkHhDW3Rs8HGLEZwIl8PB6nVf8d4BB18TfXhrU4gbWr6babmrGV26HWvPfpgvvgLL0LHEjhiByWKhoqL5FzUROT1cDR62bqxg3+6mXjiLxcSQkdH06mfzPWzx9r6tw73yPTxb1tFsDZKYeCznTcM6aRqmhJTjvm91vYv/HShn5d5SthZUt7hm5MDECCb1iue8zDhiw4JO5mOKiIi0i4KctMowDBw1br/12SrLnX5zUlplgsgoa7PQFhzSMUOMDMPAyNlHxeYNfLivig+DelASGgO+EAchbifZBV9yUeXXZPbtieWy72Me/AimyKY2bXkyLyKBU1xQx5drynAcUbE2PimEURPisEV6/wkzyooPV578AMqK/Q9gMmEeMgZL9gzMw4/f+1bX4GbtoQpW7Svjy1aWC+gRE0Z2rzgm9YwnJTLk5D+kiIjICVCQEwAMj0F1lYuKcqffOm0NzuOHNrMZomIaw1oQsXHBRMUGYe3geSFGjR3PN1/i2bqeXd8e4IPIQXyePJyGSP+n4sm1pUxz7uN7mdFETZ6KKWMeJrPmrIh0Zi6Xh22bKtmzs2k5ELPFxODh0fQZYAPDg/urNd7et81rm/e+Rcdh+c40LN+5GHPisXvfGtweNjYuF3CogvoWKtgm24KZ1DOeSb3i6Bkbfko+o4iIyMlQkDsLud0GVRWNYc0b3CrLG3C7jz+hzWo9XDmysdR/XBBRUUGY21k58kQYHg/Gwd14tnyBe+s6GvbuYG3cYN7vdg47+2U3az/cKOOSHuGMGXsOFtvUDj8/ETk1Sovq2bCmjJrqphL+cQnB3l44dwXud97AteoDKCvyf6HJhHnwaO+6b1kTMFlb/yfOYxh8XVDNqn1lfH6gjOr65sPDY0KtfKent+dtQGLzQioiIiKnk4LcGa6hwUPl4QIk5WVOKssbqKpoaFMRkuAQs9+i2tFxQdgirQH9MmNUVeDZtgH3lvV4vv4CqiuoCLLx37Rx/Hfs/6MsJNqvfZjJw+T0cGaM7kP3mLCAnaeInDy32+CbryrZvb3at81shoHDIunt2oHnb3+g/qtWet/OuwjLpIsxJ6a2enzDMPi21MHKfaV8uq+M0haWCwgPsjCxRyzZveLIStFyASIi0nkpyJ1B6uvcflUjK8oasFc3X5S2JWHhFmLigog+HNpi4oIICz91lSPbyvC4MfbuwL1lHZ6tX2Ds3+krF747sjsfDLiQ/yVl4TL7/+imRwYzY1AKU3onEB6seW4iXU1xYS0rPyqguqrpnhUTbWJ4w1rC/+81XKWFzV5jHjIay6TpmEecc8zet5zKWlbuLWPlvlLyquqb7Q+2mBjbLYZJveIYkx5DsJYLEBGRLkBBrgsyDINah9u3mHZjaDuyJPex2CKtTQVIDq/VFhJ6+sKPUVGKe+t6PFvX49n2JdQ0PY1vMFlYnTyMD9LPYXdUht/rTMDobtFcMjCZEWlRmDXsSaRL+uqLUjatL/GNFDCZDPrVbKDnJ89hdh/VaxYV29T7lpTW6jFLapys2lfKqn1lfNvKcgEj0qLJ7hnHhIxYPQASEZEuR0GukzMMg+qqhiMKkHhDm7P++EVITCZvEZLGAiTRh4uRBAWd3qfNhqsBz+5t3uC2dT3GoT3N2pQFR/FR2ng+7jaBCmuE377wIAsX9E1g+oAk0qJCA3XaItIBykudbFzXtM5bZG0eWZufJcp+yK+defBo77pvIyZisrZc5r+qzsXnB8pYubeMbYUtLxcwKMlGdq94zu0RS4yWCxARkS5MQa4T2/VNJes/L8LZhsqRFou3CEn0EXPaomKCsASgCElbGCUFh3vdvsDzzUaoa/6E3AB2JvTjP/2msjo4HTf+5949OpRLBibz3d7xhAXp6bnImSA0BEKpo95jpff+9+mz7y3MxuHRBVGxWM6diiX7YsxJ6S2+vrbBzdqDFazcV8rG3CrcLUwA7hUbxqRe8XynZxzJNi0XICIiZwYFuU7s66/KWgxxQcEmYmK9xUdi4oKJiQ0mMsqKqRNNyjecTjy7Nh/udfsCI+9Aq20bMvryvwHf431rJnuOyncmYFz3GC4ZmExWaqSqxomcYULyd/GdT36OyfBg8TgBMA8a6V33beQ5Lfa+Nbg9fJlbycq9Zaw7VEG9u/l9MiUyhOyecWT3iidDhY9EROQMpCDXiQ0fHc/XX5VjseIrQBITF0x4ROCLkLSFpyDHN1zSs+MrcDYvKgBAuA3z4FGUDhzPRyG9+PBgDVX1LnA2NbEFW7iwbyIXD0jSgrsiZzBTeiZhAwbiKSmEUd/xrvuW0q1ZO7fHYGthNSv3lrL6QDl2Z/M5wbFhQXwnM47sXnH0S9ByASIicmZTkOvEevWLole/KCoqmg9D7AyM+lo8279qmutWlNdqW1PP/piHjMU8dAzf2Lrzzs4S1hwsx2NU+rXLjA3jkoHJZPeKI9Sq4ZMiZzpTuI34R54DaHavMwyDXSU1rNpXxqf7yiirbb5cQESQhXMyY8nuGc/QlEgtFyAiImcNBTlpM8MwMPL249nyBZ6v1+PZuQVczb9YARAZjXnIGCxDx2IeMpr68CiW7y3j3e2F7Cvf7dfUbIIJGbFcMjCJIckaPilytjtYUcuqfWWs3FtKfnXznv0Qi5mx3aPJ7hXP6PRogixaLkBERM4+CnJyTIbDjuebjXi2rse99QsoK2q5ocmMqfdALMPGYR46FlOPvpjMZgqr63lvZxEf7drXbChUVIiVC/slcHH/JJJUgEDkrFZQVce7W/NZtbeUveW1zfZbTCZGpkcxqWcc4zNiCVfBIxEROcspyIkfw+PBOLTncHBbj/HtNnC3sj5dTAKWoWMwDx2LefAoTBGR3mMYBpvzq3lneyHrcyrwHFVErndcODMGJjGpZzwhWnhX5KxW7/LwwPvfsHx3cYv7hyRHMqlnHOdmxhIdquUCREREGinICYa9Es/XX/oW5aaqvOWGFivmfkMxHw5vpm69/IZB1ja4WbGnlHd3FHKwos7/pSYT5/SIZcbAJAYl2TR8UkQA2FNW0yzE9Y4LJ7tXHN/pGUdihHrrRUREWqIgdxYyPG6MfTvxbP0C95Z1GPt2QAtrLwEQn3x4uOQYzANHYgoLb9Ykv6qOd3cU8fHuEmoa/HvvYkKtXNQ/iYv6JZIQEdwRH0dEurDecRFk90mgoKqOkaneoZPdtVyAiIjIcSnInSWMijI8277AvWU9nq83QE1Vyw2tQZgHDPcOlxw6FlNq9xZ7zzyGwaa8Kt7ZXsiGnEqOjoH9EiK4ZGAS52XGqRCBiLQqxGrm0emDgeZVK0VERKR1CnJnKMPlwtjzDe6t67wLch/Y3WpbU0p333BJc/8sTCGhrbZ1NLhZ/m0J72wvIrfKf/ik1WzivMxYZgxMZkCi7ZR9FhERERER8acgdwYxSot889w832yE2pqWGwaHYh40sim8JaUd99g5lXW8t6OQj78tobbB47cvLiyIaf0TmdovibhwFSMQEREREeloCnJdmNHgxLNra9OC3Ln7W21r6tbTuyD3sDGY+w7FFHT8+Woew2BDTiXvbi/ky7zmQzEHJkYwY2Ay5/SI1fBJEREREZEAUpDrYjxFeXi2eIdLerZvAmddyw3DIjAPHoV56FgsQ8Zgik9q83vY6118/G0J7+0oarYYb5DZxKSeccwYmEzfhIiT+SgiIiIiInKCFOQ6OaO+DvfmtYd73b7AKMxpta2pR19vcBs6FlPvQZis7fu/92BFLe9uL2T5nlLqXP7DJxPCg7h4QBJT+yVqLScRERERkdNMQa4Tc/z3Lar+shCc9S03sEVhHjIay9CxmIeMwRQd1+73cHsM1udU8O72Ir7Kbz58ckhyJJcMTGJCRiwWs9Z+ExERERHpDBTkOjHHe6/4hziTCVOvgd7gNmwspsx+mMyWEzp2db2Lj3YX8/6OIgrtTr99wRYT2b3iuWRgMr3imq8bJyIiIiIip9dpDXLLly/n7rvvZtOmTb5thmHw3HPP8fLLL1NeXs7IkSP55S9/Se/evX1tnE4nTz75JO+//z4Oh4PzzjuP+fPnk5yc7GtTWVnJggUL+OSTT/B4PFxwwQX84he/wGZrKoufn5/Pb3/7W9auXUtISAiXXnopd9xxB8HBTYVAdu3axSOPPMKWLVuIjo7m6quv5qabbmpxbbVTLeKKG3C8/zLuuFQsw8ZiHjwKky36pI65r8zBO9sLWbm3jHq3//DJpIhgLh6QxIV9E4kKVcYXEREREemsTtu39Y0bN/Lzn/+82fZnn32WJUuWcPfdd5Oens7ixYuZNWsWH3zwAZGRkQD86le/YsWKFdx7772Eh4ezcOFC5s6dyxtvvIHF4u2huu2228jJyeGhhx6irq6Oxx9/nJKSEp5//nnAGwZvuOEGQkNDefzxx8nPz+fJJ5+krq6OBx98EIDS0lJmz55N3759WbRoEdu2bWPRokVYLBbmzJnT4dco7NwphJ075aQXyXV7DNYcLOfd7UVsLaxutj8rNZIZA5IZ1z1GwydFRERERLqAgAc5p9PJiy++yNNPP014eDgNDQ2+fXa7nRdeeIF58+Yxc+ZMAEaPHs3555/Pa6+9xuzZszl48CBvvfUWTz31FNOmTQNgwIABTJ06leXLl3PBBRewdu1a1q1bxyuvvEJWVhYAKSkpzJo1i23btjF48GDeffddDh48yPLly0lJSQEgJCSEhx56iFtuuYWEhASWLl2Ky+Vi8eLFhIWFMWnSJJxOJ0uWLGHmzJkEBXXuoh+VdQ18uKuYD3YWU1zjP3wyxGrmu73imTEwicxYDZ8UEREREelKAr7416effsqSJUu45557uPbaa/32bd68GYfDweTJk33boqOjGTt2LJ999hkAa9euBSA7O9vXJjMzk759+/rarFmzhvj4eF+IAxg3bhw2m83XZvXq1QwaNMgX4gCmTJmCy+VizZo1vjYTJkwgLCzMr01FRQVbt249FZejQ3xbWsPCz/cx85XNvLgx1y/EpUSGcOOY7rx0eRa3TcxUiBMRERER6YIC3iM3dOhQli9fTlRUFM8884zfvv379wPQvXt3v+3dunVjxYoVAOzbt4+EhATCw8ObtWl8/b59+8jIyPDbbzabSU9P97XZv38/mZmZfm1iY2Ox2Wx+bcaNG+fXpvHc9u/fz8iRI9v8uQFiYtoXmqxWc5tf53J7WPltCa9tzmVLC4t3j82I5fLh6YzPjNPwyWNozzWXk6frHViBuN4ncmz9HASWrndg6XoHlq63nE0CHuSOLEhyNLvdTnBwsF+xEYCIiAjsdjsANTU1REQ0X4g6IiKCgoKC47ZpPI7dbj+hNo1/b2xzupXVOHn763ze3JJHyVHDJ8ODLEwblMwPstLJVPVJEREREZEzRqcqTWgYRqvVIBu3t9bmyO2GYWA2Nx81evT21o7T0muP1pY2R2tv0ZLGp0ktvW5nsZ13thfx2f4yXB7Db19aVAgzBiTzvT4JhAdbTui9z1bHuuZy6ul6B9bJXO/ExMg2tTuRY+vnILB0vQNL1zuwAnGfE+ksOlWQi4yMxOl00tDQ4FdIpKamxlex0mazUVNT0+y1DofDr01xcXGLbRqXHzjWcY7VpvHvRy5jECgNbg+f7S/jne1F7Cppfu5jukUzY0AyI9OjMAdgeQQRERERETk9OlWQ69GjB4ZhkJOTQ8+ePX3bj/x7ZmYmJSUl1NXVERoa6tdm1KhRvjYbN270O7bH4yE3N5cZM2b42uTk5Pi1KS8vx263+73X0W0OHToEQK9evU7FR26TUoeTD3YW8+HOIsrrXH77woMsXNA3gekDkkiLCm3lCCIiIiIiciYJeNXKYxkxYgQhISEsW7bMt62yspL169czYcIEACZMmIDb7fYVPwFv4ZHdu3f7tSkuLmbLli2+NuvWrcNut/vajB8/nq+//to3rw5g2bJlBAUFMWbMGF+b1atX43A4/NrExMQwYMCADrgC/gqr63jwg2+Y9eoW/rU5zy/EdY8O5ZbxPfj7FVnMHZuhECciIiIichbpVD1yERERXHvttTz99NOYzWYyMzN57rnnsNlsXH755QBkZGQwdepUHnjgAex2O1FRUSxcuJD+/fszZcoUwBvAsrKymDdvHvfccw8ul4vHHnuM7OxshgwZAsD06dNZvHgxN954I7fffjtFRUU88cQTXHHFFSQmJgJw9dVX849//IO5c+cyZ84cduzYwZIlS7jrrruaFWTpCL/6z3a/CpQmYFz3GGYMTGJ4alSr8wlFRERE5Oxkt9t55ZVXeO+99zhw4ABut5s+ffpw+eWXc/nll59QnYfS0lLCwsKaVY2X08tkGIZx/GYd45lnnuGvf/0rmzZt8m1zuVwsWrSIN998E4fDwYgRI5g/fz69e/f2tXE4HCxYsICPPvoIj8fDxIkTmT9/vl9FzNLSUn7zm9+watUqgoODmTx5Mvfff7/f3LYDBw7w8MMPs2HDBiIjI5kxYwZ33nmn3/y8rVu38sgjj7Bt2zYSEhK46qqrmDt37gl93uLi6na1f2r1fpbvKsYWbOHCvolcPCCJlMiQE3pvaRtNSg8sXe/ACkQRgPbe50A/B4Gm6x1Yut6BdbYXO9m7dy8/+clPfNOJhgwZQn19PcuXL2fDhg3MmDGDJ554ol2dAatWreLuu+/mzTffpFu3bh149tJepzXInW3a+wUn3BbKwXIHNgxCgywddFZyJP2DG1i63oGlICeg6x1out6BdTYHufr6ev7f//t/lJeX8+KLLzabBvTrX/+af/7zn8yfP5+ZM2e2+bjPPPMMf/zjH1m+fLmCXCfTqebIib9gq5k+iTaFOBERERE5pn/+85/s27ePX/ziFy3Wcrj33nuJjo7m3//+92k4O+kICnIiIiIiIl3c+++/T3h4OBdffHGL+0NDQ3nllVd46623AO/ayf/617+47LLLGDFiBEOHDmXq1KksWbKExgF79913H3/84x8BmDx5Mtddd53veN9++y233noro0ePJisriyuvvJLPPvus2ftu3ryZmTNnMmLECM477zxfD1///v392uXm5vLzn/+c8ePHM3ToUC655BJeeeUVvzb33XcfU6dOZenSpYwZM4YxY8awbNky+vfvz9KlS5u99x133MG5556L2+1u+4XsQjpVsRMREREREWkfwzDYvn07I0eO9Kv1cLTMzEzf/160aBHPPfcc3//+97niiiuoqanhrbfe4qmnniIxMZHvf//7/OhHP8Jut/Pxxx/zi1/8gr59+wKwc+dOrr76ahISEvjxj39MUFAQ7733HnPnzuWpp55i2rRpAHz99dfMnDmThIQEbr31Vmpra3nppZeaFVw5dOgQV1xxBfX19Vx77bUkJiby3//+lwceeID9+/dzzz33+Nrm5+fz7LPPMm/ePIqKihg1ahTx8fF8+OGHXHPNNb52DoeDlStXctlll2GxnJmj2xTkRERERP4/e/cdHVXRPnD8uyWbtukNSIBQQw+hhSpBIiLFjiICUgR9ERRBRcX22lAExIIgiJ33J6BgQYrSexNpoZeQBALpZbNJNrt7f3+EbFgSerJJyPM5h3PI3Nl7Z4fNZZ87M88IUYWlp6djNpttmdevpaCggB9//JG+ffvywQcf2MoHDBhAp06dWLVqFQ888AARERGEhYXx999/Ex0dbVsj9+677+Lr68vSpUttmSwHDx7ME088wXvvvUd0dDQ6nY6PPvoInU7H4sWL8fX1BQpH9h566CG79syYMYOMjAx+/vlnmjdvDsDjjz/OmDFj+Prrr3nggQdsQWReXh5vvvkmDz74oO31ffr0YcGCBSQnJ9v6YO3ateTm5tr2kL4dydRKIYQQQgghqrCiEa7rnULo5OTE1q1befvtt+3K09PT0ev1dnsoXy49PZ2dO3fSvXt38vLySEtLIy0tjaysLO666y5SUlI4cOCAbS/o++67zxbEATRr1owuXbrYfrZYLKxfv56uXbvagrii9/T000+jKIrd/tEAXbt2tfu5X79+WK1WVq1aZSv7888/qV27NuHh4dfVJ1WRjMgJIYQQQghRhXl5eeHk5ERaWtp1v8bJyYn169ezZs0aTp8+zZkzZ8jMzATgaknt4+PjAfjhhx/44YcfSq2TmJiIs7MzVquVunXrljhev35923q69PR0jEYj9erVK1GvaPuxs2fP2pX7+fnZ/dy6dWtq167NypUrGTx4MNnZ2WzevJkRI0Zc8X3cDiSQE0IIIYQQogpTqVRERERw8OBBzGYzWm3pX/E//vhj4uPjefnll/nwww9ZtmwZbdu2JSIigkcffZT27dvzxBNPXPVaRaN+jz/+ONHR0aXWadiwIefOnQNAp9OVOO7sXLwv8tWCRqvVWuo5Slvz1q9fP7788kuSkpLYvHkzJpOJfv36XfW9VHUSyAkhhBBCCFHF3XXXXezcuZPly5dz7733ljiel5fHzz//jMViITY2lmXLljFmzBiee+45Wx2z2UxGRga1a9e+4nWCg4OBwmCqc+fOdsdOnDhBQkICrq6utnPExsaWOMeZM2dsf/f19cXNzY1Tp06VqHf69GkAatSocZV3Xqh///7Mnj2b9evXs2HDBsLCwmzr6m5XskZOCCGEEEKIKu7RRx8lODiYDz/8kGPHjtkds1gsvPXWW6SkpDBq1CjbFMqGDRva1Vu0aBG5ubmYzWZbWdH6u6KRs8DAQFq0aMHSpUu5cOGCrV5BQQGvvvoqzz77LGazGT8/PyIiIli2bJntelA4NXPjxo22nzUaDd26dWPLli3ExMTYyhVFYd68eahUKqKioq75/hs0aECzZs1YvXo127Ztu+1H40BG5IQQQgghhKjynJ2d+fzzzxkxYgQPP/ww/fv3p2XLlmRkZLBy5UoOHz5M7969GT58OGlpaej1eqZMmcK5c+fw9PRkx44dLF++HGdnZ3JycmznLUpU8tVXX3HHHXfQs2dPXnvtNZ544gkeeughHnvsMby9vfnzzz/Zt28fEydOxMfHByjchHzIkCE8/PDDDBw4EJPJxA8//FBiOuULL7zAjh07GDJkCEOGDCEgIIC///6b7du3M3z48BIB55X069ePqVOnolKprrif3u1EpVxtYqooU8nJ2TdU39u7MJ1rRsaVMweJsiV97ljS3451K/0dEOBxXfVu9D4H8jlwNOlvx5L+dixH3OcquwsXLvDtt9+yceNGzp07h6IohIWF8cgjj/Dggw+iUqkA+Oeff5g2bRpHjhxBp9NRr149hg4dyv