Lecture_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![rmotr](https://user-images.githubusercontent.com/7065401/52071918-bda15380-2562-11e9-828c-7f95297e4a82.png)\n",
    "<hr style=\"margin-bottom: 40px;\">\n",
    "\n",
    "<img src=\"https://user-images.githubusercontent.com/7065401/58563302-42466a80-8201-11e9-9948-b3e9f88a5662.jpg\"\n",
    "    style=\"width:400px; float: right; margin: 0 40px 40px 40px;\"></img>\n",
    "\n",
    "# Bike store sales\n",
    "\n",
    "In this class we'll be analyzing sales made on bike stores.\n",
    "\n",
    "[Follow this data in a Google Spreadsheet](https://docs.google.com/spreadsheets/d/1NOe_UrPx6ULF2C5MvHmZ9ODuw8t9M77Q1Y64gP-7JHA/edit?usp=sharing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![purple-divider](https://user-images.githubusercontent.com/7065401/52071927-c1cd7100-2562-11e9-908a-dde91ba14e59.png)\n",
    "\n",
    "## Hands on! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "## Loading our data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date,Day,Month,Year,Customer_Age,Age_Group,Customer_Gender,Country,State,Product_Category,Sub_Category,Product,Order_Quantity,Unit_Cost,Unit_Price,Profit,Cost,Revenue\n",
      "2013-11-26,26,November,2013,19,Youth (<25),M,Canada,British Columbia,Accessories,Bike Racks,Hitch Rack - 4-Bike,8,45,120,590,360,950\n",
      "2015-11-26,26,November,2015,19,Youth (<25),M,Canada,British Columbia,Accessories,Bike Racks,Hitch Rack - 4-Bike,8,45,120,590,360,950\n",
      "2014-03-23,23,March,2014,49,Adults (35-64),M,Australia,New South Wales,Accessories,Bike Racks,Hitch Rack - 4-Bike,23,45,120,1366,1035,2401\n",
      "2016-03-23,23,March,2016,49,Adults (35-64),M,Australia,New South Wales,Accessories,Bike Racks,Hitch Rack - 4-Bike,20,45,120,1188,900,2088\n",
      "2014-05-15,15,May,2014,47,Adults (35-64),F,Australia,New South Wales,Accessories,Bike Racks,Hitch Rack - 4-Bike,4,45,120,238,180,418\n",
      "2016-05-15,15,May,2016,47,Adults (35-64),F,Australia,New South Wales,Accessories,Bike Racks,Hitch Rack - 4-Bike,5,45,120,297,225,522\n",
      "2014-05-22,22,May,2014,47,Adults (35-64),F,Australia,Victoria,Accessories,Bike Racks,Hitch Rack - 4-Bike,4,45,120,199,180,379\n",
      "2016-05-22,22,May,2016,47,Adults (35-64),F,Australia,Victoria,Accessories,Bike Racks,Hitch Rack - 4-Bike,2,45,120,100,90,190\n",
      "2014-02-22,22,February,2014,35,Adults (35-64),M,Australia,Victoria,Accessories,Bike Racks,Hitch Rack - 4-Bike,22,45,120,1096,990,2086\n"
     ]
    }
   ],
   "source": [
    "!head data/sales_data.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales = pd.read_csv(\n",
    "    'data/sales_data.csv',\n",
    "    parse_dates=['Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "## The data at a glance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Age_Group</th>\n",
       "      <th>Customer_Gender</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>Product_Category</th>\n",
       "      <th>Sub_Category</th>\n",
       "      <th>Product</th>\n",
       "      <th>Order_Quantity</th>\n",
       "      <th>Unit_Cost</th>\n",
       "      <th>Unit_Price</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-11-26</td>\n",
       "      <td>26</td>\n",
       "      <td>November</td>\n",
       "      <td>2013</td>\n",
       "      <td>19</td>\n",
       "      <td>Youth (&lt;25)</td>\n",
       "      <td>M</td>\n",
       "      <td>Canada</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>590</td>\n",
       "      <td>360</td>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-11-26</td>\n",
       "      <td>26</td>\n",
       "      <td>November</td>\n",
       "      <td>2015</td>\n",
       "      <td>19</td>\n",
       "      <td>Youth (&lt;25)</td>\n",
       "      <td>M</td>\n",
       "      <td>Canada</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>590</td>\n",
       "      <td>360</td>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-03-23</td>\n",
       "      <td>23</td>\n",
       "      <td>March</td>\n",
       "      <td>2014</td>\n",
       "      <td>49</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>1366</td>\n",
       "      <td>1035</td>\n",
       "      <td>2401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-03-23</td>\n",
       "      <td>23</td>\n",
       "      <td>March</td>\n",
       "      <td>2016</td>\n",
       "      <td>49</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>20</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>1188</td>\n",
       "      <td>900</td>\n",
       "      <td>2088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-05-15</td>\n",
       "      <td>15</td>\n",
       "      <td>May</td>\n",
       "      <td>2014</td>\n",
       "      <td>47</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>F</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>238</td>\n",
       "      <td>180</td>\n",
       "      <td>418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Day     Month  Year  Customer_Age       Age_Group  \\\n",
       "0 2013-11-26   26  November  2013            19     Youth (<25)   \n",
       "1 2015-11-26   26  November  2015            19     Youth (<25)   \n",
       "2 2014-03-23   23     March  2014            49  Adults (35-64)   \n",
       "3 2016-03-23   23     March  2016            49  Adults (35-64)   \n",
       "4 2014-05-15   15       May  2014            47  Adults (35-64)   \n",
       "\n",
       "  Customer_Gender    Country             State Product_Category Sub_Category  \\\n",
       "0               M     Canada  British Columbia      Accessories   Bike Racks   \n",
       "1               M     Canada  British Columbia      Accessories   Bike Racks   \n",
       "2               M  Australia   New South Wales      Accessories   Bike Racks   \n",
       "3               M  Australia   New South Wales      Accessories   Bike Racks   \n",
       "4               F  Australia   New South Wales      Accessories   Bike Racks   \n",
       "\n",
       "               Product  Order_Quantity  Unit_Cost  Unit_Price  Profit  Cost  \\\n",
       "0  Hitch Rack - 4-Bike               8         45         120     590   360   \n",
       "1  Hitch Rack - 4-Bike               8         45         120     590   360   \n",
       "2  Hitch Rack - 4-Bike              23         45         120    1366  1035   \n",
       "3  Hitch Rack - 4-Bike              20         45         120    1188   900   \n",
       "4  Hitch Rack - 4-Bike               4         45         120     238   180   \n",
       "\n",
       "   Revenue  \n",
       "0      950  \n",
       "1      950  \n",
       "2     2401  \n",
       "3     2088  \n",
       "4      418  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(113036, 18)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 113036 entries, 0 to 113035\n",
      "Data columns (total 18 columns):\n",
      " #   Column            Non-Null Count   Dtype         \n",
      "---  ------            --------------   -----         \n",
      " 0   Date              113036 non-null  datetime64[ns]\n",
      " 1   Day               113036 non-null  int64         \n",
      " 2   Month             113036 non-null  object        \n",
      " 3   Year              113036 non-null  int64         \n",
      " 4   Customer_Age      113036 non-null  int64         \n",
      " 5   Age_Group         113036 non-null  object        \n",
      " 6   Customer_Gender   113036 non-null  object        \n",
      " 7   Country           113036 non-null  object        \n",
      " 8   State             113036 non-null  object        \n",
      " 9   Product_Category  113036 non-null  object        \n",
      " 10  Sub_Category      113036 non-null  object        \n",
      " 11  Product           113036 non-null  object        \n",
      " 12  Order_Quantity    113036 non-null  int64         \n",
      " 13  Unit_Cost         113036 non-null  int64         \n",
      " 14  Unit_Price        113036 non-null  int64         \n",
      " 15  Profit            113036 non-null  int64         \n",
      " 16  Cost              113036 non-null  int64         \n",
      " 17  Revenue           113036 non-null  int64         \n",
      "dtypes: datetime64[ns](1), int64(9), object(8)\n",
      "memory usage: 15.5+ MB\n"
     ]
    }
   ],
   "source": [
    "sales.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Year</th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Order_Quantity</th>\n",
       "      <th>Unit_Cost</th>\n",
       "      <th>Unit_Price</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>113036</td>\n",
       "      <td>113036.000000</td>\n",
       "      <td>113036.000000</td>\n",
       "      <td>113036.000000</td>\n",
       "      <td>113036.000000</td>\n",
       "      <td>113036.000000</td>\n",
       "      <td>113036.000000</td>\n",
       "      <td>113036.000000</td>\n",
       "      <td>113036.000000</td>\n",
       "      <td>113036.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2014-11-23 12:14:55.063519232</td>\n",
       "      <td>15.665753</td>\n",
       "      <td>2014.401739</td>\n",
       "      <td>35.919212</td>\n",
       "      <td>11.901660</td>\n",
       "      <td>267.296366</td>\n",
       "      <td>452.938427</td>\n",
       "      <td>285.051665</td>\n",
       "      <td>469.318695</td>\n",
       "      <td>754.370360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2013-12-22 00:00:00</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014-06-27 00:00:00</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>223.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016-01-09 00:00:00</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>358.000000</td>\n",
       "      <td>432.000000</td>\n",
       "      <td>800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016-07-31 00:00:00</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>2171.000000</td>\n",
       "      <td>3578.000000</td>\n",
       "      <td>15096.000000</td>\n",
       "      <td>42978.000000</td>\n",
