Iowa_Liquor_Sales.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem Statement\n",
    "\n",
    " In this study I will be investigating data about total liquor sales in the state of Iowa in order to determine if the state should create a tax on liquor. The liquor sale data from the fiscal year of 2015 will be used to predict what the total revenue of liquor sales will be in 2016. With this information we can determine how much extra money in taxes the state could potentially earn if a small tax was place on liquor. Currently Iowa does not have a tax on liquor other than basic sales tax. I believe implementing a small liquor tax would increase state revenue without detering people from still buying liquor. The extra money generated by this tax can be used to help improve schools and infrastructure in the state. \n",
    "\n",
    "Linear models will be created in order to predict the future sales of 2016. Currently we have the data for the sales of the first 2016. If we compare this data to the sales data of 2015, we will be able to make a good prediction about liquor sales for the year 2016. Then we can figure out how much money in potential tax dollars the State of Iowa could earn. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import pandas as pd\n",
    "from scipy.special import erf\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import the csv file\n",
    "iowa_csv = 'file:///Users/Andrew/Downloads/iowa_liquor_sales_sample_10pct.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Date</th>\n",
       "      <th>Store Number</th>\n",
       "      <th>City</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>County Number</th>\n",
       "      <th>County</th>\n",
       "      <th>Category</th>\n",
       "      <th>Category Name</th>\n",
       "      <th>Vendor Number</th>\n",
       "      <th>Item Number</th>\n",
       "      <th>Item Description</th>\n",
       "      <th>Bottle Volume (ml)</th>\n",
       "      <th>State Bottle Cost</th>\n",
       "      <th>State Bottle Retail</th>\n",
       "      <th>Bottles Sold</th>\n",
       "      <th>Sale (Dollars)</th>\n",
       "      <th>Volume Sold (Liters)</th>\n",
       "      <th>Volume Sold (Gallons)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11/04/2015</td>\n",
       "      <td>3717</td>\n",
       "      <td>SUMNER</td>\n",
       "      <td>50674</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Bremer</td>\n",
       "      <td>1051100.0</td>\n",
       "      <td>APRICOT BRANDIES</td>\n",
       "      <td>55</td>\n",
       "      <td>54436</td>\n",
       "      <td>Mr. Boston Apricot Brandy</td>\n",
       "      <td>750</td>\n",
       "      <td>$4.50</td>\n",
       "      <td>$6.75</td>\n",
       "      <td>12</td>\n",
       "      <td>$81.00</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03/02/2016</td>\n",
       "      <td>2614</td>\n",
       "      <td>DAVENPORT</td>\n",
       "      <td>52807</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Scott</td>\n",
       "      <td>1011100.0</td>\n",
       "      <td>BLENDED WHISKIES</td>\n",
       "      <td>395</td>\n",
       "      <td>27605</td>\n",
       "      <td>Tin Cup</td>\n",
       "      <td>750</td>\n",
       "      <td>$13.75</td>\n",
       "      <td>$20.63</td>\n",
       "      <td>2</td>\n",
       "      <td>$41.26</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02/11/2016</td>\n",
       "      <td>2106</td>\n",
       "      <td>CEDAR FALLS</td>\n",
       "      <td>50613</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Black Hawk</td>\n",
       "      <td>1011200.0</td>\n",
       "      <td>STRAIGHT BOURBON WHISKIES</td>\n",
       "      <td>65</td>\n",
       "      <td>19067</td>\n",
       "      <td>Jim Beam</td>\n",
       "      <td>1000</td>\n",
       "      <td>$12.59</td>\n",
       "      <td>$18.89</td>\n",
       "      <td>24</td>\n",
       "      <td>$453.36</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>02/03/2016</td>\n",
       "      <td>2501</td>\n",
       "      <td>AMES</td>\n",
       "      <td>50010</td>\n",
       "      <td>85.0</td>\n",
       "      <td>Story</td>\n",
       "      <td>1071100.0</td>\n",
       "      <td>AMERICAN COCKTAILS</td>\n",
       "      <td>395</td>\n",
       "      <td>59154</td>\n",
       "      <td>1800 Ultimate Margarita</td>\n",
       "      <td>1750</td>\n",
       "      <td>$9.50</td>\n",
       "      <td>$14.25</td>\n",
       "      <td>6</td>\n",
       "      <td>$85.50</td>\n",
       "      <td>10.5</td>\n",
       "      <td>2.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08/18/2015</td>\n",
       "      <td>3654</td>\n",
       "      <td>BELMOND</td>\n",
       "      <td>50421</td>\n",
       "      <td>99.0</td>\n",
       "      <td>Wright</td>\n",
       "      <td>1031080.0</td>\n",
       "      <td>VODKA 80 PROOF</td>\n",
       "      <td>297</td>\n",
       "      <td>35918</td>\n",
       "      <td>Five O'clock Vodka</td>\n",
       "      <td>1750</td>\n",
       "      <td>$7.20</td>\n",
       "      <td>$10.80</td>\n",
       "      <td>12</td>\n",
       "      <td>$129.60</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Store Number         City Zip Code  County Number      County  \\\n",
       "0  11/04/2015          3717       SUMNER    50674            9.0      Bremer   \n",
       "1  03/02/2016          2614    DAVENPORT    52807           82.0       Scott   \n",
       "2  02/11/2016          2106  CEDAR FALLS    50613            7.0  Black Hawk   \n",
       "3  02/03/2016          2501         AMES    50010           85.0       Story   \n",
       "4  08/18/2015          3654      BELMOND    50421           99.0      Wright   \n",
       "\n",
       "    Category              Category Name  Vendor Number  Item Number  \\\n",
       "0  1051100.0           APRICOT BRANDIES             55        54436   \n",
       "1  1011100.0           BLENDED WHISKIES            395        27605   \n",
       "2  1011200.0  STRAIGHT BOURBON WHISKIES             65        19067   \n",
       "3  1071100.0         AMERICAN COCKTAILS            395        59154   \n",
       "4  1031080.0             VODKA 80 PROOF            297        35918   \n",
       "\n",
       "            Item Description  Bottle Volume (ml) State Bottle Cost  \\\n",
       "0  Mr. Boston Apricot Brandy                 750             $4.50   \n",
       "1                    Tin Cup                 750            $13.75   \n",
       "2                   Jim Beam                1000            $12.59   \n",
       "3    1800 Ultimate Margarita                1750             $9.50   \n",
       "4         Five O'clock Vodka                1750             $7.20   \n",
       "\n",
       "  State Bottle Retail  Bottles Sold Sale (Dollars)  Volume Sold (Liters)  \\\n",
       "0               $6.75            12         $81.00                   9.0   \n",
       "1              $20.63             2         $41.26                   1.5   \n",
       "2              $18.89            24        $453.36                  24.0   \n",
       "3              $14.25             6         $85.50                  10.5   \n",
       "4              $10.80            12        $129.60                  21.0   \n",
       "\n",
       "   Volume Sold (Gallons)  \n",
       "0                   2.38  \n",
       "1                   0.40  \n",
       "2                   6.34  \n",
       "3                   2.77  \n",
       "4                   5.55  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iowa = pd.read_csv(iowa_csv)\n",
    "iowa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Store Number</th>\n",
       "      <th>County Number</th>\n",
       "      <th>Category</th>\n",
       "      <th>Vendor Number</th>\n",
       "      <th>Item Number</th>\n",
       "      <th>Bottle Volume (ml)</th>\n",
       "      <th>Bottles Sold</th>\n",
       "      <th>Volume Sold (Liters)</th>\n",
       "      <th>Volume Sold (Gallons)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>270955.000000</td>\n",
       "      <td>269878.000000</td>\n",
       "      <td>2.708870e+05</td>\n",
       "      <td>270955.00000</td>\n",
       "      <td>270955.000000</td>\n",
       "      <td>270955.000000</td>\n",
       "      <td>270955.000000</td>\n",
       "      <td>270955.000000</td>\n",
       "      <td>270955.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3590.263701</td>\n",
       "      <td>57.231642</td>\n",
       "      <td>1.043888e+06</td>\n",
       "      <td>256.43443</td>\n",
       "      <td>45974.963300</td>\n",
       "      <td>924.830341</td>\n",
       "      <td>9.871285</td>\n",
       "      <td>8.981351</td>\n",
       "      <td>2.372830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>947.662050</td>\n",
       "      <td>27.341205</td>\n",
       "      <td>5.018211e+04</td>\n",
       "      <td>141.01489</td>\n",
       "      <td>52757.043086</td>\n",
       "      <td>493.088489</td>\n",
       "      <td>24.040912</td>\n",
       "      <td>28.913690</td>\n",
       "      <td>7.638182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2106.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.011100e+06</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2604.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1.012200e+06</td>\n",
       "      <td>115.00000</td>\n",
       "      <td>26827.000000</td>\n",
       "      <td>750.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3722.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>1.031200e+06</td>\n",
       "      <td>260.00000</td>\n",
       "      <td>38176.000000</td>\n",
       "      <td>750.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>1.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4378.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.062310e+06</td>\n",
       "      <td>380.00000</td>\n",
       "      <td>64573.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>2.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9023.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.701100e+06</td>\n",
       "      <td>978.00000</td>\n",
       "      <td>995507.000000</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>2508.000000</td>\n",
       "      <td>2508.000000</td>\n",
       "      <td>662.540000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Store Number  County Number      Category  Vendor Number  \\\n",
       "count  270955.000000  269878.000000  2.708870e+05   270955.00000   \n",
       "mean     3590.263701      57.231642  1.043888e+06      256.43443   \n",
       "std       947.662050      27.341205  5.018211e+04      141.01489   \n",
       "min      2106.000000       1.000000  1.011100e+06       10.00000   \n",
       "25%      2604.000000      31.000000  1.012200e+06      115.00000   \n",
       "50%      3722.000000      62.000000  1.031200e+06      260.00000   \n",
       "75%      4378.000000      77.000000  1.062310e+06      380.00000   \n",
       "max      9023.000000      99.000000  1.701100e+06      978.00000   \n",
       "\n",
       "         Item Number  Bottle Volume (ml)   Bottles Sold  Volume Sold (Liters)  \\\n",
       "count  270955.000000       270955.000000  270955.000000         270955.000000   \n",
       "mean    45974.963300          924.830341       9.871285              8.981351   \n",
       "std     52757.043086          493.088489      24.040912             28.913690   \n",
       "min       168.000000           50.000000       1.000000              0.100000   \n",
       "25%     26827.000000          750.000000       2.000000              1.500000   \n",
       "50%     38176.000000          750.000000       6.000000              5.250000   \n",
       "75%     64573.000000         1000.000000      12.000000             10.500000   \n",
       "max    995507.000000         6000.000000    2508.000000           2508.000000   \n",
       "\n",
       "       Volume Sold (Gallons)  \n",
       "count          270955.000000  \n",
       "mean                2.372830  \n",
       "std                 7.638182  \n",
       "min                 0.030000  \n",
       "25%                 0.400000  \n",
       "50%                 1.390000  \n",
       "75%                 2.770000  \n",
       "max               662.540000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iowa.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(270955, 18)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iowa.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                      object\n",
       "Store Number               int64\n",
       "City                      object\n",
       "Zip Code                  object\n",
       "County Number            float64\n",
       "County                    object\n",
       "Category                 float64\n",
       "Category Name             object\n",
       "Vendor Number              int64\n",
       "Item Number                int64\n",
       "Item Description          object\n",
       "Bottle Volume (ml)         int64\n",
       "State Bottle Cost         object\n",
       "State Bottle Retail       object\n",
