CS Project, Part 2 - Descriptive, Diagnostic Analysis & Data Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4cda4765",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime as dt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba74e2d0",
   "metadata": {},
   "source": [
    ">#### reading DATA from csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8df7339f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(189085, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"Data.csv\")                  \n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4dc07a82",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Datetime</th>\n",
       "      <th>Period</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>City State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>11.95</td>\n",
       "      <td>75315</td>\n",
       "      <td>23.90</td>\n",
       "      <td>2019-04-19 08:46:00</td>\n",
       "      <td>8</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Apr</td>\n",
       "      <td>Dallas TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>59963</td>\n",
       "      <td>99.99</td>\n",
       "      <td>2019-04-07 22:30:00</td>\n",
       "      <td>22</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>Apr</td>\n",
       "      <td>Boston MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>25532</td>\n",
       "      <td>600.00</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>14</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Apr</td>\n",
       "      <td>Los Angeles CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>25532</td>\n",
       "      <td>11.99</td>\n",
       "      <td>2019-04-12 14:38:00</td>\n",
       "      <td>14</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Apr</td>\n",
       "      <td>Los Angeles CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>176561</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>89128</td>\n",
       "      <td>11.99</td>\n",
       "      <td>2019-04-30 09:27:00</td>\n",
       "      <td>9</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Apr</td>\n",
       "      <td>Los Angeles CA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Order ID                     Product  Quantity Ordered  \\\n",
       "0           0    176558        USB-C Charging Cable                 2   \n",
       "1           1    176559  Bose SoundSport Headphones                 1   \n",
       "2           2    176560                Google Phone                 1   \n",
       "3           3    176560            Wired Headphones                 1   \n",
       "4           4    176561            Wired Headphones                 1   \n",
       "\n",
       "   Price Each  Customer ID   Sales             Datetime  Period      Day  \\\n",
       "0       11.95        75315   23.90  2019-04-19 08:46:00       8   Friday   \n",
       "1       99.99        59963   99.99  2019-04-07 22:30:00      22   Sunday   \n",
       "2      600.00        25532  600.00  2019-04-12 14:38:00      14   Friday   \n",
       "3       11.99        25532   11.99  2019-04-12 14:38:00      14   Friday   \n",
       "4       11.99        89128   11.99  2019-04-30 09:27:00       9  Tuesday   \n",
       "\n",
       "  Month       City State  \n",
       "0   Apr        Dallas TX  \n",
       "1   Apr        Boston MA  \n",
       "2   Apr   Los Angeles CA  \n",
       "3   Apr   Los Angeles CA  \n",
       "4   Apr   Los Angeles CA  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confirming operation\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cca1cc86",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>Customer ID</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Datetime</th>\n",
       "      <th>Period</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>City State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>189080</th>\n",
       "      <td>259353</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>3</td>\n",
       "      <td>2.99</td>\n",
       "      <td>254883</td>\n",
       "      <td>8.97</td>\n",
       "      <td>2019-09-17 20:56:00</td>\n",
       "      <td>20</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Sep</td>\n",
       "      <td>Los Angeles CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189081</th>\n",
       "      <td>259354</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>175553</td>\n",
       "      <td>700.00</td>\n",
       "      <td>2019-09-01 16:00:00</td>\n",
       "      <td>16</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>Sep</td>\n",
       "      <td>San Francisco CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189082</th>\n",
       "      <td>259355</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>133937</td>\n",
       "      <td>700.00</td>\n",
       "      <td>2019-09-23 07:39:00</td>\n",
       "      <td>7</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Sep</td>\n",
       "      <td>San Francisco CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189083</th>\n",
       "      <td>259356</td>\n",
       "      <td>34in Ultrawide Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>379.99</td>\n",
       "      <td>189758</td>\n",
       "      <td>379.99</td>\n",
       "      <td>2019-09-19 17:30:00</td>\n",
       "      <td>17</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Sep</td>\n",
       "      <td>San Francisco CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189084</th>\n",
       "      <td>259357</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>276924</td>\n",
       "      <td>11.95</td>\n",
       "      <td>2019-09-30 00:18:00</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Sep</td>\n",
       "      <td>San Francisco CA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Order ID                 Product  Quantity Ordered  Price Each  \\\n",
       "189080    259353  AAA Batteries (4-pack)                 3        2.99   \n",
       "189081    259354                  iPhone                 1      700.00   \n",
       "189082    259355                  iPhone                 1      700.00   \n",
       "189083    259356  34in Ultrawide Monitor                 1      379.99   \n",
       "189084    259357    USB-C Charging Cable                 1       11.95   \n",
       "\n",
       "        Customer ID   Sales             Datetime  Period       Day Month  \\\n",
       "189080       254883    8.97  2019-09-17 20:56:00      20   Tuesday   Sep   \n",
       "189081       175553  700.00  2019-09-01 16:00:00      16    Sunday   Sep   \n",
       "189082       133937  700.00  2019-09-23 07:39:00       7    Monday   Sep   \n",
       "189083       189758  379.99  2019-09-19 17:30:00      17  Thursday   Sep   \n",
       "189084       276924   11.95  2019-09-30 00:18:00       0    Monday   Sep   \n",
       "\n",
       "               City State  \n",
       "189080     Los Angeles CA  \n",
       "189081   San Francisco CA  \n",
       "189082   San Francisco CA  \n",
       "189083   San Francisco CA  \n",
       "189084   San Francisco CA  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropping unwanted columns\n",
    "data.drop([\"Unnamed: 0\"], axis=1, inplace=True)\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f22225f",
   "metadata": {},
   "source": [
    "> ### Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "026ff0e9",
   "metadata": {},
   "source": [
    "#### Product Orders 2019 `pr_order`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b5299c76",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantity Ordered</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAA Batteries (4-pack)</th>\n",
       "      <td>31475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA Batteries (4-pack)</th>\n",
       "      <td>28241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USB-C Charging Cable</th>\n",
       "      <td>24301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lightning Charging Cable</th>\n",
       "      <td>23713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wired Headphones</th>\n",
       "      <td>20961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apple Airpods Headphones</th>\n",
       "      <td>15865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bose SoundSport Headphones</th>\n",
       "      <td>13548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27in FHD Monitor</th>\n",
       "      <td>7678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iPhone</th>\n",
       "      <td>7006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27in 4K Gaming Monitor</th>\n",
       "      <td>6330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34in Ultrawide Monitor</th>\n",
       "      <td>6328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Google Phone</th>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flatscreen TV</th>\n",
       "      <td>4916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Macbook Pro Laptop</th>\n",
       "      <td>4803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20in Monitor</th>\n",
       "      <td>4198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ThinkPad Laptop</th>\n",
       "      <td>4178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vareebadd Phone</th>\n",
       "      <td>2114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LG Dryer</th>\n",
       "      <td>673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LG Washing Machine</th>\n",
       "      <td>663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Quantity Ordered\n",
       "Product                                     \n",
       "AAA Batteries (4-pack)                 31475\n",
       "AA Batteries (4-pack)                  28241\n",
       "USB-C Charging Cable                   24301\n",
       "Lightning Charging Cable               23713\n",
       "Wired Headphones                       20961\n",
       "Apple Airpods Headphones               15865\n",
       "Bose SoundSport Headphones             13548\n",
       "27in FHD Monitor                        7678\n",
       "iPhone                                  7006\n",
       "27in 4K Gaming Monitor                  6330\n",
       "34in Ultrawide Monitor                  6328\n",
       "Google Phone                            5650\n",
       "Flatscreen TV                           4916\n",
       "Macbook Pro Laptop                      4803\n",
