Exploratory Data Analysis With Python and Pandas.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 align=center>Exploratory Data Analysis With Python and Pandas</h2>\n",
    "<img src=\"ad1.png\" width=\"50\" height=\"60\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import calmap\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Link to data source: https://www.kaggle.com/aungpyaeap/supermarket-sales\n",
    "\n",
    "**Context**\n",
    "\n",
    "The growth of supermarkets in most populated cities are increasing and market competitions are also high. The dataset is one of the historical sales of supermarket company which has recorded in 3 different branches for 3 months data.\n",
    "\n",
    "**Data Dictionary**\n",
    "\n",
    "1. ***Invoice id:*** Computer generated sales slip invoice identification number\n",
    "\n",
    "2. ***Branch:*** Branch of supercenter (3 branches are available identified by A, B and C).\n",
    "\n",
    "3. ***City:*** Location of supercenters\n",
    "\n",
    "4. ***Customer type:*** Type of customers, recorded by Members for customers using member card and Normal for without member card.\n",
    "\n",
    "5. ***Gender:*** Gender type of customer\n",
    "\n",
    "6. ***Product line:*** General item categorization groups - Electronic accessories, Fashion accessories, Food and beverages, Health and beauty, Home and lifestyle, Sports and travel\n",
    "\n",
    "7. ***Unit price:*** Price of each product in USD\n",
    "\n",
    "8. ***Quantity:*** Number of products purchased by customer\n",
    "\n",
    "9. ***Tax:*** 5% tax fee for customer buying\n",
    "\n",
    "10. ***Total:*** Total price including tax\n",
    "\n",
    "11. ***Date:*** Date of purchase (Record available from January 2019 to March 2019)\n",
    "\n",
    "12. ***Time:*** Purchase time (10am to 9pm)\n",
    "\n",
    "13. ***Payment:*** Payment used by customer for purchase (3 methods are available – Cash, Credit card and Ewallet)\n",
    "\n",
    "14. ***COGS:*** Cost of goods sold\n",
    "\n",
    "15. ***Gross margin percentage:*** Gross margin percentage\n",
    "\n",
    "16. ***Gross income:*** Gross income\n",
    "\n",
    "17. ***Rating:*** Customer stratification rating on their overall shopping experience (On a scale of 1 to 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 1: Initial Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('supermarket_sales.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Invoice ID</th>\n",
       "      <th>Branch</th>\n",
       "      <th>City</th>\n",
       "      <th>Customer type</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Product line</th>\n",
       "      <th>Unit price</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Tax 5%</th>\n",
       "      <th>Total</th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Payment</th>\n",
       "      <th>cogs</th>\n",
       "      <th>gross margin percentage</th>\n",
       "      <th>gross income</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>750-67-8428</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Member</td>\n",
       "      <td>Female</td>\n",
       "      <td>Health and beauty</td>\n",
       "      <td>74.69</td>\n",
       "      <td>7.0</td>\n",
       "      <td>26.1415</td>\n",
       "      <td>548.9715</td>\n",
       "      <td>1/5/19</td>\n",
       "      <td>13:08</td>\n",
       "      <td>Ewallet</td>\n",
       "      <td>522.83</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>26.1415</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>226-31-3081</td>\n",
       "      <td>C</td>\n",
       "      <td>Naypyitaw</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Female</td>\n",
       "      <td>Electronic accessories</td>\n",
       "      <td>15.28</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.8200</td>\n",
       "      <td>80.2200</td>\n",
       "      <td>3/8/19</td>\n",
       "      <td>10:29</td>\n",
       "      <td>Cash</td>\n",
       "      <td>76.40</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>3.8200</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>631-41-3108</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Male</td>\n",
       "      <td>Home and lifestyle</td>\n",
       "      <td>46.33</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.2155</td>\n",
       "      <td>340.5255</td>\n",
       "      <td>3/3/19</td>\n",
       "      <td>13:23</td>\n",
       "      <td>Credit card</td>\n",
       "      <td>324.31</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>16.2155</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>123-19-1176</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Member</td>\n",
       "      <td>Male</td>\n",
       "      <td>Health and beauty</td>\n",
       "      <td>58.22</td>\n",
       "      <td>8.0</td>\n",
       "      <td>23.2880</td>\n",
       "      <td>489.0480</td>\n",
       "      <td>1/27/19</td>\n",
       "      <td>20:33</td>\n",
       "      <td>Ewallet</td>\n",
       "      <td>465.76</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>23.2880</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>373-73-7910</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sports and travel</td>\n",
       "      <td>86.31</td>\n",
       "      <td>7.0</td>\n",
       "      <td>30.2085</td>\n",
       "      <td>634.3785</td>\n",
       "      <td>2/8/19</td>\n",
       "      <td>10:37</td>\n",
       "      <td>Ewallet</td>\n",
       "      <td>604.17</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>30.2085</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>699-14-3026</td>\n",
       "      <td>C</td>\n",
       "      <td>Naypyitaw</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Male</td>\n",
       "      <td>Electronic accessories</td>\n",
       "      <td>85.39</td>\n",
       "      <td>7.0</td>\n",
       "      <td>29.8865</td>\n",
       "      <td>627.6165</td>\n",
       "      <td>3/25/19</td>\n",
       "      <td>18:30</td>\n",
       "      <td>Ewallet</td>\n",
       "      <td>597.73</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>29.8865</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>355-53-5943</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Member</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.84</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.6520</td>\n",
       "      <td>433.6920</td>\n",
       "      <td>2/25/19</td>\n",
       "      <td>14:36</td>\n",
       "      <td>Ewallet</td>\n",
       "      <td>413.04</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>20.6520</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>315-22-5665</td>\n",
       "      <td>C</td>\n",
       "      <td>Naypyitaw</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.56</td>\n",
       "      <td>10.0</td>\n",
       "      <td>36.7800</td>\n",
       "      <td>772.3800</td>\n",
       "      <td>2/24/19</td>\n",
       "      <td>11:38</td>\n",
       "      <td>Ewallet</td>\n",
       "      <td>735.60</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>36.7800</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>665-32-9167</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Member</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.26</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.6260</td>\n",
       "      <td>76.1460</td>\n",
       "      <td>1/10/19</td>\n",
       "      <td>17:15</td>\n",
       "      <td>Credit card</td>\n",
       "      <td>72.52</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>3.6260</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>692-92-5582</td>\n",
       "      <td>B</td>\n",
       "      <td>Mandalay</td>\n",
       "      <td>Member</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.84</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.2260</td>\n",
       "      <td>172.7460</td>\n",
       "      <td>2/20/19</td>\n",
       "      <td>13:27</td>\n",
       "      <td>Credit card</td>\n",
       "      <td>164.52</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>8.2260</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Invoice ID Branch       City Customer type  Gender  \\\n",
       "0  750-67-8428      A     Yangon        Member  Female   \n",
       "1  226-31-3081      C  Naypyitaw        Normal  Female   \n",
       "2  631-41-3108      A     Yangon        Normal    Male   \n",
       "3  123-19-1176      A     Yangon        Member    Male   \n",
       "4  373-73-7910      A     Yangon        Normal    Male   \n",
       "5  699-14-3026      C  Naypyitaw        Normal    Male   \n",
       "6  355-53-5943      A     Yangon        Member  Female   \n",
       "7  315-22-5665      C  Naypyitaw        Normal  Female   \n",
       "8  665-32-9167      A     Yangon        Member  Female   \n",
       "9  692-92-5582      B   Mandalay        Member  Female   \n",
       "\n",
       "             Product line  Unit price  Quantity   Tax 5%     Total     Date  \\\n",
       "0       Health and beauty       74.69       7.0  26.1415  548.9715   1/5/19   \n",
       "1  Electronic accessories       15.28       5.0   3.8200   80.2200   3/8/19   \n",
       "2      Home and lifestyle       46.33       7.0  16.2155  340.5255   3/3/19   \n",
       "3       Health and beauty       58.22       8.0  23.2880  489.0480  1/27/19   \n",
       "4       Sports and travel       86.31       7.0  30.2085  634.3785   2/8/19   \n",
       "5  Electronic accessories       85.39       7.0  29.8865  627.6165  3/25/19   \n",
       "6                     NaN       68.84       6.0  20.6520  433.6920  2/25/19   \n",
       "7                     NaN       73.56      10.0  36.7800  772.3800  2/24/19   \n",
       "8                     NaN       36.26       2.0   3.6260   76.1460  1/10/19   \n",
       "9                     NaN       54.84       3.0   8.2260  172.7460  2/20/19   \n",
       "\n",
       "    Time      Payment    cogs  gross margin percentage  gross income  Rating  \n",
       "0  13:08      Ewallet  522.83                 4.761905       26.1415     9.1  \n",
       "1  10:29         Cash   76.40                 4.761905        3.8200     9.6  \n",
       "2  13:23  Credit card  324.31                 4.761905       16.2155     7.4  \n",
       "3  20:33      Ewallet  465.76                 4.761905       23.2880     8.4  \n",
       "4  10:37      Ewallet  604.17                 4.761905       30.2085     5.3  \n",
       "5  18:30      Ewallet  597.73                 4.761905       29.8865     4.1  \n",
       "6  14:36      Ewallet  413.04                 4.761905       20.6520     5.8  \n",
       "7  11:38      Ewallet  735.60                 4.761905       36.7800     8.0  \n",
       "8  17:15  Credit card   72.52                 4.761905        3.6260     7.2  \n",
       "9  13:27  Credit card  164.52                 4.761905        8.2260     5.9  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Invoice ID', 'Branch', 'City', 'Customer type', 'Gender',\n",
       "       'Product line', 'Unit price', 'Quantity', 'Tax 5%', 'Total', 'Date',\n",
       "       'Time', 'Payment', 'cogs', 'gross margin percentage', 'gross income',\n",
       "       'Rating'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Invoice ID                  object\n",
       "Branch                      object\n",
       "City                        object\n",
       "Customer type               object\n",
       "Gender                      object\n",
       "Product line                object\n",
       "Unit price                 float64\n",
       "Quantity                   float64\n",
       "Tax 5%                     float64\n",
       "Total                      float64\n",
       "Date                        object\n",
       "Time                        object\n",
       "Payment                     object\n",
       "cogs                       float64\n",
       "gross margin percentage    float64\n",