       "Bottles Sold               int64\n",
       "Sale (Dollars)            object\n",
       "Volume Sold (Liters)     float64\n",
       "Volume Sold (Gallons)    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iowa.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Rename all the columns to make them easier to work with.\n",
    "\n",
    "iowa.rename(columns={'Store Number':'store_number','Zip Code':'zip_code','County Number':'county_number','Category Name':'category_name',\n",
    "                    'Vendor Number':'vendor_number','Item Number':'item_number','Item Description':'item_desc',\n",
    "                    'Bottle Volume (ml)':'bottle_vol_ml','State Bottle Cost':'state_bottle_cost',\n",
    "                    'State Bottle Retail':'state_bottle_retail','Bottles Sold':'bottles_sold',\n",
    "                    'Sale (Dollars)':'sale_dollars','Volume Sold (Liters)':'volume_sold_lt',\n",
    "                    'Volume Sold (Gallons)':'volume_sold_gal'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>store_number</th>\n",
       "      <th>City</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>county_number</th>\n",
       "      <th>County</th>\n",
       "      <th>Category</th>\n",
       "      <th>category_name</th>\n",
       "      <th>vendor_number</th>\n",
       "      <th>item_number</th>\n",
       "      <th>item_desc</th>\n",
       "      <th>bottle_vol_ml</th>\n",
       "      <th>state_bottle_cost</th>\n",
       "      <th>state_bottle_retail</th>\n",
       "      <th>bottles_sold</th>\n",
       "      <th>sale_dollars</th>\n",
       "      <th>volume_sold_lt</th>\n",
       "      <th>volume_sold_gal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-11-04</td>\n",
       "      <td>3717</td>\n",
       "      <td>SUMNER</td>\n",
       "      <td>50674</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Bremer</td>\n",
       "      <td>1051100.0</td>\n",
       "      <td>APRICOT BRANDIES</td>\n",
       "      <td>55</td>\n",
       "      <td>54436</td>\n",
       "      <td>Mr. Boston Apricot Brandy</td>\n",
       "      <td>750</td>\n",
       "      <td>$4.50</td>\n",
       "      <td>$6.75</td>\n",
       "      <td>12</td>\n",
       "      <td>$81.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-03-02</td>\n",
       "      <td>2614</td>\n",
       "      <td>DAVENPORT</td>\n",
       "      <td>52807</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Scott</td>\n",
       "      <td>1011100.0</td>\n",
       "      <td>BLENDED WHISKIES</td>\n",
       "      <td>395</td>\n",
       "      <td>27605</td>\n",
       "      <td>Tin Cup</td>\n",
       "      <td>750</td>\n",
       "      <td>$13.75</td>\n",
       "      <td>$20.63</td>\n",
       "      <td>2</td>\n",
       "      <td>$41.26</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-02-11</td>\n",
       "      <td>2106</td>\n",
       "      <td>CEDAR FALLS</td>\n",
       "      <td>50613</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Black Hawk</td>\n",
       "      <td>1011200.0</td>\n",
       "      <td>STRAIGHT BOURBON WHISKIES</td>\n",
       "      <td>65</td>\n",
       "      <td>19067</td>\n",
       "      <td>Jim Beam</td>\n",
       "      <td>1000</td>\n",
       "      <td>$12.59</td>\n",
       "      <td>$18.89</td>\n",
       "      <td>24</td>\n",
       "      <td>$453.36</td>\n",
       "      <td>24.00</td>\n",
       "      <td>6.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-02-03</td>\n",
       "      <td>2501</td>\n",
       "      <td>AMES</td>\n",
       "      <td>50010</td>\n",
       "      <td>85.0</td>\n",
       "      <td>Story</td>\n",
       "      <td>1071100.0</td>\n",
       "      <td>AMERICAN COCKTAILS</td>\n",
       "      <td>395</td>\n",
       "      <td>59154</td>\n",
       "      <td>1800 Ultimate Margarita</td>\n",
       "      <td>1750</td>\n",
       "      <td>$9.50</td>\n",
       "      <td>$14.25</td>\n",
       "      <td>6</td>\n",
       "      <td>$85.50</td>\n",
       "      <td>10.50</td>\n",
       "      <td>2.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-08-18</td>\n",
       "      <td>3654</td>\n",
       "      <td>BELMOND</td>\n",
       "      <td>50421</td>\n",
       "      <td>99.0</td>\n",
       "      <td>Wright</td>\n",
       "      <td>1031080.0</td>\n",
       "      <td>VODKA 80 PROOF</td>\n",
       "      <td>297</td>\n",
       "      <td>35918</td>\n",
       "      <td>Five O'clock Vodka</td>\n",
       "      <td>1750</td>\n",
       "      <td>$7.20</td>\n",
       "      <td>$10.80</td>\n",
       "      <td>12</td>\n",
       "      <td>$129.60</td>\n",
       "      <td>21.00</td>\n",
       "      <td>5.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015-04-20</td>\n",
       "      <td>2569</td>\n",
       "      <td>CEDAR RAPIDS</td>\n",
       "      <td>52402</td>\n",
       "      <td>57.0</td>\n",
       "      <td>Linn</td>\n",
       "      <td>1041100.0</td>\n",
       "      <td>AMERICAN DRY GINS</td>\n",
       "      <td>205</td>\n",
       "      <td>31473</td>\n",
       "      <td>New Amsterdam Gin</td>\n",
       "      <td>1750</td>\n",
       "      <td>$13.32</td>\n",
       "      <td>$19.98</td>\n",
       "      <td>6</td>\n",
       "      <td>$119.88</td>\n",
       "      <td>10.50</td>\n",
       "      <td>2.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015-08-05</td>\n",
       "      <td>2596</td>\n",
       "      <td>OTTUMWA</td>\n",
       "      <td>52501</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Wapello</td>\n",
       "      <td>1051010.0</td>\n",
       "      <td>AMERICAN GRAPE BRANDIES</td>\n",
       "      <td>85</td>\n",
       "      <td>52806</td>\n",
       "      <td>Korbel Brandy</td>\n",
       "      <td>750</td>\n",
       "      <td>$6.66</td>\n",
       "      <td>$9.99</td>\n",
       "      <td>3</td>\n",
       "      <td>$29.97</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015-06-25</td>\n",
       "      <td>3456</td>\n",
       "      <td>CLEAR LAKE</td>\n",
       "      <td>50428</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Cerro Gordo</td>\n",
       "      <td>1012100.0</td>\n",
       "      <td>CANADIAN WHISKIES</td>\n",
       "      <td>65</td>\n",
       "      <td>10628</td>\n",
       "      <td>Canadian Club Whisky</td>\n",
       "      <td>1750</td>\n",
       "      <td>$15.75</td>\n",
       "      <td>$23.63</td>\n",
       "      <td>2</td>\n",
       "      <td>$47.26</td>\n",
       "      <td>3.50</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>4757</td>\n",
       "      <td>BONDURANT</td>\n",
       "      <td>50035</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Polk</td>\n",
       "      <td>1032080.0</td>\n",
       "      <td>IMPORTED VODKA</td>\n",
       "      <td>370</td>\n",
       "      <td>34006</td>\n",
       "      <td>Absolut Swedish Vodka 80 Prf</td>\n",
       "      <td>750</td>\n",
       "      <td>$11.49</td>\n",
       "      <td>$17.24</td>\n",
       "      <td>4</td>\n",
       "      <td>$68.96</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015-11-10</td>\n",
       "      <td>4346</td>\n",
       "      <td>SHELLSBURG</td>\n",
       "      <td>52332</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Benton</td>\n",
       "      <td>1081315.0</td>\n",
       "      <td>CINNAMON SCHNAPPS</td>\n",
       "      <td>65</td>\n",
       "      <td>82610</td>\n",
       "      <td>Dekuyper Hot Damn!</td>\n",
       "      <td>1000</td>\n",
       "      <td>$7.62</td>\n",
       "      <td>$11.43</td>\n",
       "      <td>2</td>\n",
       "      <td>$22.86</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2015-09-28</td>\n",
       "      <td>2648</td>\n",
       "      <td>WEST DES MOINES</td>\n",
       "      <td>50265</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Polk</td>\n",
       "      <td>1011250.0</td>\n",
       "      <td>SINGLE BARREL BOURBON WHISKIES</td>\n",
       "      <td>65</td>\n",
       "      <td>19235</td>\n",
       "      <td>Knob Creek Single Barrel Reserve</td>\n",
       "      <td>750</td>\n",
       "      <td>$26.24</td>\n",
       "      <td>$39.36</td>\n",
       "      <td>6</td>\n",
       "      <td>$236.16</td>\n",
       "      <td>4.50</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2015-10-28</td>\n",
       "      <td>2600</td>\n",
       "      <td>OSKALOOSA</td>\n",
       "      <td>52577</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Mahaska</td>\n",
       "      <td>1071100.0</td>\n",
       "      <td>AMERICAN COCKTAILS</td>\n",
       "      <td>55</td>\n",
       "      <td>57148</td>\n",
       "      <td>Chi-Chi's Margarita W/tequila</td>\n",
       "      <td>1750</td>\n",
       "      <td>$6.00</td>\n",
       "      <td>$9.25</td>\n",
       "      <td>6</td>\n",
       "      <td>$55.50</td>\n",
       "      <td>10.50</td>\n",
       "      <td>2.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2015-01-22</td>\n",
       "      <td>3858</td>\n",
       "      <td>DAVENPORT</td>\n",
       "      <td>52806</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Scott</td>\n",
       "      <td>1012100.0</td>\n",
       "      <td>CANADIAN WHISKIES</td>\n",
       "      <td>65</td>\n",
       "      <td>15248</td>\n",
       "      <td>Windsor Canadian Pet</td>\n",
       "      <td>1750</td>\n",
       "      <td>$8.92</td>\n",
       "      <td>$13.38</td>\n",
       "      <td>1</td>\n",
       "      <td>$13.38</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2016-02-29</td>\n",
       "      <td>4673</td>\n",
       "      <td>WEST POINT</td>\n",
       "      <td>52656</td>\n",
       "      <td>56.0</td>\n",
       "      <td>Lee</td>\n",
       "      <td>1081300.0</td>\n",
       "      <td>PEPPERMINT SCHNAPPS</td>\n",
       "      <td>322</td>\n",
       "      <td>75211</td>\n",
       "      <td>Kinky Liqueur</td>\n",
       "      <td>750</td>\n",
       "      <td>$10.00</td>\n",
       "      <td>$15.00</td>\n",
       "      <td>12</td>\n",
       "      <td>$180.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>2670</td>\n",
       "      <td>CORALVILLE</td>\n",
       "      <td>52241</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Johnson</td>\n",
       "      <td>1031200.0</td>\n",
       "      <td>VODKA FLAVORED</td>\n",
       "      <td>380</td>\n",
       "      <td>41705</td>\n",
       "      <td>Uv Red (cherry) Vodka</td>\n",
       "      <td>1000</td>\n",
       "      <td>$7.50</td>\n",
       "      <td>$11.25</td>\n",
       "      <td>12</td>\n",
       "      <td>$135.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>3.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015-06-10</td>\n",
       "      <td>3717</td>\n",
       "      <td>SUMNER</td>\n",
       "      <td>50674</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Bremer</td>\n",
       "      <td>1051100.0</td>\n",
       "      <td>APRICOT BRANDIES</td>\n",
       "      <td>55</td>\n",
       "      <td>54436</td>\n",
       "      <td>Mr. Boston Apricot Brandy</td>\n",
       "      <td>750</td>\n",
       "      <td>$4.50</td>\n",
       "      <td>$6.75</td>\n",
       "      <td>4</td>\n",
       "      <td>$27.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016-02-25</td>\n",
       "      <td>2130</td>\n",
       "      <td>WATERLOO</td>\n",
       "      <td>50703</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Black Hawk</td>\n",
       "      <td>1031100.0</td>\n",
       "      <td>100 PROOF VODKA</td>\n",
       "      <td>297</td>\n",
       "      <td>35913</td>\n",
       "      <td>Five O'clock Vodka</td>\n",
       "      <td>200</td>\n",
       "      <td>$1.13</td>\n",
       "      <td>$1.70</td>\n",
       "      <td>48</td>\n",
       "      <td>$81.60</td>\n",
       "      <td>9.60</td>\n",
       "      <td>2.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>2600</td>\n",
       "      <td>OSKALOOSA</td>\n",
       "      <td>52577</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Mahaska</td>\n",
       "      <td>1031200.0</td>\n",
       "      <td>VODKA FLAVORED</td>\n",
       "      <td>380</td>\n",
       "      <td>41989</td>\n",
       "      <td>Uv Cake Vodka</td>\n",
       "      <td>750</td>\n",
       "      <td>$6.50</td>\n",
       "      <td>$9.75</td>\n",
       "      <td>12</td>\n",
       "      <td>$117.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2015-12-26</td>\n",
       "      <td>4604</td>\n",
       "      <td>NEWTON</td>\n",
       "      <td>50208</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Jasper</td>\n",
       "      <td>1081700.0</td>\n",
       "      <td>DISTILLED SPIRITS SPECIALTY</td>\n",
       "      <td>434</td>\n",
       "      <td>75087</td>\n",
       "      <td>Juarez Gold Dss</td>\n",
       "      <td>1000</td>\n",
       "      <td>$4.92</td>\n",
       "      <td>$7.38</td>\n",
       "      <td>24</td>\n",
       "      <td>$177.12</td>\n",
       "      <td>24.00</td>\n",
       "      <td>6.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2015-03-04</td>\n",