       "20in Monitor                            4198\n",
       "ThinkPad Laptop                         4178\n",
       "Vareebadd Phone                         2114\n",
       "LG Dryer                                 673\n",
       "LG Washing Machine                       663"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping dataset\n",
    "pr_order = pd.DataFrame(data.groupby(\"Product\")[\"Quantity Ordered\"].sum())\n",
    "\n",
    "# sorting dataset\n",
    "pr_order.sort_values(\"Quantity Ordered\", axis=0, ascending=False, inplace=True)\n",
    "\n",
    "# Dataframe\n",
    "pr_order"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcec9562",
   "metadata": {},
   "source": [
    "***\n",
    "* **Most Ordered Product** - `USB-C Charging Cable` \n",
    "* **Least Ordered Product** - `LG Washing Machine` \n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "117734ad",
   "metadata": {},
   "source": [
    "#### Product Gross Sales 2019  `pr_gross`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "411ef53a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Macbook Pro Laptop</th>\n",
       "      <td>8165100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iPhone</th>\n",
       "      <td>4904200.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ThinkPad Laptop</th>\n",
       "      <td>4177958.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Google Phone</th>\n",
       "      <td>3390000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27in 4K Gaming Monitor</th>\n",
       "      <td>2468636.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34in Ultrawide Monitor</th>\n",
       "      <td>2404576.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apple Airpods Headphones</th>\n",
       "      <td>2379750.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flatscreen TV</th>\n",
       "      <td>1474800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bose SoundSport Headphones</th>\n",
       "      <td>1354664.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27in FHD Monitor</th>\n",
       "      <td>1151623.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vareebadd Phone</th>\n",
       "      <td>845600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20in Monitor</th>\n",
       "      <td>461738.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LG Dryer</th>\n",
       "      <td>403800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LG Washing Machine</th>\n",
       "      <td>397800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lightning Charging Cable</th>\n",
       "      <td>354509.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USB-C Charging Cable</th>\n",
       "      <td>290396.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wired Headphones</th>\n",
       "      <td>251322.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA Batteries (4-pack)</th>\n",
       "      <td>108445.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAA Batteries (4-pack)</th>\n",
       "      <td>94110.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Sales\n",
       "Product                               \n",
       "Macbook Pro Laptop          8165100.00\n",
       "iPhone                      4904200.00\n",
       "ThinkPad Laptop             4177958.22\n",
       "Google Phone                3390000.00\n",
       "27in 4K Gaming Monitor      2468636.70\n",
       "34in Ultrawide Monitor      2404576.72\n",
       "Apple Airpods Headphones    2379750.00\n",
       "Flatscreen TV               1474800.00\n",
       "Bose SoundSport Headphones  1354664.52\n",
       "27in FHD Monitor            1151623.22\n",
       "Vareebadd Phone              845600.00\n",
       "20in Monitor                 461738.02\n",
       "LG Dryer                     403800.00\n",
       "LG Washing Machine           397800.00\n",
       "Lightning Charging Cable     354509.35\n",
       "USB-C Charging Cable         290396.95\n",
       "Wired Headphones             251322.39\n",
       "AA Batteries (4-pack)        108445.44\n",
       "AAA Batteries (4-pack)        94110.25"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping dataset\n",
    "pr_gross = pd.DataFrame(data.groupby(\"Product\")[\"Sales\"].sum())\n",
    "\n",
    "# sorting dataset\n",
    "pr_gross.sort_values(\"Sales\", axis=0, ascending=False, inplace=True)\n",
    "\n",
    "# Dataframe\n",
    "pr_gross  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2da7d3c0",
   "metadata": {},
   "source": [
    "***\n",
    "* **Highest Grossing Product** - `Macbook Pro Laptop`\t\n",
    "* **Lowest Grossing Product** - `AAA Batteries (4-pack)`\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1db2370",
   "metadata": {},
   "source": [
    "### Why were `USB-C Charging Cables` the most sold, yet `Macbook Pro Laptop` the highest grossing?\n",
    "\n",
    "**The chart below renders a visualization of Product Price and Orders against Products**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a270a3c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp/ipykernel_9828/3990397979.py:18: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax1.set_xticklabels(products, rotation=\"vertical\", size=10)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# setting chart parameters\n",
    "product_Series = data.groupby('Product')\n",
    "products = [product for product, df in product_Series]\n",
    "n_orders = product_Series.sum()['Quantity Ordered']\n",
    "prices = data.groupby('Product').mean()['Price Each']\n",
    "\n",
    "# plotting\n",
    "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax1.bar(products, n_orders, color=\"darkgrey\")\n",
    "ax2.plot(products, prices, color=\"green\")\n",
    "\n",
    "# labelling plot\n",
    "ax1.set_xlabel(\"Products\")\n",
    "ax1.set_ylabel(\"Quantity Ordered\")\n",
    "ax2.set_ylabel(\"Price\", color=\"green\")\n",
    "ax1.set_xticklabels(products, rotation=\"vertical\", size=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c63d89cd",
   "metadata": {},
   "source": [
    "#### Why?\n",
    "* A `USB-C Charging Cable` is considerably less compared to a `Macbook Pro Laptop`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4877ebaa",
   "metadata": {},
   "source": [
    "***\n",
    "**The chart below renders a visualization of Product Orders and Sales, against Products**\n",
    "\n",
    "To illustrate further;\n",
    "Why `USB-C Charging Cable` was the most sold and `Macbook Pro Laptop` the highest grossing...\n",
    "* let us visualize the data...\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f910e0fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp/ipykernel_9828/160267640.py:18: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax1.set_xticklabels(products, rotation=\"vertical\", size=10)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# setting chart parameters\n",
    "product_Series = data.groupby('Product')\n",
    "products = [product for product, df in product_Series]\n",
    "n_orders = product_Series.sum()['Quantity Ordered']\n",
    "sales = data.groupby('Product').sum()['Sales']\n",
    "\n",
    "# plotting\n",
    "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax1.bar(products, n_orders)\n",
    "ax2.plot(products, sales, color=\"green\")\n",
    "\n",
    "# labelling plot\n",
    "ax1.set_xlabel(\"Products\")\n",
    "ax1.set_ylabel(\"Quantity Ordered\", color=\"blue\")\n",
    "ax2.set_ylabel(\"Total Sales, in Millions\",  color=\"green\")\n",
    "ax1.set_xticklabels(products, rotation=\"vertical\", size=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a276d146",
   "metadata": {},
   "source": [
    "### *Monthly*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b118a7b",
   "metadata": {},
   "source": [
    "#### Most Ordered/Sold Product per month `most_spm`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "64fe8265",
   "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>Quantity Ordered</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th>Product</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr</th>\n",
       "      <th>AAA Batteries (4-pack)</th>\n",
       "      <td>5872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dec</th>\n",
       "      <th>AAA Batteries (4-pack)</th>\n",
       "      <td>4241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oct</th>\n",
       "      <th>AAA Batteries (4-pack)</th>\n",
       "      <td>3363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nov</th>\n",
       "      <th>AAA Batteries (4-pack)</th>\n",
       "      <td>3025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <th>AAA Batteries (4-pack)</th>\n",
       "      <td>2927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jul</th>\n",
       "      <th>AAA Batteries (4-pack)</th>\n",
       "      <td>2365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jun</th>\n",
       "      <th>AAA Batteries (4-pack)</th>\n",
       "      <td>2162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aug</th>\n",
       "      <th>AAA Batteries (4-pack)</th>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feb</th>\n",
       "      <th>AAA Batteries (4-pack)</th>\n",
       "      <td>1972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sep</th>\n",
       "      <th>AAA Batteries (4-pack)</th>\n",
       "      <td>1923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jan</th>\n",
       "      <th>AAA Batteries (4-pack)</th>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mar</th>\n",
       "      <th>AAA Batteries (4-pack)</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Quantity Ordered\n",
       "Month Product                                 \n",
       "Apr   AAA Batteries (4-pack)              5872\n",
       "Dec   AAA Batteries (4-pack)              4241\n",
       "Oct   AAA Batteries (4-pack)              3363\n",
       "Nov   AAA Batteries (4-pack)              3025\n",
       "May   AAA Batteries (4-pack)              2927\n",
       "Jul   AAA Batteries (4-pack)              2365\n",
       "Jun   AAA Batteries (4-pack)              2162\n",
       "Aug   AAA Batteries (4-pack)              2021\n",
       "Feb   AAA Batteries (4-pack)              1972\n",
       "Sep   AAA Batteries (4-pack)              1923\n",
       "Jan   AAA Batteries (4-pack)              1600\n",