       "gross income               float64\n",
       "Rating                     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] = pd.to_datetime(df['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.set_index('Date',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unit price</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Tax 5%</th>\n",
       "      <th>Total</th>\n",
       "      <th>cogs</th>\n",
       "      <th>gross margin percentage</th>\n",
       "      <th>gross income</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>996.000000</td>\n",
       "      <td>983.000000</td>\n",
       "      <td>1003.000000</td>\n",
       "      <td>1003.000000</td>\n",
       "      <td>1003.000000</td>\n",
       "      <td>1.003000e+03</td>\n",
       "      <td>1003.000000</td>\n",
       "      <td>1003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>55.764568</td>\n",
       "      <td>5.501526</td>\n",
       "      <td>15.400368</td>\n",
       "      <td>323.407726</td>\n",
       "      <td>308.007358</td>\n",
       "      <td>4.761905e+00</td>\n",
       "      <td>15.400368</td>\n",
       "      <td>6.972682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>26.510165</td>\n",
       "      <td>2.924673</td>\n",
       "      <td>11.715192</td>\n",
       "      <td>246.019028</td>\n",
       "      <td>234.303836</td>\n",
       "      <td>8.886215e-16</td>\n",
       "      <td>11.715192</td>\n",
       "      <td>1.717647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.080000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.508500</td>\n",
       "      <td>10.678500</td>\n",
       "      <td>10.170000</td>\n",
       "      <td>4.761905e+00</td>\n",
       "      <td>0.508500</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.125000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.894750</td>\n",
       "      <td>123.789750</td>\n",
       "      <td>117.895000</td>\n",
       "      <td>4.761905e+00</td>\n",
       "      <td>5.894750</td>\n",
       "      <td>5.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.420000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>12.096000</td>\n",
       "      <td>254.016000</td>\n",
       "      <td>241.920000</td>\n",
       "      <td>4.761905e+00</td>\n",
       "      <td>12.096000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>78.085000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>22.539500</td>\n",
       "      <td>473.329500</td>\n",
       "      <td>450.790000</td>\n",
       "      <td>4.761905e+00</td>\n",
       "      <td>22.539500</td>\n",
       "      <td>8.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.960000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>49.650000</td>\n",
       "      <td>1042.650000</td>\n",
       "      <td>993.000000</td>\n",
       "      <td>4.761905e+00</td>\n",
       "      <td>49.650000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unit price    Quantity       Tax 5%        Total         cogs  \\\n",
       "count  996.000000  983.000000  1003.000000  1003.000000  1003.000000   \n",
       "mean    55.764568    5.501526    15.400368   323.407726   308.007358   \n",
       "std     26.510165    2.924673    11.715192   246.019028   234.303836   \n",
       "min     10.080000    1.000000     0.508500    10.678500    10.170000   \n",
       "25%     33.125000    3.000000     5.894750   123.789750   117.895000   \n",
       "50%     55.420000    5.000000    12.096000   254.016000   241.920000   \n",
       "75%     78.085000    8.000000    22.539500   473.329500   450.790000   \n",
       "max     99.960000   10.000000    49.650000  1042.650000   993.000000   \n",
       "\n",
       "       gross margin percentage  gross income       Rating  \n",
       "count             1.003000e+03   1003.000000  1003.000000  \n",
       "mean              4.761905e+00     15.400368     6.972682  \n",
       "std               8.886215e-16     11.715192     1.717647  \n",
       "min               4.761905e+00      0.508500     4.000000  \n",
       "25%               4.761905e+00      5.894750     5.500000  \n",
       "50%               4.761905e+00     12.096000     7.000000  \n",
       "75%               4.761905e+00     22.539500     8.500000  \n",
       "max               4.761905e+00     49.650000    10.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2: Univariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 1:** What does the distribution of customer ratings looks like? Is it skewed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1eca09ddbd0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df['Rating'])\n",
    "plt.axvline(x=np.mean(df['Rating']), c='red', ls='--', label= 'mean')\n",
    "plt.axvline(x=np.percentile(df['Rating'],25), c='green', ls='--')\n",
    "plt.axvline(x=np.percentile(df['Rating'],75), c='black', ls='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Unit price'}>,\n",
       "        <AxesSubplot:title={'center':'Quantity'}>,\n",
       "        <AxesSubplot:title={'center':'Tax 5%'}>],\n",
       "       [<AxesSubplot:title={'center':'Total'}>,\n",
       "        <AxesSubplot:title={'center':'cogs'}>,\n",
       "        <AxesSubplot:title={'center':'gross margin percentage'}>],\n",
       "       [<AxesSubplot:title={'center':'gross income'}>,\n",
       "        <AxesSubplot:title={'center':'Rating'}>, <AxesSubplot:>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 2:** Do aggregate sales numbers differ by much between branches?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adogan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Branch', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Branch'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    342\n",
       "B    333\n",
       "C    328\n",
       "Name: Branch, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Branch'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adogan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Payment', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VuGvoJHkVcGJV/W6bfg3wq1V11tAyy4BlbfLpwFdHXujoHAR8Z9xF6BHz9Xvs2t1fu1+uqonZZuw56koerapaDiwfdx2jkGSqqibHXYceGV+/x675/NrtaoeP1gOHDk0vbG2SpBHY1ULhy8DiJIcl2Rs4BVg55pokad7YpQ4fVdVDSc4CPgPsAVxYVbeNuaxxmheHyXZjvn6PXfP2tdulTjRLksZrVzt8JEkaI0NBktQxFHqSZGOS1UPDo+qyI8k5Sd7cxi9q93Rsa/nTkzz50TyntpTkl5JckuTvk6xKclWSp+3gNu5MclBfNe7Odsb/f7Ptde+lJB+Z6UEhyVt3Vs3bef7/k2SXuvR1lzrRvJv5aVUtGePznw6sBb41xhp2K0kCXAGsqKpTWttzgIOBvxtnbfPBXP//Sfasqod2dPszN802bwX+7NFV/PMeaV2j5p7CCCV5XpLL2/jSJD9NsneSfZN8vbX/qyRfTnJLksuS/KPtbPO5Sf6mfWv6TJIF7ZvPJPCxtpeyX/9/3bzwIuDBqvrQTENV3QJ8Jck1SW5OcmuSpQBJHp/kf7bXcm2S3xra1huHln/GiP+Ox6pZ//9VdV2S45Ncl2QlcHuSPZK8p72X1iR5AwyCJckHW6ebnwN+cWZbM9/ak7wL2K+9dz62eRGt086b2+t6TWs7Osn1Sb6S5P8meXprPz3JyiSfB65Jsl/b07kjyRXALvfedE+hP/slWT00/efAZcCSNv1CBt/kn8fgdbixtV9eVR8GSPJO4AzgA7M9QZK92rylVTXdPnTOrarXt0t731xVUzv1r5rfjgBWzdL+M+DkqvpBOyx0Q/twOhH4VlX9M4AkTxpa5ztVdVSSfw28GfjdLbaqzW3t/z/jKOCIqvpGBt3h3F9Vz0uyD/ClJJ8FjmTQPc7hDPYwbgcuHN5IVZ2d5KzZ9vSTTAAfBo5rz3Ngm/W3wAvbZfUvYbCX8S+G6np2Vd2X5I+An1TVryR5NnDzI/lH9MlQ6M+sh4/asdBfYdD53/uA4xjck3FdW+SIFgb7A09gcM/G1jydwRvl6sGeNXsAG3ZS/Zq7AH+W5DjgYeAQBh84twLvTfJu4NNVdd3QOpe3x1XAK0dZ7G7spqr6Rht/KfDsoXNvTwIWM3i/fbyqNgLfat/gd8QxwLUzz1NV9w1tf0WSxUABew2tc/XQcscB72/rrkmyZgefv3cePhq9axl0Df4g8Dng19ow84FxEXBWVT0LeDuw7za2FeC2qlrShmdV1Ut7q1y3Ac+dpf23gQngue2LwD3AvlX1dwy+Jd4KvDPJnw6t80B73IhfzuZqa///GT8eGg/wxqH3xmFV9dkea/sPwBeq6gjgFfz8+/bHs6+yazIURu864E3A9VU1DfwCg2/8a9v8JwIb2qGh397Otr4KTCR5PgwOJyV5Zpv3w7Yt7TyfB/ZphyYAaIcAfhm4t6oeTPKiNk0GV3/9pKr+CngPg4DQIzfr/z/JC2dZ9jPA77f3EUmeluTxDL6U/VY757CAwXmK2Tw4s+5mbgCOS3JY2+7M4aMnsamfttO38TdcC/zLtu4RwLO3sexYGAr9mTlRNTO8q7XfyODQwrVteg1wa226tfzft2W+xOA45Va135x4FfDuJLcAq4EXtNkXAR/yRPPO016jk4GXtMOAtzE4V3QVMJnkVuC1bHrdngXc1M4tvQ145+ir3n1s4///7VkW/wiD8wU3J1kL/DcGe2RXAF9r8y4Grt/K0y0H1mx+orl9kVsGXN7ec59os/4j8OdJvsK29/zOB56Q5A7gHWz7HMlY2M2FJKnjnoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSPxcr7Zrk3wy2+lzapRavz4v2P6S0qNnKEgDP213vh4B/APwe+MuaMjxbLr/ROqVoSBt6TrgqUlekeTG1vPl55IcnORxSb7WOkajTa9LMpFB3/znJ7khydfbN/wLW4+YF81sPMlLW4+aN7e9kie09juTvD1DvacmWcQgoP6w7cnMdveutNMYCtKQJHsy6JvqVuCLwDFVdSRwCfBvquph4K/Y1AXJS4Bb2p2uAAcAzwf+EFgJnAc8E3hWkiWtF9V/B7ykqo4CpoA/GirhO639fAa93N4JfAg4r+3JDHeqJ+10dsQlDQx3dX4dcAGDPqk+0frI2RuY6YHzQuBK4D8Brwc+OrSdT1VVtS4v7qmqWwFalwyLgIUMum3+UuvZdm9+vqsFe0/VWBkK0sAWXZ0n+QDwvqpameR44ByAqroryT1JTmDQBfpwx4UzvZ8+PDQ+M70ng15Rr66qU7dSh72naqw8fCRt3XDPl6dtNu8jDA4jfbL1zT9XNwDHJnkqdL/Otr3fGLbHW42MoSBt3TnAJ5OsAr6z2byVDH4E6aObr7Qt7dzD6cDH2w+sXA9s7+c4PwWc7IlmjYK9pEqPQJJJBid//ZDWbsVjltIOSnI28Pts/0eQpMcc9xQkSR3PKUiSOoaCJKljKEiSOoaCJKljKEiSOv8f6PAhuQBTIuMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Payment'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3: Bivariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 3:** Is there a relationship between gross income and customer ratings?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adogan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Rating', ylabel='gross income'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(df['Rating'],df['gross income'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Branch', ylabel='gross income'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['Branch'], y=df['gross income'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Gender', ylabel='gross income'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['Gender'],y=df['gross income'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 4:** Is there a noticeable time trend in gross income?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Invoice ID</th>\n",
       "      <th>Branch</th>\n",
       "      <th>City</th>\n",
       "      <th>Customer type</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Product line</th>\n",
       "      <th>Unit price</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Tax 5%</th>\n",
       "      <th>Total</th>\n",
       "      <th>Time</th>\n",
       "      <th>Payment</th>\n",
       "      <th>cogs</th>\n",
       "      <th>gross margin percentage</th>\n",
       "      <th>gross income</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>750-67-8428</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Member</td>\n",
       "      <td>Female</td>\n",
       "      <td>Health and beauty</td>\n",
       "      <td>74.69</td>\n",
       "      <td>7.0</td>\n",
       "      <td>26.1415</td>\n",
       "      <td>548.9715</td>\n",
       "      <td>13:08</td>\n",
       "      <td>Ewallet</td>\n",
       "      <td>522.83</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>26.1415</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-08</th>\n",
       "      <td>226-31-3081</td>\n",
       "      <td>C</td>\n",
       "      <td>Naypyitaw</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Female</td>\n",
       "      <td>Electronic accessories</td>\n",
       "      <td>15.28</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.8200</td>\n",
       "      <td>80.2200</td>\n",
       "      <td>10:29</td>\n",
       "      <td>Cash</td>\n",
       "      <td>76.40</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>3.8200</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-03</th>\n",
       "      <td>631-41-3108</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Male</td>\n",
       "      <td>Home and lifestyle</td>\n",
       "      <td>46.33</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.2155</td>\n",
       "      <td>340.5255</td>\n",
       "      <td>13:23</td>\n",
       "      <td>Credit card</td>\n",
       "      <td>324.31</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>16.2155</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-27</th>\n",
       "      <td>123-19-1176</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Member</td>\n",
       "      <td>Male</td>\n",
       "      <td>Health and beauty</td>\n",
       "      <td>58.22</td>\n",
       "      <td>8.0</td>\n",
       "      <td>23.2880</td>\n",
       "      <td>489.0480</td>\n",
       "      <td>20:33</td>\n",
       "      <td>Ewallet</td>\n",