       "      <td>2614</td>\n",
       "      <td>DAVENPORT</td>\n",
       "      <td>52807</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Scott</td>\n",
       "      <td>1012100.0</td>\n",
       "      <td>CANADIAN WHISKIES</td>\n",
       "      <td>434</td>\n",
       "      <td>13638</td>\n",
       "      <td>Lord Calvert Canadian Whisky</td>\n",
       "      <td>1750</td>\n",
       "      <td>$9.97</td>\n",
       "      <td>$14.96</td>\n",
       "      <td>6</td>\n",
       "      <td>$89.76</td>\n",
       "      <td>10.50</td>\n",
       "      <td>2.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2015-01-16</td>\n",
       "      <td>2590</td>\n",
       "      <td>CEDAR RAPIDS</td>\n",
       "      <td>52402</td>\n",
       "      <td>57.0</td>\n",
       "      <td>Linn</td>\n",
       "      <td>1031200.0</td>\n",
       "      <td>VODKA FLAVORED</td>\n",
       "      <td>971</td>\n",
       "      <td>41474</td>\n",
       "      <td>44 North Huckleberry</td>\n",
       "      <td>750</td>\n",
       "      <td>$14.25</td>\n",
       "      <td>$21.38</td>\n",
       "      <td>2</td>\n",
       "      <td>$42.76</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2015-01-13</td>\n",
       "      <td>3942</td>\n",
       "      <td>TOLEDO</td>\n",
       "      <td>52342</td>\n",
       "      <td>86.0</td>\n",
       "      <td>Tama</td>\n",
       "      <td>1012100.0</td>\n",
       "      <td>CANADIAN WHISKIES</td>\n",
       "      <td>115</td>\n",
       "      <td>11786</td>\n",
       "      <td>Black Velvet Traveler</td>\n",
       "      <td>750</td>\n",
       "      <td>$5.23</td>\n",
       "      <td>$7.85</td>\n",
       "      <td>12</td>\n",
       "      <td>$94.20</td>\n",
       "      <td>9.00</td>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2015-03-24</td>\n",
       "      <td>3981</td>\n",
       "      <td>SIOUX CENTER</td>\n",
       "      <td>51250</td>\n",
       "      <td>84.0</td>\n",
       "      <td>Sioux</td>\n",
       "      <td>1011100.0</td>\n",
       "      <td>BLENDED WHISKIES</td>\n",
       "      <td>260</td>\n",
       "      <td>25606</td>\n",
       "      <td>Seagrams 7 Crown Bl Whiskey</td>\n",
       "      <td>750</td>\n",
       "      <td>$7.00</td>\n",
       "      <td>$10.50</td>\n",
       "      <td>12</td>\n",
       "      <td>$126.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2015-11-12</td>\n",
       "      <td>4376</td>\n",
       "      <td>MASON CITY</td>\n",
       "      <td>50401</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Cerro Gordo</td>\n",
       "      <td>1022100.0</td>\n",
       "      <td>TEQUILA</td>\n",
       "      <td>395</td>\n",
       "      <td>89196</td>\n",
       "      <td>Jose Cuervo Especial Reposado Tequila</td>\n",
       "      <td>750</td>\n",
       "      <td>$10.49</td>\n",
       "      <td>$15.74</td>\n",
       "      <td>12</td>\n",
       "      <td>$188.88</td>\n",
       "      <td>9.00</td>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2016-03-14</td>\n",
       "      <td>2618</td>\n",
       "      <td>CEDAR RAPIDS</td>\n",
       "      <td>52402</td>\n",
       "      <td>57.0</td>\n",
       "      <td>Linn</td>\n",
       "      <td>1031080.0</td>\n",
       "      <td>VODKA 80 PROOF</td>\n",
       "      <td>297</td>\n",
       "      <td>35926</td>\n",
       "      <td>Five O'clock PET Vodka</td>\n",
       "      <td>750</td>\n",
       "      <td>$3.37</td>\n",
       "      <td>$5.06</td>\n",
       "      <td>12</td>\n",
       "      <td>$60.72</td>\n",
       "      <td>9.00</td>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2015-02-03</td>\n",
       "      <td>3390</td>\n",
       "      <td>MILFORD</td>\n",
       "      <td>51351</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Dickinson</td>\n",
       "      <td>1011300.0</td>\n",
       "      <td>TENNESSEE WHISKIES</td>\n",
       "      <td>85</td>\n",
       "      <td>26827</td>\n",
       "      <td>Jack Daniels Old #7 Black Lbl</td>\n",
       "      <td>1000</td>\n",
       "      <td>$17.90</td>\n",
       "      <td>$26.85</td>\n",
       "      <td>24</td>\n",
       "      <td>$644.40</td>\n",
       "      <td>24.00</td>\n",
       "      <td>6.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2015-11-23</td>\n",
       "      <td>3565</td>\n",
       "      <td>IOWA CITY</td>\n",
       "      <td>52246</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Johnson</td>\n",
       "      <td>1012100.0</td>\n",
       "      <td>CANADIAN WHISKIES</td>\n",
       "      <td>260</td>\n",
       "      <td>11290</td>\n",
       "      <td>Crown Royal Canadian Whisky Mini</td>\n",
       "      <td>300</td>\n",
       "      <td>$7.35</td>\n",
       "      <td>$11.03</td>\n",
       "      <td>1</td>\n",
       "      <td>$11.03</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2015-04-21</td>\n",
       "      <td>3963</td>\n",
       "      <td>COUNCIL BLUFFS</td>\n",
       "      <td>51501</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Pottawattamie</td>\n",
       "      <td>1011100.0</td>\n",
       "      <td>BLENDED WHISKIES</td>\n",
       "      <td>434</td>\n",
       "      <td>24156</td>\n",
       "      <td>Hawkeye Blend Whiskey</td>\n",
       "      <td>750</td>\n",
       "      <td>$3.50</td>\n",
       "      <td>$5.25</td>\n",
       "      <td>3</td>\n",
       "      <td>$15.75</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>4375</td>\n",
       "      <td>GRIMES</td>\n",
       "      <td>50111</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Polk</td>\n",
       "      <td>1031080.0</td>\n",
       "      <td>VODKA 80 PROOF</td>\n",
       "      <td>380</td>\n",
       "      <td>37338</td>\n",
       "      <td>UV Vodka PET</td>\n",
       "      <td>1750</td>\n",
       "      <td>$10.99</td>\n",
       "      <td>$16.49</td>\n",
       "      <td>6</td>\n",
       "      <td>$98.94</td>\n",
       "      <td>10.50</td>\n",
       "      <td>2.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2015-01-14</td>\n",
       "      <td>2545</td>\n",
       "      <td>IOWA CITY</td>\n",
       "      <td>52245</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Johnson</td>\n",
       "      <td>1081600.0</td>\n",
       "      <td>WHISKEY LIQUEUR</td>\n",
       "      <td>260</td>\n",
       "      <td>66199</td>\n",
       "      <td>Piehole Apple Pie Mini</td>\n",
       "      <td>500</td>\n",
       "      <td>$4.90</td>\n",
       "      <td>$7.35</td>\n",
       "      <td>1</td>\n",
       "      <td>$7.35</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.13</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270925</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>2837</td>\n",
       "      <td>WATERLOO</td>\n",
       "      <td>50702</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Black Hawk</td>\n",
       "      <td>1081200.0</td>\n",
       "      <td>CREAM LIQUEURS</td>\n",
       "      <td>260</td>\n",
       "      <td>68306</td>\n",
       "      <td>Emmets Irish Cream Liqueur</td>\n",
       "      <td>750</td>\n",
       "      <td>$6.00</td>\n",
       "      <td>$9.00</td>\n",
       "      <td>4</td>\n",
       "      <td>$36.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270926</th>\n",
       "      <td>2015-12-26</td>\n",
       "      <td>2478</td>\n",
       "      <td>ALTOONA</td>\n",
       "      <td>50009</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Polk</td>\n",
       "      <td>1022100.0</td>\n",
       "      <td>TEQUILA</td>\n",
       "      <td>410</td>\n",
       "      <td>89646</td>\n",
       "      <td>Patron Anejo</td>\n",
       "      <td>750</td>\n",
       "      <td>$31.00</td>\n",
       "      <td>$46.50</td>\n",
       "      <td>6</td>\n",
       "      <td>$279.00</td>\n",
       "      <td>4.50</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270927</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>2633</td>\n",
       "      <td>DES MOINES</td>\n",
       "      <td>50320</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Polk</td>\n",
       "      <td>1031200.0</td>\n",
       "      <td>VODKA FLAVORED</td>\n",
       "      <td>260</td>\n",
       "      <td>77699</td>\n",
       "      <td>Smirnoff Orange</td>\n",
       "      <td>1000</td>\n",
       "      <td>$9.25</td>\n",
       "      <td>$13.88</td>\n",
       "      <td>12</td>\n",
       "      <td>$166.56</td>\n",
       "      <td>12.00</td>\n",
       "      <td>3.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270928</th>\n",
       "      <td>2015-10-07</td>\n",
       "      <td>3896</td>\n",
       "      <td>IOWA CITY</td>\n",
       "      <td>52240</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Johnson</td>\n",
       "      <td>1031080.0</td>\n",
       "      <td>VODKA 80 PROOF</td>\n",
       "      <td>300</td>\n",
       "      <td>36904</td>\n",
       "      <td>Mccormick Vodka Pet</td>\n",
       "      <td>375</td>\n",
       "      <td>$1.80</td>\n",
       "      <td>$2.70</td>\n",
       "      <td>48</td>\n",
       "      <td>$129.60</td>\n",
       "      <td>18.00</td>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270929</th>\n",
       "      <td>2016-01-25</td>\n",
       "      <td>5070</td>\n",
       "      <td>WILTON</td>\n",
       "      <td>52778</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Muscatine</td>\n",
       "      <td>1031080.0</td>\n",
       "      <td>VODKA 80 PROOF</td>\n",
       "      <td>380</td>\n",
       "      <td>37346</td>\n",
       "      <td>Phillips Vodka</td>\n",
       "      <td>750</td>\n",
       "      <td>$3.57</td>\n",
       "      <td>$5.36</td>\n",
       "      <td>4</td>\n",
       "      <td>$21.44</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270930</th>\n",
       "      <td>2015-10-13</td>\n",
       "      <td>4634</td>\n",
       "      <td>CEDAR RAPIDS</td>\n",
       "      <td>52402</td>\n",
       "      <td>57.0</td>\n",
       "      <td>Linn</td>\n",
       "      <td>1031080.0</td>\n",
       "      <td>VODKA 80 PROOF</td>\n",
       "      <td>297</td>\n",
       "      <td>35914</td>\n",
       "      <td>Five O'clock Vodka</td>\n",
       "      <td>375</td>\n",
       "      <td>$1.93</td>\n",
       "      <td>$2.90</td>\n",
       "      <td>12</td>\n",
       "      <td>$34.80</td>\n",
       "      <td>4.50</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270931</th>\n",
       "      <td>2015-05-26</td>\n",
       "      <td>2602</td>\n",
       "      <td>WEBSTER CITY</td>\n",
       "      <td>50595</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Hamilton</td>\n",
       "      <td>1081900.0</td>\n",
       "      <td>MISC. AMERICAN CORDIALS &amp; LIQUEURS</td>\n",
       "      <td>65</td>\n",
       "      <td>85526</td>\n",
       "      <td>Dekuyper Signature Blue Curacao</td>\n",
       "      <td>750</td>\n",
       "      <td>$5.25</td>\n",
       "      <td>$7.88</td>\n",
       "      <td>12</td>\n",
       "      <td>$94.56</td>\n",
       "      <td>9.00</td>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270932</th>\n",
       "      <td>2015-06-18</td>\n",
       "      <td>4604</td>\n",
       "      <td>NEWTON</td>\n",
       "      <td>50208</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Jasper</td>\n",
       "      <td>1011300.0</td>\n",
       "      <td>TENNESSEE WHISKIES</td>\n",
       "      <td>85</td>\n",
       "      <td>26828</td>\n",
       "      <td>Jack Daniels Old #7 Black Lbl</td>\n",
       "      <td>1750</td>\n",
       "      <td>$29.67</td>\n",
       "      <td>$44.51</td>\n",
       "      <td>2</td>\n",
       "      <td>$89.02</td>\n",
       "      <td>3.50</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270933</th>\n",
       "      <td>2015-02-26</td>\n",
       "      <td>4987</td>\n",
       "      <td>AMES</td>\n",
       "      <td>50010</td>\n",
       "      <td>85.0</td>\n",
       "      <td>Story</td>\n",
       "      <td>1062310.0</td>\n",
       "      <td>SPICED RUM</td>\n",
       "      <td>260</td>\n",
       "      <td>43336</td>\n",
       "      <td>Captain Morgan Original Spiced</td>\n",
       "      <td>750</td>\n",
       "      <td>$9.00</td>\n",
       "      <td>$13.50</td>\n",
       "      <td>3</td>\n",
       "      <td>$40.50</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270934</th>\n",
       "      <td>2015-03-18</td>\n",
       "      <td>2623</td>\n",
       "      <td>SIOUX CITY</td>\n",
       "      <td>51104</td>\n",
       "      <td>97.0</td>\n",
       "      <td>Woodbury</td>\n",
       "      <td>1051110.0</td>\n",
       "      <td>BLACKBERRY BRANDIES</td>\n",
       "      <td>55</td>\n",
       "      <td>55068</td>\n",
       "      <td>Mr. Boston Blackberry Brandy</td>\n",
       "      <td>1750</td>\n",
       "      <td>$10.00</td>\n",
       "      <td>$15.00</td>\n",
       "      <td>2</td>\n",
       "      <td>$30.00</td>\n",
       "      <td>3.50</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270935</th>\n",
       "      <td>2015-06-08</td>\n",
