3. EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set_style('darkgrid')\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Drug1</th>\n",
       "      <th>Drug2</th>\n",
       "      <th>Drug3</th>\n",
       "      <th>Drug4</th>\n",
       "      <th>Drug5</th>\n",
       "      <th>Drug6</th>\n",
       "      <th>Drug7</th>\n",
       "      <th>Drug8</th>\n",
       "      <th>Drug1_Sales</th>\n",
       "      <th>Drug2_Sales</th>\n",
       "      <th>Drug3_Sales</th>\n",
       "      <th>Drug4_Sales</th>\n",
       "      <th>Drug5_Sales</th>\n",
       "      <th>Drug6_Sales</th>\n",
       "      <th>Drug7_Sales</th>\n",
       "      <th>Drug8_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-09-01</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.360</td>\n",
       "      <td>5.00</td>\n",
       "      <td>17.000</td>\n",
       "      <td>8.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>3488.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>10200.0</td>\n",
       "      <td>4510.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-09-02</td>\n",
       "      <td>5.33</td>\n",
       "      <td>2.087</td>\n",
       "      <td>2.50</td>\n",
       "      <td>18.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5330.0</td>\n",
       "      <td>1669.6</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>10800.0</td>\n",
       "      <td>3850.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>8100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-09-03</td>\n",
       "      <td>9.36</td>\n",
       "      <td>2.350</td>\n",
       "      <td>3.10</td>\n",
       "      <td>12.125</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9360.0</td>\n",
       "      <td>1880.0</td>\n",
       "      <td>4650.0</td>\n",
       "      <td>7275.0</td>\n",
       "      <td>4400.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-09-04</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.373</td>\n",
       "      <td>2.15</td>\n",
       "      <td>29.230</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>298.4</td>\n",
       "      <td>3225.0</td>\n",
       "      <td>17538.0</td>\n",
       "      <td>3850.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>2700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-09-05</td>\n",
       "      <td>7.00</td>\n",
       "      <td>9.530</td>\n",
       "      <td>2.00</td>\n",
       "      <td>14.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7624.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>8400.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>7200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-09-06</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2.670</td>\n",
       "      <td>1.00</td>\n",
       "      <td>28.400</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>2136.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>17040.0</td>\n",
       "      <td>3850.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-09-07</td>\n",
       "      <td>8.33</td>\n",
       "      <td>3.670</td>\n",
       "      <td>2.00</td>\n",
       "      <td>27.000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8330.0</td>\n",
       "      <td>2936.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>16200.0</td>\n",
       "      <td>7700.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-09-08</td>\n",
       "      <td>7.01</td>\n",
       "      <td>6.087</td>\n",
       "      <td>3.00</td>\n",
       "      <td>26.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7010.0</td>\n",
       "      <td>4869.6</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>15600.0</td>\n",
       "      <td>3850.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>2700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-09-09</td>\n",
       "      <td>1.18</td>\n",
       "      <td>2.124</td>\n",
       "      <td>3.00</td>\n",
       "      <td>26.000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>1699.2</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>15600.0</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-09-10</td>\n",
       "      <td>8.66</td>\n",
       "      <td>6.680</td>\n",
       "      <td>4.20</td>\n",
       "      <td>22.000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8660.0</td>\n",
       "      <td>5344.0</td>\n",
       "      <td>6300.0</td>\n",
       "      <td>13200.0</td>\n",
       "      <td>9350.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-09-11</td>\n",
       "      <td>2.67</td>\n",
       "      <td>5.076</td>\n",
       "      <td>3.00</td>\n",
       "      <td>42.000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2670.0</td>\n",
       "      <td>4060.8</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>25200.0</td>\n",
       "      <td>2750.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>2250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2019-09-12</td>\n",
       "      <td>2.33</td>\n",
       "      <td>1.340</td>\n",
       "      <td>2.00</td>\n",
       "      <td>21.975</td>\n",
       "      <td>11.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2330.0</td>\n",
       "      <td>1072.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>13185.0</td>\n",
       "      <td>6160.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019-09-13</td>\n",
       "      <td>6.34</td>\n",
       "      <td>3.350</td>\n",
       "      <td>2.00</td>\n",
       "      <td>17.000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6340.0</td>\n",
       "      <td>2680.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>10200.0</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>4500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2019-09-14</td>\n",
       "      <td>8.99</td>\n",
       "      <td>6.397</td>\n",
       "      <td>3.45</td>\n",
       "      <td>24.000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8990.0</td>\n",
       "      <td>5117.6</td>\n",
       "      <td>5175.0</td>\n",
       "      <td>14400.0</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>9000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2019-09-15</td>\n",
       "      <td>5.34</td>\n",
       "      <td>6.010</td>\n",
       "      <td>2.00</td>\n",
       "      <td>25.400</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5340.0</td>\n",
       "      <td>4808.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>15240.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>5400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2019-09-16</td>\n",
       "      <td>9.33</td>\n",
       "      <td>2.316</td>\n",
       "      <td>3.00</td>\n",
       "      <td>22.000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9330.0</td>\n",
       "      <td>1852.8</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>13200.0</td>\n",
       "      <td>2750.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2019-09-17</td>\n",
       "      <td>4.50</td>\n",
       "      <td>3.007</td>\n",
       "      <td>6.50</td>\n",
       "      <td>19.200</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>2405.6</td>\n",
       "      <td>9750.0</td>\n",
       "      <td>11520.0</td>\n",
       "      <td>4510.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019-09-18</td>\n",
       "      <td>6.33</td>\n",
       "      <td>4.330</td>\n",
       "      <td>7.00</td>\n",
       "      <td>22.000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6330.0</td>\n",
       "      <td>3464.0</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>13200.0</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2019-09-19</td>\n",
       "      <td>7.33</td>\n",
       "      <td>3.340</td>\n",
       "      <td>3.00</td>\n",
       "      <td>33.000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7330.0</td>\n",
       "      <td>2672.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>19800.0</td>\n",
       "      <td>2750.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019-09-20</td>\n",
       "      <td>5.34</td>\n",
       "      <td>3.383</td>\n",
       "      <td>2.00</td>\n",
       "      <td>51.300</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5340.0</td>\n",
       "      <td>2706.4</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>30780.0</td>\n",
       "      <td>1650.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>2700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2019-09-21</td>\n",
       "      <td>10.68</td>\n",
       "      <td>5.680</td>\n",
       "      <td>1.00</td>\n",
       "      <td>44.250</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10680.0</td>\n",
       "      <td>4544.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>26550.0</td>\n",
       "      <td>1650.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2019-09-22</td>\n",
       "      <td>3.33</td>\n",
       "      <td>3.340</td>\n",
       "      <td>1.90</td>\n",
       "      <td>56.500</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3330.0</td>\n",
       "      <td>2672.0</td>\n",
       "      <td>2850.0</td>\n",
       "      <td>33900.0</td>\n",
       "      <td>1650.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2019-09-23</td>\n",
       "      <td>5.34</td>\n",
       "      <td>4.549</td>\n",
       "      <td>3.30</td>\n",
       "      <td>55.000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5340.0</td>\n",
       "      <td>3639.2</td>\n",
       "      <td>4950.0</td>\n",
       "      <td>33000.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2019-09-24</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.340</td>\n",
       "      <td>6.00</td>\n",
       "      <td>61.300</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1072.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>36780.0</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>5400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2019-09-25</td>\n",
       "      <td>6.00</td>\n",
       "      <td>3.670</td>\n",
       "      <td>4.20</td>\n",
       "      <td>32.500</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.416667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>2936.0</td>\n",
       "      <td>6300.0</td>\n",
       "      <td>19500.0</td>\n",
       "      <td>7700.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>637.5</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019-09-26</td>\n",
       "      <td>5.50</td>\n",
       "      <td>3.033</td>\n",
       "      <td>2.00</td>\n",
       "      <td>31.000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>2426.4</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>18600.0</td>\n",
       "      <td>7150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-09-27</td>\n",
       "      <td>4.66</td>\n",
       "      <td>4.359</td>\n",
       "      <td>4.00</td>\n",
       "      <td>41.100</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4660.0</td>\n",
       "      <td>3487.2</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>24660.0</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-09-28</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1.680</td>\n",
       "      <td>4.10</td>\n",
       "      <td>48.000</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>1344.0</td>\n",
       "      <td>6150.0</td>\n",
       "      <td>28800.0</td>\n",
       "      <td>3410.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>2700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-09-29</td>\n",
       "      <td>3.51</td>\n",
       "      <td>3.867</td>\n",
       "      <td>3.00</td>\n",
       "      <td>67.800</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3510.0</td>\n",
       "      <td>3093.6</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>40680.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>1890.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.439</td>\n",