79+/n+++/ZuHEj/v7+ZGVl8dxzz7F7925CQkJYsWIFADExMXz22Wfs3r0bs9lse/0DDzxg157t27fz8ccfc+jQIby9vRk0aBAnT55k1apVHDhwwFbvzJkzzJw5k61bt5KXl0eDBg0YNGgQDz/8sK3Oyy+/zNKlSzl69OgV33tUVBTh4eH89NNPZd21lY4Ecg4kgVzlJ33uWNLfjiWBnADpb0eT/nYsCeQql0v3dbvU008/zZEjR1i/fn2ZXi8pKYnu3bvz+uuvM2jQoDI9d2UkUysd6GZvEHJjcTzpc8eS/nas8uzvWzm3fA4cS/rbsaS/HUv6u3J45JFHqF+/PvPnz7eVpaSksGPHDrp161bm11u0aBE6na5aTKsECeSEEEIIIYQQ5eDee+9lzpw5TJw4kcjISLKysli0aBFWq5VnnnmmzK4zffp0jh8/zoYNG3j88cfx8vIqs3NXZhLICSGEEEIIIcrcc889h7+/P4sWLWLNmjU4OzvTpk0bPv30U8LCwsrsOkajke3btxMdHc2ECRPK7LyVnayRE0IIIYQQQogqRvaRE0IIIYQQQogqRgI5IYQQQgghhKhiJJATQgghhBBCiCpGAjkhhBBCCCGEqGIkkBNCCCGEEEKIKkYCOSGEEEIIIYSoYiSQE0IIIYQQQpQ72fWsbEkgJ4QQQgghxG1oyJAhhIWFlfqnS5cu5XrtHTt2EBYWxoEDBwA4fvw4TzzxRLles7qRQE5USefOnePdd9/l7rvvplWrVkRGRvLII4/w3XffkZube0vn/uyzzwgLCyM5ObmMWlvs+PHjtGrVit69e5OXl1dqnXXr1hEWFsYrr7xS5td3lB07dvDEE08QGRlJhw4dePzxx9m6dWuJehs2bGDAgAG0bt2aLl268M4772AwGK543sOHD9OiRQv27t1b4tjJkyd55pln6NixI23atGHkyJHs37+/LN+WEA4l97nKrSLuc5e6cOECkZGRvPzyy7f6VsRtrk2bNixcuLDEn7lz55brdZs3b87ChQtp0KABACtXrrQFdaJsSCAnqpz169fTt29f1qxZw8MPP8zs2bN57733aNWqFTNmzODhhx8mLi6uoptZqkaNGjFx4kROnz7NjBkzShxPT0/n9ddfJzQ0lNdff70CWnjrtm3bxrBhw9BoNLz77ru8//77uLi4MHz4cP78809bvY0bN/Kf//yHoKAgZsyYwciRI/nll18YO3Zsqec9dOgQTz75JAUFBSWOJSQk8Pjjj5OQkMAbb7zBRx99hMlkYvDgwRw+fLjc3qsQ5UXuc5VbRdznLqUoCpMmTSIjI6Ms35a4TXl6etK6desSf5o3b16u19Xr9bRu3Ro3N7dyvU61pghRhZw4cUJp1aqV8vjjjyvZ2dkljsfExCht2rRR7r33XiU/P/+mrvHpp58qjRs3VpKSkm61uaWyWq3KiBEjlLCwMGXnzp12x8aNG6c0b95cOXjwYLlc2xEGDhyo9O7dWzGZTLaygoICpXfv3krfvn1tZf369VMeeughxWq12sqWL1+uNG7cWNmwYYOtzGAwKJ9//rnSokULpUOHDkrjxo2Vf//91+6a7777rtK8eXO7fzODwaC0bNlSefnll8vhXQpRfuQ+V/lVxH3uUvPmzVO6dOmiNGvWTJk0aVLZvjlxWxk8eLAyevToKx7/5ZdflMaNGyupqam2sszMTKVx48bKL7/8oihK4f3igQceUN577z2lbdu2yqOPPqrEx8crjRs3VtasWaOMGDFCadWqldK1a1fliy++sJ1n+/btSuPGjZX9+/fb7jlFf3755Re745dq27at8umnn9ra16FDB2XevHlKhw4dlF69eilGo1FRFEX57rvvlLvuuktp3ry50qdPH+XPP/8ss36rKmRETlQpX3zxBQUFBUyZMgW9Xl/ieLNmzXjuuec4cuQIy5cvB4rnaC9cuJD+/fvTqlUrPvnkEwDOnDnDuHHjaN++Pe3bt+fdd98t9UlofHw8EyZMIDIykpYtW/Lggw/y999/29UZMmQITz/9NG+//Tbt2rWja9eupKWllTiXSqXi/fffx8vLi1deeQWj0QjAsmXLWLVqFRMnTrQ9JUtLS+ONN96ga9eutGjRgr59+7Jw4cIS51y+fDmDBw+mXbt2tGjRgqioKN5++2276TvX276EhIQrzqcv+rNjx44r/hu1bt2aYcOG4eTkZCvTarXUr1+f8+fPA4VTxo4dO8Y999yDSqWy1YuOjsbNzY01a9bYyhYvXsz333/P888/zwsvvFDqNUeNGsWPP/5IQECArUyn06FWqzGZTFdsqxCVkdzn5D53NTExMXzyySdMmTIFtVq+xolrUxQFs9lc4s+NOHr0KAcOHOCzzz7j6aeftpW/8sorhIeHM2fOHHr06MHMmTPZsGFDidcPGDCAhx9+GBcXFxYuXEhUVNR1Xzs7O5slS5Ywbdo0nn/+eVxdXfn888/58MMP6dOnD3PmzKFz585MmDCBFStW3ND7quq0Fd0AIa6Xoihs3LiRJk2aULt27SvW69+/P++//z5///03999/v638/fffZ9KkSQQEBFC7dm3S0tIYOHAgLi4uvPLKK3h4ePDjjz/yzz//2J0vISGBAQMG4O7uzosvvoiPjw+///47Y8eOZcqUKTz44IO2ups3byYiIoKPP/6Y1NRUfH19S21jUFAQb7/9Ns8++yyfffYZo0aN4t1336Vbt24MGzYMgKysLAYOHEh2djbPPPMMISEhrFu3jjfeeIPk5GTb1JxffvmFV199lYEDBzJ69GgURWHdunUsWLAAZ2dnJk2adEPtCwwMZMGCBVf9twgLC7visUuvVyQ9PZ1du3bRtGlTAE6cOAFgmzdfxMnJiZCQENtxgDvvvNPW/0uWLCn1moGBgQQGBgJgMpk4c+YMn376KWazmccee+yq70WIykTuc3Kfu9J9DiA3N5eJEyfy6KOP0q1bt6u2X5Qd48a/yPxxDtZcY4W1Qe3qhtfgp3G7o9cNv3bDhg2lTqPctm3bdZ/DbDbz6quv0rJlS6DwngFwzz338OyzzwIQGRnJqlWr2LhxI927d7d7fY0aNahRowZqtZrWrVvfUPstFgvPPvus7TOflZXF3LlzefLJJxk/fjwAXbt2JScnh+nTp3PPPffc0PmrMgnkRJWRnp5OVlYWISEhV63n4+ODl5eX7SZTpE+fPgwaNMj288cff0x6ejq///47jRs3BqBHjx707duX2NhYW73PP/+c3NxcfvnlF4KDgwHo2bMnY8aM4cMPP6Rv3744OzsDUFBQwIcffkitWrWu+X7uvvtuHnjgAb777jsOHDiARqPhgw8+sD25/e6774iLi2PhwoWEh4cDEBUVhZOTE3PmzGHAgAEEBQVx/Phx7r//fv773//azt29e3d2795dYuH99bRPp9PRrl27a7b/eplMJiZNmkR2djbjxo0DCp+uAaWONri7u9uOA9SpU+eGrjdixAh27doFwNNPP02bNm1utulCOJzc5+Q+dzXvv/8+Go2GF198sQxaLa5X1i8/YD5bsWtSrUDWkh9vKpBr27ZtqYmFPD09b+g8lz+UAOyCMrVaTWBgoG0Eviw1bNjQ9ve9e/eSn59PVFSU3cjiHXfcwS+//EJ8fPxVH4TdTiSQE1XOpVNZrkSj0ZTYq+Typ6s7duygfv36ti83UDg1pk+fPnzxxRe2ss2bN9OyZUuCgoLsbhi9e/dmzZo17N+/n/bt2wPg4eFxXV9uirz22mvs3LmTXbt28dVXX+Hv7287tmXLFmrVqkXz5s3trnvPPffwww8/sG3bNu6//35bxrL8/HzOnDlDfHw8R48eJS0tDS8vL7vrXW/7rjXlQqPR2E0VupLs7Gyee+45tm7dyhtvvEGHDh0AsFqtAFc8x61MFyr6ErV161a++uorzpw5w8yZM2/6fEJUBLnPyX3ucn///TdLly5l8eLFtqBaOIbnQ0MqxYic50NDbuq1Hh4etpG0m+Xm5lZq0hIXFxe7n9VqdbnsFXfpyHpRkp+BAweWWjc5OVkCOSEqG19fX/R6fYkn0JfLzs4mPT29xND9peunoPDJd9F0vEtdXpaenk5ycvIVszsVrYcA7L6gXA+9Xk+HDh34888/S0yTSUtL4+zZs9e87oULF3jvvfdYs2YNVquVkJAQWrRogYuLS4mb6fW0LyEhgZ49e161zvfff09kZORV65w8eZIxY8Zw9uxZ3n//fbupWUVPAUtLwZ2Tk4O3t/c123klRe2KjIxErVbzxRdfMHr0aJo1a3bT5xTCUeQ+V/p1q/t97sKFC7z22muMHTuWRo0a2QWhReufrjfwFDfO7Y5eNzUSVhUUfWYu/T0qjxG16722oijX3F7Fw8MDgFmzZhEUFFTieL169cqwlZWbBHKiSunVqxe//fYbcXFxV5yKsmLFCqxWK3ffffdVz+Xr61vqHkqXL4z39PSkSZMmTJgwodTzXGsK1M3y9PSkUaNGTJkypdTjgYGBKIrCyJEjMRqNfPPNN7Rq1cr2dOyhhx4iJyfnhq8bGBjIzz//fNU617pJbt++nbFjx6LRaPjqq6/o2LGj3fGi6RmnT5+2m0dfUFBAfHz8DU952rRpEwUFBdx555125UVPIM+dOyeBnKgy5D5XTO5zhbZs2UJGRgYff/wxH3/8sd2xX3/9lV9//bXEWkYhrkfR1N+kpCT8/PwA2L17d7lc6/JR6EuvXWTv3r3XHC0PDw/HycmJ1NRUoqOjbeVLlizhr7/+Ytq0aWXY6spNAjlRpfznP/9h1apVvPTSS3z11Vcl1h4cP36c6dOnExYWds3Frl27duXTTz9l//79tGrVCih8EnRpJjGAzp07s2vXLurVq2d3vQULFrB27VrefffdMnp39jp37sz333+Pv78/NWvWtJX/9ddf/O9//+OFF17AycmJ48ePM2rUKNt0Hih82nz06FHbWpcbodPpbmkKxp49e3jqqacICgpi3rx51K1bt0SdkJAQGjZsyIoVKxg6dKjt5r569Wpyc3NLLJK+lv/7v/9jx44drF69Gh8fH1v5unXrUKvVNGnS5KbfjxCOJvc5uc9drkePHqUGno899hhdu3a1JYoR4kZFRkbi7OzMe++9x3/+8x/OnTvH7Nmz0el0ZX4tT09PcnNzWb16Na1atSIsLIygoCA++eQTtFotBoOBTz/91DbidiW+vr4MGTKEDz74gMzMTFq1asWRI0f4+OOP6dmzZ6nrUm9XEsiJKqVOnTp88sknTJgwgf79+zN48GCaNm1KQUEB27dv56effqJWrVp8/vnn17wJPfHEEyxZsoT//Oc/jBs3jsDAQBYvXsypU6fs6o0bN44BAwYwePBghg0bRkBAADt37mTevHl069bN7stHWRo2bBjLli1jyJAhjBo1itq1axMTE8MXX3xB3bp1CQsLw8nJibp167JkyRLq1atHrVq1OHbsGF999RVms9lh0yOKFBQU8OKLL2I2mxk7dizJycl2owFqtdqWfOT555/nmWeeYcyYMQwYMIC4uDg++eQTOnXqdENpiQGeeeYZtmzZwogRIxg1ahSurq789ddfLFmyhKeeekq+4IgqRe5zcp+7nI+Pj91DqiIqlQpvb+9bXv8kqi9PT09mzpzJtGnTeOqpp2jUqBFTp07lmWeeKfNr9e3bl19//ZXx48fz3HPPMWrUKGbOnMl7773H2LFjCQkJ4cUXX2T27NnXPNeLL76Ir68vixYt4tNPPyUwMJAnnnjClum2ulAp5bEiUYhylpiYyIIFC1i3bh3nzp3DycmJevXq0adPHx599FG7xbc7duxg6NChzJgxg759+9qdJykpialTp7Jx40bb1LxmzZoxdepUNm/ebFtvEhsby8yZM9m2bRtGo5FatWrRt29fnnrqKdui8yFDhpCcnMzKlStv6L28/PLL/Pnnnxw4cKDEseTkZNueLBkZGQQGBtKzZ0/Gjh1rW+B/8uRJpkyZwr59+7BYLAQHB9OnTx80Gg3Tp0/nr7/+om7dujfdvhuxZ8+eq6b71+l0du9z1apVfPHFF5w6dQpvb2969erF888/f8WnaUuWLOGVV15h4cKFJdYGHT58mE8++YR9+/aRk5NDw4YNGTp0qF1qdiGqErnPyX3uWmnaW7ZsSd++ffnggw9u6r0IIao2CeSEEEIIIYQQooq5+RzfQgghhBBCCCEqRIUEctu2bWPAgAG0atWKHj168Omnn2KxWIDCRdizZ88mKiqK8PBwhg8fzsmTJ+1ebzKZeP/99+nSpQsRERE8++yzXLhwwa5OZmYmL7/8MpGRkbRv357JkyeXSAGcmJjIM888Q9u2bencuTNTp07FZDLZ1Tl27BhPPPEEERERREVFMXfu3HLZH0MIIYQQQgghrpfDA7l//vmHUaNG0aBBA7788ksef/xx5s2bZ1vYOGvWLGbPns2IESOYMWMG2dnZDBs2jOzsbNs53nzzTX777TcmTpzIlClTOHLkCKNHj7YFg1C4cHvnzp289dZbvPrqq6xdu5aJEyfajptMJkaMGMG5c+eYOnUqY8aM4X//+5/dPPPU1FSGDx+OSqVi5syZPPLII8ycOZOvv/7aAT0lhBBCCCGEEKVzeNbK6dOn06VLF1vA1KlTJzIyMtixYwfDhg1j/vz5jB07lqFDhwLQrl07W9rd4cOHExcXx6+//sr06dPp06cPAE2aNKF3796sWbOGXr16sX37dnbs2MGiRYsIDw8HoEaNGgwbNoyYmBiaN2/OH3/8QVxcHGvWrKFGjRoAODs789ZbbzFmzBj8/f1ZsGABZrOZ2bNn4+rqSvfu3TGZTMydO5ehQ4fi5OTk6O4TQgghhBBCCMcGcmlpaezZs4dZs2bZlb/wwgtA4YaXRqORnj172o55eXnRoUMHNm3axPDhw9m+fTuAXdre0NBQGjVqxKZNm+jVqxfbtm3Dz8/PFsRB4T4Zer2eTZs20bx5c7Zu3UqzZs1sQRxAdHQ0r732Gtu2baN///5s3bqVTp064erqaldn9uzZHDhwwJZe+HolJ2dfu9IlvL3dAMjIcGxq5epM+tyxpL8d61b6OyDg6vv6FLnR+xzI58DRpL8dS/rbsRxxnxOisnBoIHf06FEURcHNzY2nn36aLVu2oNfrGTRoEM888wyxsbEA1K5d2+51ISEhrF27FoDTp0/j7++Pm5tbiTpFrz99+jR16tSxO65WqwkODrbViY2NJTQ01K6Oj48Per3erk5kZKRdnaK2xcbG3nAgV3RzuV5arfqmXidunvS5Y0l/O5Yj+vtmzi2fA8eS/nYs6W/Hkv4W1YlDA7n09HQAXnrpJfr168ewYcPYtWsXs2fPxtnZGUVR0Ol0JTY4dXd3tyUqycnJwd3dvcS53d3dOX/+/DXrFJ3HYDDcVJ2iny9PnCKEEEIIIYQQjuLQQK6goACArl27MmnSJAA6duxIeno6s2fPZvTo0ahUqlJfW1SuKEqpdS4tVxQFtbpkHpfLy690ntJee7nrqXO5Gx3ml+kYjid97ljS347liClHN3Nu+Rw4lvS3Y0l/O5ZMrRTViUOzVhaNZnXr1s2uvHPnzhiNRjw9PTGZTLaAr0hOTg4eHoW/XHq9npycnBLnNhqN11VHr9ffUp2in4vqCCGEEEIIIYSjOTSQK1q3dnmgZjabAdBqtSiKQkJCgt3xhIQE6tWrBxQmNklJSSEvL++qdeLj4+2OW61Wzp49a1fn8uukp6djMBiuWqfovPXr17+Bdy6EEEIIIUTF2LhxIyNHjiQyMpLw8HD69u3L559/TlZWVom68+fPp2PHjrRu3Zo///yTLVu2cNddd9GyZUveeecdhgwZwlNPPVXubY6JiWHs2LFERkbSokUL7rzzTt555x1SUlLK/dqlefnll+nXr5/t57CwMObPn18hbSni0ECuYcOGBAUFsXLlSrvyDRs2EBgYSN++fXF2dmb16tW2Y5mZmezcuZNOnToBhdsVWCwWW/ITKEw8cvz4cbs6ycnJ7N+/31Znx44dGAwGW52OHTty8OBB27o6gNWrV+Pk5ET79u1tdbZu3YrRaLSr4+3tTZMmTcqqW4QQQgghhCgX06dPZ9SoUej1et59913mzJnDfffdx//93//x8MMPExcXZ6ubnZ3NRx99RJcuXfjqq6/o1KkT06dPx8XFhXnz5jF06FDefPNN2xKp8nL48GEGDRqE1WrlvffeY/78+YwaNYp169YxaNCgSpGrYuHChfTv379C2+DQNXJqtZoJEyYwadIk3nzzTXr37s3WrVtZunQpb731Fnq9nsGDB/PJJ5+gVqsJDQ1lzpw56PV6BgwYABSO6vXu3ZvXX38dg8GAp6cnM2bMICwsjOjoaKAwAAsPD2fs2LG89NJLmM1mPvzwQ6KiomjRogUA/fr1Y/bs2Tz55JM899xzJCUl8dFHH/HII48QEBAAwKBBg/jxxx8ZPXo0I0eO5MiRI8ydO5eJEyeWSMgihBBCCCFEZbJixQrmzp3LK6+8wrBhw2zlnTp1ol+/fgwcOJCJEyfy008/odFoyMrKQlEUoqOjadeuHQAZGRl0796djh07Oqzd33//PSEhIcyaNcuW0yIyMpJ27drRr18/fvvtNx5//HGHtac0rVu3rtDrg4NH5ADuv/9+pk+fzp49exg9ejSrVq3iv//9LwMHDgRgwoQJDBs2jK+//poXXngBDw8PvvnmG9v6N4ApU6bQp08fpk2bxmuvvUaTJk2YO3cuGo0GKExiMnv2bNq0acPrr7/OlClT6NGjB9OnT7edw9XVlW+++YagoCBeeOEFZs+ezWOPPcYrr7xiqxMYGMg333yD2Wzm2WefZdGiRYwfP56RI0c6qLeEEEIIIYS4OV9++SWNGze2C+KK1KpVi/Hjx7N//362bNnCkiVLuPPOOwEYP348d955J2FhYZw9e5b//e9/hIWFkZCQUGJqZUZGBpMnT6Zz5860bduWESNGcPToUdtxo9HIO++8Q+fOnWnVqhVDhgzh0KFDV213amoqiqKgKIpdeaNGjXjllVcICwsDCmfchYWFceDAAbt67dq147PPPgNgyZIlREREsHnzZnr37k3r1q0ZPHgwhw8fttX/7LPPbAFijx49iIiI4KmnniqxxOpSl0+tPHPmDGPGjCEiIoJ27drx4osvkpaWZtcPkydPpmvXrrRq1YoHHniAv/7666r9cC0OHZEr0q9fP7s5ppfSarW88MILtk3CS+Pm5sY777zDO++8c8U6fn5+zJw586rtqFu37jXntrZs2ZKffvrpqnWEEEIIIYSoTNLS0jh8+DBPPvnkFevcddddvPrqq2zYsIFnnnmGzz//nLFjxzJhwgQ6duyIoiiMHTuWNm3aMGLECAIDA+1ebzabGT58OMnJyUyYMIGgoCBmz57NyJEj+fPPP/H09OQ///kPR48eZcKECQQEBPDjjz8yZMgQli5dWmLf5yJ33HEH77zzDoMHD+ahhx4iMjKSkJAQgFKD0msxmUy88MILjBkzhpCQEL744guGDh3KypUr8fPzA+Ds2bNMnTqVCRMm4O7uzvTp0xk2bBjLly+/5ky8lJQUBg0aREBAAFOnTsVkMjFz5kxGjhzJwoUL0el0fPjhh2zfvp3Jkyfj7e3NL7/8wnPPPceyZcto0KDBDb8nqKBATgghhBBCiKrgryMX+HLzaYwmc4W1wU2n5amu9ejVJOi6X1M0mhQcHHzFOh4eHnh5eXH27Fl8fX1p2rQpUDjYER4eDoBOp8Pf37/UqYTr16/n0KFDLFiwwDYVs1mzZgwYMICDBw9itVrZvn0733zzDZ07dwYKs9f37duX2bNnM2XKlFLb9fjjj3P+/Hm+/fZb/vnnH9v76NmzJ08++SRBQdffD1AYcD733HM89thjQOG0yDvvvJP/+7//Y+zYsUDhiNknn3zCHXfcARQmNrz33nv5888/eeCBB656/u+++478/Hy+/vprfH19AWjVqhV33303y5cv5/7772f37t106dKFe+65B4C2bdvi7+9vS/p4MySQE0IIIYQQ4gp+3BlHXHoF7wOYY+LHXXE3FMgV0Wqv/nX/Wsev5t9//8XDw8MWxEHhrLiipITTpk3D1dWV9u3b2wUsXbt2tUtceDmVSsULL7zAiBEjWLduHVu3bmX79u18//33LFmyhG+//ZaWLVveUFv79u1r+7uvry+tW7dm9+7dtjIPDw9bEAfQuHFjateuze7du68ZyO3YsYPWrVvj6elpe581a9akQYMGbNu2jfvvv5+IiAgWLVpEUlISPXr0ICoqipdffvmG3sPlJJATQgghhBC3BatVIT0tH7VGsSXJuFWDO9SpFCNyQ9qXPg3xSmrVqgXAuXPnrljHaDSSnp5OzZo1b6pdmZmZtqmJpcnIyCA3N9eWbPBSTk5O1zy/r68vDz30EA899BCKorBu3TpeeuklPvzwQ3788cfrbqezszOenp4lzn369Gnbz0XJDi+vk5mZec3zZ2RksG/fPpo3b17iWNF5X3vtNQIDA/ntt99Yt24darWau+66i/fff/+m96eWQE4IIYQQQlR5OQYza5fHkpFmom4Dd9p28i2T8/ZqEnRTI2EVzd/fn5YtW7JmzRqee+65UgPbdevWYbFYiIqKuqlreHh42CX0KLJ9+3ZCQkLw8PDAz8+PL7/88rrPef78eR5++GFeffVV+vTpYytXqVTceeedPPjgg/zxxx+2MsAuKYqiKOTm5tqdMz8/n9zcXFxdXW1lqamptmmQUBiMXS41NdWWWOVq9Ho9d9xxB88++2yJY+7u7gC4uLjw7LPP8uyzz3Lq1ClWrVrFF198wUcffcR///vfa16jNA7PWimEEEIIIURZSk3OZ/2KC2SkmQDINVoquEWVw5gxYzh27Bjz5s0rcSw5OZnp06fTvHlzunTpclPnj4iIICsry7aODQpH6UaNGsWWLVto27YtaWlpuLm50bJlS9ufP/74g99//73UcwYEBKBSqfjf//5X6vqxM2fO0KhRIwDbSFZSUpLt+N69e0t93bp162x/T01NZe/evURGRtrK0tLS7PagPnr0KPHx8de17ULbtm05deoUYWFhtvfYuHFjPv/8c/755x8sFgv9+vXj22+/BQrX3/3nP/+hdevWJCYmXvP8VyIjckIIIYQQosqKP53DP9vSsFoLf/bwdKJ1e+8KbVNlceeddzJ27FimT59OTEwM/fv3x9PTk0OHDjF//nx0Oh0zZsy46XVyPXr0oFmzZkyYMIHnn38eHx8f5s2bR2BgIH369LEFcKNHj2bs2LHUrFmTv/76iwULFlxxFEqj0fDaa68xfvx4HnvsMQYOHEidOnXIzMzkt99+Y+vWrfzwww9A4RYAQUFBfPLJJ2i1WgwGA59++qndtmVF3n77bXJycvD19WXWrFl4eXnZkp9A4eje+PHjbZnzP/74Y5o2bUqvXr2u2Q/Dhw/nt99+48knn2To0KE4OTnx9ddfs3fvXsaPH49Go6FVq1bMmjULZ2dn6tevz759+/jnn39uejQOJJATQgghhBBVkKIoHDmQxeH9WbayoJqu9OxTi7x8UwW2rHIZN24cbdu25bvvvuPNN9/EYDAQEhLCI488whNPPFFi7diNcHJyYv78+UydOpX3338fq9VKu3bt+Pbbb23B1Pz585k2bRofffQRBoOBunXrMmXKFB588MErnvfuu+9mwYIFzJ8/n48//piMjAz0ej3t27dn8eLFNGnSBCgM+mbOnMl7773H2LFjCQkJ4cUXX2T27Nklzvnyyy/z2WefkZaWRseOHUsEfK6urowdO5b333+fvLw8evTowauvvnpdQW6tWrX43//+x0cffcSLL76ISqWiefPmfPPNN7ZMoK+99hpubm7MmTOH1NRUgoODmTRpEgMGDLihPr+USrl8pz1RbpKTs2+ovre3GwAZGRWcKakakT53LOlvx7qV/g4IKPl0szQ3ep8D+Rw4mvS3Y0l/lw+LRWHPtjTiY4v7tU49N3r0DkajUZfrfU5ULUuWLOGVV15h27ZtdmviLvXZZ5/x9ddf8++//zq4dbdGRuSEEEIIIUSVkZ9nYduGFNKSi0fdmoV7EdbCA41G0j+I6kMCOSGEEEIIUSVkZRSwdX0yRkNhMhO1RkW7zr6E1HWr4JYJ4XgytdKBZGpl5Sd97ljS344lUysFSH87mvR32bmQmMeOjSmYCwq/ujq7qOkU5Y+vv7OtjiPuc0JUFjIiJ4QQQgghKrVTxwzs25VO0fCDp7cTnaP8cdPLV1lRfcmnXwghhBBCVEqKVeHAngxOHDHYyoJqudChqx9OOlkPJ6o3CeSEEEIIIUSlYy6wsnNzKufP5tnK6ofpadXWG7VaVYEtE6JykEBOCCGEEEJUKsYcM9vWp5CZXlBYoILwdt40CJN1bEIUkUBOCCGEEEJUGumpJratTyYv1wqA1klFh65+1Ah2reCWCVG5SCAnhBBCCCEqhbNxRnZvScNiKcxq4uquoXOUP14+ugpumRCVjwRyQgghhBCiQimKwrFD2cT8m2kr8/HX0am7Py6umgpsmRCVl6T7EUIIIYQQFcZqUdizPd0uiAuu68od0QESxN2Cl19+mbCwsKv++eyzz27pGmFhYcyfP7+MWnxlS5YsISwsjLS0tHK/VlUiI3JCCCGEEKJCmPItbN+YSsqFfFtZWAtPmoV7olJJZspbMWbMGAYOHGj7edKkSdStW5cxY8bYymrUqFERTRNlRAI5IYQQQgjhcIasArauS8GQbQZArYY2HX2pU9+9glt2e6hTpw516tSx/ezi4oKvry+tW7euuEaJMiVTK4UQQgghhEMlX8hj/cokWxCnc1bTNTpQgjgHW7ZsGf3796dly5ZER0fzww8/2B23WCzMmTOH6OhowsPDue+++1i9erVdnYyMDCZMmEBERASRkZG8//77FBQUbhuRkJBAWFgYa9euZeTIkYSHh9OtWzdmz55td460tDRee+017rjjDsLDwxk6dCgHDhy4atv//vtvHnroIVq3bk337t2ZOXOm7boAVquVzz//3HbOcePG8e233xIWFgbADz/8QLNmzUhJSbE77+uvv86DDz54Yx1ZQSSQE0IIIYQQDnPmZA6b1yRjMhVuL+DhqSWqdxD+gc4V3LLqZenSpUycOJH27dsze/Zs7r//fqZMmcJXX31lqzNlyhQ+//xzHnzwQebMmUN4eDjPPvssu3fvttX56quv8PHx4YsvvmDgwIF89913/PTTT3bXeuWVVwgPD2fOnDn06NGDmTNnsmHDBgBycnJ47LHH2Lp1KxMnTuTjjz9GURQGDx7M0aNHS237woULGTt2LC1btuTzzz9n8ODBfP3117zyyiu2OjNnzmTOnDkMGjSITz/9FIDp06fbjvft2xe1Ws2KFStsZSaTiVWrVnHffffdQs86jkytFEIIIYQQ5U5RFGL2ZnIsJttWFlDDmchu/uicK+/YwomjmezelkzBxcCzIjjp1LTrFEDDMK8yOZ/VamXGjBn079+fN954A4CuXbuiUqn44osvGDRoECaTif/9738888wztnV1nTp14vTp0+zevZt27doB0LlzZ15//XXb8bVr17Jjxw6GDBliu94999zDs88+C0BkZCSrVq1i48aNdO/enSVLlhAXF8cff/xBw4YNbW3p3bs3n3/+eYmELFarlZkzZ9K3b1/eeustW30PDw/efPNNnnzySUJCQvjmm2946qmnePrppwG44447uO+++2zBoa+vL3fccQfLli2ztXXjxo3k5OTQt2/fMunn8iaBnBBCCCGEKFdms5XdW9M4F5drKwtt6E7rDj6o1ZU7qcm+3alkppsqthE5sP+f1DIL5E6fPk1SUhJRUVGYzWZb+R133MGnn37K/v37yc/Px2KxcOedd9q99vLplxEREXY/BwcHk5WVZVd26bo8tVpNYGAgRqMRgF27dtGwYUNbEAeg0+mIjo7mt99+K9H2kydPkpaWRu/eve3K+/Xrx5tvvsnu3btJTU3FZDIRHR1tO65SqejVq5fdKN/999/PuHHjiI+Pp3bt2vz+++906dIFf3//UvutspFATgghhBBClJtco4Vt65PJSCtev9SyjRcNm3pUicyU4e38KsWIXHhbvzI7X0ZGBgATJ05k4sSJJY4nJyejKIWbsvv6+l71XK6urnY/q9Vq22uLuLi4XLFOVlZWqYGTv78/OTk5JcozMwu3qfDzs+8PvV6Ps7MzBoOB9PT0Utt++XWioqLw9vZm+fLlPP7446xfv54pU6Zc8b1WNhLICSGEEEKIcpGRbmLbuhRyjRYANBoV7bv6Uau26zVeWXk0DPMqs5GwysLDwwOAN954g1atWpU4HhISwt69ewFIT08nKCjIduzw4cMoikKzZs3KpC1eXl6cOnWqRHlycjLe3t4lyovKUlNT7cqzsrLIz8/H29ubwMBAoDCJyqVtv3wfOp1OR58+fVi1ahW1atVCq9XSs2fPW3xHjlN5JyQLIYQQQogqKzEhl42rkmxBnIurhjt6BVapIO52Vb9+fby9vblw4QItW7a0/cnIyOCTTz7BYDDQqlUrtFot69ats3vtG2+8UaabgLdt25YTJ05w8uRJW5nJZGL16tW0adOmRP169erh4+PDypUr7cqXL18OQJs2bWjatCnu7u6sWbPGrs7atWtLnO/+++8nJiaGn376ibvvvrvE6GFlJiNyQgghhBCizCiKwskjBvbvyYCLM+y8fJzo3MMfVzf56lkZaLVaxo0bxwcffAAUJilJSEhg+vTphIaGEhISgkqlYuDAgcyePRutVkuLFi1YsWIFhw8ftiVIKQsPPvgg3333HaNGjWL8+PF4eHjw7bffkpKSYktUcimNRsPYsWN555138PLyomfPnhw9epTPPvuM3r1707hxYwCeeOIJvvzyS3Q6HU2bNuW3334jJiamxHTe8PBw6tevz+7duxk3blyZvS9HkN8mIYQQQghRJqxWhX270zl9rHhtU83arrTv4otWKxPBKpPBgwfj4uLCt99+y9dff423tze9e/fm+eeftwU7r776Kj4+PixYsID09HQaNWrEvHnzaNmyZZm1Q6/Xs2DBAj788EPefvttLBYLrVu3ZsGCBVecvlnU9q+//prFixcTGBjI8OHDbdk1AcaOHYvFYuG7777DYDAQFRXFoEGDWLp0aYnzdevWDaPRSIcOHcrsfTmCSrl8NaIoN8nJ2deudAlvbzcAMjKM5dEcUQrpc8eS/nasW+nvgACP66p3o/c5kM+Bo0l/O1Z16u8Ck5Udm1JJSsyzlTVq5kGLCC+HJTVxxH1OVH4mk4nly5fTtWtXuwQnEydO5NSpUyWCub59+xIdHc3zzz/v6KbeEhmRE0IIIYQQtyTHYGbrumSyMwtT2atU0LqDD/Ua6Su4ZaI60ul0fPHFFyxevJgnn3wSV1dXtm3bxvLly3n33XeBwinAs2bNIiYmhvj4eAYOHFjBrb5xEsgJIYQQQoiblpqcz/b1KeTnF6bnd9KpiOzmT2DNqpM0Qtx+vvzyS6ZNm8Yrr7yC0WikXr16vP/++zzwwANA4b5yK1euJCUlhXfffZeaNWtWcItvnARyQgghhBDipsSfzuGfbWlYL26x5q7X0rmHPx5eThXbMFHt1atXj1mzZl21zrJlyxzUmvIhgZwQQgghhLghiqJw5EAWh/dn2cr8Ap3peIcfzi6aCmyZENWHBHJCCCGEEOK6WSwKe7alER9bnFCkTj03Ijr6otE4JqmJEEICOSGEEEIIcZ3y8yxs25BCWrLJVtYs3IuwFh4Oy0wphCgkgZwQQgghhLimrIwCtq5PxmiwAKDWqGjX2ZeQum4V3DIhqicJ5IQQQgghxFVdSMxjx8YUzAWF2w87u6jpFOWPr79zBbdMiOpLAjkhhBBCCHFFp44Z2LcrHaUwhsPT24nOUf646eVrpBAVSX4DhRBCCCFECYpV4cCeDE4cMdjKgmq50KGrH046dQW2TAgBEsgJIYQQQojLFBRY2bU5lfNn82xlDcL0tGzrjVotSU2EqAwkkBNCCCGEEDbGHDPb1qeQmV5QWKCC8HbeNAjzqNiGCSHsSCAnhBBCCCEASEvJZ9v6FPLzrABonVR06OpHjWDXCm6ZEOJyEsgJIYQQQgjOxhnZvSUNi6Uwq4mbu4ZOPfzx8tZVcMuEEKVx+ErV9PR0wsLCSvx59tlnAVAUhdmzZxMVFUV4eDjDhw/n5MmTducwmUy8//77dOnShYiICJ599lkuXLhgVyczM5OXX36ZyMhI2rdvz+TJkzEYDHZ1EhMTeeaZZ2jbti2dO3dm6tSpmEwmuzrHjh3jiSeeICIigqioKObOnYtSlLZJCCGEEKKKUxSFowez2LEx1RbE+fjriOodJEGcEJWYw0fkjhw5AsD8+fPR6/W2cm9vbwBmzZrF3LlzeeGFFwgODmb27NkMGzaM5cuX4+FRODf7zTffZO3atUyaNAk3NzdmzJjB6NGjWbJkCRqNBoBx48aRkJDAW2+9RV5eHlOnTiUlJYUvv/wSKAwGR4wYgYuLC1OnTiUxMZFp06aRl5fHG2+8AUBqairDhw+nUaNGzJw5k5iYGGbOnIlGo2HkyJGO6jIhhBBCiHJhtSj8uzOdMydzbGUhdV1p28kXjVYyUwpRmTk8kDt69Cj+/v507dq1xDGDwcD8+fMZO3YsQ4cOBaBdu3b06NGDn3/+meHDhxMXF8evv/7K9OnT6dOnDwBNmjShd+/erFmzhl69erF9+3Z27NjBokWLCA8PB6BGjRoMGzaMmJgYmjdvzh9//EFcXBxr1qyhRo0aADg7O/PWW28xZswY/P39WbBgAWazmdmzZ+Pq6kr37t0xmUzMnTuXoUOH4uTk5KBeE0IIIYQoW/n5FnZsSCUlKd9WFtbCk2bhnqhUkplSiMrO4Y9ajh49SlhYWKnH9u3bh9FopGfPnrYyLy8vOnTowKZNmwDYvn07AFFRUbY6oaGhNGrUyFZn27Zt+Pn52YI4gMjISPR6va3O1q1badasmS2IA4iOjsZsNrNt2zZbnU6dOuHq6mpXJyMjgwMHDtxKNwghhBBCVBhDVgEbVibZgji1Gtp19qV5ay8J4oSoIiokkMvNzWXgwIG0bNmSO+64g3nz5qEoCrGxsQDUrl3b7jUhISG2Y6dPn8bf3x83N7er1qlTp47dcbVaTXBwsK1ObGxsiTo+Pj7o9Xq7OnXr1rWrU9S2ojpCCCGEEFVJ8vk81q1MwpBtBkDnrKZrdCB16rtXcMuEEDfCoVMrrVYrJ0+exNXVlUmTJlGzZk02bNjAjBkzyM/Px8nJCZ1Oh05nv7DW3d3dlqgkJycHd/eSNxp3d3fOnz9/zTpF5zEYDDdVp+jnyxOnXA9vb7drV7qE9uLc9Bt9nbh50ueOJf3tWI7o75s5t3wOHEv627EqW38fO5TJ1vXJWAt3F8DLR8dd/YLx9Lo9kppUtv4Wojw5NJBTFIU5c+ZQq1Yt20hXx44dMRqNfPXVVzz99NNXHM4vKlcUpdQ6l5YrioJaXXKw8fLyK52ntNde7nrqCCGEEEJUBoqisHtbCgf2pNnKaoa4cWfvWji7aCqwZUKIm+XQQE6j0dCpU6cS5d26deOnn37C1dUVk8lEQUGBXSKRnJwcW8ZKvV5PTk5OiXMYjUa7OsnJyaXWKcqUebXzXK1O0c+XZty8XhkZxhuqX/Q06UZfJ26e9LljSX871q30d0CAx3XVu5lzy+fAsaS/Hasy9LfZbGX3ljTOxefaykIbutO6gw+5efnk5lVY08qcI+5zQlQWDh1WunDhAgsXLiQtLc2uPD+/cKGtl5cXiqKQkJBgdzwhIYF69eoBhYlNUlJSyMvLu2qd+Ph4u+NWq5WzZ8/a1bn8Ounp6RgMhqvWKTpv/fr1b+zNCyGEEEI4WK7Rwsa/kuyCuJZtvImI9EGtlqQmQlRlDg3kTCYTb7zxBr///rtd+apVqwgNDeWuu+7C2dmZ1atX245lZmayc+dO20hep06dsFgsrF271lYnNjaW48eP29VJTk5m//79tjo7duzAYDDY6nTs2JGDBw/a1tUBrF69GicnJ9q3b2+rs3XrVoxGo10db29vmjRpUlbdIoQQQghR5jLSTaxfeYGMtAIANBoVHbv706iZh2SmFOI24NCplbVr16Zfv3588sknqFQqGjRowMqVK/nrr7+YNWsW7u7uDB48mE8++QS1Wk1oaChz5sxBr9czYMAAAOrUqUPv3r15/fXXMRgMeHp6MmPGDMLCwoiOjgYKA7Dw8HDGjh3LSy+9hNls5sMPPyQqKooWLVoA0K9fP2bPns2TTz7Jc889R1JSEh999BGPPPIIAQEBAAwaNIgff/yR0aNHM3LkSI4cOcLcuXOZOHFiiYQsQgghhBCVRWJCLjs3p2IxKwC4uGnoHOWPt698fxHidqFSFEVx5AXz8vL44osvWLZsGcnJyTRo0IBnnnmGu+66CwCz2czMmTNZunQpRqORiIgIJk+eTIMGDWznMBqNTJkyhVWrVmG1WuncuTOTJ08mKCjIVic1NZV33nmHDRs2oNPp6NmzJ6+++qrd2rYzZ87w9ttvs3v3bjw8POjfvz8TJkywW5934MAB3nvvPWJiYvD39+exxx5j9OjRN/Xek5Ozb6h+ZZhXX91InzuW9LdjOWLtyI3e50A+B44m/e1Yju5vRVE4ecTA/j0ZcPEbnrevE52i/HF1c+jz+woha+REdeLwQK46k0Cu8pM+dyzpb8eSQE6A9LejObK/rVaFfbvSOX28OFFbzdqutO/ia0vLf7uTQE5UJ7f/oxkhhBBCiNucyWRl56YUkhLzbWWNmnnQIsJL1sMJcZuSQE4IIYQQogrLMZjZui6Z7EwzACoVtO7gQ71GN75VkhCi6pBATgghhBCiikpNzmf7+hTy860AOOlURN7hT2ANlwpumRCivEkgJ4QQQghRBcWfzuGfbWlYC2M43PVaOvfwx8PL6eovFELcFiSQE0IIIYSoQhRF4fD+LI4cyLKV+QU60/EOP5xdNBXYMiGEI0kgJ4QQQghRRVgsCv9sSyMhtjgrY536bkRE+qLRSFITIaoTCeSEEEIIIaqAvDwL2zekkJZsspU1a+1FWHMPyUwpRDUkgZwQQgghRCWXlVHA1nXJGHMsAKg1Ktp19iWkrlsFt6xyUQpMWC6cQ3HzR6WuHnvniepLAjkhhBBCiErswrk8dmxKwVygAODsoqZTlD++/s4V3LLKQ7FasG5bTfLSb7CmXEAT1R+nYRMqullClCsJ5IQQQgghKqlTxwzs25WOUhjD4entROcof9z08hWuiOXgLswLv0SJP2krU5LPVWCLhHAMuQsIIYQQQlQyilXhwJ4MThwx2MqCarnQoasfTjqZMghgPXMC8+IvsR7cbVeua9UehshonLj9SSAnhBBCCFGJFBRY2bU5lfNn82xlDcL0tGzrjVotSU2U1AsULPka69a/sQ1VAqqQ+niPfA5d60gyM3MrsIVCOIYEckIIIYQQlYQxx8y2dSlkZhQUFqggvJ03DcI8KrZhlYCSY8D85wIsf/0C5oLiAz7+aB8ciabLXTj7Sj+J6kMCOSGEEEKISiAtJZ9t61PIz7MCoHVS0aGbHzVquVZwyyqWUmDCsvZ3zL//ADnFm6Dj6o627yA0vR5CpZPEL6L6kUBOCCGEEKKCnY0zsntLGhZL4VRBN3cNnXr44+Wtq+CWVRzFasW6cz3mX75CSU4sPqDRornzPrT3DkHl4VVxDRSigkkgJ4QQQghRQRRF4VhMNjF7M21lvv46Onb3x8VVU4Etq1iWw3sxL/oS5fQRu3J1hyi0Dz+JOjC4glomROUhgZwQQgghRAWwWhT27Egj7pTRVhZS15W2nXzRaKtnZkrr2dOYF83Dum+bXbmqcSucHn0KdYNmFdQyISofCeSEEEIIIRwsP9/Cjg2ppCTl28qatPSkaStPVKrql5lSSU/BvPRbLJtWgGK1latq1kH7yGjUrTtXy34R4mokkBNCCCGEcKDsrAK2rUvBkG0GQK2GNh19qVPfvYJb5nhKrhHzip+wrFwMpuLtFvDyRfvAMDTd+qDSVN8ppkJcjQRyQgghhBAOknw+j+0bUykwFY466ZzVdOzuj39g9cq6qJjNWDYsw/zrd5CdUXzA2QVtn4Fo7n4ElUv1ztYpxLVIICeEEEII4QCxJw38uz3dtoe1h6eWTj0C0HtUn69jiqJg3bMZ8+J5KOfjiw+o1Wi690N73xOovH0rroFCVCHV584hhBBCCFEBFEXh4L8ZHIvJtpUF1HAm8g5/dLrqk9TEevwgBQvnoJyIsStXt+mK9uFRqGvVqaCWCVE1SSAnhBBCCFFOzAVWNvydyJlTBltZaEN3WnfwQa2uHsk7rOfjMS+eh/WfTXblqgbNcHr0adSNW1ZQy4So2iSQE0IIIYQoB4asAtavTCItpTgzZcs23jRsqq8WGRiVrHTMv32PZf0fYLHYylWBtdAOGI263R3Voh+EKC8SyAkhhBBClLG4Uzns3ZmO2Vy4IE6jVdG+ix+1at/+CTyU/Fwsq37GvPz/IC+3+ICHF9r7nkAT1Q+V1qniGijEbUICOSGEEEKIMmI2W9m7M91uk28PLyfad/HF21dXgS0rf4rVgmXTSsxLv4WMlOIDTjo0dw9A22cgKjd9hbVPiNuNBHJCCCGEEGUgM93Ejk2pGLLMtrL6jTzo3CMIozH/Kq+s2hRFwbpvO+bFc1HOxhYfUKnQdO2N9oHhqHwDKqx9QtyuJJATQgghhLgFiqJw+ngO+3enYy3cHg6NRkV4e2/C2wagUqkwGq9+jqrKevoo5oVzsB7Za1eubhVZuA6udv2KaZgQ1YAEckIIIYQQN8lksrJnexrn4orXgnl6O9Ghqx+e3k63bTIPa3Ii5l++wrp9rV25qm4jtI8+haZZ2wpqmRDVhwRyQgghhBA3IS05n52bUzHmFGdkrNfInVZtvdFob8/94RRDJuY/fsSy5jcwFxQf8AvC6eEnUUfeiUp9e753ISobCeSEEEIIIW6AoigcP5RNzN5MlMKklGidVLTp6EtIXbeKbVw5UUwmLKuXYP7jR8jNKT7gpkfbfwianvej0t3eyVyEqGwkkBNCCCGEuE55eRZ2b0kjKTHPVubjp6NDVz/cPW6/r1WK1Yp122oKlnwNqReKD2id0EQ/iLbfIFR6z4pr4GWsikJegRVFUW7baa1CFLn97jhCCCGEEOUgKTGPXVtSyc+z2soaNfWgeWsv1JrbL2iwxPyDeeEclLgTduXqTtFoHxyJOqBGBbWspDRjAatPpPDXyRTOZeYxoGUNhretXdHNEqJcSSAnhBBCCHEVVqvC4f1ZHD2YZSvTOatp19mXGsG33wbf1rgTmBfNxXpwl125umkE2kefRh3auIJaZs+qKPx7LosVx5LZEZeBpWieK3DogqECWyaEY0ggJ4QQQghxBcYcM7s2p5KabLKVBQQ5066LH65umgpsWdlTUpMoWPI11q1/wSVBkSqkPtpHnkLdsn2lmK6YajTx1/EUVh1LJinHVOJ4RLAXT7WX0Thx+5NATgghhBCiFOfic/lnWxoFpotTKVXQtKUnTVp4olJXfEBTVhSjAfOy/2H5+xcouCQw8vFH++AINF16oVJXbNBqsSr8czaTlceS2ZmQgVWxP+7prOWuhv483LY2dX3dyMi4TTfuE+ISEsgJIYQQQlzCYlE4uCeDk0eLp+e5uGno0MUX/yCXCmxZ2VLMBVjW/ob59x/AUDxtFBc3tP0GobnrIVTOFft+k3Py+et4Cn8dTyG5lNG31jU9ubuxP53r+OCkUePtfXtmDRWiNBLICSGEEEJcZMgqYMemVDLTi/dIqxHsQttOvji73B5TKRVFwbprPebFX6Eknys+oNGgufM+tP2HoPL0rrD2WawKuxIyWHEsmX/OZpYYffN2KRx9u7txALU8b5/AWogbJYGcEEIIIQQQdyqHvTvTMZsLIweVGlpGeNOgib5SrA0rC9aj+yj4aQ7K6SN25er23dE+PAp1UHAFtQwuZOez6ngyf59IIdVYUOJ4m1qe9G4cQGRtb5w0sum4EBLICSGEEKJaM5ut7N2ZTtyp4nVV7notHbr54eN3e2xybT13pjAT5d6tduWqxi1xeuRp1A2bVUi7zFYrO+MzWXEsmT1nM7ls8A0fVyd6NfLn7kYB1PBwrpA2ClFZSSAnhBBCiGorM93Ezk2pZGeZbWUhoW5EdPDBSVf1R32UjFTMv36LZcNyUIr3v1PVqI32kdGoI7pUyGhjYnYeq46l8PeJFNJz7UffVEC7EC/ubhRAh9peaNVV/99BiPIggZwQQgghqh1FUTh9PIf9u9OxXoxvNBoV4e29qdvAvcpPpVRyjZhXLsSyYhGY8ooPePqgfWAYmjv6otI4ds1fgcXK9vgMVhxNZm9iVonjfm5O3N0ogLsa+ROkl9E3Ia5FAjkhhBBCVCsmk5U929M4F5drK/P0dqJDVz88vZ0qsGW3TjGbsWxcjvnXbyErvfiAswua3o+ivedRVC6O3cT8bFYeq44Vrn3LzDPbHVOroH2IN70bB9Au2AvNbbStgxDlTQI5IYQQQlQbacn57NycijHHYisLbehOeDtvNNqqO4VPURSse7ZgXjwX5Xx88QG1Gs0dfdHe/wQqbz+HtafAYmXrmXRWHEtm//nsEscD3HXc3cifXo0C8He/PdYhCuFoEsgJIYQQ4ranKArHD2UTszcT5WJGDa2TijYdfQmpW7X3HrOeOETBojkoxw7YlasjuqAdMAp1rboOa0t8Ri4rjyez5kQqWfklR98iaxeOvrWpJaNvQtyqCgvkTCYT9913H+Hh4XzwwQdA4U12zpw5LFy4kPT0dNq0acNrr71GgwYN7F43bdo0/vzzT4xGI926dWPy5MkEBQXZ6mRmZjJlyhTWrVuH1WqlV69evPLKK+j1eludxMRE3n33XbZv346zszP3338/48ePR6crfip07Ngx3nvvPfbv34+XlxeDBg1i1KhRVX7evBBCCFGd5OVZ2L0ljaTE4rViPn46OnT1w92j6j7Ttp5PwPzzV1h3b7ArV9VvgtOjT6MOC3dIO/LNVracSWPlsRQOXig5+hak19G7cQB3NfTH101G34QoKxV29/r88885deoU4eHFN5lZs2Yxd+5cXnjhBYKDg5k9ezbDhg1j+fLleHh4APDmm2+ydu1aJk2ahJubGzNmzGD06NEsWbIEzcVFu+PGjSMhIYG33nqLvLw8pk6dSkpKCl9++SVQGAyOGDECFxcXpk6dSmJiItOmTSMvL4833ngDgNTUVIYPH06jRo2YOXMmMTExzJw5E41Gw8iRIx3cW0IIIYS4GUmJeezakkp+XnHGxkZNPWje2gu1pmo+mFWyMjD/9j2W9b+DpXiKqCqwVuFecO27O+Sh85n0XFYeS2bNyRQMJovdMY1KRac6haNvrWt5opaH4EKUuQoJ5A4dOsQPP/yAj4+PrcxgMDB//nzGjh3L0KFDAWjXrh09evTg559/Zvjw4cTFxfHrr78yffp0+vTpA0CTJk3o3bs3a9asoVevXmzfvp0dO3awaNEiW5BYo0YNhg0bRkxMDM2bN+ePP/4gLi6ONWvWUKNGDQCcnZ156623GDNmDP7+/ixYsACz2czs2bNxdXWle/fumEwm5s6dy9ChQ3FyqtqLoYUQQojbmdWqcHh/FkcPFmdH1DmradfZlxrBjk32UVaU/Dwsf/2M+c//g7ziPe/Qe6K97wk0Pfqj0pbv95M8s4XNsYVr3w4nGUocr+nhTO/GAUQ39MfHVb4rCVGeHL6q12w28+qrrzJy5Ei76ZD79u3DaDTSs2dPW5mXlxcdOnRg06ZNAGzfvh2AqKgoW53Q0FAaNWpkq7Nt2zb8/PzsRvoiIyPR6/W2Olu3bqVZs2a2IA4gOjoas9nMtm3bbHU6deqEq6urXZ2MjAwOHLCfgy6EEJWZ1apw/mwu2zde4PCB9Gu/QIgqzphjZtPfSXZBnH+QMz371qiSQZxitWDeuJz8SUMw/zK/OIhz0qHpNwjnqQvQ3vVguQZxp9OMfLH9DEMW7mPG5tN2QZxWreKOUF/evzuMeQ+2ZEDLmhLECeEADh+RmzdvHgUFBYwePZq///7bVh4bGwtA7dq17eqHhISwdu1aAE6fPo2/vz9ubm4l6hS9/vTp09SpU8fuuFqtJjg42FYnNjaW0NBQuzo+Pj7o9Xq7OpGRkXZ1itoWGxtLmzZtbuh9CyGEIylWheSkfBJijZyLy8VkKp5WFt2vRpVPsS7ElZyLz+WfbWkUFH3mVdC0pSdNWniiqmLJNRRFwbp/R2EmyoTTxQdUKjRd70b7wAhUvgHldv28AgsbY9NYcTSZoyk5JY4He7pwT+MAejb0w8tF7ilCOJpDA7mTJ08yZ84cvv32W7ukIlA4tVKn05Uod3d3x2AofOqTk5ODu7t7ifO6u7tz/vz5a9YpOo/BYLipOkU/F9W5Ud7eN5YVS3sxDfKNvk7cPOlzx5L+LluKopB0PpfTx7M5fSKbXKOlRB1vXx1BNfU4O5fPRsA3828pnwPHul3722KxsmtLMof2Z9jK3Ny1RPWqSY3ginuvN9vfBScOk/3d5xQc2G1XrmvTCY+hz+AU2qjM2ni5o0nZ/H4gkVVHkzBetvZNp1ER1TCA+1rWpHWwV6VLAHe7fr6FKI3DAjmr1crkyZN5+OGHiYiIKHFcUZQr3gyKyq9U59JyRVFQq0vOGL28/ErnKe21l7ueOkII4QiKopCanM+p41mcPp5NjsFcoo5GoyKkrjuNmnhRp54eKtf3LiFuWWaGifWrzpGanG8rqx3qTreeNXBxrVpZKc0XzmFYMIe8javsyrX1w/B4YizO4R3K5bo5JjN/H03i94PnOVJK5slQXzfua1GT3k2D8JJpk0JUCg67u/3www+cO3eOL7/8ErO5+IuGoiiYzWY8PDwwmUwUFBTYJRLJycmxZazU6/Xk5JQc2jcajXZ1kpOTS61TtP3A1c5ztTpFP1+6jcGNyMgwXrvSJYqeJt3o68TNkz53LOnvm5eVUUB8rJGEM0ZysksGbyoVBNVyIaSuGzVDXHHSqW+pvwMCPK6r3s2cWz4HjnW79Xfc6Rz27kjHbC7cHE6lhpYR3jRooicv30RevqlC23e9/a0YsjAvW4Bl9VIwFxQf8AvC6aGRqDv2JFetJrcM/90UReF4ag4rj6Ww/lQqeWar3XGdRkW3UF96Nw6gWaAelUqFkl9ARn7BFc5Y8RxxnxOisnBYILd69WouXLhAhw72T5KOHDnCr7/+yttvv42iKCQkJFCvXj3b8Ut/Dg0NJSUlhby8PFxcXOzqtG3b1lZnz549dtewWq2cPXuW/v372+okJCTY1UlPT8dgMNhd6/I68fHxANSvX/+m+0EIIW6WIbuAhNhcEs4Yycoo5YuUCgKCnAmp60ZwHVd05TR9UojKwGy2sndnOnGnir+wu+u1dOjmh49f1dmrTDGZsKxZivmPH8F4ydINNz3a/oPR9HwAla5s30+Oycz6U2msOJbMqbSSAU9db1fuCQugR30/PJyr1oimENWJw347//vf/5YY4XrhhReoV68ezzzzDPXq1eO9995j9erVjBo1Cijc2Hvnzp2MHTsWgE6dOmGxWFi7dq1t+4HY2FiOHz9uV+fLL79k//79tGrVCoAdO3ZgMBjo1KkTAB07duS///0v58+ft2WuXL16NU5OTrRv395WZ+HChRiNRltyldWrV+Pt7U2TJk3Ks6uEEMLGmGMm4YyRhFgjGWmlPwX3C9AREupGcB03XFwleBO3v8x0Ezs3pZKdVTwaHVLXjYhIH5x0VWP5g2K1Yt2+hoJf5kPqheIDWic00Q+g7fc4Kr1n2V1PUTianMOKY8lsjE0j/7LRN2eNmjvq+XJP4wDCAtwr3do3IURJDgvkShvFcnFxwdvbm5YtWwIwePBgPvnkE9RqNaGhocyZMwe9Xs+AAQMAqFOnDr179+b111/HYDDg6enJjBkzCAsLIzo6GigMwMLDwxk7diwvvfQSZrOZDz/8kKioKFq0aAFAv379mD17Nk8++STPPfccSUlJfPTRRzzyyCMEBBRmfxo0aBA//vgjo0ePZuTIkRw5coS5c+cyceLEEglZhBCiLOXlWjgbVxi8pSaXPi3M29eJ2qFuBNd1w81dnpiL6kFRFE4fz2H/7nSsF+MQjUZFeHtv6jaoOsGH5dA/mBd+iXLmuF25umNPtA89iTqgxhVeeeMM+WbWnUplxbFkYtNzSxyv7+vGPY0DiKrvi7tO7iVCVCUqRVGUirr4fffdR9OmTfnggw+Awj3mZs6cydKlSzEajURERDB58mQaNGhge43RaGTKlCmsWrUKq9VK586dmTx5st2edKmpqbzzzjts2LABnU5Hz549efXVV+3Wtp05c4a3336b3bt34+HhQf/+/ZkwYYLd+rwDBw7w3nvvERMTg7+/P4899hijR4++6febnFxy8fDV3G7rGKoC6XPHkv4ulp9v4Vxc4bTJ5Av5UMqd2dPbiZBQN0LquqL3uPFkA45YO3Kj9zmQz4GjVdX+Npms7Nmexrm44mDE09uJDl39KvV2Gpf2tzX+JOZFc7Ee2GlXR900Au2jT6EODSuTayqKwqEkAyuPJbMpNg2Txf6G4qJVE1Xfj96NA2jk51ZlAuDrIWvkRHVSoYFcdSOBXOUnfe5Y1b2/C0xWEhNyiY81kpSYR2l3Y72H9mLw5nbLX1YlkBNQNfs7LTmfnZtTMeYUp8IPbehOeDtvNNrKPZXS29sNS8oF0r79AsuWVVz6i64KqYf2kadQt+xQJsFUVp6ZtSdTWHEsmfjMvBLHG/oVjr51r++Hm9PtOQ1bAjlRncgYuhBCOJDZbOX82TwSYo2cP5trmx52KVd3DSF1C4M3b1+n2+ppuRA3QlEUjh/KJmZvpi3+0TqpaNPRl5C6lX+fMCUrg+zfvibnz0VgKt4aAW9/tA8OR9P1blTqWwuoFEXh4IVsVh5LZnNsOgVW+ydCrk5qelwcfWvoV3IPXSFE1SWBnBBClDOLReHCuTwSzhhJTMjFYi459Obsoi4M3kLd8PXXSfAmqr28PAu7t6SRlFg8suTjp6NDVz/cPSr31xclKwPzykVY1iwlP/+SkTEXN7R9H0PT62FUzi5XPsF1yMwrYM2JVFYeSyYhq+ToW5i/O/eEBdAt1BfX23T0TYjqrnLfCYUQooqyWhWSz+eTcMbIuTgjBQUlgzedTk2tOq7UDnXDP9AZlVqCNyEAkhLz2LUllfy84iHrRk09aN7aC7Wm8v6eXBrAcWkAp9GgiboX7X1DUHn63PT5rYrC/vPZrDyazNa4dMyXjb65O2m4s0Hh6Fs938o/YimEuDUSyAkhRBlRFIWUpHwSYo2cjcvFlF9y3qTWSUWt2q6E1HUjsKYLagnehLCxWhUO78/i6MEsW5nOWU27zr7UCHatwJZd3dUCONee/XF/6AkMzt43ff703AJWn0hh1bFkzmXnlzjeNFDPPY0D6Brqg4tWRt+EqC4kkBNCiFugKArpqSYSYo0knMklL9dSoo5Go6JmiAshoW4E1XJFU4lHFISoKMYcM7s2p9ptueEf5Ez7Ln64ulXO4OSqI3Dd7kHT93G8Gl3cfukGk29YFYW9iVmsPJrMtrgMLJdlQ9LrNPRs4E/vxgHU9am8Qa4QovxIICeEEDdIURQy