       "      <td>5126</td>\n",
       "      <td>DES MOINES</td>\n",
       "      <td>50310</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Polk</td>\n",
       "      <td>1011300.0</td>\n",
       "      <td>TENNESSEE WHISKIES</td>\n",
       "      <td>85</td>\n",
       "      <td>86670</td>\n",
       "      <td>Jack Daniel's Tennessee Honey</td>\n",
       "      <td>750</td>\n",
       "      <td>$15.07</td>\n",
       "      <td>$22.61</td>\n",
       "      <td>3</td>\n",
       "      <td>$67.83</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270936</th>\n",
       "      <td>2015-03-31</td>\n",
       "      <td>2552</td>\n",
       "      <td>CEDAR RAPIDS</td>\n",
       "      <td>52404</td>\n",
       "      <td>57.0</td>\n",
       "      <td>Linn</td>\n",
       "      <td>1022100.0</td>\n",
       "      <td>TEQUILA</td>\n",
       "      <td>395</td>\n",
       "      <td>89196</td>\n",
       "      <td>Jose Cuervo Especial Reposado Tequila</td>\n",
       "      <td>750</td>\n",
       "      <td>$10.49</td>\n",
       "      <td>$15.74</td>\n",
       "      <td>12</td>\n",
       "      <td>$188.88</td>\n",
       "      <td>9.00</td>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270937</th>\n",
       "      <td>2015-01-27</td>\n",
       "      <td>2553</td>\n",
       "      <td>CRESTON</td>\n",
       "      <td>50801</td>\n",
       "      <td>88.0</td>\n",
       "      <td>Union</td>\n",
       "      <td>1082900.0</td>\n",
       "      <td>MISC. IMPORTED CORDIALS &amp; LIQUEURS</td>\n",
       "      <td>85</td>\n",
       "      <td>64676</td>\n",
       "      <td>Chambord Liqueur Royale De France</td>\n",
       "      <td>750</td>\n",
       "      <td>$18.56</td>\n",
       "      <td>$27.84</td>\n",
       "      <td>1</td>\n",
       "      <td>$27.84</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270938</th>\n",
       "      <td>2015-04-29</td>\n",
       "      <td>3890</td>\n",
       "      <td>CEDAR RAPIDS</td>\n",
       "      <td>52405</td>\n",
       "      <td>57.0</td>\n",
       "      <td>Linn</td>\n",
       "      <td>1031080.0</td>\n",
       "      <td>VODKA 80 PROOF</td>\n",
       "      <td>260</td>\n",
       "      <td>37994</td>\n",
       "      <td>Smirnoff Vodka 80 Prf</td>\n",
       "      <td>375</td>\n",
       "      <td>$4.75</td>\n",
       "      <td>$7.13</td>\n",
       "      <td>3</td>\n",
       "      <td>$21.39</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270939</th>\n",
       "      <td>2015-05-04</td>\n",
       "      <td>4876</td>\n",
       "      <td>HIAWATHA</td>\n",
       "      <td>52233</td>\n",
       "      <td>57.0</td>\n",
       "      <td>Linn</td>\n",
       "      <td>1031200.0</td>\n",
       "      <td>VODKA FLAVORED</td>\n",
       "      <td>380</td>\n",
       "      <td>42002</td>\n",
       "      <td>Uv Cake Vodka</td>\n",
       "      <td>500</td>\n",
       "      <td>$4.89</td>\n",
       "      <td>$7.34</td>\n",
       "      <td>6</td>\n",
       "      <td>$44.04</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270940</th>\n",
       "      <td>2015-01-21</td>\n",
       "      <td>2523</td>\n",
       "      <td>CENTERVILLE</td>\n",
       "      <td>52544</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Appanoose</td>\n",
       "      <td>1012200.0</td>\n",
       "      <td>SCOTCH WHISKIES</td>\n",
       "      <td>305</td>\n",
       "      <td>4936</td>\n",
       "      <td>Famous Grouse Scotch</td>\n",
       "      <td>750</td>\n",
       "      <td>$12.03</td>\n",
       "      <td>$18.05</td>\n",
       "      <td>4</td>\n",
       "      <td>$72.20</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270941</th>\n",
       "      <td>2015-02-09</td>\n",
       "      <td>4856</td>\n",
       "      <td>LISBON</td>\n",
       "      <td>52253</td>\n",
       "      <td>57.0</td>\n",
       "      <td>Linn</td>\n",
       "      <td>1031080.0</td>\n",
       "      <td>VODKA 80 PROOF</td>\n",
       "      <td>55</td>\n",
       "      <td>35948</td>\n",
       "      <td>Fleischmann's Royal Vodka 80 Prf</td>\n",
       "      <td>1750</td>\n",
       "      <td>$6.68</td>\n",
       "      <td>$10.27</td>\n",
       "      <td>6</td>\n",
       "      <td>$61.62</td>\n",
       "      <td>10.50</td>\n",
       "      <td>2.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270942</th>\n",
       "      <td>2015-06-15</td>\n",
       "      <td>2512</td>\n",
       "      <td>IOWA CITY</td>\n",
       "      <td>52240</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Johnson</td>\n",
       "      <td>1011100.0</td>\n",
       "      <td>BLENDED WHISKIES</td>\n",
       "      <td>65</td>\n",
       "      <td>24466</td>\n",
       "      <td>Kessler Blend Whiskey Traveler</td>\n",
       "      <td>750</td>\n",
       "      <td>$5.51</td>\n",
       "      <td>$8.27</td>\n",
       "      <td>12</td>\n",
       "      <td>$99.24</td>\n",
       "      <td>9.00</td>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270943</th>\n",
       "      <td>2015-10-21</td>\n",
       "      <td>4438</td>\n",
       "      <td>AMES</td>\n",
       "      <td>50010</td>\n",
       "      <td>85.0</td>\n",
       "      <td>Story</td>\n",
       "      <td>1011200.0</td>\n",
       "      <td>STRAIGHT BOURBON WHISKIES</td>\n",
       "      <td>65</td>\n",
       "      <td>19061</td>\n",
       "      <td>Jim Beam Mini</td>\n",
       "      <td>500</td>\n",
       "      <td>$6.83</td>\n",
       "      <td>$10.25</td>\n",
       "      <td>1</td>\n",
       "      <td>$10.25</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270944</th>\n",
       "      <td>2016-03-22</td>\n",
       "      <td>2620</td>\n",
       "      <td>WINDSOR HEIGHTS</td>\n",
       "      <td>50311</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Polk</td>\n",
       "      <td>1081240.0</td>\n",
       "      <td>GREEN CREME DE MENTHE</td>\n",
       "      <td>434</td>\n",
       "      <td>79336</td>\n",
       "      <td>Paramount Creme De Menthe/green</td>\n",
       "      <td>750</td>\n",
       "      <td>$4.75</td>\n",
       "      <td>$7.13</td>\n",
       "      <td>2</td>\n",
       "      <td>$14.26</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270945</th>\n",
       "      <td>2015-06-11</td>\n",
       "      <td>2627</td>\n",
       "      <td>DES MOINES</td>\n",
       "      <td>50310</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Polk</td>\n",
       "      <td>1062200.0</td>\n",
       "      <td>PUERTO RICO &amp; VIRGIN ISLANDS RUM</td>\n",
       "      <td>55</td>\n",
       "      <td>44215</td>\n",
       "      <td>Barton Light Rum</td>\n",
       "      <td>100</td>\n",
       "      <td>$0.89</td>\n",
       "      <td>$1.34</td>\n",
       "      <td>48</td>\n",
       "      <td>$64.32</td>\n",
       "      <td>4.80</td>\n",
       "      <td>1.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270946</th>\n",
       "      <td>2015-11-24</td>\n",
       "      <td>5162</td>\n",
       "      <td>URBANDALE</td>\n",
       "      <td>50322</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Polk</td>\n",
       "      <td>1031080.0</td>\n",
       "      <td>VODKA 80 PROOF</td>\n",
       "      <td>301</td>\n",
       "      <td>38176</td>\n",
       "      <td>Titos Vodka</td>\n",
       "      <td>750</td>\n",
       "      <td>$9.65</td>\n",
       "      <td>$14.48</td>\n",
       "      <td>4</td>\n",
       "      <td>$57.92</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270947</th>\n",
       "      <td>2015-05-12</td>\n",
       "      <td>4375</td>\n",
       "      <td>GRIMES</td>\n",
       "      <td>50111</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Polk</td>\n",
       "      <td>1062310.0</td>\n",
       "      <td>SPICED RUM</td>\n",
       "      <td>260</td>\n",
       "      <td>43334</td>\n",
       "      <td>Captain Morgan Spiced Rum</td>\n",
       "      <td>375</td>\n",
       "      <td>$5.00</td>\n",
       "      <td>$7.50</td>\n",
       "      <td>6</td>\n",
       "      <td>$45.00</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270948</th>\n",
       "      <td>2015-04-08</td>\n",
       "      <td>2545</td>\n",
       "      <td>IOWA CITY</td>\n",
       "      <td>52245</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Johnson</td>\n",
       "      <td>1062300.0</td>\n",
       "      <td>FLAVORED RUM</td>\n",
       "      <td>35</td>\n",
       "      <td>43145</td>\n",
       "      <td>Bacardi Pineapple Fusion</td>\n",
       "      <td>750</td>\n",
       "      <td>$8.50</td>\n",
       "      <td>$12.75</td>\n",
       "      <td>3</td>\n",
       "      <td>$38.25</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270949</th>\n",
       "      <td>2015-07-21</td>\n",
       "      <td>2529</td>\n",
       "      <td>CEDAR RAPIDS</td>\n",
       "      <td>52405</td>\n",
       "      <td>57.0</td>\n",
       "      <td>Linn</td>\n",
       "      <td>1031100.0</td>\n",
       "      <td>100 PROOF VODKA</td>\n",
       "      <td>300</td>\n",
       "      <td>36887</td>\n",
       "      <td>McCormick Vodka</td>\n",
       "      <td>1000</td>\n",
       "      <td>$4.13</td>\n",
       "      <td>$6.20</td>\n",
       "      <td>3</td>\n",
       "      <td>$18.60</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270950</th>\n",
       "      <td>2015-12-22</td>\n",
       "      <td>4057</td>\n",
       "      <td>DES MOINES</td>\n",
       "      <td>50316</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Polk</td>\n",
       "      <td>1022100.0</td>\n",
       "      <td>TEQUILA</td>\n",
       "      <td>410</td>\n",
       "      <td>88291</td>\n",
       "      <td>Patron Tequila Silver Mini</td>\n",
       "      <td>300</td>\n",
       "      <td>$20.30</td>\n",
       "      <td>$30.45</td>\n",
       "      <td>4</td>\n",
       "      <td>$121.80</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270951</th>\n",
       "      <td>2015-11-04</td>\n",
       "      <td>5151</td>\n",
       "      <td>IDA GROVE</td>\n",
       "      <td>51445</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Ida</td>\n",
       "      <td>1011200.0</td>\n",
       "      <td>STRAIGHT BOURBON WHISKIES</td>\n",
       "      <td>259</td>\n",
       "      <td>17956</td>\n",
       "      <td>Evan Williams Str Bourbon</td>\n",
       "      <td>750</td>\n",
       "      <td>$7.47</td>\n",
       "      <td>$11.21</td>\n",
       "      <td>3</td>\n",
       "      <td>$33.63</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270952</th>\n",
       "      <td>2015-10-20</td>\n",
       "      <td>5152</td>\n",
       "      <td>WATERLOO</td>\n",
       "      <td>50702</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Black Hawk</td>\n",
       "      <td>1011300.0</td>\n",
       "      <td>TENNESSEE WHISKIES</td>\n",
       "      <td>85</td>\n",
       "      <td>26826</td>\n",
       "      <td>Jack Daniels Old #7 Black Lbl</td>\n",
       "      <td>750</td>\n",
       "      <td>$15.07</td>\n",
       "      <td>$22.61</td>\n",
       "      <td>6</td>\n",
       "      <td>$135.66</td>\n",
       "      <td>4.50</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270953</th>\n",
       "      <td>2015-11-20</td>\n",
       "      <td>3562</td>\n",
       "      <td>WEST BURLINGTON</td>\n",
       "      <td>52655</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Des Moines</td>\n",
       "      <td>1082900.0</td>\n",
       "      <td>MISC. IMPORTED CORDIALS &amp; LIQUEURS</td>\n",
       "      <td>192</td>\n",
       "      <td>65258</td>\n",
       "      <td>Jagermeister Liqueur</td>\n",
       "      <td>1750</td>\n",
       "      <td>$26.05</td>\n",
       "      <td>$39.08</td>\n",
       "      <td>6</td>\n",
       "      <td>$234.48</td>\n",
       "      <td>10.50</td>\n",
       "      <td>2.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270954</th>\n",
       "      <td>2015-01-27</td>\n",
       "      <td>4446</td>\n",
       "      <td>URBANDALE</td>\n",
       "      <td>50322</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Polk</td>\n",
       "      <td>1031080.0</td>\n",
       "      <td>VODKA 80 PROOF</td>\n",
       "      <td>260</td>\n",
       "      <td>37993</td>\n",
       "      <td>Smirnoff Vodka 80 Prf</td>\n",
       "      <td>200</td>\n",
       "      <td>$2.75</td>\n",
       "      <td>$4.13</td>\n",
       "      <td>8</td>\n",
       "      <td>$33.04</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>270955 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date  store_number             City zip_code  county_number  \\\n",
       "0      2015-11-04          3717           SUMNER    50674            9.0   \n",
       "1      2016-03-02          2614        DAVENPORT    52807           82.0   \n",
       "2      2016-02-11          2106      CEDAR FALLS    50613            7.0   \n",
       "3      2016-02-03          2501             AMES    50010           85.0   \n",
       "4      2015-08-18          3654          BELMOND    50421           99.0   \n",