       "      <td>2.10</td>\n",
       "      <td>49.400</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1151.2</td>\n",
       "      <td>3150.0</td>\n",
       "      <td>29640.0</td>\n",
       "      <td>4950.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Drug1  Drug2  Drug3   Drug4  Drug5  Drug6      Drug7  Drug8  \\\n",
       "0   2019-09-01   2.00  4.360   5.00  17.000    8.2    1.0   5.000000    4.0   \n",
       "1   2019-09-02   5.33  2.087   2.50  18.000    7.0    0.0   1.000000    9.0   \n",
       "2   2019-09-03   9.36  2.350   3.10  12.125    8.0    3.0   1.000000    2.0   \n",
       "3   2019-09-04   2.00  0.373   2.15  29.230    7.0    0.0   3.000000    3.0   \n",
       "4   2019-09-05   7.00  9.530   2.00  14.000    1.0    0.0   1.000000    8.0   \n",
       "5   2019-09-06   1.68  2.670   1.00  28.400    7.0    0.0   0.000000    1.5   \n",
       "6   2019-09-07   8.33  3.670   2.00  27.000   14.0    3.0   6.000000    2.0   \n",
       "7   2019-09-08   7.01  6.087   3.00  26.000    7.0    0.0   3.000000    3.0   \n",
       "8   2019-09-09   1.18  2.124   3.00  26.000    4.0    3.0   5.000000    2.0   \n",
       "9   2019-09-10   8.66  6.680   4.20  22.000   17.0    0.0   1.000000    1.0   \n",
       "10  2019-09-11   2.67  5.076   3.00  42.000    5.0    0.0   5.000000    2.5   \n",
       "11  2019-09-12   2.33  1.340   2.00  21.975   11.2    0.0  10.000000    4.0   \n",
       "12  2019-09-13   6.34  3.350   2.00  17.000   12.0    2.0   5.000000    5.0   \n",
       "13  2019-09-14   8.99  6.397   3.45  24.000   12.0    0.0   2.000000   10.0   \n",
       "14  2019-09-15   5.34  6.010   2.00  25.400    6.0    0.0   2.000000    6.0   \n",
       "15  2019-09-16   9.33  2.316   3.00  22.000    5.0    1.0   0.000000    2.0   \n",
       "16  2019-09-17   4.50  3.007   6.50  19.200    8.2    0.0   6.000000    2.0   \n",
       "17  2019-09-18   6.33  4.330   7.00  22.000    4.0    0.0   2.000000    2.0   \n",
       "18  2019-09-19   7.33  3.340   3.00  33.000    5.0    2.0   3.000000    4.0   \n",
       "19  2019-09-20   5.34  3.383   2.00  51.300    3.0    0.0   1.000000    3.0   \n",
       "20  2019-09-21  10.68  5.680   1.00  44.250    3.0    0.0   6.000000    4.0   \n",
       "21  2019-09-22   3.33  3.340   1.90  56.500    3.0    0.0   8.000000    4.0   \n",
       "22  2019-09-23   5.34  4.549   3.30  55.000    6.0    0.0   1.000000    4.0   \n",
       "23  2019-09-24   2.00  1.340   6.00  61.300   12.0    0.0   1.000000    6.0   \n",
       "24  2019-09-25   6.00  3.670   4.20  32.500   14.0    1.0   1.416667    4.0   \n",
       "25  2019-09-26   5.50  3.033   2.00  31.000   13.0    0.0  31.000000    0.0   \n",
       "26  2019-09-27   4.66  4.359   4.00  41.100    4.0    0.0   2.000000    4.0   \n",
       "27  2019-09-28   7.00  1.680   4.10  48.000    6.2    0.0   1.000000    3.0   \n",
       "28  2019-09-29   3.51  3.867   3.00  67.800    6.0    0.0   3.000000    2.1   \n",
       "29  2019-09-30   2.00  1.439   2.10  49.400    9.0    0.0   5.000000    2.0   \n",
       "\n",
       "    Drug1_Sales  Drug2_Sales  Drug3_Sales  Drug4_Sales  Drug5_Sales  \\\n",
       "0        2000.0       3488.0       7500.0      10200.0       4510.0   \n",
       "1        5330.0       1669.6       3750.0      10800.0       3850.0   \n",
       "2        9360.0       1880.0       4650.0       7275.0       4400.0   \n",
       "3        2000.0        298.4       3225.0      17538.0       3850.0   \n",
       "4        7000.0       7624.0       3000.0       8400.0        550.0   \n",
       "5        1680.0       2136.0       1500.0      17040.0       3850.0   \n",
       "6        8330.0       2936.0       3000.0      16200.0       7700.0   \n",
       "7        7010.0       4869.6       4500.0      15600.0       3850.0   \n",
       "8        1180.0       1699.2       4500.0      15600.0       2200.0   \n",
       "9        8660.0       5344.0       6300.0      13200.0       9350.0   \n",
       "10       2670.0       4060.8       4500.0      25200.0       2750.0   \n",
       "11       2330.0       1072.0       3000.0      13185.0       6160.0   \n",
       "12       6340.0       2680.0       3000.0      10200.0       6600.0   \n",
       "13       8990.0       5117.6       5175.0      14400.0       6600.0   \n",
       "14       5340.0       4808.0       3000.0      15240.0       3300.0   \n",
       "15       9330.0       1852.8       4500.0      13200.0       2750.0   \n",
       "16       4500.0       2405.6       9750.0      11520.0       4510.0   \n",
       "17       6330.0       3464.0      10500.0      13200.0       2200.0   \n",
       "18       7330.0       2672.0       4500.0      19800.0       2750.0   \n",
       "19       5340.0       2706.4       3000.0      30780.0       1650.0   \n",
       "20      10680.0       4544.0       1500.0      26550.0       1650.0   \n",
       "21       3330.0       2672.0       2850.0      33900.0       1650.0   \n",
       "22       5340.0       3639.2       4950.0      33000.0       3300.0   \n",
       "23       2000.0       1072.0       9000.0      36780.0       6600.0   \n",
       "24       6000.0       2936.0       6300.0      19500.0       7700.0   \n",
       "25       5500.0       2426.4       3000.0      18600.0       7150.0   \n",
       "26       4660.0       3487.2       6000.0      24660.0       2200.0   \n",
       "27       7000.0       1344.0       6150.0      28800.0       3410.0   \n",
       "28       3510.0       3093.6       4500.0      40680.0       3300.0   \n",
       "29       2000.0       1151.2       3150.0      29640.0       4950.0   \n",
       "\n",
       "    Drug6_Sales  Drug7_Sales  Drug8_Sales  \n",
       "0       10000.0       2250.0       3600.0  \n",
       "1           0.0        450.0       8100.0  \n",
       "2       30000.0        450.0       1800.0  \n",
       "3           0.0       1350.0       2700.0  \n",
       "4           0.0        450.0       7200.0  \n",
       "5           0.0          0.0       1350.0  \n",
       "6       30000.0       2700.0       1800.0  \n",
       "7           0.0       1350.0       2700.0  \n",
       "8       30000.0       2250.0       1800.0  \n",
       "9           0.0        450.0        900.0  \n",
       "10          0.0       2250.0       2250.0  \n",
       "11          0.0       4500.0       3600.0  \n",
       "12      20000.0       2250.0       4500.0  \n",
       "13          0.0        900.0       9000.0  \n",
       "14          0.0        900.0       5400.0  \n",
       "15      10000.0          0.0       1800.0  \n",
       "16          0.0       2700.0       1800.0  \n",
       "17          0.0        900.0       1800.0  \n",
       "18      20000.0       1350.0       3600.0  \n",
       "19          0.0        450.0       2700.0  \n",
       "20          0.0       2700.0       3600.0  \n",
       "21          0.0       3600.0       3600.0  \n",
       "22          0.0        450.0       3600.0  \n",
       "23          0.0        450.0       5400.0  \n",
       "24      10000.0        637.5       3600.0  \n",
       "25          0.0      13950.0          0.0  \n",
       "26          0.0        900.0       3600.0  \n",
       "27          0.0        450.0       2700.0  \n",
       "28          0.0       1350.0       1890.0  \n",
       "29          0.0       2250.0       1800.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"/Users/macuser/Documents/PGP AI & ML/Applied DS with Python/Datasets_4754/OSL Datasets/Lesson_04/pivot.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Drug1</th>\n",
       "      <th>Drug2</th>\n",
       "      <th>Drug3</th>\n",
       "      <th>Drug4</th>\n",
       "      <th>Drug5</th>\n",
       "      <th>Drug6</th>\n",
       "      <th>Drug7</th>\n",
       "      <th>Drug8</th>\n",
       "      <th>Drug1_Sales</th>\n",
       "      <th>Drug2_Sales</th>\n",
       "      <th>Drug3_Sales</th>\n",
       "      <th>Drug4_Sales</th>\n",
       "      <th>Drug5_Sales</th>\n",
       "      <th>Drug6_Sales</th>\n",
       "      <th>Drug7_Sales</th>\n",
       "      <th>Drug8_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-09-01</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.360</td>\n",
       "      <td>5.00</td>\n",
       "      <td>17.000</td>\n",
       "      <td>8.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>3488.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>10200.0</td>\n",
       "      <td>4510.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-09-02</td>\n",
       "      <td>5.33</td>\n",
       "      <td>2.087</td>\n",
       "      <td>2.50</td>\n",
       "      <td>18.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5330.0</td>\n",
       "      <td>1669.6</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>10800.0</td>\n",
       "      <td>3850.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>8100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-09-03</td>\n",
       "      <td>9.36</td>\n",
       "      <td>2.350</td>\n",
       "      <td>3.10</td>\n",
       "      <td>12.125</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9360.0</td>\n",
       "      <td>1880.0</td>\n",
       "      <td>4650.0</td>\n",
       "      <td>7275.0</td>\n",
       "      <td>4400.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-09-04</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.373</td>\n",
       "      <td>2.15</td>\n",
       "      <td>29.230</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>298.4</td>\n",
       "      <td>3225.0</td>\n",
       "      <td>17538.0</td>\n",
       "      <td>3850.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>2700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-09-05</td>\n",
       "      <td>7.00</td>\n",
       "      <td>9.530</td>\n",
       "      <td>2.00</td>\n",
       "      <td>14.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7624.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>8400.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>7200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Drug1  Drug2  Drug3   Drug4  Drug5  Drug6  Drug7  Drug8  \\\n",
       "0  2019-09-01   2.00  4.360   5.00  17.000    8.2    1.0    5.0    4.0   \n",
       "1  2019-09-02   5.33  2.087   2.50  18.000    7.0    0.0    1.0    9.0   \n",
       "2  2019-09-03   9.36  2.350   3.10  12.125    8.0    3.0    1.0    2.0   \n",
       "3  2019-09-04   2.00  0.373   2.15  29.230    7.0    0.0    3.0    3.0   \n",
       "4  2019-09-05   7.00  9.530   2.00  14.000    1.0    0.0    1.0    8.0   \n",
       "\n",
       "   Drug1_Sales  Drug2_Sales  Drug3_Sales  Drug4_Sales  Drug5_Sales  \\\n",
       "0       2000.0       3488.0       7500.0      10200.0       4510.0   \n",
       "1       5330.0       1669.6       3750.0      10800.0       3850.0   \n",
       "2       9360.0       1880.0       4650.0       7275.0       4400.0   \n",
       "3       2000.0        298.4       3225.0      17538.0       3850.0   \n",
       "4       7000.0       7624.0       3000.0       8400.0        550.0   \n",
       "\n",
       "   Drug6_Sales  Drug7_Sales  Drug8_Sales  \n",
       "0      10000.0       2250.0       3600.0  \n",
       "1          0.0        450.0       8100.0  \n",
       "2      30000.0        450.0       1800.0  \n",
       "3          0.0       1350.0       2700.0  \n",
       "4          0.0        450.0       7200.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Drug1</th>\n",
       "      <th>Drug2</th>\n",
       "      <th>Drug3</th>\n",
       "      <th>Drug4</th>\n",
       "      <th>Drug5</th>\n",
       "      <th>Drug6</th>\n",
       "      <th>Drug7</th>\n",
       "      <th>Drug8</th>\n",
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       "      <th>Drug2_Sales</th>\n",
       "      <th>Drug3_Sales</th>\n",