       "      <td>58074.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.781567</td>\n",
       "      <td>1.272510</td>\n",
       "      <td>11.021936</td>\n",
       "      <td>9.561857</td>\n",
       "      <td>549.835483</td>\n",
       "      <td>922.071219</td>\n",
       "      <td>453.887443</td>\n",
       "      <td>884.866118</td>\n",
       "      <td>1309.094674</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Date            Day           Year  \\\n",
       "count                         113036  113036.000000  113036.000000   \n",
       "mean   2014-11-23 12:14:55.063519232      15.665753    2014.401739   \n",
       "min              2011-01-01 00:00:00       1.000000    2011.000000   \n",
       "25%              2013-12-22 00:00:00       8.000000    2013.000000   \n",
       "50%              2014-06-27 00:00:00      16.000000    2014.000000   \n",
       "75%              2016-01-09 00:00:00      23.000000    2016.000000   \n",
       "max              2016-07-31 00:00:00      31.000000    2016.000000   \n",
       "std                              NaN       8.781567       1.272510   \n",
       "\n",
       "        Customer_Age  Order_Quantity      Unit_Cost     Unit_Price  \\\n",
       "count  113036.000000   113036.000000  113036.000000  113036.000000   \n",
       "mean       35.919212       11.901660     267.296366     452.938427   \n",
       "min        17.000000        1.000000       1.000000       2.000000   \n",
       "25%        28.000000        2.000000       2.000000       5.000000   \n",
       "50%        35.000000       10.000000       9.000000      24.000000   \n",
       "75%        43.000000       20.000000      42.000000      70.000000   \n",
       "max        87.000000       32.000000    2171.000000    3578.000000   \n",
       "std        11.021936        9.561857     549.835483     922.071219   \n",
       "\n",
       "              Profit           Cost        Revenue  \n",
       "count  113036.000000  113036.000000  113036.000000  \n",
       "mean      285.051665     469.318695     754.370360  \n",
       "min       -30.000000       1.000000       2.000000  \n",
       "25%        29.000000      28.000000      63.000000  \n",
       "50%       101.000000     108.000000     223.000000  \n",
       "75%       358.000000     432.000000     800.000000  \n",
       "max     15096.000000   42978.000000   58074.000000  \n",
       "std       453.887443     884.866118    1309.094674  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "## Numerical analysis and visualization\n",
    "\n",
    "We'll analyze the `Unit_Cost` column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    113036.000000\n",
       "mean        267.296366\n",
       "std         549.835483\n",
       "min           1.000000\n",
       "25%           2.000000\n",
       "50%           9.000000\n",
       "75%          42.000000\n",
       "max        2171.000000\n",
       "Name: Unit_Cost, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales['Unit_Cost'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "267.296365759581"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales['Unit_Cost'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales['Unit_Cost'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales['Unit_Cost'].plot(kind='box', vert=False, figsize=(14,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales['Unit_Cost'].plot(kind='density', figsize=(14,6)) # kde"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f8b17499250>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sales['Unit_Cost'].plot(kind='density', figsize=(14,6)) # kde\n",
    "ax.axvline(sales['Unit_Cost'].mean(), color='red')\n",
    "ax.axvline(sales['Unit_Cost'].median(), color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'dollars')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sales['Unit_Cost'].plot(kind='hist', figsize=(14,6))\n",
    "ax.set_ylabel('Number of Sales')\n",
    "ax.set_xlabel('dollars')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "## Categorical analysis and visualization\n",
    "\n",
    "We'll analyze the `Age_Group` column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Age_Group</th>\n",
       "      <th>Customer_Gender</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>Product_Category</th>\n",
       "      <th>Sub_Category</th>\n",
       "      <th>Product</th>\n",
       "      <th>Order_Quantity</th>\n",
       "      <th>Unit_Cost</th>\n",
       "      <th>Unit_Price</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-11-26</td>\n",
       "      <td>26</td>\n",
       "      <td>November</td>\n",
       "      <td>2013</td>\n",
       "      <td>19</td>\n",
       "      <td>Youth (&lt;25)</td>\n",
       "      <td>M</td>\n",
       "      <td>Canada</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>590</td>\n",
       "      <td>360</td>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-11-26</td>\n",
       "      <td>26</td>\n",
       "      <td>November</td>\n",
       "      <td>2015</td>\n",
       "      <td>19</td>\n",
       "      <td>Youth (&lt;25)</td>\n",
       "      <td>M</td>\n",
       "      <td>Canada</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>590</td>\n",
       "      <td>360</td>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-03-23</td>\n",
       "      <td>23</td>\n",
       "      <td>March</td>\n",
       "      <td>2014</td>\n",
       "      <td>49</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>1366</td>\n",
       "      <td>1035</td>\n",
       "      <td>2401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-03-23</td>\n",
       "      <td>23</td>\n",
       "      <td>March</td>\n",
       "      <td>2016</td>\n",
       "      <td>49</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>20</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>1188</td>\n",
       "      <td>900</td>\n",
       "      <td>2088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-05-15</td>\n",
       "      <td>15</td>\n",
       "      <td>May</td>\n",
       "      <td>2014</td>\n",
       "      <td>47</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>F</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>238</td>\n",
       "      <td>180</td>\n",
       "      <td>418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Day     Month  Year  Customer_Age       Age_Group  \\\n",
       "0 2013-11-26   26  November  2013            19     Youth (<25)   \n",
       "1 2015-11-26   26  November  2015            19     Youth (<25)   \n",
       "2 2014-03-23   23     March  2014            49  Adults (35-64)   \n",
       "3 2016-03-23   23     March  2016            49  Adults (35-64)   \n",
       "4 2014-05-15   15       May  2014            47  Adults (35-64)   \n",
       "\n",
       "  Customer_Gender    Country             State Product_Category Sub_Category  \\\n",
       "0               M     Canada  British Columbia      Accessories   Bike Racks   \n",
       "1               M     Canada  British Columbia      Accessories   Bike Racks   \n",
       "2               M  Australia   New South Wales      Accessories   Bike Racks   \n",
       "3               M  Australia   New South Wales      Accessories   Bike Racks   \n",
       "4               F  Australia   New South Wales      Accessories   Bike Racks   \n",
       "\n",
       "               Product  Order_Quantity  Unit_Cost  Unit_Price  Profit  Cost  \\\n",
       "0  Hitch Rack - 4-Bike               8         45         120     590   360   \n",
       "1  Hitch Rack - 4-Bike               8         45         120     590   360   \n",
       "2  Hitch Rack - 4-Bike              23         45         120    1366  1035   \n",
       "3  Hitch Rack - 4-Bike              20         45         120    1188   900   \n",
       "4  Hitch Rack - 4-Bike               4         45         120     238   180   \n",
       "\n",
       "   Revenue  \n",
       "0      950  \n",
       "1      950  \n",
       "2     2401  \n",
       "3     2088  \n",
       "4      418  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Adults (35-64)          55824\n",
       "Young Adults (25-34)    38654\n",
       "Youth (<25)             17828\n",
       "Seniors (64+)             730\n",
       "Name: Age_Group, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales['Age_Group'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ade8bc2e0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales['Age_Group'].value_counts().plot(kind='pie', figsize=(6,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Sales')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sales['Age_Group'].value_counts().plot(kind='bar', figsize=(14,6))\n",
    "ax.set_ylabel('Number of Sales')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "## Relationship between the columns?\n",
    "\n",
    "Can we find any significant relationship?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Year</th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Order_Quantity</th>\n",
       "      <th>Unit_Cost</th>\n",
       "      <th>Unit_Price</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007635</td>\n",
       "      <td>-0.014296</td>\n",
       "      <td>-0.002412</td>\n",
       "      <td>0.003133</td>\n",
       "      <td>0.003207</td>\n",
       "      <td>0.004623</td>\n",
       "      <td>0.003329</td>\n",
       "      <td>0.003853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>-0.007635</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.040994</td>\n",
       "      <td>0.123169</td>\n",
       "      <td>-0.217575</td>\n",
       "      <td>-0.213673</td>\n",
       "      <td>-0.181525</td>\n",
       "      <td>-0.215604</td>\n",
       "      <td>-0.208673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer_Age</th>\n",
       "      <td>-0.014296</td>\n",
       "      <td>0.040994</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.026887</td>\n",
       "      <td>-0.021374</td>\n",
       "      <td>-0.020262</td>\n",
       "      <td>0.004319</td>\n",