       "      <td>465.76</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>23.2880</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-08</th>\n",
       "      <td>373-73-7910</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sports and travel</td>\n",
       "      <td>86.31</td>\n",
       "      <td>7.0</td>\n",
       "      <td>30.2085</td>\n",
       "      <td>634.3785</td>\n",
       "      <td>10:37</td>\n",
       "      <td>Ewallet</td>\n",
       "      <td>604.17</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>30.2085</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Invoice ID Branch       City Customer type  Gender  \\\n",
       "Date                                                              \n",
       "2019-01-05  750-67-8428      A     Yangon        Member  Female   \n",
       "2019-03-08  226-31-3081      C  Naypyitaw        Normal  Female   \n",
       "2019-03-03  631-41-3108      A     Yangon        Normal    Male   \n",
       "2019-01-27  123-19-1176      A     Yangon        Member    Male   \n",
       "2019-02-08  373-73-7910      A     Yangon        Normal    Male   \n",
       "\n",
       "                      Product line  Unit price  Quantity   Tax 5%     Total  \\\n",
       "Date                                                                          \n",
       "2019-01-05       Health and beauty       74.69       7.0  26.1415  548.9715   \n",
       "2019-03-08  Electronic accessories       15.28       5.0   3.8200   80.2200   \n",
       "2019-03-03      Home and lifestyle       46.33       7.0  16.2155  340.5255   \n",
       "2019-01-27       Health and beauty       58.22       8.0  23.2880  489.0480   \n",
       "2019-02-08       Sports and travel       86.31       7.0  30.2085  634.3785   \n",
       "\n",
       "             Time      Payment    cogs  gross margin percentage  gross income  \\\n",
       "Date                                                                            \n",
       "2019-01-05  13:08      Ewallet  522.83                 4.761905       26.1415   \n",
       "2019-03-08  10:29         Cash   76.40                 4.761905        3.8200   \n",
       "2019-03-03  13:23  Credit card  324.31                 4.761905       16.2155   \n",
       "2019-01-27  20:33      Ewallet  465.76                 4.761905       23.2880   \n",
       "2019-02-08  10:37      Ewallet  604.17                 4.761905       30.2085   \n",
       "\n",
       "            Rating  \n",
       "Date                \n",
       "2019-01-05     9.1  \n",
       "2019-03-08     9.6  \n",
       "2019-03-03     7.4  \n",
       "2019-01-27     8.4  \n",
       "2019-02-08     5.3  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unit price</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Tax 5%</th>\n",
       "      <th>Total</th>\n",
       "      <th>cogs</th>\n",
       "      <th>gross margin percentage</th>\n",
       "      <th>gross income</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>54.995833</td>\n",
       "      <td>6.454545</td>\n",
       "      <td>18.830083</td>\n",
       "      <td>395.431750</td>\n",
       "      <td>376.601667</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>18.830083</td>\n",
       "      <td>6.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>44.635000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>11.580375</td>\n",
       "      <td>243.187875</td>\n",
       "      <td>231.607500</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>11.580375</td>\n",
       "      <td>6.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>59.457500</td>\n",
       "      <td>4.625000</td>\n",
       "      <td>12.369813</td>\n",
       "      <td>259.766062</td>\n",
       "      <td>247.396250</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>12.369813</td>\n",
       "      <td>8.112500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>51.743333</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>12.886417</td>\n",
       "      <td>270.614750</td>\n",
       "      <td>257.728333</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>12.886417</td>\n",
       "      <td>6.516667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>61.636667</td>\n",
       "      <td>4.583333</td>\n",
       "      <td>14.034458</td>\n",
       "      <td>294.723625</td>\n",
       "      <td>280.689167</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>14.034458</td>\n",
       "      <td>7.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-26</th>\n",
       "      <td>42.972308</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.188692</td>\n",
       "      <td>150.962538</td>\n",
       "      <td>143.773846</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>7.188692</td>\n",
       "      <td>6.623077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-27</th>\n",
       "      <td>56.841000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>13.822950</td>\n",
       "      <td>290.281950</td>\n",
       "      <td>276.459000</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>13.822950</td>\n",
       "      <td>6.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-28</th>\n",
       "      <td>45.525000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>10.616200</td>\n",
       "      <td>222.940200</td>\n",
       "      <td>212.324000</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>10.616200</td>\n",
       "      <td>7.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-29</th>\n",
       "      <td>66.346250</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>23.947875</td>\n",
       "      <td>502.905375</td>\n",
       "      <td>478.957500</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>23.947875</td>\n",
       "      <td>6.925000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-30</th>\n",
       "      <td>67.408182</td>\n",
       "      <td>5.888889</td>\n",
       "      <td>19.424500</td>\n",
       "      <td>407.914500</td>\n",
       "      <td>388.490000</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>19.424500</td>\n",
       "      <td>6.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Unit price  Quantity     Tax 5%       Total        cogs  \\\n",
       "Date                                                                  \n",
       "2019-01-01   54.995833  6.454545  18.830083  395.431750  376.601667   \n",
       "2019-01-02   44.635000  6.000000  11.580375  243.187875  231.607500   \n",
       "2019-01-03   59.457500  4.625000  12.369813  259.766062  247.396250   \n",
       "2019-01-04   51.743333  5.333333  12.886417  270.614750  257.728333   \n",
       "2019-01-05   61.636667  4.583333  14.034458  294.723625  280.689167   \n",
       "...                ...       ...        ...         ...         ...   \n",
       "2019-03-26   42.972308  4.000000   7.188692  150.962538  143.773846   \n",
       "2019-03-27   56.841000  4.500000  13.822950  290.281950  276.459000   \n",
       "2019-03-28   45.525000  4.800000  10.616200  222.940200  212.324000   \n",
       "2019-03-29   66.346250  6.750000  23.947875  502.905375  478.957500   \n",
       "2019-03-30   67.408182  5.888889  19.424500  407.914500  388.490000   \n",
       "\n",
       "            gross margin percentage  gross income    Rating  \n",
       "Date                                                         \n",
       "2019-01-01                 4.761905     18.830083  6.583333  \n",
       "2019-01-02                 4.761905     11.580375  6.050000  \n",
       "2019-01-03                 4.761905     12.369813  8.112500  \n",
       "2019-01-04                 4.761905     12.886417  6.516667  \n",
       "2019-01-05                 4.761905     14.034458  7.433333  \n",
       "...                             ...           ...       ...  \n",
       "2019-03-26                 4.761905      7.188692  6.623077  \n",
       "2019-03-27                 4.761905     13.822950  6.760000  \n",
       "2019-03-28                 4.761905     10.616200  7.050000  \n",
       "2019-03-29                 4.761905     23.947875  6.925000  \n",
       "2019-03-30                 4.761905     19.424500  6.800000  \n",
       "\n",
       "[89 rows x 8 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(df.index).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04',\n",
       "               '2019-01-05', '2019-01-06', '2019-01-07', '2019-01-08',\n",
       "               '2019-01-09', '2019-01-10', '2019-01-11', '2019-01-12',\n",
       "               '2019-01-13', '2019-01-14', '2019-01-15', '2019-01-16',\n",
       "               '2019-01-17', '2019-01-18', '2019-01-19', '2019-01-20',\n",
       "               '2019-01-21', '2019-01-22', '2019-01-23', '2019-01-24',\n",
       "               '2019-01-25', '2019-01-26', '2019-01-27', '2019-01-28',\n",
       "               '2019-01-29', '2019-01-30', '2019-01-31', '2019-02-01',\n",
       "               '2019-02-02', '2019-02-03', '2019-02-04', '2019-02-05',\n",
       "               '2019-02-06', '2019-02-07', '2019-02-08', '2019-02-09',\n",
       "               '2019-02-10', '2019-02-11', '2019-02-12', '2019-02-13',\n",
       "               '2019-02-14', '2019-02-15', '2019-02-16', '2019-02-17',\n",
       "               '2019-02-18', '2019-02-19', '2019-02-20', '2019-02-21',\n",
       "               '2019-02-22', '2019-02-23', '2019-02-24', '2019-02-25',\n",
       "               '2019-02-26', '2019-02-27', '2019-02-28', '2019-03-01',\n",
       "               '2019-03-02', '2019-03-03', '2019-03-04', '2019-03-05',\n",
       "               '2019-03-06', '2019-03-07', '2019-03-08', '2019-03-09',\n",
       "               '2019-03-10', '2019-03-11', '2019-03-12', '2019-03-13',\n",
       "               '2019-03-14', '2019-03-15', '2019-03-16', '2019-03-17',\n",
       "               '2019-03-18', '2019-03-19', '2019-03-20', '2019-03-21',\n",
       "               '2019-03-22', '2019-03-23', '2019-03-24', '2019-03-25',\n",
       "               '2019-03-26', '2019-03-27', '2019-03-28', '2019-03-29',\n",
       "               '2019-03-30'],\n",
       "              dtype='datetime64[ns]', name='Date', freq=None)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(df.index).mean().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date', ylabel='gross income'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=df.groupby(df.index).mean().index,\n",
    "             y=df.groupby(df.index).mean()['gross income'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4: Dealing With Duplicate Rows and Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Invoice ID</th>\n",
       "      <th>Branch</th>\n",
       "      <th>City</th>\n",
       "      <th>Customer type</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Product line</th>\n",
       "      <th>Unit price</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Tax 5%</th>\n",
       "      <th>Total</th>\n",
       "      <th>Time</th>\n",
       "      <th>Payment</th>\n",
       "      <th>cogs</th>\n",
       "      <th>gross margin percentage</th>\n",
       "      <th>gross income</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-02-18</th>\n",
       "      <td>849-09-3807</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Member</td>\n",
       "      <td>Female</td>\n",
       "      <td>Fashion accessories</td>\n",
       "      <td>88.34</td>\n",
       "      <td>7.0</td>\n",
       "      <td>30.919</td>\n",
       "      <td>649.299</td>\n",
       "      <td>13:28</td>\n",
       "      <td>Cash</td>\n",
       "      <td>618.38</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>30.919</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-10</th>\n",
       "      <td>745-74-0715</td>\n",
       "      <td>A</td>\n",
       "      <td>Yangon</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Male</td>\n",
       "      <td>Electronic accessories</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.803</td>\n",