       "Mar   AAA Batteries (4-pack)                 4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping dataset\n",
    "most_spm = pd.DataFrame(data.groupby([\"Month\", \"Product\"])[\"Quantity Ordered\"].sum().sort_values().groupby(level=0).tail(1))\n",
    "\n",
    "\n",
    "# sorting dataset\n",
    "most_spm.sort_values(\"Quantity Ordered\", axis=0, ascending=False, inplace=True)\n",
    "\n",
    "# Dataframe\n",
    "most_spm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c41073c9",
   "metadata": {},
   "source": [
    "#### Highest grossing product per month `hg_prodpm`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a9fb9c75",
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th>Product</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr</th>\n",
       "      <th>Macbook Pro Laptop</th>\n",
       "      <td>1540200.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dec</th>\n",
       "      <th>Macbook Pro Laptop</th>\n",
       "      <td>1093100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oct</th>\n",
       "      <th>Macbook Pro Laptop</th>\n",
       "      <td>892500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <th>Macbook Pro Laptop</th>\n",
       "      <td>793900.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nov</th>\n",
       "      <th>Macbook Pro Laptop</th>\n",
       "      <td>748000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jul</th>\n",
       "      <th>Macbook Pro Laptop</th>\n",
       "      <td>625600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jun</th>\n",
       "      <th>Macbook Pro Laptop</th>\n",
       "      <td>605200.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aug</th>\n",
       "      <th>Macbook Pro Laptop</th>\n",
       "      <td>508300.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sep</th>\n",
       "      <th>Macbook Pro Laptop</th>\n",
       "      <td>489600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feb</th>\n",
       "      <th>Macbook Pro Laptop</th>\n",
       "      <td>469200.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jan</th>\n",
       "      <th>Macbook Pro Laptop</th>\n",
       "      <td>399500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mar</th>\n",
       "      <th>ThinkPad Laptop</th>\n",
       "      <td>1999.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Sales\n",
       "Month Product                       \n",
       "Apr   Macbook Pro Laptop  1540200.00\n",
       "Dec   Macbook Pro Laptop  1093100.00\n",
       "Oct   Macbook Pro Laptop   892500.00\n",
       "May   Macbook Pro Laptop   793900.00\n",
       "Nov   Macbook Pro Laptop   748000.00\n",
       "Jul   Macbook Pro Laptop   625600.00\n",
       "Jun   Macbook Pro Laptop   605200.00\n",
       "Aug   Macbook Pro Laptop   508300.00\n",
       "Sep   Macbook Pro Laptop   489600.00\n",
       "Feb   Macbook Pro Laptop   469200.00\n",
       "Jan   Macbook Pro Laptop   399500.00\n",
       "Mar   ThinkPad Laptop        1999.98"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping dataset\n",
    "hg_prodpm = pd.DataFrame(data.groupby([\"Month\", \"Product\"])[\"Sales\"].sum().sort_values().groupby(level=0).tail(1))\n",
    "\n",
    "\n",
    "# sorting dataset\n",
    "hg_prodpm.sort_values(\"Sales\", axis=0, ascending=False, inplace=True)\n",
    "\n",
    "# Dataframe\n",
    "hg_prodpm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "033c6c69",
   "metadata": {},
   "source": [
    "#### Monthly Sales - `grouped_sales`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "44bb9663",
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr</th>\n",
       "      <td>6770999.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aug</th>\n",
       "      <td>2244467.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dec</th>\n",
       "      <td>4613443.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feb</th>\n",
       "      <td>2202022.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jan</th>\n",
       "      <td>1822256.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jul</th>\n",
       "      <td>2647775.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jun</th>\n",
       "      <td>2577802.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mar</th>\n",
       "      <td>3207.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>3163166.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nov</th>\n",
       "      <td>3199603.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oct</th>\n",
       "      <td>3736726.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sep</th>\n",
       "      <td>2097560.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Sales\n",
       "Month            \n",
       "Apr    6770999.64\n",
       "Aug    2244467.88\n",
       "Dec    4613443.34\n",
       "Feb    2202022.42\n",
       "Jan    1822256.73\n",
       "Jul    2647775.76\n",
       "Jun    2577802.26\n",
       "Mar       3207.50\n",
       "May    3163166.04\n",
       "Nov    3199603.20\n",
       "Oct    3736726.88\n",
       "Sep    2097560.13"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting total sales per month\n",
    "grouped_sales = pd.DataFrame(data.groupby(\"Month\")[\"Sales\"].sum())\n",
    "grouped_sales"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99fcd5fe",
   "metadata": {},
   "source": [
    "Setting proper `Month` Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "48914b05",
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting our DataFrame to a shorthand variable name\n",
    "sales_df = pd.DataFrame(grouped_sales)\n",
    "\n",
    "# month order\n",
    "sort_month = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \n",
    "              \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\"]\n",
    "\n",
    "# resetting index\n",
    "sales_df = sales_df.reset_index(drop=False)\n",
    "\n",
    "# adding new month index, to help with categorical sorting\n",
    "sales_df.index = pd.Categorical(sales_df['Month'], categories=sort_month, ordered=True)\n",
    "\n",
    "# sorting with the month index and dropping it right after\n",
    "sales_df = sales_df.sort_index().reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d4a0b422",
   "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>Month</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>1822256.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb</td>\n",
       "      <td>2202022.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar</td>\n",
       "      <td>3207.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr</td>\n",
       "      <td>6770999.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>3163166.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jun</td>\n",
       "      <td>2577802.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jul</td>\n",
       "      <td>2647775.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug</td>\n",
       "      <td>2244467.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sep</td>\n",
       "      <td>2097560.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oct</td>\n",
       "      <td>3736726.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nov</td>\n",
       "      <td>3199603.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec</td>\n",
       "      <td>4613443.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month       Sales\n",
       "0    Jan  1822256.73\n",
       "1    Feb  2202022.42\n",
       "2    Mar     3207.50\n",
       "3    Apr  6770999.64\n",
       "4    May  3163166.04\n",
       "5    Jun  2577802.26\n",
       "6    Jul  2647775.76\n",
       "7    Aug  2244467.88\n",
       "8    Sep  2097560.13\n",
       "9    Oct  3736726.88\n",
       "10   Nov  3199603.20\n",
       "11   Dec  4613443.34"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ed5e00e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing dataframe\n",
    "    # line plot; default plot is also line\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.plot(sales_df[\"Month\"], sales_df[\"Sales\"], linewidth=3, alpha=0.6, color=\"green\")\n",
    "\n",
    "    # setting plot characteristics\n",
    "plt.title(\"Monthly Sales Trend\", fontsize=20)\n",
    "plt.xlabel(\"Month\", size=15)\n",
    "plt.ylabel(\"Sales\", size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b66af6d",
   "metadata": {},
   "source": [
    "#### Monthly Orders - `grouped_orders`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "750168e8",
   "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>Quantity Ordered</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr</th>\n",
       "      <td>36522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aug</th>\n",
       "      <td>11961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dec</th>\n",
       "      <td>24984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feb</th>\n",
       "      <td>11975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jan</th>\n",
       "      <td>9709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jul</th>\n",
       "      <td>14293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jun</th>\n",
       "      <td>13554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mar</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>16594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nov</th>\n",
       "      <td>17573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oct</th>\n",
       "      <td>20282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sep</th>\n",
       "      <td>11621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Quantity Ordered\n",
       "Month                  \n",
       "Apr               36522\n",
       "Aug               11961\n",
       "Dec               24984\n",
       "Feb               11975\n",
       "Jan                9709\n",
       "Jul               14293\n",
       "Jun               13554\n",
       "Mar                  17\n",
       "May               16594\n",
       "Nov               17573\n",
       "Oct               20282\n",
       "Sep               11621"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting total sales per month\n",
    "grouped_orders = pd.DataFrame(data.groupby(\"Month\")[\"Quantity Ordered\"].count())\n",
    "grouped_orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e36a7019",
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting our DataFrame to a shorthand variable name\n",