       "      <td>-0.016013</td>\n",
       "      <td>-0.009326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order_Quantity</th>\n",
       "      <td>-0.002412</td>\n",
       "      <td>0.123169</td>\n",
       "      <td>0.026887</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.515835</td>\n",
       "      <td>-0.515925</td>\n",
       "      <td>-0.238863</td>\n",
       "      <td>-0.340382</td>\n",
       "      <td>-0.312895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unit_Cost</th>\n",
       "      <td>0.003133</td>\n",
       "      <td>-0.217575</td>\n",
       "      <td>-0.021374</td>\n",
       "      <td>-0.515835</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.997894</td>\n",
       "      <td>0.741020</td>\n",
       "      <td>0.829869</td>\n",
       "      <td>0.817865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unit_Price</th>\n",
       "      <td>0.003207</td>\n",
       "      <td>-0.213673</td>\n",
       "      <td>-0.020262</td>\n",
       "      <td>-0.515925</td>\n",
       "      <td>0.997894</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.749870</td>\n",
       "      <td>0.826301</td>\n",
       "      <td>0.818522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Profit</th>\n",
       "      <td>0.004623</td>\n",
       "      <td>-0.181525</td>\n",
       "      <td>0.004319</td>\n",
       "      <td>-0.238863</td>\n",
       "      <td>0.741020</td>\n",
       "      <td>0.749870</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.902233</td>\n",
       "      <td>0.956572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cost</th>\n",
       "      <td>0.003329</td>\n",
       "      <td>-0.215604</td>\n",
       "      <td>-0.016013</td>\n",
       "      <td>-0.340382</td>\n",
       "      <td>0.829869</td>\n",
       "      <td>0.826301</td>\n",
       "      <td>0.902233</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.988758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Revenue</th>\n",
       "      <td>0.003853</td>\n",
       "      <td>-0.208673</td>\n",
       "      <td>-0.009326</td>\n",
       "      <td>-0.312895</td>\n",
       "      <td>0.817865</td>\n",
       "      <td>0.818522</td>\n",
       "      <td>0.956572</td>\n",
       "      <td>0.988758</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Day      Year  Customer_Age  Order_Quantity  Unit_Cost  \\\n",
       "Day             1.000000 -0.007635     -0.014296       -0.002412   0.003133   \n",
       "Year           -0.007635  1.000000      0.040994        0.123169  -0.217575   \n",
       "Customer_Age   -0.014296  0.040994      1.000000        0.026887  -0.021374   \n",
       "Order_Quantity -0.002412  0.123169      0.026887        1.000000  -0.515835   \n",
       "Unit_Cost       0.003133 -0.217575     -0.021374       -0.515835   1.000000   \n",
       "Unit_Price      0.003207 -0.213673     -0.020262       -0.515925   0.997894   \n",
       "Profit          0.004623 -0.181525      0.004319       -0.238863   0.741020   \n",
       "Cost            0.003329 -0.215604     -0.016013       -0.340382   0.829869   \n",
       "Revenue         0.003853 -0.208673     -0.009326       -0.312895   0.817865   \n",
       "\n",
       "                Unit_Price    Profit      Cost   Revenue  \n",
       "Day               0.003207  0.004623  0.003329  0.003853  \n",
       "Year             -0.213673 -0.181525 -0.215604 -0.208673  \n",
       "Customer_Age     -0.020262  0.004319 -0.016013 -0.009326  \n",
       "Order_Quantity   -0.515925 -0.238863 -0.340382 -0.312895  \n",
       "Unit_Cost         0.997894  0.741020  0.829869  0.817865  \n",
       "Unit_Price        1.000000  0.749870  0.826301  0.818522  \n",
       "Profit            0.749870  1.000000  0.902233  0.956572  \n",
       "Cost              0.826301  0.902233  1.000000  0.988758  \n",
       "Revenue           0.818522  0.956572  0.988758  1.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = sales.corr()\n",
    "\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,8))\n",
    "plt.matshow(corr, cmap='RdBu', fignum=fig.number)\n",
    "plt.xticks(range(len(corr.columns)), corr.columns, rotation='vertical');\n",
    "plt.yticks(range(len(corr.columns)), corr.columns);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ade7c34f0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAF0CAYAAADrQ6QcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3deZxcdZ3v/9enqpeEJGQnBDohwQ4wEUKAFoIBriwiCAJziYijE8YrMPcqI6IzIo4jio4COrL8cBgRVKKOEEEkMiiEhH0JaSAJJCxpSCAdSAjZCJD0+vn9cU51qqqrupac6qpKv5+PRz+66lvnnPrU6er61Pmu5u6IiIhEIVbuAEREZPehpCIiIpFRUhERkcgoqYiISGSUVEREJDJKKiIiEpmacgfQ38aMGeOTJk0qdxgiIlXjmWeeecfdx+azbUmTipmNAG4GDgYc+D/Ay8DtwCRgNXCOu282MwOuAz4JfAD8g7s/Gx7nPODb4WF/4O63huVHAL8GBgP3Ahd7joE3kyZNorm5OboXKSKymzOz1/PdttTVX9cBf3X3g4BDgReBbwIL3H0KsCC8D3AqMCX8uRC4EcDMRgGXA0cBRwKXm9nIcJ8bgQuS9julxK9HRET6ULKkYmbDgeOAWwDcvd3dtwBnAreGm90KnBXePhOY44GngBFmNh74BDDf3Te5+2ZgPnBK+Nie7v5UeHUyJ+lYIiJSBqW8UpkMbAB+ZWbPmdnNZjYEGOfub4XbrAPGhbf3BdYk7d8alvVV3pqhXEREyqSUSaUGOBy40d0PA95nZ1UXAOEVRsknHzOzC82s2cyaN2zYUOqnExEZsEqZVFqBVndfFN6/gyDJrA+rrgh/vx0+vhaYkLR/Q1jWV3lDhvJe3P0md29y96axY/PqwCAiIkUoWVJx93XAGjM7MCw6EVgBzAPOC8vOA+4Ob88DZltgBrA1rCa7DzjZzEaGDfQnA/eFj71rZjPCnmOzk44lIiJlUOpxKv8E/M7M6oDXgC8QJLK5ZvZF4HXgnHDbewm6E7cQdCn+AoC7bzKz7wOLw+2ucPdN4e0vsbNL8V/CHxERKRMbaOupNDU1ucapiIjkz8yecfemfLbVNC0iIhIZJRUREYmMkopIBdj4XhtL12xh43tt5Q5FZJcMuAklRSrN3UvWcumdy6iNxejo7ubqs6dxxnSN45XqpCsVkTLa+F4bl965jB0d3Wxr62RHRzffuHOZrlikaimpiJRR6+bt1MZS/w1rYzFaN28vU0Qiu0ZJRaSMGkYOpqO7O6Wso7ubhpGDyxSRyK5RUhEpo9FD67n67GkMqo0xrL6GQbUxrj57GqOH1pc7NJGiqKFepMzOmL4vMxvH0Lp5Ow0jByuhSFVTUhGpAKOH1iuZyG5B1V8iIhIZJRUREYmMkoqIiERGSUVERCKjpCIiIpFRUhERkcgoqYiISGSUVEREJDJKKiIiEhklFRERiYySioiIREZJRUREIqOkIlIBtEa97C40S7FImWmNetmd6EpFpIy0Rr3sbpRURMpIa9TL7kZJRaSMtEa97G6UVETKSGvUy+5GDfUiZaY16mV3oqQiUgG0Rr3sLlT9JSIikVFSERGRyCipiIhIZJRUREQkMkoqIiISGSUVERGJjJKKiIhERklFREQio6QiIiKRUVIREZHIKKmIiEhklFRERCQyJU0qZrbazJ43syVm1hyWjTKz+Wa2Mvw9Miw3M7vezFrMbJmZHZ50nPPC7Vea2XlJ5UeEx28J97VSvh4REelbf1ypHO/u0929Kbz/TWCBu08BFoT3AU4FpoQ/FwI3QpCEgMuBo4AjgcsTiSjc5oKk/U4p/csREZFsylH9dSZwa3j7VuCspPI5HngKGGFm44FPAPPdfZO7bwbmA6eEj+3p7k+5uwNzko4lIiJlUOqk4sD9ZvaMmV0Ylo1z97fC2+uAceHtfYE1Sfu2hmV9lbdmKBcRkTIp9SJdx7j7WjPbC5hvZi8lP+jubmZe4hgIE9qFABMnTiz104mIDFglvVJx97Xh77eBuwjaRNaHVVeEv98ON18LTEjavSEs66u8IUN5pjhucvcmd28aO3bsrr4sERHJomRJxcyGmNmwxG3gZOAFYB6Q6MF1HnB3eHseMDvsBTYD2BpWk90HnGxmI8MG+pOB+8LH3jWzGWGvr9lJxxIRkTIoZfXXOOCusJdvDfDf7v5XM1sMzDWzLwKvA+eE298LfBJoAT4AvgDg7pvM7PvA4nC7K9x9U3j7S8CvgcHAX8IfEREpEws6Tg0cTU1N3tzcXO4wRESqhpk9kzQspE8aUS8iIpFRUhERkcgoqYiISGSUVEREJDJKKiIiEhklFRERiYySioiIREZJRUREIqOkIiIikVFSERGRyCipiIhIZJRUREQkMkoqIiISGSUVERGJjJKKiIhERklFREQio6QiIiKRUVIREZHIKKmIiEhklFRERCQySioiIhIZJRUREYmMkoqIiERGSUVERCKjpCIiIpFRUhERkcgoqYiISGSUVEREJDJKKiIiEhklFRERiYySioiIREZJRUREIqOkIiIikVFSERGRyCipiIhIZJRUREQkMkoqIiISGSUVERGJjJKKiIhERklFREQio6QiIiKRUVIREZHIlDypmFnczJ4zs3vC+5PNbJGZtZjZ7WZWF5bXh/dbwscnJR3jsrD8ZTP7RFL5KWFZi5l9s9SvRURE+tYfVyoXAy8m3b8KuMbdG4HNwBfD8i8Cm8Pya8LtMLOpwLnAh4FTgP8ME1Uc+BlwKjAV+Gy4rYiIlElJk4qZNQCnATeH9w04Abgj3ORW4Kzw9pnhfcLHTwy3PxO4zd3b3H0V0AIcGf60uPtr7t4O3BZuKyIiZVLqK5VrgW8A3eH90cAWd+8M77cC+4a39wXWAISPbw237ylP2ydbuYiIlEnJkoqZnQ687e7PlOo5CojlQjNrNrPmDRs2lDscEZHdVimvVGYCZ5jZaoKqqROA64ARZlYTbtMArA1vrwUmAISPDwc2Jpen7ZOtvBd3v8ndm9y9aezYsbv+ykREJKOSJRV3v8zdG9x9EkFD+0J3/xzwIDAr3Ow84O7w9rzwPuHjC93dw/Jzw95hk4EpwNPAYmBK2JusLnyOeaV6PSIikltN7k0idylwm5n9AHgOuCUsvwX4jZm1AJsIkgTuvtzM5gIrgE7gy+7eBWBmFwH3AXHgl+6+vF9fiYiIpLDgYmDgaGpq8ubm5nKHISJSNczsGXdvymdbjagXEZHIKKmIiEhklFRERCQySioiIhIZJRUREYmMkoqIiERGSUVERCKjpCIiIpFRUhERkcgoqYiISGSUVEREJDJKKiIiEhklFRERiYySioiIREZJRUREIqOkIiIikVFSERGRyCipiIhIZJRUREQkMkoqIiISGSUVERGJjJKKiIhERklFREQio6QiIiKRUVIREZHIKKmIiEhklFRERCQySioiIhIZJRUREYmMkoqIiERGSUVERCKTd1Ixs/3M7KTw9mAzG1a6sEREpBrllVTM7ALgDuDnYVED8KdSBSUiItUp3yuVLwMzgXcB3H0lsFepghIRkeqUb1Jpc/f2xB0zqwG8NCGJiEi1yjepPGxm3wIGm9nHgT8Afy5dWCIiUo3yTSrfBDYAzwP/CNwLfLtUQYmISHWqyWcjd+8GfhH+iIiIZJRXUjGzVWRoQ3H3/SOPSEREqlZeSQVoSro9CPg0MCr6cEREpJrl1abi7huTfta6+7XAaSWOTUREqky+1V+HJ92NEVy55HuVIyIiA0S+vb/+I+nnR8ARwDl97WBmg8zsaTNbambLzex7YflkM1tkZi1mdruZ1YXl9eH9lvDxSUnHuiwsf9nMPpFUfkpY1mJm3yzkhYuISPTy7f11fBHHbgNOcPf3zKwWeMzM/gJ8DbjG3W8zs/8CvgjcGP7e7O6NZnYucBXwGTObCpwLfBjYB3jAzA4In+NnwMeBVmCxmc1z9xVFxCoiIhHIt/qrHjgbmJS8j7tfkW0fd3fgvfBubfjjwAnA34XltwLfJUgqZ4a3IZhn7AYzs7D8NndvA1aZWQtwZLhdi7u/FsZ4W7itkoqISJnkW/11N8EHdifwftJPn8wsbmZLgLeB+cCrwBZ37ww3aQX2DW/vC6wBCB/fCoxOLk/bJ1u5iIiUSb6N7Q3ufkqhB3f3LmC6mY0A7gIOKvQYUTCzC4ELASZOnFiOEEREBoR8r1SeMLNDin0Sd98CPAgcDYwIJ6SEYAr9teHttcAE6JmwcjiwMbk8bZ9s5Zme/yZ3b3L3prFjxxb7MkREJId8k8oxwDNhT6tlZva8mS3rawczGxteoWBmgwka1F8kSC6zws3OI6haA5gX3id8fGHYLjMPODfsHTYZmAI8DSwGpoS9yeoIGvPn5fl6RESkBPKt/jq1iGOPB241szhB8prr7veY2QrgNjP7AfAccEu4/S3Ab8KG+E0ESQJ3X25mcwka4DuBL4fVapjZRcB9QBz4pbsvLyJOERGJiAUXA3lsaHYMMMXdf2VmY4Gh7r6qpNGVQFNTkzc3N5c7DBGRqmFmz7h7U+4t819O+HLgUuCysKgW+G1x4YmIyO4q3zaVvwXOIOxG7O5vAsNKFZSIiFSnfJNKe9ho7gBmNqR0IYmISLXKN6nMNbOfE3QHvgB4AC3YJSIiafKd++sn4dr07wIHAt9x9/kljUxERKpOvnN/fQ24XYlERET6km/11zDgfjN71MwuMrNxpQxKRESqU74rP37P3T8MfJlgUOPDZvZASSMTEZGqk++VSsLbwDqCObn2ij4cERGpZvkOfvySmT0ELCCYjv4Cd59WysBERKT65Dv31wTgq+6+pJTBiIhIdcu3TeUyYKiZfQF6ZiCeXNLIRESk6mjuLxERiYzm/hIRkcho7i8REYnMrsz9dXPpwhIRkWqkub9ERCQy+XYpJkwi8wHMLGZmn3P335UsMhERqTp9Vn+Z2Z5mdpmZ3WBmJ1vgIuA14Jz+CVFERKpFriuV3wCbgSeB84FvAQacpYGQIiKSLldS2d/dDwEws5uBt4CJ7r6j5JGJiEjVydX7qyNxw927gFYlFBERySbXlcqhZvZueNuAweF9A9zd9yxpdCIiUlX6TCruHu+vQEREpPoVup6KiIhIVkoqIiISGSUVERGJjJKKiIhERklFREQio6QiIiKRUVIREZHIKKmIiEhklFRERCQySioiIhIZJRUREYmMkoqIiERGSUVERCKjpCIiIpFRUhERkcgoqYiISGSUVEREJDJKKiIiEpmSJRUzm2BmD5rZCjNbbmYXh+WjzGy+ma0Mf48My83MrjezFjNbZmaHJx3rvHD7lWZ2XlL5EWb2fLjP9WZmpXo9IiKSWymvVDqBr7v7VGAG8GUzmwp8E1jg7lOABeF9gFOBKeHPhcCNECQh4HLgKOBI4PJEIgq3uSBpv1NK+HpERCSHkiUVd3/L3Z8Nb28DXgT2Bc4Ebg03uxU4K7x9JjDHA08BI8xsPPAJYL67b3L3zcB84JTwsT3d/Sl3d2BO0rFERKQM+qVNxcwmAYcBi4Bx7v5W+NA6YFx4e19gTdJurWFZX+WtGcpFRKRMSp5UzGwocCfwVXd/N/mx8ArD+yGGC82s2cyaN2zYUOqnExEZsEqaVMysliCh/M7d/xgWrw+rrgh/vx2WrwUmJO3eEJb1Vd6QobwXd7/J3ZvcvWns2LG79qJERCSrUvb+MuAW4EV3/2nSQ/OARA+u84C7k8pnh73AZgBbw2qy+4CTzWxk2EB/MnBf+Ni7ZjYjfK7ZSccSEZEyqCnhsWcCfw88b2ZLwrJvAVcCc83si8DrwDnhY/cCnwRagA+ALwC4+yYz+z6wONzuCnffFN7+EvBrYDDwl/BHRETKxIJmjYGjqanJm5ubyx2GiEjVMLNn3L0pn201ol5ERCKjpCIiIpFRUhERkcgoqYiISGSUVEREJDJKKiIiEhklFRERiYySioiIREZJRUREIqOkIiIikVFSERGRyCipiIhIZJRUREQkMkoqIiISGSUVERGJjJKKiIhERklFREQio6QiIiKRUVIREZHIKKmIiEhklFRERCQySipltvG9Npau2cLG99rKHYqIyC5TUolQy/pt3NG8hpb12/La/u4la5l51UI+f/MiZl61kHlL1hZ1zHwSU/px0vdpXrWRn97/Ms2rNhZ03GJiKVSh5zWb9NiiOm6u5ylmn6jOYzHHLVUsMjDUlDuA3cV3/vQ8c556o+f+7KMncsWZh2TdfuN7bVx65zJ2dHSzg24AvnHnMmY2jmH00Pq8j3n3krVceucyamMxOrq7ufrsaZwxfd8+YzumcTTNr2/u2WfiqMG8sv59AK5f2MKxjaOZ1TQh53HT5RNLoQo9r/nG1rTfSB5r2ZlAiz1urucp5rydc0QDc59p3eXz2Ou4TQ3Mbe77uKWKRQYOc/dyx9CvmpqavLm5OdJjtqzfxknXPNKr/IFLjqNx3LCM+yxds4XP37yIbW2dPWXD6mv42ecOY/jgOjo6u5j186f6PObG99qYedVCdnR09zw+qDbG45ee0JOYssWWS20Mkg7b67jp8omlUIWc143vtdG6eTsNIwf3er5MsWXS198rH8Wcg3xiK+Y8FnPcUsUi1c/MnnH3pny21ZVKBJas2ZK1PNuHVMPIwXR0p/7z7ujs4oI5zdTF4+zo7Mp5zNbN26mNxXqudABqYzFaN2/v+afPFltuBuz8wpF+3HT5xFKofM9rrquDTLHlc9xCFXMO8omtmPNYzHFLFYsMLGpTicD0CSMKKgcYPbSeq8+exqDaGMPqa6ivieHutHU629o66ejKfAU5afQePbczJaaO7m4aRg7OuH0h0q9g048LqXXt+cRSqHzOa3I14ra2TnZ0dPONO5el1P9niq2Q58tXMecgn9iKOY/FHLdUscjAoqQSgcZxw5h99MSUstlHT8z5rfeM6fvy+KUn8Nvzj+IXs5sYXNv3hWNt3KitiffcT09Mg2pjXH32tJRvkbU1cWrjlnKcmEFdTbBPXdpjEFyjXHzSAX0eN72TweMt7+SMpVCN44ZxbOPolLJjG0ennNfEt+tkiW/TCZnOU/px8/l75ZLP3yOffWYfPXGXz2Mxxy1VLOWmjgb9S20qEWpZv40la7YwfcKIgj+gdqU+u9D2hEG1Me656Bjeb+9iSF2c0294LGM7AJDxuH21HWTbpxj5tFEU0o6Rfp525e+VK+5Cz0H6PsUcI6rjliqWcihF55GBSG0qZdI4bljRH06Jb4nfyNFbJ9M/9eih9Vn/2TMd9+qzp6XEmenxxPEyHbevtoNDJ4yIJJm0bt7O1u3tOdsosr2+fM7Trvy9+tLX3yPffYo5RlTHLVUs/S2fHpYSPSWVCnLG9H2Z2Tgm5VvhxScesMvfEjMdt5DH05Wi/SQh+Ztle1cX3WkX0pmep9D4ZWAoRecRyU1tKiVUzECzdKOH1hf87T/TMaM6TuJYxbafZDpmoqxl/baURve2Tsfdqa+Jpk5/INatD8TXnFDKLz+Sna5USiSfutxiBqdF8bz57OPQ53GKuTrI9TxtXd1YWhvf4Noafva5wxk+uDbr8xRzrgdC3fpAfM3JCqkaleioob5IxTSOl3qgWVSD7+prDDDaOqMbyJj5eWJA0I06mygGXZZiYGalG4ivOZtq7mhQKdRQX2LFDLbrj4FmUQ2+i1ss6FecFsuTr25kR0dXT2+pQnoSZWp0j8cM3ICdAz0H1cbo7nbqa+J5fbMs9lzv7nXrA/E1Z1OtHQ2qlZJKgfLpUZJPXW4pBpoVO/guffR+e1cX8bSxHx90dHHR75/ruZ8+f1i2Kqdv3LEMA7rdSb8e6ep26FUK937lWN5v74qs08BArFsfiK9ZKoMa6gtU7GC7Ug40SzTGAkU1oGeqAv3Op6amjPbvSuuG9VjLxj5HsW98r42vzV1KW2c3Ozq7ae9yOrtSG91/PGsaP551aK94G8cNy7tTQbHn+uqzpwHsto3Yu9KZQmRX6EqlQPl+A8ynITvTNrNnTEoZkJeriim4GlhK3GJ0eTc/nnUo91x0TK9BfdmO07p5O4Nra1ImthxcW8PB+wzn8UtPoHXzdp57YzPf/fOKPs9LetXKk69u7JWIHPjup6YydZ/hKXHsanfgYs71Yy3vMPOqhb2utCql/j2KOM6Yvi9Tx+9ZkgGeItkoqRRoVwbbZTteYptCpx3f+F4bX5+7hKA9PajC+urtS6iNG3XxeF49ufpKkonYhtTFySU9sb6T5dt/W2c3h6bNsRVFnXch5zpbFea2HZ18/39WlL23VFS9tgZ67y8pDyWVIpRisF2mD7rEOiLZ2m6Wv/kunWnNMt0ObZ1OW2dw5fEvdywl0ZMr+ThTx+/Z026RK0km5jab8+TOdU2ObRzN4rQ2leR92tp3Xvkky1benzJ2TogZ3/vzctq7vKyjr6MaBa7R5FIuSip5Sq+OyPTNeFfmkiqkN9jm99tZsmYLnV25Z97N1JPLu52TrnmEoOMwXHvudH722cO4f8V6Tp46jhOn7g3Azx9cyZ+WvcVZ08ZzxZmHcMa0fXhk5TscN2UMTZNHZ3y9iX22t2VOHk+s2sg/Hj+lz5gXrFiXEkvzqo0pzwvFVQ9dc9+L3L1sHR8/aGzvq7MupzYejOJPiKq3VD6xJs7loNp4JL22dqX3V654K6WKUCqTkkoeilldsdCVBPPpDfZBRxe/e2o1c5/Zuexw6qonvXV5N20dqVu0hdPqJ/pefeW2JT2P3d7cyrGNo2levYnt4fiRF9/axk/uf4V4TYzaWIybHn2Nc5oauH3xmpS2nEvvWNqzTzaHTRjZ5+MnX/NQzyqUtze3MrjGeo65K6tSTrnsf0ichl88/nqvx0cOjrN1R2ovuCh6SxXz3km3ra2zqKnvt3ekJvbtHbmPkyteValJLiXr/WVmvzSzt83shaSyUWY238xWhr9HhuVmZtebWYuZLTOzw5P2OS/cfqWZnZdUfoSZPR/uc72Z9Z7DPQL5rNfRsn5brw+FOU++UdDa56OH1vcaTZ6uq9tTEgr0TigxCwYvJnr8fPqICXnHkPBoy8ZeyaHDSTkHc558g7ZO54OOLto6na/ctiRnQgEYM6Qu62MLVqzrSSgJ6cd8tGUj/zx3SZ9/j3TX3PciHTlCW7+tg6Mnj0opO6epYZdnCC7mvZPJo6+8XfDzp/9L5PoXyRVvPq9HpJRdin8NnJJW9k1ggbtPARaE9wFOBaaEPxcCN0KQhIDLgaOAI4HLE4ko3OaCpP3SnysS+XQhfqxlQ8Z9s5Vn8tsnVuX1oZzLkLoafjG7id+efxSPX3oCL721dZePGaWHVr6T9bH7V6zP6xjpZyn975Hu7mXr8jruw6+kxja3uZWW9duK7nbc13sn0Q38sZbs5yPZPc/n9xqSn3tQTWoHi0E18T7PU673ej7/CyIlq/5y90fMbFJa8ZnAx8LbtwIPAZeG5XM8GDDxlJmNMLPx4bbz3X0TgJnNB04xs4eAPd39qbB8DnAW8JeoX0c+XYjHDB2Ucd9s5Zncveyt4gJM09HdzYf3Gd7zDXvUkMqq8z79kL2zPnby1HHc3tya8xjptYS5qqnOnLY31z34Ws7j1sUhrQaMT17/aMro/kKqerK9d15Yu5XP3PRkMN9ZlmWj0/V13gp57kJXoezo7mZIXZyla7YwpC6uAZWSU38Pfhzn7olPz3XAuPD2vsCapO1aw7K+ylszlEcun0FkR39odHpbOBaW55L4xnrSgWPziidTBUZfs/heeNz+eR03XW36NC1GymDIWNrjNXm8k8bvWcdZh2evjttv9JC8YvvKiY3U18TYoy5OfU32QX2Jczt75v69Xk8mbqkvYkdHMGiz2KqeTO+dfzt9Kt//nxU9VUjtXd7rXA6uSS3Idt7SZyBOvl/M4MdM+5zT1MDpNzzG529exOk3PMY5TQ0ZjzmQZ0OWVGVrqHd3N7N+mc3SzC4kqFZj4sSJObbuLVMX4vQeStedO52v3rYEZ2ePqkwTTSYfI33gYi4TRw1i8/udKQMVh9X3nsU3Pbbhg3o3QufyTycdwE/nv9Jz/z8+Mz3lHDze8g7/cseynscvOr6Rax94ha6kv6gl/Tbgsk9O7fM5l4SzAvQlZhCLxejq6qaza+dzpL/mu5esTfl7XHfudJ5fs5l7XljHRyaO5MFX3ul1Hi88bn9ueHAlcQsaobu7PeX1eLcX3Asr/b2TqVfWkLoafvS/D2ZHRzfTJ4xg+Vvv8vXbd8aeOG/J75/HWt5Jee985iMTMs5wnavre/p7Mnmf5FVBE/HObW7tWTU0+X1cTOO9epHtnvo7qaw3s/Hu/lZYvZVofVwLJH8VawjL1rKzuixR/lBY3pBh+4zc/SbgJghmKS4m8OQuxOk9lA4cNwSHno8JB3724Mo+e8382+lTufzuF1IGLubyxqYdvS4tt3d08uF99swa28SRgwpOKHEjJaFA0EPsmW+f1DNwMdF/LPGh1tnVnfIBvHObnb+zjZNIfLiM3KM2Z2zdnjm2hNubW2kcuwctGz5IieMrty2hLg41sTh/XbGe9rRgt7V1MmpIHe5Gp3eDe6/X09bleQ0ETZfe/TxTFdLRHxqTMjAzuXktfWBme1c37Z3d4XkN/raJMUSZxqRk+8DOlgwS+yxdsyVjt+T327t63gfFjodRL7LdV38nlXnAecCV4e+7k8ovMrPbCBrlt4aJ5z7gh0mN8ycDl7n7JjN718xmAIuA2cD/1x8vIFMPpZfT7ifKFqxYx4lT9874j/e9eSt6DVzMS1ofYjNj8/vttG7ezqoN23rF9sbmHQU/RfqHacJND79KfW2cQxuGc+mdy8Ip64MPtRseasl53NpYjOVvbmX44LqMV2v5ti/kkpxQkrV3kTIOJd2//umFrI8B1MaN99t771/IN+5cMzJknjXa+N49K2hPGsCaS64xKVFNjFrMeBgNzNy9lSypmNnvCa4yxphZK0EvriuBuWb2ReB14Jxw83uBTwItwAfAFwDC5PF9YHG43RWJRnvgSwQ9zAYTNNBH3kifLPHB8edlb+a9z/0r1nPi1L1p3by910DFrhxjUrIZVGN8kNQ/NshF/NAAABmKSURBVB6znsbk97IMOIzKzx9d1XM7vR0gn4rMHZ1dnH9rMzXxYILK75w+lcvnFXa1Vk4dGa5U8vnGnamKKducXJnGl+zo6GJwXZxCJiPI1YCeTzLIZ0qiYjoEaFr+3Vspe399NstDJ2bY1oEvZznOL4FfZihvBg7elRjzlfzBsa2AD+6Tpwb9EDo6u3pdlXR57oGL6QzYkdbtOLEIU3tX/05/kr52fD69oTvCS6DE1cK/3f1Cr+NUsvq0K5V8vnEXs6qmp70zHFIWTMskHjNqYqTM+dbXB3RUE6MWs7qipuXfvWlEfQ6ZPjgyqYtBe9LD4/esY/rEkSxds4VFqzZl3a8QDlTqQp0xyLNiZqdSJpT6OLRFfPFjMSuo6ifTeyfbXGyJRLT8za29ZnfudnqdLAPqamLEY0ZXt/PjWdMKmo8uyolRC50Lr5hEJNVDSSWHfObkgtSEAvDWu+189MqF1MVjvaozEgr9TC30yqZYiQ+qQhRXmVc6s4+ezK+eWE23O2aFv56EmpgxuDbzt/9cU6FkW1Uz/Vt6aq+yzH2f6+MxtiddrQzN0OsP2KWeabvyoV7oTNOlmJRVKoOSSg75zMmVTVtnd85qi0KUMqHEDGrjQd+y06eN585ns3amqwq/eGxn+08+l3fZEvZPZh3C5LHDsn7wBVOfeNr9QKb3Tmd3Fx1pV1DJvcr2GZ55wGx6l/NgkOueKTEV00W3nEvtlvO5pXS08mMOo4fW07Rf35MgVrsYQc+mePgBOay+/75rHL1/ec7toNoYtTHYozZOfY3xd0dmHr/U2U3WVShzTYWSaTDhP51wAINqU//tBtXGetpqMvUuA3r2yzaQ8e4la5l51UI+f/MiZl61kHlLqvtLgVQvXank0LJ+G4+1bCx3GJE7Zv9RtG7dwccPGstvnl4TNvgHH2i/X7ym750jNHZY/lPZRO33F8xg9cYPmD5hBFs+aOd3T/ee2HHS6D2y7p9Pg3N6NQ/AtQtWpu7T5T2PvbMtcxfwqeOHZVzRE/ruMAD0SxWTBjJKgpJKDvmM8p599ERWbXifR5OST65R7P3VPpLNuUftx+mH7sPSNVu4rXltSr1/TczoihmdJe6aVRuHeUsLm/Nsj9rULtWFPZ8xKJzH65wjGvjcLU/3/B0uOr6RuKWO0Ykb1NZkH+yYb4NzcjVPy/ptvdp3urqdze+3BwMOWzNPADq3uZWHV27I2GMsW4eB3y16g/98qKXkAww1kFGSKankMD1t6duEa8+ZxmvvfMBxU8YweexQZl61MOXxXKPY62pigIcDCPOTLRGdccjeDK6voTYGv30694SMAOu2BlU0DSMHsyNt0OGOzi4G18Z5L+ruU2nSu87mo6ObXh/++fr9+UdRWxNnSF2cT1z3aMqH+7UPrKQmbnQl/T1qa2I9kylm+wZeaINzti8pS9ZsoXHcMI6bMobrF/YeSLrwpfV0dGceMZ/piqm9q5ufPbiSts7SrmSpgYySTkklh0xL6R7TOJqvzV2GAzcsbOGSjx+QVw+xmphRVxPr6QI6t3lN3lVrBpz30Yn8+oneVTTzCpwWHeCO5jXc+PCrHH/AGDq70r85w/b07mw51MaN8cPreWNT/iP4Tztkb+5eUtiVSkeXM3KPOJs/6DvhjRtWx/pt7T33j20czYVznmbT9m72qAleY7Iud6aMHsJLSTMS7DdqMCdd80jP/evPnZ7xG/iSNzanzDv2739+gXteWMfpB+/Nv37q4JSVK7N9Sfn1Y69y6Z3LOHzCnhzbODrlqnd6w3Be3fA+HUljpGpjMe5fvo63tu7guCljOKepIeU9etJBe/HIynd6lpVO7PPzh1/luTVbOHPaeD7/0ckZY/nTs2u45/l1nH7I3px1+IReryf58cljh9GR1hmlo7NbAxkHMPNKHfhQIk1NTd7c3FzQPrlW5stXXdyoiQVzZX394wfyw7+8tMvHrHYfGrsHr2aZVmVXFDNuJh+3zD6Cpa1be5Y2Tp5rLR/HNo5m/bYdBe0DQWN+YqBrPoygR19fV3TDB8W58//NTGmnmfHD+ax7tz37TmlG71HDxg96d5m/4x9n9Cz9LNXPzJ5x96a8tlVS6VvL+m0p31ajUhOjuLm/pGJM3XsIK9YVlhyKNXnUYFZtKmwxrEIrF2d+aBSPvxrNQN2fzJrGrKbCVx0tBXUi2HWFJBVVf+WQT0N9MdLHN0j16a+EAhScUCCokkyfjbkvUSUUyN4W2d/UiaD/aZxKDqX65+gopqVZpADd/TSx2gF7pXa7nn30xJQuz+WS3Img2IXWpHC6Usmhcdww9t6zrqB6ZpFKMG7PetZuLf0H6N8dtR/HNI7NOIamnDQbcnnoSiWH5lUblVCkKhWaUGYfPZHrz51OnOCDIU7Q4+2Cmfsxfng9sw4bn3G/YxrH0jhuGLOaJlRMQgHNhlwuulLJ4ZGV75Q7BJGSmH30RGbPmNTrCiN93M3MxjGcPr2BhpGD2WNQbUrX5Uqp6spEsyGXh5JKDvuPyT5Nh0i1+fYnD2LEHnUpSSQ9KSTPAJCxoXvaPj3jbhLdhpPH4mTqSlyuHliaDbn/KanksHZL4cvxilSqjx24F++3dzFySF3ObTONlv/6H5ZiODWxOD9/5FV+POvQlEG81y9s4djG0fzm/Bk9xyl3DyzNhty/lFREBpBTrn2EwXU1eX24Z2roTl+986u3Lek1yPTRlo00r9pI0+TRmsZlAFJDfQ6f+PDe5Q5BJDKdTt7da/NZSyjbo39dvp6la7aw/M13qY2lfswkemDJ7klJJYfGccM4tlHTTUjlM4J/6BqDvz8q92j22liM3zy5mvNvXcyfnu293EGiobsuHqw8Gc+8KGVGc558nc/fvIgL5jT3Wh2zGnpgtazfxh3Na2hZv63coVQdVX/lsPG9Nha/vrncYUgFOnzicJ59I/NU9VE76W/G8sCLG/rcxsOfboenV+ceHb+trZNrFwQzIj/w4ttc9deXePJbH09pVG9evYlg3bDM1yS1caOzy3vNDdHe1U17OGtnbdyor4G6eOqyzJU6fUr6XH+zj57IFWceUsaIqouSSg75rlEvA89zRSSUYifnWbu5sEk3Xy5wwkqAt95t59t3Pc/c5jcwYnR1d5NpZYbaWLDOTFe3c8a0vfnDs2/2edzubueWL3yE4YPrehJIuRvvs2lZv63X5LFznnyD2TMmVWzX6UqjpJJDw8jBvS7fRaC45FDsxCkv9tM8Y79dlPhAzf4l6l9P+xsOmziKhpGDOX/O4pzH7PJgOvxDwymPdqXxvmX9tpKO3M+13o3kpqSSh36aQkmkKhy8z/CeBPGR/UbldcW2tHUrJ04NOr0UO31Kf1RLZZvrr1ImyKwGaqjP4clXNyqpiITiBuve3dHTiP2P/+tDee133JQxPbeLmT4lW7VU1A3piUX5klXyrAGVSFcqOfzpufyW5xWpNpb024BLTz0o58Jx3Q4X/X5Jz/1zmhpyrg10bOPolFH2xUyfsivVUoV2CLjizEMyTl8j+VFSyWH5m+pSKLsnJ1ihsbYm3vOB+8vHX+tzAtX0i/a5za0MqYvRmbT8dH3c6CaRtIxPZ1isq9DpU4qtliq2Q0DjuGFKJkVS9VcOQ+oK6JwvUmWeePUdDp0wgtFD64uekfuD9tTLlLYup6PLae9y2ruiWcOkmGopradSHrpSyaGrW0lFdl/Nr++sVvrr8vVFHWPUHrW839FFbSxGW2cXsZixo6PvRvi7l6zlG3csIx4zurqdH8/KfAWR3Nur0GopradSHkoqOexRr4s52X1NHDmIO5rXBB/SY4cUdYwu7+bxS0+gdfN2htTFOf2Gx1IeT2+E3/heG//8h6Upq59+/Q9Le3UpztbbK99qKa2nUh76xMyhVmdIdmO/fbqVf75jGSdd8wjPrtlMrIgL85MO2qvn9sghdVx99jQG1cYYVl/DoNoYV589DYCla7aw8b02lr+5tddy2h1dzvI3d3ZNjqK3V6JDQHosukopLV2p5LByQ2EjmUWq1dzmtXwrjx5g6XZ0Okf84IGe+9efO53DG/bk6de3cOR+I3Bgxg8X0OVO3Izzj52c5Ug7M1pUgxC1nkr/U1LJ4SMTR/BQS+55lER2B4UmFIB7nl+Xcv8rt+3scvzEqi08sWrn/W53bnz4NWKWOqg4ZvDhffbsuR/lIEStp9K/VLmTg6OGepGopQ8o9rT7jeOGccC41DaeA8P7mj24sulKJYeHwxXtRKR0HPjjs62MDJc6BnglbVLMl9e/z0nXPNJzX7MHVyYlFRGpCP9+786qt4/uPyrn9sXOHpxphH2lTsNfrFJPvNkXJRURqThPvJZfO2ahDffB+JilxC1Gl3fz41mH4pDXmJlqUe71YJRURKTfDa4xtmdarCXJRz80iide7Tu55NNw37xqI4+sfIdDG4bz9blLwnnKugC45PYlmKXOXZZpzEwmlXh1UwnrwSipiEi/y5VQAK4442C2fNDOIyvf4bgpY5i37E3mPJn6DXzLB+389P6XOW7KmJRJKxM+f/NTPNZHu2hXYrnMJIkxM8cdsFfGfSD/GQH6WyWsB6OkIiIVaeFLb3P1X1/Cgf98sIUxw+pSHv/D4jU9Seb6hS0c2zia35w/o+fx5lUb+0wofXl53TaaV2/OmKzynRGgVBasWMf9K9Zz8tRxPWvUJFTCejBKKiJSkdLHzKRPdpl+tfNoy0aaV21k8tihtG7ezn0rUsfPFCLRaSBTsso2I8D85euoicd2qXE8VwP7ydc81NMr7vbmVg4cN4T7LvlYz+OJiTfTr+j6s7FeSUVEdhs/nb+Sxas3AkG1VBQSyWrnFUvmsWvfvOuFntuzj56Y1+SXyUlkzpOr+2xgX7BiXcZu1gtWrEu5Yjliv1H8ftEbYZxO0365e9JFSUlFRHYbT7yWqO6KdrnWmx55jdUbP2D6hBHsM3xQzu3nPPlGr6uF9B5Y6b20Mh0juYH9/hWZZ5H+1eOreqrDpk8cmdQZITgHX5u7hH2GD+qJv9RXLVWfVMzsFOA6IA7c7O5XljkkEdnN3P/i29z/4tsAHDV5ZMH7JxLEU6++w93L3uKjk0f2mVASkhvYs01u+9irm4BN3N7cSsPw+l6rcHZ2w6yfP9Vzv9RdjKs6qZhZHPgZ8HGgFVhsZvPcfUV5IxOR3dWiVZuL2u/U6x4hsczM4tX5HWPkHrU9t19++/0+tgy0bs29AFmpuxhX+9xfRwIt7v6au7cDtwFnljkmEZFeOrpzb5Nu8wcdPbeH1EQ3D+F/L3o9smOlq/aksi+wJul+a1iWwswuNLNmM2vesGFDvwUnIrIrkrsCr968I7Ljvrk1umOlq/akkhd3v8ndm9y9aezYseUOR0Qkp/SuwGdO27uPrQvz6SMaIjtWumpPKmuBCUn3G8KyyKy+8rQoDyciFaTWgkXF6uJGbdyoi+euYjpw3JCeafjzdeC4IQwfFM8ZywOXHMdPZk3jgUuO69WYfskn/obaPGrA0mNL3+fAcUN6DZqMUrUnlcXAFDObbGZ1wLnAvKifJD2xZLqfzzbap//2qaRYtE9+++w1JOg3tNeQmp5tRgwKPhFHDDJWX3kaH2scRV0cPtY4itVXnsbEEcEI9okj6nv2SS/7wRlT+cikkfzgjKmsvvI0Lj5+fyaN3oOLj9+flT86jTOm78uTl53IHf/3ozx52YkZn+eW2UfwmaYGbpl9BPdd8jHuu+RjKWWrrzyNWYeNZ/igOLMOG59xn6XfPSVnLI3jhjGraULWRvSVP0rdZ/WVp/Gpg/diSF2MTx28F6uvPK1XbCt/1DuWUjJPXx2nypjZJ4FrCboU/9Ld/72v7Zuamry5ublfYhMR2R2Y2TPu3pTPtlXdpRjA3e8F7i13HCIiUv3VXyIiUkGUVEREJDJKKiIiEhklFRERiYySioiIREZJRUREIqOkIiIikVFSERGRyCipiIhIZKp+mpZCmdkGoHSLCfQ2BninH59vV1RTrFBd8VZTrFBd8VZTrFBd8SZi3c/d85rifcAllf5mZs35zplTbtUUK1RXvNUUK1RXvNUUK1RXvMXEquovERGJjJKKiIhERkml9G4qdwAFqKZYobriraZYobriraZYobriLThWtamIiEhkdKUiIiKRUVKJiJlNMLMHzWyFmS03s4vD8lFmNt/MVoa/R5Y7VgAzG2RmT5vZ0jDe74Xlk81skZm1mNnt4TLNFcHM4mb2nJndE96v5FhXm9nzZrbEzJrDskp9L4wwszvM7CUze9HMjq7gWA8Mz2ni510z+2oFx3tJ+P/1gpn9Pvy/q8j3rZldHMa53My+GpYVfF6VVKLTCXzd3acCM4Avm9lU4JvAAnefAiwI71eCNuAEdz8UmA6cYmYzgKuAa9y9EdgMfLGMMaa7GHgx6X4lxwpwvLtPT+qSWanvheuAv7r7QcChBOe4ImN195fDczodOAL4ALiLCozXzPYFvgI0ufvBBEuen0sFvm/N7GDgAuBIgvfA6WbWSDHn1d31U4If4G7g48DLwPiwbDzwcrljyxDrHsCzwFEEA51qwvKjgfvKHV8YS0P4pj4BuAewSo01jGc1MCatrOLeC8BwYBVh+2olx5oh9pOBxys1XmBfYA0wimDp9nuAT1Ti+xb4NHBL0v1/A75RzHnVlUoJmNkk4DBgETDO3d8KH1oHjCtTWL2E1UlLgLeB+cCrwBZ37ww3aSX4x6gE1xK8ybvD+6Op3FgBHLjfzJ4xswvDskp8L0wGNgC/CqsWbzazIVRmrOnOBX4f3q64eN19LfAT4A3gLWAr8AyV+b59ATjWzEab2R7AJ4EJFHFelVQiZmZDgTuBr7r7u8mPeZDuK6a7nbt3eVCN0EBw2XtQmUPKyMxOB95292fKHUsBjnH3w4FTCapCj0t+sILeCzXA4cCN7n4Y8D5pVRwVFGuPsB3iDOAP6Y9VSrxh+8OZBIl7H2AIcEpZg8rC3V8kqJa7H/grsAToStsmr/OqpBIhM6slSCi/c/c/hsXrzWx8+Ph4gquCiuLuW4AHCS7FR5hZTfhQA7C2bIHtNBM4w8xWA7cRVIFdR2XGCvR8S8Xd3yao8z+SynwvtAKt7r4ovH8HQZKpxFiTnQo86+7rw/uVGO9JwCp33+DuHcAfCd7LFfm+dfdb3P0Idz+OoK3nFYo4r0oqETEzA24BXnT3nyY9NA84L7x9HkFbS9mZ2VgzGxHeHkzQ/vMiQXKZFW5WEfG6+2Xu3uDukwiqPBa6++eowFgBzGyImQ1L3Cao+3+BCnwvuPs6YI2ZHRgWnQisoAJjTfNZdlZ9QWXG+wYww8z2CD8fEue2Ut+3e4W/JwL/G/hvijmv5W4g2l1+gGMILg2XEVw6LiGolxxN0MC8EngAGFXuWMN4pwHPhfG+AHwnLN8feBpoIahaqC93rGlxfwy4p5JjDeNaGv4sB/41LK/U98J0oDl8L/wJGFmpsYbxDgE2AsOTyioyXuB7wEvh/9hvgPoKft8+SpD0lgInFnteNaJeREQio+ovERGJjJKKiIhERklFREQio6QiIiKRUVIREZHIKKmIiEhklFRkQDOzvc3sNjN7NZyn614zO6DAY5wVzkhdEcxsjJl1mNn/LXcsMvAoqciAFY5yvgt4yN0/5O5HAJdR+GSEZwH9mlTMLN7Hw58GniIYdS7Sr5RUZCA7Huhw9/9KFLj7UiCeWAgMwMxuMLN/CG9facFCbMvM7Cdm9lGCiQ1/HC4a9SEzm25mT4Xb3JVY2MjMHjKza8ysOVwM6yNm9sdwAaQfJD3f5y1YQG2Jmf08kUDM7D0z+w8zW0owT1s2nwW+DuxrZg1Jx/2imb0SHvsXZnZDWD7WzO40s8Xhz8xdPrMyYCmpyEB2MMFU5Hkxs9HA3wIfdvdpwA/c/QmC+ZH+xYPFo14F5gCXhts8D1yedJh2Dxbt+i+CeZS+HMbxD+G0438DfAaY6cEM0l3A58J9hwCL3P1Qd38sS4wTCNa/eBqYGx4LM9uHYI2MGQSTGibPSH0dwaJRHwHOBm7O95yIpKvJvYmIhLYCO4BbwiuZe9I3MLPhwAh3fzgsupXU6dnnhb+fB5Z7uFaFmb1GsH7FMQQrGi4OaucYzM6ZYbsIZsHuy2cIkgkEMzr/EvgPglmSH3b3TeHz/QFItB2dBEwNnw9gTzMb6u7v5XgukV6UVGQgW87O2WKTdZJ6FT8IwN07zexIgtlmZwEXEUzDX4i28Hd30u3E/RqCFS1vdffLMuy7w927MpQn+yywt5klrm72MbMpOfaJATPcfUeO7URyUvWXDGQLgfqklRkxs2kEH+xTzaw+XB7gxPCxoQQz494LXEKwljfANmAYgLtvBTab2bHhY38PJK5a8rEAmJU0DfkoM9svnx3DXmtD3X1fd5/kwVIBPyJINIuB/2VmI8O1PM5O2vV+4J+SjjO9gHhFUiipyIDlwRTdfwucFHYpXk7wIbyOoArphfD3c+Euw4B7zGwZ8BjwtbD8NuBfwuV4P0Sw7sSPw+2mA1cUENMK4NsESxEvI1jmeXyeu3+WoDdbsjuBz3qwaNgPCaZcfxxYTVCdB/AVoCnsWLACUFdkKZqmvhcZIBLtJOGVyl3AL909PQmJ7BJdqYgMHN81syUEV2CrCBbkEomUrlREqpCZ3QVMTiu+1N3vK0c8IglKKiIiEhlVf4mISGSUVEREJDJKKiIiEhklFRERiYySioiIROb/Bxb9QwHZM9YcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales.plot(kind='scatter', x='Customer_Age', y='Revenue', figsize=(6,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ade7a70a0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales.plot(kind='scatter', x='Revenue', y='Profit', figsize=(6,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Profit')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sales[['Profit', 'Age_Group']].boxplot(by='Age_Group', figsize=(10,6))\n",
    "ax.set_ylabel('Profit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year                 AxesSubplot(0.125,0.536818;0.227941x0.343182)\n",
       "Customer_Age      AxesSubplot(0.398529,0.536818;0.227941x0.343182)\n",
       "Order_Quantity    AxesSubplot(0.672059,0.536818;0.227941x0.343182)\n",
       "Unit_Cost               AxesSubplot(0.125,0.125;0.227941x0.343182)\n",
       "Unit_Price           AxesSubplot(0.398529,0.125;0.227941x0.343182)\n",
       "Profit               AxesSubplot(0.672059,0.125;0.227941x0.343182)\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot_cols = ['Year', 'Customer_Age', 'Order_Quantity', 'Unit_Cost', 'Unit_Price', 'Profit']\n",
    "\n",
    "sales[boxplot_cols].plot(kind='box', subplots=True, layout=(2,3), figsize=(14,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "## Column wrangling\n",
    "\n",
    "We can also create new columns or modify existing ones.\n",
    "\n",
    "### Add and calculate a new `Revenue_per_Age` column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    50.000000\n",
       "1    50.000000\n",
       "2    49.000000\n",
       "3    42.612245\n",
       "4     8.893617\n",
       "Name: Revenue_per_Age, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales['Revenue_per_Age'] = sales['Revenue'] / sales['Customer_Age']\n",
    "\n",
    "sales['Revenue_per_Age'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ade499d90>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0YAAAFpCAYAAAC4QTjSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3dfZRkdX3n8fe3qnp6ZmBmEBhBBxAUlAwhoo6wmwdPEp8wD45GjBgTSZYsJsrJiTnuCclmjXGzWXXPxk0i2Q1ZWZG4AcNGM0mIxqdkT1wfGHwCjB5HhDCIOswMAwwz0911v/tH3aqp6qru6cG+XTO33q9z+lTVrVvVv4Z7eurT39/v+4vMRJIkSZImWWPcA5AkSZKkcTMYSZIkSZp4BiNJkiRJE89gJEmSJGniGYwkSZIkTTyDkSRJkqSJV2kwiohLI+KrEbEjIq4Z8fx0RNxcPv+ZiDi7PH52RByIiC+UX/+jynFKkiRJmmytqt44IprAtcALgZ3AbRGxLTO/3HfalcDezDw3Ii4H3g68qnzu65l5UVXjkyRJkqSuKitGFwM7MvPuzJwBbgK2zjtnK3BDef8W4PkRERWOSZIkSZKGVBmMNgH39T3eWR4beU5mzgH7gFPK586JiM9HxD9GxA9VOE5JkiRJE66yqXTfpQeAszJzd0Q8B/hgRFyQmQ/3nxQRVwFXAZxwwgnPOf/888cwVEmSJEnHi9tvv/3BzNw4/3iVweh+4My+x2eUx0adszMiWsAGYHdmJnAIIDNvj4ivA08Htve/ODOvA64D2LJlS27fPvC0JEmSJA2IiHtHHa9yKt1twHkRcU5ErAIuB7bNO2cbcEV5/zLg45mZEbGxbN5ARDwVOA+4u8KxSpIkSZpglVWMMnMuIq4GPgw0gesz866IeCuwPTO3Ae8GboyIHcAeOuEJ4HnAWyNiFiiAX8rMPVWNVZIkSdJki86steOfU+kkSZIkHUlE3J6ZW+Yfr3SDV0mSJEk6HhiMJEmSJE08g5EkSZKkiWcwkiRJkjTxDEaSJEmSJp7BSJIkSdLEMxhJkiRJmngGI0mSJEkTz2AkaaSDs23+Zfdj4x6GJEnSijAYSRrpjTd/gef9l08wM1eMeyiSJEmVMxhJGuljX/kOALNtg5EkSao/g5Gk0bJzM9fO8Y5DkiRpBRiMJI2UZTKasWIkSZImgMFI0khZFoqcSidJkiaBwUjSSN0JdAYjSZI0CQxGkkbKsmRkMJIkSZPAYCRppG7F6JDtuiVJ0gQwGEka6fAaI7vSSZKk+jMYSVqUU+kkSdIkMBhJWtSsU+kkSdIEMBhJWpT7GEmSpElgMJK0KNcYSZKkSWAwkrQo1xhJkqRJYDCStCiDkSRJmgQGI0mLmrH5giRJmgAGI0mLco2RJEmaBAYjSYuamWuPewiSJEmVMxhJWpQVI0mSNAkMRpKGZB4OQ+5jJEmSJoHBSNKQ/iqRXekkSdIkMBhJGtJfJTIYSZKkSWAwkjSkv0W3a4wkSdIkMBhJGtIfjNzHSJIkTQKDkaQhgxUjg5EkSao/g5GkITPtw3sXGYwkSdIkMBhJGnLINUaSJGnCGIwkDSn6ikSuMZIkSZPAYCRpSOEGr5IkacIYjCQNaacbvEqSpMliMJI0JA1GkiRpwhiMJA0p+vottAubL0iSpPozGEkaUvSFocKCkSRJmgAGI0lDumuMWo0YWG8kSZJUVwYjSUO6WWiq2XAqnSRJmggGI0lDuu26W80wGEmSpIlgMJI0pBuGrBhJkqRJYTCSNOTwVLoY2OxVkiSprgxGkob0ptI1rBhJkqTJYDCSNKSbhVa1DEaSJGkyGIwkDTm8xsh23ZIkaTIYjCQNSafSSZKkCWMwkjSk6G++YDCSJEkTwGAkaUi3+cJUs+FUOkmSNBEqDUYRcWlEfDUidkTENSOen46Im8vnPxMRZ897/qyIeDQi3lTlOCUNGghGVowkSdIEqCwYRUQTuBZ4CbAZeHVEbJ532pXA3sw8F3gn8PZ5z/8+8HdVjVHSaL123c0wGEmSpIlQZcXoYmBHZt6dmTPATcDWeedsBW4o798CPD8iAiAiXgZ8A7irwjFKGqEoOrdWjCRJ0qSoMhhtAu7re7yzPDbynMycA/YBp0TEicCvA79T4fgkLeDwBq+BuUiSJE2CY7X5wluAd2bmo4udFBFXRcT2iNi+a9eulRmZNAF6a4xaDea65SNJkqQaa1X43vcDZ/Y9PqM8NuqcnRHRAjYAu4FLgMsi4h3ASUAREQcz8139L87M64DrALZs2eLftaVl0q0SrWo2MBdJkqRJUGUwug04LyLOoROALgd+Zt4524ArgE8BlwEfz87Okj/UPSEi3gI8Oj8USapO/1Q623VLkqRJUFkwysy5iLga+DDQBK7PzLsi4q3A9szcBrwbuDEidgB76IQnSWPW3dS1ZfMFSZI0IaqsGJGZtwK3zjv25r77B4FXHuE93lLJ4CQt6PBUuug8LpJGI8Y4IkmSpGodq80XJI1R/wavAHNWjSRJUs0ZjCQN6eagVhmMCtcZSZKkmjMYSRrSXWPUnUrnOiNJklR3BiNJQ3pd6cqKkZ3pJElS3RmMJA3pFoi6a4zabYORJEmqN4ORpCGHmy+UU+msGEmSpJozGEka0l1j1K0YFa4xkiRJNWcwkjTkcFc6K0aSJGkyGIwkDelNpWuUa4ysGEmSpJozGEkakplEQLNhu25JkjQZDEaShrQzaUQYjCRJ0sQwGEkaUiQ0I2iUwahwjZEkSao5g5GkIUV3Kl10K0ZjHpAkSVLFDEaShhSFU+kkSdJkMRhJGlIkNGy+IEmSJojBSNKQIpNGIyj3d3UfI0mSVHsGI0lDMqERQSOsGEmSpMlgMJI0pF0kjYBWucGrXekkSVLdGYwkDSkyaTaCMhcx1zYYSZKkejMYSRpSJEREr123FSNJklR3BiNJQzLTrnSSJGmiGIwkDWmX+xg1usHIipEkSao5g5GkIUXZla5VBqPCipEkSao5g5GkIZlJo0GvXfecwUiSJNWcwUjSkCI7U+maVowkSdKEMBhJGtIup9I1XWMkSZImhMFI0pCi7ErXnUpnVzpJklR3BiNJQ7KcSteyXbckSZoQBiNJQ4pi3lQ6g5EkSao5g5GkIe1MIujtY1S4xkiSJNWcwUjSkMyk2QiavTVGYx6QJElSxQxGkoYUdqWTJEkTxmAkaUi76HSl6wUjS0aSJKnmDEaShhSZRPRNpbNgJEmSas5gJGlIZqda1Ch/QxR2pZMkSTVnMJI0pLvBq2uMJEnSpDAYSRrSLsqpdO5jJEmSJoTBSNKQTGj2rzEyGEmSpJozGEkaUmTSaGDFSJIkTQyDkaQhnTVGQUQQ0XksSZJUZwYjSUPaCVFOo2s1woqRJEmqPYORpCGZSbOTi2iEwUiSJNWfwUjSkO5UOuisMzIYSZKkujMYSRpSFIen0jUj3MdIkiTVnsFI0pDuBq8AjUZQWDGSJEk1ZzCSNKTI7LXqbjWCOYORJEmqOYORpCFF0ltj1GiE7bolSVLtGYwkDSmKpMxFnTVGVowkSVLNGYwkDRnuSjfmAUmSJFXMYCRpSJH01hg1nUonSZImgMFI0pAi+6bS2XxBkiRNAIORpCFFcXgqXSOwXbckSao9g5GkIUV2mi5Ad42RwUiSJNWbwUjSkCKTRvnboRFB2zVGkiSp5ioNRhFxaUR8NSJ2RMQ1I56fjoiby+c/ExFnl8cvjogvlF9fjIiXVzlOSYOKhCgrRq2mFSNJklR/lQWjiGgC1wIvATYDr46IzfNOuxLYm5nnAu8E3l4evxPYkpkXAZcCfxIRrarGKmlQp1135777GEmSpElQZcXoYmBHZt6dmTPATcDWeedsBW4o798CPD8iIjMfy8y58vhqwE9l0goqMntrjBq265YkSROgymC0Cbiv7/HO8tjIc8ogtA84BSAiLomIu4A7gF/qC0o9EXFVRGyPiO27du2q4EeQJlNRZG8qnRUjSZI0CY7Z5guZ+ZnMvAB4LvAbEbF6xDnXZeaWzNyycePGlR+kVFOZ9Np125VOkiRNgiqD0f3AmX2PzyiPjTynXEO0Adjdf0Jm/jPwKPC9lY1U0oB2/xojg5EkSZoAVQaj24DzIuKciFgFXA5sm3fONuCK8v5lwMczM8vXtAAi4inA+cA9FY5VUp8ik2ajr2LkGiNJklRzlXV6y8y5iLga+DDQBK7PzLsi4q3A9szcBrwbuDEidgB76IQngB8EromIWaAAXp+ZD1Y1VkmD+tt1NyIorBhJkqSaq7QFdmbeCtw679ib++4fBF454nU3AjdWOTZJC8v5U+msGEmSpJo7ZpsvSBqfdpEDzRfm2gYjSZJUbwYjSUOK7OxfBJ123e5jJEmS6s5gJGlAliHIrnSSJGmSGIwkDeiGoO5UukYjMBdJkqS6MxhJGtANQd123S0rRpIkaQIYjCQN6K4nKgtGNMJgJEmS6s9gJGlAt8/C4a50GIwkSVLtGYwkDWiPar5gVzpJklRzBiNJA4qc13whgsKKkSRJqjmDkaQBWXRuu8Go1QjmDEaSJKnmDEaSBhTzptI1GlaMJElS/RmMJA3orTEqk1EzXGMkSZLqz2AkacD8NUZN9zGSJEkTwGAkacBwu+7ohSVJkqS6MhhJGjB/jVHT5guSJGkCGIwkDehOm+tv150JadVIkiTV2JKCUUT8ZUT8eEQYpKSa602laxyeSge4zkiSJNXaUoPOHwM/A3wtIt4WEc+ocEySxmjUVDrAznSSJKnWlhSMMvOjmfka4NnAPcBHI+L/RcQvRMRUlQOUtLLmT6XrBqOiGNuQJEmSKrfkqXERcQrw88AvAp8H/oBOUPpIJSOTNBbdGXPRrRiVd+ZMRpIkqcZaSzkpIj4APAO4EfjJzHygfOrmiNhe1eAkrbxuk4VupahhxUiSJE2AJQUj4E8z89b+AxExnZmHMnNLBeOSNCbF/H2MysqRa4wkSVKdLXUq3e+OOPap5RyIpGPD4TVGncd2pZMkSZNg0YpRRJwObALWRMSzgPKjEuuBtRWPTdIYHO5K122+0Pn7icFIkiTV2ZGm0r2YTsOFM4Df7zv+CPCbFY1J0hjl/Kl0ZV3ZqXSSJKnOFg1GmXkDcENEvCIz/88KjUnSGPUqRmUg6gakwoqRJEmqsSNNpfvZzPwz4OyI+LX5z2fm7494maTjWLcyFPP2MXIqnSRJqrMjTaU7obw9seqBSDo29Np1zwtGcwYjSZJUY0eaSvcn5e3vrMxwJI3b/HbdU+UiIzd4lSRJdbakdt0R8Y6IWB8RUxHxsYjYFRE/W/XgJK28Yl677la3YtS2YiRJkuprqfsYvSgzHwZ+ArgHOBf4d1UNStL4zF9j1K0YzbatGEmSpPpaajDqTrn7ceAvMnNfReORNGbdrtzdtUWtpmuMJElS/R2p+ULX30TEV4ADwC9HxEbgYHXDkjQuhzd47TxuNawYSZKk+ltSxSgzrwG+H9iSmbPAfmBrlQOTNB7dttyHp9K5xkiSJNXfUitGAOfT2c+o/zXvXebxSBqz7HWl69y27EonSZImwJKCUUTcCDwN+ALQLg8nBiOpdrpT6XprjMrbWStGkiSpxpZaMdoCbM7uzo+SamvBfYwMRpIkqcaW2pXuTuD0Kgci6dhweI1R5/HhrnROpZMkSfW11IrRqcCXI+KzwKHuwcx8aSWjkjQ2OW8q3VSvK50VI0mSVF9LDUZvqXIQko4d86fS9SpGtuuWJEk1tqRglJn/GBFPAc7LzI9GxFqgWe3QJI3D0D5GZTCadYNXSZJUY0taYxQR/xa4BfiT8tAm4INVDUrS+HSDUW8fo+5UujkrRpIkqb6W2nzhDcAPAA8DZObXgCdWNShJ49Nr1z1/Kp3NFyRJUo0tNRgdysyZ7oNyk1fn1Ug11M0/89t123xBkiTV2VKD0T9GxG8CayLihcBfAH9d3bAkjcvhqXSdx+5jJEmSJsFSg9E1wC7gDuB1wK3Ab1U1KEnj02u+UHZfaDaCCKfSSZKkeltqV7oiIj4IfDAzd1U8Jklj1G0+111jBJ0GDE6lkyRJdbZoxSg63hIRDwJfBb4aEbsi4s0rMzxJK21+u27oNGBwHyNJklRnR5pK90Y63eiem5knZ+bJwCXAD0TEGysfnaQV160YRV/FqNUI5tzHSJIk1diRgtHPAa/OzG90D2Tm3cDPAq+tcmCSxqMohitGU80Gs1aMJElSjR0pGE1l5oPzD5brjKaqGZKkcertY9SXjDpT6awYSZKk+jpSMJp5nM9JOk6NnkrXYNaudJIkqcaO1JXumRHx8IjjAayuYDySxmz0VDorRpIkqd4WrRhlZjMz14/4WpeZR5xKFxGXRsRXI2JHRFwz4vnpiLi5fP4zEXF2efyFEXF7RNxR3v7o4/0BJR2dw13p+qfSNdzHSJIk1dpSN3g9ahHRBK4FXgJsBl4dEZvnnXYlsDczzwXeCby9PP4g8JOZeSFwBXBjVeOUNKi3j1FjsCud+xhJkqQ6qywYARcDOzLz7sycAW4Cts47ZytwQ3n/FuD5ERGZ+fnM/GZ5/C5gTURMVzhWSaVuxSjmdaVzHyNJklRnVQajTcB9fY93lsdGnpOZc8A+4JR557wC+FxmHqponJL6HF5jNK8rnfsYSZKkGjtS84WxiogL6Eyve9ECz18FXAVw1llnreDIpPrqTaXrC0ZTDfcxkiRJ9VZlxeh+4My+x2eUx0aeExEtYAOwu3x8BvAB4LWZ+fVR3yAzr8vMLZm5ZePGjcs8fGkyjZpK5z5GkiSp7qoMRrcB50XEORGxCrgc2DbvnG10misAXAZ8PDMzIk4C/ha4JjM/WeEYJc2TmUTM28eo2WDWqXSSJKnGKgtG5Zqhq4EPA/8MvD8z74qIt0bES8vT3g2cEhE7gF8Dui29rwbOBd4cEV8ov55Y1VglHdbOHFhfBDDVCJsvSJKkWqt0jVFm3grcOu/Ym/vuHwReOeJ1vwv8bpVjkzRakYPri8CpdJIkqf6qnEon6ThUlFPp+rWaNl+QJEn1ZjCSNCCToal0q5oNZgxGkiSpxgxGkga0i6Qxr2K0eqrBoTmDkSRJqi+DkaQBRSaNecloutXk4Gx7TCOSJEmqnsFI0oBRU+mmpxocmrViJEmS6stgJGnAyKl0rSYz7YLCvYwkSVJNGYwkDShG7GO0eqoJ4DojSZJUWwYjSQOKZGiN0eqpzq8K1xlJkqS6MhhJGpA5qitdp2J0cM5gJEmS6slgJGlAZ43RQhUjp9JJkqR6MhhJGtAetcaoVVaMnEonSZJqymAkaUBRJM2hNUYGI0mSVG8GI0kD2slQMJp2Kp0kSao5g5GkAcWofYxsviBJkmrOYCRpQJEjptKVa4wOWTGSJEk1ZTCSNGCxrnSHrBhJkqSaMhhJGlCM6kpn8wVJklRzBiNJA9ojutJNt2y+IEmS6s1gJGlAO6Fhu25JkjRhDEaSBhRF0lyoK50VI0mSVFMGI0kDRk2lazaCqWbYrluSJNWWwUjSgPaI5gvQadntVDpJklRXBiNJA4oRFSOAE6Zb7D80N4YRSZIkVc9gJGlAe8QGrwDrVrd45KDBSJIk1ZPBSNKAYsQGr2AwkiRJ9WYwkjRg4YrRFI8cnB3DiCRJkqpnMJI0oF0wsmJ04uoWj7jGSJIk1ZTBSNKATvOF4ePrnUonSZJqzGAkaUCxQLtup9JJkqQ6MxhJGtDOpDFqjdF0i4OzBbPtYgyjkiRJqpbBSNKAokiaC3SlA5xOJ0mSaslgJGnAQl3pTlw9BeB0OkmSVEsGI0kDigW60lkxkiRJdWYwkjSgvUBXOoORJEmqM4ORpAELTaXbsKYzlW7fgZmVHpIkSVLlDEaSBhTF6HbdJ5+wCoC9j7nGSJIk1Y/BSNKAhSpGT1jbCUZ79lsxkiRJ9WMwkjSgvUDFaPVUk7WrmgYjSZJUSwYjSQOKYnTFCDpVo70GI0mSVEMGI0kDFppKB511RnseMxhJkqT6MRhJGrDQPkYATzjBipEkSaong5GkAUUmCxSMOMWKkSRJqimDkaQBi02l66wxsl23JEmqH4ORpJ7MJHPhqXQnnzDFo4fmODTXXuGRSZIkVctgJKmnXSTAwhWjcpPXh9zkVZIk1YzBSFJPOxcPRie7yaskSaopg5GknqLo3C7WlQ6wM50kSaodg5GknsMVo9HPn1wGIzvTSZKkujEYSerprjFasGK01oqRJEmqJ4ORpJ7iCM0XTlo7BcAeW3ZLkqSaMRhJ6jlS84WpZoP1q1vsdSqdJEmqGYORpJ7iCFPpoLPOyK50kiSpbgxGknqOVDGCTmc6K0aSJKluKg1GEXFpRHw1InZExDUjnp+OiJvL5z8TEWeXx0+JiE9ExKMR8a4qxyjpsN4Gr4tVjNZaMZIkSfVTWTCKiCZwLfASYDPw6ojYPO+0K4G9mXku8E7g7eXxg8B/AN5U1fgkDSsLRiySizoVI4ORJEmqmSorRhcDOzLz7sycAW4Cts47ZytwQ3n/FuD5ERGZuT8z/4lOQJK0QtpH6EoH5Rojp9JJkqSaqTIYbQLu63u8szw28pzMnAP2AadUOCZJi1jSGqO1qzg4W3Bgpr1Sw5IkSarccd18ISKuiojtEbF9165d4x6OdNxbWle6zl5Gu/cfWpExSZIkrYQqg9H9wJl9j88oj408JyJawAZg91K/QWZel5lbMnPLxo0bv8vhSlpqxQhgr5u8SpKkGqkyGN0GnBcR50TEKuByYNu8c7YBV5T3LwM+ntld/i1ppbWXuI8R4DojSZJUK62q3jgz5yLiauDDQBO4PjPvioi3AtszcxvwbuDGiNgB7KETngCIiHuA9cCqiHgZ8KLM/HJV45UERdG5PdI+RoCd6SRJUq1UFowAMvNW4NZ5x97cd/8g8MoFXnt2lWOTNOzwVLqFzzm5nEq322AkSZJq5LhuviBpeS1lKt2GNVNMNYMHH7X5giRJqg+DkaSeYgnNFxqN4JQTpnnwEYORJEmqD4ORpJ7eBq+LVIwATl23il1WjCRJUo0YjCT19PYxWqRiBHDqidNOpZMkSbViMJLUs5R9jAA2njjNg4/YfEGSJNWHwUhST1kw4gi5iFPXTbN7/6FehUmSJOl4ZzCS1FMsoSsddKbSzbaTfQdmV2JYkiRJlTMYSerpNV840lS6ddMArjOSJEm1YTCS1NNdY3TkilFnk1c700mSpLowGEnqKZZaMTqxWzGyAYMkSaoHg5GkntkyGE01lzaVbpebvEqSpJowGEnqmWsXAEw1F//VsGHNFFPNcI2RJEmqDYORpJ7ZJQajiOCUE6Z50IqRJEmqCYORpJ6Zdncq3ZF/NWxcN23FSJIk1YbBSFLP7Fy3YnSEHV7pdKazK50kSaoLg5GknrliaVPpAE7fsJpv7TtY9ZAkSZJWhMFIUs/sUUyle/KGNTz46AwHZ9tVD0uSJKlyBiNJPTNHMZVu0xPWAPDNhw5UOiZJkqSVYDCS1DPbLmg1gogjB6Mnn9QNRk6nkyRJxz+DkaSeuSKXNI0OYFMZjO5/6LEqhyRJkrQiDEaSembmiiVNo4NO84VGwP17nUonSZKOfwYjST2z7WLJFaOpZoMnn7SGe3ZbMZIkScc/g5GknqMJRgBP3XgiX9/1aIUjkiRJWhkGI0k9c+1kqrW0qXQATz31BO7etZ+iyApHJUmSVD2DkaSemaOsGD3tiSdyYLbNtx62M50kSTq+GYwk9cy2C6YaRxGMNp4AwNe+43Q6SZJ0fDMYSeqZPcqpdBc8aQMAd96/r6ohSZIkrQiDkaSeo22+sGHtFGefspYv3vdQhaOSJEmqnsFIUs/RBiOA7zvjJL60cx+ZNmCQJEnHL4ORpJ7Zdi55g9eu555zMt96+CBf37W/olFJkiRVz2AkqefxVIx+9PwnAvCJr3yniiFJkiStCIORpJ5Oxejofi1sOmkN55++jm1f/KbT6SRJ0nHLYCSpZ7ZdsOoogxHAay45izvu38dt9+ytYFSSJEnVMxhJ6pltF7SOco0RwCuecwanrZ/mzX91JzNzRQUjkyRJqpbBSFLP7NzRrzECWLuqxX962YV85VuP8K5P7KhgZJIkSdUyGEnqmS2Ofo1R1ws2n8ZPPWsTf/Txr/F3dzywzCOTJEmqlsFIUk9njdHRT6Xr+r2fupCLzjyJX735C3z67t3LODJJkqRqGYwk9czOFbQeZ8UIYPVUk3df8VzOOnkt/+Y9t3H7vTZjkCRJxweDkaSex9Oue76TT1jF+37xEp64bprXv+92HnpsZplGJ0mSVB2DkSQAMpPZ4rubStf1xPWredfPPJs9+2f49x+40/2NJEnSMc9gJAmAdpFk8l1XjLq+d9MGfvUFT+dv73iAD935rWV5T0mSpKoYjCQBcKjcf2iqtXy/Fl73vKey+Unr+e1td/Hwwdlle19JkqTlZjCSBMCjh+YAWLe6tWzv2Wo2eNsrLuTBRw/xjg99ZdneV5IkabkZjCQB8PCBTkVn/eqpZX3f7zvjJH7++8/hfZ/5Fz765W8v63tLkiQtF4ORJIDeVLflrBh1venFT+fCTRt43Z/dzq/f8iU+ffduisKGDJIk6dhhMJIEwMMHO1Pp1q9Z3ooRwNpVLW688hJe9dwz+esvfZPLr/s0W6/9JA/sO7Ds30uSJOnxMBhJAqqbSte1Yc0Uv/fyC9n+Wy/gHZd9H994cD9XXP9Z9h2wKYMkSRo/g5EkoL9itPxT6fqtXdXip7ecyXU/9xy+8eB+Xv++25ltF5V+T0mSpCMxGEkC4JGD1VaM5vv+c0/l915+IZ/csZvf+sCdtF1zJEmSxqjaPw1LOm48fGCOVc0G08u4j9GRvHLLmdy35zH+8OM7+PIDD/P6H34aL7rgdJqNWLExSJIkgRUjSaWHD86yfk2LiJUNJW984dP5g8sv4qEDM/zy+z7Hy//4k9yxc9+KjkGSJMlgJAmARw7OsW6FptH1iwi2XrSJf3jTj/DOVz2Tbz50kK3X/hO/8uef55M7HrSttyRJWhFOpZMEdLrSra9gD6OlajaClz/rDH70/NP4o499jfdvv49tX/wmm05aw/OefiprV7VoF0kEfM/p6+bxYCwAAAjsSURBVLn0wtNXbD2UJEmqv0o/BUXEpcAfAE3gf2bm2+Y9Pw28F3gOsBt4VWbeUz73G8CVQBv4lcz8cJVjlSbdPbv3c/7p68Y9DDasmeK3fmIzb3rxM/j7L3+bv/zcTj5057eYmStoNRvMtQv2z7T5nb++i59+7pm89l+fzWnrp3nosVn27J9h3eoWp61fzeqp5rh/FEmSdBypLBhFRBO4FnghsBO4LSK2ZeaX+067EtibmedGxOXA24FXRcRm4HLgAuDJwEcj4umZ2a5qvNIk2/XIIe7d/RivueSscQ+lZ/VUk5c+88m89JlPHjiemXxx5z7e88lvcOOn7uV/ffKeoddGwJM3rOGktVM0Img0gtWtBs84fR2bn7SeJ66fZsOaKU5au4rT16/mhGmL55IkTboqPw1cDOzIzLsBIuImYCvQH4y2Am8p798CvCs6K7+3Ajdl5iHgGxGxo3y/T1U4Xmlibb9nDwDPecoTxjySI4sILjrzJP7b5c/impd8Dx+68wEOzBasX9PilBNW8eihNjv3Psa9ux/jkYOztIuknbD/0Bx/+bn7ee+he4fec93qFmc8YS1nPGENa6aaREAjgqlmsKrVYFWzyfRUg1XNBqtanc59/berms3O7cCxBqunGqxZ1WJtWb2aK5J2kcwVBRHBVCOYajZoNTu3U80GjWDFG2BIkqRqg9Em4L6+xzuBSxY6JzPnImIfcEp5/NPzXrupuqEuryuu/ywPl3vC9MsF1pAvurR8gRct9pqFv88C77XImz2eMedib3jU3+dxjHnB77HwixZ8Zhl//uX8f7boax5Hr4I9+2fYuG6aC5684ehfPEanb1jNz//AOUs+vyiSnXsPsOexGfYdmGXv/hm+9fBBvvnQAXbuPcC9u/czM1eQQJHJ7Fwy0y6YmSu/VnAj2ggIOiEp6AQ1gk5wInoBKrrnxuFjnW7n3cfzzi+PNea9b/f+cmeyznc5/DMN/5xRnsfI87r3F3qfGHXwiGNaOjOqJH33zj99Pf/5py4c9zCO6LiePxIRVwFXAZx11rEzBejE6RbFAp9OF/pL8GL/9i70D/Pir1m+77PQqxb7wLDQU4u/5nF8nwX/2xz9f7SFx7zwix7fz3l032fRz2VH+/MzemzTrQa/+ENPrf26nEYjOOuUtZx1ytrH9fqiKINSf1gqA9Oh2YKZdptDfccPzRUcmGmzf2aOAJrNBq1G0IwgSWbbyVy7YLadzBYFs3NJkWUczqTITjjOpHef7IS2/mOZnUDeDXTd5yApis45RXZCc//5ndd3XtP/vssVBvp/DXZD/uCx4fP6/4TQPd7/dP8fHka//ghjWvqpj+uPPJKkYWtXHR+fL6oMRvcDZ/Y9PqM8NuqcnRHRAjbQacKwlNeSmdcB1wFs2bLlmPkX7NrXPHvcQ5BUgUYjWN1o1j5ASpI0iarcx+g24LyIOCciVtFpprBt3jnbgCvK+5cBH8/On+i2AZdHxHREnAOcB3y2wrFKkiRJmmCVVYzKNUNXAx+m0677+sy8KyLeCmzPzG3Au4Eby+YKe+iEJ8rz3k+nUcMc8AY70kmSJEmqStRlDvWWLVty+/bt4x6GJEmSpGNYRNyemVvmH69yKp0kSZIkHRcMRpIkSZImnsFIkiRJ0sQzGEmSJEmaeAYjSZIkSRPPYCRJkiRp4hmMJEmSJE08g5EkSZKkiWcwkiRJkjTxDEaSJEmSJl5k5rjHsCwiYhdw77jHUYFTgQfHPQhNJK89jYvXnsbFa0/j4rW3sp6SmRvnH6xNMKqriNiemVvGPQ5NHq89jYvXnsbFa0/j4rV3bHAqnSRJkqSJZzCSJEmSNPEMRse+68Y9AE0srz2Ni9eexsVrT+PitXcMcI2RJEmSpIlnxUiSJEnSxDMYHUMi4i0RcX9EfKH8+rG+534jInZExFcj4sV9xy8tj+2IiGvGM3LVjdeVqhYR90TEHeXvuu3lsZMj4iMR8bXy9gnl8YiIPyyvxy9FxLPHO3odTyLi+oj4TkTc2XfsqK+1iLiiPP9rEXHFOH4WHV8WuPb8rHcMMxgde96ZmReVX7cCRMRm4HLgAuBS4I8johkRTeBa4CXAZuDV5bnS4+Z1pRX0I+Xvum6L2muAj2XmecDHysfQuRbPK7+uAv77io9Ux7P30Pm3s99RXWsRcTLw28AlwMXAb3fDlLSI9zB87YGf9Y5ZBqPjw1bgpsw8lJnfAHbQ+cV8MbAjM+/OzBngpvJc6bvhdaVx2QrcUN6/AXhZ3/H3ZsengZMi4knjGKCOP5n5f4E98w4f7bX2YuAjmbknM/cCH2H0B16pZ4FrbyF+1jsGGIyOPVeX5fvr+/4atQm4r++cneWxhY5L3w2vK62EBP4+Im6PiKvKY6dl5gPl/W8Bp5X3vSa13I72WvMa1HLys94xymC0wiLioxFx54ivrXRK9k8DLgIeAP7rWAcrSdX5wcx8Np3pIW+IiOf1P5mdlqm2TVXlvNa0wvysdwxrjXsAkyYzX7CU8yLiT4G/KR/eD5zZ9/QZ5TEWOS49Xotdb9KyyMz7y9vvRMQH6EwX+XZEPCkzHyinL32nPN1rUsvtaK+1+4Efnnf8H1ZgnKqZzPx2976f9Y49VoyOIfPmzL8c6HYx2QZcHhHTEXEOnUWhnwVuA86LiHMiYhWdRXvbVnLMqiWvK1UqIk6IiHXd+8CL6Py+2wZ0u31dAfxVeX8b8NqyY9i/Avb1TYOSHo+jvdY+DLwoIp5QTn16UXlMOip+1ju2WTE6trwjIi6iU9K/B3gdQGbeFRHvB74MzAFvyMw2QERcTeeXcxO4PjPvGsfAVR+ZOed1pYqdBnwgIqDz79D/zswPRcRtwPsj4krgXuCny/NvBX6MzmLkx4BfWPkh63gVEX9Op9pzakTspNNd7m0cxbWWmXsi4j/S+ZAK8NbMXOqiek2oBa69H/az3rErOlNrJUmSJGlyOZVOkiRJ0sQzGEmSJEmaeAYjSZIkSRPPYCRJkiRp4hmMJEmSJE08g5EkSZKkiWcwkiRJkjTxDEaSJEmSJt7/ByrR5TSA9Hf5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales['Revenue_per_Age'].plot(kind='density', figsize=(14,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ade3f9100>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales['Revenue_per_Age'].plot(kind='hist', figsize=(14,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add and calculate a new `Calculated_Cost` column\n",
    "\n",
    "Use this formula\n",
    "\n",