       "      <th>Drug4_Sales</th>\n",
       "      <th>Drug5_Sales</th>\n",
       "      <th>Drug6_Sales</th>\n",
       "      <th>Drug7_Sales</th>\n",
       "      <th>Drug8_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019-09-26</td>\n",
       "      <td>5.50</td>\n",
       "      <td>3.033</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>2426.4</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>18600.0</td>\n",
       "      <td>7150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-09-27</td>\n",
       "      <td>4.66</td>\n",
       "      <td>4.359</td>\n",
       "      <td>4.0</td>\n",
       "      <td>41.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4660.0</td>\n",
       "      <td>3487.2</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>24660.0</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-09-28</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1.680</td>\n",
       "      <td>4.1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>1344.0</td>\n",
       "      <td>6150.0</td>\n",
       "      <td>28800.0</td>\n",
       "      <td>3410.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>2700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-09-29</td>\n",
       "      <td>3.51</td>\n",
       "      <td>3.867</td>\n",
       "      <td>3.0</td>\n",
       "      <td>67.8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3510.0</td>\n",
       "      <td>3093.6</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>40680.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>1890.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.439</td>\n",
       "      <td>2.1</td>\n",
       "      <td>49.4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1151.2</td>\n",
       "      <td>3150.0</td>\n",
       "      <td>29640.0</td>\n",
       "      <td>4950.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Drug1  Drug2  Drug3  Drug4  Drug5  Drug6  Drug7  Drug8  \\\n",
       "25  2019-09-26   5.50  3.033    2.0   31.0   13.0    0.0   31.0    0.0   \n",
       "26  2019-09-27   4.66  4.359    4.0   41.1    4.0    0.0    2.0    4.0   \n",
       "27  2019-09-28   7.00  1.680    4.1   48.0    6.2    0.0    1.0    3.0   \n",
       "28  2019-09-29   3.51  3.867    3.0   67.8    6.0    0.0    3.0    2.1   \n",
       "29  2019-09-30   2.00  1.439    2.1   49.4    9.0    0.0    5.0    2.0   \n",
       "\n",
       "    Drug1_Sales  Drug2_Sales  Drug3_Sales  Drug4_Sales  Drug5_Sales  \\\n",
       "25       5500.0       2426.4       3000.0      18600.0       7150.0   \n",
       "26       4660.0       3487.2       6000.0      24660.0       2200.0   \n",
       "27       7000.0       1344.0       6150.0      28800.0       3410.0   \n",
       "28       3510.0       3093.6       4500.0      40680.0       3300.0   \n",
       "29       2000.0       1151.2       3150.0      29640.0       4950.0   \n",
       "\n",
       "    Drug6_Sales  Drug7_Sales  Drug8_Sales  \n",
       "25          0.0      13950.0          0.0  \n",
       "26          0.0        900.0       3600.0  \n",
       "27          0.0        450.0       2700.0  \n",
       "28          0.0       1350.0       1890.0  \n",
       "29          0.0       2250.0       1800.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 17)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "df.set_index(['Date'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Drug3</th>\n",
       "      <th>Drug4</th>\n",
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       "      <th>Drug6</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-01</th>\n",
       "      <td>2.00</td>\n",
       "      <td>4.360</td>\n",
       "      <td>5.00</td>\n",
       "      <td>17.000</td>\n",
       "      <td>8.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>3488.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>10200.0</td>\n",
       "      <td>4510.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-02</th>\n",
       "      <td>5.33</td>\n",
       "      <td>2.087</td>\n",
       "      <td>2.50</td>\n",
       "      <td>18.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5330.0</td>\n",
       "      <td>1669.6</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>10800.0</td>\n",
       "      <td>3850.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>8100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-03</th>\n",
       "      <td>9.36</td>\n",
       "      <td>2.350</td>\n",
       "      <td>3.10</td>\n",
       "      <td>12.125</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9360.0</td>\n",
       "      <td>1880.0</td>\n",
       "      <td>4650.0</td>\n",
       "      <td>7275.0</td>\n",
       "      <td>4400.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-04</th>\n",
       "      <td>2.00</td>\n",
       "      <td>0.373</td>\n",
       "      <td>2.15</td>\n",
       "      <td>29.230</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>298.4</td>\n",
       "      <td>3225.0</td>\n",
       "      <td>17538.0</td>\n",
       "      <td>3850.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>2700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-05</th>\n",
       "      <td>7.00</td>\n",
       "      <td>9.530</td>\n",
       "      <td>2.00</td>\n",
       "      <td>14.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7624.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>8400.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>7200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Drug1  Drug2  Drug3   Drug4  Drug5  Drug6  Drug7  Drug8  \\\n",
       "Date                                                                  \n",
       "2019-09-01   2.00  4.360   5.00  17.000    8.2    1.0    5.0    4.0   \n",
       "2019-09-02   5.33  2.087   2.50  18.000    7.0    0.0    1.0    9.0   \n",
       "2019-09-03   9.36  2.350   3.10  12.125    8.0    3.0    1.0    2.0   \n",
       "2019-09-04   2.00  0.373   2.15  29.230    7.0    0.0    3.0    3.0   \n",
       "2019-09-05   7.00  9.530   2.00  14.000    1.0    0.0    1.0    8.0   \n",
       "\n",
       "            Drug1_Sales  Drug2_Sales  Drug3_Sales  Drug4_Sales  Drug5_Sales  \\\n",
       "Date                                                                          \n",
       "2019-09-01       2000.0       3488.0       7500.0      10200.0       4510.0   \n",
       "2019-09-02       5330.0       1669.6       3750.0      10800.0       3850.0   \n",
       "2019-09-03       9360.0       1880.0       4650.0       7275.0       4400.0   \n",
       "2019-09-04       2000.0        298.4       3225.0      17538.0       3850.0   \n",
       "2019-09-05       7000.0       7624.0       3000.0       8400.0        550.0   \n",
       "\n",
       "            Drug6_Sales  Drug7_Sales  Drug8_Sales  \n",
       "Date                                               \n",
       "2019-09-01      10000.0       2250.0       3600.0  \n",
       "2019-09-02          0.0        450.0       8100.0  \n",
       "2019-09-03      30000.0        450.0       1800.0  \n",
       "2019-09-04          0.0       1350.0       2700.0  \n",
       "2019-09-05          0.0        450.0       7200.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution and Skewness using plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    sns.distplot(df[col])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug1</th>\n",
       "      <th>Drug2</th>\n",
       "      <th>Drug3</th>\n",
       "      <th>Drug4</th>\n",
       "      <th>Drug5</th>\n",
       "      <th>Drug6</th>\n",
       "      <th>Drug7</th>\n",
       "      <th>Drug8</th>\n",
       "      <th>Drug1_Sales</th>\n",
       "      <th>Drug2_Sales</th>\n",
       "      <th>Drug3_Sales</th>\n",
       "      <th>Drug4_Sales</th>\n",
       "      <th>Drug5_Sales</th>\n",
       "      <th>Drug6_Sales</th>\n",
       "      <th>Drug7_Sales</th>\n",
       "      <th>Drug8_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30.00000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.00000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.36900</td>\n",
       "      <td>3.714567</td>\n",
       "      <td>3.116667</td>\n",
       "      <td>32.816000</td>\n",
       "      <td>7.593333</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>4.047222</td>\n",
       "      <td>3.636667</td>\n",
       "      <td>5369.000000</td>\n",
       "      <td>2971.653333</td>\n",
       "      <td>4675.000000</td>\n",
       "      <td>19689.60000</td>\n",
       "      <td>4176.333333</td>\n",
       "      <td>5333.333333</td>\n",
       "      <td>1821.250000</td>\n",
       "      <td>3273.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.68217</td>\n",
       "      <td>1.962529</td>\n",
       "      <td>1.488191</td>\n",
       "      <td>15.166121</td>\n",
       "      <td>3.941812</td>\n",
       "      <td>1.008014</td>\n",
       "      <td>5.658703</td>\n",
       "      <td>2.282993</td>\n",
       "      <td>2682.170184</td>\n",
       "      <td>1570.022885</td>\n",
       "      <td>2232.286596</td>\n",
       "      <td>9099.67288</td>\n",
       "      <td>2167.996824</td>\n",
       "      <td>10080.138660</td>\n",
       "      <td>2546.416409</td>\n",
       "      <td>2054.693785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.18000</td>\n",
       "      <td>0.373000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.125000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1180.000000</td>\n",
       "      <td>298.400000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>7275.00000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.83500</td>\n",
       "      <td>2.324500</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2835.000000</td>\n",
       "      <td>1859.600000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>13200.00000</td>\n",
       "      <td>2750.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>450.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.34000</td>\n",
       "      <td>3.366500</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>27.700000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5340.000000</td>\n",
       "      <td>2693.200000</td>\n",
       "      <td>4500.000000</td>\n",
       "      <td>16620.00000</td>\n",
       "      <td>3850.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1125.000000</td>\n",
       "      <td>2700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.00750</td>\n",
       "      <td>4.501750</td>\n",
       "      <td>3.862500</td>\n",
       "      <td>43.687500</td>\n",
       "      <td>10.650000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7007.500000</td>\n",
       "      <td>3601.400000</td>\n",
       "      <td>5793.750000</td>\n",
       "      <td>26212.50000</td>\n",
       "      <td>5857.500000</td>\n",
       "      <td>7500.000000</td>\n",
       "      <td>2250.000000</td>\n",
       "      <td>3600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.68000</td>\n",
       "      <td>9.530000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>67.800000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10680.000000</td>\n",
       "      <td>7624.000000</td>\n",
       "      <td>10500.000000</td>\n",
       "      <td>40680.00000</td>\n",
       "      <td>9350.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>13950.000000</td>\n",
       "      <td>9000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          Drug1      Drug2      Drug3      Drug4      Drug5      Drug6  \\\n",
       "count  30.00000  30.000000  30.000000  30.000000  30.000000  30.000000   \n",
       "mean    5.36900   3.714567   3.116667  32.816000   7.593333   0.533333   \n",
       "std     2.68217   1.962529   1.488191  15.166121   3.941812   1.008014   \n",
       "min     1.18000   0.373000   1.000000  12.125000   1.000000   0.000000   \n",
