Sales-Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas_profiling import ProfileReport"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making list of all csv files "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['April_2019.csv',\n",
       " 'August_2019.csv',\n",
       " 'combined_data.csv',\n",
       " 'December_2019.csv',\n",
       " 'February_2019.csv',\n",
       " 'January_2019.csv',\n",
       " 'July_2019.csv',\n",
       " 'June_2019.csv',\n",
       " 'March_2019.csv',\n",
       " 'May_2019.csv',\n",
       " 'November_2019.csv',\n",
       " 'October_2019.csv',\n",
       " 'September_2019.csv']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = \"Datasets\"\n",
    "all_filenames = os.listdir(path)\n",
    "all_filenames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging 12 months data into single csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'E:\\\\eCommerce-Sales-Analysis\\\\Datasets'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.chdir('..\\eCommerce-Sales-Analysis\\Datasets')\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of merged data frame:  (1307943, 6)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1307943, 6)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialising empty data frame to store 12 months data\n",
    "all_data = pd.DataFrame()\n",
    "\n",
    "for f in all_filenames:\n",
    "    df = pd.read_csv(f) \n",
    "   \n",
    "    all_data = pd.concat([all_data,df])\n",
    "    # pd.concat([ 1. df: where to concatenate into, 2. df which you want to add in first data frame ])\n",
    "\n",
    "# changing a data frame to csv file using .to_csv\n",
    "all_data.to_csv( \"combined_data.csv\", index=False)\n",
    "# dataframe_name.to_csv('csv_file_name')\n",
    "\n",
    "print(\"Size of merged data frame: \",all_data.shape)\n",
    "\n",
    "all_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning or data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Columns with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order ID            True\n",
       "Product             True\n",
       "Quantity Ordered    True\n",
       "Price Each          True\n",
       "Order Date          True\n",
       "Purchase Address    True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values = all_data.isna().any()\n",
    "missing_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rows with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1432</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1552</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1570</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10012</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10274</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10878</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11384</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11662</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3808 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Order ID Product Quantity Ordered Price Each Order Date Purchase Address\n",
       "355        NaN     NaN              NaN        NaN        NaN              NaN\n",
       "734        NaN     NaN              NaN        NaN        NaN              NaN\n",
       "1432       NaN     NaN              NaN        NaN        NaN              NaN\n",
       "1552       NaN     NaN              NaN        NaN        NaN              NaN\n",
       "1570       NaN     NaN              NaN        NaN        NaN              NaN\n",
       "...        ...     ...              ...        ...        ...              ...\n",
       "10012      NaN     NaN              NaN        NaN        NaN              NaN\n",
       "10274      NaN     NaN              NaN        NaN        NaN              NaN\n",
       "10878      NaN     NaN              NaN        NaN        NaN              NaN\n",
       "11384      NaN     NaN              NaN        NaN        NaN              NaN\n",
       "11662      NaN     NaN              NaN        NaN        NaN              NaN\n",
       "\n",
       "[3808 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_val_2 = all_data[all_data.isnull().any(axis=1)]\n",
    "missing_val_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of rows with mising values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3808"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows,columns = missing_val_2.shape\n",
    "rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deleting only those rows which are totally NAN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data.dropna(axis=0,how='all',inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking NAN values  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order ID            False\n",
       "Product             False\n",
       "Quantity Ordered    False\n",
       "Price Each          False\n",
       "Order Date          False\n",
       "Purchase Address    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_nan = all_data.isnull().any()\n",
    "check_nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Converting Order Date(string datatype) to DateTime format\n",
    "## It gives parser error when we are trying to convert month column to DateTime format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new = os.path.dirname(os.getcwd())\n",
    "os.chdir(new)\n",
    "\n",
    "from IPython.display import Image\n",
    "Image(filename =r'Images\\error1.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## There is still some problematic rows, we have to clean them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9027       02\n",
       "1084052    11\n",
       "1034495    06\n",
       "751267     05\n",
       "901        07\n",
       "112828     08\n",
       "1090279    10\n",
       "8775       08\n",
       "424530     03\n",
       "937779     10\n",
       "829610     12\n",
       "675869     12\n",
       "853477     01\n",
       "445608     11\n",
       "1099626    10\n",
       "903513     05\n",
       "579094     03\n",
       "211607     02\n",
       "173921     08\n",
       "14659      10\n",
       "Name: month, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['month'] = all_data['Order Date'].str[0:2]\n",
    "all_data['month'].sample(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## There is presence of string 'Or' in month column, we have to clean it \n",
    "## That's why we are facing errors while converting data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename =r'Images\\error2.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning Data - 2\n",
    "## Removing 'Or' from month column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only putting those rows which doesn't contain 'Or' in month section\n",
    "all_data = all_data[~all_data['month'].str.contains(\"Or\")]\n",
    "# Putting ~ works as an invert\n",
    "\n",
    "# Another way: all_data = all_data[all_data['Month'].str[0:] != \"Or\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['month'].str.contains(\"Or\").any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting datatype of columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['month'] = all_data['month'].astype('int32')\n",
    "all_data['Price Each'] = all_data['Price Each'].astype('float')\n",
    "all_data['Quantity Ordered'] = all_data['Quantity Ordered'].astype('int32')\n",
    "all_data['Order ID'] = all_data['Order ID'].astype('int32')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What was the best month for sales?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating total sales  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12755797.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15414156.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19649702.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23734691.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>22068247.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18044615.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18534430.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15711275.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14682920.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>26157088.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>22397222.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>32294103.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_sales\n",
       "month             \n",
       "1      12755797.11\n",
       "2      15414156.94\n",
       "3      19649702.66\n",
       "4      23734691.68\n",
       "5      22068247.25\n",
       "6      18044615.82\n",
       "7      18534430.32\n",
       "8      15711275.16\n",
       "9      14682920.91\n",
       "10     26157088.16\n",
       "11     22397222.40\n",
       "12     32294103.38"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making a total_sales column\n",
    "all_data['total_sales'] = all_data['Quantity Ordered'] * all_data['Price Each']\n",
    "val = all_data.groupby(['month']).agg({'total_sales':'sum'})\n",
    "val.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>month</th>\n",
       "      <th>total_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>462176</th>\n",
       "      <td>294684</td>\n",
       "      <td>AA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>3.84</td>\n",
       "      <td>11/04/19 22:21</td>\n",
       "      <td>759 1st St, Los Angeles, CA 90001</td>\n",
       "      <td>11</td>\n",
       "      <td>3.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253517</th>\n",
       "      <td>219366</td>\n",
       "      <td>ThinkPad Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>999.99</td>\n",
       "      <td>06/20/19 09:04</td>\n",
       "      <td>830 Washington St, Austin, TX 73301</td>\n",
       "      <td>6</td>\n",
       "      <td>999.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405757</th>\n",
       "      <td>215325</td>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>06/30/19 14:48</td>\n",
       "      <td>475 Forest St, Seattle, WA 98101</td>\n",
       "      <td>6</td>\n",
       "      <td>14.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>845916</th>\n",