    "$$ Calculated\\_Cost = Order\\_Quantity * Unit\\_Cost $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     360\n",
       "1     360\n",
       "2    1035\n",
       "3     900\n",
       "4     180\n",
       "Name: Calculated_Cost, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales['Calculated_Cost'] = sales['Order_Quantity'] * sales['Unit_Cost']\n",
    "\n",
    "sales['Calculated_Cost'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(sales['Calculated_Cost'] != sales['Cost']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the relationship between `Cost` and `Profit` using a scatter plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ade3df790>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales.plot(kind='scatter', x='Calculated_Cost', y='Profit', figsize=(6,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add and calculate a new `Calculated_Revenue` column\n",
    "\n",
    "Use this formula\n",
    "\n",
    "$$ Calculated\\_Revenue = Cost + Profit $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     950\n",
       "1     950\n",
       "2    2401\n",
       "3    2088\n",
       "4     418\n",
       "Name: Calculated_Revenue, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales['Calculated_Revenue'] = sales['Cost'] + sales['Profit']\n",
    "\n",
    "sales['Calculated_Revenue'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(sales['Calculated_Revenue'] != sales['Revenue']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Age_Group</th>\n",
       "      <th>Customer_Gender</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>Product_Category</th>\n",
       "      <th>...</th>\n",
       "      <th>Product</th>\n",
       "      <th>Order_Quantity</th>\n",
       "      <th>Unit_Cost</th>\n",
       "      <th>Unit_Price</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Revenue_per_Age</th>\n",
       "      <th>Calculated_Cost</th>\n",
       "      <th>Calculated_Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-11-26</td>\n",
       "      <td>26</td>\n",
       "      <td>November</td>\n",
       "      <td>2013</td>\n",
       "      <td>19</td>\n",
       "      <td>Youth (&lt;25)</td>\n",
       "      <td>M</td>\n",
       "      <td>Canada</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>...</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>590</td>\n",
       "      <td>360</td>\n",
       "      <td>950</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>360</td>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-11-26</td>\n",
       "      <td>26</td>\n",
       "      <td>November</td>\n",
       "      <td>2015</td>\n",
       "      <td>19</td>\n",
       "      <td>Youth (&lt;25)</td>\n",
       "      <td>M</td>\n",
       "      <td>Canada</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>...</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>590</td>\n",
       "      <td>360</td>\n",
       "      <td>950</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>360</td>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-03-23</td>\n",
       "      <td>23</td>\n",
       "      <td>March</td>\n",
       "      <td>2014</td>\n",
       "      <td>49</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>...</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>1366</td>\n",
       "      <td>1035</td>\n",
       "      <td>2401</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>1035</td>\n",
       "      <td>2401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-03-23</td>\n",
       "      <td>23</td>\n",
       "      <td>March</td>\n",
       "      <td>2016</td>\n",
       "      <td>49</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>...</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>20</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>1188</td>\n",
       "      <td>900</td>\n",
       "      <td>2088</td>\n",
       "      <td>42.612245</td>\n",
       "      <td>900</td>\n",
       "      <td>2088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-05-15</td>\n",
       "      <td>15</td>\n",
       "      <td>May</td>\n",
       "      <td>2014</td>\n",
       "      <td>47</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>F</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>...</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>238</td>\n",
       "      <td>180</td>\n",
       "      <td>418</td>\n",
       "      <td>8.893617</td>\n",
       "      <td>180</td>\n",
       "      <td>418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Day     Month  Year  Customer_Age       Age_Group  \\\n",
       "0 2013-11-26   26  November  2013            19     Youth (<25)   \n",
       "1 2015-11-26   26  November  2015            19     Youth (<25)   \n",
       "2 2014-03-23   23     March  2014            49  Adults (35-64)   \n",
       "3 2016-03-23   23     March  2016            49  Adults (35-64)   \n",
       "4 2014-05-15   15       May  2014            47  Adults (35-64)   \n",
       "\n",
       "  Customer_Gender    Country             State Product_Category  ...  \\\n",
       "0               M     Canada  British Columbia      Accessories  ...   \n",
       "1               M     Canada  British Columbia      Accessories  ...   \n",
       "2               M  Australia   New South Wales      Accessories  ...   \n",
       "3               M  Australia   New South Wales      Accessories  ...   \n",
       "4               F  Australia   New South Wales      Accessories  ...   \n",
       "\n",
       "               Product Order_Quantity  Unit_Cost  Unit_Price  Profit  Cost  \\\n",
       "0  Hitch Rack - 4-Bike              8         45         120     590   360   \n",
       "1  Hitch Rack - 4-Bike              8         45         120     590   360   \n",
       "2  Hitch Rack - 4-Bike             23         45         120    1366  1035   \n",
       "3  Hitch Rack - 4-Bike             20         45         120    1188   900   \n",
       "4  Hitch Rack - 4-Bike              4         45         120     238   180   \n",
       "\n",
       "   Revenue  Revenue_per_Age  Calculated_Cost  Calculated_Revenue  \n",
       "0      950        50.000000              360                 950  \n",
       "1      950        50.000000              360                 950  \n",
       "2     2401        49.000000             1035                2401  \n",
       "3     2088        42.612245              900                2088  \n",
       "4      418         8.893617              180                 418  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ade39afa0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales['Revenue'].plot(kind='hist', bins=100, figsize=(14,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modify all `Unit_Price` values adding 3% tax to them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    120\n",
       "1    120\n",
       "2    120\n",
       "3    120\n",
       "4    120\n",
       "Name: Unit_Price, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales['Unit_Price'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sales['Unit_Price'] = sales['Unit_Price'] * 1.03\n",
    "\n",
    "sales['Unit_Price'] *= 1.03"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    123.6\n",
       "1    123.6\n",
       "2    123.6\n",
       "3    123.6\n",
       "4    123.6\n",
       "Name: Unit_Price, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales['Unit_Price'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "## Selection & Indexing:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get all the sales made in the state of `Kentucky`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Age_Group</th>\n",
       "      <th>Customer_Gender</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>Product_Category</th>\n",
       "      <th>...</th>\n",
       "      <th>Product</th>\n",
       "      <th>Order_Quantity</th>\n",
       "      <th>Unit_Cost</th>\n",
       "      <th>Unit_Price</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Revenue_per_Age</th>\n",
       "      <th>Calculated_Cost</th>\n",
       "      <th>Calculated_Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>2013-11-04</td>\n",
       "      <td>4</td>\n",
       "      <td>November</td>\n",
       "      <td>2013</td>\n",
       "      <td>40</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>United States</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>...</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>123.60</td>\n",
       "      <td>63</td>\n",
       "      <td>45</td>\n",
       "      <td>108</td>\n",
       "      <td>2.700</td>\n",
       "      <td>45</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>2015-11-04</td>\n",
       "      <td>4</td>\n",
       "      <td>November</td>\n",
       "      <td>2015</td>\n",
       "      <td>40</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>United States</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>...</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>123.60</td>\n",
       "      <td>63</td>\n",
       "      <td>45</td>\n",
       "      <td>108</td>\n",
       "      <td>2.700</td>\n",
       "      <td>45</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23826</th>\n",
       "      <td>2014-04-16</td>\n",
       "      <td>16</td>\n",
       "      <td>April</td>\n",
       "      <td>2014</td>\n",
       "      <td>40</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>United States</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>...</td>\n",
       "      <td>Fender Set - Mountain</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>22.66</td>\n",
       "      <td>142</td>\n",
       "      <td>96</td>\n",
       "      <td>238</td>\n",
       "      <td>5.950</td>\n",
       "      <td>96</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23827</th>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>16</td>\n",
       "      <td>April</td>\n",
       "      <td>2016</td>\n",