       "25%     2.83500   2.324500   2.000000  22.000000   5.000000   0.000000   \n",
       "50%     5.34000   3.366500   3.000000  27.700000   7.000000   0.000000   \n",
       "75%     7.00750   4.501750   3.862500  43.687500  10.650000   0.750000   \n",
       "max    10.68000   9.530000   7.000000  67.800000  17.000000   3.000000   \n",
       "\n",
       "           Drug7      Drug8   Drug1_Sales  Drug2_Sales   Drug3_Sales  \\\n",
       "count  30.000000  30.000000     30.000000    30.000000     30.000000   \n",
       "mean    4.047222   3.636667   5369.000000  2971.653333   4675.000000   \n",
       "std     5.658703   2.282993   2682.170184  1570.022885   2232.286596   \n",
       "min     0.000000   0.000000   1180.000000   298.400000   1500.000000   \n",
       "25%     1.000000   2.000000   2835.000000  1859.600000   3000.000000   \n",
       "50%     2.500000   3.000000   5340.000000  2693.200000   4500.000000   \n",
       "75%     5.000000   4.000000   7007.500000  3601.400000   5793.750000   \n",
       "max    31.000000  10.000000  10680.000000  7624.000000  10500.000000   \n",
       "\n",
       "       Drug4_Sales  Drug5_Sales   Drug6_Sales   Drug7_Sales  Drug8_Sales  \n",
       "count     30.00000    30.000000     30.000000     30.000000    30.000000  \n",
       "mean   19689.60000  4176.333333   5333.333333   1821.250000  3273.000000  \n",
       "std     9099.67288  2167.996824  10080.138660   2546.416409  2054.693785  \n",
       "min     7275.00000   550.000000      0.000000      0.000000     0.000000  \n",
       "25%    13200.00000  2750.000000      0.000000    450.000000  1800.000000  \n",
       "50%    16620.00000  3850.000000      0.000000   1125.000000  2700.000000  \n",
       "75%    26212.50000  5857.500000   7500.000000   2250.000000  3600.000000  \n",
       "max    40680.00000  9350.000000  30000.000000  13950.000000  9000.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Inference from the describe function:\n",
    "- Most number of units sold were of Drug4\n",
    "- Least number of units sold were Drug6\n",
    "- Most sales happened with Drug4 while Drug2 contributed the least."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking the cost of each drug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drug 1\n",
    "\n",
    "np.mean(df['Drug1_Sales']/df['Drug1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "800.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drug 2\n",
    "\n",
    "np.mean(df['Drug2_Sales']/df['Drug2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1500.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drug 3\n",
    "\n",
    "np.mean(df['Drug3_Sales']/df['Drug3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "600.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drug 4\n",
    "\n",
    "np.mean(df['Drug4_Sales']/df['Drug4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "550.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drug 5\n",
    "\n",
    "np.mean(df['Drug5_Sales']/df['Drug5'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drug 6\n",
    "\n",
    "np.mean(df['Drug6_Sales']/df['Drug6'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "450.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drug 7\n",
    "\n",
    "np.mean(df['Drug7_Sales']/df['Drug7'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "900.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drug 8\n",
    "\n",
    "np.mean(df['Drug8_Sales']/df['Drug8'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Weekly Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdf = df.resample('W').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug1</th>\n",
       "      <th>Drug2</th>\n",
       "      <th>Drug3</th>\n",
       "      <th>Drug4</th>\n",
       "      <th>Drug5</th>\n",
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       "      <th>Drug7</th>\n",
       "      <th>Drug8</th>\n",
       "      <th>Drug1_Sales</th>\n",
       "      <th>Drug2_Sales</th>\n",
       "      <th>Drug3_Sales</th>\n",
       "      <th>Drug4_Sales</th>\n",
       "      <th>Drug5_Sales</th>\n",
       "      <th>Drug6_Sales</th>\n",
       "      <th>Drug7_Sales</th>\n",
       "      <th>Drug8_Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-01</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.360000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>3488.000000</td>\n",
       "      <td>7500.000000</td>\n",
       "      <td>10200.000000</td>\n",
       "      <td>4510.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>2250.000000</td>\n",
       "      <td>3600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-08</th>\n",
       "      <td>5.815714</td>\n",
       "      <td>3.823857</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>22.107857</td>\n",
       "      <td>7.285714</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>2.142857</td>\n",
       "      <td>4.071429</td>\n",
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       "      <td>964.285714</td>\n",
       "      <td>3664.285714</td>\n",
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       "    <tr>\n",
       "      <th>2019-09-15</th>\n",
       "      <td>5.072857</td>\n",
       "      <td>4.425286</td>\n",
       "      <td>2.807143</td>\n",
       "      <td>25.482143</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>4.285714</td>\n",
       "      <td>4.357143</td>\n",
       "      <td>5072.857143</td>\n",
       "      <td>3540.228571</td>\n",
       "      <td>4210.714286</td>\n",
       "      <td>15289.285714</td>\n",
       "      <td>5280.000000</td>\n",
       "      <td>7142.857143</td>\n",
       "      <td>1928.571429</td>\n",
       "      <td>3921.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-22</th>\n",
       "      <td>6.691429</td>\n",
       "      <td>3.628000</td>\n",
       "      <td>3.485714</td>\n",
       "      <td>35.464286</td>\n",
       "      <td>4.457143</td>\n",
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       "      <td>1671.428571</td>\n",
       "      <td>2700.000000</td>\n",
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       "    <tr>\n",
       "      <th>2019-09-29</th>\n",
       "      <td>4.858571</td>\n",
       "      <td>3.214000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>48.100000</td>\n",
       "      <td>8.742857</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>5.773810</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>4858.571429</td>\n",
       "      <td>2571.200000</td>\n",
       "      <td>5700.000000</td>\n",
       "      <td>28860.000000</td>\n",
       "      <td>4808.571429</td>\n",
       "      <td>1428.571429</td>\n",
       "      <td>2598.214286</td>\n",
       "      <td>2970.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Drug1     Drug2     Drug3      Drug4     Drug5     Drug6  \\\n",
       "Date                                                                      \n",
       "2019-09-01  2.000000  4.360000  5.000000  17.000000  8.200000  1.000000   \n",
       "2019-09-08  5.815714  3.823857  2.250000  22.107857  7.285714  0.857143   \n",
       "2019-09-15  5.072857  4.425286  2.807143  25.482143  9.600000  0.714286   \n",
       "2019-09-22  6.691429  3.628000  3.485714  35.464286  4.457143  0.428571   \n",
       "2019-09-29  4.858571  3.214000  3.800000  48.100000  8.742857  0.142857   \n",
       "\n",
       "               Drug7     Drug8  Drug1_Sales  Drug2_Sales  Drug3_Sales  \\\n",
       "Date                                                                    \n",
       "2019-09-01  5.000000  4.000000  2000.000000  3488.000000  7500.000000   \n",
       "2019-09-08  2.142857  4.071429  5815.714286  3059.085714  3375.000000   \n",
       "2019-09-15  4.285714  4.357143  5072.857143  3540.228571  4210.714286   \n",
       "2019-09-22  3.714286  3.000000  6691.428571  2902.400000  5228.571429   \n",
       "2019-09-29  5.773810  3.300000  4858.571429  2571.200000  5700.000000   \n",
       "\n",
       "             Drug4_Sales  Drug5_Sales   Drug6_Sales  Drug7_Sales  Drug8_Sales  \n",
       "Date                                                                           \n",
       "2019-09-01  10200.000000  4510.000000  10000.000000  2250.000000  3600.000000  \n",
       "2019-09-08  13264.714286  4007.142857   8571.428571   964.285714  3664.285714  \n",
       "2019-09-15  15289.285714  5280.000000   7142.857143  1928.571429  3921.428571  \n",
       "2019-09-22  21278.571429  2451.428571   4285.714286  1671.428571  2700.000000  \n",
       "2019-09-29  28860.000000  4808.571429   1428.571429  2598.214286  2970.000000  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug1</th>\n",
       "      <th>Drug2</th>\n",
       "      <th>Drug3</th>\n",
       "      <th>Drug4</th>\n",
       "      <th>Drug5</th>\n",
       "      <th>Drug6</th>\n",
       "      <th>Drug7</th>\n",
       "      <th>Drug8</th>\n",
       "      <th>Drug1_Sales</th>\n",
       "      <th>Drug2_Sales</th>\n",
       "      <th>Drug3_Sales</th>\n",
       "      <th>Drug4_Sales</th>\n",
       "      <th>Drug5_Sales</th>\n",
       "      <th>Drug6_Sales</th>\n",
       "      <th>Drug7_Sales</th>\n",
       "      <th>Drug8_Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-08</th>\n",
       "      <td>5.815714</td>\n",
       "      <td>3.823857</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>22.107857</td>\n",
       "      <td>7.285714</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>2.142857</td>\n",
       "      <td>4.071429</td>\n",
       "      <td>5815.714286</td>\n",
       "      <td>3059.085714</td>\n",
       "      <td>3375.000000</td>\n",
       "      <td>13264.714286</td>\n",
       "      <td>4007.142857</td>\n",
       "      <td>8571.428571</td>\n",
       "      <td>964.285714</td>\n",
       "      <td>3664.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-15</th>\n",
       "      <td>5.072857</td>\n",
       "      <td>4.425286</td>\n",
       "      <td>2.807143</td>\n",
       "      <td>25.482143</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>4.285714</td>\n",
       "      <td>4.357143</td>\n",
       "      <td>5072.857143</td>\n",
       "      <td>3540.228571</td>\n",
       "      <td>4210.714286</td>\n",
       "      <td>15289.285714</td>\n",
       "      <td>5280.000000</td>\n",
       "      <td>7142.857143</td>\n",
       "      <td>1928.571429</td>\n",
       "      <td>3921.428571</td>\n",
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       "    <tr>\n",
       "      <th>2019-09-22</th>\n",
       "      <td>6.691429</td>\n",
       "      <td>3.628000</td>\n",
       "      <td>3.485714</td>\n",
       "      <td>35.464286</td>\n",
       "      <td>4.457143</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6691.428571</td>\n",
       "      <td>2902.400000</td>\n",
       "      <td>5228.571429</td>\n",
       "      <td>21278.571429</td>\n",
       "      <td>2451.428571</td>\n",
       "      <td>4285.714286</td>\n",
       "      <td>1671.428571</td>\n",
       "      <td>2700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-29</th>\n",
       "      <td>4.858571</td>\n",
       "      <td>3.214000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>48.100000</td>\n",
       "      <td>8.742857</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>5.773810</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>4858.571429</td>\n",
       "      <td>2571.200000</td>\n",
       "      <td>5700.000000</td>\n",
       "      <td>28860.000000</td>\n",
       "      <td>4808.571429</td>\n",
       "      <td>1428.571429</td>\n",
       "      <td>2598.214286</td>\n",
       "      <td>2970.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-06</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.439000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>49.400000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>1151.200000</td>\n",