    "order_df = pd.DataFrame(grouped_orders)\n",
    "\n",
    "# month order\n",
    "sort_month = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \n",
    "              \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\"]\n",
    "\n",
    "# resetting index\n",
    "order_df = order_df.reset_index(drop=False)\n",
    "\n",
    "# adding new month index, to help with categorical sorting\n",
    "order_df.index = pd.Categorical(order_df['Month'], categories=sort_month, ordered=True)\n",
    "\n",
    "# sorting with the month index and dropping it right after\n",
    "order_df = order_df.sort_index().reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "917a9246",
   "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>Month</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>9709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb</td>\n",
       "      <td>11975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr</td>\n",
       "      <td>36522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>16594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jun</td>\n",
       "      <td>13554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jul</td>\n",
       "      <td>14293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug</td>\n",
       "      <td>11961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sep</td>\n",
       "      <td>11621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oct</td>\n",
       "      <td>20282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nov</td>\n",
       "      <td>17573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec</td>\n",
       "      <td>24984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month  Quantity Ordered\n",
       "0    Jan              9709\n",
       "1    Feb             11975\n",
       "2    Mar                17\n",
       "3    Apr             36522\n",
       "4    May             16594\n",
       "5    Jun             13554\n",
       "6    Jul             14293\n",
       "7    Aug             11961\n",
       "8    Sep             11621\n",
       "9    Oct             20282\n",
       "10   Nov             17573\n",
       "11   Dec             24984"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "66ed8687",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing dataframe\n",
    "    # line plot; default plot is also line\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.plot(order_df[\"Month\"], order_df[\"Quantity Ordered\"], linewidth=3, alpha=0.6, color=\"pink\")\n",
    "\n",
    "    # setting plot characteristics\n",
    "plt.title(\"Monthly Orders Trend\", fontsize=20)\n",
    "plt.xlabel(\"Month\", size=15)\n",
    "plt.ylabel(\"Number Of Orders\", size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0e5984f",
   "metadata": {},
   "source": [
    "***\n",
    "The `Monthly Sales Trend` and `Monthly Orders Trend` charts seem quite similarly, don't they?\n",
    "* We will look closely at just why, using `statistical concepts` like `Correlation Co-efficient` later on \n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccdf934e",
   "metadata": {},
   "source": [
    "### *Daily*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "391c4a1a",
   "metadata": {},
   "source": [
    "***\n",
    "### Peak Period \n",
    "* At what time of the day were orders most likely to come in? \n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bb67044e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing data\n",
    "order_p = data.groupby([\"Period\"])[\"Quantity Ordered\"].count()\n",
    "peaks = [period for period, df in data.groupby(\"Period\")]\n",
    "\n",
    "# plotting\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(peaks, order_p, color=\"indigo\")\n",
    "plt.xticks(peaks)\n",
    "plt.xlabel(\"Periods of the Day\")\n",
    "plt.ylabel(\"Number Of Orders\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e08a5e59",
   "metadata": {},
   "source": [
    "The data shows **2 peaks**, At `12hrs (12 PM)` and `19hrs (7 PM)`.\n",
    "* This tells us that most customers did their shopping by mid-day or mid-evening\n",
    "* This would be the best or ideal time of the day to push out adverts promoting company products"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46e043d6",
   "metadata": {},
   "source": [
    "**Total Orders Per Day**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "82affba8",
   "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>Quantity Ordered</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>26202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>27503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>26419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>26616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>27019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>28276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>27050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Quantity Ordered\n",
       "Day                        \n",
       "Friday                26202\n",
       "Monday                27503\n",
       "Saturday              26419\n",
       "Sunday                26616\n",
       "Thursday              27019\n",
       "Tuesday               28276\n",
       "Wednesday             27050"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# counting orders per day\n",
    "daily_orders = pd.DataFrame(data.groupby(\"Day\")[\"Quantity Ordered\"].count())\n",
    "daily_orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c256820c",
   "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>Day</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>26616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monday</td>\n",
       "      <td>27503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>28276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>27050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>27019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Friday</td>\n",
       "      <td>26202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>26419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Day  Quantity Ordered\n",
       "0     Sunday             26616\n",
       "1     Monday             27503\n",
       "2    Tuesday             28276\n",
       "3  Wednesday             27050\n",
       "4   Thursday             27019\n",
       "5     Friday             26202\n",
       "6   Saturday             26419"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# setting our DataFrame to a shorthand variable name\n",
    "daily_df = pd.DataFrame(daily_orders)\n",
    "\n",
    "# month order\n",
    "sort_day = [\"Sunday\", \"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\"]\n",
    "\n",
    "# resetting index\n",
    "daily_df = daily_df.reset_index(drop=False)\n",
    "\n",
    "# adding new month index, to help with categorical sorting\n",
    "daily_df.index = pd.Categorical(daily_df['Day'], categories=sort_day, ordered=True)\n",
    "\n",
    "# sorting with the month index and dropping it right after\n",
    "daily_df = daily_df.sort_index().reset_index(drop = True)\n",
    "\n",
    "daily_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "516b4bff",
   "metadata": {},
   "source": [
    "**Total Sales per Day**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "48e9d07d",
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>4831476.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>5024344.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>4885674.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>4950319.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>4967299.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>5280222.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>5139695.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Sales\n",
       "Day                  \n",
       "Friday     4831476.71\n",
       "Monday     5024344.47\n",
       "Saturday   4885674.00\n",
       "Sunday     4950319.05\n",
       "Thursday   4967299.71\n",
       "Tuesday    5280222.53\n",
       "Wednesday  5139695.31"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summing sales per day\n",
    "daily_sales = pd.DataFrame(data.groupby(\"Day\")[\"Sales\"].sum())\n",
    "daily_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b741e90f",
   "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>Day</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>4950319.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monday</td>\n",
       "      <td>5024344.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>5280222.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>5139695.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>4967299.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Friday</td>\n",
       "      <td>4831476.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>4885674.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Day       Sales\n",
       "0     Sunday  4950319.05\n",
       "1     Monday  5024344.47\n",
       "2    Tuesday  5280222.53\n",
       "3  Wednesday  5139695.31\n",
       "4   Thursday  4967299.71\n",
       "5     Friday  4831476.71\n",
       "6   Saturday  4885674.00"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# setting our DataFrame to a shorthand variable name\n",
    "sales_df = pd.DataFrame(daily_sales)\n",
    "\n",
    "# month order\n",
    "sort_day = [\"Sunday\", \"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\"]\n",
    "\n",
    "# resetting index\n",
    "sales_df = sales_df.reset_index(drop=False)\n",
    "\n",
    "# adding new month index, to help with categorical sorting\n",
    "sales_df.index = pd.Categorical(sales_df['Day'], categories=sort_day, ordered=True)\n",
    "\n",
    "# sorting with the month index and dropping it right after\n",
    "sales_df =sales_df.sort_index().reset_index(drop = True)\n",
    "\n",
    "sales_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82264b84",
   "metadata": {},
   "source": [
    "**The chart below renders some visualizations;**\n",