       "      <td>141800</td>\n",
       "      <td>27in FHD Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>149.99</td>\n",
       "      <td>01/26/19 12:11</td>\n",
       "      <td>775 West St, Seattle, WA 98101</td>\n",
       "      <td>1</td>\n",
       "      <td>149.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233442</th>\n",
       "      <td>226933</td>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>07/31/19 14:19</td>\n",
       "      <td>40 Lakeview St, San Francisco, CA 94016</td>\n",
       "      <td>7</td>\n",
       "      <td>14.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651434</th>\n",
       "      <td>259083</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>09/08/19 11:36</td>\n",
       "      <td>474 Lincoln St, Los Angeles, CA 90001</td>\n",
       "      <td>9</td>\n",
       "      <td>99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210143</th>\n",
       "      <td>153046</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>02/26/19 11:49</td>\n",
       "      <td>345 Hickory St, Los Angeles, CA 90001</td>\n",
       "      <td>2</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Order ID                     Product  Quantity Ordered  Price Each  \\\n",
       "462176    294684       AA Batteries (4-pack)                 1        3.84   \n",
       "253517    219366             ThinkPad Laptop                 1      999.99   \n",
       "405757    215325    Lightning Charging Cable                 1       14.95   \n",
       "845916    141800            27in FHD Monitor                 1      149.99   \n",
       "233442    226933    Lightning Charging Cable                 1       14.95   \n",
       "651434    259083  Bose SoundSport Headphones                 1       99.99   \n",
       "210143    153046      AAA Batteries (4-pack)                 1        2.99   \n",
       "\n",
       "            Order Date                         Purchase Address  month  \\\n",
       "462176  11/04/19 22:21        759 1st St, Los Angeles, CA 90001     11   \n",
       "253517  06/20/19 09:04      830 Washington St, Austin, TX 73301      6   \n",
       "405757  06/30/19 14:48         475 Forest St, Seattle, WA 98101      6   \n",
       "845916  01/26/19 12:11           775 West St, Seattle, WA 98101      1   \n",
       "233442  07/31/19 14:19  40 Lakeview St, San Francisco, CA 94016      7   \n",
       "651434  09/08/19 11:36    474 Lincoln St, Los Angeles, CA 90001      9   \n",
       "210143  02/26/19 11:49    345 Hickory St, Los Angeles, CA 90001      2   \n",
       "\n",
       "        total_sales  \n",
       "462176         3.84  \n",
       "253517       999.99  \n",
       "405757        14.95  \n",
       "845916       149.99  \n",
       "233442        14.95  \n",
       "651434        99.99  \n",
       "210143         2.99  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.sample(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "months = [month for month,data in all_data.groupby(['month'])]\n",
    "# Method2: months = range(1,13)\n",
    "plt.style.use('ggplot')\n",
    "plt.bar(months,val['total_sales'])\n",
    "plt.xlabel('Months')\n",
    "plt.ylabel('Sales in million USD')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### December has highest sales of about 4.6 million USD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What city sold the most product ?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['city'] = all_data['Purchase Address'].apply(lambda x: x.split(',')[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' Dallas', ' Boston', ' Los Angeles', ..., ' San Francisco',\n",
       "       ' San Francisco', ' San Francisco'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['city'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(all_data.values == 'Los Angeles').any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## There are some whitespace character in 'city' column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(all_data.values == ' Los Angeles').any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing whitespace character in 'city' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['city'] = all_data['city'].apply(lambda y: y.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Dallas', 'Boston', 'Los Angeles', ..., 'San Francisco',\n",
       "       'San Francisco', 'San Francisco'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['city'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Many states have same city names, so we have to report cities with their state name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Dallas(TX)', 'Boston(MA)', 'Los Angeles(CA)', ...,\n",
       "       'San Francisco(CA)', 'San Francisco(CA)', 'San Francisco(CA)'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def finding_state(address):\n",
    "    return address.split(',')[2].split(' ')[1]\n",
    "\n",
    "all_data['city'] = all_data['city'] + all_data['Purchase Address'].apply(lambda x:'(' + finding_state(x) + ')')\n",
    "all_data['city'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Total sales city wise in USD "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Atlanta(GA)          19568490.06\n",
       "Austin(TX)           12737072.25\n",
       "Boston(MA)           25631494.07\n",
       "Dallas(TX)           19375827.80\n",
       "Los Angeles(CA)      38167995.60\n",
       "New York City(NY)    32650222.01\n",
       "Portland(ME)          3148307.89\n",
       "Portland(OR)         13095126.38\n",
       "San Francisco(CA)    57835427.37\n",
       "Seattle(WA)          19234288.36\n",
       "Name: total_sales, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales = all_data.groupby(['city']).agg({'total_sales':'sum'})\n",
    "sales['total_sales'].round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Atlanta(GA)',\n",
       " 'Austin(TX)',\n",
       " 'Boston(MA)',\n",
       " 'Dallas(TX)',\n",
       " 'Los Angeles(CA)',\n",
       " 'New York City(NY)',\n",
       " 'Portland(ME)',\n",
       " 'Portland(OR)',\n",
       " 'San Francisco(CA)',\n",
       " 'Seattle(WA)']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities = [city for city,data in all_data.groupby(['city'])]\n",
    "cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(cities,sales['total_sales'])\n",
    "plt.xticks(cities,rotation='vertical',size=12)\n",
    "plt.xlabel('City Name')\n",
    "plt.ylabel('Sales in million USD')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### San Francisco,California have highest sales "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What time should we display advertisements to maximize the likelihood of purchases?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting column to date time format\n",
    "all_data['Order Date'] = pd.to_datetime(all_data['Order Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Order ID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hours</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>28077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>43792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>61236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>76608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>86877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>88109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>84903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>76888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>71225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>72688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>76293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>85960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>90335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>85596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>76447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>61754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>43925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Order ID\n",
       "hours          \n",
       "0         27370\n",
       "1         16450\n",
       "2          8701\n",
       "3          5817\n",
       "4          5978\n",
       "5          9247\n",
       "6         17374\n",
       "7         28077\n",
       "8         43792\n",
       "9         61236\n",
       "10        76608\n",
       "11        86877\n",
       "12        88109\n",
       "13        84903\n",
       "14        76888\n",
       "15        71225\n",
       "16        72688\n",
       "17        76293\n",
       "18        85960\n",
       "19        90335\n",
       "20        85596\n",
       "21        76447\n",
       "22        61754\n",
       "23        43925"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['hours'] = all_data['Order Date'].dt.hour\n",
    "\n",
    "no_of_sales = all_data.groupby(['hours']).agg({'Order ID':'count'})\n",
    "no_of_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hours = [hour for hour,data in all_data.groupby(['hours'])]\n",
    "# Method2: hours = range(0,24)\n",
    "\n",
    "# line graph\n",
    "plt.plot(hours,no_of_sales['Order ID'])\n",
    "plt.xticks(hours)\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Number of sales')\n",
    "plt.title('Hourly number of sales')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sellers should advertise their product between or just before 11am to 1pm and 6pm to 8pm to increase their sales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What time should we display advertisements to increase sales in Portland,ME "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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></th>\n",
       "      <th>Order ID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th>hours</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Atlanta(GA)</th>\n",
       "      <th>0</th>\n",
       "      <td>2170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Seattle(WA)</th>\n",
       "      <th>19</th>\n",
       "      <td>6923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>6083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>240 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Order ID\n",