       "      <td>121.863</td>\n",
       "      <td>20:46</td>\n",
       "      <td>Ewallet</td>\n",
       "      <td>116.06</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>5.803</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-26</th>\n",
       "      <td>452-04-8808</td>\n",
       "      <td>B</td>\n",
       "      <td>Mandalay</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Male</td>\n",
       "      <td>Electronic accessories</td>\n",
       "      <td>87.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.478</td>\n",
       "      <td>640.038</td>\n",
       "      <td>15:17</td>\n",
       "      <td>Cash</td>\n",
       "      <td>609.56</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>30.478</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Invoice ID Branch      City Customer type  Gender  \\\n",
       "Date                                                             \n",
       "2019-02-18  849-09-3807      A    Yangon        Member  Female   \n",
       "2019-03-10  745-74-0715      A    Yangon        Normal    Male   \n",
       "2019-01-26  452-04-8808      B  Mandalay        Normal    Male   \n",
       "\n",
       "                      Product line  Unit price  Quantity  Tax 5%    Total  \\\n",
       "Date                                                                        \n",
       "2019-02-18     Fashion accessories       88.34       7.0  30.919  649.299   \n",
       "2019-03-10  Electronic accessories         NaN       2.0   5.803  121.863   \n",
       "2019-01-26  Electronic accessories       87.08       NaN  30.478  640.038   \n",
       "\n",
       "             Time  Payment    cogs  gross margin percentage  gross income  \\\n",
       "Date                                                                        \n",
       "2019-02-18  13:28     Cash  618.38                 4.761905        30.919   \n",
       "2019-03-10  20:46  Ewallet  116.06                 4.761905         5.803   \n",
       "2019-01-26  15:17     Cash  609.56                 4.761905        30.478   \n",
       "\n",
       "            Rating  \n",
       "Date                \n",
       "2019-02-18     6.6  \n",
       "2019-03-10     8.8  \n",
       "2019-01-26     5.5  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.duplicated()==True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Invoice ID                 0.000\n",
       "Branch                     0.000\n",
       "City                       0.000\n",
       "Customer type              0.079\n",
       "Gender                     0.000\n",
       "Product line               0.043\n",
       "Unit price                 0.006\n",
       "Quantity                   0.019\n",
       "Tax 5%                     0.000\n",
       "Total                      0.000\n",
       "Time                       0.000\n",
       "Payment                    0.000\n",
       "cogs                       0.000\n",
       "gross margin percentage    0.000\n",
       "gross income               0.000\n",
       "Rating                     0.000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Date'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gpAbmS5Gmq7ciifJcImnDslLMXslaD+OxLyvprNXHPbY3KX773QaE4uJk8fg94xcsXfbFr52ASIJgijKB1YWFJfc3kn4UXSVpru1bCpf9O3CW7W/m5fh5wJqd/A6V4mKuqz8D2FlpB8GW/Rw6czdJoOcpp82FbiMNGqropA64aqcYa9jXUVys6uM69p1ibOq3l37oRxtjcCguBkEwCCZ2JqHOBoAm6f1AUrO9p5vTGa+4mLPi1ynEtCNwq+2FBftu+x78kDSLgKSVScsPt3e4/nxgW0kr5SS6bYHz8zT6w5Jm51j2rLj/ucCeOfbZwEPZttRvhX1ZH1f5HaFLjE399tIP/WgjCIJg8DSQZZa0n6T5hWO/Nm91lpMPA/aQdDdpFuHD3UKc8YqLkpYATpG0PCDgeuADZQFLOhJ4J7Bc7sTjbR/G6IPoFtJSx8dt31d147bvl/RZ0sAFYI7t+/PrDwInkxIoz8sHrXyEPOCZR8q9WAA8Buzdza/S/hPH2p5f1cdVfrP9dblSozLGpn576Yc+tRH0gVgqCILOeFH95QbbxwHHjbPJ3YGTbX9Z0j+Q1G1f6Q7bEITiYhAUCMXFIAjqMl7FxceOPbD2981y+3+tY1v5oX+Y7e3y+0MAbH+xcM3NwPa2/5Df3w7M7lSx15fqhiAIgiAI2pjYEsg6GwDeBbwBQNLLgWcCf+3kdFgUFw/I55xzCqrsS68ri02pJLOV+PgnSX8svF+mQyxrSboinz8z/2WWxXJIvua3krbrdo9tts/Ivhfkttbs5rfNvjTGXvw27Yd+tDEMPH7Pr8Z9BEEwySxy/aMLWUeoteT+G1IVw82S5ihtIAjwUeB9kq4Hvgu8x12WE4ZFcfFSYBvSGncnqq5bLDbb93lUcfFY4KuF9ws7xHJEvnYdUqXGPiX3sgFpFPgKkljSMUrVGJ3uscg+wAO5ja/mNiv9lthXxdjIb5d4B9lGEATB4Hn66fpHDWzPs72e7bVtfz6f+4zT7sDYvsX2q50UiDex/bNuPictcTFnl9+bXz8iqai4uFW+7BTgYuCTeU3kL5L+uc3ViOIigKSW4uKRbdeNlH/k61rlH7fYvjaf6xZz6XUdYquiNJbcB1uTkiMh3f9hLC6PvBNwhu0ngDuUtk1uyUWX3mOJ/WH59dnA0Uo3VeX3spZhvq4qxqZ+S+Pt0g/9aGPGE0mDQTANmAY5gUOjuNhnqmJ5HvBgnhYqnkfSjhrVa+hFcbE4pTRyXW7rodx2nT6qjLEHv437oU9tBEEQDJ5psHfD0CguTnXydFB7kkkT+25aD0EQBMFUokEJ5KAYKsXFkvjOz/bHN7+7jlTFch9pY6Kl2s7Xta97jyPX5bZWyG3Xse8UY1O/vfRDP9oYgwoiJYsW/b3skiAIgolnGmzwNGkzCXkduZPi4uE0UFy0/ReNKi7OzoJKmxSuWYpc/kF6GOzG6Hp0Ka160klgpBSlGEueObkI2IUkmVl1/3OB0yV9BXgxSQL6SpIYVJ17bPXxZbmtX+S2q/yO0CXGpn5L450CbYyhKFISOgkTR+zdEARdmAYzCTNecTH7/gjwCeCFwA2S5rlkQ6Sq67rEthidYgE+CZwh6XPAtaSBFDmfYFbORL1Z0lmkhMSn8z0vzNdV3eMcYH5etjiBpKS1gFSZsVuOq5PfecC+tu+pirFHv436oU9tBH0gHvBB0Bk/PfVXzkNxMQgKxExCEAR1Ga/i4t8/t0ft75tn/ft3xtVWr0x64mIQBEEQBCUM+XJDEARBEARVDLC0sS4hyzzW/rRsf5OkE3N1BpK2kvRQoZriMwpZ5pBlDoIgGA8TKMs8WYQs81hOA9YHNiRtL1xMbvxVS3bZ9pyQZQ5Z5iAIgnExDUogJ22QYPte29fk14+QNpxoyTKfki87BfiXfM1fbF9FqmAoMiLLnNXzWrLM7YxIIdt+klT2tlP2fa3tO2vEPM8ZUnndqg1uuWss0ojk8dn5upH7b2NEgtj2HUBLgrjyHkvsW318NvCG3HaV3xG6xNjUby/90I82giAIBo6fXlj7GBQhy1we79Kk8s2fFk7/g6TrJZ0n6RVdXIQsc8gyB0EQdGYaLDeELHM5xwCX2G6pwVwDrGH7UUk7AD8kiflMGCHLPDgk7QfsB6AlV2CJJZ414IiCIBgKpkF1Q8gyt8kySzoUWAU4uND+w7Yfza/nAUurQwJkh1hClnkKyjLbPs72LNuzYoAQBEHfmAY5CSHLPLadfYHtgDfYo38rSoqLf84zH1uQBlf3dXAdsszTSJY5mBxCljkIujANZhJClnksx+aYLsvLIufYnkN60HxA0tPA48Bu7iBV2SkWQpY5ZJmHhHjAB0Fn/PTU10kIWeYgKBCyzEEQ1GW8ssyPHLBD7e+b5xw9L2SZgyAIgmBomAbLDcOiuHhCLl+8QdLZueKizP7zkv4g6dG282tIujDbXyxpVUkbFhIn75d0R37982yzV77H30naq+Brc0k35hi/rmK5x+g1yp8tyG1uVvis1G+bfVUfV/ptsy+NsRe/TfuhH20EQRBMCaZBCeSwKC4eZHtj2xsBdwEHVMR8Lm3iQpkvAadm+znAF23f6FGFxbnAx/P7bSQ9FzgU2DL7O7T1sAO+me9n3XxsX9Lemwqf75dt6OK3SFUfl/otoSrGRn577Id+tBEEQTBwbNc+BsWwKC4+DCMVF8sCpT1u+/KC0FORDYBf5NcXUa5yWGQ74ALb99t+ALgA2F6p5HP53I6BU6lWXDzVictJpXwvqvJbYb9YH3fwO0KXGJv67aUf+tFGEATB4BnymYQRNAUUFyWdlNtbHziq4S1cz+jA5K3Ac5SqLaropA54d1mMkvZX0nvoZl+luHi8pFn5fFUf11FcrIyxB7+N+6FPbQRBEAwcP72o9jEoJn2QoDbFxeJn+RdeV8VF0kY9PyPJJF9HD4qLtvcm1df/Bti1ofnHgH+SdC3wT6R6/AlVfbR9rO1jx2G/r+35Jee79nGP7U2K3363AUlxUdJ8SfMXLfr7ZDcXBEGQGPaZBE0xxcVcV38GsLPSDoIt+zl0wPY9tt9me1Pg0/ncgx1MOqkDrlpyvol9HcXFqj6uY98pxqZ+e+mHfrQxBofiYhAEg2BRg2NATGZ1QzfFRWiguJj/bCkunm77Dx7duvlYCiqHkpYhifDMzVnx6xRi2hG41fbCgn3HfQ8krSyp1VeHACd2Cfl8YFtJK+Ukum2B8/M0+sOSZudY9qy4/7nAnjn22cBD2bbUb4V9WR9X+R2hS4xN/fbSD/1oIwiCYOB4kWsfg2LGKy7mh/spkpYnSfheD3ygLGBJR5JkjpeTdDdwvO3DgK2ALyptRnUJ8KFON277fkmfJQ1cAObYvj+//iBwMimB8rx80MpHyAOeeaTciwXAY8De3fwq7T9xbF5yKO3jKr/Z/rpcqVEZY1O/vfRDn9oIgiAYPNNAJyEUF4OgQCguBkFQl/EqLj646+trf9+seOZFobgYBEEQBMOCn576v0likBAEQRAEA2CQuQZ1GRZZ5gPyOUtauYP9WpKuyNeemRMgW5+9o3AvpytkmUOWOQiCYDwMc3UDU0uW+VJgG1IiXCeOAL5qex1SqeU+2fe6pKqGV9t+BUnzIWSZQ5Y5CIKgZ7yo/jEohkWW+Vrbd3aKN//K3Bo4uz020oPmG1n2F9vdtB1CljlkmYMgCDoz5DMJI2gKyDLX4HnAg3kg0m6/HrCepEslXS6p2y/SkGUOWeYgCIKO+On6x6CY9MRFtckyF5eFbVtJe6AS27+R1JJl/js9yjKPk6VI09VbkZT7LpG0YRfVxUZ4HJLM2X7fivNd+7jH9ibFb7/bgCTLTFrCQEuuQKguBkHQDyZ6GSH/gP0aSSvoeNuHl1zzDuAwkuT99bbf2cnnUMkyl8R3frY/HriPNIXdGjgV7e8G5tp+yvYdwG2kQUMVIcscssxBEAQdmcichC55ea1rFsuv6+Z3xssyd/Jre7tsv29et74I2KUkth+SZhFQqo5YD7i9g+uQZQ5Z5iAIgo5McOJiZV5egab5dTNfljn7/gjwCeCFwA2S5lVMz38SOEPS54BrSYMcGH0Q3UJa6vi47fuqbrwXqWCFLHPIMgdBMFy4flV2cVk0c5zt4wrvy/Kztmxzs172dSnpOXmY7Z92bNchyxwEI4QscxAEdRmvLPOfXrdV7e+bF15ycce2JO0CbN/6ASzp3cCWtg8oXPNj0o/td5Dz64CO+XWhuBgEQRAEA2DR0xOq71Yn5+xukqTAU8Adklr5dVdRQSgujrUvvU7STkoqf9dJmi/pNQrFxVBcDIIgGAe2ah81qJOX90Oa5deF4mIbVdddCGyc1+zfSyotCcXFUFwMgiDomYlMXMwaP628vN8AZ9m+WdIcSTvmy84H7lPKr7uILvl1EIqL7TGXXmf7UY8mbzyLVF/aiVBcDMXFIAiCjniRah+1/NnzbK9ne23bn8/nPmN7bn5t2wfb3sD2hrbP6OYzFBdrIumtkm4FfkKaTehEKC6G4mIQBEFH7PrHoAjFxZrY/gHwA0mvAz5LWpaYSP+huDiANiAUF4MgGAx1ZwgGSSgujiou1sL2JcBL1SEBskMsobgYiotBEAQALFqo2segCMXFrLjYpf11Ctn3mwHPIMk4VxGKi6G4GARB0JGJzkmYDEJxsUCH63YmPaCeAh4Hdi0kMi6GQ3ExFBcHzOP3/GrcPpZ98WsnIJIgCKqoWdo4UEJxMQgKhOJiEAR1Ga/i4oINtqv9fbPOLecPZEQRiotBEARBMAAWTYOZhNqDBElrAOva/rmkZYGlnPQPgiAIgiBoyKKFfVEhGBe1IpT0PuBs4L/zqVVJ8o6dbKaSLPNp+fxNkk7MVRdl9mtJuiLbn5kTIJF0cL6PGyRdKGkNhSxzyDIHQRCMg+mgk1B3GPMhUiLiwwC2fwc8v4vNVJJlPg1YH9iQlMRWVc1wBPBV2+uQSi33yeevBWbZ3og0WDrSIcscssxBEATjYDpUN9QdJDzhJHUMgKSl6CJN7Kklyzwvy1EauJKx9fOtexKwNWkQ0B7bRbYfy+cvL7NvI2SZQ5Y5CIKgI4us2segqDtI+KWkfwOWlfRG4HvAuXUb0RSRZVZaZng38NMS++cBD+aBSKl9Zh+6l9KFLHPIMgdBEHRkgneBnBTqJi5+ivRwvBF4PzDP9rfqGGpqyTIfA1xiu6cickl7ALOAf+qx/UocsswDaQNCljkIgsEwHRQI6s4kfNj2t2y/3fYutr+lnIjYCU0hWWZJhwKrAAcXzhVlme8jTWEvVWG/DfBpYEfbT3QJOWSZQ5Y5CIKgIwsXLVH7GBR1Wy7Lon9PJ4O8xj8lZJkl7Utau97dHt2Z2wVZ5rxufRGwS3tskjYlVXbsaLvroIaQZQ5Z5iAIgi5Mh+qGjssNknYH3gmsJam4D8JzSNUInZgysszAsbmty/Jyxzm255TE/EngDEmfI1U0nJDP/yfwbOB72f4u2ztW3bhDljlkmYMgCLowHcSUOsoyKwkorQV8kdFSNIBHgBsKSX5BMCMIWeYgCOoyXlnmq17y1trfN6/64w+mniyz7d+Tfs39Q3/CCYIgCILhYDrMJNRVXJwt6SpJj0p6UtJCSQ93sZlJiotf1WiS5G2SHlQoLobiYhAEwThwg2NQ1E1cPBrYHfgdo4qF3+hiM2MUF20f5FF1xaNIOQ2huBiKi0EQBD0zk6obsL0AWNL2Qtsn0eUL1zNIcbGN3YHvdrp3QnExFBeDIAi6sKjBMSjqDhIey1Pv10k6UtJBDWxnjOKiRhM5f9El5lBcDMXFIAiCjhjVPgZFXcXFd5MGBQcAB5Ee0jvXMdQMUlwkaS+cbbvX9itxKC4OpA0IxcUgCAbDomlQS1VrkGD795JWya//X13n6qC4aPteNVBcJGsWSPoCcLek1RjdP+JY4HrqKS6+v3DufNIv1fmktesVJS2VZxPKFPp2I+2I2Y0/Alu1xXIxE6O4WOa3nao+nhDFxQZ+e+mHfrQxBtvHAcfBzCmBfPyeXsfBoyz74tdOQCRBEFSxaIAzBHXpJqYkUlLYAaSZBEl6GjiqQoyo3baT4uLhNFBctP0XjSouzs6CSpsUrlmKrLhIehjsRhKCKiouvsFtiott7bQUF89oj03S+sBKwGXd4iUJOn2hkEC3LXBIFv55WEkx8AqSCuBRJfZzgQMknUFKyHsoPzRL/VbYl/Vxqd+iYW6nKsZGfqvi7dIP/Wgj6AMxUAmCziyc7oME0tLCq4FX2b4DQNJLgW9KOsj2VzvYziTFRUiDjjPcSX1qNJZQXOyxH/rUxoxnKjxcp0IMQTCVGWSuQV26KS5eC7zR9t/azq8C/Mz2ppMcXxD0lZmy3BAEweQzXsXFn75gt9rfN9v/+Yypp7gILN0+QACw/VdVCBIFQRAEQdCdQZY21qVbGeOTPX42XRUXS69TYozan0JxMRQXgyAIxsF0KIHsNkjYWCn5q/14hKRe2InpqLhYdd1ian8OxcVQXAyCIBgHi1T/GBQdBwm2l7S9fMnxHNsdlxs8zRQXu1zXVamwjVBcDMXFIAiCjixEtY9B0RdBaE0PxcVO19VRKqwTSyguhuJiEAQBMD1kmesqLvaMpqfi4niVGRvjUFwcSBtBEASDYtE0SJOa1JkEdVBczJ/XVly0vbnt15F2Z7xNKTGylTi4P13UBDWquHhw4dz52f74Ttd1811CJ3XA8Sou1omjqo8nRHGxgd9e+qEfbYxB0n6S5kuav2jR38suCYIgmHBm0lbRjcmZ5J0UF6GB4mL+s6W4eLrtP7QSB/Ov8KvIiotKm1HtltsqKi7u7jbFxWy/b6frsp89c4b9bEqUCts4H9hW0ko5iW5b4Pxs87Ck2bl/9qy4/6r2Sv1W2Jf1cdf76BJjU7+99EM/2hiD7eNsz7I9K/ZtCIKgXwz7csN0VFysuq5SqbAMh+JiKC4GQRB04ekJXm6QtD3wNdIz8Hjbh1dctzNwNklNeX5Hn+6uMhwEQ0MoLgZBUJfxKi5+58V71P6+2eOe73RsS0kG4DbgjaRE7atIs+K3tF33HOAnwDLAAd0GCX2pbgiCIAiCYCwTrJNQKQPQxmeBI4D/q+N00qsbgiDoP7EDYxBMfZrkGkjajyQk1+I4p23uW5SVg2/Z5mMzYDXbP5H08TrtTmbi4nSUZT4g21rSym2fbZUrIW6W9Esl3YZWdcWfJP2x8H6ZDrGsJemKfP5MpSTLslgOydf8VtJ2hfOlfttsn5F9L8htrdnNb5t9aYy9+G3aD/1oIwiCYCrQpLqhmGCdj+Mq3JYiaQngK8BHm9hN5nLDdJRlvhTYhpQwV/S9Ikk7YUfbrwDebvs+j8oyHwt8tfB+YYdYjsjXrkMq59yn5F42IFVnvIKkqHiMpCW73GORfYAHchtfzW1W+i2xr4qxkd8u8Q6yjSAIgoEzwcsN3UrcnwO8ErhY0p2k5/JcjYrwlTJpgwRPT1nma23fWfLRO0mVDne1Yu1y+6WxSBKwNSmrdMz9t7ETcIbtJ2zfQcro36LTPZbYt/r4bOANue0qvyN0ibGp3176oR9tBEEQDJynGxw1qJQBALD9kO2Vba9pe03gctIP346Ji33JSdD4ZZk/r1QC+TipDK7spuqsx7Tklg9seAvrAUtLupg0Gvua7VM7XF8Vy/OAB/Ngp3W+Jau8IzDL9mfyucvb7FuSwqX3KGkOMN/23GL7uTT0odx2J78tKmPs0W+jfuhTGzOeyCcIgqmPJ7ACskoGoO3Z0JiQZa7HUsDmwBtIyxWXSbrc9m09xrEY+S+wp7/EbP+ZiYpl2FAhIUhLrkAIKgVB0A8mWiTJ9jySpkzxXOmzwfZWdXyGLHObLHMFd5NU/P5u+2/AJaT8iCqqYrmPtHvhUm3n69rXlWUeuS63tUJuu459pxib+u2lH/rRxhgciotBEAyA6aC4GLLMBVnmDvwIeI2kpZR2otySlGNRRWksOSfiImCXLvc/F9gtZ/qvBaxLyqXouObUZt/q412AX+S2q/yO0CXGpn576Yd+tBEEQTBwpsPeDSHLXEDSR4BPAC8EbpA0z/a+ecnjp8ANpEHd8bZvqrrxLrF8EjhD0ueAa0kDqTE5CXkd6SzgFlLOyodsL8zXlfptW3c6Afi2pAWkqpHdclyd/M4D9rV9T1WMPfpt1A99aiMIgmDg1KxaGCghyxwEBUKWOQiCuoxXlvnLq9eXZf7oXZ1lmSeLUFwMgiAIggEwHX6RDIvi4gmSrpd0g6Szc8VFmf3mkm7M9l/PeRVI+k9Jt2b7H0haUdJ2hcTJR3O710k6NdtMK8XEmv3Y2G/TfuhHG0EQBFOBCRZTmhSGRXHxINsb294IuAs4oCLmb+a21s3H9vn8BcArs/1twCG2z/eowuJ84F35/Z6aZoqJDfpxpigxBkEQDJzpUN0wacsNWTDp3vz6EUlFxcWt8mWnABcDn3RSMfyLpH9uczWiuAggqaW4eGTbdSPKe/m6lhrhLTn5sVVxsSwlszxK5ZjL2748vz+VpNB3nu2fFS69nNGM+SpG1AGBO5SS8FrKhqUxltgfll+fDRydY6/ye1nhPlpKg+/Mp07Jvr5Z5ddjE1NK+zH//TXy27Qf+tFGSV/PSGKDpyCY+kyH5YahUVyUdFK2vYXyDS5ekm2K9mUKfe8FzuwSc98VE5WrE4Anaa5m+Le22KeiSuJEtTEUxAM+CKY+T0+DYcKkiinB4oqLxc/yL9iuioukqeWfAT+lR8VF23sDLybpG+za1B5A0qdJyyin9WLfiVz6OB7FxR1y+WLQEEn7SZovaf6iRX8fdDhBEAwJ00EnYWgUF7OfhaQNgHbO69Yt+zn52lWr7CW9B3gzKfeg29/ZdFNMrBP7oFUSJ6qNxQjFxSAIBsF0yEmY