0wtsG3VfmkmviFoNQbVcCQl1o2awC1qnyp1ZT4iKdC4+l3+2pVFgujiKrYKmLT1p0sKzUk45vp4A7mb3gkszmvj7RAqrjqVw3lBy9K1FkAf3NA6gc10fnCt5xk4hRPmSQE4IIa5TdmYB8bFGEs4YMWSZSxxXqSCwpgshdd2oWdsVnU6+ZAlxNRaLwsE9GZw8arCVubhpaN/Fl4CgW0sGUh7KK4CzWBX2nMtk5bFkdsRncNnSNzydtfRs6EfvRgHU9pbRNyFEIQnkhBDiKnIM5ovTJo1kpheUWsc/yJmQum4E13HF2aVyTgETorIxZBWwY1Oq3e9VjWAX2nbyrXS/R+UVwKXkmPjr4tq35BxTieOtahSPvjlp5MGQEMKeBHJCCHGZXKOZs2dyiT9jJD2l5JcrAF9/HSGhbgTXcau063eEqKziTuewd0c65otbcajU0DLCmwZN9JVq643yCOAsVoXdZwtH33YllBx983LRcldDf+5uHECwZ+UblRRCVB4SyAkhBJCfZ+FsXC4JsUZSkkquSwHw8nGidqgbwXXdcNfL7VOIG2U2W9m7M524U8WJP9z1Wjp088PHT1eBLbNXHgFckiGfv46nsOp4MqnGkqP7EbU86d04gI61vWX0TQhxXeSbiBCi2jKZrJyLN5IQm0vy+TyUklu94eGpJSTUjZC6bnh4OTm+kULcJjLTTezclEr2JetLQ+q6ERHpg1MlWU9a1gGcxaqw+VQKS/cnsj02jctvMT4uWu5qFMDdjfypKaNvQogbdEuB3MmTJ0lISKBDhw64usriWyFE5WcusJKYkEt8rJELiXkoJbd6w12vsQVvnt5OlWqqlxBVjaIonD6ew/7d6Vgv/r5pNCrC23tTt4F7pfj9KusALivPzF8nkvnzSBIXDPbTs1VAm2Av7mkcQIfaXmjVlSOIFUJUPdcdyBkMBl5//XWaNm3K6NGj+f3335k0aRIANWrU4IcffiAkJKTcGiqEEDfLYrZy/lweCbFGzp/Nw2IpOfTm4qYhpG7hRt0+frpK8eVSiKrOZLKyZ3sa5+JybWWeXlo6dPPH07viR7jLOoA7mZrDH0eSWH8qFdNl9xk/Nyd6NQqgV0N/gjycy+otCCGqsesO5D7++GPWrFlD9+7dAfjss89o2rQpY8aMYfr06cycOZNp06aVW0OFEOJGWC0KSecLg7dzCbmYC0oGb84uaoLruBES6oZfgARvQpSltOR8dm5OtdtnMbShO63aeaOt4P3PyjKAK7BY2XomnT+OJHEoyVDieNva3jwcHkwLX1c0lXBPPCFE1XXdgdzatWuZMGEC999/PydPniQ+Pp4ZM2YQHR2N0Wjkgw8+KM92CiHENSlWheQL+SScMXI2Lrd4c+FLOOlU1KrtRu1QN/yDnFHLFyshypSiKBw/lE3M3kzbulOtk4o2HX0JqetWsW0rwwAuzWhixbFkVhxNJi3XPnmJi1ZNz4b+9G8SSKtQPwAyMoylnUYIIW7adQdyKSkpNGnSBICtW7eiUqno1KkTAAEBAeTk5JRPC4UQ4ioURSEt2UT8GSNnzxjJzysZvGm1KmqGuBIS6kZQTRfUGgnehCgPeXkWdm9JIymxOEjy8dPRoasf7h4Vl19Nyc7EvGLhLQdwiqJwONnAH4eT2BybjuWyDEm1PJ3p3ySI6IZ+uOskn5wQonxd913G39+f8+fPA7Bx40YaN26Mj48PADExMQQGBpZPC4UQ4jKKopCRVmDbqDvXaClRR61RUSPYhdp13QgKdqnwqVxC3O6SEvPYtSXV7mFKo6YeNG/tVWEPT5TszMIRuNVLbimAyzdb2XA6lT8OJ3EyzX5kTQW0D/Hi3qZBtK7liVqmaAshHOS6A7mOHTvy8ccfs2fPHjZt2sT48eMB+Pbbb5k1axYDBw4srzYKIQQAmRkmEmIL93rLMZhLHFepIaimCyGhbtQMccXJSYI3JS8X65G9WA/uIuXUIbR1G6IMeR6VWjYxF2XDalU4vD+LowezbGU6ZzXtOvtSI7hiMlqXVQB3ITufP48msep4Mtn59g+M9DoNdzcKoE+TAGp6yNYBQgjHu+5AbvLkyTz//PP89ttvREdHM3z4cAC+//572rVrxzPPPFNujRRCVF+GrAISzhhJiDWSlVkyeEMFgUHOhIS6Uau2Kzrn6h2gKFYrypnjWA/uwnJwN8qJGLAU95v51FF0UfejqtuwAlspbhfGHDO7NqeSmlycYt8/yJn2XXxxdXP81MKyCOAUReHfxCyWHU5iR3xGib3f6vu40q9pEFH1fXHRVu/7jRCiYl33XVav1zNv3rwS5YsXL8bPz69MGyWEqN6MBnNh8HbGSEZaQal1/AKdCanrSnAdN1xcq/eXKSUtGcvBXVgP7sZ66B8wZJVeUaXCpetdKLXqOLaB4rZ0Lj6Xf7alFScVUkHTlp40aeGJysFJhK4awHXtjabf4GsGcEaThTUnU/jjcBIJWXl2xzQqFV3q+tC/aSDNAvWS4VYIUSnc8OOy5ORktm7dyoULF3jggQdITk7G29sbjaZ6f5EStwdFKZmiXjhGXq6Fs2eMxJ8xkpZsKrWOj5+OkLpuBNd1xc29+iYSUPJzsR7dXxi4HdyFcu7MlSv7BqJp0R51i3b4dOqC2sNLsueJW2KxKBzck8HJo8Wp9l3cNLTv4ktAkGOnGJZFABefkcuyI0msPpFCrtk+WZKPi5Z7wgLp3TgAf3ddebwFIYS4aTf0TWjWrFnMmTOHgoICVCoVXbp0Ydq0aWRmZvLdd9/h4eFRXu0UotwdP5TNoX0Jts2iVaqiPypUKuCSv196jEvrUbhOS0Upx67jfFz6Mxf/fnGZV2nnKLUNVzuf+mJ5WZyv6O+U9l6ufT5UhSNv8WcMHDucQfKFfErMYQK8vJ0ICXUjuK4b+grMeleRFKsVJf6kLXCzHj8I5tJHKnF2Qd0kAnWLdqhbtENVo7Zt9EDtUbGp30XVZ8gqYMemVDLTiz9/NYJdaNvJF2cXxz3QvdUAzmJV2JmQwR+Hk9ibWHIEu2mAO/2aBtG1rg9OGllrK4SonK77W9GiRYv47LPPGDJkCL179+bxxx8H4LHHHmPSpEnMmjWLl19+udwaKkR5Onk0mwN7MuzKFIWLeyBdGl3IiJ0j6D20hIS6EVLXDU9vp4puToVQ0lOwxuzGcnA31ph/IDuj9IoqFarQxqibt0PToh2qhs1Raatnn4nyFXc6h7070jGbLz7sUkOLCG8aNnHcVMNbDeCy8sysOp7Mn0eSSMqxH/l3UquIqu9HvyaBNPJ3L6+3IIQQZea6A7nvv/+exx57jMmTJ2OxFGdu6tWrF2fOnOGnn36SQE5USWdO5rBvV4btZ29fHYqiXAzklOKA7pK/X3qMor8DivWSv188Jq6Pq7uG2nXdCAl1w8vHqdqtQVFM+ViPXTJdMuH0lSv7+KNp0Q51i/aom7VF5eHluIaKasdstrJ3Zzpxp4qn5LrrtXTo5oePn2OmG95qAHcyNYffDyex4XQqJov9jTnAXUe/JoH0auSPl4s8BBFCVB3XHcidOXOGF198sdRjLVq0IDk5ucwaJYSjnI0z8s/2NNvPLVr70L5LAJmZuWVy/qI1d1cM/i4LDCn6O8rFoPDi35XCILHonKW+5mrnKzpW9Hrbua4vWC31fJRWfv3nQ1HQaDX4+DkTUMMJX39dtQreFEVBSTh1MXDbjfXovitPl9Q5o27SGnXzi9Mla9WtVn0lKk5muomdm1LJzirOfBpS142ISB+cdOU/5fBWArgCi5UtZ9L540gSh5MMJY63rulJ/6aBdAjxRuPg5CxCCFEWrjuQ8/b25uzZs6Uei4+Px8tLngiLquXCuVx2bk61jZqFNnSnfZeAMv2CXHSu4lPKl4VLeXsXrtmqLsk3lMw0rDH/FGaYjPkHMtOuWFdVt5EtcFM3aoHKSRItCMdRFIXTx3PYvzsd68UHPxqNivD23tRt4F7uDxJuJYBLNZpYcTSZFceSSc+1fzjiqlXTs6E//ZoEUse7Yva4E0KIsnLdgVyPHj2YNWsWLVu2pFmzZkDhl9QLFy4wb948oqKiyquNQpS5lKR8tm9ItY1MhdR1I6KDj4xyiDKlmExYjx8oTFBycDdK/MkrV/byRd2iXWGGyeZtUXn6OK6hQlzCZLKyZ3sa5+KKZyZ4emnp0M2/3Nes3mwApygKh5IM/HE4iS1n0rFcloE42NOF/k0DiW7gj5tOsmwLIW4P1x3IjR8/nu3bt/Poo49Ss2ZNVCoVkyZNIiEhAV9fX8aPH1+OzRSi7KSnmti6LtmWnbJGsAvtuvg6fN8jcftRFAXlXKz9dElTfumVnXSow1qhbl64NYAqpJ48SBAVLi05n52bUzHmFK+FD23oTqt23mi15TeV8mYDuDyzhQ2n0vjjSBKn0uxH9lVAZG1v+jUNpHVNT9Ty+yWEuM1cdyDn6+vLL7/8wrfffsuWLVvQarW4uLgwdOhQhg0bho+PPD0WlV9WZgFb1iZjLigM4gKCnIns5odagjhxk5SsDKyH/sF6sDDDJBkpV6yrql3fFripG7dEpXN2YEuFuDJFUTh+KJuYvZkUDWZpnVS0ifQlJLT8tq242QDufHY+fx5N4q/jyWTnW+yO6XUa7m4cQN+wQGp4yO+YEOL2dUObMnl4eDBu3DjGjRtXXu0RotzkGMxsXpOMKb9wPqWPn46OUf5oyvEps7j9KAUmrCdiirNLnjl+5cqePqibty2eLunt57iGCnGd8vIs7N6SRlJicSDl46ejQ1c/3Mtp78abCeCsisLec1n8cSSJnfEZJZIC1/dxpX/TILrX98VFK9MnhRC3v6veoffv339DJ2vVqtUtNUaI8pJrtLB5dRJ5xsInt57eTnS50x8nJwnixNUpioKSGGdb52Y9sg9MeaVX1jqhbtyycMSteXtUteujUstnTFReSYl57NqSSn6e1VbWqKkHzVt7odaU/UyFmwngjCYLq0+msOxwEglZ9r97GpWKrqE+9GsSSLNAx+1nJ4QQlcFVA7lHHnnkum6KiqKgUqk4fPhwmTVMiLKSn29hy5okcgyFQZy7h5auPQPQOcsTW1E6xZCJ9dCe4umSaUlXrKsKDrUFbuqwVqicXRzYUiFujtWqELM3k6MHs2xlOmc17Tr7UiO47LM53kwAF5eRy7IjSaw5kUKu2Wp3zMfViXsaB3BPWAB+bpLRVQhRPV01kJsyZYqj2iFEuSgwWdmyJpmszMI9kFzdNHTrGYCLqwRxophiLkA5eQjLxSQlSuxRUC6fuHWRhxfq5m1RN2+PpkVbVD4Bjm2sELfIkF3Ahr8SuZBYnJXSP8iZ9l18cXUr26mUNxrAWawKO+IzWHYkib2JWSXO1zRQT/8mgXSp64OTRka7hRDV21Xv2A888ICj2iFEmTObrWxbn0JGWuE+Qs7Oarr2DMBNXz5rPkTVoSgKyoWE4uySR/6FvCtsAq/Rom7UonDUrUV7VHUaynRJUeUoikJ6qolTxwycPZNry9qLCpq29KRJC88yzdx7owFcZl4Bfx1PYdmRJJJzTHbn0mlURNX3o1+TQBr6uZdZG4UQoqq7oW+0ZrOZEydOYDIV32StViu5ubns3LmT5557rswbKMTNsFoUdmxMJSWpMPW7k5OKLj0D8PAq3z2QROWl5GQXTpeMuTjqlnL+inVVNetcnC7ZDnWT1qhcZONgUTWZzVYSYo2cOmawPdQq4uKqoX1XXwKCym468I0GcCdSc/j9cBIbTqVSYLUfBQ9019G3SSB3NwrA00UewAkhxOWu+864f/9+xo0bR1JS6WtF1Gq1BHKiUlCsCru2pHLhXOGXCI1WRec7A/D2lXUU1YliNqOcPlw8XfLUEWw7wF/O3ePidMl2aFq0Q+UX5NjGClHGsrMKOH3MwJlTORSY7AMkJ52aRk08qdfYDWeXsplmfiMBXIHFyuYz6Sw7fIHDyTklztW6pif3Ng2kfYg3GtkaRgghrui6A7lPPvkEs9nMiy++yPr163FxcaF79+6sXbuWbdu28X//93/l2U4hrouiKOzZkc7ZuMJpcmo1dOruj1+A7CVUHViTzhZPlzz8L+SW/JIIgEaDqkFzNEXTJUMboVLLuklRtVmtCokJuZw+ZiDpfMmN6L28nagfpqdFuD9OOjUZGcZSznJjbiSASzWaWHE0mRVHk0jPM9udx1WrJrqhP32bBFLHW0bAhRDietzQiNz48eN5/PHHcXNzY+XKlTz++OM8/vjjjBw5ku+//55p06aVZ1uFuCpFUTjwTwZnThZ+eVepoEM3fwJrShbB25ViNGA9vNe2NYCSfO6KdVVBIRfXuV2cLukqa23E7SHXaCH2hIHTJ3JsW6wUUashuI4b9cP0+PrrUKlUOOlufY3n9QZwiqJw8EI2fxy+wNYzGVguSyIU4ulC/6aB9Gzgj5tOHqYIIcSNuO5ALjc3lwYNGgBQr149jh49ajv28MMP89FHH5V964S4AYf3Z3HiiMH2c9tOvtSqLU92byeKxYJy+ijWmF1YDu5GOXkIrFeYLunqXjhdsmitW0BNxzZWiHKkKAopSfmcOmbgXFxuiSSrbu4a6jXSU7ehOy5lNH0Srj+AyzNbWH8smWWHL3Aq3T6RkAqIrO1N/6aBtK7pKXu/CSHETbruQM7Pz4+0tDQA6tSpQ0ZGBmlpafj6+uLt7U1qamq5NVKIazl+KJsjB4pTVbfu4EOd+jLicjuwJp/HGnNxM+5De8BoKL2iWo2qflM0LdoXTpesF4ZKI0/4L2W2WNFKyvYqrcBkJe50DqeOGcjONJc4HlTLhfqN9dSo5VIhWSgTs/NYviueVceTMZjsRwc9nDXc3SiAvmGBBHnIdHchhLhV1x3IRUZG8tVXXxEREUHNmjXx8/Pjjz/+4IknnmD9+vX4+vqWZzuFuKLTxw0c2JNh+7l5hBf1G+srrkHilii5RqxH/rWtdVMuJFyxriqgJuoW7S9Ol4xA5S7/7pcyma0cSjLwb2Im+xKzOZGaQ10fNz7q3USmsVUxmemFWwfEnTZiMdsPv+l0auo2dKdeIz16D8fvA4d/EP+ey+KP1cfYlZDJ5TswNvB1o3/TQLrX88NZKw8ShBCirFz3Hf+ZZ57hkUce4fnnn+enn35iyJAhTJkyhXnz5pGamsrIkSPLs51ClCoh1si/O9JtP4e18CCsuWcFtkjcKMViwXrqCNaDu7Ec3IVyMgYsltIru7ihbtameK1bYLBjG1vJWawKJ9Ny+PdcFvsSs4m5kF0ipfvpNCMpRhN1dDLtuLKzWBTOxRVuHZCabCpx3MdfR/3GekLquKIp4wDpegK4XC9/Vp9IZdmmg5zNyrN7vUalomuoD/2bBtI0QC/TJ4UQohxcdyBXt25dVq5cyeHDhwF46qmn0Gg07Nq1i4iICJ588slya6QQpUlMyGXXluIpvfXD9DQL96rAFonrpSgK1ph/yNjxF/l7d6BkZ5VeUaVGVb8J6hYXtwWo1xSVVvaTKqIoCmez8i4GblnsP59dYjrbper4uHJ/y1rU9pIEQJWZ0WDm9AkDsSdyyM+zXwOq1qioHepG/cZ6fPzKfkuV6wngEpy8+OPwBdac3Eee2b59Pq5O9AkL4J7GAfi6yZYvQghRnq7rG9HJkydxcXEhODiYzp07k52dzbx58zh27BhNmjThscceQytfroQDJZ/PY8emVNsC/zr13Qhv5y1PfSs5JdeIZcsqLGt+RUmMo6C0Sn5Btm0B1E0jUOllhPVSqUYTexOz2Hcui72JWaQYS+1FAHxdnQiv6UlELU9a1/SkYbA3QJmknRdlS1EUkhLzOHXMQOLZPC6fn6j30FK/sZ469d3ROZf99MRrBXD0eZxdeS78secC+xLjS7y+WaCe/k0D6VzHBydZhymEEA5x1ehLURTeeOMNfv75ZyZMmMCoUaMwm80MHTqUI0eO4Ofnx9atW/n7779ZtGgRer2sTxHlLy0ln23rU7BaCr/p1KrtSpuOvhLEVWLW8wlY1izFsnlVib3dVC5uqJq0tk2XVAWFyL/lJXJMZg6cz2ZvYmHgFpeRd8W6bk4aWtXwoPXFwK22l4v0ZSWXn28h7mQOp47lkGO4LHmJCmqFuFK/sZ6AGs7l8m95rQDO0Osx/k5V8+fGJJJz7Kd36jQqour70b9JIA38JLmUEEI42lUDuV9//ZXFixczePBgevfuDcDixYs5fPgwQ4YMYfLkyVy4cIGBAwfy1VdfMX78+Gte0GQyMWvWLH7//XfS09Np1aoVkyZNonnz5kBh8DhnzhwWLlxIeno6bdq04bXXXrNtfVB0jmnTpvHnn39iNBrp1q0bkydPJigoyFYnMzOTKVOmsG7dOqxWK7169eKVV16xCzYTExN599132b59O87Oztx///2MHz8ena54OsixY8d477332L9/P15eXgwaNIhRo0bJl6MKkpluYsvaFMwXF/sH1nShfVc/1GWYnU2UDcVqxXpgJ5bVS7Ee2FniuKpBMzzvHYhL5x5k5pTMvlddFVisHE4y8G9i4XTJYyk5WC/PHnGRVq2iWaCe1jULA7dG/u5o5HehSkhLKdw6IOFMru2hVBFnFzX1GukJbeiOm3v5zHaxZmVQsPi7KwZwp7s9wrJEMxvWJJZYZxmk19G3SSC9Ggbg6SKzcYQQoqJc9Q68dOlSHn74YV577TVb2d9//41Wq2Xs2LEABAUFMWTIEJYsWXJdgdyUKVP47bffeOGFF6hTpw4//PADQ4cO5ffffyc4OJhZs2Yxd+5cXnjhBYKDg5k9ezbDhg1j+fLleHh4APDmm2+ydu1aJk2ahJubGzNmzGD06NEsWbIEzcV04+PGjSMhIYG33nqLvLw8pk6dSkpKCl9++SVQGAyOGDECFxcXpk6dSmJiItOmTSMvL4833ngDgNTUVIYPH06jRo2YOXMmMTExzJw5E41GI8ldKoAhu4DNa5IpMBWuyfAL0NGxux8ajXxxrUwUowHLppVY1v6KcuGs/UGtE+rIHmijH0RdLwxXb7eLB6pvIGdVFE6lGW3r3GIuGMi3lL43ngpo4OdmC9yaBelx0Ur2yarCbLaScMbIqaMGMtJKTon1D3SmfmM9tWq7oi6n+5qSm0P2su8wLluIknfJ/m4aDdau97At4n7+PJvPkc0XSry2TS1P+jcNol2wlzwwEEKISuCqgdzRo0cZOnSo7WeLxcK///5LixYt8PIqTirRtGlTEhKunCK8SHZ2NosXL2bixIkMGjQIgHbt2hEZGclvv/3G0KFDmT9/PmPHjrVdt127dvTo0YOff/6Z4cOHExcXx6+//sr06dPp06cPAE2aNKF3796sWbOGXr16sX37dnbs2MGiRYsIDw8HoEaNGgwbNoyYmBiaN2/OH3/8QVxcHGvWrKFGjRoAODs789ZbbzFmzBj8/f1ZsGABZrOZ2bNn4+rqSvfu3TGZTMydO5ehQ4fi5OR0I30tboExx8zm1cm2hf9ePk506hGAVlJZVxrWs7GF0ye3/GX/hB/Axx9tj/vQRPVF5elTMQ2sJBRFITE73xa47TufRXb+lROU1PJwtk2VbFXDU0ZAqiBDVgGnjhs4c9JoexBVROukok49d+o1dsfLu/ySgyhmM5b1f2D+7XvyszOKD2g0ZHS9l1WN7mZVgpGMvWl2r3N1UnNXQ3/6hgVS21synQohRGVy1W8ERqMRd/fiee9HjhwhNzeXdu3a2dUzm83XNdXQ1dWVRYsWERxcnDJcq9WiUqkwmUzs27cPo9FIz549bce9vLzo0KEDmzZtYvjw4Wzfvh2AqKgoW53Q0FAaNWrEpk2b6NWrF9u2bcPPz88WxEHhPnh6vZ5NmzbRvHlztm7dSrNmzWxBHEB0dDSvvfYa27Zto3///mzdupVOnTrh6upqV2f27NkcOHCANm3aXPM9i1uXl2dh8+pkjDmFX3Y9PLV07RmATidBXEVTrBase7cVTp88tKfEcVXjVmijH0Ddpmu1zjaZnlvAvotr3P49l1VirdGlvF20tsAtvKYnQXrZOLkqsloVzp/N5dSxHJISS65r9PR2on5jPbXrueHkVH73MkVRsO7ZgnnxXJTzxUlKFI2Go10fZXmtTmw9n4v1hH3m2NpeLvRvGsSdDfxwc5JRXyGEqIyu+s0qICCAc+fO2X7eunUrKpWK9u3b29U7dOgQgYGB176YVkuzZs0AsFqtnD17ls8++wyVSsW9997Ltm3bAKhdu7bd60JCQli7di0Ap0+fxt/fHzc3txJ1YmNjbXXq1Kljd1ytVhMcHGyrExsbS2hoqF0dHx8f9Hq9XZ3IyEi7OkVti42NlUDOAUwmK1vWJGPILpx65+auoUvPAJxd5ItFRVIMWVg2Lsey9jeUlPP2B510aDpFo+n5AOq6DSumgRXMWGDh4CUJSmLTc69Y11WrpkUNDyJqehJey5NQb1dZg1uF5eVaiD1h4PTxHHKN9iOtKjUE1yncOsAvQFfu/87WU4cp+GkOyrH9tjILKrZ2HsTvAe05mWmCxOLPploFkbW96d8kiPCaHvI5FEKISu6qgVzHjh1ZsGABffv2xWKx8PPPP+Pm5kbHjh1tddLT01mwYAHdu3e/oQt/8cUXfPbZZwA8++yz1K9fn7///hudTmeXbATA3d0dg8EAQE5Ojt0o4aV1zp8/f806RecxGAw3Vafo56I6N8Lb2+3alS5RNG3wRl93uygwWdm8Op7M9MK1JK5uGvo8WAdPr/KbflTd+/xaCmKPY/xzMXkbVoIp3+6YOqAmbvc8hFv0vag9r28/v9ulvwssVmLOZ7E7LoPd8enEnM/GcoUMJVq1iuY1PWlf25t2dXxoFuSB1kHp2h3R3zdz7qr+OVAUhQuJuRw+kMGZk9lYL1vi6K7XEtbCm8ZNvcotecmlzBfOYfjxC0yb/rYrP9LiTr6u14cT2VbILB4V9nTRcm+LmjzQshY1ZY/BMlfVP99VjfS3qE6u+j/K008/zUMPPUSXLl1Qq9VkZ2fz0ksv4excONVn7ty5/PTTT2RlZTFixIgbunB0dDQdOnRgx44dfPHFFxQUFODicuVU2UXliqKUWufSckVRUKtLfjG6vPxK5ynttZe7njri5pnNVtasOEvS+cIpSc7OanrfV7tcgzhROsViJn/HBnL+XExBzL8ljutatsOt7yM4t++KSlM9RkqtisLJlBx2xaXzT3wGe89mkFtQeoISgEb+7rSr40O72t6EB3vjpqse/XS7M5ksnDyaxZEDGaSnlZwuG1zHjSYtvKkdqndIZl2rIQvD4m8w/rkYzMXJVJJCmvJjuyfYnKGG7OLPaeMAPQ+3DuausACcJWmOEEJUOVcN5OrUqcNPP/3E/PnzSUtLIzo6mocffth2/LvvvsPV1ZXZs2dTr169G7pwkyZNAOjQoQM5OTnMnz+fF154AZPJREFBgV0ikZycHFvGSr1eT05OTonzGY1GuzrJycml1inafuBq57lanaKfb2bPvBvdhLfoaVJ127zXalXYsSmVxPjCKT9aJxWdevij0ljKvS+qa5+XRsnKwLJhGeZ1v0PaZb9POhc0XXqhib4fdXA98oC87PxSz3M1Vam/z2fnF06VvLgRd1b+lTNt1tAXJigJr+lB65qeeLkU389MxnxMFfR2b6W/AwI8rqvezZy7Kn0OADIzTJw6ZiD+lNG2FUoRJ52a0Abu1Gvkjt6z8N89K+vKU2vLgmIuwLLmN8y/fw852bbyHE9/lnR7ij9yvTFnFLeznp8b47o1IMyrcG+6XEM+5dvC6q2qfb6rOkfc54SoLK45x6NBgwa8//77pR5bsmQJgYGB1z2PPjk5mY0bN3L33XfbBUJNmzbFZDLh5eWFoigkJCTYBYaX/hwaGkpKSgp5eXm4uLjY1Wnbtq2tzp499okXitbk9e/f31bn8kyb6enpGAwGu2tdXic+vnCxeP369a/rPYsboygK/2xLswVxao2KzlH++PpLwgdHscYexfz3Uqw71to91QdQBdRCE30/mq73oHK/8YcZVUlmXgH7ErNtwdt5w5UDVU9nbeGWABeDt5oeMj3tdmO1KJyNz+XUMQOpSSU/C96+TjQI8yCkrisaB2XTVRQF664NmBfPQ0kuXs9u0TqzJmok/1M3ICvHAhQGcZ7OWoZEBPNoh7po1SoJLIQQooq7pcn6l27AfT2ysrJ49dVXAXjooYds5Vu2bMHPz4/o6GicnZ1ZvXo1o0aNAgo39t65c6dt37pOnTphsVhYu3atbfuB2NhYjh8/blfnyy+/ZP/+/bRq1QqAHTt2YDAY6NSpE1C4/u+///0v58+ft2WuXL16NU5OTrZkLh07dmThwoUYjUZbcpXVq1fj7e1tG1EUZUdRFPbuSif+dOGXC5UKOt7hh3+QfCkub4q5AOvujZhXL0U5EVPiuLpFezTRD6BuFYnqNp1WnFdg4eAFA3sTM9mbmM2ptCt/yXXWqmkZ5GEL3kJ9XFFLYojbkjHHzOnjOcSeMNi2Pymi1qgIqetK/cZ6hz9ssh4/SMHCOSV+X/d1HMA3/p2IM5iBwmQrWrWK+5oG8WirmuidtWhlDzghhLgtODQfeIMGDbj77rv58MMPKSgooHbt2vz111/89ttvvP/+++j1egYPHswnn3yCWq0mNDSUOXPmoNfrGTBgAFA43bN37968/vrrGAwGPD09mTFjBmFhYURHRwOFAVh4eDhjx47lpZdewmw28+GHHxIVFUWLFi0A6NevH7Nnz+bJJ5/kueeeIykpiY8++ohHHnmEgIAAAAYNGsSPsWTKcwAAOpRJREFUP/7I6NGjGTlyJEeOHGHu3LlMnDixREIWceti9mZy+tjFqawqaN/VjxrBsm9ReVIy0gr3llr3O2Ta7x+Fixuarnej6Xk/6pp1Sj9BFWa2WjmWknNxqmQ2R5INmK+QoEStgiYBelvgFubvjpODEpQIx1MUhaTEfE4dM5B4NrdoQMvG3UNL/Ubu1GngjrOzY9eWWS+cxbx4LtbdG+3KzzbrwvdNHmRXmgUMxdN+O9fxZkS72tTylAdiQghxu1EpilL6N5dykpuby+eff86KFStISkqiYcOGPP300/Tu3Rso3JNu5syZLF26FKPRSEREBJMnT6ZBgwa2cxiNRqZMmcKqVauwWq107tyZyZMn240Qpqam8s4777BhwwZ0Oh09e/bk1VdftZvSeebMGd5++212796Nh4cH/fv3Z8KECXbr8w4cOMB7771HTEwM/v7+PPbYY4wePfqm3ntycva1K12iOs2rP3owi5i9mbaf23T0IbSh46fuVYc+VxQF5eRhzKuXYN21ASz2a71UNWqjiX4ATZdeqFxLZnYtS47sb0VROJORa1vjduB8NrnmKycoqevtSsTF/dxa1PC4LfbScsTakRu9z0Hl+b0z5Vs4c9LIqeMGcrIvWwOpgprBhaNvgTWdHZ6aX8nOxPz791jW/gaW4m0NDDUbsLjzSJanOWG55L/z+r5ujG5fm1Y1PUucq7L0d3Uh/e1YskZOVCcOD+SqMwnkSnfyaDb7dmXYfm7V1puGTSvmZno797liMmHdta5w+uTpo/YHVSrUrTqiuesB1M3aOmz6ZHn3d5Ih37aX275zWaTnXTlBSYC7johahZtwh9fwxNfN6Yp1qyoJ5EqXnnoxeUmsEavF/r9EZxc1oQ311Gvk7pCtAy6nmExYVi/B/MePkFucfMvs4c3fd47h/3L9MJiKAzsfFy1D24QQ3dAfzRWmUFZ0f1c30t+OJYGcqE4c/7+SEJeIO5VjF8Q1beVZYUHc7UpJS8a89jcsG/6E7Az7g67uaO7og6bnfagDgyukfWUpK8/M/vNZtgQl566SSdPDWUN4jcLALaKWJzU9HD/KIiqOxWwl4Uxh8pL01JJbB/gF6KjfWE+tOm5oNI7/XChWK9Ydayn4+StIvVBcrnVib8+RfKNrQkKmiaJ1cE5qFQ80r8EjrWreFqPHQgghrk0COVFhzsYZ+Wdb8bqshk31NGlZchqQuHGKoqAcO4D57yVY92zi8h2KVcGhhdMnO9+FyrnqrkPMM1s4dMFgG3U7mWq8fDmTjbNGTfMgvS1wq+/rJglKqiFDdgGnj+UQezKHApP974VWq6J2PTfqN9bj5VNx66CtR/YWJjK5bOQ8vvP9fBd8J3uS8yC/OPjsFurDiLa1CfKQ7L5CCFGdSCAnKsSFxDx2bU6laGJvaEN3WrbxlhGRW6Tk52HZvgbL6qUo8SftD6rUqNt0Kcw+2aR1lexri1XheGqObZ3boaSrJyhp7O9eGLjV9KRpoF4SlFRTilUh8Wwep44ZSErMK3Hcw0tL/cZ66tRzx0lXcZ8R67k4zIu/xPrvVrvy7GYdWdj6UVYmFmBNLm5/Iz83RneoQ/MgmcUghBDVkQRywuFSk/LZvj7FNkgUUteViA4+VTKwqCysyeexrP0Vy8YVkJNlf9DdE033vmjvvBeVf42KaeBNUhSF+Mw821TJ/eezMRZYrli/jrdLYWbJmp60rOGBu05ucdVZXq6F2JM5nD5uIDfH/nOjUkGtOq40aKzHL7Bip9UqWemYf/0Wy/pldqPn5uB6rOg+mkXJOnLOFe/p6OfmxLC2IfSo7yejykIIUY3JtxzhUBlpJrauS8ZyMaFAjWAX2nXxQyX7Gt0wRVGwHt6D5e+lWPduA+Wy6ZN1GhZOn+zYE5Wu6ky5Sskx2aZK7j2XRVpuwRXr+rk5EVHTk/CL2SX93GRbkOpOURRSkwuTl5yNM17+a4Grm4Z6jdyp20CPq1vFriVT8vOwrPoZ8/L/g7zixAyKpw//3P0U3+TVJPFcPkXr4Jw1ah5qUYOHW9TARdbBCSFEtSeBnHCY7MwCNq9JpqCgMIjzD3ImspsfagniboiSl4tl61+F0yfPnbE/qFajbncH2ugHUTVqUSVGObPyCth6Jt0WuCVklZz6VkSv09CqhodtnVuwp0uVeI+i/BUUWIk/beTUMQNZGSWD/8CaztRvrKdGsGuF33MUqwXr1r8p+GU+pKcUH9A5c+auoXztHs6BJCNQnKynR30/hrcNwd9dHlYIIYQoJIGccIgcg5lNa5Ix5Rc+Hvfx09Epyh+NVtYsXS/rhbNY1vyKZdMKuzTkAHj6oOneD+2d/VH5BFRMA6+DxaoQn5nL8RQjx1NzOJGey7GkbK6wzA0ntYrmQR6E1/QgopYnDXzdr5hSXVRPWRkFnDpmIO50DuYC+w+Sk05F3fru1Gusx8OzcmwnYYn5B/NPs+3XsKpUZHW9l//Vu5u/4wwoOcWjc00D3BndoQ5hAY7fV1MIIUTlJoGcKHe5RgubVyeTZyycHuTp7USXO/1xcpIg7loUqxXrwd1YVi/BemAnXLbto6peE7TRD6DuEIXKqXI9qbcqCmcz8ziemmML3E6mGcm/yibcKqCRv3th4FbTi6aBepwl2BeXsVoUziXkcuqogZSkkltMePk40SBMT0ioG9pK8vmxJpzGvHBO4e/xJcwtOrCsw2AWx5nIjTPYygPcdYxoF8Idob4y6iyEEKJUEsiJcpWfb2HLmiRyDIUbMbvrtXS5MwCds6zvuBolNwfL5pVY1vyGcj7e/qBGi7pDVGEA16BZxTTwMoqikJidz/GUHFvgdiIth9yCKwdtRer4uNIyyIOIiwlKPJzltiRKZ8wxE3uiMHlJfp79Z0uthpDQwq0DfPx0lSb4UTJSMS/5pnAk/dIFeyH12X7XU3yb5EzSqeLpxC5aNY+0rMkDzWvIQwwhhBBXJd+YRLkpKPj/9u48voky8R/4Z5L0StODpimF3vSEUkqBUsohR7tQVJT1/MliBRHWA3U9lkVwxdUVRJGFVSnURWDB13cFF0QXEKUgV0sRUSgVKL2AcvUuTdM01/z+CE0J5YYmTft5v177R2aemTydjQ/zmecYE7K3V+JinTnEucmlGJqqsvsCA+2Z6ewpGLM2wLh3K6BttN7prYRs5DhIh4+D4O1jnwrCHNrK1bpLga0BJ6o0KKxqgFp37dUkm/nKnRDp645IpTsifd3Rv4cvvN2cUFurueGx1DmJooiK800oLlDjXFnjlZ3ScFdIERalQEi4O1za0QMiUdsIw5YvYdzyJaC7bN6ntxIl907Dcn0gfitpAGB+H5wAIDXCF0/1C4APF+0hIqKbwCBHbcJoMCFnRyVqqsw3KS4uEgxNUcFdwZ/clUSTEaZDuTBu2wBT/oFW+4WIWMhSH4JkwDAIMtvO8xFFEVUaPQou9bQVXgpuF5sMNzy2i6vMHNouBbcIpTt85Nb193ZrH/OWqP3RNZlwqrgBxQVqqOtb/978A1zRI1qBrt3a14I3otEI4+4tMGxYAdRVt+xwcUVd2pNY3SUR20trAbTMc+3d1QPTBgYhQulu8/oSEZHj4l013XUmo4h9u6osc1ecnAQMSVHBw4s37ZcTG+ph3LUFxu0bIVactd4pc4J0UIr55d2hUTarU7VGf1lPmzm41WhvHNo8XS6FNqXcEtyUcqd2dYNNjqGmyvzqgLJSjeU1Jc2cXSQIjXBHWKSi3T0UEkURprz9MHy5FOKZ0pYdggSG4eOwMeZ+fFVYh6a6Wssufw8XTBkQiMHBfI8mERHduvb1LyE5PNEk4qfsKlw4ax5KJJUKGDxKBW8fDhVqZiorgXHbehizt1kPuQIAHxVkox6E9J77IHh6t2k96rR6yyIkzcGtSnPtd7Y1UzhLEaF0R6Sv3DJE0s+9/cxJIsdjMJhQUliPI79Wo6ZS12q/UuWMsCgFAoLlkErb3+/MdLIQhi8zYPrtoPWO+EHYO+wprCrWovJYjWWzm5MET8R3x4M9u8JJynlwRER0exjk6K4RRREHc2tw5qR5bpdEAiSP8IVS5Tgvo24rotEI0y97zcMnj/3aar8QHW9evKTfUAjSuz/Pp77JgMLLVo88UdmA8obWN8xXcnOSmMOa0h0RvnJEKd3h7+HC0EZ3TVOTEVmbTqKuxvr3KJUJCA6TIyxKAe8u7fNBkFhVDv365TBl/2C1oqwQEonCe/+IzyrcUHCk3rJdIgBjIlV4MiGAw4qJiOiOMcjRXSGKIvJ+rsXJIvO8D0EABg5Twq+bq51rZl9ifR2MOzfBsOMboOqC9U5nF0iTU83DJ4PC79p3anRGc2i7LLidq2+9RPuVXGQSRPjIL1uMRI7unq6QMLRRG2qoN1iFOA8vGXpEKRAc5g4n5/bZWyU2NsCw6f9g3LoO0F8WQH38UPPAVKxCKHYdrcHl8+D6dvPE1IFBCOsit32FiYioQ2KQo7viWN5FFB5reQdS/2QfdA/qvDcsppMnzMMnc7IAg/VwRcHXH9KU8ZAOGwtB4XlH36PVG1FUrbFa9r/sovaGxzlLBfTwaRkaGal0R6CXK1+2TTbXRemMfkm+aFDr4dfNGb5d22+Pr2gwwLjzfzB8vQqor23Z4SqH/r6JWO8/GBuOVUBnbBlGGeDpimcSgzAw0Kvd/l1EROSYGOTojp04Wo+jhy9aPscneiO4R+dbfU00GGD6eRcM2zZAPHGk1X5JbH9z71v8IAiSWx8+2WQwoaRaY7Xs/+m6RpjE6x8nkwgI6+Jmtex/sLcrZJL22dtBnYsgCOibqASAdvsaClEUYfplLwxrM63f6yiVQhj+AHb2fRCrjtagJr/cskvhLMWEvt1xX7Qf58EREVGbYJCjO1JaqEbez7WWz7F9vRAe7WG/CtmBWFcN44//g2HHt0BtpfVOF1dIh4wxB7juITd9Tr3RhJKaRqvVI0trbxzaJAIQ2kVutXpkaBc33kgS3SZT8THov8yAePyw1XZJ/2E4mvIU/lWoRdHBlgAnEYD7Y/wwIT4Anq78J5aIiNoO/5Wh21Z2UoOD+1qGEEXFeiC6950NFXQkpuKjMPywAaaffmw9fLJrgHn45NA0CHLFdc9jMJlwskZrtXpkaU0jDDdIbRIBCPJys1r2P6yLHC4yhjaiO2WqOA/Dfz+Dad92q+1CWAwqxz+LFdXuyN5fbbUvMdALUwYEIdjbzZZVJSKiTopBjm7L+TON+GlPleVzjygFYvt62bFGtiHqdTD9tBOGbeshFh9rtV/SJ8nc+9Y7EcJVhi4aTSJO1zVarR5ZXK2B/gahTQAQ6OVqtex/uI8crk53f4VLos5MbFDD8L81MP6w3uoBjeDrj6bfT8U65whsPFQOg6nlIVawtyumJgajf0DHbwOJiKj9YJCjW1ZxQYt9u6osq20Hh8kRn+jdoSfyizWVMOz4BsYf/wdcrLHe6eYO6dA0SFPGQ+IfaNlsEkWcqdNarR5ZVK1Bk8F0w+/r5uFiWTkyUumOCKU75M4MbURtRTToYdy+EYaNq4GGljm/kCsg3P8kskKHYfXh86jTtqw+6+kiw8SEAIyNUnGhICIisjkGObol1ZVNyNlRCZPRnOK6B7mhX7JPhwxxoihCLDxiHj758y7AaLTaL3QPMQ+fHDwacHXD+fomFJRUobC5t62qAY36G4c2P3dnqyX/I5Tu8HDhf5pEtiCKIkwHdsGwLhNi+dmWHVIZpKnjkTfwIfzrSBVK95dZdskkAsb19MMTfbpDwf9WiYjITvgvEN20ulodsrdXwmAwhzi/bi5IHKqEpIM9iRabtDDs3gLjtg0QT56w3ikIEPomo3rY71Hk08Mc2HadRmFVA9Q649VPeBml3Mlqyf9IXzm8XPliYCJ7MBXmQ/+fDIiF+VbbJQNH4ELaJHxeokPu7jKrfYOCvDElMQgBnp37HZlERGR/DHJ0U9T1euzZVgGdztzD5KNyxqDhvpBKO0aIEw16iCXHUX/0J2h+2Aixvs68HUC1syeKVJEojrkHRT6hOHHRiIuHDQCKrntOb1eZ1ZL/kUo5fOTObf/HENF1mS6cgWHdZzAd2Gm1XYiIRdMjz+I/Fz3x7e5yGMWWuathXdwwdWAw+nbrPAs6ERFR+8YgRzekaTBgz7YKNGnNIc6rixMGj1RB5sCrI4raRpgK82EqOAyxIA+mot8AvQ7lTgoUegSiKCQRRR6BKPIOQY3s0jvxjAAqmq56Pk8XGSKVckRcCm5Rvu5Qyp065JBTIkclqutg2Lgaxu0bAaPBsl3oGgA8PBVbFTH44pczqG9qmQfn7SpDer9A/C7Cl/PgiIioXWGQo+vSao3Ym1UBTYN52KDCU4YhKSo4OztWiBPVdTAV5MF0/DBMBXkQTxYAJnMw1Ulk2OfbGz90T0K+d/gNz+XuJLVa8j9S6Q4/hTNDG1E7Jep0MGZtgOGb1UBjQ8sOhSdkD6bjl6gR+NfBszhdd8qySyYR8PteXfF4n+5caIiIiNolBjm6Jp3OhL1ZFai/aH5yLXeXYmiKCq6u7f+mRqwqh6ng8KXwdgji2ZOtypTJ/fBDt4HY4d8faif3q57HTSaxWvI/0tcd3TxcGNqIHIBoMsG0fwf06z4Dqlp62SBzgnT0wzgz9BEsP1KJn3cUWx03NLQLnu4fBH8PFxvXmIiI6OYxyNFVGQwmZO+oQF2N+T1KLq4SDE1VQe7e/n4yoihCPH/6Um+beaikWHn+qmV1EhlyfOPwQ+g9+E0e0Gp/dy9XDAlTIsTDGZFKdwR4uULC0EbkcEzHfoX+y6UQS45bbZckp0IzbhK+OGXAlh+KcfkrHCOUckwbGIzeXT1sXFsiIqJb1/7uysnujEYR+3ZWorpCBwBwdjaHOIVH+1hdUTQZIZ4qaulxK8hr/W63ywkCysL64YeQYfhR0g31RutgJhUEJId4Y2yUCsN7+kMiCKit1bTxX0FEbcF07hQMazNh+mWv1XYhOh547Fn8T+uN/9txFg36llVmfdycMKl/IEaFK/nghoiIHAaDHFkxmUT8tKcK5efMi3rIZAIGj/KFl7f9VlsU9TqIJcdhOn7IHNwK863nuVxJKoMQFg19ZDz2qeKwVaNAfmWjeQnKy94Q0M3DBWlRKqRG+KKLmzmk8iaOyDGJF2tg+HoVjD9+a5n/CgCCfxCkjz2L/coYfH6gDGfrT1v2OUsFPNy7Gx7p7Q83p/Y/ZJyIiOhyDHJkIYoiDuZU4+zpRgCARCogeaQvfHxtO09EbNTAVHjEsjiJWHwUMOivfYCLKyQRsZBE9YEQ1QdlylB8V3IRWUWVUJ8yAmi0FJVJBCQHe2NslB/6dPNgcCNycGKTFsbvv4Jh0/8B2st60j28Ifv9JJyMG4nPfj6Lw4etXxcyoocPJvULhJ+C8+CIiMgxMcgRAHOIO/RTLU6VmG+EBAEYdI8Sqq5t/9Jb8WLtZcMkD0M8WQiIpmsfoPCEJDIOkqg+kET3gRAcAR0k2HmyGt8dr0B+7olWhzT3vv0uwhfebu1jiCgR3T7RZIIp5wfo/7scqK5o2eHsAumYR1E/6hGsOVqDrZuO47JpcIhRuWNaYjBi/BQ2rzMREdHdxCBHAID8X+tQXKA2fxCAxKFK+Ae4tcl3iZXnLatJmgryIJ47df0DfFTm0BbVB5LoOAjdQiBIzK8/OFnTiO9+PmvufdMZrQ6z9L5F+6GPP3vfiDoK428/w/CfpRBPFbZsFARIh4yB6cFJ+Pq8Cf/ZXIhGfcsDIZW7Myb3D8TwMB+uOktERB0CgxzheP5FFOTXWz73S+qCwBD5XTm3KIoQz5687FUAh4Hq8useI/gHtfS2RfWB4NvV6saryWDCnuJKbCmowG/l6lbHd/dwQVq0Cqnh7H0j6khMZSUwrF0G0+Fcq+2S2P6QPvpH5ECJ5TtP44JaZ9nnIpPgsbhu+H1sV7jKOA+OiIg6Dga5Tq64QI38X+osn+P6eyM04vaHHIlGI8RThebgdvwwTCfygPq6ax8gSCAEh1/qcYuDJCoOgpfPVYuerGnEloJybC+qumrv25CQLkiLUiGOvW9EHYpYWwXDhhUw7tpiNexaCAyD7LFnUdS9Fz776TTyL1jPg0uN8MVT/QKglNtvsSYiIqK2wiDXiZ0qbsCv+1uW7e/ZxxORPW/t/Umirgli8VHL/DZTYT6gbbz2ATInCD1iWoZKRsZCcLv6y7iBS71vpdXYXFCBo1frffN0wdhLK096ubL3jagjEZsaYdyyFoYt/wGatC07vHwge+hp1PYfhX//cg7bDh61Oi62qwLTEoMR6XvttoWIiMjRMch1UmdPa/BzTrXlc0SMAjFxnjc8TtSoYSrMb3n5dsnx668o6eoGSUTvlqGSYTEQnG/8dLy0RoPvCiqu2/s29lLvG+e7EHUsoskI4+7vYNiwAqitatnh4grZ2P8HfeojWFtUh3Vf/4YmQ0sPnb/CBU8PCMSQkC5sF4iIqMNjkOuEys9psX93FcRLS7mFhLsjrr/3VW98xLrqlt6244chni6+/oqSHl7WC5MERUCQ3ty8FK3BiD2lNdhyvBxHK1q/Jy7A0xVjo1RIiVCy942oAxJFEaa8/TCsXQaxrKRlhyCB9J6xkI6fhJ01AlZsPoFKTcsDJDcnCf5fn+54sGdXOMskdqg5ERGR7THIdTJVFU3I+bHS8r7cgBA39EsyP70WRRFi5XmIzS/eLsiDeP709U+o7GoJbZKoeAjdgm75SXhJjQbfHTf3vjXo2ftG1BnpiwugX74IpvyfrbZL+iRB9vgfUeCkwrK9p3C8suUhj0QARkeq8GRCALpwYSMiIupkGOQ6kdpqHbK3V8BoNHfFde3ugv4h9TD+uNcyVBI1ldc9h9A9pGVhkug+EJRdb6suWoMRu0tqsKWgHMeu0vsW6OmKtGgVUsLZ+0bUkYmiiIuffQTN5nWwDBMAIARHQPb4s6gK6Y0VP5dhZ4n1PLj4bh6YmhiMHj53Z4VdIiIiR8Mg10nU1+mxJ6sCer35RslHfxZ9N3wIQ33VtQ+SSCCERF5aTTLevKKkh9cd1aOkWoMtBRXYcY3et6Gh5t633l3Z+0bUGYhFR6HZtLZlg48KTg9PQdOAUVj7Wzn+uyEPOmNLwOvu6YJnBgQhKejqw8GJiIg6Cwa5Dkxs0sJUfBQNRwuwpz4OOql5RUqvuiL0PzgfUqPW+gCZE4TwXi1DJcNjIbjd+dNurd6IXaXV2HK8wmpYVLNAT1eMjVZhFHvfiDodwT8AspAImGqrIEl9CMLvHsb20w1Y9XU+qhtb5sG5O0kxoW933B/jBycp58ERERExyHUgYkM9TCeOtCxMUlqAJqk7cgbMhlZuDnEK9Wkk/voRnIxawM0dksjeLQuThEZDcLp771sqvqz3TXNF75vTpd63tCg/9O6q4JN1ok5KUHhBuWgNBEHAnuMXkPlDMQqrNJb9EgG4N9oPf+jbnQ96iIiILsMg58DEmkpzaLu0qqRYVmI1x0Qnc0duvxnQyP0BAHJtBQYZd0P+2CTzwiRBYRAkN7ei5M26Ue9bkJd55clR4b7wdOXPj4iAcxe1+HRPMXacsJ6j2z/AC1MTgxDs7WanmhEREbVfvJN2EKIoQiw/aw5sze9wKz97zfIGqSsOJL4BtXsQAMDVBRj6YBwUHv3apH7F1RpsOV6OHcXV1+h988HYaBVi/dj7RkQtqjQ6TP8mH3Vag2VbkJcrnkkMQmKgt/0qRkRE1M7ZPMgZjUb8+9//xtq1a3Hu3Dl0794dEyZMwB/+8AfLEvhLly7Fl19+iZqaGvTr1w9vvvkmwsPDLefQ6XRYsGABNm3aBI1Gg2HDhmH27Nno2rVlBcW6ujrMmzcPO3bsgMlkwujRo/HGG29AoVBYypw7dw5///vfsW/fPri4uGD8+PH405/+BOfLXlhdUFCA9957D4cPH4aXlxcmTJiAqVOn2iSMiE1aNGb9D7pff4KpIM/6xbhXIQSGQRIVBzEiHgdro1FbbX7HgIuLBMNG+0HhcXeHJTXqjdhVUo0tBRUouFbvW7QKKeG+8HDhMwMiaq22UW8JcR4uUkzsG4Cx0SrIJJwHR0REdD02v7tesmQJMjMz8fzzz6Nv3744cOAA5s6di8bGRkydOhWffvopMjMz8frrryMgIAAZGRmYNGkSNm/eDA8P8zyvOXPmYPv27fjLX/4CuVyOhQsXYtq0aVi/fj2kl14+/eKLL6KsrAxvv/02tFotPvjgA1RWVmLZsmUAzGHw6aefhqurKz744AOcO3cOCxYsgFarxVtvvQUAqKqqwuTJkxEZGYlFixYhPz8fixYtglQqxZQpU9r8WtV+OAtNB/ZefadUemlFyXjzwiSRvSEovGAyidi3sxKV1eaFTJycBAxJUcHD6+6FuKIqDb4rKMf24io06q1fDu4kETAszAdjo1Toxd43IrqBcKU73k7ridpGHZK7e/KhDxER0U2y6b+YJpMJK1aswJQpU/Dcc88BAJKTk1FdXY3PP/8cTzzxBJYvX47p06cjPT0dADBgwACMHDkSX331FSZPnoxTp07h66+/xkcffYR7770XABATE4O0tDRkZWVh9OjR2LdvH3Jzc7F27VrEx8cDAPz9/TFp0iTk5+cjNjYW3377LU6dOoWsrCz4+5vnkLm4uODtt9/G888/D19fX3zxxRcwGAzIyMiAm5sbhg8fDp1Oh8zMTKSnp8PJqW0n3os6XcsHZxdIwntBuPT+Nkl4Lwgu1vNGRJOIA3urcf6MOcRJpQIGj1TB2+fOFzBp1Buxs6Qa312j9y3Y2xVpUex9I6JbNzrGDwBQW6u5QUkiIiJqZtM77vr6eowfPx6jR4+22h4WFobq6mrs27cPGo0GKSkpln1eXl4YOHAgdu/ejcmTJ2Pfvn0AgBEjRljKhIaGIjIyErt378bo0aORk5MDpVJpCXEAkJSUBIVCgd27dyM2NhbZ2dno1auXJcQBQGpqKt58803k5ORg3LhxyM7ORnJyMtzc3KzKZGRkIC8vD/36tc18s2ber72Lpryf0ejqBSE0CoLs2sFRFEX8sr8GZSfNN0ISCTBohC+Ufi53VIeiqgbzypNX6X1zlgoYFuqDNPa+ERERERHZlE2DnJeXl2XY4uV27NgBf39/XLhwAQAQFBRktT8wMBDbt28HAJSUlMDX1xdyubxVmdLSUkuZ4OBgq/0SiQQBAQGWMqWlpQgNDbUq06VLFygUCqsySUlJVmWa61ZaWtrmQU7i1QVuQ1PRdIOn1KIo4sjBOpQWmnvKBAFIHKpE126ut/W9zb1vW46X40RV6+8O9nbF2Cg/jApXsveNiIiIiMgO7H4Xvm7dOmRnZ+PNN9+EWq2Gs7Oz1WIjAODu7g61Wg0AaGhogLu7e6vzuLu74/z58zcs03wetVp9W2WaPzeXuRXe3rf2cm2ZTHJTx/36UyVOHK23fB6W4o+IGK9brt/x8npszDuH74+Vt1p50lkqQUqUCuPjuqF3N88O2/t2s9ec7g5eb9uyxfW+nXPzd2BbvN62xettW7ze1JnYNch98803mDNnDsaMGYOJEydi2bJl1wwIzdtFUbxqmcu3i6IIyVVWPLty+7XOc7Vjr3QzZWwh/1ANDua2rGaZPNzvlkJcg86AbccrsDHvLI6Vtw6nYUo5xsd1x5gYP3jyZbxERERERO2C3YLcypUr8f7772PUqFFYsGABBEGAh4cHdDod9Hq91UIiDQ0NlhUrFQoFGhpaL7ah0WisylRUVFy1TPPrB653nuuVaf58+WsMbtatTuRvfpp0reNKC9U4uK/G8jm2rxe6Bbnc1PecqGzAdwUV+LG4Co2G1nPf7gn1QVq0Cj1V5rlvJq0etVr9LdXfEd3omtPdxettW3dyvVUqj5sqdzvn5u/Atni9bYvX27Zs0c4RtRd2CXILFy7EsmXLMH78eLz33nuQyczVCAkJgSiKKCsrQ1hYmKX85Z9DQ0NRWVkJrVYLV1dXqzL9+/e3lDl48KDVd5pMJpw5cwbjxo2zlCkrK7MqU1NTA7VabfVdV5Y5ffo0AKBHjx53fB3uRNlJDQ7mtoS4qFgPRPf2vO4xGr0RO4ursKWgAoVXmfsW4u2Ge6NVGNGDc9+IiIiIiNozm48PXLVqFZYtW4b09HS8//77lhAHAAkJCXBxccG2bdss2+rq6rB//34kJycDML+uwGg0WhY/AcwLj5w4ccKqTEVFBQ4fPmwpk5ubC7VabSkzaNAgHDlyxDKvDgC2bdsGJycnJCYmWspkZ2dDo9FYlfH29kZMTMzdvCy35PyZRvy0twoQzZ97RCkQ2/fawykLKhvwz+xSTPzyV3ycc9IqxLlIJUiN8MXCe3tiyYOxGNezK0McEREREVE7Z9M79vLycixYsABRUVG47777cOjQIav9vXv3xsSJE7F48WJIJBKEhoZi6dKlUCgUePTRRwEAwcHBSEtLw1//+leo1Wp4enpi4cKFiI6ORmpqKgBzAIuPj8f06dMxY8YMGAwGzJ8/HyNGjEDv3r0BAPfffz8yMjLwzDPP4OWXX0Z5eTk+/PBDPPbYY1CpVACACRMmYM2aNZg2bRqmTJmCY8eOITMzE6+99lqrBVlspfKCFvt2VUG8NBoyKEyO+ETvVvP9NDojfiypwnfX6H0L7eKGsVEqjOyhhILBjYiIiIjIoQiiKIq2+rL169fjjTfeuOb+nJwceHp6YtGiRdiwYQM0Gg0SEhIwe/ZshIeHW8ppNBrMmzcPW7duhclkwuDBgzF79mx07drVUqaqqgrvvvsudu7cCWdnZ6SkpGDWrFlWc9tOnjyJd955BwcOHICHhwfGjRuHV1991Wp+Xl5eHt577z3k5+fD19cXTzzxBKZNm3Zbf39FRf2NC13mynHeNVU67N5WDoPe/H9ZtyA3JA1TQiJpWeTlRFUDthyvwM6SamivmPvmIpXgnjAfjI1SIVrl3mFXnrwTnMtgW7zetmWLuSO32s4B/B3YGq+3bfF62xbnyFFnYtMg19ndSZC7WKvHru/LodOZw5mfvwuSR6oglQrQ6IzYUWzufSuqbt1whXVxw9hoP4zs4QN3Z/a+XQ//wbUtXm/bYpAjgNfb1ni9bYtBjjoT3tU7gIZ6A/ZktYQ4H19nJA1XoqimAZsv9b41Xdn7JpNgeJgP0qJUiPZl7xsRERERUUfCINfONaj12L2tHNpGc1Dz8JZBHQi8suUYiq/S+9bjUu/bCPa+ERERERF1WLzTb8caGw34bmMZNA1GAIDeScTntedQd8BoVa65921slApR7H0jIiIiIurwGOTasb07LqCuRgcAqBeN+F9jFRrQMoSyh2XumxJyZ6m9qklERERERDbGINeOFZyphysEaEQjthir0QATXJt736JViFSy942IiIiIqDNikGvHjiu00Gj0KBG18PdxxZOX3vvG3jciIiIios6NQa4dm31/T+wtqUKA3AmRSjl734iIiIiICACDXLvm5+GC3/fpznfPEBERERGRFYm9K0BERERERES3hkGOiIiIiIjIwTDIERERERERORgGOSIiIiIiIgfDIEdERERERORgGOSIiIiIiIgcDIMcERERERGRg2GQIyIiIiIicjAMckRERERERA6GQY6IiIiIiMjBMMgRERERERE5GAY5IiIiIiIiB8MgR0RERERE5GAY5IiIiIiIiBwMgxwREREREZGDYZAjIiIiIiJyMAxyREREREREDoZBjoiIiIiIyMEwyBERERERETkYBjkiIiIiIiIHwyBHRERERETkYBjkiIiIiIiIHAyDHBERERERkYNhkCMiIiIiInIwDHJEREREREQOhkGOiIiIiIjIwTDIERERERERORgGOSIiIiIiIgfDIEdERERERORgGOSIiIiIiIgcDIMcERERERGRg2GQIyIiIiIicjAMckRERERERA6GQY6IiIiIiMjBMMgRERERERE5GAY5IiIiIiIiB8MgR0RERERE5GDsGuSysrKQkJBgtU0URWRkZGDEiBGIj4/H5MmTUVRUZFVGp9Nh7ty5GDJkCBISEvDSSy/hwoULVmXq6uowc+ZMJCUlITExEbNnz4ZarbYqc+7cObzwwgvo378/Bg8ejA8++AA6nc6qTEFBAZ566ikkJCRgxIgRyMzMhCiKd/EqEBERERER3RqZvb744MGD+POf/9xq+6efforMzEy8/vrrCAgIQEZGBiZNmoTNmzfDw8MDADBnzhxs374df/nLXyCXy7Fw4UJMmzYN69evh1QqBQC8+OKLKCsrw9tvvw2tVosPPvgAlZWVWLZsGQBzGHz66afh6uqKDz74AOfOncOCBQug1Wrx1ltvAQCqqqowefJkREZGYtGiRcjPz8eiRYsglUoxZcoUG10pIiIiIiIiazYPcjqdDqtWrcLixYshl8uh1+st+9RqNZYvX47p06cjPT0dADBgwACMHDkSX331FSZPnoxTp07h66+/xkcffYR7770XABATE4O0tDRkZWVh9OjR2LdvH3Jzc7F27VrEx8cDAPz9/TFp0iTk5+cjNjYW3377LU6dOoWsrCz4+/sDAFxcXPD222/j+eefh6+vL7744gsYDAZkZGTAzc0Nw4cPh06nQ2ZmJtLT0+Hk5GTjq0dERERERGSHoZW7du1CZmYmZsyYgYkTJ1rtO3ToEDQaDVJSUizbvLy8MHDgQOzevRsAsG/fPgDAiBEjLGVCQ0MRGRlpKZOTkwOlUmkJcQCQlJQEhUJhKZOdnY1evXpZQhwApKamwmAwICcnx1ImOTkZbm5uVmVqa2uRl5d3Ny4HERERERHRLbN5kIuLi0NWVhbS09MhCILVvtLSUgBAUFCQ1fbAwEDLvpKSEvj6+kIul1+3THBwsNV+iUSCgIAAS5nS0tJWZbp06QKFQmFVJiQkxKpMc92ayxAREREREdmazYdWdu3a9Zr71Go1nJ2d4ezsbLXd3d3dslBJQ0MD3N3dWx3r7u6O8+fP37