       "5      2015-04-20          2569     CEDAR RAPIDS    52402           57.0   \n",
       "6      2015-08-05          2596          OTTUMWA    52501           90.0   \n",
       "7      2015-06-25          3456       CLEAR LAKE    50428           17.0   \n",
       "8      2016-01-04          4757        BONDURANT    50035           77.0   \n",
       "9      2015-11-10          4346       SHELLSBURG    52332            6.0   \n",
       "10     2015-09-28          2648  WEST DES MOINES    50265           77.0   \n",
       "11     2015-10-28          2600        OSKALOOSA    52577           62.0   \n",
       "12     2015-01-22          3858        DAVENPORT    52806           82.0   \n",
       "13     2016-02-29          4673       WEST POINT    52656           56.0   \n",
       "14     2016-01-04          2670       CORALVILLE    52241           52.0   \n",
       "15     2015-06-10          3717           SUMNER    50674            9.0   \n",
       "16     2016-02-25          2130         WATERLOO    50703            7.0   \n",
       "17     2015-12-29          2600        OSKALOOSA    52577           62.0   \n",
       "18     2015-12-26          4604           NEWTON    50208           50.0   \n",
       "19     2015-03-04          2614        DAVENPORT    52807           82.0   \n",
       "20     2015-01-16          2590     CEDAR RAPIDS    52402           57.0   \n",
       "21     2015-01-13          3942           TOLEDO    52342           86.0   \n",
       "22     2015-03-24          3981     SIOUX CENTER    51250           84.0   \n",
       "23     2015-11-12          4376       MASON CITY    50401           17.0   \n",
       "24     2016-03-14          2618     CEDAR RAPIDS    52402           57.0   \n",
       "25     2015-02-03          3390          MILFORD    51351           30.0   \n",
       "26     2015-11-23          3565        IOWA CITY    52246           52.0   \n",
       "27     2015-04-21          3963   COUNCIL BLUFFS    51501           78.0   \n",
       "28     2015-12-01          4375           GRIMES    50111           77.0   \n",
       "29     2015-01-14          2545        IOWA CITY    52245           52.0   \n",
       "...           ...           ...              ...      ...            ...   \n",
       "270925 2015-01-05          2837         WATERLOO    50702            7.0   \n",
       "270926 2015-12-26          2478          ALTOONA    50009           77.0   \n",
       "270927 2015-08-31          2633       DES MOINES    50320           77.0   \n",
       "270928 2015-10-07          3896        IOWA CITY    52240           52.0   \n",
       "270929 2016-01-25          5070           WILTON    52778           70.0   \n",
       "270930 2015-10-13          4634     CEDAR RAPIDS    52402           57.0   \n",
       "270931 2015-05-26          2602     WEBSTER CITY    50595           40.0   \n",
       "270932 2015-06-18          4604           NEWTON    50208           50.0   \n",
       "270933 2015-02-26          4987             AMES    50010           85.0   \n",
       "270934 2015-03-18          2623       SIOUX CITY    51104           97.0   \n",
       "270935 2015-06-08          5126       DES MOINES    50310           77.0   \n",
       "270936 2015-03-31          2552     CEDAR RAPIDS    52404           57.0   \n",
       "270937 2015-01-27          2553          CRESTON    50801           88.0   \n",
       "270938 2015-04-29          3890     CEDAR RAPIDS    52405           57.0   \n",
       "270939 2015-05-04          4876         HIAWATHA    52233           57.0   \n",
       "270940 2015-01-21          2523      CENTERVILLE    52544            4.0   \n",
       "270941 2015-02-09          4856           LISBON    52253           57.0   \n",
       "270942 2015-06-15          2512        IOWA CITY    52240           52.0   \n",
       "270943 2015-10-21          4438             AMES    50010           85.0   \n",
       "270944 2016-03-22          2620  WINDSOR HEIGHTS    50311           77.0   \n",
       "270945 2015-06-11          2627       DES MOINES    50310           77.0   \n",
       "270946 2015-11-24          5162        URBANDALE    50322           77.0   \n",
       "270947 2015-05-12          4375           GRIMES    50111           77.0   \n",
       "270948 2015-04-08          2545        IOWA CITY    52245           52.0   \n",
       "270949 2015-07-21          2529     CEDAR RAPIDS    52405           57.0   \n",
       "270950 2015-12-22          4057       DES MOINES    50316           77.0   \n",
       "270951 2015-11-04          5151        IDA GROVE    51445           47.0   \n",
       "270952 2015-10-20          5152         WATERLOO    50702            7.0   \n",
       "270953 2015-11-20          3562  WEST BURLINGTON    52655           29.0   \n",
       "270954 2015-01-27          4446        URBANDALE    50322           77.0   \n",
       "\n",
       "               County   Category                       category_name  \\\n",
       "0              Bremer  1051100.0                    APRICOT BRANDIES   \n",
       "1               Scott  1011100.0                    BLENDED WHISKIES   \n",
       "2          Black Hawk  1011200.0           STRAIGHT BOURBON WHISKIES   \n",
       "3               Story  1071100.0                  AMERICAN COCKTAILS   \n",
       "4              Wright  1031080.0                      VODKA 80 PROOF   \n",
       "5                Linn  1041100.0                   AMERICAN DRY GINS   \n",
       "6             Wapello  1051010.0             AMERICAN GRAPE BRANDIES   \n",
       "7         Cerro Gordo  1012100.0                   CANADIAN WHISKIES   \n",
       "8                Polk  1032080.0                      IMPORTED VODKA   \n",
       "9              Benton  1081315.0                   CINNAMON SCHNAPPS   \n",
       "10               Polk  1011250.0      SINGLE BARREL BOURBON WHISKIES   \n",
       "11            Mahaska  1071100.0                  AMERICAN COCKTAILS   \n",
       "12              Scott  1012100.0                   CANADIAN WHISKIES   \n",
       "13                Lee  1081300.0                 PEPPERMINT SCHNAPPS   \n",
       "14            Johnson  1031200.0                      VODKA FLAVORED   \n",
       "15             Bremer  1051100.0                    APRICOT BRANDIES   \n",
       "16         Black Hawk  1031100.0                     100 PROOF VODKA   \n",
       "17            Mahaska  1031200.0                      VODKA FLAVORED   \n",
       "18             Jasper  1081700.0         DISTILLED SPIRITS SPECIALTY   \n",
       "19              Scott  1012100.0                   CANADIAN WHISKIES   \n",
       "20               Linn  1031200.0                      VODKA FLAVORED   \n",
       "21               Tama  1012100.0                   CANADIAN WHISKIES   \n",
       "22              Sioux  1011100.0                    BLENDED WHISKIES   \n",
       "23        Cerro Gordo  1022100.0                             TEQUILA   \n",
       "24               Linn  1031080.0                      VODKA 80 PROOF   \n",
       "25          Dickinson  1011300.0                  TENNESSEE WHISKIES   \n",
       "26            Johnson  1012100.0                   CANADIAN WHISKIES   \n",
       "27      Pottawattamie  1011100.0                    BLENDED WHISKIES   \n",
       "28               Polk  1031080.0                      VODKA 80 PROOF   \n",
       "29            Johnson  1081600.0                     WHISKEY LIQUEUR   \n",
       "...               ...        ...                                 ...   \n",
       "270925     Black Hawk  1081200.0                      CREAM LIQUEURS   \n",
       "270926           Polk  1022100.0                             TEQUILA   \n",
       "270927           Polk  1031200.0                      VODKA FLAVORED   \n",
       "270928        Johnson  1031080.0                      VODKA 80 PROOF   \n",
       "270929      Muscatine  1031080.0                      VODKA 80 PROOF   \n",
       "270930           Linn  1031080.0                      VODKA 80 PROOF   \n",
       "270931       Hamilton  1081900.0  MISC. AMERICAN CORDIALS & LIQUEURS   \n",
       "270932         Jasper  1011300.0                  TENNESSEE WHISKIES   \n",
       "270933          Story  1062310.0                          SPICED RUM   \n",
       "270934       Woodbury  1051110.0                 BLACKBERRY BRANDIES   \n",
       "270935           Polk  1011300.0                  TENNESSEE WHISKIES   \n",
       "270936           Linn  1022100.0                             TEQUILA   \n",
       "270937          Union  1082900.0  MISC. IMPORTED CORDIALS & LIQUEURS   \n",
       "270938           Linn  1031080.0                      VODKA 80 PROOF   \n",
       "270939           Linn  1031200.0                      VODKA FLAVORED   \n",
       "270940      Appanoose  1012200.0                     SCOTCH WHISKIES   \n",
       "270941           Linn  1031080.0                      VODKA 80 PROOF   \n",
       "270942        Johnson  1011100.0                    BLENDED WHISKIES   \n",
       "270943          Story  1011200.0           STRAIGHT BOURBON WHISKIES   \n",
       "270944           Polk  1081240.0               GREEN CREME DE MENTHE   \n",
       "270945           Polk  1062200.0    PUERTO RICO & VIRGIN ISLANDS RUM   \n",
       "270946           Polk  1031080.0                      VODKA 80 PROOF   \n",
       "270947           Polk  1062310.0                          SPICED RUM   \n",
       "270948        Johnson  1062300.0                        FLAVORED RUM   \n",
       "270949           Linn  1031100.0                     100 PROOF VODKA   \n",
       "270950           Polk  1022100.0                             TEQUILA   \n",
       "270951            Ida  1011200.0           STRAIGHT BOURBON WHISKIES   \n",
       "270952     Black Hawk  1011300.0                  TENNESSEE WHISKIES   \n",
       "270953     Des Moines  1082900.0  MISC. IMPORTED CORDIALS & LIQUEURS   \n",
       "270954           Polk  1031080.0                      VODKA 80 PROOF   \n",
       "\n",
       "        vendor_number  item_number                              item_desc  \\\n",
       "0                  55        54436              Mr. Boston Apricot Brandy   \n",
       "1                 395        27605                                Tin Cup   \n",
       "2                  65        19067                               Jim Beam   \n",
       "3                 395        59154                1800 Ultimate Margarita   \n",
       "4                 297        35918                     Five O'clock Vodka   \n",
       "5                 205        31473                      New Amsterdam Gin   \n",
       "6                  85        52806                          Korbel Brandy   \n",
       "7                  65        10628                   Canadian Club Whisky   \n",
       "8                 370        34006           Absolut Swedish Vodka 80 Prf   \n",
       "9                  65        82610                     Dekuyper Hot Damn!   \n",
       "10                 65        19235       Knob Creek Single Barrel Reserve   \n",
       "11                 55        57148          Chi-Chi's Margarita W/tequila   \n",
       "12                 65        15248                   Windsor Canadian Pet   \n",
       "13                322        75211                          Kinky Liqueur   \n",
       "14                380        41705                  Uv Red (cherry) Vodka   \n",
       "15                 55        54436              Mr. Boston Apricot Brandy   \n",
       "16                297        35913                     Five O'clock Vodka   \n",
       "17                380        41989                          Uv Cake Vodka   \n",
       "18                434        75087                        Juarez Gold Dss   \n",
       "19                434        13638           Lord Calvert Canadian Whisky   \n",
       "20                971        41474                   44 North Huckleberry   \n",
       "21                115        11786                  Black Velvet Traveler   \n",
       "22                260        25606            Seagrams 7 Crown Bl Whiskey   \n",
       "23                395        89196  Jose Cuervo Especial Reposado Tequila   \n",
       "24                297        35926                 Five O'clock PET Vodka   \n",
       "25                 85        26827          Jack Daniels Old #7 Black Lbl   \n",
       "26                260        11290       Crown Royal Canadian Whisky Mini   \n",
       "27                434        24156                  Hawkeye Blend Whiskey   \n",
       "28                380        37338                           UV Vodka PET   \n",
       "29                260        66199                 Piehole Apple Pie Mini   \n",
       "...               ...          ...                                    ...   \n",
       "270925            260        68306             Emmets Irish Cream Liqueur   \n",
       "270926            410        89646                           Patron Anejo   \n",
       "270927            260        77699                        Smirnoff Orange   \n",