       "      <td>40</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>United States</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>...</td>\n",
       "      <td>Fender Set - Mountain</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>22.66</td>\n",
       "      <td>165</td>\n",
       "      <td>112</td>\n",
       "      <td>277</td>\n",
       "      <td>6.925</td>\n",
       "      <td>112</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31446</th>\n",
       "      <td>2014-04-16</td>\n",
       "      <td>16</td>\n",
       "      <td>April</td>\n",
       "      <td>2014</td>\n",
       "      <td>40</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>United States</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>...</td>\n",
       "      <td>Sport-100 Helmet, Blue</td>\n",
       "      <td>29</td>\n",
       "      <td>13</td>\n",
       "      <td>36.05</td>\n",
       "      <td>537</td>\n",
       "      <td>377</td>\n",
       "      <td>914</td>\n",
       "      <td>22.850</td>\n",
       "      <td>377</td>\n",
       "      <td>914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31447</th>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>16</td>\n",
       "      <td>April</td>\n",
       "      <td>2016</td>\n",
       "      <td>40</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>United States</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>...</td>\n",
       "      <td>Sport-100 Helmet, Blue</td>\n",
       "      <td>31</td>\n",
       "      <td>13</td>\n",
       "      <td>36.05</td>\n",
       "      <td>574</td>\n",
       "      <td>403</td>\n",
       "      <td>977</td>\n",
       "      <td>24.425</td>\n",
       "      <td>403</td>\n",
       "      <td>977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79670</th>\n",
       "      <td>2014-04-16</td>\n",
       "      <td>16</td>\n",
       "      <td>April</td>\n",
       "      <td>2014</td>\n",
       "      <td>40</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>United States</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>...</td>\n",
       "      <td>ML Mountain Tire</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>30.90</td>\n",
       "      <td>32</td>\n",
       "      <td>22</td>\n",
       "      <td>54</td>\n",
       "      <td>1.350</td>\n",
       "      <td>22</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79671</th>\n",
       "      <td>2014-04-16</td>\n",
       "      <td>16</td>\n",
       "      <td>April</td>\n",
       "      <td>2014</td>\n",
       "      <td>40</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>United States</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>...</td>\n",
       "      <td>ML Mountain Tire</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>30.90</td>\n",
       "      <td>336</td>\n",
       "      <td>231</td>\n",
       "      <td>567</td>\n",
       "      <td>14.175</td>\n",
       "      <td>231</td>\n",
       "      <td>567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79672</th>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>16</td>\n",
       "      <td>April</td>\n",
       "      <td>2016</td>\n",
       "      <td>40</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>United States</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>...</td>\n",
       "      <td>ML Mountain Tire</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>30.90</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>0.675</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79673</th>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>16</td>\n",
       "      <td>April</td>\n",
       "      <td>2016</td>\n",
       "      <td>40</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>United States</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>...</td>\n",
       "      <td>ML Mountain Tire</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>30.90</td>\n",
       "      <td>288</td>\n",
       "      <td>198</td>\n",
       "      <td>486</td>\n",
       "      <td>12.150</td>\n",
       "      <td>198</td>\n",
       "      <td>486</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  Day     Month  Year  Customer_Age       Age_Group  \\\n",
       "156   2013-11-04    4  November  2013            40  Adults (35-64)   \n",
       "157   2015-11-04    4  November  2015            40  Adults (35-64)   \n",
       "23826 2014-04-16   16     April  2014            40  Adults (35-64)   \n",
       "23827 2016-04-16   16     April  2016            40  Adults (35-64)   \n",
       "31446 2014-04-16   16     April  2014            40  Adults (35-64)   \n",
       "31447 2016-04-16   16     April  2016            40  Adults (35-64)   \n",
       "79670 2014-04-16   16     April  2014            40  Adults (35-64)   \n",
       "79671 2014-04-16   16     April  2014            40  Adults (35-64)   \n",
       "79672 2016-04-16   16     April  2016            40  Adults (35-64)   \n",
       "79673 2016-04-16   16     April  2016            40  Adults (35-64)   \n",
       "\n",
       "      Customer_Gender        Country     State Product_Category  ...  \\\n",
       "156                 M  United States  Kentucky      Accessories  ...   \n",
       "157                 M  United States  Kentucky      Accessories  ...   \n",
       "23826               M  United States  Kentucky      Accessories  ...   \n",
       "23827               M  United States  Kentucky      Accessories  ...   \n",
       "31446               M  United States  Kentucky      Accessories  ...   \n",
       "31447               M  United States  Kentucky      Accessories  ...   \n",
       "79670               M  United States  Kentucky      Accessories  ...   \n",
       "79671               M  United States  Kentucky      Accessories  ...   \n",
       "79672               M  United States  Kentucky      Accessories  ...   \n",
       "79673               M  United States  Kentucky      Accessories  ...   \n",
       "\n",
       "                      Product Order_Quantity  Unit_Cost  Unit_Price  Profit  \\\n",
       "156       Hitch Rack - 4-Bike              1         45      123.60      63   \n",
       "157       Hitch Rack - 4-Bike              1         45      123.60      63   \n",
       "23826   Fender Set - Mountain             12          8       22.66     142   \n",
       "23827   Fender Set - Mountain             14          8       22.66     165   \n",
       "31446  Sport-100 Helmet, Blue             29         13       36.05     537   \n",
       "31447  Sport-100 Helmet, Blue             31         13       36.05     574   \n",
       "79670        ML Mountain Tire              2         11       30.90      32   \n",
       "79671        ML Mountain Tire             21         11       30.90     336   \n",
       "79672        ML Mountain Tire              1         11       30.90      16   \n",
       "79673        ML Mountain Tire             18         11       30.90     288   \n",
       "\n",
       "       Cost  Revenue  Revenue_per_Age  Calculated_Cost  Calculated_Revenue  \n",
       "156      45      108            2.700               45                 108  \n",
       "157      45      108            2.700               45                 108  \n",
       "23826    96      238            5.950               96                 238  \n",
       "23827   112      277            6.925              112                 277  \n",
       "31446   377      914           22.850              377                 914  \n",
       "31447   403      977           24.425              403                 977  \n",
       "79670    22       54            1.350               22                  54  \n",
       "79671   231      567           14.175              231                 567  \n",
       "79672    11       27            0.675               11                  27  \n",
       "79673   198      486           12.150              198                 486  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.loc[sales['State'] == 'Kentucky']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the mean revenue of the `Adults (35-64)` sales group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "762.8287654055604"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.loc[sales['Age_Group'] == 'Adults (35-64)', 'Revenue'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many records belong to Age Group `Youth (<25)` or `Adults (35-64)`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73652"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.loc[(sales['Age_Group'] == 'Youth (<25)') | (sales['Age_Group'] == 'Adults (35-64)')].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the mean revenue of the sales group `Adults (35-64)` in `United States`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "726.7260473588342"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.loc[(sales['Age_Group'] == 'Adults (35-64)') & (sales['Country'] == 'United States'), 'Revenue'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Increase the revenue by 10% to every sale made in France"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50     787\n",
       "51     787\n",
       "52    2957\n",
       "53    2851\n",
       "60     626\n",
       "Name: Revenue, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.loc[sales['Country'] == 'France', 'Revenue'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sales.loc[sales['Country'] == 'France', 'Revenue'] = sales.loc[sales['Country'] == 'France', 'Revenue'] * 1.1\n",
    "\n",
    "sales.loc[sales['Country'] == 'France', 'Revenue'] *= 1.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50     865.7\n",
       "51     865.7\n",
       "52    3252.7\n",
       "53    3136.1\n",
       "60     688.6\n",
       "Name: Revenue, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.loc[sales['Country'] == 'France', 'Revenue'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![purple-divider](https://user-images.githubusercontent.com/7065401/52071927-c1cd7100-2562-11e9-908a-dde91ba14e59.png)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}