BM83nUavVtlWn+fOXCKTfD21t+40KXkckkt3Uc3T5ec9vi9bYtW1zv2zk3fwe2xettW7zetsXrTZ1Ju3r9gCiKrXrpmjVvv1aZy7dfr4xE0vIn30yZa7mZMkRERERERG3BbqtWXo2Hhwd0Oh30er3VQiINDQ2WFSsVCgUaGhpaHavRaKzKVFRUXLWMQqG44XmuV6b5c3OZW1Fbq7ml8s1Pk271OLp9vOa2xettW3dyvVUqj5sqdzvn5u/Atni9bYvX27Zs0c4RtRftqlspJCQEoiiirKzMantZWRnCwsIAmBc2qayshFarvW6Z06dPW+03mUw4c+aMVZkrv6empgZqtfq6ZZrP26NHjzv5U4mIiIiIiG5buwpyCQkJcHFxwbZt2yzb6urqsH//fiQnJwMAkpOTYTQasX37dkuZ0tJSnDhxwqpMRUUFDh8+bCmTm5sLtVptKTNo0CAcOXLEMq8OALZt2wYnJyckJiZaymRnZ0Oj0ViV8fb2RkxMTBtcASIiIiIiohtrV0Mr3d3dMXHiRCxevBgSiQShoaFYunQpFAoFHn30UQBAcHAw0tLS8Ne//hVqtRqenp5YuHAhoqOjkZqaCsAcwOLj4zF9+nTMmDEDBoMB8+fPx4gRI9C7d28AwP3334+MjAw888wzePnll1FeXo4PP/wQjz32GFQqFQBgwoQJWLNmDaZNm4YpU6bg2LFjyMzMxGuvvdZqQRYiIiIiIiJbEURRFO315R9//DE+//xz/PLLL5ZtBoMBixYtwoYNG6DRaJCQkIDZs2cjPDzcUkaj0WDevHnYunUrTCYTBg8ejNmzZ1utiFlVVYV3330XO3fuhLOzM1JSUjBr1iyruW0nT57EO++8gwMHDsDDwwPjxo3Dq6++ajU/Ly8vD++99x7y8/Ph6+uLJ554AtOmTbutv7eiov6WynNcve3xmtsWr7dt2WLuyK22cwB/B7bG621bvN62xTly1JnYNch1Ngxy7R+vuW3xetsWgxwBvN62xuttWwxy1Jm0qzlyREREREREdGMMckRERERERA6GQY6IiIiIiMjBMMgRERERERE5GAY5IiIiIiIiB8MgR0RERERE5GAY5IiIiIiIiBwMgxwREREREZGDYZAjIiIiIiJyMAxyREREREREDoZBjoiIiIiIyMEwyBERERERETkYBjkiIiIiIiIHI4iiKNq7EkRERERERHTz2CNHRERERETkYBjkiIiIiIiIHAyDHBERERERkYNhkCMiIiIiInIwDHJEREREREQOhkGOiIiIiIjIwTDIERERERERORgGOSIiIiIiIgfDIEdERERERORgGOSIiIiIiIgcDIMcERERERGRg2GQcwBZWVlISEiwdzU6NKPRiBUrVmDs2LHo27cv7r33XqxZswaiKNq7ah2STqfDP/7xD4wcORJ9+/ZFeno68vPz7V2tTkGn02Hs2LGYOXOmvatihe1c22M7Z3ts6+yjvbZzRHcbg1w7d/DgQfz5z3+2dzU6vCVLlmDhwoV44IEHkJGRgbFjx2Lu3Ln417/+Ze+qdUjz5s3D6tWrMXXqVHzyySdwc3NDeno6zpw5Y++qdXiffPIJiouL7V0NK2znbIPtnO2xrbOP9tjOEbUFmb0rQFen0+mwatUqLF68GHK5HHq93t5V6rBMJhNWrFiBKVOm4LnnngMAJCcno7q6Gp9//jmmTp1q5xp2LPX19Vi3bh1ee+01TJgwAQAwYMAAJCUlYePGjXj++eftXMOO67fffsPq1avRpUsXe1cFANs5W2I7Z3ts6+yjvbVzRG2JPXLt1K5du5CZmYkZM2Zg4sSJ9q5Oh1ZfX4/x48dj9OjRVtvDwsJQXV0NjUZjp5p1TG5ubli7di0eeughyzaZTAZBEKDT6exYs47NYDBg1qxZmDJlCrp27Wrv6gBgO2dLbOdsj22d7bXHdo6oLTHItVNxcXHIyspCeno6BEGwd3U6NC8vL7z11lvo1auX1fYdO3bA398fcrncTjXrmGQyGXr16gUvLy+YTCacPn0as2bNgiAIeOCBB+xdvQ7rs88+g16vx7Rp0+xdFQu2c7bDds722NbZXnts54jaEodWtlN8kmRf69atQ3Z2Nt588017V6VDW7JkCT7++GMAwEsvvYQePXrYuUYdU1FREZYuXYqVK1fC2dnZ3tWxYDtnX2znbIdtXdtrr+0cUVtikCO6wjfffIM5c+ZgzJgxHO7VxlJTUzFw4EDk5uZiyZIl0Ov1+NOf/mTvanUoJpMJs2fPxiOPPMJVIcmC7Zxtsa1rW2znqLNikCO6zMqVK/H+++9j1KhRWLBgAYd7tbGYmBgAwMCBA9HQ0IDly5fjhRdegJOTk51r1nGsXr0aZ8+exbJly2AwGCzbRVGEwWCATMZ/BjobtnO2x7aubbGdo86Kc+SILlm4cCHmzZuHBx98EP/85z85NKONVFRU4L///S/UarXV9p49e0Kn06G2ttY+Feugtm3bhgsXLmDgwIGIjY1FbGwsjh07hq+//hqxsbEoKyuzdxXJhtjO2Q7bOtthO0edFR9REAFYtWoVli1bhvT0dMtkdGobFy9exKxZswAADz/8sGX73r17oVQqoVQq7VW1Dulvf/sbGhoarLa9/vrrCAsLwwsvvAA/Pz871Yxsje2cbbGtsx22c9RZMchRp1deXo4FCxYgKioK9913Hw4dOmS1v3fv3hyWcReFh4djzJgxmD9/PvR6PYKCgvD9999j48aNmDt3LiQSDhS4m662qIKrqyu8vb0RFxdnhxqRPbCdsz22dbbDdo46K7ba1Ont2bMHOp0OBQUFePzxx1vtz8nJgY+Pjx1q1nHNnz8fn3zyCTIzM1FeXo6IiAgsXrwYaWlp9q4aUYfEds4+2NYRUVsSRFEU7V0JIiIiIiIiunns1yciIiIiInIwDHJEREREREQOhkGOiIiIiIjIwTDIERERERERORgGOSIiIiIiIgfDIEfUSXHBWiLq6NjOEVFHxiBH1MYKCwvxt7/9DaNHj0Z8fDySk5Px5JNP4quvvoLRaLR5fYqKipCeno7KykqbfzcRdUxs54iIbI8vBCdqQ+vWrcM777yDkJAQPPXUU+jRowfq6uqwc+dOvPXWW1i/fj0++eQTm76Id/PmzcjNzbXZ9xFRx8Z2jojIPhjkiNrIgQMHMGfOHKSmpmLBggVwdna27EtLS8N9992HP/7xj5g5cyYyMzPtWFMiotvDdo6IyH44tJKojXz88ceQy+WYO3eu1c1Ns6FDh2LixInYuXMnDhw4gNzcXERHR2PTpk1W5TIzMxEdHY2ysjLLtuzsbEyZMgVJSUmIjY3F0KFDMWPGDFRUVFjKzJw5Ew8++CCWLFmCQYMGISkpCdOnT8cnn3xi+f6ZM2daym/duhWPPvoo+vTpg4EDB+KVV17BmTNnLPvLysoQHR2N5cuX4/HHH0efPn2sjieizoftHBGR/bBHjqgN1NXVITc3F2PGjIFCobhmuQceeAArV67E1q1bkZqaelPnzsnJwZQpU/C73/0O77//PmQyGQ4cOIDMzExotVr885//tJQtKirCli1bMH/+fFy4cAEDBw6Eh4cH1q9fjyVLliA8PBwAsGbNGrz77rsYM2YMnn32WdTW1uLTTz/F448/jvXr18PPz89yzn/84x947rnn8Nxzz133byOijo3tHBGRfTHIEbWBM2fOQBRFBAUFXbdcaGgoAFg9hb6R/Px8DBkyBIsXL4YgCACAYcOG4dixY8jOzrYqq9fr8dZbbyExMdGyrXv37gCAPn36QKVSQa1WY+HChbjnnnusbo6GDh2KMWPGICMjA3PmzLFs79evH1544YWbri8RdUxs54iI7ItBjqgN3OyS1xKJeXSzyWS66XM/88wzeOaZZ6DX63H69GmcPn0aJ06cQHFxMXQ6Xavy0dHR1z3fr7/+ioaGBowZMwYGg8GyXalUon///ti1a9ctnY+IOge2c0RE9sUgR9QGAgMDIQgCTp06dd1yJ0+eBAAEBATc9LnVajXee+89bN68GVqtFt26dUOvXr0gl8tb3Vg5OzvD09Pzuuerrq4GAMyePRuzZ89utd/Jycnqs0qluum6ElHHxXaOiMi+GOSI2oCXlxcGDRqEPXv24OLFi1Y3GYcOHUJcXBwkEollwv+IESMsw4eufOeSWq22+vzKK6/g0KFDWLhwIZKSkizzN1566SUUFxffVl0B8w1OQkLCLR9PRJ0T2zkiIvviqpVEbeTll19GU1MT3njjDctQoIsXL2LSpEkYM2YMVq5ciZUrVyIxMRH33HOP5Ubl3LlzVufZv3+/1eeffvoJQ4YMQUpKiuWYixcv4ueff76poU7Nw5yaJSQkwNXVFSdPnkRcXJzlfz179sTSpUtbrS5HRNSM7RwRkf2wR46ojSQkJODvf/875syZg4ceeghPPPEEevTogTlz5mDevHmYN28eZDIZ3nzzTQBATEwMAgMD8fnnn8PPzw8+Pj5Yv359q2FLCQkJ2LFjB9asWYPw8HCcOnUKK1asQFVVFURRhF6vbzVM6HLNT6Y3bdqEIUOGIDIyEi+99BI+/PBD6HQ6jBo1CkajEatXr0Zubi4WLlzYdheJiBwa2zkiIvsRxJudrUxEt6WoqAirV6/G3r17ceHCBcjlckRERCAlJQXff/89jh07hvT0dLzyyis4duwY3n//ffzyyy9wc3PD2LFjMWDAALz66qvIyspCYGAgLly4gLlz5yI3NxdarRb+/v4YNWoUIiMjMXPmTKxYsQKDBw/GzJkzsWnTJuTl5VnVp6KiAi+++CKOHDmCIUOGYNmyZQDMNzwrVqxAQUEBnJ2dER0djalTp2LEiBEAzCvOpaSk4LXXXsO0adNsfRmJqB1jO0dEZHsMckR2JIoisrKycObMGTz11FP2rg4R0V3Hdo6IqG0wyBERERERETkYLnZCRERERETkYBjkiIiIiIiIHAyDHBERERERkYNhkCMiIiIiInIwDHJEREREREQOhkGOiIiIiIjIwTDIEREREREROZj/D0N4aUB77yUaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 884.975x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "\n",
    "sns.relplot(data=seasonality_grouped, \n",
    "            kind='line', \n",
    "            x='Quarter', y='Sales', \n",
    "            col='Order Year',\n",
    "            col_wrap=2,\n",
    "            hue='Category');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So this clearly shows us that there is a clear trend in the increase of sales every year. As the year passes the Sales go up when looked from a quarterly point of view. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us save and upload our work to Jovian before continuing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inferences and Conclusion\n",
    "\n",
    "So here are some of the insights that I have derived from this data exploration. \n",
    "- Global superstore operates globally in almost 150 Countries (147) and has wide range of products available for sales. \n",
    "- The major categories of products supplied are Office supplies, Technology and furniture. \n",
    "- Office supplies category accounts for the substantial amount of orders from all the order with other two being relatively close in terms of orders placed. \n",
    "- Majority of the profit for the store comes from Technology Products. The distribution of profits variable shows us why. The technology category operates in high price range. \n",
    "- Among other categories, Tables which is a sub-category of Furniture products seems to be operating at a Loss. This needs further investigation as why that is happening. \n",
    "- The top 10 countries in terms of sales are \n",
    "     1. United States\n",
    "     2. Australia\n",
    "     3. France\n",
    "     4. China\n",
    "     5. Germany\n",
    "     6. Mexico\n",
    "     7. India\n",
    "     8. United Kingdom\n",
    "     9. Indonesia\n",
    "     10. Brazil\n",
    "- while these might be top 10 countries the gap between first two countries in terms of sales in huge. United states accounts for majority of the sales from global superstore and some effort can be taken to increase sales in other countries. \n",
    "- among top 10 countries china and india seem to be good countries to target for increasing sales as they are producing good profits \n",
    "- The orders priority column is correlated with shipping duration as exprected. Higher the order priority lower the shipping time it takes. \n",
    "- After investigation it can be seen that the sales figures increase as the year passes. \n",
    "- Finally, the sales figures have shown linear growth over the 4 years and the business looks like its doing good. \n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References and Future Work\n",
    "\n",
    "#### **Here are some links that I found useful when using these tools**\n",
    "- (Jupyter markdown)[https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github]\n",
    "- (pandas Documentation)[https://pandas.pydata.org/docs/user_guide/index.html#user-guide]\n",
    "- (matplotlilb tutorial][https://matplotlib.org/stable/tutorials/index.html]\n",
    "- (seaborn documentation)[https://seaborn.pydata.org/api.html ]\n",
    "\n",
    "#### **Future work**\n",
    "- This dataset can be used for sales forecasting\n",
    "- Geospatial analysis \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jovian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "window.require && require([\"base/js/namespace\"],function(Jupyter){Jupyter.notebook.save_checkpoint()})"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[jovian] Updating notebook \"anuragpatil888825/global-superstore-sales-analysis-visualization\" on https://jovian.ai\u001b[0m\n",
      "[jovian] Committed successfully! https://jovian.ai/anuragpatil888825/global-superstore-sales-analysis-visualization\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'https://jovian.ai/anuragpatil888825/global-superstore-sales-analysis-visualization'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jovian.commit()"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
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