       "270928            300        36904                    Mccormick Vodka Pet   \n",
       "270929            380        37346                         Phillips Vodka   \n",
       "270930            297        35914                     Five O'clock Vodka   \n",
       "270931             65        85526        Dekuyper Signature Blue Curacao   \n",
       "270932             85        26828          Jack Daniels Old #7 Black Lbl   \n",
       "270933            260        43336         Captain Morgan Original Spiced   \n",
       "270934             55        55068           Mr. Boston Blackberry Brandy   \n",
       "270935             85        86670          Jack Daniel's Tennessee Honey   \n",
       "270936            395        89196  Jose Cuervo Especial Reposado Tequila   \n",
       "270937             85        64676      Chambord Liqueur Royale De France   \n",
       "270938            260        37994                  Smirnoff Vodka 80 Prf   \n",
       "270939            380        42002                          Uv Cake Vodka   \n",
       "270940            305         4936                   Famous Grouse Scotch   \n",
       "270941             55        35948       Fleischmann's Royal Vodka 80 Prf   \n",
       "270942             65        24466         Kessler Blend Whiskey Traveler   \n",
       "270943             65        19061                          Jim Beam Mini   \n",
       "270944            434        79336        Paramount Creme De Menthe/green   \n",
       "270945             55        44215                       Barton Light Rum   \n",
       "270946            301        38176                            Titos Vodka   \n",
       "270947            260        43334              Captain Morgan Spiced Rum   \n",
       "270948             35        43145               Bacardi Pineapple Fusion   \n",
       "270949            300        36887                        McCormick Vodka   \n",
       "270950            410        88291             Patron Tequila Silver Mini   \n",
       "270951            259        17956              Evan Williams Str Bourbon   \n",
       "270952             85        26826          Jack Daniels Old #7 Black Lbl   \n",
       "270953            192        65258                   Jagermeister Liqueur   \n",
       "270954            260        37993                  Smirnoff Vodka 80 Prf   \n",
       "\n",
       "        bottle_vol_ml state_bottle_cost state_bottle_retail  bottles_sold  \\\n",
       "0                 750             $4.50               $6.75            12   \n",
       "1                 750            $13.75              $20.63             2   \n",
       "2                1000            $12.59              $18.89            24   \n",
       "3                1750             $9.50              $14.25             6   \n",
       "4                1750             $7.20              $10.80            12   \n",
       "5                1750            $13.32              $19.98             6   \n",
       "6                 750             $6.66               $9.99             3   \n",
       "7                1750            $15.75              $23.63             2   \n",
       "8                 750            $11.49              $17.24             4   \n",
       "9                1000             $7.62              $11.43             2   \n",
       "10                750            $26.24              $39.36             6   \n",
       "11               1750             $6.00               $9.25             6   \n",
       "12               1750             $8.92              $13.38             1   \n",
       "13                750            $10.00              $15.00            12   \n",
       "14               1000             $7.50              $11.25            12   \n",
       "15                750             $4.50               $6.75             4   \n",
       "16                200             $1.13               $1.70            48   \n",
       "17                750             $6.50               $9.75            12   \n",
       "18               1000             $4.92               $7.38            24   \n",
       "19               1750             $9.97              $14.96             6   \n",
       "20                750            $14.25              $21.38             2   \n",
       "21                750             $5.23               $7.85            12   \n",
       "22                750             $7.00              $10.50            12   \n",
       "23                750            $10.49              $15.74            12   \n",
       "24                750             $3.37               $5.06            12   \n",
       "25               1000            $17.90              $26.85            24   \n",
       "26                300             $7.35              $11.03             1   \n",
       "27                750             $3.50               $5.25             3   \n",
       "28               1750            $10.99              $16.49             6   \n",
       "29                500             $4.90               $7.35             1   \n",
       "...               ...               ...                 ...           ...   \n",
       "270925            750             $6.00               $9.00             4   \n",
       "270926            750            $31.00              $46.50             6   \n",
       "270927           1000             $9.25              $13.88            12   \n",
       "270928            375             $1.80               $2.70            48   \n",
       "270929            750             $3.57               $5.36             4   \n",
       "270930            375             $1.93               $2.90            12   \n",
       "270931            750             $5.25               $7.88            12   \n",
       "270932           1750            $29.67              $44.51             2   \n",
       "270933            750             $9.00              $13.50             3   \n",
       "270934           1750            $10.00              $15.00             2   \n",
       "270935            750            $15.07              $22.61             3   \n",
       "270936            750            $10.49              $15.74            12   \n",
       "270937            750            $18.56              $27.84             1   \n",
       "270938            375             $4.75               $7.13             3   \n",
       "270939            500             $4.89               $7.34             6   \n",
       "270940            750            $12.03              $18.05             4   \n",
       "270941           1750             $6.68              $10.27             6   \n",
       "270942            750             $5.51               $8.27            12   \n",
       "270943            500             $6.83              $10.25             1   \n",
       "270944            750             $4.75               $7.13             2   \n",
       "270945            100             $0.89               $1.34            48   \n",
       "270946            750             $9.65              $14.48             4   \n",
       "270947            375             $5.00               $7.50             6   \n",
       "270948            750             $8.50              $12.75             3   \n",
       "270949           1000             $4.13               $6.20             3   \n",
       "270950            300            $20.30              $30.45             4   \n",
       "270951            750             $7.47              $11.21             3   \n",
       "270952            750            $15.07              $22.61             6   \n",
       "270953           1750            $26.05              $39.08             6   \n",
       "270954            200             $2.75               $4.13             8   \n",
       "\n",
       "       sale_dollars  volume_sold_lt  volume_sold_gal  \n",
       "0            $81.00            9.00             2.38  \n",
       "1            $41.26            1.50             0.40  \n",
       "2           $453.36           24.00             6.34  \n",
       "3            $85.50           10.50             2.77  \n",
       "4           $129.60           21.00             5.55  \n",
       "5           $119.88           10.50             2.77  \n",
       "6            $29.97            2.25             0.59  \n",
       "7            $47.26            3.50             0.92  \n",
       "8            $68.96            3.00             0.79  \n",
       "9            $22.86            2.00             0.53  \n",
       "10          $236.16            4.50             1.19  \n",
       "11           $55.50           10.50             2.77  \n",
       "12           $13.38            1.75             0.46  \n",
       "13          $180.00            9.00             2.38  \n",
       "14          $135.00           12.00             3.17  \n",
       "15           $27.00            3.00             0.79  \n",
       "16           $81.60            9.60             2.54  \n",
       "17          $117.00            9.00             2.38  \n",
       "18          $177.12           24.00             6.34  \n",
       "19           $89.76           10.50             2.77  \n",
       "20           $42.76            1.50             0.40  \n",
       "21           $94.20            9.00             2.38  \n",
       "22          $126.00            9.00             2.38  \n",
       "23          $188.88            9.00             2.38  \n",
       "24           $60.72            9.00             2.38  \n",
       "25          $644.40           24.00             6.34  \n",
       "26           $11.03            0.30             0.08  \n",
       "27           $15.75            2.25             0.59  \n",
       "28           $98.94           10.50             2.77  \n",
       "29            $7.35            0.50             0.13  \n",
       "...             ...             ...              ...  \n",
       "270925       $36.00            3.00             0.79  \n",
       "270926      $279.00            4.50             1.19  \n",
       "270927      $166.56           12.00             3.17  \n",
       "270928      $129.60           18.00             4.76  \n",
       "270929       $21.44            3.00             0.79  \n",
       "270930       $34.80            4.50             1.19  \n",
       "270931       $94.56            9.00             2.38  \n",
       "270932       $89.02            3.50             0.92  \n",
       "270933       $40.50            2.25             0.59  \n",
       "270934       $30.00            3.50             0.92  \n",
       "270935       $67.83            2.25             0.59  \n",
       "270936      $188.88            9.00             2.38  \n",
       "270937       $27.84            0.75             0.20  \n",
       "270938       $21.39            1.12             0.30  \n",
       "270939       $44.04            3.00             0.79  \n",
       "270940       $72.20            3.00             0.79  \n",
       "270941       $61.62           10.50             2.77  \n",
       "270942       $99.24            9.00             2.38  \n",
       "270943       $10.25            0.50             0.13  \n",
       "270944       $14.26            1.50             0.40  \n",
       "270945       $64.32            4.80             1.27  \n",
       "270946       $57.92            3.00             0.79  \n",
       "270947       $45.00            2.25             0.59  \n",
       "270948       $38.25            2.25             0.59  \n",
       "270949       $18.60            3.00             0.79  \n",
       "270950      $121.80            1.20             0.32  \n",
       "270951       $33.63            2.25             0.59  \n",
       "270952      $135.66            4.50             1.19  \n",
       "270953      $234.48           10.50             2.77  \n",
       "270954       $33.04            1.60             0.42  \n",
       "\n",
       "[270955 rows x 18 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# format date in order to split data frame later on.\n",
    "\n",
    "iowa[\"Date\"] = pd.to_datetime(iowa[\"Date\"], errors='raise', format=\"%m/%d/%Y\")\n",
    "iowa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# remove '$' sign from the three columns with them to change them from objects to floats.\n",
    "\n",
    "iowa['state_bottle_cost'] = iowa['state_bottle_cost'].str.replace('$', '').astype('float64')\n",
    "iowa['state_bottle_retail'] = iowa['state_bottle_retail'].str.replace('$', '').astype('float64')\n",
    "iowa['sale_dollars'] = iowa['sale_dollars'].str.replace('$', '').astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Filtering out-liers out of the dataframe. \n",
    "\n",
    "for col in ['bottle_vol_ml', 'bottles_sold', 'sale_dollars', 'volume_sold_lt',\n",
    "            'state_bottle_retail', 'state_bottle_cost']:    \n",
    "    df = iowa[(iowa[col] < np.mean(iowa[col]) + 3 * np.std(iowa[col]))\n",
    "              & (iowa[col] > np.mean(iowa[col]) - 3 * np.std(iowa[col]))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>store_number</th>\n",