       "      <td>3150.000000</td>\n",
       "      <td>29640.000000</td>\n",
       "      <td>4950.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2250.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Drug1     Drug2     Drug3      Drug4     Drug5     Drug6  \\\n",
       "Date                                                                      \n",
       "2019-09-08  5.815714  3.823857  2.250000  22.107857  7.285714  0.857143   \n",
       "2019-09-15  5.072857  4.425286  2.807143  25.482143  9.600000  0.714286   \n",
       "2019-09-22  6.691429  3.628000  3.485714  35.464286  4.457143  0.428571   \n",
       "2019-09-29  4.858571  3.214000  3.800000  48.100000  8.742857  0.142857   \n",
       "2019-10-06  2.000000  1.439000  2.100000  49.400000  9.000000  0.000000   \n",
       "\n",
       "               Drug7     Drug8  Drug1_Sales  Drug2_Sales  Drug3_Sales  \\\n",
       "Date                                                                    \n",
       "2019-09-08  2.142857  4.071429  5815.714286  3059.085714  3375.000000   \n",
       "2019-09-15  4.285714  4.357143  5072.857143  3540.228571  4210.714286   \n",
       "2019-09-22  3.714286  3.000000  6691.428571  2902.400000  5228.571429   \n",
       "2019-09-29  5.773810  3.300000  4858.571429  2571.200000  5700.000000   \n",
       "2019-10-06  5.000000  2.000000  2000.000000  1151.200000  3150.000000   \n",
       "\n",
       "             Drug4_Sales  Drug5_Sales  Drug6_Sales  Drug7_Sales  Drug8_Sales  \n",
       "Date                                                                          \n",
       "2019-09-08  13264.714286  4007.142857  8571.428571   964.285714  3664.285714  \n",
       "2019-09-15  15289.285714  5280.000000  7142.857143  1928.571429  3921.428571  \n",
       "2019-09-22  21278.571429  2451.428571  4285.714286  1671.428571  2700.000000  \n",
       "2019-09-29  28860.000000  4808.571429  1428.571429  2598.214286  2970.000000  \n",
       "2019-10-06  29640.000000  4950.000000     0.000000  2250.000000  1800.000000  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdf.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug1</th>\n",
       "      <th>Drug2</th>\n",
       "      <th>Drug3</th>\n",
       "      <th>Drug4</th>\n",
       "      <th>Drug5</th>\n",
       "      <th>Drug6</th>\n",
       "      <th>Drug7</th>\n",
       "      <th>Drug8</th>\n",
       "      <th>Drug1_Sales</th>\n",
       "      <th>Drug2_Sales</th>\n",
       "      <th>Drug3_Sales</th>\n",
       "      <th>Drug4_Sales</th>\n",
       "      <th>Drug5_Sales</th>\n",
       "      <th>Drug6_Sales</th>\n",
       "      <th>Drug7_Sales</th>\n",
       "      <th>Drug8_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.406429</td>\n",
       "      <td>3.481690</td>\n",
       "      <td>3.240476</td>\n",
       "      <td>32.925714</td>\n",
       "      <td>7.880952</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>4.319444</td>\n",
       "      <td>3.454762</td>\n",
       "      <td>4406.428571</td>\n",
       "      <td>2785.352381</td>\n",
       "      <td>4860.714286</td>\n",
       "      <td>19755.428571</td>\n",
       "      <td>4334.523810</td>\n",
       "      <td>5238.095238</td>\n",
       "      <td>1943.750000</td>\n",
       "      <td>3109.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.971683</td>\n",
       "      <td>1.099502</td>\n",
       "      <td>1.089829</td>\n",
       "      <td>13.668698</td>\n",
       "      <td>1.850883</td>\n",
       "      <td>0.400680</td>\n",
       "      <td>1.276431</td>\n",
       "      <td>0.876236</td>\n",
       "      <td>1971.682955</td>\n",
       "      <td>879.601850</td>\n",
       "      <td>1634.743942</td>\n",
       "      <td>8201.218891</td>\n",
       "      <td>1017.985773</td>\n",
       "      <td>4006.796946</td>\n",
       "      <td>574.393813</td>\n",
       "      <td>788.612188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.439000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>4.457143</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.142857</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>1151.200000</td>\n",
       "      <td>3150.000000</td>\n",
       "      <td>10200.000000</td>\n",
       "      <td>2451.428571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>964.285714</td>\n",
       "      <td>1800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.714643</td>\n",
       "      <td>3.317500</td>\n",
       "      <td>2.389286</td>\n",
       "      <td>22.951429</td>\n",
       "      <td>7.514286</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>3.857143</td>\n",
       "      <td>3.075000</td>\n",
       "      <td>2714.642857</td>\n",
       "      <td>2654.000000</td>\n",
       "      <td>3583.928571</td>\n",
       "      <td>13770.857143</td>\n",
       "      <td>4132.857143</td>\n",
       "      <td>2142.857143</td>\n",
       "      <td>1735.714286</td>\n",
       "      <td>2767.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.965714</td>\n",
       "      <td>3.725929</td>\n",
       "      <td>3.146429</td>\n",
       "      <td>30.473214</td>\n",
       "      <td>8.471429</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>4.642857</td>\n",
       "      <td>3.650000</td>\n",
       "      <td>4965.714286</td>\n",
       "      <td>2980.742857</td>\n",
       "      <td>4719.642857</td>\n",
       "      <td>18283.928571</td>\n",
       "      <td>4659.285714</td>\n",
       "      <td>5714.285714</td>\n",
       "      <td>2089.285714</td>\n",
       "      <td>3285.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.630000</td>\n",
       "      <td>4.225964</td>\n",
       "      <td>3.721429</td>\n",
       "      <td>44.941071</td>\n",
       "      <td>8.935714</td>\n",
       "      <td>0.821429</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.053571</td>\n",
       "      <td>5630.000000</td>\n",
       "      <td>3380.771429</td>\n",
       "      <td>5582.142857</td>\n",
       "      <td>26964.642857</td>\n",
       "      <td>4914.642857</td>\n",
       "      <td>8214.285714</td>\n",
       "      <td>2250.000000</td>\n",
       "      <td>3648.214286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.691429</td>\n",
       "      <td>4.425286</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>49.400000</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.773810</td>\n",
       "      <td>4.357143</td>\n",
       "      <td>6691.428571</td>\n",
       "      <td>3540.228571</td>\n",
       "      <td>7500.000000</td>\n",
       "      <td>29640.000000</td>\n",
       "      <td>5280.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>2598.214286</td>\n",
       "      <td>3921.428571</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          Drug1     Drug2     Drug3      Drug4     Drug5     Drug6     Drug7  \\\n",
       "count  6.000000  6.000000  6.000000   6.000000  6.000000  6.000000  6.000000   \n",
       "mean   4.406429  3.481690  3.240476  32.925714  7.880952  0.523810  4.319444   \n",
       "std    1.971683  1.099502  1.089829  13.668698  1.850883  0.400680  1.276431   \n",
       "min    2.000000  1.439000  2.100000  17.000000  4.457143  0.000000  2.142857   \n",
       "25%    2.714643  3.317500  2.389286  22.951429  7.514286  0.214286  3.857143   \n",
       "50%    4.965714  3.725929  3.146429  30.473214  8.471429  0.571429  4.642857   \n",
       "75%    5.630000  4.225964  3.721429  44.941071  8.935714  0.821429  5.000000   \n",
       "max    6.691429  4.425286  5.000000  49.400000  9.600000  1.000000  5.773810   \n",
       "\n",
       "          Drug8  Drug1_Sales  Drug2_Sales  Drug3_Sales   Drug4_Sales  \\\n",
       "count  6.000000     6.000000     6.000000     6.000000      6.000000   \n",
       "mean   3.454762  4406.428571  2785.352381  4860.714286  19755.428571   \n",
       "std    0.876236  1971.682955   879.601850  1634.743942   8201.218891   \n",
       "min    2.000000  2000.000000  1151.200000  3150.000000  10200.000000   \n",
       "25%    3.075000  2714.642857  2654.000000  3583.928571  13770.857143   \n",
       "50%    3.650000  4965.714286  2980.742857  4719.642857  18283.928571   \n",
       "75%    4.053571  5630.000000  3380.771429  5582.142857  26964.642857   \n",
       "max    4.357143  6691.428571  3540.228571  7500.000000  29640.000000   \n",
       "\n",
       "       Drug5_Sales   Drug6_Sales  Drug7_Sales  Drug8_Sales  \n",
       "count     6.000000      6.000000     6.000000     6.000000  \n",
       "mean   4334.523810   5238.095238  1943.750000  3109.285714  \n",
       "std    1017.985773   4006.796946   574.393813   788.612188  \n",
       "min    2451.428571      0.000000   964.285714  1800.000000  \n",
       "25%    4132.857143   2142.857143  1735.714286  2767.500000  \n",
       "50%    4659.285714   5714.285714  2089.285714  3285.000000  \n",
       "75%    4914.642857   8214.285714  2250.000000  3648.214286  \n",
       "max    5280.000000  10000.000000  2598.214286  3921.428571  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 6 artists>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,3), dpi = 100)\n",
    "plt.tick_params(axis = 'x', rotation = 45)\n",
    "plt.title(\"Drug1 weekly sales overview\")\n",
    "plt.bar(x = wdf.index, height = wdf.Drug1_Sales)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Monthly Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug1</th>\n",
       "      <th>Drug2</th>\n",
       "      <th>Drug3</th>\n",
       "      <th>Drug4</th>\n",
       "      <th>Drug5</th>\n",
       "      <th>Drug6</th>\n",
       "      <th>Drug7</th>\n",
       "      <th>Drug8</th>\n",
       "      <th>Drug1_Sales</th>\n",
       "      <th>Drug2_Sales</th>\n",
       "      <th>Drug3_Sales</th>\n",
       "      <th>Drug4_Sales</th>\n",
       "      <th>Drug5_Sales</th>\n",
       "      <th>Drug6_Sales</th>\n",
       "      <th>Drug7_Sales</th>\n",
       "      <th>Drug8_Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>5.369</td>\n",
       "      <td>3.714567</td>\n",
       "      <td>3.116667</td>\n",
       "      <td>32.816</td>\n",
       "      <td>7.593333</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>4.047222</td>\n",
       "      <td>3.636667</td>\n",
       "      <td>5369.0</td>\n",
       "      <td>2971.653333</td>\n",
       "      <td>4675.0</td>\n",
       "      <td>19689.6</td>\n",
       "      <td>4176.333333</td>\n",
       "      <td>5333.333333</td>\n",
       "      <td>1821.25</td>\n",
       "      <td>3273.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Drug1     Drug2     Drug3   Drug4     Drug5     Drug6     Drug7  \\\n",
       "Date                                                                          \n",
       "2019-09-30  5.369  3.714567  3.116667  32.816  7.593333  0.533333  4.047222   \n",
       "\n",
       "               Drug8  Drug1_Sales  Drug2_Sales  Drug3_Sales  Drug4_Sales  \\\n",
       "Date                                                                       \n",
       "2019-09-30  3.636667       5369.0  2971.653333       4675.0      19689.6   \n",
       "\n",
       "            Drug5_Sales  Drug6_Sales  Drug7_Sales  Drug8_Sales  \n",
       "Date                                                            \n",
       "2019-09-30  4176.333333  5333.333333      1821.25       3273.0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf = df.resample('M').mean()\n",
    "mdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>name</th>\n",
       "      <th>school_rating</th>\n",
       "      <th>size</th>\n",
       "      <th>reduced_lunch</th>\n",
       "      <th>state_percentile_16</th>\n",
       "      <th>state_percentile_15</th>\n",
       "      <th>stu_teach_ratio</th>\n",
       "      <th>school_type</th>\n",