8YqLSqxTiGlH4FbbCwv2n8lLIA9Lmp2v27MVm6TtSSJLO7ZyI7ow3RQTi0xVlcQJaWOxng6CIBgQ06G6YcYrLkpaAjhF0vKAgOuBD1TE/EHgZFJy43n5ADgaeAZwQRo/cLnt/atufLopJkp6MUlFcode1CJ7jHeQbcx4InExCKY+i6ZBTkIoLgZBgVBcDIKgLuNVXPz0mu+s/X3z+TtPD8XFIAiCIBgWpkN1QwwSgiAIgmAATP0hwvDIMp+Wz98k6cRcdVFmv5bKJYEPzvdxg6QLJa0hacNCdcT9ku7Ir3+ebfbK9/g7SXsV2iiVfm6LQ/mzBbnNzQqflfpts6/q40q/bfZV8tSN/Tbth360EQRBMBUY6uoGppYs82nA+sCGpKTEfStirpIEvpa0jfNGJLW/I23fWJBlngt8PL/fRtJzgUNJ4j1bAIe2HnZUSz8XeVPh8/2yDV38Fqnq41K/JVTF2Mhvj/3QjzaCIAgGziJc+xgUkzZIsH2v7Wvy60dIIkYtWeZT8mWnkKSPsf0X21eRKhiKjMgyZyW+lixzOyNywrafJOkh7JR9z3OGVB63artx/pW5NWkQ0B7bRYXSx8vL7NvYDrjA9v22HyDt/bC9CtLPOZaW9HM7OwGn5pAvJ2kHvKjKb4X9Yn3cwW+xHzrF2NRvL/3QjzaCIAgGztCLKbXQ+GWZXyvpeZKWI0krr1ZyXZmc8BhZZaVlhneTlBvb6SQJXGQfRksjq6iKpVL6WdL+SqJQ3exL71HS8ZJm5fNVfdy1jzrF2IPfxv3QpzaCIAgGznRYbpj0xEW1yTIXl4VtW1JXWWZJLVnmv9OjLHPmGOAS2z0VkUvaA5gF/FOP7VeSBaHGY1+6hFKnj3tsb1L89rsNSLLMpCUMtOQKhOpiEAT9YOE0SF0cGllmSYcCqwAHF86dn+2Pp7MkMJK2AT5NUl18okvInaSCK6Wfa9rXkRqu6uM69p1ibOq3l37oRxtjcMgyB0EwAKZDTsKkzSTkNf5OssyH00CW2fZfNCrLPDurLm5SuGYpsgwv6WGwG3nLYUn7ktau32B7ZObG9nZt7bQkgc8oxiZpU+C/ge2dtrTuxvnAFwoJdNsCh9i+X9LDkmaTll72BI4qsZ8LHKC0vfGWwEO275VU6rfCvqyPS/0WDXM7VTE28lsVb5d+6EcbM55QXAyCqc/Un0cYAlnm/PGxua3L8nLHObbnlMRcJQn8n8Czge9l+7ts71h14/kB9VnS/gEAc2zfn1+XSj+38hHyssM8Uu7FAuAxYO9ufvNsyLG251f1cZXfbH9drtSojLGp3176oU9tBEEQDJyQZQ6CaUbIMgdBUJfxyjK/b8231/6++dad3wtZ5iAIgiAYFoY6cVFTS3HxBEnXKyn1nZ0rLsrsPy/pD5IeLfnsHYV7OV2huBiKi0EQBOPADf4bFMOiuHiQ7Y2dFBPvAg6oiPnc3E6773VJCYKvtv0KUjlnKC6G4mIQBEHPTAedhGFRXHwYRioulqUiqTSr891b8tH7gG9kRT9qVDiE4mIoLgZBEHRkkV37GBRDo7go6aTc3vo0L4VbD1hP0qWSLpfU7RdpKC6G4mIQBEFHpoMs89AoLtreOy9JHAXsCpzUwHwp0nT1ViRRnkskbVhWitkrobg4mDZgZiouhk5CEEx9pkMJ5KQOEtRBcTEL4dRWXCRrFkj6AnC3pNVIOQSQdBCup4uaoO2FSmI8n5B0KnB1/miu7c90COFu0pLHU8Adkm4jDRquqrj+j6QBRTGWi5kYxcUyv+1U9fGEKC428NtLP/SjjTHYPg44DmZOCWQ84INg6jPs1Q3dFBehgeJi/rOluHi67T+0Egfzr/CryIqLkpYhKS7OzVnx6xRi2hG41fbCgn2nAQLAD8kPIkkrk5Yfbu9w/fnAtpJWykl02wLn52n0hyXNzrHsWXH/c4E9c+yzGVVGLPVbYV/Wx1V+R+gSY1O/vfRDP9oIgiAYOEMty8wUUVyUtARwiqTlAZFmHD5QFrCkI0lSzstJuhs43vZhjD6IbiEtdXzc9n1VN96LCqBCcTEUF4MgGCoGWdpYl1BcDIICM2W5IQiCyWe8iotvW2PH2t835/x+bte2clL910g/lI+3fXjb5wcD+5IkCv4KvNf27zv57Et1QxAEQRAEY7Fd++iGOmsFtbgWmOWkGXQ2cGQ3vzFICIIgCIIBMME5CZVaQS1sX2T7sfz2csYmd5cyLLLMB+RzzomHVfanZfubJJ2oVJ2BpJ2UpICvkzRf0msUsswhyxwEQTAOFuLah6T98vOndezX5q6ODk6RfaiRpzUsssyXAtuQEuE6cRpJbGlDUrJbS3vgQmDjnNj3XtJaT8gyhyxzEARBzzSZSbB9nO1ZheO4XtuVtAcwC/jPbtcOiyzztbbvrBHzvCz7a+BK8lSM7Uc9uij0LLoLYIUsc8gyB0EQdGQicxKop4ODpG2ATwM72n6im9OhkWVuGO/SpPLNnxbOvVXSrcBPSLMJnQhZ5pBlDoIg6MgEb/BUqhVUvEDSpsB/kwYIXYUMYYhkmRtyDHCJ7RFtW9s/AH4g6XXAZ0nLFxOGQ5Z5IG3AzJRlDoJg6jOROgmu1gqaA8y3PZe0vPBs4Hv5WXyX7R07+Z3UmQR1kGXOn9eWZba9ue3XAQ8AtyklRrYSB/en5lRLW3znZ/vjC+cOBVYBDq6I5RLgpeqQANkhlomQZa5zj1V9PCGyzA389tIP/WhjDMW1vhggBEHQLxZ6Ue2jDnnJfD3ba9v+fD73mTxAwPY2tl/gUbXhjgMEGAJZ5k5+bW+X7ffN/vclrXHvbo/+rUhap5B9vxnwDKBScZGQZQ5Z5iAIgi6ELPMUkGXOvj8CfAJ4IXCDpHkV0/PH5pguy2OCc2zPAXYmPaCeAh4Hdi0kMi6GQ5Y5ZJkHTOwCGQRTn5BlDoJpRsgyB0FQl/HKMr/uJW+o/X1zyR8vHIjOy6QnLgZBEARBsDjT4RdJKC6OtV9L0hX52jNzbgOSVs/3cq2S2t8OkrYrJE4+mtu9TtKp2eaQ7Oe3krbrFmNbHM/I7S/I8axZ+KzUb837qPRbsx8b+23aD/1oYxh4/J5fjfsIgmBymQ45CZO23KCUmf4i29dIeg5wNUnM5j3A/bYPz1/cK9n+pFJy4hr5mgdsfyn7eSVJGGkL4EmSdsH+the0tbckcBvwRlJN/FWkBMRblGpDHwAuJm1u8beKmM8i5SGcIelY4Hrb35R0HHBtfr0BMM/2mgW7i4GP5XwA8jXfzTG/GPg5sF6+vDTGtjg+CGxke39JuwFvtb1rlV/bC9vsq+6j1G+Dfmzkt5d+6EcbJX/1I8RyQxAEdRnvcsPsF29V+/vm8nsuHshyQyguZiQJ2Jq0M9aY2EizQsvn1ysA93Tylds9w/YTtu8gJdtt0SnGEvtWH50NvCHHV+W37n1U+S1SGmOPfhv1Qz/aIAiCYIowHWYSQnFxlOcBD+aBSLv9YcAeku4mZdl/uIuvXhQT50jasd0+x/NQjq+T/TxJL+5yH1V+68Tei9+m/dCPNoIgCKYEbvDfoAjFxXrsDpxs+8uS/gH4tqRXFrUUxovtz4zTfgcAdRZ5CkrQDFRcnAolkFMhhiCYykyH6sJJHSSog+Ki7XvVQHGRJMyEpC8Ad0taDTg3X3IscD09KC6SZjLmk3YLXFHSUvmXatF+H/JGSrYvk/RMYOUOsXdSNqwTY8v+bklLkZY47uvit8V9He6jym+d2Hvx27Qf+tHGYjjtpnYczJychKnwcJ0KMQTBVGaQywh1CcXFrLiYxZEuAnYpie0u4A05hpcDzwT+2sH1XGC3nJG/Fmmb4isbxFjso12AX+T4qvwW76nTfVT5LVIaY49+G/VDP9pYrKeDIAgGxETLMk8Gobg4lk8CZ0j6HHAtefYC+CjwLUkHkZIY31PycC3GcnPO0r8FeDrHvDDHUhVjcROOE0hLGguA+0kPuG5+5wH72r6nw32U+s25DMfb3qFTPzb120s/9KmNYIiIZY9gqhKKi0EwzZgpyw3xYBwl+iKYLMZbAvnKF8yu/X1z058vD8XFIAgmhniojRJ9EUxVpsNMQgwSgiCY0cRMQjBVWTQNZvInbZCQqw9OJVUPGDjO9tckPRc4E1gTuBN4h+0HJK0PnARsBnzaWXEx+zqQVH0g4Fu2/6uize2Br5HWoI+3fXg+fxowi5TTcCXwftvtok3kxLczSPX3VwPvtv2kpK8Cr8+XLQc8H3gt8O18bnVS3f5DwN9sbyNpL+Df8+efs31KbmNzRncmnAcc2J7fkJM+v0bShHiMlANxTf6s1G+bfVUfV/ptsy+NsRe/TfuhH220328wOUyVh3M84IOpynSYSRgWWeYdGN0m+HTgEtvfLIm5VBK47ZoPA5vafm/h3MnAj22fnd8/l5SEOYs0QLoa2Dw/7K4EPkISlpoHfN32eW1t7EASbNoB2BL4mu0tO/ltsz+yoo9L/Zb0Q2mMTf320g/9aKP9fovMlJyEIAgmn/HmJKy7yua1v29+99erQ5bZkyfLPM8Z0kzCqu3G+ZdqlSRwkd1JewV0YjvgAtv35wf4BcD2eeC0vO3LcyynVrSxE3BqDvlyknbAi6r8Vtgv1scd/Bb7oVOMTf320g/9aCMIgmDgLLJrH4NiqGSZlcSd3k2ajWinkyRwy34NYC3gF11i7iQVfHdZjJL2l7R/DfsqWebjJc3K56v6uI5UcWWMPfht3A99aiMIgmDghCwzU06W+RjSUkOvi6W7AWe7bdfFicBJEGo89mW6D7X6uMf2JsVvv9uAmSnLHATB1GcClf0njUmdSVAHWeb8eW1ZZtub234dacvn2yStJum6fOxPF8liSYcCqwAHF86dn+2PpyAJXGaf2Y3uSw10iOWPjF3q6CbLXGZfR2q4qo/r2HeKsanfXvqhH22MwfZxtmfZnhUDhCAI+sVQ7wKZ1/inhCyzpH1Ja9e7uzB0c31ZZnL1xUrAZTVu/3xgW0krSVoJ2BY4P0+jPyxpdu6fPSvufy6wpxKzgYeybanfCvuyPq7yO0KXGJv67aUf+tFGEATBwLFd+xgUQyHLTNoA6vfAZXm54xzbc0pirpIEhjToOMM1/rZs3y/ps6SBC8Ac2/fn1x9ktCzvvHzQykfIA555pNyLBaRyv727+c2zIcfank9FH1f5zfbX2d6kU4xN/fbSD31qIwiCYOAMck+GuoQscxAUiBLIIAjqMt4SyBetuEHt75t7H7wlZJmDIAiCYFiYDmJKk5mTsJqkiyTdIulmJdVEJD1X0gWSfpf/XCmfX1/SZZKekPSxNl8HSrop+/nXDm1uL+m3khYoCfG0zp8g6XpJN0g6O1dclNl/XtIfJD3adn71fC/XZh87SNqukDj5aG73OkmnZptDchy/lbRdtxjb2nuGpDPzNVcolZDSyW+b/VrZbkH2s0w3vzX7sbHfpv3QjzaCIAimAtMhJ2EyqxueBj5qewNgNvAhSRsAnwIutL0ucGF+D2n7348AXyo6UVJcfB9JLGlj4M2S1mlvTElx8RvAm4ANgN1zewAH2d7Y9kbAXcABFTGfm9tp59+Bs2xvSspNOMb2+a3ESVIuxbvy+z1zu7sBryCJHR0jackuMRbZh6Q6uQ7wVeCIfI+lfkvsjwC+mu0fyP4q/RbpEmMjvz32Qz/aCIIgGDhDXd3gqaW4+DCMVFwsC+U9ntX57i37iJRQCbACcE/nu2cnUpLjE7bvICXbbdEpxhL7Vh+dDbwhx17ld4R8XZVyZJXfIqUx9ui3UT/0ow2CIAimCAsXLap9DIqhUVyUdFJub33gqIa3cBiwh6S7SVn2H+5yfS+KiXMk7dhunwdGD5EUITvZz5P0YjorR1b5rRN7L36b9kM/2giCIJgSDPtyA7C44mLxs1xO2FVxkTS1/DOSnPJ19KC4aHtv4MWkGY1dG5rvDpxse1XSIOXbkia072x/xvbccdjvYLvbDEdQgqT9JM2XNH/Ror8POpwgCIaEoV5ugKmluJj9LCRNO++c161b9mWaCUX2Ac7KPi4Dngms3OH68SomjlynpAC5AkkRso59J+XIKr91Yu/Fb9N+6Ecbi+FQXAyCYABMh5mESSuBzOvFnRQXD6eB4qLtv2hUcXG2k6DSJoVrliIrLpIeBrsB78xxrG17QX69I3BrHjBsQj3uAt4AnCzp5aRBwl87XD8XOF3SV0izF+uSdp9UWYwV9nuR1B13AX5h25Kq/I6Qr2spR57B4qqFi/lta3tEubIYYy9+O8Rb2g/9aKOkr4MZzuP39LpVyyjLvvi1ExBJEIxlkLs71mXGKy7mZYFTJC1PenBcD3ygLGBJR5IeJMvl/IPjbR8GfBT4lqSDSMsj7yl5uBZjuVnSWcAtpCqPD+VBCWUx5vNzgPl5yeEE0pLGAlLVx241/M4D9s1LDlXKkaV+cy7D8XnJopNyZSO/vfRDn9oIgiAYONNBJyEUF4OgQCguBkFQl/EqLj7zmavX/r75v/+7KxQXgyAIgmBYmA4zCX0pgQyCIAiCYCwTnbjYTWVWNVV3i8RMQhAEU5ZIOgxmMhO53F9QmX0jSRfmKklzbd9SuGxEuVbSbiR5gY6SADFICIJgyhIP+GAmM8GLDSMqswCSWiqzxUHCTiRxQEjKtUdLUqdE/EbTHXHEMewHsN9M8DEVYpgqPqZCDHEfM7MvJvIA9iNVALaO/do+34VUpdZ6/27g6LZrbgJWLbz/X2DlTu1GTkIQNGO/GeJjKsQwVXxMhRgmwsdUiGGq+JgKMUwoLoi+5eO4frQbg4QgCIIgmP7UUZmto7o7hhgkBEEQBMH0Z0QtV9IyJMG59v2AWsq1UK26O4ZIXAyCZkzEFN9U8DEVYpgqPqZCDBPhYyrEMFV8TIUY+oqrVYe7qvl2IhQXgyAIgiAoJZYbgiAIgiAoJQYJQRAEQRCUEoOEIAiCIAhKiUFCEFQg6WWSvizpJ/n4kqSXDSiW543Tfj1JF0q6Kb/fSNK/N/SxpKSLxhnHcpL+Q9K38vt1Jb25oY9x38tEIWkdSd+R9H1J/1DTZrNOR4O2XyDpBEnn5fcbSNqnh3t4dZ1zk4kSe0j6TH6/uqQtGthPSF8EixOJi0FQQv7CPwf4b+BaQMCmwPuAt9m+vKG/ZwA7A2tSqCqyPaem/e+A64CTgPO6lS2V2P8S+Djw37Y3zedusv3Khn4uJN3/Q03sCvZnAlcDe9p+paTlgP+xvUkDH+O+F0nnkDK9z7O9qIHdM23/X+H9d4FP5Lfn1rmPLgMt2966Ziznkf49fNr2xrnu/VrbG9axL/i5xvZm3c51sH81cJ3tv0vaA9gM+Jrt3zeI4ZvAImBr2y+XtBLwM9uvqmk/UX3xtpLTDwE32v5LE18zhSiBDIJyPgPsbvviwrkfSvoFcCjwpob+fkT6srkaeKKHeNYDtgHeC3xd0lnAybZvq2m/nO0rpTFb0j/dQxyPAjdKugD4e+uk7Y/UtF/b9q6Sds92j6ktqBpMxL0cA+xN6svvASfZ/m0Nu3Mlfdv2qfn9U6SBn4GFdRq2/fqGsVaxsu2zJB2S/T4tqVYMMDIQ/kdgFUkHFz5anlRCV5dvAhtL2hj4KHA8cCrwTw18bGl7M0nXAth+INf612VcfVFgH+AfgNZAbivS/7NrSZpj+9s9+JzWxCAhCMpZu22AAIDtX0rqpX56Vdvb9xpMnjm4ALhA0uuB7wAflHQ98Cnbl3Vx8TdJa5P3lJG0C3BvD6Gck49eeVLSsoU41qb5oGnc92L758DPJa0A7J5f/wH4FvAd209VmG4PfEDST4EvAB8DPgIsC7yr4X0g6ZXABsAzC7GdWm0xhr/nZahWP8wmDUTrsgzwbNJz4DmF8w+ThHbq8rRtS9qJtFfACT1M9T+ltIth615WIc0s1GW8fdFiKeDltv+c/byANODZErgEGLpBwsA3rYgjjql4AFd3+OyaHvwdB2w4jnieBxxI2tjlJ8DbSF9os4A7ati/FPg58BhJmvXXwBo9xrIs8LIebd8I/BL4K3AacCewVUMfZfey5jj7dC5py9yjgItr2K4AfAn4LmlA2UtfHEr6xfpn0lT5n4CzG9hvBlxKehheCtwGbNRDHD39OyjY/xI4JLf/QlKu240Nfbwr/x3cDXwe+C3w9gH0xS1t79U6R1q+6LmfpusROQlBUIKkvwBnlH0EvMP2C2r6uZH062YpYF3gdtIvZ5EmCDaq6ec20q+Yk2zf3fbZJ20fUdPPs4AlbD9S5/oS+7eQHo7L2F5L0ibAHNs7NvDxPGA2qQ8ut/23HmPp+V4k/QB4GalPT7Z9b+Gz+bZnVdhtScqHeJI0k/A46aH2R+Czth9sEMONwMakh8/G+Vfrd2y/sYGPpfJ9CPitq2dAOvlYjzQjsiZj82Xq5ka8EHgncJXtX0lanTTwqzsj0vKzPvAG0r1caPs3De0noi+OAVYHvpdP7UwauHwc+LEnbqlo2hCDhCAoQdJenT63fUpNP2t08VMruUvSO2yf1Xbu7ba/V2XTdu3zSL9cX0MatPya9HDvuLlLiZ+rga1Jv7Z7TRp8CbAGYx9IlzSw/wJwZOuBnJPcPmq7doWDpB1sz2s79wzbHZc+JF0H7ECapj/J9qvz+X8C/s32dg1iuNL2FrlPXw88AvzG9vo17ZcE/pnFH+5fqRtD9nM9cCxp7X1kHd/21U38jAdJzy05/UjdB/0E9oVIA4NWdcelwPc9xA/KyEkIghLqDgJq+Pk9jKyR3tz61StpeeDlQN0M8E8BZ7WdO4TRXzzdOIO0prpzfv8u4ExSMmQTnrL9UFvSYJPqgCNI0/o3F+ycY6vLm2z/W+uNU5LbDkCTMsjPAfPazl1GmrbuxNOkB9GzSLMJrRh+SZp2b8J8SSuS8iCuJiWFdsstKXIu8H/AjTRbv2/nadvf7NVY0iPkXIACD5GWcT5q+/Yabq4h7U74AGkmYEXgT5L+DLyvxoBlQvoiDwbOzkdADBKCoBRJ57L4F98ITabXM99k7APo0ZJzZXG8ifTL9SWSvl74aHmaZfS/yPZnC+8/J2nXBvYtbpb0TmBJSeuSkvb+p4H9v5DyGXqp8GixZPFXf06EfEYdwzw1/hJgWUmbkh5IkPpzuRou3gm8nzRA2LNp4EVsfzC/PDYnQi5v+4YGLlatu1zVhXMlfRD4AYUkUtv317T/L9KU/Omk/twNWJv04D+RVCHQjQtI+RjnA0jaljSgPYlUibJlF/sJ6YtcAnkE8HzSvbSWBZcfr+/pSiw3BEEJefq4kvzLsYm/69xWQy/phm5fbLmsbBNgDqkss8UjwEW2H6jZ/leAKxmdjdgF2ML2x2rdwKif5YBPA9uSvkDPJ63F/19Hw1H780gJaY82abfNxyeBt5AeIJBKGefaPrKG7V7Ae0gJn/MLHz1Cyk0YT+VGIyRdaPsN3c51sD+CtHb/s3HGcUfJadt+aU37621v3HbuOtublH1W4eNGt2katP7/KPt/p8R+ovpiAfCWpvkQM5kYJARBH1AS77mYNHsA8EHg9bb/pab9UrZ70TVo2T9CmiJvrTkvyajOQd9+KUn6PilZ70LG/mqtq7PQ8vMmUpIbwAWtX6AN7He2/f0mNtnuvbZPzK9XBU4BNgduAd7jGroVkp5JmrW4iPQruzib8dMGOQlvJZXCLkHSaxjIr15JlwFfZXSKfhfgYNuz6zzgs4+fkf5NtJKFdyVVwmxPSojsNuM2IX0h6dJWnkmQiEFCEPQBSc8Hvk5K+jPpC/Ff3UXFTdJZtt9RqJIYwwRNN3dlopZfqhJCJyoHpEb7e9j+jqSPUt6fHRPdVFAiVBK0+jlJPGgn4IA6swCSDgT+FXgxcE/ho4eBb9k+uua93JHbvXE8iXV5duhgYHXb++VlpJfZ/nFN+5cCXyOJEEHKqziIVPGxue1f1/CxMqOJtZASBv8fKbdhddsLuthPVF98jVTG+UPGDmL7NsM01YhBQhBMYSS9lpSIdXfbR6sBf+r25Vnw832SDPFP3UCGuGDfWn55G+lL9Dv5/e7An20f1NRnDzH82vZrShLlav9qlPR+2/8t6dCSj+0uMtltg4Qxv5IlXduq+KiDpA/bPqru9SX2l5BKDceTtIgmQCp70ExgX5xUctq23zsev9OZGCQEQQ0kLWf7sXHYr0La92FNxpZodfzykfRj4BDbN7ad3xD4gu231Gx/G9La/WxSRcRJridD3O5nMQ2BsnMldlNiRqQQz6ttX9rtXIldSz9DpAHTmq0yvR5KQZcB9gdel09dTNqPom7Z38kkYanzGPurt2nZ33zbs4qDnLq5BPnaVUkiVK1p+l8BB7pNz6OLj1VIe2C8grHqk3W1Gk5mAvoiWJyobgiCDkj6R9J08rOB1XMi4fsLmel1+RHpy/Pn1NT4z7ygfYAAYPtGSWvWdeLeZYjbeZaklzqXtUlai5Tr0I0D85+NdnxsJ9fD31x33b4DR7F4ZUnZuXY+Xng9n/Tv4oFcNTG3YQzHAEvnPwHeTcpZ2bem/R35WCYfvTJeqeyTSJUNb8/v98jnaotCkdQ3zyT9+9gf2IukylmXcfWFpE/YPlLSUZQPYhvlzMwkYiYhCDog6QpSItZcj2/3xFoJXCV2v7O9bsVnC2yv08DX80hf4O8mrYWfRloD3tD2VjV9bE+SmL6d9Gt6DdKgqVHi4HiQ9CPgw7bv6sG2tanRv5KS7VosD7y17q/n8dBKQq2oCqj9C34C43kjSWNiA+BnpBmB97hk75IK+7LKnUb/3iVdbXvzYsWPpKtccxfIgp9nA7hh9Yykt9g+d9A5M1ORmEkIgi7Y/oPGigf1srvcj1Wi8leD+ZLeZ/tbxZOS9iWtI9dCY2WI3+JRGeIzJc2vthyL7Z/mxLbWL/lbXUPzoCSPYOQjmmehr0TSa7iSsTtR1kmenKhNjcbDlaQZi4WS1rb9vzCSANj135ako20fUJVMWjeJtHD9BZKuYVQq+0A3k8q+T2mL6O/m97sDjZQ8SRUJAPdK+mfSILZMhbEUpY2yvt2ykfQ3Uo7FzXXsbZ+bXz7mNhVTSW8vMRkaYiYhCDog6WzgK8DRJEGXA4FZtndr6KdVgvgEDUq0lPT8f0AS72kNCmaRHnZvtf2nmu33JENc4esfWTy3opFO/3hQhYaFG2hXSFrDNSWxJ5rW2r+krYGTSbMykPp0b9sXVdlm+4dtLz8R/VDwuRGL/53WyuhXkh4/ilTdYJK41odt/6FB+28mLcetln0tDxxWeHh3s/8f4NOtvpO0FSln5x/rxpDtRhJTO50bJmKQEAQdyKVZXyPJF4s0HXugG+55MAFxvB5oLXHcbPsXDe0n5MtP0rdJanrXMfqr1/1es805AFuQHkpX1R0sFezHtanReJB0N2ngCWlHzSXz64XA492S7ZpWUdSI50RgI9qksutm9Es6hVTO+0B+/1zgS00qAnpNJC1cO66lG40qm76DlBvRYnlgA9tb1PEzE4nlhiDoQJ52fddE+FLaiGhdxmZv19qzIP9C6vgLs6LN8coQtzOL9KU5sF8XeanlM8AvSPdzlKQ5ziJHNfkeaVOj4+lh+SgPlg6w/VB+vwZwouupJS5JWvJQ2/n2JZAqVpF0cNWHPWT0z7a9QUObIhu5oPxp+/78b60JvSaStrhd0n+Qlhwg5d7U2TOixT2kRNQdGbuM9whJ82FoiUFCEHQg/0o60GN3HPxyk19J2W5f0lLFqqRf4bNJojOT/ct1O5IM8arAlxl9MD0M/FuFTSduIukk3Nvtwknk48CmrdmcnJD5P6R9Auoyrk2NSLtoXpEf1i/JMX20pu297qLH0IWqQUavXCZpA9u39Gi/hKSV2mYSaj1bComk7QOf5RmdYanDe0niS+eQZpd+lc/Vwvb1wPWSTm9Q6TMUxCAhCDqzUWuAACM7DvYy1Xsg8Crgctuvl7Q+8IUJirGSnJV9inqUIS5hZeCWnDRYrEevq7h4hO1PdjvXhftIv/BaPELzRLlxbWrkJMh0M2l252+kQUvdJY/xPtzHO8ho51TSQOFPpL5o5cvU1a74crZvJfy9Hfh8TdsJSSTNA5SJWPJaU9IXSZUexRm/WvtYzEQiJyEIOiDpepKSW/FX0i/dthlNDT9X2X6VpOuALW0/Ielm26+Y+Kgnj/Emy1XkRnTd6Krt+lOBDUnaEybJ8d6Qj1rT7Rr/pkbvBv6DJCW8EWnGZu/8i7Sb7XPrDkYq7Cc6J2EBSZZ5zDbLTRI7JW3A6KzYL5rOSow3kVTSBaSNwx7M71cCzrC9XUM/vyb9nX6VtInY3sAStj/T0XAGEzMJQdCZ4q8kkX7d1P2VVORuSSuSNOEvkPQAMJDs+vFg+5d5/X1d2z9XkvDtOi0s6QOkTa3WllTcDvk5NNtqGuB/89HiRwVftbC9VsM229kZeI3T3hvfzSWmp5B27OzWds8DhEytXSIb8FfbTYWgxpAHBb0uVwA8Q9Jx9J5IunLJjN/ze4hjWdsXSlIetBwm6WrG7sA6VMRMQhB0Yby/kkr8/ROwAmkfhSfHG1+N9pYgJac1fRiX+XofsB/wXNtrZ82EY7sl7CkpPa4EfBH4VOGjRybgodkTuba+fVq551JOScv04+9zopF0DLAicC4D2tQoz9gdS0oaHEkktV1LCyQ/yN/qLLCVB7I/6KF6539IAmNnkxJj/wgcbvtlTfzMJGKQEAQlSFre9sN5eWExmjzYNHFSwj0zUVPUeblkC+AKjypQ3lh3+UXSbFJfPJLfLw+83PYV442tCUobPG1FGiTMA94E/Np2rXVwpe2e92HxvQam3UZAmgKbGikrLo7DvqUE+kvSjN9rgf3cfAvxVwG/IQ2aPksazB/R73+fU4lYbgiCck4n6chfTVr3LiabmbSZTC1sL5T0W0mruwcp4QniQkk7A+eMs3zxCdtPKitQSlqKDltIl/BNxpa1PVpyrh/sAmwMXGt7byXRqu90sSnybeBWUi7CHFKZ7G8mPMr+8DH3WfejhPEmkv5U0makqiFIug1NVCNbfq7KLx8F9s4D/N2AoR0kxExCEPQBpa1sNyVJ8jaVEp6I9luKjwuBx+lNDhlJRwIPAnsCHyblGdxi+9M17ct0/psmLo5LeCdff6XtLfI09etJFRK/6Tbbo9F9F651Uk28wfZGkpYGfmV7dif7qYik35HKck8CzhvnILLXGMaVSJp9vIS0l0gxp6GWDkme0foQqZx1LnBBfv9R4AbbO9WNY6YRMwlB0AVJO1LYztf2j3tw8x8TGFJjbNdO6uvCp0jT7DeSchN+Yvv4Bva3S/oIafYA0iCjiegNjF94B9KeGCuSdsG8mvTL8bIadq19F1q19A/m3IY/Ab0kyk0F1iMpir4X+Lqks4CTbd/WrwDGm0gq6QhgV9pUI4FagwTSzNADpH8D+5I0RETKc7huPLFNd2ImIQg6IOlwkr7BafnU7iQZ4F6EiFo+Vwbu6+cvNqX1gXcBa9n+rKTVgBfZvrKm/U7Aqra/kd9fCaxC+iL+hO2za/p5PvB1UiKogQtJU8N/qWE7KTs4Km25vbztG2pce43tzZTEsb5PKsU8mVTr/x+2/7uXGKYKSvLf3yHNOl0PfMp2ncHTeNtdjlSGubrt/XJC7MvqDsgl/ZakadJ4L5JsP5JXk5cY7s2x/F8v/mYSMZMQBJ3ZAdjE9iIYUWC8lppqhTlR73DgflIi1LdJgkRLSNrT9k8nJerFOYb0C2vrHMejwDdIA6A6fIK0NttiGWBz0sPxJFI2eFfyYKDR5lhtbU7IDo6SXld2rsb09PM1qgy4d/7zG/nPZzWJYaqgsVuI/5m0jDSXVM75PWC85aJ1OIk0o9PakOmPue26s3a3A0tTyGdoyIjKYs4hujsGCIkYJARBd1YkPeQhZTs34WjSgGIFUknVm2xfrqS4+F2gX4OELfMv4GthpI58mQb2y3jsrn6/zkll90uq/XDMmfRl2xt3zaR3Emz6paSTxyO8k/l44fUzSRUbV9NdJruTJPJ0nZa9jDR4/RfbdxfOz5d0bJ9iWNv2rpJ2B7D9mKSyPq7iMeA6SRcyNvGxrgrjxpIezq9F2uvkYXrM3ZlJxCAhCDrzReBaSReRvjBex9g6/24sZftnAEqbEF0OYPvWZt+B4+apPI3qHMsqFNT1arBS8Y3tAwpvV2ngp/jL8JnAW0mb63RF0n/Z/lfgaEllA43aSaC239LmezXgv2qYTrQk8lTgZVVLX7aP6FMMT0paltF/n2vTbFZgbj56wnaTfSKGihgkBEEHbH9X0sWMTst/0s22JS4+iB9vdz+e2BrydVJ52fMlfZ40Pf/vDeyvkPQ+298qnpT0flIyXy3ctn+EpO+SNkuqQ2uHvy/Vba8BdwMvr3FdX0d2fWJlSZ9gcc2HSd82u8ChpFm11SSdBryatDFZLZz2KAkmgUhcDIIOSDqXpJkw1/bfu11fYr+QVPIoYFnStCj5/TNtLz1RsdaIZX2SpK+AC23XruvPCYc/JP26uyaf3hx4Bmma+s89xvQyUoXEOr3Y94qkoxgdpC1BKk+9w/YeXezGte/CVETSz4AzgY8B+wN7kaSam2y6NRFxPI+kcyDSRmhddQ4knWX7HZJupHwZq3ZpbVBODBKCoANKEsq7Av8MXAWcAfx4OiY1KW16sxpj68ivqbYo9bE16RcnJOXEXzS0f4RRcSqTSgcPaZ9h6OLj1cBhjNbEt9aNm9TUf4jRPSfuA+5sorMwk2ipHRb1KpQ3JOtjDG8lSZ4/lN+vSNpY7Ydd7F5k+14lGebFmIDclaEnBglBUIO8nr818D5g++mWyCTps6Tp2/9l9BeX+zylPCFIuhU4iMV1/ruqBmbRo/8kiUHdmU+/ADjK9uGSNhm2unhJl9ueLel80rLUPcDZttfuYwxlIlvXegJ3uwx6I3ISgqALOaHqLaQZhc1Iu/1NN95ByiAfyAZEWTK3koYzGg/ZPq/HUL4MLAes4bH7R3xJ0jeB7elPyd9U4nNKG3B9lCRKtTxpENZPlig5F8+nKUDMJARBB7L63BakpKozgV+2NBOmE5K+D3ygjmjRJLV/UX75TGAWSahHwEbAfNv/0MDX4aSlgnMYW+7WdaAhaQFpm2u3nV8S+Bu5RLVuLNMZpU2q9gfWISlonmD76QHFciJJ7rulOfEh0k6j7xlEPMEoMUgIgg5I2g74ue2FXS+ewkiaBfwIuImxD9a+7B1RiOMc4FDbN+b3rwQOc83dF7PNRSWnay2dSLrN9npNP5uJSDqTJCL0K9IumL+3feCAYnkWSbp8G9Jy2AXA53tMFl4JWK2OgmbQnRgkBEEXJP0jsCZjE/5OHVhAPSDpZuC/Sb8YR2ZCskBRX+Ow/Ypu5yax/R+SdsI8te38HsDbPUQb+bRJES8FXGm737txtmZxfm779ePwcTGwI+n/0auBvwCX2j64k13QnVjzCYIOSPo2sDZpl7zWbIKBaTVIAB6z/fVBBwHcIOl4RrdlfhdQ6xdfQQ65hUlLBL+2XbaLYBkfAs6R9F7SwwTS8seyJGGnYaIoRfx0n8W9RsgyyIskrdCqbuiBFWw/nPfUONX2oZJiJmECiEFCEHRmFrBBlSLdNOJXkr5IUqVrtI4/wewNfABoTWtfwuiOkN0o28lyTeDTkg6zfUY3B7b/CGzZVso5z/aFNWOYSUwlKeJHgRslXcDYrdTryiovJelFpATdWtuWB/WI5YYg6ICk7wEfsX3voGMZD+NZx5+EWJYBXkaaCfit7ae6mHTz91zSdHXfp8qDiUHSXmXn6yopSno7Kafh17Y/KOmlwH/a3nkCwxxKYpAQBB3ID9dNSNLDA0v4mylI2opUQnon6RfrasBe7r77Yje/UVM/zcmlxqvb/u2gYwlGieWGIOjMYYMOYCLIdfCHkjaoAvglMGcca8C98mVg29aDQNJ6pN0wN+/VoaTXAw9MTHjBIJD0FtKeHMsAa0nahPTvs9ZgXNKRwOdI+6P8lFRae5Dt73Q0DLoSMwlBMARknYSbGBWCejewse239TmOEenfTucqbMv0+Z9LUgjc0/atExdp0E8ktbbpvrg1IyTpJtuvrGl/ne1Nsrzzm4GDgUtsbzxpQQ8JMZMQBCUU9hhY7COm5/7ya7etz/4/SdcNII6rS6ob5te0fXPbewP39VJLH0w5nrL9UFuFRRPRstaz7J+B75X4CnokBglBUILtskz66czjkl5j+9cwsklS+9bV/WB/UhliK2v9V8AxdQxjs54Zzc2S3gksKWld0r+P/2lg/+O8p8fjwAckrQJMu03YpiKx3BAEQ4CkjUnaDivkUw+QEgb7VkueRXNutr1+v9oMpgeSliOVLm5Lmq07H/hsk91Wc5XLQ1l3YTlgedt/mpSAh4gYJATBECBpLdt35M2MyMIzazUQIZqoOH4EfNj2Xf1sN5ge5H+fbm2+1cBuaZL+RjEx99jxltcGMUgIgqFA0jXtOgKSrrbdc1VBj3FcAmxKKiktiuZESekQI+lVwImMCmY9BLzX9tXVVmPsjweWZmxi7kLb+050rMNG5CQEwQxG0vokZcEVJBUrGZYn7cjYb/5jAG0GU58TgA/a/hWApNcAJ5FKGevwqrZKhl9Iun6CYxxKYpAQBDObl5GqAlYE3lI4/wjwvn4FMZW2JQ6mJAtbAwQA27+W1OTfx0JJa9v+X4CsuDitd26dKsRyQxAMAZL+wfZlA2x/ymxLHEw9JP0XaZOt75JKW3clVSd8B7rvMZL34jgZuJ2U+LgGsLftMjnyoAExkxAEw8Fb83bRg1Kk26CwLfEJpJyEIGjRWio4tO38pqRBQ+UeI7lqZmNgXdLMGaQ9QZ6osgnqEzMJQTAEDFqRrj1xsiyRMgh6RdKVtrcYdBwzkZhJCILhYOn856AU6abStsTBzONSSUcDZzK2aqbfW6HPOGKQEATDwbmDVKSzvWS/2gqGkk3yn3MK5zouUwT1iOWGIBgSQpEuCIKmxExCEAwBkvYsvC5+dGr/owmCxZH0j8CaFJ5Ltmv9+5R0cMnph4CrbV83EfENKzFICILh4FWF188E3gBcQwwSgimApG8DawPXMapvYOr/+5yVj3Pz+zcDNwD7S/qe7SMnLtrhIpYbgmAIkbQicIbt7QcdSxBI+g2pTLanB1KW+97B9qP5/bOBnwDbk2YTNpiwYIeMJQYdQBAEA+HvwFqDDiIIMjcBLxyH/fOBoi7CU8ALbD/edj5oSCw3BMEQIOlc0vQtpB8HGwBnDS6iIBjDysAtkq6k8FBvsPHXacAVeZdRSBLkp0t6FnDLhEY6ZMRyQxAMAZL+qfD2aZIs8t2DiicIirT9+xzB9i8b+JgFvDq/vdT2/ImIbdiJQUIQzGAkrUOadr207fyrgT+1NsQJgiAoI3ISgmBm81/AwyXnH86fBcHAkPTr/Ocjkh4uHI8UFDqDARIzCUEwg5F0le1XVXx2Y2vTpSAIgjIicTEIZjYrdvhs2X4FEQSdyGqg7Txi+6m+BxOMIZYbgmBmM1/S+9pPStoXuHoA8QRBGdcAfwVuA36XX98p6RpJmw80siEnlhuCYAYj6QXAD4AnGR0UzAKWAd4aezcEUwFJ3wLOtn1+fr8tsDNwEvA121sOMr5hJgYJQTAESHo98Mr89mbbvxhkPEFQpCw/RtINtjeSdJ3tTQYU2tATOQlBMATYvgi4aNBxBEEF90r6JHBGfr8r8GdJSwKLBhdWEDMJQRAEwUCRtDJwKPAakjLopcAc0k6Oq9teMMDwhpoYJARBEAQDI88WnGr7XYOOJVicqG4IgiAIBobthcAakpYZdCzB4kROQhAEQTBobgculTSXtEMpALa/MriQAohBQhAEQTB4/jcfSwDPGXAsQYHISQiCIAiCoJSYSQiCIAgGiqRVgE8ArwCe2Tpve+uBBRUAkbgYBEEQDJ7TgFuBtYD/B9wJXDXIgIJELDcEQRAEA0XS1bY3b6ks5nOVO5gG/SOWG4IgCIJB09rt8V5J/wzcA5TtDBn0mRgkBEEQBIPmc5JWAD4KHAUsDxw02JACiOWGIAiCIAgqiJmEIAiCYKBIWgv4MLAmheeS7R0HFVOQiEFCEARBMGh+CJwAnEvs+jiliOWGIAiCYKBIusL2loOOI1icGCQEQRAEA0XSO4F1gZ8BT7TO275mYEEFQCw3BEEQBINnQ+DdwNaMLjc4vw8GSMwkBEEQBANF0gJgA9tPDjqWYCwhyxwEQRAMmpuAFQcdRLA4sdwQBEEQDJoVgVslXcXYnIQogRwwMUgIgiAIBs2hgw4gKCdyEoIgCIIgKCVyEoIgCIIgKCUGCUEQBEEQlBKDhCAIgmDKIGklSRsNOo4gEYOEIAiCYKBIuljS8pKeC1wDfEvSVwYdVxCDhCAIgmDwrGD7YeBtwKl5H4dtBhxTQAwSgiAIgsGzlKQXAe8AfjzoYIJRYpAQBEEQDJo5wPnAAttXSXop8LsBxxQQOglBEARBEFQQMwlBEATBQJF0ZE5cXFrShZL+KmmPQccVxCAhCIIgGDzb5sTFNwN3AusAHx9oRAEQg4QgCIJg8LT2Efpn4Hu2HxpkMMEoscFTEARBMGh+LOlW4HHgA5JWAf5vwDEFROJiEARBMAXIQkoP2V4oaTlgedt/GnRcw07MJARBEAQDRdLSwB7A6yQB/BI4dqBBBUDMJARBEAQDRtLxwNLAKfnUu4GFtvcdXFQBxCAhCIIgGDCSrre9cbdzQf+J6oYgCIJg0CyUtHbrTVZcXDjAeIJM5CQEQRAEg+ZjwEWSbgcErAHsPdiQAohBQhAEQTBAJC0JbAysC7wsn/6t7ScGF1XQInISgiAIgoEi6UrbWww6jmBxYpAQBEEQDBRJXyVVN5wJ/L113vY1AwsqAGKQEARBEAwYSReVnLbtrfseTDCGGCQEQRAEQVBKJC4GQRAEA0XSwSWnHwKutn1dn8MJCsRMQhAEQTBQJJ0OzALOzafeDNwArEnaFfLIAYU29MQgIQiCIBgoki4BdrD9aH7/bOAnwPak2YQNBhnfMBOKi0EQBMGgeT5Q1EV4CniB7cfbzgd9JnISgiAIgkFzGnCFpB/l928BTpf0LOCWwYUVxHJDEARBMHAkzQJend9eanv+IOMJEjFICIIgCIKglMhJCIIgCIKglBgkBEEQBEFQSgwSgiAIgiAoJQYJQRAEQRCU8v8Bb+LDwpl/p9YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5: Correlation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.04"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(np.corrcoef(df['gross income'], df['Rating'])[1][0],2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Unit price</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Tax 5%</th>\n",
       "      <th>Total</th>\n",
       "      <th>cogs</th>\n",
       "      <th>gross margin percentage</th>\n",
       "      <th>gross income</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unit price</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.63</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantity</th>\n",
       "      <td>0.01</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.70</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tax 5%</th>\n",
       "      <td>0.63</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.63</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cogs</th>\n",
       "      <td>0.63</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gross margin percentage</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gross income</th>\n",
       "      <td>0.63</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rating</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Unit price  Quantity  Tax 5%  Total  cogs  \\\n",
       "Unit price                     1.00      0.01    0.63   0.63  0.63   \n",
       "Quantity                       0.01      1.00    0.70   0.70  0.70   \n",
       "Tax 5%                         0.63      0.70    1.00   1.00  1.00   \n",
       "Total                          0.63      0.70    1.00   1.00  1.00   \n",
       "cogs                           0.63      0.70    1.00   1.00  1.00   \n",
       "gross margin percentage         NaN       NaN     NaN    NaN   NaN   \n",
       "gross income                   0.63      0.70    1.00   1.00  1.00   \n",
       "Rating                        -0.01     -0.02   -0.04  -0.04 -0.04   \n",
       "\n",
       "                         gross margin percentage  gross income  Rating  \n",
       "Unit price                                   NaN          0.63   -0.01  \n",
       "Quantity                                     NaN          0.70   -0.02  \n",
       "Tax 5%                                       NaN          1.00   -0.04  \n",
       "Total                                        NaN          1.00   -0.04  \n",
       "cogs                                         NaN          1.00   -0.04  \n",
       "gross margin percentage                      NaN           NaN     NaN  \n",
       "gross income                                 NaN          1.00   -0.04  \n",
       "Rating                                       NaN         -0.04    1.00  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(df.corr(),2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helpful Links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8sRNhj98jwECCS2inS+q0XbEbgP9nZt2A04B/lBeeJ9Do+72kmcAsgiRc/P/0l8L/fgp0qMxJzcyAscCZkpoDhwITty8n6bywBTxj05Y1lY/eVVhql97kzfs4qSMPoxBDlOKIgqh+FldffTF5eXk8+2zcHVflq74WaE9gkZl9Y2ZbgP8C2/dbG9A0fN6MClzq8gSaOAuAg4vvkNQUSAdWUPL/iwbh8T2Bq4CjzOxA4PXCY6HCPs984rue/SRwJnA68LyZ7XDXspmNMbPuZta9Qb1mZZ5oyZKltC3WldO2bTpLFi/doUy7zAwAUlJSaNq0CStXrooj7NrD1qxAzVsVbatZK2zNylLLpnY5jLw5H5R6rLbHEKU4oiDRn8X555/FtGkTmTZtIrm5y8gM/x0CtGuXzuLFuSXKL16cS7t26WWWGTr0ZAYOPIpzzvlzleIql1mFH8V/7IeP84qdqR3wY7Ht7HBfcSMJGhTZBD10l5QXnifQxJkCNJJ0FhR1KdwHPAx8C3SVFJPUnuDXEgS/hn4G1khqQ9D9UJ51QFkX+kocC0fjLibouniyjNdUyKxP57HXXh3YfY9M0tLSOPGk45j0xpQSZSa98TannX4iAINPGMAH731clSprhYLsRcRaZaAWu0FKKqldepP/edYO5dS6HWrYmILvF/4iY4hSHFGQ6M/isceeoVevgfTqNZDx4yczZMhJAPTs2Y01a9aRm7usRPnc3GWsXbuenj27ATBkyElMmPAmAMcc8xuuuOJCTj55OBs3bqpSXOWqRAu0+I/98DGm/ApKOB14yswygWOBsZJ2miN9FG6CmJlJOhF4RNJfgdbAc2Z2ezhC91uCVurnwMzwNXMkzQK+IPj1NLUCVU0AXpD0W3b8BfUUMFrSRuBQM9sIjANam9nnVXl/+fn5XDPiFp5/+XFiKSn8Z+wLLPxiEddc/2dmz5zPpIlvM+6Z5/nHmHuYPvstVq9awx+HXV70+pnz3qZJ08akpaVx7HFHc/IJw0qM4K0OI266i6xZc1m9ei1HnXAmFw0fykmD+ldrHTsoKGDzq/+i4fAbIRZja9YUCpb+SL1jTiM/++uiL820LjU4YCYKMUQpjihI4mcxadLbDBhwJAsWfMCGDRs577xti8NPmzaRXr2C3+mXXnpD0W0skye/w+TJ7wDw97/fSv369Xj99XFAMBDpkkuuq9YYi1TfVH45QPti25nhvuKGAwMAzOxjSQ2AXYFllEHBpTCXaJJ+DTwLnGhmM5MYx8PALDN7vLyyuzbdNxJ/LEu+mZTsENh8z1XlF3IJ1fhvL5VfqIatv/p3yQ4BgF1HzUh2CGza9IOqeo4NYy6v8HdOo/MeKLM+SanAl8BRBIkzCzjDzD4rVmYiQaPmKUm/Iug1bGc7SZLeAk2ScKKDPZIZg6RPCbqIr0xmHM45V6pqmkjBzPIkXQxMJpjE5gkz+0zSLcAMMxtP8D34T0mXEwwoOmdnyRM8gdZpZnZw+aWccy5JqnHksZm9QTA4qPi+G4s9XwAcVplzegJ1zjkXTQWRuGpUJk+gzjnnoinP1wN1zjnnKi/ig1w9gTrnnIsmX43FOeeci4NfA3XOOefiELH5f7fnCdQ551w0eQvU/VJ81b9tskMAojELUP0RCVyU29UaUfm7yHvg8GSHUC0sL9oLansCdc45F03eheucc87FwbtwnXPOuTj4bSzOOedcHLwF6pxzzsXBr4E655xzleejcJ1zzrl4eBeuc845FwdPoK48kloBU8LNdCAfWB5u9zSzLZU83znAPUBOuOthM/uXpP8D/gOkAeeb2ceSUoFJwGAz21CV95HatSeNhl0MsRQ2T3mdza/8Z4cyaYf2oeHvzwEz8r//mp9H3UZs1zbsMuJWiMVQSgqbJr7MlrfGVyUUAFL27Ub9wX8Axdia9T+2vvtyieP1jh9Gyt4HAKC0+qhxM34eObTK9Zbnhjvu5/2p02nZojmv/Ht0jdcX1RiiFEcUJPKzeOD+Wxg4oC8bNm5k+PDLmTV7/g5lDurWmccff4CGDRowcdLbXH7FjSWOX37