    "* `Total Orders per Day` and `Total Sales per Day` against `Days` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cf995eed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X7PskEREJW0FNLM0sFm9S+bJzbpJv8xVA9ePXgOrBO2uBdjVOb4u3mXyt7/Hu23c5x8xi8Datb9s9DufcU865Ps65PjExGggvoVXpcby5ogDn4PxOacRG7b33RrhWK6slREfROzOB5TvKySs5sA9tBzU9iPcufY/8snxOH3c6W4u3BjhKERGpL8EcFW54q5HfOOceqrFrPXCC7/FA4Hvf46nAMN9I7054B+nMdc5tAArM7GjfNS8HptQ45wrf4yHAR04zL0uY+2hdERuKKzmzQwpN42uvQIZztbJanxaJxEbBnE37N0K8piNaHsHUYVP5aftPnD3+bIrKiwIYoYiI1BcLVh5mZscCnwJfAx7f5r8A+cCjeOfQLAVuds7N953zV+BqvCPKb3fOvevb3gd4HkgE3gVudc45M0sAXsLbf3MbMMw591NtcSUnJ7uiIv3RktBYtr2MqSsL6NsikYFtkms91uM89BzbE4/z8PVNX4dtYgnw4dpC5m0p5fpuTfeZLNdm8jeTGfLaEE4/+HSmDJtCbPSeJ4oXEWkszKzYOVf7H4wwErTEMlwpsZRQySut5IXlO8hKjGF45yZE72MCg9eWvsbFr1/M+AvHM6zHsHqK8sAUVFTxxNLt9MiI54z2qXW61tPzn+b6t69nRM8RvHDeC0SZVp4VkcYr0hJLdTgUqQflVd5+lbFRxuCOqftMKqv7Vh6WeRgXdbuonqI8cKmx0fRslsCiraUc0zKJtLgDr1pe1/s6NhVt4u8f/50WSS3492n/9nvZSBERCS0lliJB5pxj+ppC8kqrGHZwGql+JF3VfSvHXzg+rJvAa+rXIpFFeaV8ublknyPd9+Wvx/2VzUWbeeiLh8hKyeKPx/wxQFGKiEgwKbEUCbKFW0tZur2M41ol0TFt3+tqR1q1slp6fDTdM+JZlFfKgKwkkmMPvAnbzHhk0CNsKd7Cnz74Ey2SW3BlrysDF6yIiASFOi+JBNHG4ko+WFtEp9RYBmQl+nXOzyPBTwjfkeB70z8riUoHX20+8BHi1aIsihfOe4FTDzqVa6dey1vL3wpAhCIiEkxKLEWCpLTSw+QV+STHRHFOx1S/+glGarWyWkZCNIelx7Egr5SSSs++T9iHuOg43rj4DbJbZXPx6xfz+erPAxCliIgEixJLkSBwzvH2qkIKKjwM7pRKUox/v2qRXK2s1r9lEuUex/wtpQG5Xmp8KtMumUb7Ju05e/zZLNm8JCDXFRFpKMxspZl9bWYLzWzeHvZfamaLfV+zzeyIYMWixFIkCL7cXMIP+eUMbJ1Mm2T/5mKM9GpltRaJMRzSJI55W0ooq6p71RKgeXJzpo+YTlJsEqePO51VO1YF5LoiIg3ISc65Xs65PnvYtwI4wTnXE7gXeCpYQSixFAmw1QUVfLK+mK7pcfRunuD3eQ2hWlltQFYipVWO3LzAVC0BOqZ35L1L36O4opjTxp3GlqItAbu2iEhD5pyb7Zzb7vv2C3ZdKjugGt0E6e3atXMvvfRSqMOQBsrjIK+0iiiDZgnR+Dv7osd5uGbeNTgcz/Z5lmiL7MQSYHtZFRUeaJ7o//Pgj693fs2di+/koOSDeOiIh0iM9m9QlIhIJDrppJPK8a5iWO0p59wuFUczWwFsBxzw5O77dzv2TqCrc+7aYMTb6BJLrbwjweJxjgk/5LO+qILLu6TTItH/2bwiaZUdf60urOCV73dyattkejcPbPI3dflULnj1AgZ2Gsjbl7xNXPS+p3ESEYlE/qy8Y2atnXPrzawF8D7epa9n7eG4k4DHgWOdc1uDEa+awkUC5NMNxawurOD0din7lVQ2lL6Vu2ufEkvb5Bi+2FRClSewH2DP7XIuT53zFO//9D5XvnklHheYvpwiIpHIObfe9+9mYDLQd/djzKwn8AwwOFhJJSixFAmIH3aWM2dTCUc0i+fwZv73q4SG1bdydwNaJlFQ4WHJ9rKAX/vq7Ku57+T7GL9kPL9773c0ttYXEREAM0s2s9Tqx8BpwJLdjmkPTAIuc859F8x4tPKOSB3tKKvi7VUFtEiM3u+lDBtqtbJap9RYWibGMGdjMYdnxBMV4DW//3TMn9hctJmHv3iYrJQs/nLcXwJ6fRGRCJAFTPbNlRwDvOKce8/MbgRwzj0B3AU0Ax73HVe5l9HjdabEUqQOKj2ON1cW4IDzO6URE7V/iVMkrgm+P8yMAS0TmbSigG+2l9E9Y/+quf5c/9+n/ZstxVv460d/pUVyC649Mij90UVEwpJz7ifgV/NS+hLK6sfXAvXy5qjEUqQOPlpXxMbiSi7olErT+P1LDBt6tbJa5yZxZCZEM2dTCd2axvu1AtH+iLIonjv3OfKK87jh7RvITMrkvK7nBfQeIiLiH/WxFDlAS7eVsiCvlL4tEjk0PX6/z2/IfStrMjMGZCWRV1rFdzvLg3KP2OhYXr/odY5qfRTDXh/GrFW/GgwpIiL1QImlyAHIK63kvTWFtE2O4YTWSft9fmOpVlbr2jSOpvFRzN5YHLRBNslxybxzyTt0atqJc8efy+JNi4NyHxER2TslliL7qbzKMXlFAbFRxuBOqUQfQNNuY6lWVosy4+isJDaVVLGioCJo92mW1IzpI6aTGp/K6eNOZ8X2FUG7l4iI/JoSS5H94Jxj+ppCtpVWcW7HVFJj9z8pbGzVymo9msaTFhvcqiVA+ybtmT5iOmWVZZw27jQ2F20O2r1ERGRXSixF9sPCraUs3V7Gsa2S6Jh6YKu9NLZqZbXoKKNfViJriypZU1gZ1Ht1a96Ndy55h3X56zjj5TPIL8sP6v1ERMRLiaWInzYUV/DB2iIOSotlQNaBLVHYWKuV1Xo2SyA5xpi9qTjo9+rfrj+vXfQaizYu4vxXz6esMvCTtIuIyK6UWIr4oaTSw+QVBSTHRHFOh9QDnjKnsVYrq8VGGX1bJLKyoIL1RcHra1ntrEPP4rnBz/HRio+4bPJlVHmqgn5PEZHGTImlyD4453h7VQGFFR7O65RKYsyB/do09mpltV6ZCSREG7M3ltTL/S4/4nIePPVBXlv2Gr9997da+lFEJIg0QbrIPny5uYQf8ys4tW0yrZNjD/g6DX2VHX/FR0fRp3kin20sZlNxJVlJwX8bunPAnWwu2syDsx8kKyWLu064K+j3FBFpjFSxFKnFqoJyPllfzGHpcRyZeeDLEapauas+zROIizLm1ENfy2r3n3I/VxxxBTkzc3hi3hP7PkFERPabKpYie1FY4WHqygKaxkczqH1KnZYiVLVyVwkxURzZPIEvNpWwtbSSZgnBfysyM54+52nyivO4+Z2baZ7UnAu7XRj0+4qINCaqWIrsgcc5pqzMp6zKcX6nVOKjD/xXRdXKPTuqeSIxBnM21U9fS/Au/Tjxoon0b9efSyZdwscrPq63e4uINAZKLEX2YNaGYtYUVjKofQrNE+tWTWvsI8H3Jjk2il6ZCSzdVsaOsvobrZ0Um8Rbw9+ic0ZnBk8YTO6G3Hq7t4hIQ6fEUmQ33+8s44tNJfRqlkCPjAPvVwmqVu5L3xaJRJl3gFR9ykjM4L0R75GekM4ZL5/Bj9t+rNf7i4g0VEosRWrYUVbF26sKyUqM5pS2yXW+nqqVtUuLi+bwjAQWby2loKJ+55hsm9aWGZfNoNJTyWnjTmNj4cZ6vb+ISEOkxFLEp9LjeHNFAQDnd0ojJurAB+uAqpX+OjorEY+DufXY17Ja18yuvHPJO2ws3MigcYPYWbqz3mMQEWlIlFiK+Hy4roiNJZWc3SGF9Pi6VxdVrfRPenw03ZrGs3BrKcUVnnq/f7+2/Zh08SSWblnK4AmDKa0srfcYREQaCiWWIsDSbaXk5pXSr0UinZvE1/l6qlbun/4tE6nwwFdb6r9qCXD6Iafz/ODn+WTVJ1w66VIt/SgicoCUWEqjl1dSyXtrCmmbHMPxrZMCck1VK/dPZkIMXdLjWLCllNLK+q9aAlza81IePv1hJn0ziZvfuVlLP4qIHAAlltKolVc5Jq8oIDbKGNwpleg6TIJeTdXKAzMgK4kyj2N+Xuiaom8/+nb+fOyfeWrBU+TMzAlZHCIikUor70ij5ZzjvTWFbCurYtghaaTGBqayqFV2DkxWUgwHp8Uyb3MJRzVPJC667kn+gfjnwH+yuWgz9866lxbJLbil7y0hiUNEJBKpYimNVm5eKcu2l3FcqyQ6pMYF5JqqVtbNgJZJlFQ5cvNC09cSvEs/PnH2EwzuMpjfvvtbJi6dGLJYREQijRJLaZQ2FFXw4boiDk6LpX9WYsCuq76VddMmOZYOKbHM3VxCpSd0fRxjomIYf+F4jm1/LCMmjeCDnz4IWSwiIpFEiaU0OiWVHiavLCA5JoqzO6RiAehXCapWBsqAlokUVToWbw3ttD+JsYlMHT6VrpldOf/V85m3fl5I4xERiQRKLKVRcc7x9qoCCis8nNcplcSYwP0KqFoZGO1TYmmTHMMXm0qoCvHI7PSEdN4b8R6ZSZmc+fKZfL/1+5DGIyIS7pRYSqPyxaYSfsyv4OQ2ybROjg3YdVWtDBwzY0BWEvkVHpZuKwt1OLRObc30EdNxOE4bdxrrC9aHOiQRkbClxFIajVUF5czaUMxh6XEcmZkQ0GurWhlYB6XFkpUYzZxNxXjCYD7JQ5sdyruXvktecR6Dxg1iR+mOUIckIhKWlFhKo1BY4WHqygIy4qMZ1D4lYP0qQdXKYDAz+rdMYnuZh293lIc6HAD6tO7D5KGT+TbvW84Zfw4lFaEbuS4iEq6UWEqD53GOKSvzKfc4zuuUSnx0YF/2qlYGR5cmcTRLiGbOxuKwWQXnlINO4aXzX+Lz1Z8z7I1hVHoqQx2SiEhYUWIpDd6s9cWsKaxkULsUmicGdk0AVSuDx8zon5XIltIqvt8ZHlVLgKE9hvKfM/7D1OVTueGtG8Im6RURCQdaeUcatO93lvHF5hJ6NUuge0Zg+1WCVtkJtm5N4/lsQzFzNpXQuUlcQLsw1MUtfW/5eXWerJQs/nXyv0IdkohIWFBiKQ3WjrIq3l5VSFZiNKe0TQ749VWtDL4oM47OSuK9NYWsLKigU1pgVkgKhFEnjmJT4Sbu++w+spKzuO3o20IdkohIyCmxlAap0uOYvCIfgPM7pRETFfhKl6qV9aNHRjyfbyxm9qbisEoszYzHz3qcvJI8bp9+O82Tm3PJ4ZeEOiwRkZBSH0tpkD5YW8SmkirO7pBCenzgkz5VK+tPTJTRr0UiaworWVNYEepwdhEdFc3LF7zMCR1O4Jqp17B40+JQhyQiElJKLKXBWbKtlIVbSzm6RSKdm8QH5R4aCV6/jshMICnGmL2xONSh/EpCTAITL5pIekI6Q18fSmF5YahDEhEJGSWW0qBsKalk+ppC2qXEcHzrpKDcQ9XK+hcbZfRtkciKggo2FIVX1RKgRXILXrngFZbnLeeWabeEOhwRkZBRYikNRlmVh8krCoiLMgZ3TCMqSCOIVa0MjezMBOKjjdmbwnNi8pM6ncRdJ9zFC4te4IWFL4Q6HBGRkLDGNgdbcnKyKyoqCnUYEmDOOaauLODbHeUMOySNDqnBGeThcR56ju2Jx3n4+qavlVjWs083FPH5xhKu6Zoe8DlJA6HKU8UpL53C3HVzmX/9fLpmdg11SCIS4cys2DkX+KlN9na/UXYMsNDluCIbZSOAI4FHXY5b5c/5qlhKg7Agr5RvdpRzfKukoCWVoGplqPVpnkhclDEnTKuW1YN5kmKTuPi1i7Xso4hEorFAsY2yI4A/AquAF/09WYmlRLz1RRV8uK6Ig9NiOTorMWj3Ud/K0EuMiSI7M4FvtpexrbQq1OHsUevU1rx0/kt8vflrfjf9d6EOR0Rkf1W6HOeAwXgrlY8Cqf6erMRSIlpJpYc3VxSQEhvF2R1Sg7oyi6qV4eGoFolEG3yxKfxGiFcbdMgg/nTMn3hy/pO8uuTVUIcjIrI/CmyU/RkYAbxjoywaiPX3ZCWWErGcc7y9qoCiSg/nd0olMSZ4L2dVK8NHSmwUPZslsGRbGTvLw7NqCXDvSffSv21/rnvrOn7c9mOowxER8ddQoAy4xuW4jUAb4EF/T9bgHYlYszcWM2tDMae1TebI5sFrAgeYuHQiQ18fyvgLxzOsx7Cg3kv2bWd5FU8u206vZgmc1i4l1OHs1aodq8h+MpuDmh7E51d/TnxMcOZVFZGGq74H79SVEkuJSCsLynn1h3wOaxrPOR1SgtoE7nEeDh97OM45jQQPI9NWF7B0Wxk3dc8gJTZ8G1+mfDuF8149j9v63cYjgx4JdTgiEmFCMCr8AuB+oAVgvi/nclyaP+eH33wdIvtQUFHF1JUFZCREM6hdcJNKgNeXvc6yLcu0JniY6Z+VxNdby5i7uYSBbcL3w/zgroO5rd9tPPrlo5zY8UTO63peqEMSEanNA8A5Lsd9cyAnh+/HfJE9qHKOKSsKqPA4zu+YSlx0cJNK9a0MX03jozmsaTy5eSWUVHpCHU6t7j/lfnq36s1VU65i1Q6/poITEQmVTQeaVIIqlhJhZq0vZm1RJed0SCGzHibIVrUyvPXPSmTZ9jK+2lLC8a3Ct2oZHxPPq0NeJfvJbIa/MZxPrvyE2Gi/B1mKiNSneTbKXgXexDuIBwCX4yb5c7IqlhIxvttRxpebS8jOTKB7RkLQ76dqZfhrnhjDoU3imL+llNKq8K5aHpxxMM+c+wxz1s7h7x//PdThiIjsTRpQDJwGnOP7Otvfk/da8hmdm1cA7HVkz8jszFo7cZpZO7wztbcEPMBTzrlHzexVoIvvsHRgh3Oul5l1BL4Blvv2feGcu9F3rd7A80AiMA24zTnnzCzed4/ewFZgqHNuZW1xSWTaUVbFO6sLaZkUw8n11J9O1crIMKBlEt8t30HullL6t0wKdTi1urj7xXy04iPu//x+Tux4IoMOGRTqkEREduFy3FV1OX+fo8JH5+bdA2wEXsI7MuhSIHVkduYDtV7YrBXQyjm3wMxSgfnAec65ZTWO+T9gp3PuHl9i+bZzrscerjUXuA34Am9i+R/n3LtmdjPQ0zl3o5kNA853zg2tLS6NCo88lR7HS9/tYGe5hyu7pJMeH/wkTyPBI8vEH3ayoaSSm7plBL3fbV2VVJTQ75l+bCjcwKIbF9E6tXWoQxKRMBaCUeFtgceAY/AWGD8DbnM5bq0/5/vTFH76yOzMx0dmZxaMzM7MH5mdORa4cF8nOec2OOcW+B4X4K1Gtvk5cO9Q3ouB8bVdx5egpjnn5jhvFvwicJ5v92DgBd/j14GTLdhDhKXefbiuiE0lVZzdIbVekkr4pVqpVXYiw4CWSZRUOhZtLQ11KPuUGJvIxIsmUlxRzCVvXEKVJ3wneReRRul/wFSgNd687S3fNr/4M/qhanRu3qXABLyZ63Bgv94JfdXIbODLGpuPAzY5576vsa2TmeUC+cDfnHOf4v2hambJa/klQW0DrAFwzlWa2U6gGZC32/2vB64HiIuL25/QJcRW5JeTm1dK3xaJHNKkfv7v1Lcy8rRNiaVdSszPfXBjosL782XXzK6MPWssV7x5BffOupe7T7w71CGJiFRr7nJczUTyeRtlt/t7sj+J5SXAo74vB3zu2+YXM0sB3gBud87l19g1nF2rlRuA9s65rb4+lW+aWXe8ze+7q26/r23fLxucewp4CrxN4f7GLqFVWuXh3dWFNIuP5vhW9dd3Tn0rI9MxWUlM+DGfr7eVkp0Z3JWYAuHyIy7noxUfcc8n93B8h+MZ2GlgqEMSEQHIs1E2gl9ytOF4x7H4ZZ+J5cjszJV4m5z3m5nF4k0qX3bul2HqZhYDXIB30A0AzrkyfMPanXPzzexH4FC8Fcq2NS7bFljve7wWaAes9V2zCbDtQGKV8PPxuiIKKjxcdmiTeqtAqVoZuTqkxtIqKYYvNpXQs1kC0RHQK2bMmWP4ct2XXDrpUhbduIgWyS1CHZKIyNXAGOBhvMW62b5tftlnYjk6N+9QYCyQNTI7s8fo3LyewLkjszP/Udt5vr6OzwLfOOce2m33KcC3zv3SEdTMmgPbnHNVZnYQ0Bn4yTm3zcwKzOxovE3pl+PtVArePgBXAHOAIcBHrrGtUdlArcgvZ9HWMvq1SKR1cv3N96dqZeQyMwa0TOSNnwpYtq2Mw5sFf0qqukqJS+HVIa/S75l+XDb5Mt699F2iTLPAiUjouBy3Gjj3QM/35x3saeDPQAXAyOzMxcAwP847BrgMGGhmC31fZ/r2DePXg3aOBxab2SK8A3FudM5VVx9vAp4BfgB+BN71bX8WaGZmPwB3ACP9iEvCXM0m8OPqsQlc1crId0haHM0TovliUwmeCPmM2TOrJ48OepQZP87ggc9rnWxDRCRobJT90ffvYzbK/rP7l7/X8aePZdLI7My5o3N3GQ9Tua+TnHOfsec+kDjnrtzDtjfwNpvv6fh5wK+mIXLOlQLKABqYUDSBg6qVDYG3apnElJUFfLejnK5N40Mdkl+uO/I6PlrxEX/76G8c1/44jml/TKhDEpEIYmYrgQK8g6srnXN9dtvfFe/I7iOBvzrn/r2Hy1Qv4zivLrH4k1jmjc7NOxjfoJjRuXlD8A60EQm4UDWBq1rZcHRJjyMjPprZm4rpkh5HJMxAZmY8dc5TzFs/j+FvDCf3hlyaJTULdVgiEllOcs7l7WXfNuC3/DJd46+4HPeW798X9naMP/xJLH+Dd0R119G5eeuAFXgnSRcJqFA1gYOqlQ1JlBn9sxJ5Z3UhP+ZX1Ns0VXWVFp/Gq0Nepf+z/blqylVMGTYlIpJiEQl/zrnNwGYzO2tvx9goe4taVlx0Oc6vfpe1Jpajc/OigZtGZmeeMjo3LxmIGpmdWeDPhUX2V6iawFWtbHi6ZcTz2cZiZm8s5uC02IhJ0Hq37s2/T/s3t713G49++Si3H317qEMSkcjggBlm5oAnfdMs7q89NY/vt1oTy5HZmVWjc/N6+x43iHUQMzIymDlzZqjDkN2Uexw7y6roFhPFdzuj+K4e7/3x5o9ZtmUZfz/s73w669N6vLMEU8dKx86KKt5fF01cmE+YXtPh7nCOaXYMf5jxBxI2J9A1rWuoQxKR0Ioxs5r9Hp/aQ+J4jHNuvZm1AN43s2+dc7P25yYux31S50jxb63w/8M79c9rwM/J5cjszEl7PSmMaa3w8FNa5eG5b3YQG2Vc1TW93quVWhO8Yar0OJ5Yup2MhGgu6dwk1OHsl20l28h+Mptoiyb3hlyaJERW/CISOPu7VriZ3Q0U7mmATq37RtnX1N4U3tOf+/vTxzID74zrNZeFcEBEJpYSfkLVBA7qW9mQxUQZfbMS+WhdEWsLK2ibUn+DweoqIzGDCRdO4Lj/Hce1b13LxCETI6Y5X0Tql5klA1HOuQLf49OAew7gUmcHJJ7GNp+4KpbhZUV+Oa/+mE+/Fomc1MbvD2QBoWplw1de5Ri7bButk2K46ODIq/o98PkD/OmDPzH2rLHc2OfGUIcjIiGwr4qlb1GZyb5vY4BXnHP/NLMbAZxzT5hZS7zTCKUBHqAQ6LbbUtsBEbSVd0T2JZSjwEHVysYgLto4qnkiszYUs7G4kpZJ/jTShI87B9zJxys/5vb3bqd/2/4c0fKIUIckImHGOfcT8Ks3B+fcEzUeb2TX5bF/xUbZZy7HHWujrIBdm8QNcC7HpfkTjz99LD8B/gA8OTI7M9u3bcnI7MxfTVgeCVSxDB/vri5g8dYyLju0Sb3OWQmqVjYmpVUexi7dTsfUWM7v5Nf7YljZUrSFXk/2IiUuhfnXzyclLiXUIYlIPdrfPpahFrSVd0RqE6qJ0KupWtl4JERH0TszgdmbSsgrqSQzMbKqls2Tm/PKBa8w8MWB3PTOTbx43ovqbykiAWejLKO2/S7n52W2a+XPWuFaeUcCKtRN4Jq3svHp0yKR2CiYs6kk1KEckBM6nkDOCTmMWzyOFxbVaVEMEZG9yQMW4u2LOQ+YX+PL72UeD3TlnRH7GazIz0I5Chx+qVZOuHCCqpWNRFJMFNmZiXy1uYRjWyXRND7y/t//etxfmblyJr+Z9hv6tulLt+bdQh2SiDQsjwEnAp8D44HPXM7+j/D2e1R4Q1l5R30sQyuUo8Dhl76VAItvXKzEshEprPAwduk2emTEc0b71FCHc0A2FGzgiCeOICsliy+v/ZKk2Pqv+ItI/arPPpY2ygxvcjkc6AvMAMa6HLfC32vstWI5Ojfvjr1sB2BkduZD+xGrSMibwEHVysYsJTaKns0SWLS1lGNaJpEWF3n//61SWzHugnGcPu50bn/vdp4650BWbRMR2TNfhfJjG2W5wDDgXuB74Gl/r1FbH8tU31cf4Cagje/rRkBtMLLfqpvAz+qQEpIm8Oq+ld2ad2NItyH1fn8JvX4tEsHBl5sjs68lwGkHn8afj/0zTy94mvFfjw91OCLSQNgoS7ZRdomNsinANCAFONLlOL+TSqilYjkyO3MUwOjcvBnAkdVN4KNz8+7Gu7yjiN9CPQocVK0USI+PpntGPIvyShmQlURyrD/jF8PPPSfdw6xVs7j+7evp07oPnZt1DnVIIhL5NuOtTo4HfsA7aPsoG2VHAbgc59eKi/68q7YHymt8Xw503J9IpXELhyZwVSulWv+sJCodfBXBVcuYqBjGXzieuOg4hr4+lLLKslCHJCKR7zUgF+iKd3nHc2p8+b3coz+jwl8C5o7OzZuMN3s9H9B8F+K3UI8CB1Ur5RcZCdEclh7HgrxS+mUlkhgTmVXLdk3a8fzg5zl3wrn84f0/8J8z/hPqkEQkgrkcd2UgrlPrO+ro3DwDXgSuArYDO4CrRmZn3heIm0vDV90E3jeETeCqVsru+rdMotzjmL+lNNSh1Mk5Xc7hd0f/jsfmPsbkbybv+wQRkSCrtWI5MjvTjc7Ne3NkdmZvYEE9xSQNRDg0gYOqlfJrLRJjOKRJHPO2lHBUiwTioyOzagkw+pTRfLr6U66eejXZrbLpmN4x1CGJSCPmz7vpF6Nz844KeiTS4IR6FDioWil7NyArkdIqR25eZFct46LjeHXIq3ich2GvD6OiqiLUIYlII+ZPH8uTgBtH5+atBIoAA9zI7MyewQxMIls4jAIHVStl71onx9IxNZa5m0vo3TyR2BB9+AmEg5oexDPnPMPFr1/MXz/6Kw+c+kCoQxKRCGajbADegdo/54kux73oz7n+JJZnHFhY0liFSxO4qpWyLwNaJvHK9ztZtLWUPs0TQx1OnVzU/SJuWnkTD85+kBM7nsiZnc8MdUgiEoFslL0EHIx33fAq32aHd8zNPvmTWB4EdPdddNnI7MyP9z9MaUzCYRQ4qFop+9Y+JZa2yTF8uamE7GYJREdw1RLgodMfYvaa2Vw++XIW3riQtmltQx2SiESePkC3A1knHGrpYzk6N6/N6Ny8L4G78SaXhwB3j87Nmzs6N6/NgdxMGr5wGAUOqlaK/wa0TKKgwsOSbZE/F2RCTAITL5pIaWUpl7xxCZWeylCHJCKRZwnQ8kBPrq1iOQYYOzI78/maG0fn5l0OPA4MPtCbSsMULk3goGql+K9TaiwtE2OYs6mYw5vFE2WRXbU8tNmhPHn2k4yYPIJ7PrmHe066J9QhiUhkyQSW2SibC/z8idvluHP9Obm2xLLbyOzM83ffODI788XRuXl/3e8wpcELlyZwVStlf5gZA1omMmlFAd9sL6N7RkKoQ6qzS3teykcrPuIfs/7BCR1O4OSDTg51SCISOe6uy8m1JZZ7LPOMzs2L2ts+abzCZRQ4qFop+69zkzgyE6KZs6mEbk3jsQivWgL854z/8MW6L7h00qUsunERWSlZoQ5JRCKAy3Gf1OX82uaxfGt0bt7To3Pzkqs3+B4/AUyry02lYQmnJnBVK+VAmBkDspLIK63iu53loQ4nIJLjkpk4ZCL5ZfmMmDwCj/OEOiQRCWM2yj7z/Vtgoyy/xleBjbJ8f69TW8Xyj8B9wKrRuXmr8I4K74B3nfC/1CF2aWDCpQkcVK2UA9e1aRyfboxi9sZiDm0S1yCqlt1bdOexMx7j2reuZfRno/nLcXrrFpE9cznuWN+/qXW5jrl9jCYfnZuXiHdEuAE/jMzOLK7LDUMtOTnZFRUVhTqMBmNFfjmv/phPvxaJnNQmed8nBJHHeTh87OEALL5xsRJL2W+Ltpby7upCLjoojYObxIU6nIBwzjFi8ggmLJnAzCtmclyH40IdkojsBzMrds6F9g/sfthnYtnQKLEMnNIqD899s4PYKOOqrukhr1ZOXDqRoa8PZcKFExjaY2hIY5HIVOVxPLlsO6lxUYzo3KRBVC0BCsoKOPKpIympKGHhjQvJTMoMdUgi4qdISyz9WStcZI/CYS3waupbKYEQHWX0y0pkXVElqwsbzprbqfGpTBwykS3FW7jyzStpbAUFEak/SizlgITLROjVqvtW3nX8XWoClzrp2SyB5BhjzqaSUIcSUNmtsnnotId45/t3ePiLh0MdjoiEKRtlyTbKonyPD7VRdq6NMr//0O8zsRydm/fG6Ny8s3zTDImE1ShwULVSAis2yujbIpGVBRWsL2o4VUuAm4+6mQsOu4A/ffAn5q6bG+pwRCQ8zQISbJS1AT4ErgKe9/dkf5LFscAlwPejc/NGj87N63ogUUrDEU5N4KBqpQRedmYiCdHG7I0Nq2ppZjx77rO0TWvL0NeHsqN0R6hDEpHwYy7HFQMXAI+5HHc+0M3fk/eZWI7MzvxgZHbmpcCRwErg/dG5ebNH5+ZdNTo3L/RtoFKvwq0JXNVKCYa4aOOoFon8kF/OpuKGtd52ekI6Ey6cwNr8tVw79Vr1txSR3ZmNsv7ApcA7vm21TU+5C7+at0fn5jUDrgSuBXKBR/Emmu/vT6QS2cKtCRxUrZTg6Z2ZQFyUMWdTRM+wtkf92vZj9MmjeeObNxg7b2yowxGR8HI78GdgsstxS22UHQR87O/J/sxjOQnoCrwEPD8yO3NDjX3zRmZn9jmQqENF0w0duHdXF7B4axmXHdokbKqVmrdSgumT9UXM2VTCdYel0yzB7w/sEcHjPJw7/lze/+l9vrz2S3q17BXqkERkD0I13ZCNsmSX4/Y7YfKnYvnMyOzMbiOzM++rTipH5+bFA0RaUikHLtyawEHVSgm+o5onEmM0uBHiAFEWxfPnPU/zpOZc/NrFFJQVhDokEQkDNsr62yhbBnzj+/4IG2WP+3u+P4nlP/awbY6/N5DIF45N4OpbKfUhKTaKXpkJLN1Wxo6yqlCHE3CZSZmMv3A8P27/kRvfuVH9LUUE4BHgdGArgMtxi4Dj/T15r207o3PzWgJtgMTRuXnZeJd0BEgDwiO7kHoRTmuBV9Oa4FJf+rVIJDevlC83l3B6u5RQhxNwx3U4jntOvIe/ffw3Tu50MldnXx3qkEQkxFyOW2Ojdvl77/cn69o6DZ2Od8BOW+ChGtsLgL/sR3wSwaqbwPuFURO4qpVSn1Ljojk8I4HFW0sZkJVIalzD+yAz8tiRzFw1k1um3UK/Nv3o3qJ7qEMSkdBZY6NsAOBslMUBv8XXLO4PfwbvXDgyO/ONusUYPjR4x3/hthZ4Na0JLvVtR1kVTy7bTp/mCZzctuFVLQE2Fm6k1xO9aJbUjK+u+4qkWDVMiYSD+h68Y6MsE+/sP6fgba2eAdzmctxWv87fW2I5OjdvxMjszHGjc/N+D/zqoJHZmQ/t4bSwp8TSf+E2Chw0ElxC5+1VBXy7vYybu2eQFNswFyL74KcPOO2l07g6+2qeOfeZUIcjIoRuVPiBqq0pvPqH2NPHc/XwbuDCsQkc1LdSQqd/ViJLtpXx1ZYSTmgdMe/x++WUg07hr8f9lX98+g9O6ngSl/a8NNQhiUg9sVH2GLXkdy7H/daf6+w1sRyZnfmk7+EHI7MzP6+5b3Ru3jH+XFwiUziOAgf1rZTQapYQQ5f0OBZsKaVfi0QSYhpm1TLnxBw+WfUJN75zI0e1OYpDmx0a6pBEpH7MC8RF/Jnx9zG8q+zsa5s0EOE4ChxUrZTQG5CVxPIdO5ifV8oxLcPnQ1cgxUTF8MqFr9DriV4MfX0oc66ZQ0JMQqjDEpEgcznuhUBcp7bphvoDA4Dmo3Pz7qixKw3QX/UGKlybwFWtlHCQlRTDwWmxfLW5hKOaJxIXHT4fvAKpbVpbXjjvBc4efzZ3zriTMWeOCXVIIlJPbJQ1B/4EdAN+/lTpctxAf86vrS0nDm//yhggtcZXPqC/7A1QuDaBg1bZkfAxoGUSpVWO3LyGtxpPTWcdehZ39r+T/371X95Y1mAmBhGRfXsZ7/RCnYBRwErgK39P9me6oQ4jszNX1SHAsKJR4XsXjqPAQSPBJfyM/34neaWV3NQ9I6y6iwRaeVU5x//veL7N+5bcG3Lp1LRTqEMSaXRCMN3QfJfjetsoW+xyXE/ftk9cjjvBn/P96WMZPzo37ymgY83jR2Zn+lUSlcgQrk3goL6VEn4GtExk/A/5LN5aypHNE0MdTtDERccxYcgEej3Ri2FvDOPTqz4lLjou1GGJSHBV+P7dYKPsLGA93sVy/OLPsMbXgFzgb8AfanxJAxGuTeCllaXc9+l9XD3lavWtlLDSPiWWNskxfLGphKoGvr52x/SOPDf4Oeaum8ufP/hzqMMRkeD7h42yJsDvgTuBZ4Df+XuyPxXLypHZmWMPMDiJAOE2Ctw5x5vfvsnvZ/yeFTtWcF7X83jk9EdUrZSwYWYMyEritZ/yWbqtjJ7NGvao6QsOu4BbjrqFh754iBM7nsg5Xc4JdUgiEiQux73te7gTOGl/z/enj+XdwGZgMlBWvX1kdua2/b1ZOFAfy12tyC/n1R/z6dcikZPahH7S5683fc3t02/noxUf0b15dx4Z9AinHHRKqMMS+RXnHM8v30G5x3HdYU2JstB/KAum0spSBjw7gFU7V7HwhoW0a9Iu1CGJNAr11cfSRtl1wEyX4763UWbAc8AFwCrgCpfjcv25jj9N4VfgbfqeDcz3fQVkEk0JrXBqAt9avJXfvPMbej3Zi9wNuYw5YwwLb1yopFLClpnRv2US28s8fLujPNThBF1CTAKvDnmV8qpyhr8xnEpPZahDEpHAug3vCHCA4UBP4CDgDuA//l5kn03hI7MzNQywgQqHJvCKqgqemPcEOTNzyC/L5+Y+N3P3iXfTLKlZSOIR2R9dmsTRLCGaORuLOSw9DmvgVcvOzTrz1NlPccmkS8j5OId/nvzPUIckIoFT6XJc9cCds4EXXY7bCnxgo+wBfy/iTx9LRufm9WC3iTJHZme+uB/BSpj5KQxGgX/w0wfc9t5tLNuyjJM7ncwjgx6hR4seIYlF5ECYGf2zEnl7VSHf7yzn0PT4UIcUdMMPH85HKz7ivs/u44SOJ3DawaeFOiQRCQyPjbJWwHbgZKDmJ0e/p7/YZ2I5OjcvBzgRb2I5DTgD+AxQYhmhfm4CTwhNE/gP237gzhl3MmX5FA5qehBvDn2Tc7uc2+CrPdIwdWsaz2cbipm0ooD46ELiooz4aCMuyoir8W/1ttr2xUUb8VFRxEYR1r8Pj57xKHPWzuGyyZex8IaFtEptFeqQRKTu7sLb1TEamOpy3FIAG2UnAD/5exF/KpZDgCOA3JHZmVeNzs3Lwjv0XCLUx+uKKKzwcFmn+m0CLygr4J+f/pOHv3iY2KhY7jv5Pn539O+Ij2n4VR5puKLMGHJQGsu2l1HucZRVOco9jnLfv4UVnp8fl1U5/J2cKL46+axOOquT0r0kqLXtC3SSmhSbxMSLJtLnqT6MmDyCGSNmaNYGkQjnctzbNso6AKkux22vsWseMNTf6/iTWJaMzM70jM7Nqxydm5eGd4T4QfsXroSLUDSBe5yHFxe9yJ8//DMbCzdyxRFX8K+T/0Xr1Nb1cn+RYMtMjOH4xH2/nTrnqHTskmj+8tjzc0JaVp2Y1jzO4ygu8+yyz+NnfL9KQP1ISHdPbON3S1K7Ne/Gf8/8L1dPvZp/ffov/n7C3+vwDIpIOHA5rhJvU3jNbfs1lY4/ieW80bl56cDTeEeEFwJz9+cmEh5C0QQ+Z80cfvveb5m3fh792vRjyrAp9G3Tt17uLRJuzIxYg9goo65zhzjnqHL8nHTuUindLVnd074dZbsmsh4/S6mxURAfFUVctBGbeB7HdHyPnE/uJia+D0e2Oe6XhLRGYhq/e7Lq+7ehT9Ek0hjtcx7Lmkbn5nUE0kZmZy7e54XN2uHth9kS8ABPOeceNbNXgS6+w9KBHc65Xr5z/gxcA1QBv3XOTfdt7w08j7fz6DTgNuecM7N43z16A1uBoc65lbXF1ZjnsazPtcDX5a9j5IcjGbd4HK1TW3P/KfdzyeGXEGX+zHAlIvWt0vPrCmnN6mnNimrNfTtL8/n79IGUVhTyh4EziYttRtV+JKm7Vk+jfq6OJsYY/bISSY1VE7s0bvW9Vnhd+TNB+vF72j4yO3NWrRc2awW0cs4tMLNUvNXO85xzy2oc83/ATufcPWbWDRgP9AVaAx8AhzrnqsxsLt75lb7Am1j+xzn3rpndDPR0zt1oZsOA851ztfYDaKyJ5U/55Uysh4nQSypKeGjOQ/zrs39R5anizgF3MvLYkaTEpQTtniISWgs3LuToZ45mYKeBvH3J23ic/ZJ87tbntLZkteax+RUeWiXFcGnnJqpsSqNWjxOkH1nbfpfjFvhzHX+awmuuC56AN/GbDwysNQDnNgAbfI8LzOwboA2wDMC8HXUurnGdwcAE51wZsMLMfgD6mtlKIM05N8d33ovAecC7vnPu9p3/OjDGzMztTxm2EaiPJnDnHJO+mcSd79/Jyh0rufCwC3nw1Afp1FTToIo0dL1a9uLh0x/m5mk383+z/48/HPMHYqKMJL8mtNuzZdvKmLqqgM83FnNcq4gp1ohEsv+rZZ9jH3lfNX8mSN9lUdjRuXntAL8nygQws45ANvBljc3HAZucc9/7vm+DtyJZba1vW4Xv8e7bq89ZA+CcqzSznUAzIG9/4mvogj0KfPGmxdz23m3MXDmTw1sczoeXf8jATn69/kSkgbixz418tPIj/vLRXzi2/bH0b9e/TtfrlhHPTwXlzN5YQofUONqnhGa+XZFI4CvCFeDtSljpnOuz234DHgXOBIqBK53btQLpctx+rwu+JwfyeXIt4Pcs1maWArwB3O6cy6+xazjepu+fD93D6a6W7bWds3sM1wPXA8TFxfkRdcMRzFHgecV5/P2jv/PUgqdomtCUx898nOt6X0dMVB3KFCISkcyMp895mvnr5zPsjWEsvGEhTROb1umap7ZNZl1RBW+tLODqrukkxqiPtkgtTnLO7a2wdgbQ2ffVDxjr+3ePbJT9amEcl+P8mr/cnwnSH+OXZC0K6AUs8ufiZhaLN6l82Tk3qcb2GLwLm/eucfhaoF2N79sC633b2+5he81z1vqu2QTYtnsczrmngKfA28fSn9gbgmA1gVdUVfD4V49z9yd3U1BWwC1H3ULOiTlkJGYE7B4iEnnSE9J5dcirHPPcMVw99WomXTypTnNoxkdHMbhjGi9+t4N3VxdyfqfUsJ44XiSMDQZe9HUV/MLM0s2sla/b4i5slNVpYRx/Pv7Nw9uncj4wB/jTyOzMEfs6yVd2fRb4xjn30G67TwG+dc7VbOKeCgwzs3gz64Q3q57r+6ELzOxo3zUvB6bUOOcK3+MhwEfqX/mL6ibws9qnBKwJfPoP0zniiSO4ffrtHNX6KBbftJhHz3hUSaWIAHBUm6O4/5T7efPbNxkzd0ydr9cyKYYTWiXx3U5v64uI7JEDZpjZfF8r7e5+7jroU7Nb4e6G4F3ScaPLcVfhXSTH75VM/GmzfA04xPd4+cjsTH9/s48BLgO+NrOFvm1/cc5NA4axazM4zrmlZjYR7+CeSuA3zrkq3+6b+GW6oXd9X+BNXF/yDfTZ5rturTIyMpg5c6afP0LkKvc4dpZV0S0miu92RvFdHa+3tngtY38ay+yts2md0Jp/dv8n/Zv1Z/PSzWxmc0BiFpGGoZfrRf+M/vx++u+J2xxHl9Qu+z5pH7qUVbF8oyMvIYYYFS2lcYkxs3k1vn/K1xJb0zHOufVm1gJ438y+dc7VnL3Hr66DPiUux3lslFXaKNvvhXH2Ot3Q6Ny8WOBBvMnhSrzVzRbAYyOzM0ePzs3LHpmdmevvjcJFY5huqLTKw7Pf7CAu2riqS3qdqpX5Zfn8Y9Y/eOSLR0iISeDvx/+d3/b7rZZhFJFabS3eSq8nexEfHc+CGxaQFp9Wp+sVVnh47tvtJMdEcUUd39dEIsn+TjdkZncDhc65f9fY9iQw0zk33vf9cuDEvTSFPw78BW+x7vd4F8ZZ6Kte7lNtTeH/B6QAHUdmZ/YemZ2ZDRwGHDQ6N28sMKmWcyWEAtEE7nEenst9js6Pdebfs//NZT0v47tbv+MPx/xBSaWI7FOzpGaMv3A8K3es5Pq3rqeuvZRSYqM4q30qW0qr+Hh9wy4OiOwPM0v2zReOmSUDpwFLdjtsKnC5eR2Ndw7xXyWVAC7H3exy3A6X454ATgWu8DephNoTyzOB60ZmZxZUbxiZnZmPt1l6GN5R3RJmqkeB963DKPDPV39O36f7cs3Uazgk4xDmXjeXZwc/S8uUlgGOVkQasmPbH8u9J93Lq0tf5ZkFz9T5egc3iaNP8wTmbynl+53qbynikwV8ZmaL8C65/Y5z7j0zu9HMbvQdMw34CfgB7xLdN+/tYjbKPqx+7HLcSpfjFtfcti+1NYV/NzI789D93RfuGnJTeF2bwNfmr+WP7/+R8UvG0ya1DQ+c+gDDewzXKEwROWAe52HQuEF8uvpT5l47l8OzDq/T9So9jhe/20FBuYerD0vXko/S4NXjyjsJQBLwMd5R4dV//NOAd12OO8yf69RWsVw2Ojfv8t03js7NGwF8s1/RSr040CbwkooS7v3kXrqM6cLkbyfz9+P/zvJblnPJ4ZcoqRSROomyKF46/yXSE9K5+PWLKSqv2wf7mChjcMdUKp3j7ZWFdW5iF5Gf3YB3BqCuwAJ+mRFoCvBffy9SW8WyDd5+lCW+CzvgKLwjs88fmZ25rg7Bh0xDrVgeyFrgzjleX/Y6d75/J6t3ruaibhfxwKkP0DG9Y3CDFZFG56MVH3HKi6dwRa8r+N/g/9X5eou2lvLu6kJObJ3E0VnBWapWJBzUV8Xy5/uNsltdjnvsgM/f16e90bl5A4HueEuiS0dmZ/rdzh6OGmJieSBN4As3LuS2925j1qpZHJF1BI8OepQTOp5QD9GKSGOV83EO98y6hxfOe4HLj/hVg9h+cc4xZWUB3+0oZ8ShTQK+sphIuAhBYhkH3Agc79s0E3jS5bgKv85vbM0IDTGxfHd1AYu3lnGZH2+uW4q28LeP/sbTC56mWVIz/jnwn1yTfQ3RUeqnJCLBVeWp4uQXT2be+nnMu34eXTO71ul6pZUenlu+gyjgqq7pxEdryUdpeEKQWD4DxAIv+DZdBlS5HHetP+frtzDC+TsKvLyqnIfnPEznxzrz3MLnuK3fbXx/6/dc3/t6JZUiUi+io6J5+YKXSYxNZOjrQympKKnT9RJioji3Qyo7yz3MWNOwCgYi9c1GWfWiOUe5HHeFy3Ef+b6uwtsV0i9KLCOYv2uBv/fDe/Qc25M7ZtzB0W2PZvGNi3l40MOkJ6TXX7AiIkCbtDa8eN6LLN60mDum31Hn67VNieWYlkks3V7Gkm2lAYhQpNGa6/u3ykbZwdUbbZQdBFTt+ZRf82dJRwlT1aPAL+vUZI/9Kr/b+h13TL+Dd75/h84ZnXl7+Nuc2flMjfQWkZA6o/MZ/HHAH3lg9gOc1OkkLu5+cZ2uN6BlIqsKy5mxpog2ybE0jVcrjMgBqE4O7gQ+tlH2k+/7joDfE6Srj2WEqm0U+M7Sndw7614e/fJRkmKTuOv4u7i1363ERceFKFoRkV1VVFVw/PPHs2zLMhZcv4CDMw7e90m1yC+v4rlvd9A0PpoRnZsQrSUfpYGox3ks1wIP+b5NBKKBIiAB7/rhD+3t3JrUFB6B9tYEXuWp4pkFz9D5sc48NOchrjziSr675Tt+P+D3SipFJKzERscy4cIJRFkUQ18fSlll3VbSSYuL5oz2KWwormTWhuIARSnSqETjXco7FW+Ltvm+j/Ft84uawiPQnprAP131Kbe9dxu5G3M5tv2xvDfoPY5sdWSIIxUR2bsO6R343+D/cf6r5zPyg5E8POjhOl2vS3o8vZpV8OXmEjqlxtIxTR+oRfbDBpfj7qnrRVSxjDC7jwJfvXM1w14fxvHPH8+W4i2Mv3A8s66cpaRSRCLCeV3P47d9f8sjXz7C1OVT63y9k9smk5kQzVurCiiu8AQgQpFGIyD9R5RYRpCaTeC9M2HUzFF0HdOVKcunkHNCDstvWc6wHsM0OEdEIsoDpz7Aka2O5Mo3r2T1ztV1ulZslHFux1RKqxzvrC7Qko8i/js5EBfR4J0I8u7qAhblldLEzeC+WX9mTf4ahnYfygOnPkD7Ju1DHZ6IyAH7YdsPHPnkkRyedTgzr5hJbHTdVtKZv6WE99cWcUqbZPq0SAxQlCL1r74nSK8rVSwjxE/55Uz7cS6vzB3MzW+PIDMpk1lXzmLCkAlKKkUk4h2ScQhPnfMUs9fM5q6P76rz9Y7MTOCQtDg+Xl/EpuLKAEQoIv5QYhkBVudv5LLJ1/LfWaeyseAHnj7nab667iuO63BcqEMTEQmYYT2Gcf2R1zP689FM/2F6na5lZpzZIYXE6CimriygvKpxtc6JhIqawsNYeVU5j335GHfNvIfSymKuPfIW7j8lRyvmiEiDVVJRQt9n+rKpcBMLblhA27S2dbreyoJyJvyQzxHN4jmjvd8zpoiEDTWFS0BM+34ah489nDvfv5N2Gf147sK5PHm2lmEUkYYtMTaRiUMmUlJZwunjTievOK9O1+uYGsfRWYks2lrGt9vrNlemiOybEssw823et5z58pmc9cpZgHHTMa/y+xNe5dLDeoU6NBGRenFY88N4a