       "      <th>avg_score_15</th>\n",
       "      <th>avg_score_16</th>\n",
       "      <th>full_time_teachers</th>\n",
       "      <th>percent_black</th>\n",
       "      <th>percent_white</th>\n",
       "      <th>percent_asian</th>\n",
       "      <th>percent_hispanic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Allendale Elementary School</td>\n",
       "      <td>5.0</td>\n",
       "      <td>851.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>90.2</td>\n",
       "      <td>95.8</td>\n",
       "      <td>15.7</td>\n",
       "      <td>Public</td>\n",
       "      <td>89.4</td>\n",
       "      <td>85.2</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>85.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anderson Elementary</td>\n",
       "      <td>2.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>32.8</td>\n",
       "      <td>37.3</td>\n",
       "      <td>12.8</td>\n",
       "      <td>Public</td>\n",
       "      <td>43.0</td>\n",
       "      <td>38.3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>86.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Avoca Elementary</td>\n",
       "      <td>4.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>78.4</td>\n",
       "      <td>83.6</td>\n",
       "      <td>16.6</td>\n",
       "      <td>Public</td>\n",
       "      <td>75.7</td>\n",
       "      <td>73.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>91.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bailey Middle</td>\n",
       "      <td>0.0</td>\n",
       "      <td>394.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.1</td>\n",
       "      <td>Public Magnet</td>\n",
       "      <td>2.1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>80.7</td>\n",
       "      <td>11.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Barfield Elementary</td>\n",
       "      <td>4.0</td>\n",
       "      <td>948.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>85.3</td>\n",
       "      <td>89.2</td>\n",
       "      <td>14.8</td>\n",
       "      <td>Public</td>\n",
       "      <td>81.3</td>\n",
       "      <td>79.6</td>\n",
       "      <td>64.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>71.2</td>\n",
       "      <td>7.1</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>Winfree Bryant Middle School</td>\n",
       "      <td>3.0</td>\n",
       "      <td>611.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>59.1</td>\n",
       "      <td>65.2</td>\n",
       "      <td>16.9</td>\n",
       "      <td>Public</td>\n",
       "      <td>61.4</td>\n",
       "      <td>57.7</td>\n",
       "      <td>36.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>66.3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>15.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>Winstead Elementary School</td>\n",
       "      <td>5.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>93.9</td>\n",
       "      <td>97.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>Public</td>\n",
       "      <td>92.0</td>\n",
       "      <td>89.3</td>\n",
       "      <td>36.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>87.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>Woodland Elementary</td>\n",
       "      <td>4.0</td>\n",
       "      <td>424.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>84.8</td>\n",
       "      <td>76.7</td>\n",
       "      <td>14.1</td>\n",
       "      <td>Public</td>\n",
       "      <td>69.4</td>\n",
       "      <td>79.4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>70.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>Woodland Middle School</td>\n",
       "      <td>5.0</td>\n",
       "      <td>866.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>93.3</td>\n",
       "      <td>97.1</td>\n",
       "      <td>19.2</td>\n",
       "      <td>Public</td>\n",
       "      <td>89.8</td>\n",
       "      <td>84.9</td>\n",
       "      <td>45.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>77.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>Wright Middle</td>\n",
       "      <td>0.0</td>\n",
       "      <td>829.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>16.5</td>\n",
       "      <td>Public</td>\n",
       "      <td>2.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>50.0</td>\n",
       "      <td>22.3</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>49.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>347 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             name  school_rating   size  reduced_lunch  \\\n",
       "0     Allendale Elementary School            5.0  851.0           10.0   \n",
       "1             Anderson Elementary            2.0  412.0           71.0   \n",
       "2                Avoca Elementary            4.0  482.0           43.0   \n",
       "3                   Bailey Middle            0.0  394.0           91.0   \n",
       "4             Barfield Elementary            4.0  948.0           26.0   \n",
       "..                            ...            ...    ...            ...   \n",
       "342  Winfree Bryant Middle School            3.0  611.0           57.0   \n",
       "343    Winstead Elementary School            5.0  515.0            8.0   \n",
       "344           Woodland Elementary            4.0  424.0           55.0   \n",
       "345        Woodland Middle School            5.0  866.0            2.0   \n",
       "346                 Wright Middle            0.0  829.0           89.0   \n",
       "\n",
       "     state_percentile_16  state_percentile_15  stu_teach_ratio    school_type  \\\n",
       "0                   90.2                 95.8             15.7         Public   \n",
       "1                   32.8                 37.3             12.8         Public   \n",
       "2                   78.4                 83.6             16.6         Public   \n",
       "3                    1.6                  1.0             13.1  Public Magnet   \n",
       "4                   85.3                 89.2             14.8         Public   \n",
       "..                   ...                  ...              ...            ...   \n",
       "342                 59.1                 65.2             16.9         Public   \n",
       "343                 93.9                 97.0             14.3         Public   \n",
       "344                 84.8                 76.7             14.1         Public   \n",
       "345                 93.3                 97.1             19.2         Public   \n",
       "346                  4.5                  1.2             16.5         Public   \n",
       "\n",
       "     avg_score_15  avg_score_16  full_time_teachers  percent_black  \\\n",
       "0            89.4          85.2                54.0            2.9   \n",
       "1            43.0          38.3                32.0            3.9   \n",
       "2            75.7          73.0                29.0            1.0   \n",
       "3             2.1           4.4                30.0           80.7   \n",
       "4            81.3          79.6                64.0           11.8   \n",
       "..            ...           ...                 ...            ...   \n",
       "342          61.4          57.7                36.0           15.2   \n",
       "343          92.0          89.3                36.0            3.3   \n",
       "344          69.4          79.4                30.0           11.6   \n",
       "345          89.8          84.9                45.0            4.5   \n",
       "346           2.7           9.6                50.0           22.3   \n",
       "\n",
       "     percent_white  percent_asian  percent_hispanic  \n",
       "0             85.5            1.6               5.6  \n",
       "1             86.7            1.0               4.9  \n",
       "2             91.5            1.2               4.4  \n",
       "3             11.7            2.3               4.3  \n",
       "4             71.2            7.1               6.0  \n",
       "..             ...            ...               ...  \n",
       "342           66.3            1.5              15.7  \n",
       "343           87.4            3.1               4.1  \n",
       "344           70.5            2.1               9.7  \n",
       "345           77.6           10.0               4.4  \n",
       "346           21.0            6.6              49.5  \n",
       "\n",
       "[347 rows x 15 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"/Users/macuser/Documents/PGP AI & ML/Applied DS with Python/Datasets_4754/OSL Datasets/Lesson_05/middle_tn_schools.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school_rating</th>\n",
       "      <th>size</th>\n",
       "      <th>reduced_lunch</th>\n",
       "      <th>state_percentile_16</th>\n",
       "      <th>state_percentile_15</th>\n",
       "      <th>stu_teach_ratio</th>\n",
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       "      <th>percent_black</th>\n",
       "      <th>percent_white</th>\n",
       "      <th>percent_asian</th>\n",
       "      <th>percent_hispanic</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>347.000000</td>\n",
       "      <td>347.000000</td>\n",
       "      <td>347.000000</td>\n",
       "      <td>347.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>347.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>347.000000</td>\n",
       "      <td>347.000000</td>\n",
       "      <td>347.000000</td>\n",
       "      <td>347.000000</td>\n",
       "      <td>347.000000</td>\n",
       "      <td>347.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.968300</td>\n",
       "      <td>699.472622</td>\n",
       "      <td>50.279539</td>\n",
       "      <td>58.801729</td>\n",
       "      <td>58.249267</td>\n",
       "      <td>15.461671</td>\n",
       "      <td>57.004692</td>\n",
       "      <td>57.049856</td>\n",
       "      <td>44.939481</td>\n",
       "      <td>21.197983</td>\n",
       "      <td>61.673487</td>\n",
       "      <td>2.642651</td>\n",
       "      <td>11.164553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.690377</td>\n",
       "      <td>400.598636</td>\n",
       "      <td>25.480236</td>\n",
       "      <td>32.540747</td>\n",
       "      <td>32.702630</td>\n",
       "      <td>5.725170</td>\n",
       "      <td>26.696450</td>\n",
       "      <td>27.968974</td>\n",
       "      <td>22.053386</td>\n",
       "      <td>23.562538</td>\n",
       "      <td>27.274859</td>\n",
       "      <td>3.109629</td>\n",
       "      <td>12.030608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>420.500000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.950000</td>\n",
       "      <td>27.100000</td>\n",
       "      <td>13.700000</td>\n",
       "      <td>37.600000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>40.600000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>3.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>595.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>66.400000</td>\n",
       "      <td>65.800000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>61.800000</td>\n",
       "      <td>60.700000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>68.700000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>6.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>851.000000</td>\n",
       "      <td>71.500000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.600000</td>\n",
       "      <td>16.700000</td>\n",
       "      <td>79.600000</td>\n",
       "      <td>80.250000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>28.350000</td>\n",
       "      <td>85.950000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>13.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>2314.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>99.800000</td>\n",
       "      <td>99.800000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>98.900000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>97.400000</td>\n",
       "      <td>99.700000</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>65.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       school_rating         size  reduced_lunch  state_percentile_16  \\\n",
       "count     347.000000   347.000000     347.000000           347.000000   \n",
       "mean        2.968300   699.472622      50.279539            58.801729   \n",
       "std         1.690377   400.598636      25.480236            32.540747   \n",
       "min         0.000000    53.000000       2.000000             0.200000   \n",
       "25%         2.000000   420.500000      30.000000            30.950000   \n",
       "50%         3.000000   595.000000      51.000000            66.400000   \n",
       "75%         4.000000   851.000000      71.500000            88.000000   \n",
       "max         5.000000  2314.000000      98.000000            99.800000   \n",
       "\n",
       "       state_percentile_15  stu_teach_ratio  avg_score_15  avg_score_16  \\\n",
       "count           341.000000       347.000000    341.000000    347.000000   \n",
       "mean             58.249267        15.461671     57.004692     57.049856   \n",
       "std              32.702630         5.725170     26.696450     27.968974   \n",
       "min               0.600000         4.700000      1.500000      0.100000   \n",
       "25%              27.100000        13.700000     37.600000     37.000000   \n",
       "50%              65.800000        15.000000     61.800000     60.700000   \n",
       "75%              88.600000        16.700000     79.600000     80.250000   \n",
       "max              99.800000       111.000000     99.000000     98.900000   \n",
       "\n",
       "       full_time_teachers  percent_black  percent_white  percent_asian  \\\n",
       "count          347.000000     347.000000     347.000000     347.000000   \n",
       "mean            44.939481      21.197983      61.673487       2.642651   \n",
       "std             22.053386      23.562538      27.274859       3.109629   \n",
       "min              2.000000       0.000000       1.100000       0.000000   \n",
       "25%             30.000000       3.600000      40.600000       0.750000   \n",
       "50%             40.000000      13.500000      68.700000       1.600000   \n",
       "75%             54.000000      28.350000      85.950000       3.100000   \n",
       "max            140.000000      97.400000      99.700000      21.100000   \n",
       "\n",
       "       percent_hispanic  \n",
       "count        347.000000  \n",
       "mean          11.164553  \n",
       "std           12.030608  \n",
       "min            0.000000  \n",
       "25%            3.800000  \n",
       "50%            6.400000  \n",
       "75%           13.800000  \n",
       "max           65.200000  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                      school_rating\n",
       "reduced_lunch  count  0.0              43.000000\n",
       "                      1.0              40.000000\n",
       "                      2.0              44.000000\n",
       "                      3.0              56.000000\n",
       "                      4.0              86.000000\n",
       "                      5.0              78.000000\n",
       "               mean   0.0              83.581395\n",
       "                      1.0              74.950000\n",
       "                      2.0              64.272727\n",
       "                      3.0              50.285714\n",
       "                      4.0              41.000000\n",
       "                      5.0              21.602564\n",
       "               std    0.0               8.813498\n",
       "                      1.0              11.644191\n",
       "                      2.0              11.956051\n",
       "                      3.0              13.550866\n",
       "                      4.0              16.681092\n",
       "                      5.0              17.651268\n",
       "               min    0.0              53.000000\n",
       "                      1.0              53.000000\n",
       "                      2.0              37.000000\n",
       "                      3.0              24.000000\n",
       "                      4.0               4.000000\n",
       "                      5.0               2.000000\n",
       "               25%    0.0              79.500000\n",
       "                      1.0              65.000000\n",
       "                      2.0              54.750000\n",
       "                      3.0              41.000000\n",
       "                      4.0              30.000000\n",
       "                      5.0               8.000000\n",
       "               50%    0.0              86.000000\n",
       "                      1.0              74.500000\n",
       "                      2.0              62.500000\n",
       "                      3.0              48.500000\n",
       "                      4.0              41.500000\n",
       "                      5.0              19.000000\n",
       "               75%    0.0              90.000000\n",
       "                      1.0              84.250000\n",
       "                      2.0              74.000000\n",
       "                      3.0              63.000000\n",
       "                      4.0              50.000000\n",
       "                      5.0              29.750000\n",
       "               max    0.0              98.000000\n",
       "                      1.0              98.000000\n",
       "                      2.0              88.000000\n",
       "                      3.0              78.000000\n",
       "                      4.0              87.000000\n",
       "                      5.0              87.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data distribution of reduced lunch with respect to school rating\n",
    "\n",
    "df[['reduced_lunch','school_rating']].groupby(['school_rating']).describe().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>reduced_lunch</th>\n",
       "      <th>school_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>reduced_lunch</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.815757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>school_rating</th>\n",
       "      <td>-0.815757</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               reduced_lunch  school_rating\n",
       "reduced_lunch       1.000000      -0.815757\n",
       "school_rating      -0.815757       1.000000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['reduced_lunch', 'school_rating']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[ 0.03807591,  0.05068012,  0.06169621, ..., -0.00259226,\n",
       "          0.01990842, -0.01764613],\n",
       "        [-0.00188202, -0.04464164, -0.05147406, ..., -0.03949338,\n",
       "         -0.06832974, -0.09220405],\n",
       "        [ 0.08529891,  0.05068012,  0.04445121, ..., -0.00259226,\n",
       "          0.00286377, -0.02593034],\n",
       "        ...,\n",
       "        [ 0.04170844,  0.05068012, -0.01590626, ..., -0.01107952,\n",
       "         -0.04687948,  0.01549073],\n",
       "        [-0.04547248, -0.04464164,  0.03906215, ...,  0.02655962,\n",
       "          0.04452837, -0.02593034],\n",
       "        [-0.04547248, -0.04464164, -0.0730303 , ..., -0.03949338,\n",
       "         -0.00421986,  0.00306441]]),\n",
       " 'target': array([151.,  75., 141., 206., 135.,  97., 138.,  63., 110., 310., 101.,\n",
       "         69., 179., 185., 118., 171., 166., 144.,  97., 168.,  68.,  49.,\n",
       "         68., 245., 184., 202., 137.,  85., 131., 283., 129.,  59., 341.,\n",
       "         87.,  65., 102., 265., 276., 252.,  90., 100.,  55.,  61.,  92.,\n",
       "        259.,  53., 190., 142.,  75., 142., 155., 225.,  59., 104., 182.,\n",
       "        128.,  52.,  37., 170., 170.,  61., 144.,  52., 128.,  71., 163.,\n",
       "        150.,  97., 160., 178.,  48., 270., 202., 111.,  85.,  42., 170.,\n",
       "        200., 252., 113., 143.,  51.,  52., 210.,  65., 141.,  55., 134.,\n",
       "         42., 111.,  98., 164.,  48.,  96.,  90., 162., 150., 279.,  92.,\n",
       "         83., 128., 102., 302., 198.,  95.,  53., 134., 144., 232.,  81.,\n",
       "        104.,  59., 246., 297., 258., 229., 275., 281., 179., 200., 200.,\n",
       "        173., 180.,  84., 121., 161.,  99., 109., 115., 268., 274., 158.,\n",
       "        107.,  83., 103., 272.,  85., 280., 336., 281., 118., 317., 235.,\n",
       "         60., 174., 259., 178., 128.,  96., 126., 288.,  88., 292.,  71.,\n",
       "        197., 186.,  25.,  84.,  96., 195.,  53., 217., 172., 131., 214.,\n",
       "         59.,  70., 220., 268., 152.,  47.,  74., 295., 101., 151., 127.,\n",
       "        237., 225.,  81., 151., 107.,  64., 138., 185., 265., 101., 137.,\n",
       "        143., 141.,  79., 292., 178.,  91., 116.,  86., 122.,  72., 129.,\n",
       "        142.,  90., 158.,  39., 196., 222., 277.,  99., 196., 202., 155.,\n",
       "         77., 191.,  70.,  73.,  49.,  65., 263., 248., 296., 214., 185.,\n",
       "         78.,  93., 252., 150.,  77., 208.,  77., 108., 160.,  53., 220.,\n",
       "        154., 259.,  90., 246., 124.,  67.,  72., 257., 262., 275., 177.,\n",
       "         71.,  47., 187., 125.,  78.,  51., 258., 215., 303., 243.,  91.,\n",
       "        150., 310., 153., 346.,  63.,  89.,  50.,  39., 103., 308., 116.,\n",
       "        145.,  74.,  45., 115., 264.,  87., 202., 127., 182., 241.,  66.,\n",
       "         94., 283.,  64., 102., 200., 265.,  94., 230., 181., 156., 233.,\n",
       "         60., 219.,  80.,  68., 332., 248.,  84., 200.,  55.,  85.,  89.,\n",
       "         31., 129.,  83., 275.,  65., 198., 236., 253., 124.,  44., 172.,\n",
       "        114., 142., 109., 180., 144., 163., 147.,  97., 220., 190., 109.,\n",
       "        191., 122., 230., 242., 248., 249., 192., 131., 237.,  78., 135.,\n",
       "        244., 199., 270., 164.,  72.,  96., 306.,  91., 214.,  95., 216.,\n",
       "        263., 178., 113., 200., 139., 139.,  88., 148.,  88., 243.,  71.,\n",
       "         77., 109., 272.,  60.,  54., 221.,  90., 311., 281., 182., 321.,\n",
       "         58., 262., 206., 233., 242., 123., 167.,  63., 197.,  71., 168.,\n",
       "        140., 217., 121., 235., 245.,  40.,  52., 104., 132.,  88.,  69.,\n",
       "        219.,  72., 201., 110.,  51., 277.,  63., 118.,  69., 273., 258.,\n",
       "         43., 198., 242., 232., 175.,  93., 168., 275., 293., 281.,  72.,\n",
       "        140., 189., 181., 209., 136., 261., 113., 131., 174., 257.,  55.,\n",
       "         84.,  42., 146., 212., 233.,  91., 111., 152., 120.,  67., 310.,\n",
       "         94., 183.,  66., 173.,  72.,  49.,  64.,  48., 178., 104., 132.,\n",
       "        220.,  57.]),\n",
       " 'frame': None,\n",
       " 'DESCR': '.. _diabetes_dataset:\\n\\nDiabetes dataset\\n----------------\\n\\nTen baseline variables, age, sex, body mass index, average blood\\npressure, and six blood serum measurements were obtained for each of n =\\n442 diabetes patients, as well as the response of interest, a\\nquantitative measure of disease progression one year after baseline.\\n\\n**Data Set Characteristics:**\\n\\n  :Number of Instances: 442\\n\\n  :Number of Attributes: First 10 columns are numeric predictive values\\n\\n  :Target: Column 11 is a quantitative measure of disease progression one year after baseline\\n\\n  :Attribute Information:\\n      - age     age in years\\n      - sex\\n      - bmi     body mass index\\n      - bp      average blood pressure\\n      - s1      tc, T-Cells (a type of white blood cells)\\n      - s2      ldl, low-density lipoproteins\\n      - s3      hdl, high-density lipoproteins\\n      - s4      tch, thyroid stimulating hormone\\n      - s5      ltg, lamotrigine\\n      - s6      glu, blood sugar level\\n\\nNote: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1).\\n\\nSource URL:\\nhttps://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\\n\\nFor more information see:\\nBradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\\n(https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)',\n",