       "city        hours          \n",
       "Atlanta(GA) 0          2170\n",
       "            1          1491\n",
       "            2           791\n",
       "            3           399\n",
       "            4           560\n",
       "...                     ...\n",
       "Seattle(WA) 19         6923\n",
       "            20         6916\n",
       "            21         6083\n",
       "            22         4851\n",
       "            23         3857\n",
       "\n",
       "[240 rows x 1 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "citywise_data = all_data.groupby(['city','hours']).agg({'Order ID':'count'})\n",
    "citywise_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hourly sales in Portland,ME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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></th>\n",
       "      <th>Order ID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th>hours</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">Portland(ME)</th>\n",
       "      <th>0</th>\n",
       "      <td>336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>448</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Order ID\n",
       "city         hours          \n",
       "Portland(ME) 0           336\n",
       "             1           238\n",
       "             2            98\n",
       "             3            77\n",
       "             4            77\n",
       "             5            98\n",
       "             6           280\n",
       "             7           392\n",
       "             8           581\n",
       "             9           742\n",
       "             10         1106\n",
       "             11         1239\n",
       "             12         1127\n",
       "             13         1043\n",
       "             14         1001\n",
       "             15          987\n",
       "             16          966\n",
       "             17         1071\n",
       "             18         1141\n",
       "             19         1232\n",
       "             20         1092\n",
       "             21          966\n",
       "             22          847\n",
       "             23          448"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hourly_sales = citywise_data.iloc[citywise_data.index.get_level_values('city') == 'Portland(ME)']\n",
    "# Another method: y = check.iloc[check.index.get_level_values(grouped_data_name='city' or grouped_data_level=0) == ' Atlanta (GA)']\n",
    "\n",
    "hourly_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hours = [hour for hour,data in all_data.groupby(['hours'])]\n",
    "plt.xlabel('Hours')\n",
    "plt.xticks(hours)\n",
    "plt.ylabel('Number of sales')\n",
    "plt.title('Hourly number of sales in Portland,ME')\n",
    "plt.plot(hours,hourly_sales.values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What products are most often sold together?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Order ID's with multiple products:\n",
      " 0        176558\n",
      "1        176559\n",
      "2        176560\n",
      "3        176560\n",
      "4        176561\n",
      "          ...  \n",
      "11681    259353\n",
      "11682    259354\n",
      "11683    259355\n",
      "11684    259356\n",
      "11685    259357\n",
      "Name: Order ID, Length: 1301650, dtype: int32\n"
     ]
    }
   ],
   "source": [
    "multi_prod = all_data[all_data.duplicated(subset='Order ID',keep = False)]\n",
    "print(\"Order ID's with multiple products:\\n\",multi_prod['Order ID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>month</th>\n",
       "      <th>total_sales</th>\n",
       "      <th>city</th>\n",
       "      <th>hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>600.00</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>600.00</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30395</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>600.00</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30396</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60788</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>600.00</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60789</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91181</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>600.00</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91182</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121574</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>600.00</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121575</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151967</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>600.00</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151968</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Order ID           Product  Quantity Ordered  Price Each  \\\n",
       "2         176560      Google Phone                 1      600.00   \n",
       "3         176560  Wired Headphones                 1       11.99   \n",
       "2         176560      Google Phone                 1      600.00   \n",
       "3         176560  Wired Headphones                 1       11.99   \n",
       "30395     176560      Google Phone                 1      600.00   \n",
       "30396     176560  Wired Headphones                 1       11.99   \n",
       "60788     176560      Google Phone                 1      600.00   \n",
       "60789     176560  Wired Headphones                 1       11.99   \n",
       "91181     176560      Google Phone                 1      600.00   \n",
       "91182     176560  Wired Headphones                 1       11.99   \n",
       "121574    176560      Google Phone                 1      600.00   \n",
       "121575    176560  Wired Headphones                 1       11.99   \n",
       "151967    176560      Google Phone                 1      600.00   \n",
       "151968    176560  Wired Headphones                 1       11.99   \n",
       "\n",
       "                Order Date                      Purchase Address  month  \\\n",
       "2      2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4   \n",
       "3      2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4   \n",
       "2      2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4   \n",
       "3      2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4   \n",
       "30395  2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4   \n",
       "30396  2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4   \n",
       "60788  2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4   \n",
       "60789  2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4   \n",
       "91181  2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4   \n",
       "91182  2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4   \n",
       "121574 2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4   \n",
       "121575 2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4   \n",
       "151967 2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4   \n",
       "151968 2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4   \n",
       "\n",
       "        total_sales             city  hours  \n",
       "2            600.00  Los Angeles(CA)     14  \n",
       "3             11.99  Los Angeles(CA)     14  \n",
       "2            600.00  Los Angeles(CA)     14  \n",
       "3             11.99  Los Angeles(CA)     14  \n",
       "30395        600.00  Los Angeles(CA)     14  \n",
       "30396         11.99  Los Angeles(CA)     14  \n",
       "60788        600.00  Los Angeles(CA)     14  \n",
       "60789         11.99  Los Angeles(CA)     14  \n",
       "91181        600.00  Los Angeles(CA)     14  \n",
       "91182         11.99  Los Angeles(CA)     14  \n",
       "121574       600.00  Los Angeles(CA)     14  \n",
       "121575        11.99  Los Angeles(CA)     14  \n",
       "151967       600.00  Los Angeles(CA)     14  \n",
       "151968        11.99  Los Angeles(CA)     14  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_prod[multi_prod['Order ID'] == 176560]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_prod['m_order'] = multi_prod.groupby(['Order ID'])['Product'].transform(lambda x: ','.join(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_prod.drop_duplicates(subset='Order ID',keep='last',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>month</th>\n",
       "      <th>total_sales</th>\n",
       "      <th>city</th>\n",
       "      <th>hours</th>\n",
       "      <th>m_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>151968</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Los Angeles(CA)</td>\n",
       "      <td>14</td>\n",
       "      <td>Google Phone,Wired Headphones,Google Phone,Wir...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Order ID           Product  Quantity Ordered  Price Each  \\\n",
       "151968    176560  Wired Headphones                 1       11.99   \n",
       "\n",
       "                Order Date                      Purchase Address  month  \\\n",
       "151968 2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001      4   \n",
       "\n",
       "        total_sales             city  hours  \\\n",
       "151968        11.99  Los Angeles(CA)     14   \n",
       "\n",
       "                                                  m_order  \n",
       "151968  Google Phone,Wired Headphones,Google Phone,Wir...  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_prod[multi_prod['Order ID'] == 176560]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('USB-C Charging Cable', 'USB-C Charging Cable') : 462315\n",
      "('Lightning Charging Cable', 'Lightning Charging Cable') : 457464\n",
      "('AAA Batteries (4-pack)', 'AAA Batteries (4-pack)') : 435813\n",
      "('AA Batteries (4-pack)', 'AA Batteries (4-pack)') : 433832\n",
      "('Wired Headphones', 'Wired Headphones') : 398237\n",
      "('Apple Airpods Headphones', 'Apple Airpods Headphones') : 327705\n",
      "('Bose SoundSport Headphones', 'Bose SoundSport Headphones') : 281148\n",