/hb/LT9JwaNG0R+WX6drndcqyRaJcXw7ppCdpb7veSxiBwAJZZhYkfpDu6YfgeHjz2c2Wtm89BpD/HgWZ/TodlAzmqfsse1wEVEGqoTO57I6xe9zqJNizhn/DkUVxz4ajrR5p2CyDl4a2UBnkbWBUykPimxDLEqTxVPzX+Kzo915pEvHuHqXlfz3a3fMbj7b1i2w/08EbqISGNz1qFnMe78cXy66lOGTBxCeVX5AV+raXw0p7dLZm1RJbM3lgQwShGpSYllCH2y8hN6P9WbG96+gW7NuzH/+vk8ec6TpCVk7nEtcBGRxmZoj6E8efaTvPvDu4yYNIIqz4E3ZXfPSKB703g+31jMmsKKAEYpItW0VngIrNqxij+8/wdeW/Ya7Zu0Z+KQiQzpNuTnFXP2tBa4iEhjdV3v6ygoL+D3M35PalwqT5/7NFF2YHWR09ols66ogrdWFnB113QSYlRfEQkkJZb1qKi8iAc+f4AHZj+AYYw6cRR3DriTpNhfqpLVa4H3UxO4iMjP7uh/BztLd3LPrHtIi0/jodMfOqDla+OjoxjcMZWXvtvJu2sKOa9jqpbBFQkgJZb1wDnHhCUT+OMHf2Rt/lqG9xjO/afcT7sm7XY5ruZa4GoCFxHZ1d0n3s3Osp088uUjNElowt0n3n1A12mVHMvxrZOYub6YxVvLOCIzIbCBijRiSiyDbP76+dz23m18vuZzjmx1JOMvHM+x7Y/d47FqAhcR2Tsz46HTHyK/LJ9Rn4yiSXwTftf/dwd0rX4tEllZUMH7awtpkxJDZoL+HIoEgjqXBMnGwo1cM+Uajnr6KL7f9j3PnvssX1331V6TyuomcI0CFxHZuyiL4ulznmZItyHcMeMOnl3w7AFdx8w4u0MqsdHG1JUFVHo0BZFIIOgjWoCVVZbxny//w72z7qW0spQ7B9zJ347/G2nxaXs9R03gIiL+i46K5uULXqawvJDr3rqOlLgUhvYYut/XSYmN4qz2qbz+Uz4z1xdxStuUIEQr0rioYhlgE5dO5I8f/JETOp7A0puX8sCpD9SaVMIvTeCaCF1ExD9x0XG8cfEbHNv+WEZMHsE7371zQNc5pEkcvZsnMG9LKT/sPPB5MkXEy1wjW4EgOTnZFRUVBe36VZ4qPl39KSd2PNGv43/KL2fij/n0a5HISW0iZo15EZGwkF+Wz8AXBrJ0y1Leu/Q9Tuh4wn5fo9LjePG7HRRUeLima1NSYlVzkfBhZsXOuYhJEPTbE2DRUdF+J5VqAhcRqZu0+DTeG/EeBzU9iLPHn81X677a72vERHmXfKyocry9qoDGVnARCSQlliGkJnARkbrLTMrk/cvep3lScwa9PIglm5fs/zUSYjilbQorCyqYu1lLPoocKCWWIaJR4CIigdM6tTUfXP4BCTEJnPrSqfy47cf9vsYRzeLpkh7HJ+uL2VCkJR9FDoQSyxBQE7iISOAd1PQg3r/sfSqqKjjlpVNYm792v843M85ol0JKbBRTVhZQVuUJUqQiDZcSyxBQE7iISHB0a96N6SOms7V4K6e+dCpbirbs1/kJMVGc0zGVneUe3l8bvIGeIg2VEst6piZwEZHg6t26N+9c8g6rdqzi9HGns6N0x36d3y4llgEtE1myrYyl20qDE6RIA6XEsh6pCVxEpH4c1+E4Jg2dxJLNSzj7lbMpKt+/6uMxLZNomxzD9DVF7CirClKUIg2PEst6pCZwEZH6M+iQQbxy4SvMWTuHCyZeQFllmd/nRplxTsdUzGDKygKqNAWRiF+UWNYTNYGLiNS/Id2G8Mw5zzDjxxlcMukSKj2Vfp/bJC6aM9qlsKG4kk83FAcxSpGGQ4llPVATuIhI6FyVfRWPnP4Ik76ZxLVTr8Xj/B/t3bVpPEc0i+eLTSWsLNCSjyL7osSyHqgJXEQktG47+jbuOfEeXlj0Are9e9t+ra5zcpsUmsVH8/bKQoorNQWRSG2UWAaZmsBFRMLD347/G7/v/3vGfDWGv3/8d7/Pi4v2LvlYUuVh2qpCLfkoUouYUAfQkKkJXEQkfJgZD576IPll+fzz03/SJL4JfzjmD36dm5UUw4mtk/lwXREL8krp3TwxyNGKRCYllkFU3QR+WacmagIXEQkDZsbYs8aSX5bPHz/4I2nxadzQ5wa/zu3TPIGVBeV8tK6IdimxtEjUn1CR3akpPEjUBC4iEp6io6J56fyXOKvzWdz0zk2M/3q8X+eZGWe1TyUh2pi6soAKj5rERXanxDII1AQuIhLeYqNjee2i1zih4wlcNvkypi6f6td5SbFRnN0hlbzSKj7Uko8iv6LEMgg0ClxEJPwlxiYyddhUerfuzcWvXcyHP33o13md0uLo1yKRhVtLWb7D/0nXRRoDJZYBpiZwEZHIkRqfyruXvkvnZp0ZPGEwX6z9wq/zjm+VRMukGN5dXUh+uZZ8FKmmxDLAmiVE06tZgprARUQiREZiBjNGzKBlSkvOePkMFm9avM9zoqOMwR1T8Th4a1UBHk1BJAIosQy4JnHRDFITuIhIRGmV2ooPLv+AlLgUTnvpNL7f+v0+z2kaH82pbZNZU1jJnE0l9RClSPhTYikiIgJ0TO/I+5e9j8d5OOWlU1i9c/U+z+mREU/3pvF8tqGYtYUV9RClSHhTYikiIuLTNbMr00dMZ2fpTk596VQ2FW6q9Xgz47R2yTSJi2LqygJKteSjNHJKLEVERGrIbpXNO5e8w9r8tZw27jS2l2yv9fj46CjO7ZhKYYWH99ZoyUdp3JRYioiI7OaY9sfw5tA3+TbvW8585UwKywtrPb51cizHtUri2x3lLN6mKYik8VJiKSIisgenHnwqEy6cwFfrvuK8CedRWlla6/FHZyXSISWWD9YWsrW0sp6iFAkvSixFRET24vzDzue5wc/x4YoPGfb6MCqq9j5Ax8w4u2MKMWZMWVlApZZ8lEZIiaWIiEgtLj/icsacMYYpy6dw9dSr8bi9D9BJjY3mzA4pbC6p4pP1WvJRGp+YUAcgIiIS7n7T9zfsLNvJXz/6K6lxqfz3zP9ituf5ijs3iefIzAq+2lJKx9Q4Dm4SV8/RioSOKpYiIiJ++POxf+ZPx/yJsfPG8ucP/1zrsQPbJNM8IZp3VhdQWKEpiCT4zCzazHLN7O097GtqZpPNbLGZzTWzHsGKQ4mliIiIH8yM+06+j5v63MT9n9/PfZ/et9djY3xLPpZXOd5ZVaApiKQ+3AZ8s5d9fwEWOud6ApcDjwYrCCWWIiIifjIzxpw5hksPv5S/fPQX/jv3v3s9NjMxhpPbJrOioIK5m7XkowSPmbUFzgKe2csh3YAPAZxz3wIdzSwrGLEELbE0s3Zm9rGZfWNmS83sthr7bjWz5b7tD/i2dTSzEjNb6Pt6osbxvc3sazP7wcz+Y76OLWYWb2av+rZ/aWYdg/XziIiIAERZFP8b/D8GdxnMLe/ewkuLXtrrsb2aJXBokzg+2VDMxmJNQSRB8wjwR2Bv/S4WARcAmFlfoAPQNhiBBHPwTiXwe+fcAjNLBeab2ftAFjAY6OmcKzOzFjXO+dE512sP1xoLXA98AUwDBgHvAtcA251zh5jZMOB+YGhtQWVkZDBz5sy6/WQiItLo3dziZtZsWsOVb17Jyu9XclzmcXs8rinQqbSKzzZCs4Ro9jzkR2SvYsxsXo3vn3LOPVX9jZmdDWx2zs03sxP3co3RwKNmthD4GsjFm6cFnNVXvw8zmwKMAa7D+6R8sNv+jsDbzrkeu21vBXzsnOvq+344cKJz7gYzmw7c7ZybY2YxwEaguavlh0pOTnZFRZoCQkRE6q6wvJBTXzqVBRsW8Pbwtzn14FP3eNzqggrG/7CTHhnxnNUhtZ6jlEhmZsXOueRa9t8HXIY3UUwA0oBJzrkReznegBV4C3z5gY63XvpY+pLGbOBL4FDgOF/T9SdmdlSNQzv5RjR9YmbVH/3aAGtrHLPWt6163xoA51wlsBNotof7X29m88xsXmWlmiJERCQwUuJSmHbJNLpmduW8V8/j89Wf7/G49qmx9G+ZyNfbylimJR8lgJxzf3bOtXXOdQSGAR/tnlSaWbqZVc97dS0wKxhJJdRDYmlmKcAbwO2+HyIGb8vA0cAfgIm+7HkD0N45lw3cAbxiZmmwx1aD6opkbft+2eDcU865Ps65PjExmrpTREQCp2liU2aMmEHbtLac9cpZ5G7I3eNxx7ZMok1yDNPXFLKjrKqeo5TGxsxuNLMbfd8eBiw1s2+BM/COIA+KoCaWZhaLN6l82Tk3ybd5Ld4SrXPOzcXb0TTTOVfmnNsK4JybD/yIt7q5ll07mLYF1te4VjvfvWKAJsC2YP5MIiIiu8tKyeL9y94nLT6N08edzrd53/7qmCgzzumQCgZTVxZQpSmIJMCcczOdc2f7Hj/hnHvC93iOc66zc66rc+4C59z2YMUQzFHhBjwLfOOce6jGrjeBgb5jDgXigDwza25m0b7tBwGdgZ+ccxuAAjM72nfNy4EpvmtNBa7wPR6Ct/yr31QREal37Zu054PLP8DMOPWlU1m1Y9WvjkmPj2ZQuxTWF1fy2YbiEEQpElzBrFgeg7cz6cAaUwidCTwHHGRmS4AJwBW+ZPB4YLGZLQJeB250zlVXH2/COzfTD3grme/6tj8LNDOzH/A2n48M4s8jIiJSq0ObHcr7l71PYXkhp7x0ChsLN/7qmMOaxtMzI545m0pYVVAegihFgqfeRoWHC40KFxGRYPti7Rec8uIpdGraiU+u/ISMxIxd9pdXOZ5fvoNyj+PqrukkxWi9EtmzfY0KDzd6JYuIiATY0W2PZsqwKXy/9XvOePkMCsoKdtkfF22c2zGVkkoP01YXasnHCPPd1u+47d3b9joLQGOmxFJERCQITj7oZCZeNJH56+dz7oRzKanYdVnHlkkxnNA6mR92lpObVxqiKMVfVZ4q3v7ubQaNG0SXMV0YO28sCzYsCHVYYUdN4SIiIkH0ytevMGLSCM469CwmXTyJ2OjYn/c553jtx3xWF1ZwRZd0midqSrxws71kO8/lPsfj8x7np+0/0SqlFTf2uZHre19Py5SWQb9/pDWFK7EUEREJsifnPcmN79zIsB7DGHf+OKKjon/eV1Th4blvt5MYE8UVXdKJjdKij+Fg8abFjJk7hnGLx1FSWcKx7Y/l1r63cn7X83f5cBBskZZY6qORiIhIkN3Q5wZ2lu3kTx/8idS4VJ48+0m8M+hBcmwUZ3dI5dUf8/loXRGnt0sJcbSNV0VVBVOWT+GxuY8xa9UsEmISuPTwS7ml7y30atnrV8c7537+fxQvJZYiIiL14I/H/JGdpTv512f/Ii0+jQdPffDnpKRTWhx9WyQyd3MJnVJjOTQ9PsTRNi6bizbz1PyneGLeE6wrWEfH9I48eOqDXJ199a9G9JdUevh2RxlLtpXRp3kihzXV/1VNSixFRETqyT8G/oP8snz+b87/0SS+CX8/4e8/7zuhVRKrCyqYtrqQlkkxpMVF13IlCYS56+by2NzHmLh0IuVV5Zx60KmMPWssZ3Y+c5fuClUex08F5SzZVsYPO8upcpCZEI2Klb+mxFJERKSemBmPnvEoBeUF3DXzLtLi07jtaO+yzdFR3imI/rd8O2+tKmD4IU2IUuYScGWVZUxcOpHH5j7GV+u/IjUulRt638DNR91M18yuPx/nnGNjcSVfbyvjmx1llFQ6kmKM7MwEemQkkJUYrWbwPVBiKSIiUo+iLIpnzn2GgvICbp9+O2nxaVyVfRUAGQnRnNo2hWmrC/liUwkDWiaFONqGY23+WsZ+NZanFzzNluItdM3sypgzxnDZEZeRFp/283H55VUs3eZt6t5aVkW0QecmcfTISKBTWizRSiZrpVHhIiIiIVBWWca5E87lg58+4NUhrzKk2xDAWyl7a1Uh32wvY8ShTWiTXH8jkBsa5xyzVs3isbmP8ea3b+JxHs7pcg639r2Vkzud/HPFsazKw3c7vE3dqworAGibHEOPjAS6pseREMKVkSJtVLgSSxERkRApKi/i9HGnM3fdXKYOn8qgQwYBUFrl4X/f7gDgqq7pJERrPZP9UVRexMtfv8yYuWP4evPXNE1oyrVHXstNfW6iU9NOAHicY1VBBUu2lfHdzjIqPJAeF0WPjAS6Z8TTND48+rgqsQxzSixFRCSc7CjdwcAXBvJt3rdMHzGd4zocB8C6ogrGfbeTrulxnNsxVf35/PDjth95/KvHeW7hc+wo3cERWUdwa99bGX74cJJivd0KtpRUsmRbGUu3l1FY4SE+2jgsPZ4eGfG0SY4Ju+dZiWWYU2IpIiLhZkvRFo5//njW5a/j4ys+pnfr3gDM3ljMrA3FnNk+hZ7NEkIcZXjyOA8zfpzBmLljmPb9NKKjornwsAu5te+tDGg3ADOjqMLDsu1lLNlWyqaSKgw4KC2WwzMSOKRJHDFhPCm9Esswp8RSRETC0dr8tRz73LEUlhcy66pZdGveDY9zTPghnw3FFVzVpSkZCeHRPBsOdpbu5PmFz/Pfr/7L99u+Jys5ixt638ANfW6gdWprKj2OH3aW8/W2Un7Kr8ABLRNj6JERz2FN40mOjYzuBUosw5wSSxERCVc/bPuB4/53HFEWxWdXfUanpp0oKK/i2W930CQuissOTQ/r6lp9WLp5Kf/96r+8uOhFiiqK6N+2P7f2vZULu11IbFQsa4sqWeqbIqisypEaG0X3pvF0z4iPyLXYlViGOSWWIiISzpZsXsIJz59AekI6n171Ka1TW/PdjjImrSigb4tEBraJmBwjYCo9lby1/C3GfDWGj1Z8RHx0PMMPH84tR91C79a92V5WxZJtpSzdVsaOcg+xUXBok3gOz4infWpsRM8HqsQyzCmxFBGRcPfVuq8Y+OJA2jdpzydXfkJmUiYz1hSyIK+Uiw9O46C0uFCHWC/yivN4ZsEzjJ03ltU7V9MurR03H3Uz1x55LSlxGXy7o5wl20pZW1QJQIeUWHpkxNMlPZ646MhNJmtSYhnmlFiKiEgkmLlyJme8fAbdm3fnw8s/JCkujReX76Co0sM1XZtGTB/BAzF//XzGfDWG8V+Pp6yqjIGdBnJr31s5o/NZrCl0LNlWyve+pRWbJUTTw9fU3RCXwVRiGeaUWIqISKSY9v00Bk8YTP+2/XlvxHsUVcbxwvIdtE+J5aKD08Juapy6KK8q5/VlrzNm7hjmrJ1Dcmwylx9xOb856jc0S+nCkm2lLNteRnGlIzHG6NbUO0VQy8TwmyIokJRYhjklliIiEkleXfIqw98YzumHnM6UYVNYsq2KGWuLGNgmmb4tEkMdXp2tL1jPk/Oe5Mn5T7KpaBOdMzrzm6N+w4XdLmNtSQJLtpWRV+pdWvGQJnH0yIjnoLS4RrO0YqQllpE3PEpERKQRGdpjKAXlBVz31nWMmDSCVy54hRUFccxcX0T7lFhaJkXen3LnHJ+v+Zwxc8fwxjdvUOWp4szOZ3Jjn1to3+x4lm2vYNyPFUAxbZJjOL1dMoelx4d0aUXxT+S9GkVERBqZa4+8lvyyfH4/4/ekxqXynzOf5PnllUxdWcCVXdIjZqBKSUUJr3z9CmO+GsPCjQtJT0jn1r63MrjbdRR42rJ8RxlLVhfTJC6KY1om0r1pgubujDBKLEVERCLAHf3vYGfpTu6ZdQ+p8ancPuB+JvxYwAdrCzmzQ2qow6vVyh0refyrx3k291m2lWzj8BaH89DpY+na6gJ+KohhTp6H+KhyX7/JBNqG4dKK4h8lliIiIhHi7hPvJr8sn0e+fIQm8U04ucsfmb2phE5pcRzWND7U4e3COccHP33AmK/G8Nbyt4iyKM7tch6Dut5AbHwfNpVUsXgbHJQWzcA2yRzSJI7YRj75e0OgxFJERCRCmBkPnf4Q+WX5vsplGq2bXcN7qwtplRRDenzom40Lygp4YdEL/Per//Jt3rc0T2rO9Uf9kex2V7CzsjnbPJAFDGyTTLem8aQ04GmTGiONChcREYkwVZ4qhr8xnNeWvcajZzxJVfyFZCZGc2nnJiFbZWZ53nLGzB3DC4teoKC8gF4tj+KUQ6+jRfpZVBFHSkwU3TO88022iMClFUNFo8JFREQkqKKjohl3wTgKywu5/d0befD0JNZ5BvHZhmKOb11/OUiVp4pp30/jsbmP8f5P7xMXHcdJB11Ir/ZXkZ6cTYxBl3TvfJMdInxpRfGPKpYiIiIRqriimEHjBjFn7Rz+etIrJCSfxCWHNKF9amxQ77utZBvP5T7H4189zoodK2iR3JrjD76KLq0uJSW+Oe1/XloxjvhoNXXXRaRVLJVYioiIRLD8snwGvjCQpVuWctMxr3Jw5rFc3TWdxCDM+bho4yIem/sYL3/9MqWVpXTPOpYj219Nl6wzaJ7orUx2z4inSQNcWjFUlFiGOSWWIiLS0OQV53HC8yewasdqrur/Bid2OJoLOqUGZMqeiqoKJn87mce+fIzP1nxGQkwSR7YdwlEdr6Fj0+4/L63YKklTBAWDEsswp8RSREQaovUF6znuf8eRV7ydq/pP4YrufcjOPPAlHzcWbuTp+U8zdt4TbChcT/OUTvTtcDV92g+jZ2YLemTEc3BaHNGaIiiolFiGOSWWIiLSUK3YvoJj/3csxRVV3HDMVH7f+0ia78cIbOccX677kv98OYbXlk2k0lPBoS0G0r/TtZzY8XR6NkvksKbxQWlmlz1TYhnmlFiKiEhDtmzLMo773/FgSYwcOI3bs7vvc+Lx0spSJiyZwENfPMbXmxaQEJPKke2Hc2rnaxnYvhs9MrS0YqgosQxzSixFRKShm79+Pie+MJDEuCzGnDOdiw/ttMfjVu9czcNfPM7/Fj7DztKttEjtwnEHXcPwHiM4KiuTdinqNxlqSizDnBJLERFpDD5d9SmnvnQ6zVIOYerw9+mdlQV4m7un//gRD8x5jJkr3gIH3VqewQXdb2DoYadyaNN4La0YRpRYhjklliIi0li88927DH51MO3Ts5l+6Zu8suQNnl3wOGt2fkNSXAYnHHQ5N/S+kZM7dNbSimFKiWWYU2IpIiKNyfOLJnL1m8MBh8PRNr0nww+/id/0GUGHtJRQhyf7EGmJpZZ0FBERacCuPOJitpeV8+4PM7gu+1ou6HIs0VGqTkpwqGIpIiIiEqYirWKpjywiIiIiEhBKLEVEREQkIJRYioiIiEhAKLEUERERkYBQYikiIiIiAaHEUkREREQCQomliIiIiASEEksRERERCQglliIiIiISEEosRURERCQglFiKiIiISEAosRQRERGRgFBiKSIiIiIBYc65UMdQr8zMA5QE+TYxQGWQ79HY6DkNPD2ngaXnM/D0nAaWns/Aq4/nNNE5FzGFwEaXWNYHM5vnnOsT6jgaEj2ngafnNLD0fAaentPA0vMZeHpOfy1iMmARERERCW9KLEVEREQkIJRYBsdToQ6gAdJzGnh6TgNLz2fg6TkNLD2fgafndDfqYykiIiIiAaGKpYiIiIgEhBLL3ZjZX81sqZktNrOFZtYvANe828zuDER8kcTMnJm9VOP7GDPbYmZvB+j6jeZ5NbNmvtfjQjPbaGbranwfF8D7nBio/59QMrOHzez2Gt9PN7Nnanz/f2Z2hx/X6WhmS4IUZvU9CoN5/WCq5XW5w8yW1cP9rzSzMcG+T7gxs6oaz/tCM+u4h2OmmVn6HrY3mvfN3e3P33ffa6t1AO650swy63qdSBIT6gDCiZn1B84GjnTOlfleDAH7o90IFQE9zCzROVcCnAqsC3FMEck5txXoBd4/DEChc+7foYwpzM0GLgIeMbMoIBNIq7F/AHB7COJqUPb2uvQlOgf8AcXMYpxzmm9x70qcc732tMPMDG83tzPrN6TwdgB/368ElgDr9+Meet2iiuXuWgF5zrkyAOdcnnNufc1PHGbWx8xm+h7fbWbPmdlMM/vJzH5bfSHfJ6PlZvYB0KXG9uvM7CszW2Rmb5hZkpmlmtkKM4v1HZPmu2dsPf7swfIucJbv8XBgfPUOM8swszd9nx6/MLOevu16Xv1gZs+b2ZAa3xfWePwH3/Ox2MxG+bYlm9k7vudoiZkN9W0fZGbfmtlnwAU1rtHXzGabWa7v3y6+7Z+aWa8ax31e/X8XRj7HmzwCdMf7B6LAzJqaWTxwGICZfWJm830VzVa+bb19z9Ec4DfVF/RVMCaZ2Xtm9r2ZPVBj32lmNsfMFpjZa2aW4ts+2syW+f4f/u3b1sl37Fdmdm+Na6SY2Ye+a3xtZoN92+81s9tqHPfPmr8TYSzazJ42b4VohpklAvh+r/v4Hmea2Urf4yt9z91bwAwza2Vms8xbWVpiZsf5jrvKzL4zs0+AY6pvZmbnmNmXvtfrB2aWZWZRvv+r5r5joszsB2tgFSTzVta/MbPHgQVAO9v175beN/f+9/0u33OwxMyeMq8hQB/gZd/rL9FqzwOeMrMZwIvmreLP8L0OnwSsOgDz/r2b7/uduN637Roze7jGMdeZ2UP19aQEhXNOX74vIAVYCHwHPA6c4Nu+Esj0Pe4DzPQ9vhtvZSQeb0VkKxAL9Aa+BpLwVkl+AO70ndOsxv3+Adzqe/w/4Dzf4+uB/wv18xGA57MQ6Am8DiT4ntsTgbd9+x8DcnyPBwIL9bz69bzeDdwJPA8Mqfl8+/49De9IRcP74fFt4HjgQuDpGsc38f2/rAE6+46fWOP/Jw2I8T0+BXjD9/gK4BHf40OBeaF+TvbyPK0E2gM3ADcC9wJn4k1G5vheY819xw4FnvM9Xswvv/sPAkt8j68EfqrxvK0C2vleo7OAZN9xfwLuAjKA5fwySDLd9+9U4HLf49/U+H+LAdJ8jzN9r28DOgILfNujgB9rvt7D5av6del73BHvaiS9fN9PBEb4Hs8E+tT4OVfWeH7XAhm+738P/NX3OBpIxZscrAaa4602fQ6M8R3TtMZzfS2+33UgB7i9xu/GG6F+rgLwXFfhfT9dCEz2Pd8e4OjdXv+Z6H2z+ufb29/3jBrHvAScs/vrtObz6Xu8ex4wH+/qOAD/Ae7yPT4LcDXOq35tJ+L9sNsMSPb9Tsf69s0GDg/181WXL1Usa3DOFeL9Jbwe2AK8amZX7uO0d5xzZc65PGAzkAUcB0x2zhU75/Lx/iGp1sNX8fkauBRvNQXgGeAq3+Or8P5iRzzn3GK8b3rDgWm77T4W7y8yzrmPgGZm1sS3T8/rgTvN95WLt3rRFW/i+DVwipndb2bHOed2+vatcM5977zvauNqXKcJ8Jp5+xg+zC/P6WvA2b4KxtV4E9xwVF21HIA3kZxT4/t1QA/gfTNbCPwNaOt7/aU75z7xXeOl3a75oXNup3OuFFgGdACOBroBn/uudYVvez5QCjxjZhcAxb5rHMMvlfua1zfgX2a2GPgAaANkOedWAlvNLBvf/6vzNkGHuxXOuYW+x/Pxvg/sy/vOuW2+x18BV5m3if1w51wB0A/vH/Qtzrly4NUa57YFpvveA/7AL6/X54DLfY+vpmG8B5Q453r5vs73bVvlnPtiD8fqfZNa/76f5Kt0f423wNF971fZq6nO290LvB/ix/nu+Q6wvcZxvzWzRcAXeD+UdnbOFQEf4X1P7Yo3wfz6AGIIG+pjuRvnXBXeTyozfS+0K/B+8q5OwhN2O6WsxuMqfnlO9zaP0/N4Pwku8r2oT/Td93Nfc8YJQLRzLqgDBurZVODfeH/WZjW22x6OrX7e9Lzu28+vSzMzfukvZMB9zrkndz/BzHrjrdrd52u6mcren9N7gY+dc+ebt8/cTADnXLGZvQ8MBi7G++k9HM3Gm0Qejrc6sAZvFSwf7xt5G+dc/5onmHewQ21zsO3pdWl4E6Lhux9sZn2Bk4FhwC14/3Cxl3tcircS19s5V+FrIq5+v3kGb0WvJd5EKRLs/lwl+h7X9n5aVP3AOTfLzI7HW/V5ycwexPt/t7f/n8eAh5xzU83sRLyVJJxza8xsk5kNxJuYXnqgP1CYK6pln9432ePf9xvwtqr18b1O7ubXr8lqfr1uq2+1+8m+1+QpQH/fe+hMdv39/gvwLQ0giVfFsgYz62JmnWts6oW3uWsl3k864G1O3JdZwPm+fhmpwDk19qUCG3zVnt3f4F7EW8mI+BfWbp4D7tnDp7BZ+J4D3y9dnu8T9d7oed3VSn55XQ7G210AYDpwtf3Sz6+NmbUw7wjHYufcOLyJ/pF438g6mdnBvnNrJkdN+GWw1ZW73fsZvE0+X9WoMIWbz/F21t/mnKvyxZkO9Mdb6Wpu3g79mFmsmXV3zu0AdprZsb5r+JOEfAEcY2aH+K6VZGaH+p7/Js65aXgHCvWqEdewPVy/CbDZl1SehLfqWW0yMAg4Cu//byRbyS+v2yF7O8jMOuB9Pp4GnsX7ev0SONHXjy0W7wCtajVfr1fsdrln8FaRJvqSi8ZE75vs9e/7ct/jPN/va83XYwHe56faSvzLA2r+XTsDbxcN8L4+t/uSyq54WzoAcM59ibeCeQk1xiFEKlUsd5UCPOarWlTi7YtyPd6O/s+a2V/wvrHVyjm3wMxexdufYxXwaY3df/ddYxXepsmaL9yX8fZzifgXVk3OubXAo3vYdTfwP1/TXzG//mOw+3X0vO7qaWCKmc0FPsT3qdk5N8PMDgPmeAuZFAIjgEOAB83MA1QANznnSn2dyN8xszzgM7xNxAAPAC+Yd1qej2re2Dk338zyCe8/Ol/j7WP2ym7bUpxzm83bQf8/vubvGOARYCnepr/nzKwYP5I459wWX7VnvHkHBoG3ab0A7/9PAt6q5u98+24DXjHvgJw3alzqZeAtM5uH9zX+bY17lJvZx8COBpAY/RuYaGaXsdvrajcnAn8wswq8r+HLnXMbfFWlOcAGvF09on3H342368Y6vMl+pxrXmor3tRrOr9eg0Pvmz/b2930H3p99Jd7uF9WeB54wsxK8H0ZH4V8eMArve8EC4BO8fYIB3gNu9P29W473NVrTRLx9krcT4bTyThjx/aEb7Jy7LNSxNCR6XgPPV/2cCXR1znlCHE6DZ94pkxYAFznnvg91PJHGvKPQH3bOHRfqWCKF3jfrl3nnD37YOfdhqGOpK1Usw4SZPQacgbf/mwSIntfAM7PLgX8CdyipDD4z64Z3ZP9kJZX7z8xGAjfRcPtWBpzeN+uPr4I6F1jUEJJKUMVSRERERAJEg3dEREREJCCUWIqIiIhIQCixFBEREZGA0OAdEZG9MLMqvFORxOKdouQFvMtZatCSiMgeKLEUEdm7EudcLwAza4F3TswmeNefFhGR3agpXETED865zXgnVL7FvDqad53lBb6vAQBm9pKZDa4+z8xeNrNzzay7mc01s4Vmtni3VUBERBoETTckIrIXZlbonEvZbdt2oCvelXU8vtWLOgPjnXN9fOsr/845d55vVZ+FQGfgYeAL59zLZhaHdw3mknr9gUREgkxN4SIi+8d8/8YCY8ysF1AFHArgnPvEzP7razq/AHjDOVdpZnOAv5pZW2CSJjsXkYZITeEiIn4ys4PwJpGb8a79vQk4AugDxNU49CW8K71chW99aufcK8C5QAkw3cwG1l/kIiL1Q4mliIgfzKw58AQwxnn7EDUBNvhGiF8GRNc4/HngdgDn3FLf+QcBPznn/gNMBXrWW/AiIvVETeEiInuXaGYL+WW6oZeAh3z7HgfeMLOLgI+BouqTnHObzOwb4M0a1xoKjDCzCmAjcE/QoxcRqWcavCMiEmBmloR3/ssjnXM7Qx2PiEh9UVO4iEgAmdkpwLfAY0oqRaSxUcVSRERERAJCFUsRERERCQglliIiIiISEEosRURERCQglFiKiIiISEAosRQRERGRgFBiKSIiIiIB8f8RvJgwJ9dNxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# setting chart parameters\n",
    "days = sales_df[\"Day\"]\n",
    "orders_pd = daily_df['Quantity Ordered']\n",
    "sales_pd = sales_df['Sales']\n",
    "\n",
    "# plotting\n",
    "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax1.plot(days, orders_pd, color=\"skyblue\")\n",
    "ax2.plot(days, sales_pd, color=\"green\")\n",
    "\n",
    "# labelling plot\n",
    "ax1.set_xlabel(\"Days\")\n",
    "ax1.set_ylabel(\"Quantity Ordered\", color=\"skyblue\")\n",
    "ax2.set_ylabel(\"Total Sales in Millions\",  color=\"green\")\n",
    "\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd138bdc",
   "metadata": {},
   "source": [
    "### Cities\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bee9e5a",
   "metadata": {},
   "source": [
    "***\n",
    "* Which cities spent the most, `City Sales Trend`\n",
    "* Which cities made the most orders, `Orders per City Trend`\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "32f58de8",
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City State</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Atlanta GA</th>\n",
       "      <td>2848142.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austin TX</th>\n",
       "      <td>1837719.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boston MA</th>\n",
       "      <td>3714458.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dallas TX</th>\n",
       "      <td>2797839.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Los Angeles CA</th>\n",
       "      <td>5573987.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York City NY</th>\n",
       "      <td>4740706.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portland ME</th>\n",
       "      <td>461778.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portland OR</th>\n",
       "      <td>1913025.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Francisco CA</th>\n",
       "      <td>8387121.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seattle WA</th>\n",
       "      <td>2804253.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Sales\n",
       "City State                   \n",
       " Atlanta GA        2848142.13\n",
       " Austin TX         1837719.06\n",
       " Boston MA         3714458.62\n",
       " Dallas TX         2797839.19\n",
       " Los Angeles CA    5573987.54\n",
       " New York City NY  4740706.99\n",
       " Portland ME        461778.47\n",
       " Portland OR       1913025.22\n",
       " San Francisco CA  8387121.37\n",
       " Seattle WA        2804253.19"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sales per city\n",
    "spenddata = pd.DataFrame(data.groupby(\"City State\")[\"Sales\"].sum())\n",
    "spenddata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2ef72169",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extrapolating data\n",
    "spend_df = data.groupby(\"City State\").sum()\n",
    "spend_df = spend_df.sort_index().reset_index(drop=False)\n",
    "\n",
    "# plotting\n",
    "spend_df.plot.bar(\"City State\", \"Sales\", width=0.5, figsize=(10, 6))\n",
    "plt.title(\"City Sales Trend\", fontsize=20, pad=10)\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.xlabel(\"City, State\", size=10)\n",
    "plt.ylabel(\"Sales per City in Millions\", size=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e9c705a3",
   "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>Quantity Ordered</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City State</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Atlanta GA</th>\n",
       "      <td>15108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austin TX</th>\n",
       "      <td>10038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boston MA</th>\n",
       "      <td>20204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dallas TX</th>\n",
       "      <td>14968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Los Angeles CA</th>\n",
       "      <td>30175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York City NY</th>\n",
       "      <td>25329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portland ME</th>\n",
       "      <td>2510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portland OR</th>\n",
       "      <td>10209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Francisco CA</th>\n",
       "      <td>45572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seattle WA</th>\n",
       "      <td>14972</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Quantity Ordered\n",
       "City State                         \n",
       " Atlanta GA                   15108\n",
       " Austin TX                    10038\n",
       " Boston MA                    20204\n",
       " Dallas TX                    14968\n",
       " Los Angeles CA               30175\n",
       " New York City NY             25329\n",
       " Portland ME                   2510\n",
       " Portland OR                  10209\n",
       " San Francisco CA             45572\n",
       " Seattle WA                   14972"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# orders per city\n",
    "orderdata = pd.DataFrame(data.groupby(\"City State\")[\"Quantity Ordered\"].count())\n",
    "orderdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "16bf86ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extrapolating data\n",
    "city_orders = data.groupby(\"City State\").count()\n",
    "city_orders = city_orders.sort_index().reset_index(drop=False)   \n",
    "\n",
    "# plotting\n",
    "spend_df.plot.bar(\"City State\", \"Quantity Ordered\", width=0.5, color=\"orange\", figsize=(10, 6)) \n",
    "plt.title(\"Orders per City Trend\", fontsize=20, pad=10)\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.xlabel(\"City, State\", size=10)\n",
    "plt.ylabel(\"Orders per City\", size=10) \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a91ed39",
   "metadata": {},
   "source": [
    "> #### Monthly Sales Average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cace3dd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2923252.6483333334"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping data based on average monthly sales for the year\n",
    "mth_average = data.groupby(\"Month\")[\"Sales\"].sum().mean()\n",
    "mth_average"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f05a4c8e",
   "metadata": {},
   "source": [
    "> #### Sales Average per City"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c9699311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3507903.1780000003"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping data based on average city sales for the year\n",
    "city_average = data.groupby(\"City State\")[\"Sales\"].sum().mean()\n",
    "city_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8731150",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1839c729",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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