       "      <th>City</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>county_number</th>\n",
       "      <th>County</th>\n",
       "      <th>Category</th>\n",
       "      <th>category_name</th>\n",
       "      <th>vendor_number</th>\n",
       "      <th>item_number</th>\n",
       "      <th>item_desc</th>\n",
       "      <th>bottle_vol_ml</th>\n",
       "      <th>state_bottle_cost</th>\n",
       "      <th>state_bottle_retail</th>\n",
       "      <th>bottles_sold</th>\n",
       "      <th>sale_dollars</th>\n",
       "      <th>volume_sold_lt</th>\n",
       "      <th>volume_sold_gal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-11-04</td>\n",
       "      <td>3717</td>\n",
       "      <td>SUMNER</td>\n",
       "      <td>50674</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Bremer</td>\n",
       "      <td>1051100.0</td>\n",
       "      <td>APRICOT BRANDIES</td>\n",
       "      <td>55</td>\n",
       "      <td>54436</td>\n",
       "      <td>Mr. Boston Apricot Brandy</td>\n",
       "      <td>750</td>\n",
       "      <td>4.50</td>\n",
       "      <td>6.75</td>\n",
       "      <td>12</td>\n",
       "      <td>81.00</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-03-02</td>\n",
       "      <td>2614</td>\n",
       "      <td>DAVENPORT</td>\n",
       "      <td>52807</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Scott</td>\n",
       "      <td>1011100.0</td>\n",
       "      <td>BLENDED WHISKIES</td>\n",
       "      <td>395</td>\n",
       "      <td>27605</td>\n",
       "      <td>Tin Cup</td>\n",
       "      <td>750</td>\n",
       "      <td>13.75</td>\n",
       "      <td>20.63</td>\n",
       "      <td>2</td>\n",
       "      <td>41.26</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-02-11</td>\n",
       "      <td>2106</td>\n",
       "      <td>CEDAR FALLS</td>\n",
       "      <td>50613</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Black Hawk</td>\n",
       "      <td>1011200.0</td>\n",
       "      <td>STRAIGHT BOURBON WHISKIES</td>\n",
       "      <td>65</td>\n",
       "      <td>19067</td>\n",
       "      <td>Jim Beam</td>\n",
       "      <td>1000</td>\n",
       "      <td>12.59</td>\n",
       "      <td>18.89</td>\n",
       "      <td>24</td>\n",
       "      <td>453.36</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-02-03</td>\n",
       "      <td>2501</td>\n",
       "      <td>AMES</td>\n",
       "      <td>50010</td>\n",
       "      <td>85.0</td>\n",
       "      <td>Story</td>\n",
       "      <td>1071100.0</td>\n",
       "      <td>AMERICAN COCKTAILS</td>\n",
       "      <td>395</td>\n",
       "      <td>59154</td>\n",
       "      <td>1800 Ultimate Margarita</td>\n",
       "      <td>1750</td>\n",
       "      <td>9.50</td>\n",
       "      <td>14.25</td>\n",
       "      <td>6</td>\n",
       "      <td>85.50</td>\n",
       "      <td>10.5</td>\n",
       "      <td>2.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-08-18</td>\n",
       "      <td>3654</td>\n",
       "      <td>BELMOND</td>\n",
       "      <td>50421</td>\n",
       "      <td>99.0</td>\n",
       "      <td>Wright</td>\n",
       "      <td>1031080.0</td>\n",
       "      <td>VODKA 80 PROOF</td>\n",
       "      <td>297</td>\n",
       "      <td>35918</td>\n",
       "      <td>Five O'clock Vodka</td>\n",
       "      <td>1750</td>\n",
       "      <td>7.20</td>\n",
       "      <td>10.80</td>\n",
       "      <td>12</td>\n",
       "      <td>129.60</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  store_number         City zip_code  county_number      County  \\\n",
       "0 2015-11-04          3717       SUMNER    50674            9.0      Bremer   \n",
       "1 2016-03-02          2614    DAVENPORT    52807           82.0       Scott   \n",
       "2 2016-02-11          2106  CEDAR FALLS    50613            7.0  Black Hawk   \n",
       "3 2016-02-03          2501         AMES    50010           85.0       Story   \n",
       "4 2015-08-18          3654      BELMOND    50421           99.0      Wright   \n",
       "\n",
       "    Category              category_name  vendor_number  item_number  \\\n",
       "0  1051100.0           APRICOT BRANDIES             55        54436   \n",
       "1  1011100.0           BLENDED WHISKIES            395        27605   \n",
       "2  1011200.0  STRAIGHT BOURBON WHISKIES             65        19067   \n",
       "3  1071100.0         AMERICAN COCKTAILS            395        59154   \n",
       "4  1031080.0             VODKA 80 PROOF            297        35918   \n",
       "\n",
       "                   item_desc  bottle_vol_ml  state_bottle_cost  \\\n",
       "0  Mr. Boston Apricot Brandy            750               4.50   \n",
       "1                    Tin Cup            750              13.75   \n",
       "2                   Jim Beam           1000              12.59   \n",
       "3    1800 Ultimate Margarita           1750               9.50   \n",
       "4         Five O'clock Vodka           1750               7.20   \n",
       "\n",
       "   state_bottle_retail  bottles_sold  sale_dollars  volume_sold_lt  \\\n",
       "0                 6.75            12         81.00             9.0   \n",
       "1                20.63             2         41.26             1.5   \n",
       "2                18.89            24        453.36            24.0   \n",
       "3                14.25             6         85.50            10.5   \n",
       "4                10.80            12        129.60            21.0   \n",
       "\n",
       "   volume_sold_gal  \n",
       "0             2.38  \n",
       "1             0.40  \n",
       "2             6.34  \n",
       "3             2.77  \n",
       "4             5.55  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Drop any missing values. \n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(267878, 18)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                   datetime64[ns]\n",
       "store_number                    int64\n",
       "City                           object\n",
       "zip_code                       object\n",
       "county_number                 float64\n",
       "County                         object\n",
       "Category                      float64\n",
       "category_name                  object\n",
       "vendor_number                   int64\n",
       "item_number                     int64\n",
       "item_desc                      object\n",
       "bottle_vol_ml                   int64\n",
       "state_bottle_cost             float64\n",
       "state_bottle_retail           float64\n",
       "bottles_sold                    int64\n",
       "sale_dollars                  float64\n",
       "volume_sold_lt                float64\n",
       "volume_sold_gal               float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Andrew/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:517: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    },
    {
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       "      <td>3717</td>\n",
       "      <td>SUMNER</td>\n",
       "      <td>50674</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Bremer</td>\n",
       "      <td>1051100.0</td>\n",
       "      <td>APRICOT BRANDIES</td>\n",
       "      <td>55</td>\n",
       "      <td>54436</td>\n",
       "      <td>Mr. Boston Apricot Brandy</td>\n",
       "      <td>750</td>\n",
       "      <td>4.50</td>\n",
       "      <td>6.75</td>\n",
       "      <td>12</td>\n",
       "      <td>81.00</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.38</td>\n",
       "      <td>750ml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-03-02</td>\n",
       "      <td>2614</td>\n",
       "      <td>DAVENPORT</td>\n",
       "      <td>52807</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Scott</td>\n",
       "      <td>1011100.0</td>\n",
       "      <td>BLENDED WHISKIES</td>\n",
       "      <td>395</td>\n",
       "      <td>27605</td>\n",
       "      <td>Tin Cup</td>\n",
       "      <td>750</td>\n",
       "      <td>13.75</td>\n",
       "      <td>20.63</td>\n",
       "      <td>2</td>\n",
       "      <td>41.26</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.40</td>\n",
       "      <td>750ml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-02-11</td>\n",
       "      <td>2106</td>\n",
       "      <td>CEDAR FALLS</td>\n",
       "      <td>50613</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Black Hawk</td>\n",
       "      <td>1011200.0</td>\n",
       "      <td>STRAIGHT BOURBON WHISKIES</td>\n",
       "      <td>65</td>\n",
       "      <td>19067</td>\n",
       "      <td>Jim Beam</td>\n",
       "      <td>1000</td>\n",
       "      <td>12.59</td>\n",
       "      <td>18.89</td>\n",
       "      <td>24</td>\n",
       "      <td>453.36</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.34</td>\n",
       "      <td>1000ml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-02-03</td>\n",
       "      <td>2501</td>\n",
       "      <td>AMES</td>\n",
       "      <td>50010</td>\n",
       "      <td>85.0</td>\n",
       "      <td>Story</td>\n",
       "      <td>1071100.0</td>\n",
       "      <td>AMERICAN COCKTAILS</td>\n",
       "      <td>395</td>\n",
       "      <td>59154</td>\n",
       "      <td>1800 Ultimate Margarita</td>\n",
       "      <td>1750</td>\n",
       "      <td>9.50</td>\n",
       "      <td>14.25</td>\n",
       "      <td>6</td>\n",
       "      <td>85.50</td>\n",
       "      <td>10.5</td>\n",
       "      <td>2.77</td>\n",
       "      <td>1750ml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-08-18</td>\n",
       "      <td>3654</td>\n",
       "      <td>BELMOND</td>\n",
       "      <td>50421</td>\n",
       "      <td>99.0</td>\n",
       "      <td>Wright</td>\n",
       "      <td>1031080.0</td>\n",
       "      <td>VODKA 80 PROOF</td>\n",
       "      <td>297</td>\n",
       "      <td>35918</td>\n",
       "      <td>Five O'clock Vodka</td>\n",
       "      <td>1750</td>\n",
       "      <td>7.20</td>\n",
       "      <td>10.80</td>\n",
       "      <td>12</td>\n",
       "      <td>129.60</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.55</td>\n",
       "      <td>1750ml</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  store_number         City zip_code  county_number      County  \\\n",
       "0 2015-11-04          3717       SUMNER    50674            9.0      Bremer   \n",
       "1 2016-03-02          2614    DAVENPORT    52807           82.0       Scott   \n",
       "2 2016-02-11          2106  CEDAR FALLS    50613            7.0  Black Hawk   \n",
       "3 2016-02-03          2501         AMES    50010           85.0       Story   \n",
       "4 2015-08-18          3654      BELMOND    50421           99.0      Wright   \n",
       "\n",
       "    Category              category_name  vendor_number  item_number  \\\n",
       "0  1051100.0           APRICOT BRANDIES             55        54436   \n",
       "1  1011100.0           BLENDED WHISKIES            395        27605   \n",
       "2  1011200.0  STRAIGHT BOURBON WHISKIES             65        19067   \n",
       "3  1071100.0         AMERICAN COCKTAILS            395        59154   \n",
       "4  1031080.0             VODKA 80 PROOF            297        35918   \n",
       "\n",
       "                   item_desc  bottle_vol_ml  state_bottle_cost  \\\n",
       "0  Mr. Boston Apricot Brandy            750               4.50   \n",
       "1                    Tin Cup            750              13.75   \n",
       "2                   Jim Beam           1000              12.59   \n",
       "3    1800 Ultimate Margarita           1750               9.50   \n",
       "4         Five O'clock Vodka           1750               7.20   \n",
       "\n",
       "   state_bottle_retail  bottles_sold  sale_dollars  volume_sold_lt  \\\n",
       "0                 6.75            12         81.00             9.0   \n",
       "1                20.63             2         41.26             1.5   \n",
       "2                18.89            24        453.36            24.0   \n",
       "3                14.25             6         85.50            10.5   \n",
       "4                10.80            12        129.60            21.0   \n",
       "\n",
       "   volume_sold_gal consolidated_bottle_ml  \n",
       "0             2.38                  750ml  \n",
       "1             0.40                  750ml  \n",
       "2             6.34                 1000ml  \n",
       "3             2.77                 1750ml  \n",
       "4             5.55                 1750ml  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a new column with labeled bottle volume.\n",
    "\n",
    "df.loc[df.bottle_vol_ml == 750, 'consolidated_bottle_ml'] = '750ml'\n",
    "df.loc[df.bottle_vol_ml == 1000, 'consolidated_bottle_ml'] = '1000ml'\n",
    "df.loc[df.bottle_vol_ml == 1750, 'consolidated_bottle_ml'] = '1750ml'\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Andrew/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py:3660: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    }