      "('27in FHD Monitor', '27in FHD Monitor') : 158088\n",
      "('iPhone', 'iPhone') : 143780\n",
      "('27in 4K Gaming Monitor', '27in 4K Gaming Monitor') : 131075\n"
     ]
    }
   ],
   "source": [
    "from itertools import combinations\n",
    "from collections import Counter\n",
    "\n",
    "sublist_to_count = list()\n",
    "holding_values = dict()\n",
    "\n",
    "count = Counter()\n",
    "\n",
    "for prod in multi_prod['m_order']:\n",
    "    sublist_to_count = prod.split(',')\n",
    "    count.update(combinations(sublist_to_count, 2))\n",
    "\n",
    "for key,val in count.most_common(10):\n",
    "    print(key,':',val)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### When a customer is buying a phone it is most likely that they will buy a charging cable and headphone with it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Which product has maximum sales?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "product_list = all_data.groupby('Product')\n",
    "products = [i for i,j in product_list]\n",
    "\n",
    "total_quantity = all_data.groupby('Product').sum()['Quantity Ordered']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6uhqnTp1CRkYGnjx5ovTYihUreKkYIYSQpoNVsPn++++Rnp4OiUSCH3/8EWPHjsXp06fh7e3N6kWio6Nx5coV2NjYYP369QAAqVSKiIgI5Ofnw8HBAXPnzmUySx8+fBgJCQkQCoUIDAyEh4cHACAzMxNRUVGQyWTo0aMHAgMDIRAIUFlZicjISGRmZqJFixaYM2cOHB0dAQBJSUk4dOgQACAgIAC+vr56fUCEEEK4Y9WNduHCBSxatAhDhw6FiYkJhg4digULFuCvv/5i9SK+vr5YtGiR0rEjR46gW7du2LRpE7p164YjR44AAB49eoTU1FRs2LABixcvRkxMDORyOQBg27ZtCAoKwqZNm5CTk4OrV68CABISEtC8eXNs3rwZw4YNw549ewDUBLQDBw5g9erVWL16NQ4cOACpVMrukyGEEGI0rIKNTCaDvb09AMDc3BzPnz9Hq1atWG8x0KVLF6bVUuvSpUsYMGAAAGDAgAG4dOkSc9zb2xtmZmZwdHSEs7Mz7ty5g+LiYjx79gwdO3aEQCCAj48PU+b3339nWix9+/ZFeno6FAoFrl69iu7du0MkEkEkEqF79+5MgCKEENJwWHWjtWrVCnfv3oWbmxvatWuH/fv3w9LSEnZ2dga/cGlpKWxtbQEAtra2KCsrAwAUFRWhQ4cOzPPs7OxQVFQEExMTJuABgL29PYqKipgytY+ZmJjAysoKT548UTpe91zqxMfHIz4+HgAQGhoKsVhs8HvTxNTUlNN5uZZvqDqwW6GlrP45uZ7DGHVQ52X5Dl6E8nx9B/rUgWt5fd+Dsev/In+G+mIVbCZPngyhsKYRNGnSJGzfvh3Pnj3Dp59+avQKKRQKvY5rekwgEKh9rqbjEokEEomE+X9BQYG2ahpELBZzOi/X8i9KHdQxxjm5noNN+abwHTR2eW3YnvdFfQ8NVf8XtQ4uLi4aH2MVbMRiMVq2bAkAeO2117B06VIAQElJicGVsrGxQXFxMWxtbVFcXAxra2sANS2WwsJC5nlFRUWws7NTOV5YWMi0rGofs7e3R3V1NcrLyyESiWBnZ4eMjAylc3Xp0sXgOhNCCDEMqzGbL774Qu3x2o3UDOHp6Ylz584BAM6dO4fevXszx1NTU1FZWYm8vDxkZ2fDzc0Ntra2sLS0xK1bt6BQKJCcnAxPT08AQK9evZCUlAQASEtLg7u7OwQCATw8PPDnn39CKpVCKpXizz//ZGa2EUIIaTisWjbquqnKy8uZrjVdvv32W2aNzvTp0zF69GiMGDECERERSEhIgFgsxrx58wAArVu3hpeXF+bNmwehUIipU6cyrzNt2jRER0dDJpPBw8MDPXr0AAD4+fkhMjISs2bNgkgkYjZ1E4lEGDlyJIKDgwEAo0aNUpmoQAghhH9ag82MGTMA1MxGq/13LalUinfeeYfVi2ja0XPZsmVqjwcEBCAgIEDlePv27Zl1OnWZm5szwao+Pz8/+Pn5saonIYQQfmgNNrNmzYJCocCaNWswa9YspcdatmypdTCIkJcZbVFAiHFpDTa1g+kxMTEq20ITQgghbLEaszExMcG+ffuQnJzMzCDz8fFBQEAATE1p/zVCCCHasYoUu3fvxt27d/HJJ5/AwcEB+fn5OHjwIMrLyzF58mSeq0gIIeRlxyrYpKWlITw8HC1atABQs3DnjTfewIIFCyjYEEII0YnV3GVtq/cJIYQQXbQGm5SUFACAl5cX1q5di6tXr+LRo0e4evUqwsPD4eXl1SCVJIQQ8nLT2o22bds29OvXD+PHj8fBgwcRExPDTBB45513MHLkyIaqJyGEkJeY1mBT231mamqKMWPGYMyYMQ1SKUIIIU2L1mAjl8uRnp6u9QRdu3Y1aoUIIYQ0PVqDTWVlJb777juNEwQEAgEiIyN5qRghhJCmQ2uwsbCwoGBCCCGEM3ZpmwkhhBAOtAYbWl9DCCHEGLQGm9jY2IaqByGEkCaMutEIIYTwjoINIYQQ3lGwIYQQwjsKNoQQQnhHwYYQQgjvKNgQQgjhHQUbQgghvKNgQwghhHcUbAghhPCOgg0hhBDeUbAhhBDCOwo2hBBCeEfBhhBCCO8o2BBCCOEdBRtCCCG8o2BDCCGEdxRsCCGE8I6CDSGEEN5RsCGEEMI7CjaEEEJ4R8GGEEII7yjYEEII4R0FG0IIIbyjYEMIIYR3po1dgc8//xwWFhYQCoUwMTFBaGgopFIpIiIikJ+fDwcHB8ydOxcikQgAcPjwYSQkJEAoFCIwMBAeHh4AgMzMTERFRUEmk6FHjx4IDAyEQCBAZWUlIiMjkZmZiRYtWmDOnDlwdHRszLfMu+pPhmt8LFfDcZNtx/ipDCGE4AVp2fzrX/9CeHg4QkNDAQBHjhxBt27dsGnTJnTr1g1HjhwBADx69AipqanYsGEDFi9ejJiYGMjlcgDAtm3bEBQUhE2bNiEnJwdXr14FACQkJKB58+bYvHkzhg0bhj179jTOmySEkFfYCxFs6rt06RIGDBgAABgwYAAuXbrEHPf29oaZmRkcHR3h7OyMO3fuoLi4GM+ePUPHjh0hEAjg4+PDlPn999/h6+sLAOjbty/S09OhUCga5X0RQsirqtG70QBg1apVAIDBgwdDIpGgtLQUtra2AABbW1uUlZUBAIqKitChQwemnJ2dHYqKimBiYgJ7e3vmuL29PYqKipgytY+ZmJjAysoKT548gbW1tVId4uPjER8fDwAIDQ2FWCw2+vs0NTXldF625TV1lWnDtl5s6mCM1+d6jsYur01D/R00dvmm8Bnq+x6MXf8X+TPUV6MHm5CQENjZ2aG0tBQrV66Ei4uLxudqapFoa6moe0wgEKgck0gkkEgkzP8LCgq0VdsgYrGY03m5lteG7Xn5qoMxzsn1HA1VvrH/Dhq7vDYvy2eoSWP/jhq7Dtqu340ebOzs7AAANjY26N27N+7cuQMbGxsUFxfD1tYWxcXFTCvE3t4ehYWFTNmioiLY2dmpHC8sLGTOW/uYvb09qqurUV5ezkw2IISQpuxFmizUqGM2FRUVePbsGfPva9euoU2bNvD09MS5c+cAAOfOnUPv3r0BAJ6enkhNTUVlZSXy8vKQnZ0NNzc32NrawtLSErdu3YJCoUBycjI8PT0BAL169UJSUhIAIC0tDe7u7mpbNoQQQvjTqC2b0tJSrFu3DgBQXV2Nfv36wcPDA+3bt0dERAQSEhIgFosxb948AEDr1q3h5eWFefPmQSgUYurUqRAKa+LltGnTEB0dDZlMBg8PD/To0QMA4Ofnh8jISMyaNQsikQhz5sxpnDdLCCGvsEYNNk5OTggPD1c53qJFCyxbtkxtmYCAAAQEBKgcb9++PdavX69y3NzcnAlWhBBCGkejj9kQVZr6WbXNTDFmP+uL1M9LCGkaXsh1NoQQQpoWCjaEEEJ4R8GGEEII7yjYEEII4R1NECDkBUSTNEhTQy0bQgghvKNgQwghhHcUbAghhPCOgg0hhBDe0QQBQnhAA/zc0WfYtFDLhhBCCO8o2BBCCOEdBRtCCCG8ozEbHjR21mZCCI35vGioZUMIIYR3FGwIIYTwjoINIYQQ3lGwIYQQwjsKNoQQQnhHwYYQQgjvKNgQQgjhHQUbQgghvKNgQwghhHcUbAghhPCOgg0hhBDeUbAhhBDCOwo2hBBCeEdZnwkhhCeUAf5/qGVDCCGEdxRsCCGE8I6CDSGEEN5RsCGEEMI7CjaEEEJ4R8GGEEII7yjYEEII4R0FG0IIIbyjRZ2ENFFcFxTSgkRiTNSyIYQQwrtXpmVz9epV7Ny5E3K5HIMGDcKIESMau0qEEPLKeCVaNnK5HDExMVi0aBEiIiJw/vx5PHr0qLGrRQghr4xXItjcuXMHzs7OcHJygqmpKby9vXHp0qXGrhYhhLwyBAqFQtHYleBbWloarl69iunTpwMAkpOTcfv2bUydOpV5Tnx8POLj4wEAoaGhjVJPQghpql6Jlo26eCoQCJT+L5FIEBoaymug+frrrxu1/ItQh1e9/ItQh5e9/ItQh5e9vLHOoY9XItjY29ujsLCQ+X9hYSFsbW0bsUaEEPJqeSWCTfv27ZGdnY28vDxUVVUhNTUVnp6ejV0tQgh5ZZgsX758eWNXgm9CoRDOzs7YvHkzTp48if79+6Nv376NUpd27do1avkXoQ6vevkXoQ4ve/kXoQ4ve3ljnYOtV2KCACGEkMb1SnSjEUIIaVwUbAghhPCOgg1PqqurkZqaisrKykYrv337dshkMoPKE25+/vlnSKXSxq4GIS8MCjY8MTExwXfffQczM7NGK5+ammpw+erqakyaNIlTsMrKykJCQgKOHTuGhIQE3Lt3z+BzGerGjRvIz88HAJSWliIqKgrfffcdysrKDDrfX3/9hRs3buh83qlTpxAUFIRNmzbhv//9r0GvVZdCoUBxcbHe5YzxPQI1KZ9u3ryJ1NRUAMDz58/1Omdubi4OHTqE7du3AwAeP36Mhw8fsi6fmpqKVatWYf78+Vi1ahXOnz+v3xsAUFJSgrt376KkpESvcnK5HAcPHsTs2bMxadIkADW5Fk+fPq3Xebh8BidPnkRWVhYAIDMzE5999hlmzZqFu3fv6lWHxvTKJOJsDO3bt8f9+/fh6uraKOU9PT1x4cIFg2bemZiYwNraGtXV1XqVq6qqwsmTJ3HixAmUlJTgtddeg6WlJZ49e4bs7Gy0bNkS7733Hv7xj3/A1FT7n19ubi5+//133Lt3D1KpFCKRCG3btoWnpyecnZ1Z1Wf79u348ssvAQB79uxBUVERzMzMEBMTg7lz5+osv3LlSgQEBKBLly745ZdfEBcXB6FQiNGjR+P999/XWG7z5s24du0a4uPjERISAicnJwwePBg+Pj4QiUSs6g4AFRUV2LlzJ1JSUiAUCvHDDz/g4sWLuH//Pj788EOd5Q39HuvKycnB2rVrUVxcjOrqanh7e+PPP/9EWloaZs+erbP8tWvXsG7dOri7uyMjIwPTpk1DWVkZDh06hEWLFuksf+TIERw/fhyDBg2Cl5cX8vLysGvXLuTn57NKqFtWVsZ8H7W6d++OmTNnwsbGRmf5//znP7h+/TrGjRuHLVu2AABcXFwQFxeHIUOG6CwPcP8Mfv75Z3h7ezP18fb2hqWlJWJjY7FixQpWdWhsFGx45O7ujrVr10IikUAsFkMo/F9Dsl+/fryXr6qqwubNm3HmzBk4ODgoZU0ICgrSWf7DDz/Etm3bMH78eNjZ2el8PgAsWLAAb7zxBoKCgtClSxelgFJVVYWMjAwkJSVhwYIFiIiIUHuOx48f44cffkB6ejrc3NzQunVriMVilJeX4/Lly9i7dy+6du2KCRMmwMXFRWt9CgsL4ezsDIVCgcuXL2PDhg1o1qwZZs6cyer9ZGVloWPHjgCAs2fPYsmSJbCyskJ4eLjWYAPUXNC6d++OsrIyJCQk4NSpU4iLi8Pbb7+NwYMHo1OnTjpfPzY2FqWlpQgJCUFISAgAwM3NDT/++COrYAMY9j3WtXPnTnh7e2PkyJFMiid3d3fs2rWLVfk9e/Zg3rx58PDwQGBgIICaKbdsW7qnTp1CcHCw0jTdt99+G2FhYayCzdatW9GsWTNs3LgRjo6OyMvLw549e7B161YsXLhQZ/nz589j5cqVsLW1xXfffQcAcHBwYFrMbHD9DJ48eQJra2tUVlbi5s2b+PLLL2Fqaoqff/6ZdR0AoLy8HFeuXEFhYSH8/f1RUlICuVxu0N+FvijY8CgxMRECgQBnz55VOi4QCFgFC67lTU1N8c477wCo6QrQ15YtWyCXy3H+/HmlQAcAP/74o9oy8+fPx+uvv66xPrUXYG1Zt9euXYsPPvgAs2fPhqWlpcrjFRUVSElJwdq1a7Fx40at70EoFEImk+HRo0do2bIlbGxsIJfLWXcBVVVVwdTUFCUlJSgtLWUChD7dcNbW1hgxYgRGjBiBa9euYdu2bUhJScHevXt1lr18+TIiIiJgZWXFHLOzs9OrS82Q77GuO3fuYOHChUplmzdvjqdPn7J6/dzcXHh4eCgdMzc3R1VVFavyMplMpXXfpk0b1t9hRkYGtmzZAgsLCwCAs7MzZsyYgc8//5xV+YqKCpUWUI/PtMIAACAASURBVFVVlV5d1Fw/AwsLCxQVFeHBgwdwdXVlyurzu87MzMSqVatga2uLvLw8+Pv74/79+zhz5gzT+ucTBRseRUVFNWr5zz77jFP5pUuX6l2mbqCRyWQwNzdXeU5hYaHGgAQA69ev19rFZmFhAYlEAl9fX531cXd3x4YNGyCVStG7d28AYLrz2HByckJSUhJyc3PRtWtXADV3mfqOhRUXFyMxMREJCQkoKyuDn58f67L1P8OKigrmwsmGId9jXZaWlnj69Cmsra2ZY0VFRaw/Q3t7ezx48ABt2rRhjmVlZcHR0ZFVeR8fHxw/fhzDh/9v59Djx49jwIABrMpbW1tDJpMpfWaVlZVK70ebdu3aITExEYMGDWKOpaSkwM3NjVV5gPtn4Ovri8WLF6OyshJjx44F8L9s9mzt2rUL48ePx8CBA5nW1Ztvvono6GjW5+CCgk0DKCkpQWFhIcRiMas+YmOWr6iowJUrV1BQUACxWIyePXuyvlB16dJF77rWtWHDBnz11VdK3XelpaVYuXKlxi40AEqBJicnR+0PKj09nbn4axMUFIRjx47B1NSUuVjl5OTgH//4B6v3MG7cOERFRcHMzIzpcrly5Qrat2+vs6xCocAff/yB+Ph4/PHHH2jTpg2GDx+O/v37q22xqfPmm2/i8OHDSl1mJ06cgLu7O6vyAPfvsU+fPtiyZQumTZsGoCbY7tq1ixlD0OW9997D+vXrMXLkSMjlcqSlpWH//v3w9/dnVf7Bgwc4efIkTp06BbFYjIKCAhQXF6Nz585YuXIl87wlS5aoLe/v748NGzbgww8/hIODA/Ly8nDw4EH4+/ujqKiIeZ6mrqQJEyZgxYoVSE1NhUwmQ2hoKDIzM/Gvf/2LVf2N8RmMHTsW7u7uMDU1Zb5PMzMzTJgwgXUdHj58qHKDZmFhgefPn7M+BxeUQYBHUqkUkZGR+OOPP5hjPXr0wMyZM1kNEnMt//DhQ6xcuRJCoZDpY5bL5Vi8eLHSHZY2aWlpSEhIQGFhIezt7eHn58d6wkFYWBjs7e2Zfv6nT59ixYoV6Nq1KyZOnMjqHHPmzMHKlSuV3u+tW7ewdu1axMTEsDqHsdV2fWhrfe3btw+JiYmQSqV45513IJFI9LoTrlVQUIBvvvkGAJCfnw8XFxdUV1dj2bJlevWzJycn49y5cygtLcW6deuQkZGBJ0+e4O2339ZZViaTYcuWLcxMNKBmzDAoKEhty1Wd+Ph4nDhxAnl5eXB0dMTQoUOVWgra7N+/n9XzNI1hjRkzhlV5bd2aZWVlOHfuHPLy8uDg4AAfHx/WLbtaXD6DWgqFAiUlJQYlEv7iiy+wZMkSODg4IDAwEDt37kROTg7CwsKwYcMGvc+nLwo2PIqKisKTJ08wceJEODs7IycnBz/88ANEIhGr/mKu5UNCQtCpUyeMGjUKAoEACoUCBw8eREZGBpYtW6azfHx8POLi4iCRSJjXP3v2LMaMGcNqFs7z58+xfPlyeHl54d1338U333yDNm3asJqcUOvgwYO4evUqli1bBjMzM9y/fx8hISGYNm0a66CXnJyM5ORklJSU6H2hBQwbVF24cCEkEolerRhNKisrcfnyZeZC16tXL9YXeaBmJtOpU6fw7rvv4sCBA9i1axcePXqELVu2YNWqVazPU1ZWhvz8fDg4OLDugnoRsB3Id3Bw4LkmhquoqMCuXbvw66+/GjQrEQAOHTqEy5cvY8KECVi7di2WLl2K2NhY9O7dG8OGDeP5HVA3Gq+uXbumNLjr4uKCzz//HPPmzWuQ8llZWQgODma6sQQCAUaMGIFffvmFVfnjx48jODgYHTp0YI716dMHUVFRrIJNs2bN8NVXX2Hp0qVITk7WO9AAwMiRI5Gbm4vNmzfjo48+wqpVqzB+/HjWgab+hRao6cPfs2cPq2Bj6KBqdXU162mxupiZmXFKHHv69GkEBwfDxcUFBw8eBFDzt5STk6PXeaytrRstyBQUFCAlJYVpYffr1w9isZhVWUOCSEpKCqvnsZmoU9ezZ8/w7NkzpWNsWqixsbEoKSnhNCtxxIgRqKiowJo1a1BRUYEVK1bgvffew3vvvafXezAUBZsGVn82EJ/lrayskJ+fj9dee405lp+fz/pOu7i4WGVsol27dloXxan7kXp7eyMpKQlvvfUW87g+P9KgoCCsXr0aX331FcaNG8dqYkAtrhdaQwdVCwoKWNdRG4VCgfPnzyMzM1PlIsU2cEulUpUp4vp0aJSUlGDfvn24e/cuKioqlB7TNBuwdhBbFzaz4W7cuIFVq1bB1dUVTk5OyMrKwsGDB7Fo0SJ07txZZ3mFQoGff/4ZCQkJzNiln58fhg0bpvH3xKZebGeFAjVdv1FRUWr/7hpqVqJQKMTHH3+Mjz/+GGVlZQ1+40DBhkfdunVDZGQkJk2axIyZxMbGonv37g1SfsCAAQgNDYW/vz+zvuDYsWOsL9atWrXCr7/+qjTrJyUlRSl41afpR2pqaop9+/YB0P0j3bp1q8qx5s2bw9LSEvfv32ceZ3Ox5XqhbexB1W3btiEtLQ1du3bVawZaXa+//jouX76MXr16MceuXr2Ktm3bsiofFRWFiooK+Pn5oVmzZqzKcJ0BV9fu3bsRGBioNIMvKSkJu3fvZtUNePjwYSQmJsLf3x9OTk7Izc3F0aNHIZPJMHLkSLVluM4Ere/f//43evbsiUGDBrH+DOvjOiuxrsZooVKw4dHkyZOxceNGpVXWb731Fus7Uq7lAwICYGJigqNHjzLdD76+vkpTSLUZN24cVq9ejbNnz8LR0RH5+fnIzMxEcHCwxjLG+JGqW+1uYWEBDw8PvVfCc73QWltbo6CgQKkrJicnR2fXR2VlJdNtp8moUaN0vn5aWhpWr16t1xTX+saOHYvQ0FB4eXmhqqoKO3bswPnz51lvC3zr1i1s3bpVrwsb1xlwdf39998qAd/Hxwfff/89q/KJiYn4+uuv0apVKwA1N3FdunTB6tWrNQYbTQxtEeTl5WHixIkq29GzZYxZiY8fP8bOnTtx9+5dlVYym5YcVxRseCQSibB48WIUFxczF3t9ZpFwLS8UCvHPf/4T//znPw2pPrp06YINGzbg/PnzKCwshIeHBz7//HPWawOys7NhaWmpNGunpKQEFRUVWi+eXNcH1cX1QjtgwAB8++23zBTTzMxMxMbG6pxFpFAotOZEY3vRadasGeuxCU1qpwifOXMG7u7ukMvlWL58OVq3bs2qvL29PevFh5rcunULSUlJKCoqgp2dHXx9fZnMDLrY2Njg3r17Sl26mZmZrC/6UqkUTk5OSsccHR1ZL0qtqKhAbGwskpOTUVlZCTMzM/j4+GDixImsA3CHDh3w+PFjJuDpa+LEifjmm2/w66+/oqKiAvPnz2dmJbIVHR0NOzs7zJw50+AWERc0G41HW7duVdsK2bZtGz755BPey8+dO1ftepb58+dj/fr1OstnZGSovUP973//y6qvPDg4GDNmzFCaZv3gwQNs2bIFa9as0VkeAC5evAhnZ2eVc+Tk5KBPnz6szvHw4UOcOXMGeXl5EIvFePfdd1lfaOVyOf7zn//g1KlTTLfFe++9h9GjR2sdP5s0aRLrO29tTpw4gdLSUowZM8bgu2KukpOT8dtvv+HDDz9Ume7LZnA7KSkJ27dvx9tvv810B1+8eBFTp05l1aV7+vRp7Nu3DxKJBE5OTsjLy0N8fDxGjRqFd999V2f5b775Bl27dkVAQABz7PDhw7h27RqrtTLR0dHIzs7G6NGjmXU6Bw4cgLOzM+sbo8OHDyMpKQmDBw9W+QzZjvtwnZU4adIkxMTE6MxJyBdq2fAoNTVVbbD47bffWAULruULCwvVHq+7kE2btWvXqr1ghoWFYefOnTrLZ2dnq6znad26NbKzs1m9PlDTX19/53KRSIQ9e/awDjatW7fGlClTWL9mXY09qHrixAnk5+fj5MmTKgt6daXqqfXFF19g0KBB8PX1Naj+tV2jV65cUXmMzeD2oUOHsHDhQqWxxvT0dGzdupVVsBkyZAisrKxw7tw5XLhwAWKxGJMmTWJ9kZ40aRJWrlyJ+Ph4ODg4oKCgADKZTOMi0PpqB+drPztnZ2e4urqySuRaKz4+HkDN91mXPpMMuM5KdHFxQVlZWYPkQVOHgg0Pbt68CaDmrvjWrVtKA9LZ2dk6Bwi5lj906BCAmrGP2n/XysnJgb29Pav3oa7R++zZM9Yz4qysrFQu0E+ePNFrgLS0tFTlx6HvLJyCggJkZWWpzKTSd9qqPhdqY3UY1L0bN5S/vz8SExOxd+9e9OzZExKJBG+99Rbr8pGRkZxev7S0VCXbQ5cuXVjnl1MoFOjXr5/K96VQKFi19lxdXbFx40ZmrZS9vT169uypNLNLGwsLC5UWhLm5uV7rp7iOZRpjVuLAgQOxfv16DB8+XKV19eabb3KqHxsUbHhQtx+1/qwcW1tbfPTRR7yWv379OoCaYFP7b6DmLqply5aYMWOG1vKzZ8+GQCCATCbDF198ofRYaWkp69lw3bt3x/bt2/HZZ5/BwsICFRUV2LFjh14XOltbWzx8+FCp2+vhw4esV2/Hx8cjJiYGzZs3VwpybO8oDR1UNdaKbH2meWvi5+cHPz8/PHr0CAkJCYiMjIS5uTn8/PwwcOBAnXe6XBc79u7dG6mpqUqfd1paGpOrTpfJkyerbWFPmTKFVQs7OTkZPj4+Kt/3r7/+iv79++ssP3r0aERHR2P8+PFMupy4uDjWmQnqMjT1lDFmJdZm3FD3t8mmhcoVjdnwaMGCBQgPD2+08jt27DCo+ygpKQmA6tiQUChEy5Yt0bVrV1atm7KyMoSFhSEzMxM2NjYoLS1Fu3btsHDhQtathEOHDuH8+fOYMGECk8Vg9+7d8PLyYjWTaMaMGQgMDGTd5VbfkiVLmAHt+j9yY8640ubOnTtITExk7soHDhxoUOqbWo8fP8bGjRuRlZUFoVCIPn36YMKECUoTEY4dO8bMWqzfOq5LU8ur7vT158+fIy0tDR06dGDGbG7fvg0vLy/MmjVLZ30nTpyI2NhYpWMKhYJ1sNE0flabskWd+uuE1GVXFgqFrGdxcU09NWXKFM6zEhsbtWx4xCVQGKO8oeMUtXfTLi4urGcMqWNtbY2QkBDcvXuXSXPSvn17vQa6/f39UV5ejg0bNuD58+ewsLDA4MGDWe1jAtTMJDI00AA1rajly5c32qDqxYsXsXHjRvTp0wdt27ZFXl4eli9fjtmzZ+v1vqqqqnDx4kUkJCTg5s2b6NWrFyZOnAgHBwccOXIEa9euVfp7++uvv5hgU7d1XJ+mYFN3irqpqalSq8LJyUlldpg6tQGrqqpKZe1VXl6ezr2Maqm7n87Ly4OJiYnGMsZcJwSACXYRERFKqae+//57VqmnjDErsa7GGH+klo2Rcb0j5Fo+LCyMyU5cNyNufZoGR0tKSpguKm0TCRpjkNGQH8jWrVvRu3dv9OzZ06DXDA4OxoIFCxptUHXhwoX46KOPlOr/xx9/IC4ujvXNSG1OrRYtWsDPz09lokB1dTUmT56MH374wej156I2S0P97q7a7uBBgwZpnYZf2zqRy+UqLXG5XI53333X4BsyfQUFBalkAJBKpZg3bx7+/e9/6yxvjFmJMpkMP/zwA5KSkpjtPwYOHIjx48frNavNUNSyMTKud4Rcy9fNY2bIoN8XX3zB3IVpG9th08erUChw9uxZpKeno6ysTOkOU5/07LUMuROrrKxEREQE3N3dVdYosRlYbexB1fz8fJVNt9566y3WM9GAmhuIuXPnatySwcTEpEG2FpbJZCqTArTdrddOK3799ddZL0Sua+nSpVAoFFizZo3S1su1wUpbJoz6/v77b/z1118q9WezMFcTfVJPGWNWYlxcHO7cuYMvv/ySyaSwb98+xMXFYfLkyfpU3SDUsiFKanNHAdqz5bIZNI6Li0NiYiJ8fHxw+vRpDBkyBMnJyejXrx8mTZrEqj5lZWXYtWsXrl+/rvJDZxPwtOUwY7NGQtsgMNuAy2UW0YIFCzB+/HilSRXXrl1DbGws1q1bp7N8dXU1wsLCMH/+fIPvXg3JjVZXbSLV27dvqzzGdmBaoVDg9u3bKCoqgr29Pdzc3Fjf4RcXFxuUkr9WSkoKoqOj4erqivv378PV1RVZWVno0qUL6+62yMhIlJeXq6SesrS0ZLVFee04qjpsJ5F89tlnTFLZWkVFRVi8eDG2bNnC6hxcUMvmFaBPptm6d5pcZyGdP38eixcvRtu2bZGQkIAJEybg7bffxrFjx1ifY8eOHSgpKcGMGTOwceNGfPHFFzhy5Ai8vLxYleeajYDrLB2us4hGjhyJsLAw9O3bl1nQeOHCBdZbGpuYmCAzM1Pr+IQuhuRGq2vHjh2wt7fHp59+imXLliEkJAR79+6Fp6cnq/K1e67k5ubC2toaZWVlcHJywsKFC1kNmNva2nLKYHD48GHMnDkT3t7eCAwMxJo1a5CQkIDHjx+zKg9oTj01ffp0VuWNMSvx+fPnaN68udIxkUjEenttrijY8IjrHSHX8lwzzdaeQ920XzbrP6RSKZODTCgUQi6Xo2PHjvjrr79YvTZQ0624bt062NjYQCAQoGfPnmjTpg3Wr1/POjU6l91K6zJkzIhrbrO+ffvCzs4OSUlJuHv3Luzt7bF06VK9Jm74+Pjg5MmTBu9ZYkhutLpu376NqKgoZl1K69at8emnn2L58uWsLqL//ve/0bVrV4SGhsLc3BzPnz/Hnj17sHXrVlbdsXUzGLRt2xb5+fkICQlhncGgoKBA5eZmwIABmDFjBsaPH6+zPPC/1FNFRUVM60zf1hbXWYlvvvkmYmNjMXHiRJibm0MmkyE2NpbTJCB9ULDhEdc7Qq7luWaa3bdvHw4fPoy2bduqXGjYBBs7OzvmAu/o6Ig//vgDLVq00Osuu6qqirnAm5ubo6KiAmKxmPVdpbrdSr///nvWu5VyHVQ1xiyijh07crog3Lt3D7/88gtOnjwJR0dHpe4nNqvoueZGEwgEzGdlYWGBp0+fQiQSsd6G4e7duwgODoaZmRmAms90/PjxrLJoANwzGFhZWaG8vBzNmzeHjY0NHj16hBYtWhiU+dvOzs6gySbGmJU4ZcoUrFmzBoGBgcxSBGdnZ9Z5ArmiYMMjrneEXMtzzTR75swZhISEGLymY/DgwcjMzIRYLMawYcMQHh4OhUKB0aNHsz6Hi4sL7t27h3bt2sHV1RWHDx+GlZUV6wVxu3btgkQiUdmtdNeuXaySGHIdVB0+fDgOHDjAaRYR1wwInTt3ZpXLrq66MxFHjBiBqKgog3OjtW7dGjdv3kSXLl3g5uaG77//Hs2aNWOd0NXJyQnFxcVKz6//f224ZjDo1q0bLl68iIEDB8LLywshISEwNTVVmbihDddeigMHDmD+/PlqZyWyDTZisRjh4eG4ffs2s7DUzc2N8x5bbFGw4RHXO0Ku5blmmgVqNksz1NChQ5l/9+vXD507d0ZFRYVe9Rk7diwqKyuZf2/cuBHPnj3Dp59+yqo8191KL168qDSo6uzsjNatW2Px4sWsgg3XWURcMyAAYL2TY13qZiIamhstMDCQ+fwnTJiAbdu26fUd+vr6Yu3atfjggw8gFouRn5+P48ePw8/Pj0ntBGieHcg1g0Hdcb/Ro0ejVatWKC8v12schWsvhTFmJQI13dkNMYtSHQo2POJ6R8i1fNeuXREWFmZwplk/Pz8kJCRAIpHofK42tSk67O3t9Q58de9I27Vrp/ePi+tupVwHVbnmNjt48CDmzp3LaWHq9evXcf36dUilUohEInTr1g3dunXTWoZrPrS6XF1dmX87OTmxToBZq3Yqfv0ZU/WzAmgKfHK5HNHR0Thz5oxKBoO6i0XZzA7UJ8jXxbWXQiwW4/r160qzEtPT0/Xqoq2oqMCJEyfUtq70/U4MQcGGR1yz5XItzzXT7J07d/DTTz/hxIkTKoOZbP44y8rKsHnzZly7do051r17d8ycOVOvvFAVFRW4fPky0/Tv0aMH62DBdbdSroOqXGcRccmAoFAoEBERgQsXLsDW1hb29vYoLCzE0aNH0bt3b8yfP19j1x7XmYhAzZ4zFy9eVJvLb+/evcyAvS5cZwRqy2CgbTO++Ph4/P7772rHNEJDQ9GnTx+l3UO14dpLwXVWIlATrLOystC7d2+DdwvlgtbZ8IjrOhWu5bnav3+/xsfYdM2Eh4dDIBBg/PjxzIV+z549qK6uZrIc6HL37l2sWbMG5ubmTBJEmUyG4OBgpc20NJHL5Th69CiSkpJUditlM1GhoKAAa9asQU5OjsqgKtvv4MaNGzh37hyKi4vx9ddfIzMzExUVFaxyq3HJgJCQkID9+/dj9uzZSmM2N27cwObNmxEQEKBzEzgAWLVqFfz9/ZVamenp6Th69CgWL16ssdy3336LXr16qU12ef78eVy6dAlz5szR8101nCVLliAwMFDt31lmZiZ27tyJkJAQjeXrjnulp6dz2hMI+N8GdHX/jvWZOBIYGIiNGzc2ypbQALVseMU1IBgroBiaadaQvv66MjIysGXLFqbrwNnZGTNmzNDrbmz79u14//33lXKhHT16FNu3b2e1ARvX3Uq5DqqmpKQgJiYG/fv3V9q5c9++fSr79NSq27XDJQPC+fPnERgYqDI5oFOnTpg0aRJOnDjBKthkZmaqPYe6jfnqun37tsYsFJ6enoiLi9P52oDxMlHom8EgNzdX4w1Nu3bt1C4pqMuY414A91mJIpGoUXborEXBhkcKhQI///wzEhISmCnAfn5+GDZsGKuLFdfyXDPNAjV39ikpKczdVL9+/Vj3E1tbW0Mmkyn9gVdWVup1Z/X333/jgw8+UDr2/vvv4+DBg6zPAei3sLU+LoOqhw4dwpIlS9C+fXv8+uuvAIA2bdrg4cOHGsvU7doRCoXMGg9tXT7qPHjwQON2Dm+99RarnFxATbdrdXW1UktQLpfr3LPnyZMnGqeHm5mZ4cmTJ6xe/8cff2QyUVy+fFkpEwUbhmYweP78OZ4/f662y6miokJl3KM+Y457AdxnJY4dOxY7duzA+PHjWf/+jYmCDY8OHz6MxMRE+Pv7M9Nmjx49CplMxio9PtfyXDPN3rhxA6tWrYKrqyucnJyQlZWFgwcPYtGiRaym0vr7+2PDhg348MMPme10Dx48CH9/f6UuBm0X/bZt2+Lhw4dKffsPHjxg1dcPcF/YynVQtbi4WOXuuHaBqyZcsx7Uqqys1Ng336xZM2aWny7t2rXDiRMn4O/vzxw7efIk3njjDa3lWrRooXE2ZHZ2tsrEC024ZqIwNIOBi4sLrl+/rvZ56enpOrNO1/ZMPHv2DDk5OXjttdcMblkYOitR3VYJiYmJKjerbLdK4IKCDY8SExPx9ddfMz+2bt26oUuXLli9ejWrYMG1/LVr15Qyzbq4uODzzz/HvHnzWNV/9+7dCAwMVBoETUpKwu7du7Fq1Sqd5Wu7g7755hul4xkZGUr/13bR7969O0JDQ+Hn58fMJEpMTMSgQYOQkpLCPE/TD47rwlaug6pOTk64efOmUsvo1q1bOi9UVVVVOHToELKysuDm5gZ/f3+9U84Yazh23LhxWL58OS5cuIDXXnsNOTk5ePz4sc4uLA8PD+zevRsLFixQurjJ5XLExcWhV69erF6fayYKQzMY+Pn5ISYmBiKRCJ06dWKO37hxAzt27GC1zUVGRgbWrl2LiooKWFlZITg42KCuMENnJRp7qwQuKNjwSCqVquzb4ejoiKdPnzZIeXX0WcD1999/q/wYfXx81G5EpY4xuhESExNhYmKCc+fOMceEQiESExOZ/2u7u+O6sPXatWucBlUDAgIQFhaGoUOHorq6Gj/99BN++eUXnWtMdu/ejfPnz6Nz58745ZdfUFFRgY8//liv166oqFDZabUutivgXV1dERERgeTkZOTl5eHtt99G//79daZb+fDDDxEcHIz58+czaXeKioqQlpaGiooKVmNuAPdMFIZmMBgyZAju3LmDf/3rX8zK/9p0MwMGDMCQIUN0vvaPP/4IX19fSCQSnDlzBnv37jUoABg6K7HuJJTaqe+NhYINj9544w0cO3ZMaa3FTz/9xLoLiGv5bt26ITIyUiXTLNttnW1sbHDv3j2lbqDMzEzWF966Exxqx3z0xXXvdq4LW7kOqvbp0wcWFhb45Zdf4ODggPT0dMyYMUPnd3Dp0iUsWbIErq6uuHfvHr799lu9g42u7b/10bJlS73T/Lds2RKrV6/G3r17kZCQwOSW69GjB0aPHs16a29NmSjYTmDhksHgs88+g5+fH/744w+UlZWha9eu6NGjh1JLR5u///4by5Ytg5mZGT7++GOlRJz66Nu3L65cuWLQrMT79+8jLCwMBQUFTALT119/3aB6cEFTn3l0//59rFy5EmZmZnBwcGCm7dZeRPguL5VKsXHjRqV1Lm+99RZmzZqFFi1a6Cx/+vRp7Nu3DxKJhJnbHx8fj1GjRuHdd9/VWb4uTVvz6iMlJYXVYGjd7rX8/HwkJSUZvLA1NTUV165da/BB1fqf15QpU7Bjx44Ge/36+NjPxVCFhYV6ZaK4f/8+BAIB2rRpg9zcXCaDwaRJk3hPQln/e9S2FXV99WclXrhwwaBZiatWrYKlpSV8fX2RmJio19IDY6KWDY9cXV2xceNGXLlyhbmz79mzp9JufXyW55ppdsiQIbCyssK5c+dw4cIFiMViTJo0yaAV1Ma4p9m2bRur11Y32KnPwlZjD6rm5ubi/PnzKC4uxtSpU/H48WNUV1ejdevWrMoDxht/MYS2/VwaQ+0CyfHjx2P37t06n68ug4FCocBvv/3Ge7CpqqpSuvmprq5W+j+g+abHWLMS7927h82bN8PS0hIdQV6GiQAAIABJREFUO3bEl19+qVd5Y6FgwzMrKyuDLs7GKg8YnmkWqPkhcH39hsa1682Yg6rXrl3DunXr4O7ujoyMDEydOhVlZWU4dOiQ0u6R9dUfb3n27JnK+Iu+qXsMZYz9XIxNoVCwnk2nTlVVFTZu3Ahvb28j1kpVy5YtlW5KWrRoofR/bTc9xpyVWDs5QiQSGZSt2hgo2PDgwIEDOp+jrfuBa3k2W/xqm0nEJvW7vmnztV1Y2eJyd183P5uusQJj3rHv2bMH8+bNg4eHBwIDAwHUTCW+d++e1nLGHG/hyhj7ubyquN741GVo2ia5XI5bt24xv5/6/wcaZotzCjY82L9/P1xcXGBvb6/2AqlrZhTX8hkZGXj99dfRp08fZg8QfbBZg6NvvqpOnTrhypUr6Natm0F1AoDY2Fi9yxgjP1tqaioSExOVdnl85513WJXNzc1VydZrbm6uM0+WMXZmNBZj7udCDKMubdOOHTtYpW2SyWQqrfX6/+eaf44NCjY8kEgk+O233+Dk5ASJRIKePXvqNeWYa/kvvvgCZ8+exenTp+Hj44NBgwbpNfvEysoKtra2kEgk8PT0hKmpcf5MoqOjER4ezmk/+Li4OIwYMYL1uNXWrVvRrFkzbNy4USk/29atW1kNkh45cgTHjx/HoEGD4OXlhby8POzatQv5+fms1lnY29vjwYMHShu1ZWVlsd6LxZjqt1jZtk6NsZ+LIeqPbdTFJamlvupOu6/LzMyswfaE4ZK2qSECCRs0G40nMpkMqampOHv2LPLz8+Hr64tBgwaxznfGtTxQs8bk7NmzSEpKgqOjIwYPHgxvb2+dwaOyshJpaWk4e/YsHj9+DB8fH0gkEtZbG9cfYK8ll8uZH6WuwfW6+5TUFRoais8//xwtWrRg1fQPDAxUys8GAOXl5fj8889ZzQqaMWMGFixYoLSvz7179xAWFqaS8l6d+Ph4/PTTTxg5ciRiYmIwY8YM7N+/H/7+/vDx8dFZnouLFy/i4sWLmDlzJoCaxZl1L9ILFizQuYq+PoVCgZSUFDx79gy+vr6sdisdM2aM2uOmpqZwcHBAv379MGLECJW/SzYtbG3dVCEhIRp7ARQKBdLT01ldiGfPno2CggIIBAJYW1sz+dlsbW1RVFQER0dHfPXVVzoX6nIxceJE7Ny5U2ltUXV1NQIDA7W2+I8dO8ZMWT906JDa5wgEAoNzB+qDWjY8MTc3h6+vL3x9ffHw4UOcOnUKs2fPxooVK1jNgOFaHqhZADp27FiMGTMGBw4cQHR0NOzs7FR2LazPzMwM/fv3R//+/fH48WOcPXsWS5cuRevWrTF9+nSdd+VisRhOTk5Kq94VCgXCw8Mxffp0Vut0tO2iGR4eDoDdHRvX/GwymUxlmnmbNm1Y72dTuxfQ0aNHIZfLsX//fgwdOpT3QAMAZ8+eVdrAztzcHJs3bwZQswr+zJkzegcbgUCgNouzNpMmTcLZs2cxbNgwZr3XiRMn4OPjAwsLCxw7dgwVFRUq4z9cxzt0rYVhu1ZGIpGgsLAQ48aNY7aZiIuLg52dHYYMGYIdO3Zg586dWjNgc2Vo2qa//vqLCTbXr1/X+DwKNk1ATk4OM3X4zTffZL2QzRjlnz9/jvPnzyM+Ph7FxcUICAjQe+dNFxcXDBw4EM+fP0d8fDxKSkp0Bpt169Zhz5492L17N4KCgpjXNDExQceOHVnNjOvbty+kUimCgoKUXm/q1KkIDw9nPbuOa342Hx8fHD9+XGlB4/HjxzFgwACdr11dXY2ff/4Z7733HucN6Azx8OFDuLu7Kx2rfZ99+vTROW2Y60SVWklJSfjqq6+UvseuXbti3bp1CAsLQ8eOHREWFmb0yQZcs5bXOn78OCIjI5mxRnNzc3z88ceYNWsWhg8fjsmTJ2PWrFlGeS1NDE3bFBwczPxbnwzZfKBgw4OqqiqmG+rRo0cYMGAAVqxYwbqZzbX83bt3cfbsWaSlpaFz584YNWoUPDw89OpXlslk+O233xAfH4+8vDz4+vpi06ZNrMYamjVrhilTpiAjIwObNm2Cp6cnRo8ezfq1AWDu3Lm4ePEiQkJC8N577yndoeuDa362Bw8e4OTJkzh16hQzMFtcXIzOnTtj5cqVzPPUJeU0MTHB4cOHlRJYGqK8vJxZa+Xv74+SkhLI5XKdAVcqlSp1u9S92AiFQp1Zl7lOVKmVm5urUldbW1vk5uYCqFkHU3+x6ItELpejpKREqQu7pKSEWe/SrFkztWNI0dHRrM7PZooz17RNLwIKNjwICgpCy5Yt4efnhxkzZjB90WwzHXMtv2jRIrz++usYMWIE7OzsUF5ejtTUVKXnaPujjImJQWpqKjp06IDhw4ejV69eBg2AdunSBWFhYYiLi8OCBQtYdz3V6tOnDzp37owdO3Zg8eLFmD59ut45zrjmZ+vUqRPr7hZ1atfXGDqdOjMzE6tWrYKtrS3y8vLg7++P+/fv48yZMzoX51lZWSE3N5cZa6vb5ZKXl6dzkgXXiSq13njjDezevRvjxo2DmZkZKisrERcXx9QnNzeXVUaLxvLOO+9g9erV8Pf3Z244jh07xvyGrl+/rvZGsO5nVVlZidTUVLi5uTETVe7cucN6VqMxp1A3FpogwANNA6J1aRtv4Fpe18CqQCDQehEeM2YMXnvtNdjb22u8uOu7Z/mNGzdw/fp1fPDBBwblGrt48SJ27dqF4uJiREVFGbxItaHt2rULSUlJ6Nu3LxwcHJQ+z7o57zRZtmwZBg4ciIEDBzKpTmoXfNZNZ6JOVFQUTExMMH36dJXHvvvuO1RVVTGTBzQxxkSV7OxsrF27Fvn5+bCxsUFZWRnEYjEWLlwIFxcX3Lx5EwUFBawvvA2turoahw8fRnJyMjP93cfHh5nU8PTpU8jlcq0BMzIyEt27d1caq0tOTsa1a9d0fgdNBQUbHmjbzrmWth8r1/JcadsOupax+sP1IZVK8eDBA3Ts2FHrjDqu3Rf1u9g0YdNa0bbAlk0femBgIHbs2AGBQKCUV2vy5MnYtWuX1rI5OTn4+uuv4ebmhnfeeQd2dnYoLCxEamoqbt++jf9j79wDYsr///9suuhKdNWW2iJFZWVLkpDL7mJ37Vq3LUvshl23RdLadUuixGKTZSUt1p21cuviFpsQHyWSElmaGt2kpmZqfn/0m/OdaaaZM7ea8n78VTOdmXc157zO+3V5Pjds2IDu3btLXQMffqNKSkqKTI0qwP8NEvIv1o6OjipvF1YnZsyYgbi4OBGrhZkzZ7b4f/zhhx9o7eSVbdKmKkgaTQWoix20vCgSSI4cOYLPPvtM4u6FzWbj9OnTMtdxDA0NaV3gFU1fNPeVF2d0xmAwaGmjKVqU7dy5M1gsltBnori4mNbOztLSEmvXrkV8fDx27dpFtZ47OztjzZo1MgUaRRtdGAyG3OlIRR1rlYUibq9GRkbIyckR6gTNycmRKO46ZcoU6msmk4mLFy/Cz88P5ubmYDKZuHz5Mi2bA3WBBBuCUuFwOJg3bx4+/PBDuLm5wdramppAf/HiBbKysnDr1i0hQ7bmrFq1CpMnT5YYWHJycnDkyBGsXr1a5DnBtNFvv/2GuXPnik1ftIRgELly5Qpu3bqFgIAA6iT/66+/aLcM5+bmwtjYWMiXiMlkoqKigtac0NChQ/Hrr79i2rRpAJpqOAkJCRgxYgSt9+/RowdWrlyJ+vp6ys+EPxtTWVkpUUVB0UYVPoq6ncrjWEsnFQ3Qa59X1O0VaGot3rBhA7y8vKibnps3b1ISRuIQbDFftWoVQkJChNQCBg4ciH379tEyUlQHSBqNoHRYLBYuXryIW7duCYk1WllZwcPDA6NHj5Y4vX7v3j0kJCSgoaEBrq6usLa2hp6eHmpra6mApampiW+++UbqFLs86QtBfvjhB2zatElIg6qmpgbBwcG0irZLlixBcHCw0EBscXExoqKiEB0dLfX4xsZGHDp0CBcuXACbzYauri4++eQTTJo0SaG7eg6Hg4CAAIkXy1mzZlGNKh4eHmJTl3Tu7Lds2dKi2ymdXfT8+fOFHGsB4OXLl1i/fn2LKaRHjx5RXxcUFCApKQnjxo2jbhjOnj2LkSNH0upyXLp0KVxdXcW6vcqShXj48CFV9+natSuGDh1Ky14daJpViouLExnqnDlzpsLWHa0F2dkQlI6pqSm+/vprfP3116ivr8fbt29hYGBAa9ocaLIT/uCDD3Dv3j3cvn0b165do+7K7ezsEBgYSFsqRZ70hSC1tbVCqrlA04W6pqaG1vEsFktEecHS0pKW2CnQlH7i/y355mOtRXV1Naqrq5GQkNDilDqdO3tF3U7lcawVTNnFxcUhJCSE+j+4urrCxcUFmzdvphVsFHV75ePs7Ew7uDTHyspK7LyXLKnQtoYEG4JK0dHRoR1kmsMPOoogT/pCEHd3d0RFRWHKlCnUUOjRo0cxYMAAWsfzay6CO7nS0lK5jNhaM9AAyis8K+p2qqhjLZPJFNlJd+vWjZrzkYa8bq8taao1h86A8KxZsxARESE078Vms7F8+XKZ1tSWkDSaimGxWCgsLBTJVdMZvlJEALCkpASHDx9GdnY23rx5AyMjI7i4uGDixIm0Nc6ApjROXl4eXr9+DW9vb9TV1Ql5urcHFElfsNls7N27F2lpaeByudDS0sLgwYMRGBhIS949ISEB+fn5CAoKQvfu3fHq1Sv88ccfsLOzw/Tp08Ues2DBAlp30Yr42dBJoykLRd1OFXWsXbNmDWxsbBAQECAkN1NYWCi25teckydPyuX22twCmr+b7dKlCyorKwE0peHo/h9rampw584dqqNvwIABtAVp1QESbFRIcnIy9uzZAwMDA6Fcr7Q5Fz7yCgBWVFQgODgYRkZG8PT0RLdu3VBWVoaMjAxUVVXRVl4uLi7Gxo0bUV5ejoaGBvz555/IyMhAenq63F7q7RUej0elsWRJp7DZbMTGxiI9PZ16bODAgfj+++9bvNu/fPkyrdeWZkPQkvAiAEqnrTWCzdSpU6mOPnndTgVVFGR1rOV/jktKSqgLvZmZGTXnI42W5tbonsdAkzZeaWkppk2bhk6dOoHNZmP//v0wNzcXSo11ZEiwUSFz585FYGAgPD095Tr+9OnTUgUAy8vLRQQAExISUFJSgkWLFgkVdRsaGrB161aYmprim2++kfr+ERER6NmzJyZMmIBZs2Zh7969ePv2LYKDg2nPsqgDb968QX5+PhWs+dBJXyiLqqoqlJSUwNzcvNXSYYqa6CkLSXNL0lrZ+UXw3bt3K7Sbbus5n9mzZwvpqwFNA7Pz58+XOpzL5/79+8jKyhL5HCvL0VPVkJqNCmGz2XIHGkB+AcCsrCzMmzdPpHtIU1MTEyZMoJR/pfHkyRMsW7ZM6KQ0MDCQWJhVN+7fv4/o6GhoaWmhpqaGasM2NzenFWyqqqoQHx9PneSCyLIr6Ny5Mzp37owHDx5AU1OT9sxJSxdqfkpJ0rxLWwsv8lHE+VRTUxOdO3emdMjkRZE5H2XQ2NiI8vJyIW1BfsaADmfPnsXBgwfh7u6OO3fuYMCAAbh79y4GDhyoqiUrHRJsVIiXlxcyMzPh7u4u1/HyCgCyWKwWc9k9evTA69evab2/np4e3r59K3QnXlZWRnug7/z583BycoKdnR0KCgqwadMmaGpqYtGiRVLdBVuipKQEDAaDtvHXwYMH8dVXX+HTTz9FYGAg9uzZg2PHjtEuWMfFxaGiogJz587F1q1bsXDhQpw6dUrEJrkl1q1bhy+//BJ9+vShLhgMBgOTJk3CuHHjpB4fFhYmdqiUT58+fbBgwQKFDOlUwb///kv9jSSZoNGpXU6cOBG7d+9GQECAXDJFPB4PKSkpyM7OFtkVtBSQIyMjKXM9Sb44dGWbfHx8sH79eowfPx6mpqYoLS0V0leTxvnz5xEaGoq+ffsiMDAQixcvRmZmJm7evEnreHWABBsVwuFwsGXLFvTt21fkYjB79mypx8srACjp4qShoSHxeUE8PT0RGxuLb7/9FkBTOio+Ph7e3t60jj9z5gz1s4cOHYK3tzf09PSQkJBAK8UDNEnP+Pn5wcnJCWlpadi+fTs0NDQwb948Wifqq1evMHbsWACgLjKff/45FixYQOti/+DBA2zatAldunSBhoYG3N3d0aNHD0RHR+OTTz6RenxhYSEl65KSkoKff/4Z+vr6iIqKovX+c+fORWZmJqZOnUpJyx86dAj9+vWDo6Mj9u3bh/j4ePz4449SX0te5GlUOXbsGBVsWqrL0FUpjo2NRWNjI65fvy5Xzeevv/7CpUuX4Ovrizt37mD06NG4evWqxPfu1asX9bUydkQBAQEwMDDAyZMn8fr1ayF9NTpUVFSI2EX079+/XQl0kmCjQhgMBnXCyZMGmDZtGgwNDXHixAkRAUCg6YT46aefRI7j56dbKsfRDTZTpkxBbGwslRP+9ttv4ePjQ9to6c2bN+jcuTM4HA5yc3OxdOlSaGlp4cyZM7SOB5oGPPnBLjExEUuWLIG+vj727t1L60LVqVMncDgcdOrUCUZGRmCxWDKlArlcLrWz09HRAZvNhqmpqdCwqrTjtbS0UFFRgcrKSurCRVdS//Dhw4iOjqZ2YhYWFggKCsLSpUsRExNDOYmqkuPHj8vcqCI4sKroBfGXX35R6Pjr169jxYoVsLOzQ2pqKqZNm4aBAwfi9OnTLR4j+BlXhg6gpqYmvvrqK1r+P+Lo0qULKioqYGxsDBMTE+Tl5cHIyIj2uawOkGCjQhQt3En7gBoYGIh9vL6+XuETFGi6uC5cuBCBgYEoLS2FmZmZTMVtXV1dlJWV4fnz57C1tYWOjg64XK5MJ0hdXR10dHRQXV2N4uJieHh4QENDg/ZQpKOjI27dugUfHx/0798fGzduhLa2Nm0RSSsrKzx9+hT29vawtbXFyZMnoa+vL1HmRRALCwtcvnwZTCaTGix98+aNUKFYEvyh0uZOo/yhUr4TqSppS6fK4uJiVFdXw9bWVmSwky7V1dXUTA6DwUBjYyMcHR3x4MEDWse39FnT0tKSWSOuvr5e5EaDTkrY29sb2dnZ8PHxgZ+fH9asWQNNTU2ZXVPbEhJs2gGyCgAqu52VX9yWlWHDhmHFihXgcDiYOnUqgKamA1nmfLp164acnBy8ePECzs7O0NDQQE1NjZBshyQWLFhABbeAgACcOXMGtbW1tFJYQFPbLofDob7eunUramtrERQUROt4f39/xMTEQFtbm6oBZGZm0q5Zubu7Y9OmTZg8eTKV6xccKn3y5Ant+pW8KOpU2VLKlJ+G8/HxEdtEcPPmTfz6669obGyElpYWlixZIlf9s1u3btRgrbm5Oe7evQsjIyPanyFJlh3a2trw9vbGjBkzJLZiM5lMbN++HXl5eSLP0Tlf+ecPAIwZMwYODg6ora1Fv379pB6rLpDWZyUjKAMv+AFpDp1cszIEAGVF2bLm9+/fh5aWFnUxyc/PR21trZB8jCSuX7+O3377DVpaWggNDUWfPn1w8+ZNXLhwAStXrqT1GuoGl8uFhoYGrYsdm81GXFwcrl+/LjRUOnPmTOjq6qKkpAT19fWwtrZW2Xq/++47rF+/XqhRpaSkBD/99BP++OMPqRpd+/btQ2pqKjw8PKja461btzB06FC8efMGGRkZ+O6770TmhkJCQjB48GCMHj0a58+fx507d0QUuelw9uxZmJqawtPTE2lpafjtt9/A4/EwadIkWiKWqampuHbtGiZMmECt/8SJExg0aBDMzMxw+PBh2NnZSazDRkREQFdXFxMmTMDKlSsRFhaGw4cP48MPP5Q6LyUIj8dDRUWF2jWE0IHsbJRMSEgI9bWiqaxdu3bB3d1drACgJCQN8/FpybhL2bLmbm5u4PF4KC8vR9euXWXuQhs8eDA8PDwAgJqzcHJyoqWYzOfq1au4cuUKKisrsWnTJuTk5ODNmze020bltWUGmuytt2zZIvQY/y6djhCnrq4uvv/+e8ydO1fsUCkdm25FkbdRhU9xcTGCg4OFbjAePHiAM2fOICQkBLdu3cKhQ4dELrolJSUYN24cGAwGxo0bh8TERLnW7+vrSykX+Pj4wNnZGWw2m7b8zOnTp7F27Vpqd29paQkbGxusWrUKv/76K7p37y61zTwvLw8xMTGU6oSNjQ2CgoKwevVqWsFGUMmCwWBQA9bPnj1rE28peSDBRskIdq4oMl8AyC8AmJWVJfT9o0ePRDpqWgo2ypQ1Z7PZiI+Px7Vr1xQ6QbS0tIQkc3R1dWn/Tc6cOYMLFy7go48+wrFjxwA0pQUPHDhAK9goYssMoMU2c0GLb2nweDw8efIEr1+/hqmpKRwcHBQWhZQFeRtV+OTk5Ig0MTg7OyMyMhIAMGDAALE7Zb7/DtD0GRDX5i+JZ8+eITIyEiwWCxYWFli2bBmsra1hYmIi0+tUVFSIDJR26tQJFRUVAJrqctKEWQUlnnR1dfH27VsYGhrSrj0mJCSgsrISYWFh1O6uZ8+e+Ouvv0iweddhs9nIyMhAYWEhamtroaenBzs7O3h6etKe8ZBXALD5XVZgYKBcA36FhYUiYoe2trYoLCykdXxCQgIqKioUOkGaS+Z4e3vjf//7H23JnIsXLyI0NBRWVlY4fvw4gKaiv7jUpDji4+MREBBA2TIDQO/evaUqKPB3lw0NDSI7zeLiYtoXPBaLhY0bN+K///6jpFbee+89hISEqLxWw0feRhU+JiYmSE9PF2qZz8jIoHaGtbW1Yu0LuFyu0N+Ow+GI/C0lWWvv378fDg4OmDVrFi5duoSDBw9SdTNZcHJywo4dOzBt2jSYmJiAxWLh4MGD1A3c8+fPpTYK2NjYIDc3F3369EHPnj2xb98+dOrUifbO9M6dO9iyZYtQXahbt24oLy+X+fdpK0iwUQEFBQWIiIiAtrY27OzsYGBggOLiYty8eRP79+/HTz/9REux1sXFBZGRkTILACoLRWXNlXGC7N27F97e3pRkDgD07duXlhcN0NSJ1DzFI0uZsqioSCTNoauri7q6OonH8XeXDQ0NQjtNDQ0NGBsbY+7cubTePz4+Hg4ODggLC4Ouri7YbDYSEhIQFxcn14VTEeR1qgwICEB0dDQuXrxIpeHy8vKwePFiAE07b3GppF69egn97Zp/D0gONk+fPsX27duhp6cHR0dHWjtRccyZMwfbtm3DvHnzqMf69u1L3ezweDypc3OBgYHUbnTatGnYtWsX2Gw27UYTACK7K76/UXuBBBsVsGfPHnz66adiBfZOnz6N3bt3Izw8XOrrJCcnAwDOnTsn9DjdYThFUYasuaIniKKSOdbW1pS8B5979+7RlqeX15aZv5OMi4vDzJkzab2XOB49eoQdO3YIpWCmT58usUNK2SjaqOLu7o7Nmzfj+vXrKCsrg5ubG+bOnUu1Mg8YMECsZQMdRWZJCPoQGRoaSr1BEEdjYyNYLBZ++uknVFVVUWlEwf8/HeVpwZ+xsLCQuZ7bu3dvnDx5UigjcO7cOZFBT3WGBBsV8Pz58xbbPceMGYOjR4/Sep22ng7u2bMntm/fLresuTJOEEUlc6ZOnYoNGzZg0KBB4HK5VGcX3YCpqC2zIoEGaGqtrampEQraNTU1YtNOqkLeRhWgaWe3bNkyRERESNyFqILmw83ihp2lNZowGAysWbMGCQkJIkFGVlgsFtLS0ijl6sGDB9N2+vzmm2+wdu1aXLt2DWw2G0uWLEFDQ0O76sgkwUYFGBsb4+HDh3B1dRV57tGjRzIPgslK87y2rLluQfT19eUeHFPGCaKoZI6zszPWrVuHpKQk9O3bF42NjVi9ejVsbGxoHT9+/Hiw2WxERESAzWZjzZo1+OSTT2hJ1QBNQ3zHjx8Xq8tFp33c09MTUVFRmDp1KmX+dvjw4VYVYFTEqVJTUxM1NTWt2tDAR9xwc/Pv6ezMrK2tUVpaqlDn37179xAVFQUHBweYmZnh2bNnOHr0KIKDg2kZBJqamiI6Ohp37txBSUkJzMzMMGDAgHblK0XmbFTAlStX8Mcff2DgwIGwt7enlIafPn2KjIwMzJo1C76+vmKPVYYAoCLS8qdPn6bSf5JaqOkGKw6Ho9AJUl9fj9jYWNy4cYN6zMfHB7Nnz271E00eW+Zdu3YhNzcXo0aNwoEDB+Dv748LFy7Ax8eHVkdffX094uPjcfXqVXA4HGhpaWHo0KGYMWNGq/3+YWFhmDlzpsyNKnxOnz6NiooK+Pv70x6kVCfOnj2LS5cu4bPPPoOZmZnQOUm3BX/JkiX44osvhNLfaWlpOHHiBDZv3qz0NasjJNioiEePHiElJQXPnj2jutFsbW0xYsQIicJ+J0+epHSZJKXbVNXuGBERgdDQUACSg1Zry9dXVVXJJJmTm5sr9WdkmdWRl9mzZ2Pt2rWwsLDAjBkzEB8fjxcvXiAuLk6mHZ685m3KQF6nSj4LFixAaWkpJe8iWH9TxG2ULsXFxWJVK7Kzs2kNF0+ePLnF5+gOV3/zzTeIj48X+t0bGxsxY8YMJCQktHicIjNz6gZJo6kIJycnudRilSEAmJOTI/eMDz/QAPIFFGXvjPjIKplD50Iu6UJBx5qZzoWSzWZThXD+rIi1tTXy8/OlHsunredsFG1UUfRimJ+fL3YYOC0tjdb7b9iwAevWrROypH78+DG2bNmCPXv2SD1eGWodffr0QU5OjlBwo3OeKjIzp26QYNMOkLXldOPGjULSIcuXL8eGDRtkft/09HS4urpKnaMQ5MGDB1SwaX6iCCLpBFFGKrH5BUJQRogOzde3Z88eqvVaFszNzfHixQtYW1vjvffeQ2pqKgwMDGg3WajDnI2ijSqyyLGIY9OmTVi7dq1QMf3WrVu0lb+HDBmCjRs3YuXKldDW1qaGPb/77juF1iUNwZstS0tLREVFwcPDg6q93bomH7SCAAAgAElEQVR1C35+fhJfQ1kzc+oACTYqoLq6Gjt27MDDhw/Ro0cPBAYGCrXaTp8+vUUdKUEePXqE2NhYmVtOm2dGmUwm/cU3e4+tW7fCzs4Orq6ucHV1hZOTk0TFYkV3RoDyvUTkofkFct++fXJdNL/44guwWCxYW1tjwoQJiIqKApfLpRoepKFOczaKoIg196RJk7B+/XqEh4dDX18fWVlZ2LlzJ21rhQkTJlBCmFOmTEF4eDgCAgLg5eVF63h5zNcA0Zste3t7vH79mlKVsLe3pz0g3REgwUYFHDhwAA0NDZg/fz6ysrIo2Rf+lplumez333+Hl5cXhgwZIlPLqbJSLFu2bEFZWRmysrKQlZWFmJgYVFdXw8nJSapDoSLe8fxUYmNjI/r37w87O7tWbfVVJoJdc25ubti7dy+4XC7tWaO2mrNRplOlotbcw4cPR0lJCSIjIzFx4kRs2bIFCxculOlGZPbs2Vi/fj1CQkLg7+8v042DPOZrgPrYcqsL7fMMVnPu3r2LqKgoGBkZwd3dHb1790Z0dDRCQkLg6OhIOxiUlZVhypQpMgcPvvClYFBr/j3deYFu3brB09MT+vr60NPTw7Vr11BUVCT1OGV4xwvOOHQUtLS0ZAqcbTVno8zdpaLW3EBTkX779u0IDw/HokWLpErr//777yKPGRgYQE9PD8+ePaOep+OYK4/5mjgaGxuFNP7q6uqENNM6OiTYqIDa2lqhOoeXlxd4PB42btwok8GUm5sb8vPz0bNnT5nev66uDnPmzBF6rPn3dIqeR48exf379/Hff//B0dERrq6uWLt2LXr06EFrHYp6xwPyzzg070aTZ6BPXug0FwD0Ggzaas5GmU6V8lhzi9tNcTgc6Ovr4+LFi7h48SKAlndW4m5ydHV18cEHH8h8A6So+Rogv8afMmfm2hoSbFSAqakpnj9/LlSnGTRoENhsNsLDwykzLmnMnj0bERERcHBwEGk5lfQBo+s1I41jx47B0tIS06ZNQ//+/WUeRlXUOx5oKu5GRUXJPOMgrhtNloG+5gGjtrYWCxcuFPqZloKFMk9+f39/xMfHY8OGDUJzNv7+/kp7D7rIq40mjzV3S7spuj5IirrkCqKo+Rogv8Zf87qPrPpw6gQJNirA3d0d6enpIvpbw4cPR21tLa3mAAA4deoUCgsLwePxRGo2kj5gdCUwpLF9+3bcv38fd+/exYEDB2BsbAw3Nze4ubnRmnpWhjU1/28lLoBKChaKtqsqcgIr2n0liI6ODoKCgvDdd9+12ZyNotpo8lhzq2KOTN5gOWrUKBQUFMDU1BRjx45FVFQUZb5GF3k1/jpS3YcMdaox06dPR1hYGO20lSphs9lITEzEP//8g9raWpU5hXYUfv/9d6F6wJMnT2ROh6oLS5cuhaurq1htNDo3NvX19WhsbISuri7q6+uFrLm7dOlCaw3NdcV8fHxot34r2/H29evXMpmvAcC8efOwfv16dO7cmWrDLysrw5o1a1plsFUdIDsbNUZfX19uiRBlkJeXh/v37yMrKwt5eXkwNjaGp6cn3NzcaL+Goi6ZfNqbHe6NGzeEgk14eLhMcz7Z2dm4desW5aHj7+8vZB62