       " 'feature_names': ['age',\n",
       "  'sex',\n",
       "  'bmi',\n",
       "  'bp',\n",
       "  's1',\n",
       "  's2',\n",
       "  's3',\n",
       "  's4',\n",
       "  's5',\n",
       "  's6'],\n",
       " 'data_filename': '/Users/macuser/opt/anaconda3/lib/python3.8/site-packages/sklearn/datasets/data/diabetes_data.csv.gz',\n",
       " 'target_filename': '/Users/macuser/opt/anaconda3/lib/python3.8/site-packages/sklearn/datasets/data/diabetes_target.csv.gz'}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = load_diabetes()\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([151.,  75., 141., 206., 135.,  97., 138.,  63., 110., 310., 101.,\n",
       "        69., 179., 185., 118., 171., 166., 144.,  97., 168.,  68.,  49.,\n",
       "        68., 245., 184., 202., 137.,  85., 131., 283., 129.,  59., 341.,\n",
       "        87.,  65., 102., 265., 276., 252.,  90., 100.,  55.,  61.,  92.,\n",
       "       259.,  53., 190., 142.,  75., 142., 155., 225.,  59., 104., 182.,\n",
       "       128.,  52.,  37., 170., 170.,  61., 144.,  52., 128.,  71., 163.,\n",
       "       150.,  97., 160., 178.,  48., 270., 202., 111.,  85.,  42., 170.,\n",
       "       200., 252., 113., 143.,  51.,  52., 210.,  65., 141.,  55., 134.,\n",
       "        42., 111.,  98., 164.,  48.,  96.,  90., 162., 150., 279.,  92.,\n",
       "        83., 128., 102., 302., 198.,  95.,  53., 134., 144., 232.,  81.,\n",
       "       104.,  59., 246., 297., 258., 229., 275., 281., 179., 200., 200.,\n",
       "       173., 180.,  84., 121., 161.,  99., 109., 115., 268., 274., 158.,\n",
       "       107.,  83., 103., 272.,  85., 280., 336., 281., 118., 317., 235.,\n",
       "        60., 174., 259., 178., 128.,  96., 126., 288.,  88., 292.,  71.,\n",
       "       197., 186.,  25.,  84.,  96., 195.,  53., 217., 172., 131., 214.,\n",
       "        59.,  70., 220., 268., 152.,  47.,  74., 295., 101., 151., 127.,\n",
       "       237., 225.,  81., 151., 107.,  64., 138., 185., 265., 101., 137.,\n",
       "       143., 141.,  79., 292., 178.,  91., 116.,  86., 122.,  72., 129.,\n",
       "       142.,  90., 158.,  39., 196., 222., 277.,  99., 196., 202., 155.,\n",
       "        77., 191.,  70.,  73.,  49.,  65., 263., 248., 296., 214., 185.,\n",
       "        78.,  93., 252., 150.,  77., 208.,  77., 108., 160.,  53., 220.,\n",
       "       154., 259.,  90., 246., 124.,  67.,  72., 257., 262., 275., 177.,\n",
       "        71.,  47., 187., 125.,  78.,  51., 258., 215., 303., 243.,  91.,\n",
       "       150., 310., 153., 346.,  63.,  89.,  50.,  39., 103., 308., 116.,\n",
       "       145.,  74.,  45., 115., 264.,  87., 202., 127., 182., 241.,  66.,\n",
       "        94., 283.,  64., 102., 200., 265.,  94., 230., 181., 156., 233.,\n",
       "        60., 219.,  80.,  68., 332., 248.,  84., 200.,  55.,  85.,  89.,\n",
       "        31., 129.,  83., 275.,  65., 198., 236., 253., 124.,  44., 172.,\n",
       "       114., 142., 109., 180., 144., 163., 147.,  97., 220., 190., 109.,\n",
       "       191., 122., 230., 242., 248., 249., 192., 131., 237.,  78., 135.,\n",
       "       244., 199., 270., 164.,  72.,  96., 306.,  91., 214.,  95., 216.,\n",
       "       263., 178., 113., 200., 139., 139.,  88., 148.,  88., 243.,  71.,\n",
       "        77., 109., 272.,  60.,  54., 221.,  90., 311., 281., 182., 321.,\n",
       "        58., 262., 206., 233., 242., 123., 167.,  63., 197.,  71., 168.,\n",
       "       140., 217., 121., 235., 245.,  40.,  52., 104., 132.,  88.,  69.,\n",
       "       219.,  72., 201., 110.,  51., 277.,  63., 118.,  69., 273., 258.,\n",
       "        43., 198., 242., 232., 175.,  93., 168., 275., 293., 281.,  72.,\n",
       "       140., 189., 181., 209., 136., 261., 113., 131., 174., 257.,  55.,\n",
       "        84.,  42., 146., 212., 233.,  91., 111., 152., 120.,  67., 310.,\n",
       "        94., 183.,  66., 173.,  72.,  49.,  64.,  48., 178., 104., 132.,\n",
       "       220.,  57.])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['feature_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.038076</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.061696</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>-0.044223</td>\n",
       "      <td>-0.034821</td>\n",
       "      <td>-0.043401</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.019908</td>\n",
       "      <td>-0.017646</td>\n",
       "      <td>151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.001882</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.051474</td>\n",
       "      <td>-0.026328</td>\n",
       "      <td>-0.008449</td>\n",
       "      <td>-0.019163</td>\n",
       "      <td>0.074412</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.068330</td>\n",
       "      <td>-0.092204</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.085299</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.044451</td>\n",
       "      <td>-0.005671</td>\n",
       "      <td>-0.045599</td>\n",
       "      <td>-0.034194</td>\n",
       "      <td>-0.032356</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.002864</td>\n",
       "      <td>-0.025930</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.089063</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.011595</td>\n",
       "      <td>-0.036656</td>\n",
       "      <td>0.012191</td>\n",
       "      <td>0.024991</td>\n",
       "      <td>-0.036038</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>0.022692</td>\n",
       "      <td>-0.009362</td>\n",
       "      <td>206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005383</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.036385</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>0.003935</td>\n",
       "      <td>0.015596</td>\n",
       "      <td>0.008142</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.031991</td>\n",
       "      <td>-0.046641</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>0.041708</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.019662</td>\n",
       "      <td>0.059744</td>\n",
       "      <td>-0.005697</td>\n",
       "      <td>-0.002566</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.031193</td>\n",
       "      <td>0.007207</td>\n",
       "      <td>178.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>-0.005515</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.015906</td>\n",
       "      <td>-0.067642</td>\n",
       "      <td>0.049341</td>\n",
       "      <td>0.079165</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>-0.018118</td>\n",
       "      <td>0.044485</td>\n",
       "      <td>104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>0.041708</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.015906</td>\n",
       "      <td>0.017282</td>\n",
       "      <td>-0.037344</td>\n",
       "      <td>-0.013840</td>\n",
       "      <td>-0.024993</td>\n",
       "      <td>-0.011080</td>\n",
       "      <td>-0.046879</td>\n",
       "      <td>0.015491</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>-0.045472</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>0.039062</td>\n",
       "      <td>0.001215</td>\n",
       "      <td>0.016318</td>\n",
       "      <td>0.015283</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>0.026560</td>\n",
       "      <td>0.044528</td>\n",
       "      <td>-0.025930</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>-0.045472</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.073030</td>\n",
       "      <td>-0.081414</td>\n",
       "      <td>0.083740</td>\n",
       "      <td>0.027809</td>\n",
       "      <td>0.173816</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.004220</td>\n",
       "      <td>0.003064</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>442 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          age       sex       bmi        bp        s1        s2        s3  \\\n",
       "0    0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
       "1   -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
       "2    0.085299  0.050680  0.044451 -0.005671 -0.045599 -0.034194 -0.032356   \n",
       "3   -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
       "4    0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "437  0.041708  0.050680  0.019662  0.059744 -0.005697 -0.002566 -0.028674   \n",
       "438 -0.005515  0.050680 -0.015906 -0.067642  0.049341  0.079165 -0.028674   \n",
       "439  0.041708  0.050680 -0.015906  0.017282 -0.037344 -0.013840 -0.024993   \n",
       "440 -0.045472 -0.044642  0.039062  0.001215  0.016318  0.015283 -0.028674   \n",
       "441 -0.045472 -0.044642 -0.073030 -0.081414  0.083740  0.027809  0.173816   \n",
       "\n",
       "           s4        s5        s6  target  \n",
       "0   -0.002592  0.019908 -0.017646   151.0  \n",
       "1   -0.039493 -0.068330 -0.092204    75.0  \n",
       "2   -0.002592  0.002864 -0.025930   141.0  \n",
       "3    0.034309  0.022692 -0.009362   206.0  \n",
       "4   -0.002592 -0.031991 -0.046641   135.0  \n",
       "..        ...       ...       ...     ...  \n",
       "437 -0.002592  0.031193  0.007207   178.0  \n",
       "438  0.034309 -0.018118  0.044485   104.0  \n",
       "439 -0.011080 -0.046879  0.015491   132.0  \n",
       "440  0.026560  0.044528 -0.025930   220.0  \n",
       "441 -0.039493 -0.004220  0.003064    57.0  \n",
       "\n",
       "[442 rows x 11 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data=np.c_[dataset['data'],dataset['target']], columns = dataset['feature_names']+['target'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age       False\n",
       "sex       False\n",
       "bmi       False\n",
       "bp        False\n",
       "s1        False\n",
       "s2        False\n",
       "s3        False\n",
       "s4        False\n",
       "s5        False\n",
       "s6        False\n",
       "target    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any()\n",
    "\n",
    "# there are no missing data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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qiss7UmN2Az8C9gN3MHG3L805Rl9qzM3Aiymla4E9wD1tno9UiMs7UmP+E3gqIsaAcWBzm+cjFeIum5KUEZd3JCkjRl+SMmL0JSkjRl+SMmL0JSkjRl+SMmL0JSkj/w8oIQNbRvbDTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vdBGS1GPeA9RmN/ZC6EuSThOndySpIIa+JBXE0Jekghj6klQQQ1+SCmLoS1JBqq6nLxUvIr7I1D7QDWBtZv6wwyVJ8zL0pQoi4g+AC6b/ew/wn8DKjhYlNcHQl5oQEcuAB4FJ4CjwV8ClmdmIiPcAL3WwPKlpzulLzbkE2MnUdM4Xgd/LzKPTUzzfBv69k8VJzTL0peZsAn4NPAbcxNRon8z8LPAu4PaIeG/nypOaY+hLzbkS+EFmfgjYDGyNiK9PH3uVqWmfY50qTmqWC65JTZgexT/E1Aj/GLAOWAO8H+gHNmXmfZ2rUGqOoS9JBXF6R5IKYuhLUkEMfUkqiKEvSQUx9CWpIIa+JBXE0Jekgvw/W2O2OPeSTc8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EHqiW91R/1oFfAf41IgA+BDzV92mlM+SZuhaKk7SO968B/5KZ1wLPAAOn7QNwAPidavni6s+3gR8BGzNzA60z9GcmPa80L3gwaqH4KbAE+E1gV0TsAT4J/FKbfb8O/F5EPANsAk5k5kngBuDfI+IF4M9oxf+nwJKIuGsW/g5SR75RKp0mIn4X+Flm7ouIK4FbMvPyuZ5L6obX1KX3exO4LyImgBqwZY7nkbrmmbokFcRr6pJUEKMuSQUx6pJUEKMuSQUx6pJUkP8HTimr2RiAnd8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing the outliers\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "for column in df:\n",
    "    plt.figure()\n",
    "    df.boxplot([column])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}