Z+dxz9420yTiAFStW1UygEb8GGkt2AA7MbIWZdTWzrsBo4IHC7comz2KeK3aOf4X7zgcuBY4FCqfzuRD4d1WTJ7EYjYZfyvrbr2bt5WdT77C+xDL3KFkkvR0NThzCuhsuZu0Vw9jw5MMAFKxewbrr/8S6Eeey9rqLaHDCGahFqyqFg2LUP+GPbHziNjbcfympXQ5Hu2WWKLLltSfZOOpKNo66kq0fvUHe/E+qVmcFnXDsMYy+/7aE1BXlGKIURxQk6rMYOKAv+3Tck/069ebCC6/mkYfvLLXcIw/fyQUX/IX9OvVmn457MqD/kUXHMjPbcszRR/D999k1G2yBVfyRBJ5AI0rSHyVlSZoj6UVJjcL9r0o6K3x+vqRxlTjtVqBR+NgqqTkwCHimqvGmdNyPgtwcCpYtgbw8tk59m3rdDytRpv7Rx7N50ivYz+sBsLWrgwN5eZC3FQClpqGYqhoOsfYdKVixBFu5FPLzyJvzIamdepZZPrVrb/LmfFjleiuie9fONGvaJCF1RTmGKMURBYn6LAYN6s/YcS8AMG36TJo1b0Z6+m4lyqSn70aTpk2YNn0mAGPHvcDgwQOKjt9370iuue52rIbX67S8ggo/ksG7cKPrJTP7J4Ck24DhwEPAecBUSd8CVwKHlPH6kyQdAXwJXG5mPwKPECTL+gSt0b8Cd5hVvZ8k1rI1BSuWF20XrFxOyj6dSpbJaA9Ak1sfglgKG59/irzZ0wFQq9Y0vvYuUtLbsXHsaGzViirFo2atsNXbzmFrVhDbfZ/SyzZvjVq0IX/RvCrV6Vxt0K5tOtk/Li7azsleQru26eTmLitRJid7yQ5lAAYN6kdOzhLmzl1Q88FGfCIFb4FG1wGSPpA0DxgC7A9gZkuBG4F3gCvNbGUpr50AdDCzA4G3gKfD1/5gZn3M7FBgA5AJfC5prKTnJO27/YkknSdphqQZT32zePvDlZOSQiwjk3UjL+PnUbewy/lXoUaNAbAVy1l31XDWXDKEen36o2YtqlZXJaR26U3evI8jf73FuWRr2LAB1159CSNvTtCk+d6F6+L0FHCxmXUGbgYaFDvWGVgBlLo8SnhNdXO4+S/g4FKK3Q7cAPw5LPMX4KZSzjXGzLqbWfdz9ip7NZaClcuJtWpdtB1r2Ror1iKFIEluzZoK+fkULMslf8mPxDLalSyzagX5P3xL6q8OLLOuirA1K1DzbddR1awVtqa03xqQ2uUw8uZ8UKX6nIuyCy84mxlZbzIj602W5C4ls/22f8vtMjPIWZxbonzO4lzaZWbsUGbvvTvQocPuzJzxFou+/ITMzAyypk2mTZvW1AhPoC5OTYAlktIIWqAASOoJDAS6AVdJ2nP7F0rKKLY5GPh8u+O/ARab2VcE10MLwkejeIPNX7SQWEYmsd3SITWVtMP6smXGRyXKbMn6kNT9uwYxNGlGSkZ7CpYuQS1bQ716wf5dGpO6X2fyF/8QbygAFGQvItYqA7XYDVJSSe3Sm/zPs3Yop9btUMPGFHy/sEr1ORdlj45+mu49+tG9Rz/Gj5/M0CEnA9Cr50GsXbO2RPctQG7uMtatXUevngcBMHTIyUyYMJn587+gbWYXOu57CB33PYTs7CX06NWfpUuX71BndTCzCj+Swa+BRtdfgWkEt7NMA5pIqg/8ExhmZoslXQk8IamvlfwL+rOkwUAesBI4p/CAJBG0PE8Nd40BxhH8LVwYd7QF+Wx4fBSNr78HYjG2vDORguzvaHDqMPK/XsjWGR+RN3s6aV260/SBp6CggA1jR2Pr15J64ME0POsiMAOJTROeo+CHb+MOJYingM2v/ouGw2+EWIytWVMoWPoj9Y45jfzsr4uSaVqXxA0eKjTiprvImjWX1avXctQJZ3LR8KGcNKh/nYshSnFEQaI+izcmTmHAgL4s/HwqGzZu5Nxzryg6NiPrTbr36AfAxZdcV3Qby6TJ7zBx0tvVHku5In4fqJKVuV3ts+qUPpH4Y0nbq2WyQ4jMwslum7Rd90p2CGz96ZtkhwBAw7bJX1A7b0tOlYfTrxl2dIW/c5o9+b+qD9+vJG+BOueci6aIt0A9gTrnnIumiA+M9wTqnHMuksxboM4551wcPIE655xzcfAuXOecc67yLM9boM4551yl+TVQ55xzLh4R78L1qfycc85FkhVU/FEeSQMkLZS0SNI1ZZT5vaQFkj6T9J/yzuktUOecc9FUTS1QSSkEyzkeA2QDWZLGm9mCYmX2Aa4FDjOzVZJ2K/1s23gCdc45F0mWV22n6gksMrNvACT9F/gtUHxR0z8Cj5jZKgAzW7bDWbbjXbjOOeciqTJduMXXLg4f5xU7VTvgx2Lb2eG+4vYF9pU0VdInkgaUF5+3QJ1zzkVSZda4N7MxBKtLxSsV2AfoA2QC70vqbGary3qBt0Cdc85FUjUOIsoB2hfbzgz3FZcNjDezrWb2LfAlQUItkydQ55xz0WSq+GPnsoB9JO0pqR5wGjB+uzKvELQ+kbQrQZfuTten8y5c55xzkVSZLtydnscsT9LFwGQgBXjCzD6TdAsww8zGh8f6SVoA5AMjzGzFzs7rCdQ551wkFeRV3xrZZvYG8MZ2+24s9tyAK8JHhXgCdc45F0lWftdsUnkCrYUktQKmhJvpBN0Ny8Ptnma2pVjZy4AxZrahnHO+C1xlZjPijSu1a08aDbsYYilsnvI6m1/ZcSKPtEP70PD354AZ+d9/zc+jbiO2axt2GXErxGIoJYVNE19my1vbX56ovJR9u1F/8B9AMbZm/Y+t775c4ni944eRsvcBACitPmrcjJ9HDq1yveW54Y77eX/qdFq2aM4r/x5d4/VFNYYoxREFifwsHrj/FgYO6MuGjRsZPvxyZs2ev0OZg7p15vHHH6BhgwZMnPQ2l19xY4njl192PvfcfSNtMg5gxYpVNRJndXXh1hQfRFQLmdkKM+tqZl2B0cADhdvFk2foMqBRjQcVi9Fo+KWsv/1q1l5+NvUO60ssc4+SRdLb0eDEIay74WLWXjGMDU8+DEDB6hWsu/5PrBtxLmuvu4gGJ5yBWrSqWjyKUf+EP7LxidvYcP+lpHY5HO2WWaLIlteeZOOoK9k46kq2fvQGefM/qVqdFXTCsccw+v7bElJXlGOIUhxRkKjPYuCAvuzTcU/269SbCy+8mkcevrPUco88fCcXXPAX9uvUm3067smA/kcWHcvMbMsxRx/B999n12isVqAKP5LBE+gvhKSjJM2SNE/SE5LqS/oz0BZ4R9I7YblHw5uMP5N0c3XVn9JxPwpycyhYtgTy8tg69W3qdT+sRJn6Rx/P5kmvYD+vB8DWrg4O5OVB3tbgfaSmoVjV/zHE2nekYMUSbOVSyM8jb86HpHbqWWb51K69yZvzYZXrrYjuXTvTrGmThNQV5RiiFEcUJOqzGDSoP2PHvQDAtOkzada8GenpJWetS0/fjSZNmzBt+kwAxo57gcGDt80rcN+9I7nmutsJLhvWHLOKP5LBu3B/GRoATwFHmdmXkp4BLjSzv0u6AjjSzH4Ky15vZivDuSGnSDrQzOZWNYBYy9YUrFhetF2wcjkp+3QqWSYjuA2rya0PQSyFjc8/Rd7s6QCoVWsaX3sXKent2Dh2NLZqp4PfyqVmrbDV285ha1YQ2730W7rUvDVq0Yb8RfOqVKdztUG7tulk/7i4aDsnewnt2qaTm7usRJmc7CU7lAEYNKgfOTlLmDu3+Cx4NSNZLcuK8hboL0MK8K2ZfRluPw0cUUbZ30uaCcwC9gc6lVEOKDk91lPfLN5Z0QpEmUIsI5N1Iy/j51G3sMv5V6FGjQGwFctZd9Vw1lwyhHp9+qNmLapWVyWkdulN3ryPo3/Bxbkka9iwAddefQkjb743IfUV5KvCj2TwBFqHSNoTuIqgpXog8DpB67VMZjbGzLqbWfdz9mpbZrmClcuJtWpdtB1r2Ror1iKFIEluzZoK+fkULMslf8mPxDJKTkdpq1aQ/8O3pP7qwMq+vZLnWbMCNd92HVXNWmFrVpZaNrXLYeTN+aBK9TkXZRdecDYzst5kRtabLMldSmb7bf+W22VmkLM4t0T5nMW5tMvM2KHM3nt3oEOH3Zk54y0WffkJmZkZZE2bTJs2rakJfg3UJUI+0EFSx3B7KPBe+HwdUHhhpSnwM7BGUhtgYLUFsGghsYxMYrulQ2oqaYf1ZcuMj0qU2ZL1Ian7dwVATZqRktGegqVLUMvWUK9esH+XxqTu15n8xT9UKZ6C7EXEWmWgFrtBSiqpXXqT/3nWDuXUuh1q2JiC7xdWqT7nouzR0U/TvUc/uvfox/jxkxk65GQAevU8iLVr1pbovgXIzV3GurXr6NXzIACGDjmZCRMmM3/+F7TN7ELHfQ+h476HkJ29hB69+rN06fId6qwOZqrwIxn8GugvwyZgGPC8pFSCaasKx8GPASZJWmxmR0qaBXxBsDLB1GqLoCCfDY+PovH190AsxpZ3JlKQ/R0NTh1G/tcL2TrjI/JmTyetS3eaPvAUFBSwYexobP1aUg88mIZnXRSMBJDYNOE5Cn74torxFLD51X/RcPiNEIuxNWsKBUt/pN4xp5Gf/XVRMk3rkrjBQ4VG3HQXWbPmsnr1Wo464UwuGj6Ukwb1r3MxRCmOKEjUZ/HGxCkMGNCXhZ9PZcPGjZx77rZ5A2ZkvUn3Hv0AuPiS64puY5k0+R0mTnq72mMpT9SvqqimR1G5X45Vp/SJxB9L2l4tkx0C9Uck5hqQq7i0XfdKdghs/WmnU6cmTMO2hyc7BPK25FS5WfjlrwZU+Dtn388nJbwZ6i1Q55xzkVSQH+2rjJ5AnXPORVLUO0g9gTrnnIukqN8H6gnUOedcJBX4ZPLOOedc5flqLM4551wc/Bqoc845F4f8Ah+F65xzzlWat0Cdc865OPggIueccy4OPojIOeeci4O3QJ1zzrk4RPwSqCdQ55xz0eSjcJ1zzrk4RHw1M0+gzjnnosnwa6DOOedcpRVE/CKoJ1DnnHORVOAtUOecc67y8j2BOuecc5Xn10BdnZHatSeNhl0MsRQ2T3mdza/8Z4cyaYf2oeHvzwEz8r//mp9H3UZs1zbsMuJWiMVQSgqbJr7MlrfGVzmelH27UX/wH0Axtmb9j63vvlzieL3jh5Gy9wEAKK0+atyMn0cOrXK95bnhjvt5f+p0WrZoziv/Hl3j9UU1hijFEQWJ/CweuP8WBg7oy4aNGxk+/HJmzZ6/Q5mDunXm8ccfoGGDBkyc9DaXX3FjieOXX3Y+99x9I20yDmDFilU1EmfUR+FG+yYbV3vEYjQafinrb7+atZefTb3D+hLL3KNkkfR2NDhxCOtuuJi1Vwxjw5MPA1CwegXrrv8T60acy9rrLqLBCWegFq2qFo9i1D/hj2x84jY23H8pqV0OR7tlliiy5bUn2TjqSjaOupKtH71B3vxPqlZnBZ1w7DGMvv+2hNQV5RiiFEcUJOqzGDigL/t03JP9OvXmwguv5pGH7yy13CMP38kFF/yF/Tr1Zp+OezKg/5FFxzIz23LM0Ufw/ffZNRprQSUeyeAJtBaTdJakuZLmSBorqYOkt8N9UyTtHpbbW9InkuZJuk3S+nB/hqT3Jc2WNF/S4fHGktJxPwpycyhYtgTy8tg69W3qdT+sRJn6Rx/P5kmvYD+vB8DWrg4O5OVB3tbgPaWmoVjVu21i7TtSsGIJtnIp5OeRN+dDUjv1LLN8atfe5M35sMr1VkT3rp1p1rRJQuqKcgxRiiMKEvVZDBrUn7HjXgBg2vSZNGvejPT03UqUSU/fjSZNmzBt+kwAxo57gcGDBxQdv+/ekVxz3e1YDS+XYqjCj2TwBFpLSdofuAHoa2ZdgEuBh4CnzexAYBzwYFh8FDDKzDoDxX8yngFMNrOuQBdgdrzxxFq2pmDF8qLtgpXLUavWJctktCelbSZNbn2IJrf/g9Su2xKaWrWmyb2P02z0/2PTK89iq1bEG0pwvmatsNXbzmFrVqBmLUsv27w1atGG/EXzqlSnc7VBu7bpZP+4uGg7J3sJ7dqm71AmJ3tJqWUGDepHTs4S5s5dUOOxFqjij2TwBFp79QWeN7OfAMxsJXAoUHjhcSzQO3x+KPB8+Lz4hcksYJikkUBnM1u3fSWSzpM0Q9KMp75ZvP3hyklJIZaRybqRl/HzqFvY5fyrUKPGANiK5ay7ajhrLhlCvT79UbMWVaurElK79CZv3sdgUb/i4lxyNWzYgGuvvoSRN9+bkPryUYUf5ZE0QNJCSYskXbOTcidJMkndyzunJ9A6zMzeB44AcoCnJJ1VSpkxZtbdzLqfs1fbMs9VsHI5sWItzljL1lixFikESXJr1lTIz6dgWS75S34kltGuZJlVK8j/4VtSf3Vg1d7bmhWo+bbrqGrWCluzstSyqV0OI2/OB1Wqz7kou/CCs5mR9SYzst5kSe5SMttv+7fcLjODnMW5JcrnLM6lXWbGDmX23rsDHTrszswZb7Hoy0/IzMwga9pk2rQp2dtUXarrGqikFOARYCDQCThdUqdSyjUh6M2bVpH4PIHWXm8Dp0hqBSCpJfARcFp4fAhQmBU+AU4KnxceR9IewFIz+yfwL+CgeIPJX7SQWEYmsd3SITWVtMP6smXGRyXKbMn6kNT9uwZ1N2lGSkZ7CpYuQS1bQ716wf5dGpO6X2fyF/8QbygAFGQvItYqA7XYDVJSSe3Sm/zPs3Yop9btUMPGFHy/sEr1ORdlj45+mu49+tG9Rz/Gj5/M0CEnA9Cr50GsXbOW3NxlJcrn5i5j3dp19OoZfCUMHXIyEyZMZv78L2ib2YWO+x5Cx30PITt7CT169Wfp0uU71FkdCqQKP8rRE1hkZt+Y2Rbgv8BvSyl3K/A3YFNF4vPbWGopM/tM0u3Ae5LygVnAJcCTkkYAy4FhYfHLgH9Luh6YBKwJ9/cBRkjaCqwHdmiBVlhBPhseH0Xj6++BWIwt70ykIPs7Gpw6jPyvF7J1xkfkzZ5OWpfuNH3gKSgoYMPY0dj6taQeeDANz7oIzEBi04TnKPjh27hDCeIpYPOr/6Lh8BshFmNr1hQKlv5IvWNOIz/766JkmtYlcYOHCo246S6yZs1l9eq1HHXCmVw0fCgnDepf52KIUhxRkKjP4o2JUxgwoC8LP5/Kho0bOffcK4qOzch6k+49+gFw8SXXFd3GMmnyO0yc9Ha1x1KeygxRknQecF6xXWPMbEz4vB3wY7Fj2UCv7V5/ENDezF4Pv0PLr7OmR1G55JPUCNhoZibpNOB0Myvt19dOrTqlTyT+WNL2Kn0wUCLVH5GYa0Cu4tJ23SvZIbD1p2+SHQIADdvGPaC+2uRtyany0J7nMoZU+Dvn1CXjyqxP0snAADM7N9weCvQys4vD7RhBr945ZvadpHeBq8xsxs7q9BZo3XAw8LAkAauBPyQ3HOecK19e+V2zFZUDtC+2nRnuK9QEOAB4N/iaJB0YL2nwzpKoJ9A6wMw+ILhNxTnnao1q7PLKAvaRtCdB4jyN4Da+oB6zNcCuhdveAnXOOVerVdf9nWaWJ+liYDKQAjwRjiO5BZhhZnHNHeoJ1DnnXCRV553ZZvYG8MZ2+24so2yfipzTE6hzzrlIisSoxZ3wBOqccy6SkjVFX0V5AnXOORdJeckOoByeQJ1zzkWSeQvU/VK0eP7dZIfgXKRFYTIHgLwtOeUXqgWivryDJ1DnnHOR5AnUOeeci4OPwnXOOefi4KNwnXPOuTj4KFznnHMuDt6F65xzzsXBu3Cdc865OPgoXOeccy4O3oXrnHPOxSEv4ik0luwAkk3SR8mOIR6S+kj6dbLjcM65mmKVeCRDtSRQSZFtyZYXm5klLAlV8+fUB/AE6pz7xSqoxCMZyk2gkv4qaaGkDyU9K+mqcP+7kv4uaQZwqaSjJM2SNE/SE5Lqh+XukrRA0lxJ94b7TpE0X9IcSe+XUmcfSe9JelXSN+E5hkiaHp5/77DcIEnTwnr/J6lNuH+kpLGSpgJjJbWW9JakzyT9S9L3knYNy64vVue7kl6Q9IWkcZJ2GAMWlhklaXb4HnqG+3cJ3/f0MJ7fhvvPkTRe0tvAFEmNJT0Zvo+5kk4Ky/WT9LGkmZKel9Q43P+dpJvD/fMk7SepA3ABcHkYx+E7+Sx29t7PDOOdLekxSSkV/cNxzrmaVqCKP5JhpwlUUg/gJKALMBDovl2RembWHXgEeAo41cw6E1xbvVBSK+BEYH8zOxC4LXzdjUB/M+sCDC6j+i4ESeJXwFBgXzPrCfwLuCQs8yFwiJl1A/4L/KXY6zsBR5vZ6cBNwNtmtj/wArB7GXV2Ay4LX7sXcFgZ5RqZWVfgIuCJcN/1YR09gSOBeyTtEh47CDjZzH4D/BVYY2adw8/k7TCh3RDGexAwA7iiWH0/hfsfBa4ys++A0cADZtbVzD7YyWdR6nuX9CvgVOCw8L3kA0PKeL/OOZdwBViFH8lQXgv0MOBVM9tkZuuACdsdfy787/8B35rZl+H208ARwBpgE/C4pN8BG8LjU4GnJP0RKKvVk2VmS8xsM/A18Ga4fx7QIXyeCUyWNA8YAexf7PXjzWxj+Lw3QVLBzCYBq8qoc7qZZZtZATC7WD3bezY81/tAU0nNgX7ANZJmA+8CDdiWqN8ys5Xh86MJfnAQnmMVcAhB0p4avv5sYI9i9b0U/vfTncRU1mdR1ns/CjgYyArrPIrgR0MJks6TNEPSjDFjxpRRtXPOVb+oXwOt6jW5n3d20Mzywi7Oo4CTgYuBvmZ2gaRewHHAp5IONrMV2718c7HnBcW2C4rF/RBwv5mNl9QHGFnR2MpQvM58yv58tv//ywABJ5nZwuIHwvdZXiwiSLKnlxPXzmLa2WdRVp1Pm9m1OytkZmOAwswZ7SFxzrlflNo+CncqMEhSg/Ca3PFllFsIdJDUMdweCrwXvqaZmb0BXE7QLYukvc1smpndCCwH2scZfzOgcOG7s8t5H78P6+4HtIizvkKnhufqTdAduwaYDFxSeN1UUrcyXvsW8KfCDUktgE+Awwo/v/B66r7lxLAOaFJsu6zPoqz3PgU4WdJu4bGWkoq3ep1zLqmi3gLdaQI1syxgPDAXmEjQfbqmlHKbgGHA82EXYgHBNbomwGuS5hJcoyu8rndPOCBmPvARMCfO+EeGdX4K/LSTcjcD/cL6TgFyCRJQvDZJmkXwHoeH+24F0oC5kj4Lt0tzG9AiHIA0BzjSzJYD5wDPhp/Vx8B+5cQwATixcBARZX8Wpb53M1tAcN31zbDOt4CMCn8CzjlXw6I+CldmO8/dkhqb2XpJjYD3gfPMbGZCoqsmCkYE54ddyocCj4YDZ+I517sEA3lmVGOINaY63zveheucq7gqj429osNpFf7Ouf+7/yZ8LG5FroGOkdSJYFDM07UteYZ2B/6fpBiwBfhjkuNJpLr83p1ztVjUf7GXm0DN7IxEBFKTzOwrgltUquNcfarjPIlSne/dOecSySeTd8455+KQH/E2qCdQ55xzkZSsCRIqyhOoc865SIp2+vQE6pxzLqK8Beqcc87FwQcROeecc3HwQUTuF2PVKX2SHQIAaXu1THYI1B9xb7JDcNtJ23WHtRASbutP3yQ7BAAatj082SGQtyWn/ELlsIgn0GpZUNs555yrbtU5lZ+kAQrWtl4k6ZpSjl+hbWtXT6nI3OCeQJ1zzkVSgVmFHzsjKYVgGcmBBEtHnh7OsFfcLKB7uE7zC8Dd5cXnCdQ551wkVeNqLD2BRWb2jZltIVgj+bcl6jJ7x8wK16z+hGCN5Z3yBOqccy6SCrAKPySdJ2lGscd5xU7VDvix2HZ2uK8swwlWINspH0TknHMukiozCtfMxgBjqlqnpDOB7sBvyivrCdQ551wkVeNECjlA+2LbmeG+EiQdDVwP/MbMNpd3Uu/Cdc45F0lWif+VIwvYR9KekuoBpwHjixeQ1A14DBhsZssqEp+3QJ1zzkVSdc1EZGZ5ki4GJgMpwBNm9pmkW4AZZjYeuAdoDDwvCeAHMxu8s/N6AnXOORdJVs7tKZU81xvAG9vtu7HY86Mre05PoEkmqS3woJmdnOxYqiq1a08aDbsYYilsnvI6m1/5zw5l0g7tQ8PfnwNm5H//NT+Puo3Yrm3YZcStEIuhlBQ2TXyZLW+N37GCSkrZtxv1B/8BFGNr1v/Y+u7LJY7XO34YKXsfAIDS6qPGzfh55NAq11ueG+64n/enTqdli+a88u/RNV5fVGOIUhxRkMjP4oH7b2HggL5s2LiR4cMvZ9bs+TuUOahbZx5//AEaNmjAxElvc/kVN5Y4fvll53PP3TfSJuMAVqxYVSNxRn0yeb8GWkmSqvVHh5kt/iUkT2IxGg2/lPW3X83ay8+m3mF9iWWWnMgjlt6OBicOYd0NF7P2imFsePJhAApWr2Dd9X9i3YhzWXvdRTQ44QzUolXV4lGM+if8kY1P3MaG+y8ltcvhaLeSt3Vtee1JNo66ko2jrmTrR2+QN/+TqtVZQSccewyj778tIXVFOYYoxREFifosBg7oyz4d92S/Tr258MKreeThO0st98jDd3LBBX9hv0692afjngzof2TRsczMthxz9BF8/312jcaaj1X4kQyeQIuR9NdwqqcPJT0r6apw/7uS/i5pBnCppKMkzZI0T9ITkuqH5e4qNhXUveG+UyTNlzRH0vul1NlB0vzw+TmSXpI0SdJXku4uVm6ApJnheaaE+1pKeiWs7xNJB4b7R0p6WtIHkr6X9DtJd4fxTpKUFpY7WNJ7kj6VNFlSRryfXUrH/SjIzaFg2RLIy2Pr1Lep1/2wEmXqH308mye9gv28HgBbuzo4kJcHeVuD95mahmKKN4wisfYdKVixBFu5FPLzyJvzIamdepZZPrVrb/LmfFjleiuie9fONGvaJCF1RTmGKMURBYn6LAYN6s/YcS8AMG36TJo1b0Z6+m4lyqSn70aTpk2YNn0mAGPHvcDgwQOKjt9370iuue72au1iLU1l7gNNBu/CDUnqAZwEdAHSgJnAp8WK1DOz7pIaAF8BR5nZl5KeAS6UNBY4EdjPzExS8/B1NwL9zSyn2L6d6Qp0AzYDCyU9BGwC/gkcYWbfSiqcTf1mYJaZnSCpL/BM+HqAvYEjCaat+hg4ycz+Iull4DhJrwMPAb81s+WSTgVuB/5Q0c+suFjL1hSsWF60XbByOSn7lJwpK5YRjCJvcutDEEth4/NPkTd7OgBq1ZrG195FSno7No4dja1aEU8YRdSsFbZ62zlszQpiu+9TetnmrVGLNuQvmlelOp2rDdq1TSf7x8VF2znZS2jXNp3c3GUlyuRkL9mhDMCgQf3IyVnC3LkLajzWmk7QVeUt0G0OA141s01mtg6YsN3x58L//h/wrZl9GW4/DRwBrCFIdI9L+h1QOCXUVOApSX8kGP1VnilmtsbMNgELgD2AQ4D3zexbADNbGZbtDYwN970NtJLUNDw20cy2AvPCeieF++cBHcL3cQDwlqTZwA2UMnVV8dk9nvpm8faHKyclhVhGJutGXsbPo25hl/OvQo0aA2ArlrPuquGsuWQI9fr0R81aVK2uSkjt0pu8eR+DRX31QeeSq2HDBlx79SWMvDkxqxFV52TyNcETaMX9vLODZpZHMN/iC8DxhAnLzC4gSE7tgU8llXdxr/jNu/nE30uwOay/ANhq237KFYTnFPCZmXUNH53NrF8p72uMmXU3s+7n7NW2zMoKVi4n1qp10XasZWusWIsUgiS5NWsq5OdTsCyX/CU/EssoOZuWrVpB/g/fkvqrA+N600XnWbMCNd/2UatZK2zNylLLpnY5jLw5H1SpPuei7MILzmZG1pvMyHqTJblLyWy/7d9yu8wMchbnliifsziXdpkZO5TZe+8OdOiwOzNnvMWiLz8hMzODrGmTadOmNTWhGu8DrRGeQLeZCgyS1EBSY4IkWJqFQAdJHcPtocB74WuahUOlLyfoCkbS3mY2LRwuvZySs2FU1CfAEZL2DM9Z2IX7ATAk3NcH+MnM1lbwnAuB1pIODV+fJmn/OGIDIH/RQmIZmcR2S4fUVNIO68uWGR+VKLMl60NS9+8KgJo0IyWjPQVLl6CWraFevWD/Lo1J3a8z+Yt/iDcUAAqyFxFrlYFa7AYpqaR26U3+51k7lFPrdqhhYwq+X1il+pyLskdHP033Hv3o3qMf48dPZuiQYNxir54HsXbN2hLdtwC5uctYt3YdvXoeBMDQISczYcJk5s//graZXei47yF03PcQsrOX0KNXf5YuXb5DndUh3woq/EgGvwYaMrMsSeOBucBSgq7ONaWU2yRpGMHNtqkEM1yMBloCr4bXSAVcEb7kHkn7hPumAHPiiG15ODHyS5JiwDLgGGAk8ISkuQRdxmdX4pxbJJ0MPCipGcHfwt+BzyobHwAF+Wx4fBSNr78HYjG2vDORguzvaHDqMPK/XsjWGR+RN3s6aV260/SBp6CggA1jR2Pr15J64ME0POsiMAOJTROeo+CHb+MKY1s8BWx+9V80HH4jxGJszZpCwdIfqXfMaeRnf12UTNO6JG7wUKERN91F1qy5rF69lqNOOJOLhg/lpEH961wMUYojChL1WbwxcQoDBvRl4edT2bBxI+eee0XRsRlZb9K9R9ARdfEl1xXdxjJp8jtMnPR2tcdSnqjfxqKoX6RNJEmNzWy9pEbA+8B5ZjYz2XFFxapT+kTijyVtr5blF6ph9Uck5hqQq7i0XfdKdghs/embZIcAQMO2hyc7BPK25FR5OH2fzKMr/J3zbvb/qj58v5K8BVrSmHCR1QbA0548nXMuecpbKDvZPIEWY2ZnJDsG55xzgWinT0+gzjnnIirq10A9gTrnnIukZI2urShPoM455yLJW6DOOedcHJI1QUJFeQJ1zjkXSVG/zdITqHPOuUiKeheuT6TgKqxxoz0j8ceSV5Cf7BAiEYMrKW9LTrJDILVeu/ILJcDGxcmf2zlt172qPLFBl/RfV/g7Z07uRz6RgnPOOQd+DdQ555yLi89E5JxzzsXBW6DOOedcHLwF6pxzzsXBW6DOOedcHHwqP+eccy4O3oXrnHPOxcG7cJ1zzrk4WMS7cGPJDsDtSFK+pNmS5kuaIKl5OeW7Sjq22PZgSdfUeKCluOfem5gz7x0+mTaRLl33L7VM124HMG36RObMe4d77r2paP+JJx5L1ozJrF3/Nd0O6hx3DPfddzOfffY+WVmT6dr1gFLLdOvWmRkz3uSzz97nvvtuLtp/xx3XMWfO22RlTea558bQrFnTuON44P5b+GLBh8z89C26lRHHQd06M2vm//hiwYc8cP8tOxy//LLzyduSQ6tWLWptDFGKIwqi/FnccMf9HHHcaZxw5gXVet54FWAVfiSDJ9Bo2mhmXc3sAGAl8KdyyncFihKomY03s7tqML5S9evfh707dqBL5yO55OJr+fuo20ot9/dRt3Hxn66lS+cj2btjB47p9xsAFixYyBmnX8jUD6fHHUP//kfSsWMH9t//CP70p2t48MHbSy334IO3c9FFV7P//kfQsWMH+vXrA8Dbb3/AQQcdQ48e/fnqq28ZMaK8j750Awf0ZZ+Oe7Jfp95ceOHVPPLwnaWWe+ThO7nggr+wX6fe7NNxTwb0P7LoWGZmW445+gi+/z671sYQpTiiIOqfxQnHHsPo+0v/d5sMZlbhRzJ4Ao2+j4F2AJJ6SvpY0ixJH0n6P0n1gFuAU8NW66mSzpH0cPiapyQ9GJb/RtLJ4f6YpH9I+kLSW5LeKDwWr+OPP4Znx70EQFbWbJo1a0qb9NYlyrRJb03TJo3JypoNwLPjXmLQoH4ALFz4NV999U1VQmDQoH6MG/ciANOnz6J586akp+9Wokx6+m40bdqY6dNnATBu3IsMHtwfgP/97wPy8/PD188kMzM9zjj6M3bcCwBMmz6TZs2blRpHk6ZNmDZ9JgBjx73A4MEDio7fd+9Irrnu9ri/HKIQQ5TiiIKofxbdu3amWdMm1X7eeOVbQYUfyeAJNMIkpQBHAePDXV8Ah5tZN+BG4A4z2xI+fy5stT5XyqkygN7A8UBhy/R3QAegEzAUOLSq8Wa0bUN29pKi7cU5S2jbtmQCats2nZycbWVycnLJaNumqlWXOH/xGHJycsuIIXenZQDOPvtUJk9+N6442rVNJ/vHxdvqyF5Cu+3qaNc2nZzisRYrM2hQP3JyljB37oK46o9KDFGKIwr8s6icArMKP5LBBxFFU0NJswlanp8Db4X7mwFPS9oHMCCtgud7xYKr8QskFWar3sDz4f5cSe+U9kJJ5wHnAdRLa0VaanR+ndakq6++mLy8PJ599uWE192wYQOuvfoSBhx7RsLrjlIMUYojCuriZxH1UbjeAo2mjWbWFdgDENuugd4KvBNeGx0ENKjg+TYXe16pJX/MbIyZdTez7qUlz/POH8pHn7zOR5+8Tm7ucjIzM4qOtW2XweLFuSXKL16cS7t228q0a5fOksVLKxPSDs4//yymTZvItGkTyc1dViKGdu3Sy4ghvcwyQ4eezMCBR3HOOX+uVBwXXnA2M7LeZEbWmyzJXUpm+7bb6sjMIGe7OHIW59KueKxhmb337kCHDrszc8ZbLPryEzIzM8iaNpk2bUp2h0c1hijFEQX+WcTPr4G6uJnZBuDPwJWSUglaoIWLHp5TrOg6oLJNw6nASeG10DZAn3hiHPPYWH59yHH8+pDjeG3Cm5w+5HcA9OjRlbVr17E0d3mJ8ktzl7N23Xp69OgKwOlDfsdrr721/Wkr5bHHnqFXr4H06jWQ8eMnM2TISQD07NmNNWvWkZu7rET53NxlrF27np49uwEwZMhJTJjwJgDHHPMbrrjiQk4+eTgbN26qVByPjn6a7j360b1HP8aPn8zQIcEl5V49D2LtmrWlxrFu7Tp69TwIgKFDTmbChMnMn/8FbTO70HHfQ+i47yFkZy+hR6/+LF26fIc6oxhDlOKIAv8s4ledo3AlDZC0UNKi0u5SkFRf0nPh8WmSOpR3Tk+gEWdms4C5wOnA3cCdkmZRsvv9HaBT4SCiCp76RSAbWAD8G5gJrKlKrJMnvcN33/7I3Pnv8vAjd3L5ZX8tOvbRJ68XPb/8sr/yyD/uYu78d/n2mx94M7zOOGhwPxZ+9RE9e3XjxRef4JVXn650DJMmvc233/7AggUf8I9//I1LL72h6Ni0aROLnl966Q08+ujdLFjwAd988z2TJwc92H//+600abILr78+jmnTJvLQQ3dUOgaANyZO4Ztvf2Dh51MZPfpuLr7kuqJjM7LeLHp+8SXX8dhj97Dw86l8/c33TJz0dlz1RTWGKMURBVH/LEbcdBdDzr+c737I5qgTzuTFCZMTUm9Z8gsKKvzYmXA8ySPAQIJxH6dL6rRdseHAKjPrCDwA/K28+FTbR7W5+ElqbGbrJbUCpgOHmVluWeUbN9ozEn8seQX5yQ4hEjG4kvK25JRfqIal1muX7BAA2Lj4g2SHQNque1XqclFpWjTuWOHvnFXrF5VZn6RDgZFm1j/cvhbAzO4sVmZyWObjsMcvF2htO0mSPoiobnstnKShHnDrzpKnc84lWmUmSCg+4DE0xszGhM/bAT8WO5YN9NruFEVlzCxP0hqgFfBTWXV6Aq3DzKxPsmNwzrmyVKaHNEyWY8otWI08gTrnnIukary/MwdoX2w7k20DMrcvk11s0OaKnZ3UBxE555yLJKvE/8qRBewjac9w9rbT2DZBTaHxwNnh85OBt3d2/RO8Beqccy6iyhtdW1HhNc2LgclACvCEmX0m6RZghpmNBx4HxkpaRDAH+WnlndcTqHPOuUiqzpmIzOwN4I3t9t1Y7Pkm4JTKnNMTqHPOuUiK+m2WnkCdc85FkidQ55xzLg7RTp8+E5FLMEnnFbu5uU7HEYUYohKHxxCtOKIQQ23gt7G4RDuv/CIJEYU4ohADRCMOj2GbKMQRhRgizxOoc845FwdPoM4551wcPIG6RIvKdZUoxBGFGCAacXgM20QhjijEEHk+iMg555yLg7dAnXPOuTh4AnXOOefi4AnUOeeci4MnUFejJO0raYqk+eH2gZJuSHZcySSpo6R/S3pR0qEJqvOgnT0SEUPUKHCmpBvD7d0l9UxCHG0kPS5pYrjdSdLwRMfhKs8HEbkaJek9YATwmJl1C/fNN7MDEhzHSwTLFU00s+pZI6nidTcIV3oo3H4W+Eu4OcHMuiYghnd2ctjMrG9Nx1CcpMOA2Wb2s6QzgYOAUWb2fQJjeBQoAPqa2a8ktQDeNLMeiYohjGMi8CRwvZl1CRdznmVmnRMcx+9K2b0GmGdmyxIZS23hc+G6mtbIzKZLKr4vLwlx/AMYBjwo6XngSTNbmKC6J0gaa2bPhNtbgQ4EU33mJyIAMzsyEfVUwqNAF0ldgCuBfwHPAL9JYAy9zOwgSbMAzGxVuNhyou1qZv9P0rVhHHmSEvJ3sZ3hwKFA4Y+tPsCnwJ6SbjGzsUmIKdI8gbqa9pOkvQnnhZZ0MrAk0UGY2f+A/0lqBpwePv8R+CfwbzPbWoPVDwAulDQJuAO4Cvgz0BAYUoP1lkrSAUAnoEHhvmLJPVHyzMwk/RZ42MweT0K35VZJKWz722xN0CJNtJ8ltSoWxyEELb9ESwV+ZWZLwzjaEPyo6QW8D3gC3Y534boaJWkvgpuyfw2sAr4FzjSz75IQSyvgTGAosBgYB/QGOptZnwTU3wz4K9AOuMHMvq7pOkuJ4SaClkUngsWFBwIfmtnJCY7jPWASQa/AEcAyYE4iuy0lDQFOJeg+fho4meD/l+cTFUMYx0HAQ8ABwHygNXCymc1NcBwLzKxTsW0Bn5lZJ0mzCi/BuG08gbqEkLQLEDOzdUmq/2Xg/wh+RT9lZkuKHZthZt1rsO5eBNeBtxC0QDcCtwM5wK1mtrqm6i4llnlAF4JrbF3CVsa/zeyYRMUQxpEOnAFkmdkHknYH+iS6JSxpP+AoQMAUM/s8kfUXiyOV4O9TwMIa7hEpK4Z/ALsDhT8gTgKyCf52X4vgZYCk8wTqapSkO4C7C5NEOFDjSjNL6EhcScea2Rvb7atvZpsTUPds4FigMcG118PC/b8BrjOz/jUdQ7FYpptZT0mfAkcC64DPzWy/RMUQFZJalrJ7XaKTV9iNfBzBdfGiy2pmdn+C4xBB0jws3DUVeNE8SZTJr4G6mjbQzK4r3AgHahwLJPpWltsIuiyL+5ig+66m5RF8Oe5C0AoFwMzeA95LQP3FzZDUnODa76fAeoLPIaEkrWPH9ZLXADMIfmB9k4AwZgLtCS4tCGgO5EpaCvzRzD5NQAwAE4BNwDyScw0WCIZiAy+ED1cBnkBdTUsp3tKT1BCon6jKw67CdkBDSd0IvigBmgKNEhTGGcD5BMnzrATVWSozuyh8Ojoc1NQ00dfaQn8n6B78D8H/J6cBexMktScIrtPWtLeAF8xsMoCkfgQtsCcJRm33SkAMAJlmdmCC6ipTeBvL34DdCP4/EUFebZrUwCLMu3BdjZJ0NTCI4EsJgkEj483s7gTVfzZwDtCdoHVTaB3BtdCXEhFHVEiaYmZHlbcvAXHMMbMu2+2bbWZdSztWQzHM237QkqS5ZnZgYSw1HUNY598Irr++mYj6dhLHImBQsq4D10beAnU1ysz+JmkuwUANCAbNTE5g/U8DT0s6ycxeTFS9xUn6g5k9ET7PJBjxeTCwADjHzL5MQAwNCFrcu4bXoYu3xNvVdP2l2CDp92zrLjyZoBsTduzarSlLwh94/w23TwWWhtckE9mV+gnwsqQYwT3CyWr5LfXkWTneAnW/aJLONLN/S7qSUr6YEzFQQ9JMMzsofP7/gP8RTBzwW+DiRLT+JF0KXAa0JbiFp9Ba4J9m9nBNx7BdPHsBowhu3IfgOuzlBCOTDzazDxMQw67ATQS3MkEwaOZmgmuxu5vZopqOIYzjW4K/hXnJHLAjaRSQDrwCFA2uq2u9NJXhCdTVCEkfmlnvUgaLJPTXtaTzzeyx8P7H7ZmZ3ZKAGIon0BJdg4m+v07SJWb2UKLqc+WT9D7BLTxJG0AUxvFkKbvNzP6Q8GBqCU+grk6QdJiZTS1vXw3VvYygm1DA74AOhbdKJHpe4HCqugsIJi8AeJdgnuJE37qRSTB5QOEtEx8Al5pZdgJjaE0wJ/H+lJyVKdHzAj8F7AVMpGTLL6G3sbjK82ugrsaE15I+i8g9hg+x4y0rpe2rCSOKPZ9BcD/oqnCE8PgE1F/cP4C08L8QzMr0KHBuguN4kmAE7inh9pnhvkRO6DAOeA44nuBHxdnA8gTWX+jb8FEvfCSUpL+Y2d2SHqL0yxx/TnRMtYW3QF2NkvQqcImZ/ZCk+g8lmEbwMuCBYoeaAicmYrRnFEhKDScpL230a0JGvW5X5w6jXBM58jWs71MzO7hw5G24L8sSvBpLsXgaA5jZ+gTXO8jMJoQj1ncQDsRzpfAWqKtpLYDPJE0Hfi7caWaDE1R/PYIWXyrQpNj+tQQjP+uK6QSt7XxJexfOwxsO5knGyh8rFCxj9my4fTqwIsExFHZbL5F0HMHgqtJmJ6pR4eT+YwvrlvQTcJaZfZaI+s1sQvh0w/bzAEs6pZSXuJC3QF2NCqer20E4C08i49jDErjWZNQUDlaS1Bd4Ciic6acDMMzMdrZeaE3EswdBF/qhBN2GHxH0VPyYwBiOJ7j22j6MpSkwslhCSVQcHxGsBfpOuN0HuMPMfp3gOIoGu+1sn9vGW6CuRpnZe+G1vp4EX5RZZpabhFDqSxrDjvONJnTASBK1lnRF+PwxICV8ng90Y9sakIlyC3C2ma2Conlp7wUSOeJzlZmtIbht5cgwjsN2/pIasUvxHzBm9m64+EJCSBpIMFdzO0kPFjvUlOSs3VtrxJIdgPtlk3QuQffh7wi6TD+RlIxh8c8Dswjm4B1R7JEwksYqWNKscHsPSVMSVH0KQVd2E4IfEIVTtW3ftZ0oBxYmTwAzW0mQyBOptNt5knGLzzeS/iqpQ/i4gW09BImwmGBw2yaC+ZELH+OBhC10UBt5C9TVtBFANzNbAUVrcn5EMN9pIuWZ2aMJrnN7HwLTwpZgO4LP5soE1b0kEfe8VkJMUovtWqAJ+T4qNrCseKscghZXSumvqlF/IJjA4SWCXpoPSGBL3MzmAHMk/SfRtzPVdp5AXU1bQTDvbKF1JH6wCMAESRcBL1PyXruViQognNDhM4Lu0p8Iflgkqjtb5RdJqPuAjyUVDlo5hWCN1ESI1MCy8EdEFG4V6SDpToLF1ovfF7tX8kKKNh9E5GqUpGeAzsCrBL+ufwvMDR8Ju1k8nC5te5bILwdJQ4G/EkwfdyBB99iwsAVQ03W3TOSPhYqQ1AkovAb9tpktSHD9kRhYJukt4BQruWbufy2B68SG9X5I8Lf5AMECEMOAmJndmMg4ahNPoK5GlTGFXhEzuzlRsSSbpFeA88xsWbjdExiTyHsf3TaS9gWuIskDy0qbzjHRUzyGdRbeF1u0Sk3hvkTGUZt4F66rUVFKkOH9dtt3Tz2TqPrN7ITttqeHSdQlx/PAaIKJ/ZNxL2yhAkm7F042Et7ik4yWzeZwRZivJF1MMLF/4yTEUWt4C9TVCWFLuA9BAn0DGAh8aGYJu+YVLik2nB3nXvXJupMgKq0rSQOAMcB7BNeqDyfoqUjYsn9hHD2Az4HmwK1AM+BvZjYtkXHUJp5AXZ0gaR7QBZhlZl0ktQH+bWYJm3s1HDDzBXAGwX2QQ4DPzezSRMXgtpE0ElhGEgeWFYtlV+CQcPMTM/sp0TFsL5zL+jQzG5fsWKLK7wN1Naq0G9OTdLP6xnC5qDxJTQm+ONsnomJJhZdKOprZX4Gfw/lFjwN6JSIGV6qzCW4l+oht9z7OSFIs9YGVBCOBO0k6opzy1UZSU0nXSnpYUj8FLgYWAb9PVBy1kV8DdTUtmaugFDdDUnPgnwRflOsJFnFOhMJ5aAvvsVsdXo/NBXZLUAxuO2a2Z7JjAJD0N+BU4DOgcE1QA95PUAhjgVUE/x7OBa4j6Eo+0cxmJyiGWskTqKsRUbtZ3cwuCp+OljQJaGpmcxMcxpjwFoUbCGZ5aUxwW4tLAkmNgCuA3c3sPEn7AP9nZq8lOJQTwno3l1ewhuxVbNTtv4AlBJ/JpiTFU2t4AnU1JVI3q5fWJSbpCDNLxK/83Yr9iBgW/veR8L8Jm/PU7eBJgt6IwknbcwhG5iY6gX5DsEZrshJo0exDZpYvKduTZ8V4AnU1Ilxt5T1JT0XhZnVKznvbgGBy+0/ZdiN/TSqch7a02YB8FF/y7G1mp0o6HcDMNkhKxoxNG4DZ4bzIxQczJWp2oi6S1obPBTQMtxWEYU0TFEet4wnU1QhJfzezy4CHJZW2yn2i1gMtrG9Q8W1J7YG/J6j6qM1D6wJbJDUk/BEjaW+S0wocHz6SwsySMf/vL4InUFdTxob/vTepUZQtG/hVguqK2jy0LnATMAloL2kccBhwTqKDCEdku1rI7wN1dYKkh9jWXRojWDrrWzM7MwF1R24eWhcIVwc6hOBHTkLvv5T0/8zs9+E9yqX10hyYqFhcfDyBuhoV3vM5EtiDbetQJnQS9zCOP7Ft9O8K4Dszm5rIGFy0SDqRYBL7NeF2c6CPmb2SoPozzGxJOHXfDiIydsDthCdQV6MkfQFcTjBgp2i+0cL1QRNQfxpwD3AW8F24uw3wkJndJamr3+tWN0mavf1E/smYxN3VXn4N1NW0NWY2MYn13wc0AvYws3UQzLwC3CvpUWAAEIkb6l3ClTYTm38nugrzFqirUZLuIug6fYmSQ/RnJqj+RcA+tt0fejjP50/AQDP7JBGxuGiR9ASwmm335P4JaGlm5yQrJle7eAJ1NUrSO6XstkStuSjpSzPbt7LH3C+fpF0IZoI6mmAQz1vA7Wb2cxJjagG0T8IsWS4O3l3hapSZHZnkEBZIOmv7dT8lnUmwdJOrg8IeiNci8PeJpHeBwQTfx58CyyRNNbMrdvpCl3SeQF2N2G7+Wwh+4f9EsAbntwkM5U/AS5L+QPDlBNAdaAicmMA4XISEU9YVSGpWOAo3iZqZ2VpJ5wLPmNlNkrwFWgt4AnU1pUkp+zoA10saaWb/TUQQZpYD9JLUl2Aha4A3zGxKIup3kbYemCfpLaCo2zaBU+gVSpWUQbB02PUJrttVgV8DdQklqSXwPzNL9HJmzpUg6ezS9id6ZiBJpxBci/3QzC6StBdwj5mdlMg4XOV5AnUJ5/fauagI58Ld3cwWJjsWV/uUdh+UczVG0pEEi/c6l1SSBgGzCebDRVJXSQmf1F3S3ZKaSkqTNEXS8nCQm4s4b4G6GlHG/J4tgcXAWWb2ReKjcm4bSYXL2b1b2CMiab6ZHZDgOGabWddwasHjCRb5ft/MuiQyDld5PojI1ZTjt9s2YEUy77FzbjtbzWzNdkuAFiQhjsLv4eOA50uJyUWUJ1BXI3wibFcLfCbpDCBF0j7An4GPkhDHa+Gc0RuBCyW1BjYlIQ5XSd6F65yrkyQ1IrhtpB/BKkGTgVvNLOHJKxydvia8P7UR0NTMchMdh6scT6DOuTotXFzAChcbSEL9acCFwBHhrveA0Wa2NRnxuIrzBOqcq5Mk9QCeYNukH2uAP5jZp2W/qkbi+BeQBhTefzoUyDezcxMZh6s8T6DOuTopnC7vT2b2QbjdG/iHmR2Y4DjmbD/itrR9Lnr8PlDnXF2VX5g8AczsQyAvGXFI2rtwI5yJKH8n5V1EeAvUOVcnSfo7waICzxLcZnUqwejXf0NC16ztCzwFfEMwmGkPYJiZlbYUoIsQT6DOuTqpjLVqCyVkzdpwWbU/A/8A/i/cvdDMNpf9KhcVnkCdcy6JJE03s57JjsNVnidQ55xLIkkPEIzCfY6Sy6olpAvZxc8TqHPOJVEZXckJ6UJ2VeMJ1DnnnIuDz4XrnKuzJP0a6ECx70IzeybBMVxRyu41wKdmNjuRsbjK8Raoc65OkjQW2JtgTdDC+y7NzP6c4Dj+A3QHJoS7jgfmEiT2583s7kTG4yrOE6hzrk6S9DnQyZL8JSjpfeBYM1sfbjcGXgcGELRCOyUzPlc2n4nIOVdXzQfSkx0EsBtQ/L7PrUAbM9u43X4XMX4N1DlXV+0KLJA0nWKJyswGJziOccA0Sa+G24OA/0jaBViQ4FhcJXgXrnOuTpL0m9L2m9l7SYilO3BYuDnVzGYkOgZXeZ5AnXPOuTh4F65zrk6R9KGZ9Za0jmAS+aJDBKNwmyYpNFfLeAvUOeeci4O3QJ1zdZKklqXsXmdmWxMejKuVvAXqnKuTJH0HtAdWEXTfNgdygaXAH83s06QF52oFvw/UOVdXvUUwgcGuZtYKGAi8BlxEsD6nczvlLVDnXJ0kaZ6Zdd5u31wzO1DSbDPrmqTQXC3h10Cdc3XVEklXA/8Nt08FlkpKAQqSF5arLbwF6pyrkyTtCtwE9Ca4nWUqcAvBSii7m9miJIbnagFPoM65OidsZT5jZkOSHYurvXwQkXOuzjGzfGAPSfWSHYurvfwaqHOurvoGmCppPPBz4U4zuz95IbnaxBOoc66u+jp8xIAmSY7F1UJ+DdQ555yLg7dAnXN1kqTWwF+A/YEGhfvNrG/SgnK1ig8ics7VVeOAL4A9gZuB74CsZAbkahfvwnXO1UmSPjWzgwtnHwr3ZZlZj2TH5moH78J1ztVVhauuLJF0HLAYKG2FFudK5QnUOVdX3SapGXAl8BDQFLg8uSG52sS7cJ1zzrk4eAvUOVcnSdoTuAToQLHvQjMbnKyYXO3iCdQ5V1e9AjwOTMBXX3Fx8C5c51ydJGmamfVKdhyu9vIE6pyrkySdAewDvAlsLtxvZjOTFpSrVbwL1zlXV3UGhgJ92daFa+G2c+XyFqhzrk6StAjoZGZbkh2Lq518Kj/nXF01H2ie7CBc7eVduM65uqo58IWkLEpeA/XbWFyFeAJ1ztVVNyU7AFe7+TVQ55xzLg5+DdQ555yLgydQ55xzLg6eQJ1zdZ6kFpIOTHYcrnbxBOqcq5MkvSupqaSWwEzgn5LuT3ZcrvbwBOqcq6uamdla4HfAM+G8uEcnOSZXi3gCdc7VVamSMoDfA68lOxhX+3gCdc7VVbcAk4FFZpYlaS/gqyTH5GoRvw/UOeeci4O3QJ1zdZKku8NBRGmSpkhaLunMZMflag9PoM65uqpfOIjoeOA7oCMwIqkRuVrFE6hzrq4qnAv8OOB5M1uTzGBc7eOTyTvn6qrXJH0BbAQulNQa2JTkmFwt4oOInHN1VjiJwhozy5fUCGhqZrnJjsvVDt4Cdc7VSZLSgDOBIyQBvAeMTmpQrlbxFqhzrk6S9C8gDXg63DUUyDezc5MXlatNPIE65+okSXPMrEt5+5wri4/Cdc7VVfmS9i7cCGciyk9iPK6W8Wugzrm66irgHUnfAAL2AIYlNyRXm3gCdc7VOZJSgC7APsD/hbsXmtnm5EXlahu/Buqcq5MkTTeznsmOw9VenkCdc3WSpAcIRuE+B/xcuN/MZiYtKFereAJ1ztVJkt4pZbeZWd+EB+NqJU+gzjnnXBx8EJFzrk6SdEUpu9cAn5rZ7ASH42ohb4E65+okSf8BugMTwl3HA3OBDgSrs9ydpNBcLeEJ1DlXJ0l6HzjWzNaH242B14EBBK3QTsmMz0Wfz0TknKurdgOK3/e5FWhjZhu32+9cqfwaqHOurhoHTJP0arg9CPiPpF2ABckLy9UW3oXrnKuzJHUHDgs3p5rZjGTG42oXT6DOOedcHPwaqHPOORcHT6DOOedcHDyBOuecc3HwBOqcc87F4f8DfFLbUBKT/rAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(np.round(df.corr(),2),annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. More visualizations: https://www.data-to-viz.com/\n",
    "2. Seaborn gallery: https://seaborn.pydata.org/examples/index.html\n",
    "3. Pandas profiling documentation: https://pypi.org/project/pandas-profiling/"
   ]
  }
 ],
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