atUqhc356KKINhogrPyto6Mj867x0aNHCA8Ph62tLSwsLFBYWIjjx4/jp59+grOzs9TjFRESFYes5muA4hp/HQESbNSYcePG4eTJky2aVqma9evXo2/fvhg0aBCCgoIkWv+KQ1GXTKD92uEqmjC4ePGiUGuttrY2lZbMy8vDuXPnWi3YyGviJ8jjx4+Rn58vksaiE3j279+PwMBAoQHIy5cvY//+/bSsOhQNlhUVFThy5Ajy8/PBZrOFnqO7K5kyZQpiY2OpWtK3334LHx8foUaMjg4JNmrMhQsXUFpaijNnzoikG1pj671nzx6Rwr4sKOqSCbRfO1xF6yoFBQVCRW4NDQ2qaG5vb091Y6mKtLQ06mtFTfyOHDmCkydPws7OTqTdmU6w+e+//0TqYL6+vrRrn4oGy5iYGLDZbPj5+cm9M9LR0cHChQsxc+ZMSpRWVlHX9g4JNmqMMrpMWCwWCgsLRe7I6Fwk+G2er169EpmcpnNXrahLJtB+7XAVtTR+8+aN0IVZcIhTR0cHlZWVSlytKOK6BeXVRktKSkJYWJjcNasuXbrg6dOnQvpoBQUFtC/WigbLx48f4/fff1eKKyaHw0FDQ8M7F2gAEmzUGkW7mpKTk7Fnzx4YGBgI3ZHRvUgUFBQgOjoaLBZL6HEGg0GrdVlRl0w+irh9nj9/Hk5OTrCzs0NBQQE2bdoETU1NLFq0SKy4Y3N+/PFHbNmyReTxJUuWIDo6usXjFLU01tHRoYrhAPDhhx9Sz71+/Vrlg4DKNu6zt7eX+9gxY8YgIiICI0eOhIWFBUpKSpCcnEw7vayokKiJiYlQvUweKisrsW3bNmRnZ0NHRwd//vknbty4gZycnBali+hYhQDtp2ONBBs1RxFNqePHj+PHH3+Ep6enXO+9b98+eHh4YPLkyfj+++8RGxuL/fv3054oV9QlE1Dc7fPMmTNUEfbQoUPw9vaGnp4eEhISaJ3MfB2r5pSVlUk8TlFLYycnJ1y4cAFff/21yHMXLlxoM804oElsVVNTk/Ya/Pz8kJqaipEjR8r1fqNHj4a+vj6uXLmCmzdvwtTUFNOnT6dtjS5P4BT8/44fPx4xMTGYOHGiyM6I7rDy3r17YWZmhj/++IMa4nRxcZF40yZovlhVVYWUlBQhIc/bt2/TdoxVB0iwUWMU1ZRis9lyBxqgyd76559/hra2Nng8HnR1dREQEIDg4GBaJ7qiLpmA4m6fb968QefOncHhcJCbm4ulS5dCS0sLZ86ckXgcP+XV0NAgkv4qLi6WqyNJFr744gv88ssvqK6uhre3N7p164aysjJcv34dV69epepXrcG6devw5Zdfok+fPjh79iwOHjwIBoOBSZMm0VIA4PF4OH36NM6dOyfSTUhHWw1oSnfRDS7KYO7cuSKPZWZmijxGt3X6wYMHiImJEdqRdu7cGVVVVS0eI7jz3bhxI4KDg4U6Qe/fv4+zZ8/Sen91gAQbNUZRTSkvLy9kZmbC3d1drvcXnJDW19dHZWUl9PX1UVFRQev4kpIS2NjYYObMmUKP803Q6KCoHa6uri7Kysrw/Plz2NraQkdHB1wuF42NjRKP46e8GhoahNJfGhoaMDY2FnsxUib29vYICQnBH3/8gZSUFOpxc3NzBAcHK5SWkpXCwkJqADMlJQU///wz9PX1ERUV1WKwab7rUXQnJuucjaJCospS4eCjpaUl8pmrrq4W8tiRRE5OjojKtYuLi8RUrrpBgo0aI4+mlCAcDgdbtmxB3759Re4o6QgQ2tvb4/79+xgwYAD69OmD3377DTo6OrC1taW1/uDgYLEdQ8uWLaM9c3L16lX4+vqKyMFfu3YNQ4YMkXr8sGHDsGLFCnA4HEydOhVA04ClOOdGQfh58Li4OJFg2Vq4ublh27ZtVIOGkZGRzO3nyoDL5UJLSwsVFRWorKykAoeku3JldgrKM2ejqJCoslQ4+PTr1w8JCQlCn6UjR47QvhE0MTHBjRs3hHZ36enpcmsOtgUk2Kgx8mhKCcJgMDBo0CAA4oUJpTFnzhzqbmz69Ok4ePAgamtraetOies843K5MrUF79mzB76+viKPx8XF0Qo2U6dORd++faGlpUV10Glra2PatGlSj21oaMCVK1cQEBDQpsq83bt3R/fu3dvs/S0sLHD58mUwmUxKm+zNmzcSfY0A4OnTp7h58yamTJki8hxfTJROs4g8czbKEBJNTk7G7du3xdYYN2zYAE9PT6nmZ3wCAgIQFRWFwMBAcDgczJgxA3Z2drQ9eQICAhAdHY2kpCSYmZmhtLQUT548weLFi2X6ndoSEmzUGHk0pQRRRIywoaEB165dwyeffAIAMDIyorUbAv4vbcHhcLBu3Tqh51gsFt5//33a6xAXsEpKSmQSQWyueECnCw1Qjk1CR8Df3x8xMTHQ1tamUlOZmZlS/45///23UCeiINbW1jh16hQWLVok9f0VnbPhI6s22uXLlykFh+ZMmjQJe/fupRVseDweGhoasGrVKhQWFqKkpERme293d3ds3rwZ169fR1lZGfr164fvv/+eshxvD5Bgo8YsWLCA2lkEBAQIaUqpGk1NTZw8eRKff/65zMfy0xY5OTlCysz8egd/tyUJfsqrsbGR+ppPY2MjPvrooxaPVWbLqDJsEto7bm5uIv4wgwcPxuDBgyUel5eX12Jt68MPP8TBgwdpvb+iczbyaqMxmcwWA6q9vT3t4WQej4fvv/8eCQkJsLe3l7veZmFh0W4UnsVBgo0aI4+mFF/kD4DIRVoQOnMyffv2RU5OjsyyKPy0xXvvvSe39tMvv/wCHo+HiIgI/PTTT9Tj/IAlKa2kzJZRZdgkdBTq6+tF6jSSivRv3rxpMf2ora2NN2/e0HpfReds5NVGq6urQ11dndhj2Gy2yKB0SzAYDFhYWKC6upq28Kg4rl69iqtXr6KiokIhncG2ggQbNaSmpgbPnz+ndggnTpwQ6mT55JNPhMzZBAkJCaG+VlTi38zMDJGRkfDy8hLxkqET+I4ePSo22EgbiAT+T6Fg5cqVYtOGkjralNkyqgybhPYOk8nE9u3bkZeXJ/KcpJ2BkZFRizIxr169avEz3BxF52zk1UazsrJCVlaW0EAtn+zsbJmaNcaNG4dt27Zh4sSJIucSnR2zMnQG2xoSbNSQ8+fPo6amhgo2J0+epC645eXl0NTUbFHAT7DzRlGhxmfPnuH9998Hk8kEk8kUeo5OsGlp8FHaQKQg4eHhCnW0Kdoy2lpil+pMXFwcTExMEBQUhJUrVyIsLAyHDx8WexEW5IMPPsD+/fsRHBwstCtsbGzEwYMHW6zniEORORt5tdH8/PywZ88eGBoaCp1Xjx49QlxcHMaPH0/7tfhpyOzsbJHn6MzqKENnsK0hwUYNuXnzppArpJaWFnWH/fLlS2zbtk2iWmxiYiIGDx4ss7Nmc+SVweAPQTbXBwNkH4hUtKNNGS2j6enpSE1NpWY8/Pz8RFqxOzJ5eXmIiYmBnp4eAMDGxgZBQUFYvXq1REmliRMnIjQ0FEuWLIGXlxc1mJqeng42m42IiAjaa2g+Z+Pt7S3RKlwZQqKjR4/GkydPsGrVKnTr1o1af1lZGYYOHYrRo0fTXr+iczvK0Blsa0iwUUPKysqEPljW1tbU11ZWVi1KqPBJSEjAgQMHMGDAAIwePVqohiEL4eHhWLFihcjjERERCA0NbfE4ZQxEKqujTdGW0eTkZBw8eBAjR46El5cXiouLsXv3blRVVcl0sWnPaGhoULUXXV1dvH37FoaGhiKaec0xNjbG+vXrcfjwYaSmplJOo/3798ekSZNo3wzdu3cPUVFRcHBwgJmZGZ49e4ajR48iODi4RcdYZQmJfv/99/Dz88Pdu3dRVVUFFxcX9O/fX+bZHUXndpSlM9iWkGCjhrDZbNTX11MnuKA0SV1dndTCZKdOnbB06VIkJSUhIiICpqamGDlyJIYPHw4jIyPa63j8+LHYx8Xl7gVRxkCkMjraAMVbRhMTExEaGio0JOjp6YmYmJh3JtjY2NggNzcXffr0Qc+ePbFv3z506tRJ4s6CT9euXTFnzhyF3v/PP//E3LlzhQJDWloaEhISWgw2yhQSdXJyUooWHd+IsLnOIZ0RBWXoDLY1JNioIZaWlnj8+DE1QCfI48ePpZ7kGhoacHNzg5ubGyoqKpCamooLFy5Qg3SjR4+WePLwUxANDQ1C6QhAtsKuIpP3yuho46NIy2h5eblI+6u9vT1tyZ6OQGBgIJW2nDZtGnbv3o3a2loEBQW1yvuXlpaKfAa8vb2xa9cuicdNnz5d5lkcVcHXlHN3d6d2KHfv3qVd3FeGzmBbQ4KNGjJ48GDs3bsXISEhQoGlpKQE8fHxMhVKjY2N8eWXX+KLL77A3bt3kZycjDVr1khs2+U/x+FwhH6OwWDA2NiYdhCpr6/H8ePHkZ2dLXI3RzeHzb/IyNp2K4giNZf33nsP165dExI+TUtLa9OJ/tZi8+bNcHNzg4uLCyXvY2FhQVs8U1n06dMHOTk5QjdfdFry1ammcf78eYSGhqJv374IDAzE4sWLkZmZiZs3b0o9tqGhAZGRkViyZEmbSScpAxJs1JBx48YhKysLixcvhoODA1WYzM/Ph6OjIz799FOJx4s7yTQ0NODu7g53d3ep3WD8FMTGjRuFWqllJT4+Hrm5uRg1ahQOHDgAf39/XLhwQaZgKW/bLR9Fay7+/v5Yv349UlJSYG5ujtLSUhQUFEisWXUUdHR0cPz4cezevRumpqZwdXWlgo+qzb8EG0ssLS0RFRUlNCt169YtqdP7irqlKpOKigoRW4z+/fvTSvdpamqioKBAJtUMdYQEGzVES0sLK1asQFpaGu7du4eqqip069YNI0aMgI+Pj1SrZmmaYXQ7sRQJNECTy+batWthYWGBQ4cO4eOPP4aLiwvi4uIwYcIEWq8hb9stH0VrLn369KFqPq9fv8YHH3yAH374gVa9or0zb948AE0dkFlZWcjOzsaePXvw9u1b9OjRA25ubggICFDJezc3mrO3t8fr16+p5hh7e3sUFhZKfI36+nrs2LFD4s8oIukkC126dEFFRQWMjY1hYmKCvLw8GBkZSVUf5+Pr64vz589TwrztERJs1BQGgwFfX1+xIpTS+O677+R+3+ayJC1BRyeNzWZThXgtLS1wuVxYW1sjPz+f9nrkbbvlo4yai7m5ucRW846OlZUVrKys8NFHH4HL5SI5ORknT57EP//8o7Jgoyz3SWk3ZpLIzs5Genq6WCfNPXv2YNCgQbTnsLy9vZGdnQ0fHx/4+flhzZo10NTUpCUmCzSJmp49exbnz5+Hubm50K6ttdOa8kKCDUEIZYpOmpub48WLF7C2tsZ7772H1NRUGBgYQF9fn/ZryNt2y0cZNZcbN27g0qVLKCsrQ7du3TBs2DCpumAdiefPnyM7OxtZWVl49OgRTExMMHDgQLENLOKYPHmy2Me1tLRgZmYGHx8fjB8/Hlpa4i9Hubm5MDY2FuogZDKZqKioEOpUbI6Ojo5CnXBnzpxp0V20f//++Oeff6QGm7i4OIwaNUpIOmrMmDFwcHBAbW0t+vXrR2stzs7OLdoptBdIsOmAVFdXY8eOHXj48CF69OiBwMBAoX58SV06ykwrfPHFF2CxWLC2tsaECRMQFRUFLpfboue6OBRpuwUUr7mcOnUKiYmJGDFiBAYNGkQ1aZSWlso0Qd4e2bp1K7Kzs2FgYIA+ffpgyJAhmDt3rsz1munTpyMlJQVjx46lZp3OnTsHX19f6Orq4vTp02Cz2S3uknbt2iWiAsHj8bBr1y6Vmoc9e/asxdZqNzc37N69W+prFBUVITg4GL169cKoUaPg5eUFHR0diUFSHMr0B2orSLDpgBw4cAANDQ2YP38+srKysGrVKoSEhFB3Ya3VpSPYrurm5oa9e/eCy+XSdhoFFG+7VbTmcuHCBYSGhgop9Q4cOBCRkZEdPtjcuHED1tbWGDJkCFxcXGSSxBfk8uXLIp2VLi4u2LRpEyIjI+Ho6IjIyMgWgw2LxRIxu7O0tJS6u1X0c/727dsWf18NDQ1UV1dLfY1Vq1bh1atXSE5Oxp9//on4+Hj4+vpi5MiRQsPa4uDXeADJEk/tRY2cBBs1hO+M2BL//fefRJ2nu3fvIioqCkZGRnB3d0fv3r0RHR2NkJAQODo6Sr1giFOL1tLSgrm5OYYPH46xY8fKfNHhcrkoKiqS2X9D0BVU3rZbRWou9fX1Is6kPXr0QH19vVyv157Yu3cvHjx4gKysLOzYsQPl5eVwdnaGi4sLXFxc0KNHD1qvw2QyRS6IXbt2pfT2bG1tJbp+du7cGSwWS6jVvbS0VKqlckJCAq31tUTXrl3x7NkzsZYAz549o62A0L17d0ybNg1Tp05FRkYGUlJSsGTJEvTu3RsjR45ssS67cOFCKgMhSXWDTlemOkCCjRqybdu2FuVUmEwmwsLCsHPnzhaPr62tFRq89PLyAo/Hw8aNG8XKzzRHnNIxl8tFcXEx/vnnHwCQ6KnD4XBw/PhxPH/+HM7Ozhg+fDh+/vlnvHr1Crq6uggNDZVrIvu///7D3bt3YWdnR7teAChWc/H19UViYiI+++wz6rHExEShGlBHRV9fHx4eHvDw8ADQdKfNr92cOnUKPB6PVirp/fffx/79++Hv7w9tbW1wOBwcPHiQSu0ymUyJyhYeHh7Yvn07goKC0L17d7x69Qp//PEHPD09lfJ7SnrfvXv3IjQ0VKjOWFNTg/j4eJn18bS0tODt7Q1vb28UFhYiOjoaMTExLQYbwRShotpq6oAGT50mnwgAmiT4XVxcRFwCX79+jVWrVsHFxUVi4XPJkiWYP3++iG7SpUuXsH//ftTW1tI2rmpOYWEhtm3bhs2bN7f4M7t27UJeXh769euHu3fvQl9fHw4ODhgxYgRSUlLw7Nkzqd1GxcXFiImJQVFREZycnDBx4kSsXr0aOjo6qK6uxuzZs2m5JArWXPgzGvz6AZ00WFhYGHJyctCtWzeYmpqCxWJRd/jtsSNIXurr6/Hw4UMq2BQWFkJPT4+W8varV6+wceNGlJaWokuXLqiqqoKpqSmWLVsGKysr5ObmgsVitXgDwGazERsbi/T0dOoxLy8vzJ07V6aUrKzU1tbil19+QXl5Ofr370/Nu927dw9du3ZFWFiYzO//5MkTJCcn499//4WhoSFGjBghUd0iMzMTffr0Uenv2VqQYKOGsFgs/PLLL/j4448pp8yKigqsXr0a77//vpAitDgOHDgATU1Nsd7vZ8+exb59+xTaes+YMQPx8fEtPj979mxs2LABXbt2RWlpKebNm4d9+/ZBV1cXdXV1mDt3LuLi4iS+x4YNG2BoaAhvb2+kpaXh4cOH8Pf3h4+PD27cuIETJ05g06ZNUtc6d+5cBAcHC6VCnj59isjISMTGxko9/ujRo1J/BugYBdzm5ObmUvM1eXl50NDQQO/eveHq6ipzDaexsRGPHz+mdpeOjo4ytyVXVlaitLQU5ubmKh8q5cNms5GYmEjNu/GFRMeMGUM7ANTU1ODq1atISUnBixcv4O7ujlGjRqFfv35S/37+/v7g8Xjo2bMnXF1d4erqKtffTh0gaTQ1xNTUFKGhoZS0ef/+/bFu3TpYWVlh/vz5Uo/39/dv8bkxY8ZgzJgxcq+toqJCqjYam81G165dATSp3erq6lInZqdOnWi1V+fl5SE2NhY6OjpwcnLCzJkzqYYDLy8v2vNAitZcOmIQocuqVavw/vvvw9XVFRMmTICTkxO0tbXlei0Gg6GwmGWXLl0UcrqUB11dXUyYMIH2EHJzfvvtN9y8eROdO3eGn58f/Pz8qHODDnv37sWjR4+QnZ2Nu3fv4sSJE9DR0YGzszPc3Nzg6urabvTRSLBRU3r06IGlS5ciMjISJiYmMDExweLFi9vsjobH44HJZCIhIYH2bAAfedbM5XKp+Rp9fX3o6elRryPL68lbc2loaACPxxNq1Lh8+TIKCwvh7OzcbtwRFWHPnj20RVclwWazce7cOeTn54soltNJPypDY09ZSLPXaE5NTQ0WL16MDz74QK5OPh0dHUpUl/96OTk5yMrKwt9//42EhAQcOnRI5tdtC0iwUUMElZb79euHrKwsjB07VihnLa9rIR3EdaPxeDzweDy4ubmJfV6Q+vp6IQ8aNpst9H1rdnI9f/4c58+fx4ULF0RqLoJran7R27JlCz744ANqqO/48eM4duwYbG1tkZSUhFmzZtGqGbVnlBFoACA2NhaFhYXw8PBAp06dZD5eGRp7yuLRo0cy/fyyZcuU9t7V1dV48OABVTerr6+Hu7u70l5f1ZBgo4Y0V2TW19cXEiakY/qkCOK60TQ1NWFmZkarp795wbP5ABudgTY2my0U1BobG0W+p4O8XiQFBQVCCrvnz5/HnDlzMHToUKSnp+PkyZMdPtgoi/v372Pr1q1y11mUobGnLFq7xP2///0PWVlZyMrKApPJRO/evdG3b18sWLAA77//vlqJjUqDBBs1RJnGT/JAV++pJZRR51CGNlZDQwPee+89eHh4yFxrePv2LRVYX7x4gZqaGsqwzdPTU6qXCuH/MDQ0VKibShkae8qCrpaZsli/fj1sbGwwceJEfPjhh+1a+ZkEm3bE27dvlZbaUHcUDXhA025s586dcpmvderUCWw2G7q6uigoKICNjQ1VQ+LxeErVkOvoTJ06FXFxcQgICJA6iCkOZWjsKQtFRG7lYfr06Xjw4AF27tyJrl27UgO1ffr0ketv2ZaQYNOOWLhwIaKiomTqZnnXcXBwwLNnz0Q60qTh5OSEQ4cOYdSoUUhKShLSyHr58uU7+T9oLg9D17xu+/btaGxsxKVLl0SaOySZ+PFRhsaevLx8+RIvXryAg4MDTExMcPToUWRkZMDW1hYzZ85UecDjd482NjaioKAA2dnZuHjxImJiYmBpaQlXV1eVKW8rGzJno4YIFq4FycnJQa9evaCtrd3hhwiVxbFjx5CamoqRI0fC1NRU6GInqe5VUlKCiIgIvHz5EjY2Nli9ejV1J3nw4EFUVVUppCjcHsjIyEBGRgbla+Pv7w8ul0s9HxwcTMtXKCcnp8Xn5NnBcrlcmTX25CEtLQ0xMTEwMDBAXV0dxo8fj7S0NLi7uyMzMxNOTk60rDaUzYsXL5CZmYnExERUVFQQuRqC/OTk5MDZ2VmksJ2bmwsHBwfK24UgnUuXLkFDQwMpKSlCj0trsjA3N8eWLVtQXV0tkq74/PPPJWrXdRRSUlKEZrJ0dHSwfft2AE1dWUlJSbSCjTJSoo/OPAYAABy5SURBVIJoaWm1yt//5MmTWLJkCT788ENkZGRgy5Yt2LJlCywtLTF69GisXr1a5WsAmnaU/A607OxsVFRUwMrKCh4eHlRLdHug458x7ZCIiAjs3LkTlZWVCAgIoO7gzp8/j3HjxrWqyiuLxUJhYaHIfIQ83XD8Serk5GRa0/+SEFTElYSizRbi8uLvSt2sqKhIxMqY/9nz9PTE/v37Wzz233//pRoqBFv5myPpc8TfUUlClXM2LBaLCqYffvghGAwGpT5tYWGBt2/fquy9+SxYsABMJhPGxsZwcXHB1KlT4erqChMTE5W/t7IhwUYNsbW1RXh4OE6ePIlly5ZhxowZbdJPn5ycTA32Cc5HyNp6nZeXh6SkJPz777/Q1tbGqFGj5F7T/fv3kZSUhDt37sit7yaNH374gVZLaUcQR5REdXW1UPeTYIcgg8HAmzdvWjz22LFjVLBpqS4j7XNUWloKa2trDBs2jLbCsqpgMBhUgwif1mg7/vjjj+Hm5ibVjqA9QIKNmsJgMDBhwgR4eHggNjYWaWlprd4Bdfz4cfz4449yqevW1tZSu5jnz5/D3t4eGhoaiIyMpF1Y5lNZWYlLly4hJSUFJSUlcHV1xfLly1v8+cjISGqYLiwsrMWLQkt1L0FNOSaTiYsXL8LPzw/m5uZgMpm4fPkyRo8eLdPv0B7R19cHk8mk7uYFhV1LSkokFscFFYvl3V2Gh4cjOTkZJ06cQN++fTFixAi5J/HlgcvlCu3KGhoaRL5XNYpIS6kbJNioOT169EB4eDhOnTqF8vJyubWp5IHNZssVaHbu3IkbN25AR0cHPj4+mD9/Pnr06IGgoCCZ1p+VlYWkpCTcvn0bhoaG8PX1RWpqKubPny9RI6tXr17U1/IMdArOUvCN5xwcHKjHBg4ciH379rX6QGFr4+rqilOnTolthDh16hRcXV1V+v49e/ZEz549MWPGDKSlpeHIkSPYvXs3/Pz8MGbMGJV3ghkbGwvtyoyMjIS+b22dtvYOCTbtAAaDgS+//FKiFLkq8PLyQmZmpswpvEuXLsHQ0BCBgYHw8vKSaxBtwYIFKC0tRf/+/bFo0SIMGDAAmpqauHr1qtRjBY3SWhowFeyqkkRhYaGIVYOtrS0KCwtpHd+emTBhApYvX07J/3fr1g2vX7/GjRs3kJeXhw0bNtB6HUW10XR1dTFy5Ej4+fnh2LFjOHbsGJycnGTyNJKHth6u7miQYENoEQ6Hgy1btqBv374icyWSWj7XrFmD5ORkxMbGIj4+HkOGDMGwYcNkSn9UVVVBV1cXpqamMDc3V9rkNN+i98qVK/jjjz+k/ryVlZVYIc/u3bsrZT3qjKWlJdauXYv4+Hjs2rULjY2NYDAYcHZ2xpo1a2j/DRTVRispKUFqaiouX74MMzMzzJkzh5bkkap4l4arlQmZsyG0yI4dO1p87vvvv5d6/Nu3b3H58mWkpqbixYsXAIBvv/0Wfn5+UoNHfX09rl+/juTkZDx58gR2dnYYPnw4jh49is2bN8uUwuByucjIyEBSUhJycnJgZWWFsWPHUiKbknjy5AkiIiKowMdiscBms7F8+XKhdF1Hp76+nmoD5xfKKysraf0fAgMD5dJGS09PR0pKCp4+fQofHx+MGDGiVeX0//33XxgYGFDtxUVFRZQJnI2NDUJCQmBmZtZq62nvkGBDaBUePnyIpKQkZGRkwMDAgLYfDdCUykpOTkZaWhpqa2sxZMgQfP7551IvPMXFxdQupqamBgMGDKBEIWUJVjU1Nbhz5w5l/DVgwIA2kUpRJzgcDgICAmgNFM6fPx/R0dEi3VzSmDx5MqytreHp6dlirU+VqeXQ0FBMnz6dqvutWbMGGhoaGDt2LJKSkmBgYEDLX4rQBEmjEVoFZ2dnODs7o7q6GleuXJHpWDs7O3z77beYNm0a0tLSkJycjKVLl0q80IWFhSE7Oxs9evTA+PHj4evrCyMjIwQFBcm8dn19/VYXYOxIyKuNxrfeliTrr8pgw2QyqcaQmpoaPHz4EJs3b4aVlRXs7OyIioeMkGCj5ihzqJIOgYGBlK+8JN8aOppW4igvLweTyZTr2E6dOmHEiBEYMWKE1AJ9dnY2DA0NMXToUAwZMgRGRkZyvSePx8P169dRUFCA2tpaoefaQqqkPSKvNlprTei3RENDA7WjKigoQOfOnWFlZQUAMDExaZWhzo4ECTZqjLKGKmUhJCSE+lqcr408cDgc3LhxA8nJyXj8+DEcHR3lfi26KgQxMTFITU1FYmIiDh48CHd3dwwfPlxmP5Ldu3cjPT0dLi4uKtfi6qgo63PU2piamiI/Px8ODg7Izs4Wakp48+aNXM0O7zIk2KgxigxVyovgXIqimlYvXrxAcnIyrl69irq6OvB4PKxYsUIuPSdZVQhMTU0xadIkfPXVV7h9+zZSUlIQFRUFHo+Hc+fOYcyYMbQK1unp6Vi/fj012PguIWjY1xy65nV8xH2WHj58KPOaWpOPPvoIkZGRcHZ2xu3bt7F48WLquQcPHnSIqf7WhAQbNUbeoUpVsX37dgQGBkrNu/N3Hrm5ubC1tcXEiRMxZMgQLF68WCapf2WoEDAYDHh6esLT0xOlpaVISUnB5cuXkZiYiD///FPq8Z06dZJZ8aCjkJWVJfF5ujcjGzduxL59+0Qej4yMpFK26sjo0aOhr6+Px48fY968eULzZrW1tfjkk0/acHXtDxJs1Bh5hyoVpSXhxNu3b6NXr14wNDSUmMaLiYmBoaEhli9fjv79+8u1BmWpEAhiZmaGKVOmYNKkSbh9+zatYz777DMcO3YMkydPblcWvMpAGW6pgHgr5draWpH6jTri4+Mj9rM+fPjwNlhN+4YEGzVG3qFKRdm+fTu6du0qMgtTV1eHv//+G5qamhKDzaRJk3Dp0iVERUWhf//+GD58eJuoELQEf7dDh3PnzqG0tBTnz58XaZfeunWr0tbUEVmwYAE0NDRQX1+PhQsXCj1XWVmp9vL44m66NDU1YWFhATs7u3YRLNUJEmzUGAaDQSnntqYI56effor//e9/CAoKEhpcnDVrFsLDw6VaHEyYMAFffvkl7t27h+TkZERHR8PIyAhv375FeXk5rRkXZagQKIPWlgjqSPD/drt37xaSEGIwGJRkvjojrlOuoaEBlZWVsLGxwfLly1vV7qO9Q4Y6CWJ58uQJdu7cCVdXV0ydOhU6OjqYNWsWoqKiZD7BysvLkZKSgkuXLqGsrAweHh5CxVZJKKJCQFAPFO1AVDfYbDb27t0LDoeDBQsWtPVy2g0k2BBahMvl4siRI7h58yZmzZqFbdu2ITIyUu67OR6Ph7t37yI5OZmyAJAFRVQI5EVe4y+CMPX19SguLhaZVWpLjTNFqK6uRnBwMGJjY9t6Ke0GkkZTM1Q9VCkLWlpa+PrrrzFw4EDExsaiurpaodfT0NCAu7u73A0P8qoQ1NTUIDMzE69fv8bnn3+OiooKNDY20gqazf/OVVVVaGhoQLdu3Uiwocnt27cRExODmpoakefoyN2oI/r6+iKD1gTJkGCjZqhiqFJRHBwcsGHDBpSXl7e5YyIgmwpBQUEBwsPD0bVrV5SUlODzzz/Hs2fPkJSUhKVLl0o9vrnMfENDA/766y+Ym5vLtfZ3kYSEBHz11VcYOXJkhxmEfPz48TvbEi8vJNioGcocqlQmdXV1qKura7P3l1eFID4+HgEBARg+fDgCAwMBNKVuJClaS0JTUxNTpkzB/Pnz3wm3TmVQWVmJsWPHtvUyZEZcCpXL5aK4uBgpKSkSMw8EUUiwUUMSExMxePDgNttFsFgsbN26FS9fvsSkSZNgY2ODDRs2oK6uDubm5vjll19a7c5eURWCoqIiDBs2TOgxXV1dhQJnWVkZSaHIgJubW7tsEhCXqtbS0oKZmRmmT59O0qgyQoKNGpKQkIADBw5gwIABGD16tMrtd8W9f69evdCrVy/s27cPEydOxG+//QYul4s9e/bg8OHDKpdWV5YKQefOncFisYR8R4qLi2k3OTRvQqirq0NWVha8vLxor+FdRFDqxszMDJGRkfD29ha5gVLn1nLi1KlcSLBRQzp16oSlS5ciKSkJERERMDU1xciRIzF8+HC51Ytl4eHDh4iJiUFjYyMSExPh5+dH6YjNnDmzVaTVlaFCAABDhw7Fr7/+imnTpgFoquEkJCRgxIgRtI5vPt9kaGgIf39/YjkgheZSNzY2NigqKkJRUZHQ4+ocbAjKhQQbNURDQwNubm5wc3NDRUUFUlNTceHCBRw+fBgDBw7E6NGjhWo7yqa+vp4yutLT0xMawmwtaXVlqBAAwPjx48FmsxEREQE2m401a9bgk08+oa1rRceRlCCKsqRuCB0HEmzUHGNjY3z55Zf44osvqBmVNWvWqLT12cjICLW1tdDT08O3334r9FxVVVWrdBQpQ4UAaJpW//rrr/H111+jqqpKZmtioGmILzMzEywWC6ampnB3dyd2AwSCjJChTjXkm2++QUJCQovP8+2JVcWhQ4fg6+tLGUUJcv78edy9exehoaEqe39xKKJCoAhFRUVYt24dGAwGzMzMUFpaisbGRqxYsQI9evRQ+ft3BCZPniz2cX6x3cfHB+PHj4eWFrn37ciQYKOG7N69G999911bL0Ms/I9LWykg01Eh4AtASoOOkGZYWBicnJzw1VdfQUNDAzweD8ePH0dOTg5Wrlwp8/rfRc6ePYuUlBSMHTuWCtjnzp2Dr68vdHV1cfr0aQwcOBABAQFtvVSCCiHBhtDhuHz5Mq2fa94SLY5Zs2bh999/F7rr5nK5CAoKQlxcnJwrfLdYtmwZli5dKtQuX1JSgk2bNiEyMhLPnj1DZGQk6f7q4JB9K6HDQSeI0EVfXx+lpaXo3r079VhpaSn09PSU9h4dHSaTKZL27dq1K6UCYWtri6qqqrZYGqEVIcFGDamursaOHTvw8OFD9OjRA4GBgbCzs6Oenz59uljnQ4J4WCwW0tLS8Pr1a5iYmGDw4MFCczeSGDp0KDZs2IDPP/8c5ubmKCkpwenTp5Ua0Do677//Pvbv3w9/f39oa2uDw+Hg4MGD1GeayWS2Sks/oW0haTQ15Pfff0dZWRk++ugjZGVlITU1FSEhIZR8jbQGAsL/ce/ePURFRcHBwYGqF+Tn5yM4OBgffPCB1OMbGxvx999/4/Lly1SwGjZsGD777DNicUCTV69eYePGjSgtLUWXLl1QVVUFU1NTLFu2DFZWVsjNzQWLxcLgwYPbeqkEFUKCjRoyZ84cREVFUXd76enp2L17N0JCQuDo6Eh2NjKwZMkSfPHFF0LSImlpaThx4gQ2b97chit7t2hsbMTjx4+pTkpHR0fidPmOQdJoakhtbS0MDAyo7728vMDj8bBx40asWLGiDVfW/igtLYW3t7fQY97e3ti1a5fMr/XgwQMUFhbCyckJDg4OylriOwGDwVDpIDJB/SHBRg0xNTXF8+fPheo0gwYNApvNRnh4ODgcTtstrp3Rp08f5OTkCFkQ5+TkSFXU3rlzJ3r27ImRI0cCAJKTk7F7924YGhqipqYGP/74Izw9PVW69vZMXFwcZs6cCUBUX06Q2bNnt9aSCG0MCTZqiLu7O9LT04WCDQAMHz4ctbW1JIUmA5aWloiKioKHhwdV4L916xb8/PyExCKba3Q9ePBA6LETJ07A398fn332GS5evIh//vmHBBsJCGrKNdeXI7ybkJoNoUOzZs0aWj/XXMtLsC7GZDKxaNEixMXFQU9PDxwOB3PmzMGePXuUvt53icrKStqyQ4T2D9nZEDo08gpC8lt0tbW1kZ+fDysrK2q2RlNTE1wuV5nLfOfgcDgICgpqt7bQBNkh7SCEDg+Px8Pjx4+Rnp6OvLw80NnMOzg44Ny5c6ivr8fVq1eFPIVKSkrkEvQkEN5lyM6G0KEpLi5GZGQkmEwmOnfujKqqKlhYWGDZsmWwtLRs8bipU6ciLCwMBw4cgLGxsZD69Y0bN9CrV6/WWD6B0GEgNRtCh2bt2rWwtrZGQEAAdHR0UFdXhwMHDqCoqEhqio3NZuPly5ewsrISshR4+fIldHV1Vaq83dHhcDgICAggabR3CLKzIXRo8vPzERoaCm1tbQBNLqgBAQG0VLV1dXVhb28v8rg46wWCKILdfs1pbGxsxZUQ1AESbAgdGgsLC5SXlwspDjf/nqAamltDN0farBOhY0HSaIQODd9L5dNPP4WpqSlKS0uRmJgIPz8/IRWA3r17t+EqCYSODwk2hA5NSy6RzSG1AwJBtZBgQyAQCASVQ+ZsCO8cRUVFtF02Gxsbcfz4cSxYsADTp08H0GRbcPHiRVUukUDocJBgQ3gn4HA4uHLlCn755RcsXboUT58+pXXcoUOHcPv2bfj7+0NDQwNAUzdacnKyKpdLIHQ4SDcaoUPz4sULJCcn4+rVq6irqwOPx8OKFSvg5uZG6/jr169j3bp16Nq1K3bu3AkAlAkbgUCgDwk2hA7J1atXkZycjNzcXNja2mLixIkYMmQIFi9eDFtbW9qvw2azRcQiuVwuNbdDIBDoQYINoUMSExMDQ0NDLF++HP3795f7dezt7XHp0iWMGDGCeiwtLQ09e/ZUxjIJhHcGzdWrV69u60UQCMqGwWDgxYsXSElJQUFBATp16gRLS0ucOXMGo0ePFpKfkYSdnR1+++033L9/H0wmE3l5ebh27Rrmz59PxDgJBBkgrc+EDguPx8O9e/eQnJyMzMxMGBkZ4e3btwgPDxcxppNEVVUVrly5gpKSEpiZmcHX1xfGxsaqWziB0AEhwYbwTlBeXo6UlBRcunQJZWVl8PDwwOLFi9t6WQTCOwMJNoR3Ch6Ph7t37yI5ORnLli2T+vPnz5+Hk5MT7OzsUFBQgOjoaDAYDCxatEhI7oZAIEiGzNkQ3ik0NDTg7u5OK9AAwJkzZygrgUOHDmHQoEEYNmwYEhISVLlMAqHDQbrRCAQJvHnzBp07dwaHw0Fubi6WLl0KLS0tnDlzpq2XRiC0K0iwIRAkoKuri7KyMjx//hy2trbQ0dEBl8slfiwEgoyQYEMgSGDYsGFYsWIFOBwOpk6dCgB48uSJREtpAoEgCmkQIBCkcP/+fWhpaVFmX/n5+aitrYWLi0sbr4xAaD+QYEMg0KSqqooMchIIckLSaASCBOrr67F//35cunQJ9fX10NHRwfDhwxEQEAAdHZ22Xh6B0G4gOxsCQQLx8fHIzc3FlClTYGFhASaTiSNHjqBXr16YMWNGWy+PQGg3kDkbAkECGRkZWLZsGfr16wdLS0v069cPS5Yswc2bN9t6aQRCu4IEGwJBAnV1dTAwMBB6zNDQEPX19W20IgKhfUKCDYEggd69eyMhIYEKLvX19UhISICjo2Mbr4xAaF+Qmg2BIAEWi4WIiAgUFxejS5cuqKyshKWlJZYvXw4zM7O2Xh6B0G4gwYZAkEJjYyOePHkCFosFU1NT9OzZEwwGSQoQCLJAgg2BQCAQVA6ZsyEQWuD+/fsoKipC79694eDggB07diAjIwN2dnZYsGABTExM2nqJBEK7gexsCAQxnD17Fn/99Resra3x8uVLDBkyBEVFRRg0aBDS09NhbGyMRYsWtfUyCYR2A9nZEAhiuHDhAlauXIlevXrh0aNHWLVqFWJjY9GtWzd4eXnR9sMhEAhNkCongSCGiooK9OrVCwDg5OQEHR0dykTN2NgYdXV1bbk8AqHdQYINgUADLS2SBCAQFIGcQQSCGLhcLk6cOEF9z+FwhL7ncrltsSwCod1Cgg2BIIZevXohKyurxe+JggCBIBukG41AIBAIKofUbAgEAoGgckiwIRAIBILKIcGGQCAQCCqHBBsCoRVYvXo1UlJS2noZBEKbQYINgfD/+eGHH+Dv749p06bhu+++w47/197dhTT59gEc/869NOl2OqUXyMIi0FYNMYNkYXiwg6wQDZcEQUVGhRlGIXQQESG9LRFRsuxleTCLBF2QJ4YHdlIGKyl7oaIQwnRrpreibuZzIM+NPn///7+Pz7Onh/p9jnZdv3vXde0e7Lfr2u77qq1ldHT0Zw9L8+rVKw4dOvSXx9TU1OByuXj//r1W19vbi8vlivbwhPhLkmyEmKa8vJyGhgYuXLjAhw8faGpq+sMxExMTP2Fkc6coCo2NjT97GELMINfZCDGLxMRE0tPT6enpAcDlcrF//34ePnzIxMQENTU1tLW10dLSgqqqpKWlUVxcrN3Spquri5s3bxIKhcjOzmb6FQb37t2jt7eX0tJSAPr6+igpKcHr9aLX61FVlTt37vDixQvGx8dZs2YNpaWlVFRUEIlE2LNnDwBVVVVaf9Nt2bKFx48f093djc1m+0O8vb0dn89HMBjEYrGQl5eH0+kEpmZP1dXVbN26lQcPHhATE8OBAwcwGAx4PB4GBwfZsWMHBQUFwNRePz6fj0ePHjE8PMy6des4ePAgiqL8F98N8SuQmY0QswgEAvj9flJSUrS6zs5OKioqqKys5OXLl3i9XsrKyrh27RqLFi2iqqoKgMHBQdxuN0VFRdy4cYMlS5bw9u3bOfddXV3N2NgYbreb69evs337dsxmM6dOncJqtdLQ0EBDQ8OsiQZgwYIF5Ofn4/V6Z43Hx8dTXl6Ox+PhyJEjeDwePn78qMUHBgYIh8NcvXoVl8tFXV0dHR0dnD9/nrNnz9LU1MTXr18BaG1tpbOzkzNnzlBXV4eiKNTX18/5tYrfhyQbIaa5dOkSe/fu5fTp09hsNu0bPEB+fj6KomAymejo6CAnJ4dVq1ZhNBrZvXs37969o6+vD7/fT3JyMps2bcJgMLBt2zYSEhLm1H8oFOL58+cUFxejKAoGg2HW2cnfcTqdWsL8VxkZGSxduhSdTofNZsNut/PmzRstrtfrKSgowGAw4HA4GBoaIjc3l9jYWJYvX05ycjKfP38GoK2tjaKiIpKSkjAajRQWFvLkyZP/+6VG8b8ny2hCTHPy5EnsdvussembpYVCIVauXKmVzWYziqLw7ds3QqHQjGN1Ot2cN1oLBoMoivIfL0MZjUZ27tzJ3bt3OXbs2IyY3+/n/v37fPnyhcnJScbGxlixYoUWj4uL07a9NplMwNRs6J9MJpP2x4n+/n4uX76MTqfT4jExMXz//v1PZ17i9yTJRog5mv6BarVaCQQCWnl0dBRVVUlMTCQhIYFgMKjFJicnZ5TNZjPj4+NaeWBgQHuclJSEqqoMDw+zcOHCP+1/LnJycvD5fDx9+lSrC4fDuN1uSkpKyMzMxGAwcPHixX+r3emSkpI4fPgwaWlp825D/B5kGU2Iedi8eTPt7e18+vSJcDiM1+tl9erVLF68mIyMDHp6erTlpNbW1hkJJSUlhdevXxMIBBgZGaG5uVmLWa1W0tPTqa+vR1VVIpEI3d3dwNTsYmhoiJGRkTmNUa/XU1hYSEtLi1YXiUQIh8NYLBb0ej1+v5+urq55nwen00ljYyP9/f3A1O9VnZ2d825P/LpkZiPEPKxfv55du3bhdrtRVZXU1FRtm2iLxcLx48e5desWtbW1ZGdnk5qaqj3XbreTlZXFiRMniIuLIy8vj2fPnmnxo0ePcvv2bcrKyohEIqxduxabzcayZctwOByUlJTw48cPrly58rdLVQ6Hg+bmZlRVBSA2NpZ9+/ZRWVlJOBxmw4YNZGZmzvs85ObmAnDu3DlCoRDx8fFkZWWxcePGebcpfk1y12chhBBRJ8toQgghok6SjRBCiKiTZCOEECLqJNkIIYSIOkk2Qgghok6SjRBCiKiTZCOEECLqJNkIIYSIun8A4tu2RoAuw/MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(products,total_quantity)\n",
    "plt.xticks(products,rotation='vertical',size=13)\n",
    "plt.xlabel('Product Name')\n",
    "plt.ylabel('Total sales')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AAA Batteries (4-pack) have maximum sales because it's cheaper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 12 Months Profile Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile = ProfileReport(all_data, title=\"Pandas Profiling Report\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "24ef3a1678b14562a15b11d42812c2a8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Summarize dataset', max=25.0, style=ProgressStyle(descrip…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "55e582e2c0cb41728656f051f5308297",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Generate report structure', max=1.0, style=ProgressStyle(…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Render widgets', max=1.0, style=ProgressStyle(description…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6ca037be668f49fe9bf4501a90015c70",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Tab(children=(Tab(children=(GridBox(children=(VBox(children=(GridspecLayout(children=(HTML(valu…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "profile.to_widgets()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}