   ],
   "source": [
    "# change NaN values in new column with \"Other_ml\n",
    "\n",
    "df['consolidated_bottle_ml'].fillna('Other_ml', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Andrew/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# create a column for net profit. \n",
    "df['net_profit'] = (df['state_bottle_retail'] - df['state_bottle_cost']) * df['bottles_sold']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Split data frame into two different df's based on year. \n",
    "\n",
    "df_2015 = df[df['Date'].dt.year==2015]\n",
    "df_2016 = df[df['Date'].dt.year==2016]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((216099, 20), (51779, 20))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2015.shape, df_2016.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_2015 = df.sort_values('store_number')\n",
    "df_2016 = df.sort_values('store_number')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a column for sales from January through March for 2016.\n",
    "df_2016['sales_jan_mar'] = [y if ((x.month >=1) & (x.month <=3)) else 0.0 for x, y in zip(df['Date'],df['sale_dollars'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Group sums of columns to be graphed.\n",
    "\n",
    "df_top_cat_2015=df.groupby(df_2015.category_name,as_index=False).agg({\n",
    "        \"sale_dollars\": np.sum,\n",
    "        \"category_name\": lambda x: x.iloc[0]\n",
    "                }).sort_values(by='sale_dollars', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a column for sales from January through March for 2015.\n",
    "df_2015['sales_jan_mar']=[y if ((x.month >=1) & (x.month <=3)) else 0.0 for x, y in zip(df['Date'],df['sale_dollars'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Group data by store name(number)\n",
    "\n",
    "sales_2015 = df_2015.groupby(by=[\"store_number\"], as_index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# get sums of all the columns needed. \n",
    "\n",
    "sales_2015 = sales_2015.agg({'sale_dollars': [np.sum],\n",
    "                   'volume_sold_lt': [np.sum],\n",
    "                   'net_profit': [np.sum],\n",
    "                   'state_bottle_cost': [np.sum],\n",
    "                   'state_bottle_retail': [np.sum],\n",
    "                   'bottles_sold': [np.sum],\n",
    "                   'sales_jan_mar': [np.sum]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>store_number</th>\n",
       "      <th>sale_dollars</th>\n",
       "      <th>volume_sold_lt</th>\n",
       "      <th>net_profit</th>\n",
       "      <th>state_bottle_cost</th>\n",
       "      <th>state_bottle_retail</th>\n",
       "      <th>bottles_sold</th>\n",
       "      <th>sales_jan_mar</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>sum</th>\n",
       "      <th>sum</th>\n",
       "      <th>sum</th>\n",
       "      <th>sum</th>\n",
       "      <th>sum</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2106</td>\n",
       "      <td>174332.60</td>\n",
       "      <td>11825.60</td>\n",
       "      <td>58188.57</td>\n",
       "      <td>6230.99</td>\n",
       "      <td>9354.41</td>\n",
       "      <td>12555</td>\n",
       "      <td>35388.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2113</td>\n",
       "      <td>11325.72</td>\n",
       "      <td>836.10</td>\n",
       "      <td>3785.73</td>\n",
       "      <td>1960.62</td>\n",
       "      <td>2942.86</td>\n",
       "      <td>829</td>\n",
       "      <td>7985.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2130</td>\n",
       "      <td>139727.54</td>\n",
       "      <td>8448.27</td>\n",
       "      <td>46613.49</td>\n",
       "      <td>5099.22</td>\n",
       "      <td>7651.68</td>\n",
       "      <td>9156</td>\n",
       "      <td>21337.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2152</td>\n",
       "      <td>9097.51</td>\n",
       "      <td>773.50</td>\n",
       "      <td>3048.97</td>\n",
       "      <td>1461.46</td>\n",
       "      <td>2198.51</td>\n",
       "      <td>709</td>\n",
       "      <td>5781.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2178</td>\n",
       "      <td>29860.72</td>\n",
       "      <td>2436.17</td>\n",
       "      <td>10017.14</td>\n",
       "      <td>2799.30</td>\n",
       "      <td>4205.54</td>\n",
       "      <td>2407</td>\n",
       "      <td>11385.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  store_number sale_dollars volume_sold_lt net_profit state_bottle_cost  \\\n",
       "                        sum            sum        sum               sum   \n",
       "0         2106    174332.60       11825.60   58188.57           6230.99   \n",
       "1         2113     11325.72         836.10    3785.73           1960.62   \n",
       "2         2130    139727.54        8448.27   46613.49           5099.22   \n",
       "3         2152      9097.51         773.50    3048.97           1461.46   \n",
       "4         2178     29860.72        2436.17   10017.14           2799.30   \n",
       "\n",
       "  state_bottle_retail bottles_sold sales_jan_mar  \n",
       "                  sum          sum           sum  \n",
       "0             9354.41        12555      35388.66  \n",
       "1             2942.86          829       7985.91  \n",
       "2             7651.68         9156      21337.01  \n",
       "3             2198.51          709       5781.07  \n",
       "4             4205.54         2407      11385.33  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_2015.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>volume_sold_sum</th>\n",
       "      <th>net_profit</th>\n",
       "      <th>state_bottle_cost</th>\n",
       "      <th>state_bottle_retail</th>\n",
       "      <th>bottles_sold</th>\n",
       "      <th>sales_jan_mar</th>\n",
       "      <th>store_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11825.60</td>\n",
       "      <td>58188.57</td>\n",
       "      <td>6230.99</td>\n",
       "      <td>9354.41</td>\n",
       "      <td>12555</td>\n",
       "      <td>35388.66</td>\n",
       "      <td>2106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>836.10</td>\n",
       "      <td>3785.73</td>\n",
       "      <td>1960.62</td>\n",
       "      <td>2942.86</td>\n",
       "      <td>829</td>\n",
       "      <td>7985.91</td>\n",
       "      <td>2113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8448.27</td>\n",
       "      <td>46613.49</td>\n",
       "      <td>5099.22</td>\n",
       "      <td>7651.68</td>\n",
       "      <td>9156</td>\n",
       "      <td>21337.01</td>\n",
       "      <td>2130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>773.50</td>\n",
       "      <td>3048.97</td>\n",
       "      <td>1461.46</td>\n",
       "      <td>2198.51</td>\n",
       "      <td>709</td>\n",
       "      <td>5781.07</td>\n",
       "      <td>2152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2436.17</td>\n",
       "      <td>10017.14</td>\n",
       "      <td>2799.30</td>\n",
       "      <td>4205.54</td>\n",
       "      <td>2407</td>\n",
       "      <td>11385.33</td>\n",
       "      <td>2178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   volume_sold_sum  net_profit  state_bottle_cost  state_bottle_retail  \\\n",
       "0         11825.60    58188.57            6230.99              9354.41   \n",
       "1           836.10     3785.73            1960.62              2942.86   \n",
       "2          8448.27    46613.49            5099.22              7651.68   \n",
       "3           773.50     3048.97            1461.46              2198.51   \n",
       "4          2436.17    10017.14            2799.30              4205.54   \n",
       "\n",
       "   bottles_sold  sales_jan_mar  store_number  \n",
       "0         12555       35388.66          2106  \n",
       "1           829        7985.91          2113  \n",
       "2          9156       21337.01          2130  \n",
       "3           709        5781.07          2152  \n",
       "4          2407       11385.33          2178  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create an X data set to be used later, while fixing column names. \n",
    "\n",
    "X = sales_2015['volume_sold_lt'].rename(columns={'sum':'volume_sold_sum'})\n",
    "X['net_profit'] = sales_2015['net_profit']\n",
    "X['state_bottle_cost'] = sales_2015['state_bottle_cost'].rename(columns={'sum':'state_bottle_cost_sum'})\n",
    "X['state_bottle_retail'] = sales_2015['state_bottle_retail']\n",
    "X['bottles_sold'] = sales_2015['bottles_sold']\n",
    "X['sales_jan_mar'] = sales_2015['sales_jan_mar']\n",
    "X['store_number'] = sales_2015['store_number']\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>store_number</th>\n",
       "      <th>sale_dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2106</td>\n",
       "      <td>174332.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2113</td>\n",
       "      <td>11325.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2130</td>\n",
       "      <td>139727.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2152</td>\n",
       "      <td>9097.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2178</td>\n",
       "      <td>29860.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  store_number sale_dollars\n",
       "                           \n",
       "0         2106    174332.60\n",
       "1         2113     11325.72\n",
       "2         2130    139727.54\n",
       "3         2152      9097.51\n",
       "4         2178     29860.72"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create my y variable.\n",
    "\n",
    "y = sales_2015[['store_number']].rename(columns={'sum':'store_number'})\n",
    "y['sale_dollars'] = sales_2015['sale_dollars']\n",
    "\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>store_number</th>\n",
       "      <th>sales_jan_mar</th>\n",
       "      <th>volume_sold_lt</th>\n",
       "      <th>net_profit</th>\n",
       "      <th>state_bottle_cost</th>\n",
       "      <th>state_bottle_retail</th>\n",
       "      <th>bottles_sold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>sum</th>\n",
       "      <th>sum</th>\n",
       "      <th>sum</th>\n",
       "      <th>sum</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2106</td>\n",
       "      <td>35388.66</td>\n",
       "      <td>11825.60</td>\n",
       "      <td>58188.57</td>\n",
       "      <td>6230.99</td>\n",
       "      <td>9354.41</td>\n",
       "      <td>12555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2113</td>\n",
       "      <td>7985.91</td>\n",
       "      <td>836.10</td>\n",
       "      <td>3785.73</td>\n",
       "      <td>1960.62</td>\n",
       "      <td>2942.86</td>\n",
       "      <td>829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2130</td>\n",
       "      <td>21337.01</td>\n",
       "      <td>8448.27</td>\n",
       "      <td>46613.49</td>\n",
       "      <td>5099.22</td>\n",
       "      <td>7651.68</td>\n",
       "      <td>9156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2152</td>\n",
       "      <td>5781.07</td>\n",
       "      <td>773.50</td>\n",
       "      <td>3048.97</td>\n",
       "      <td>1461.46</td>\n",
       "      <td>2198.51</td>\n",
       "      <td>709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2178</td>\n",
       "      <td>11385.33</td>\n",
       "      <td>2436.17</td>\n",
       "      <td>10017.14</td>\n",
       "      <td>2799.30</td>\n",
       "      <td>4205.54</td>\n",
       "      <td>2407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  store_number sales_jan_mar volume_sold_lt net_profit state_bottle_cost  \\\n",
       "                         sum            sum        sum               sum   \n",
       "0         2106      35388.66       11825.60   58188.57           6230.99   \n",
       "1         2113       7985.91         836.10    3785.73           1960.62   \n",
       "2         2130      21337.01        8448.27   46613.49           5099.22   \n",
       "3         2152       5781.07         773.50    3048.97           1461.46   \n",
       "4         2178      11385.33        2436.17   10017.14           2799.30   \n",
       "\n",
       "  state_bottle_retail bottles_sold  \n",
       "                  sum          sum  \n",
       "0             9354.41        12555  \n",
       "1             2942.86          829  \n",
       "2             7651.68         9156  \n",
       "3             2198.51          709  \n",
       "4             4205.54         2407  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create my X variable\n",
    "\n",
    "X = sales_2015[['store_number','sales_jan_mar', 'volume_sold_lt', 'net_profit', \n",
    "                'state_bottle_cost', 'state_bottle_retail', 'bottles_sold']]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sales_2016 = df_2016.groupby(by=[\"store_number\"], as_index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sales_2016 = sales_2016.agg({'sale_dollars': [np.sum, np.mean],\n",
    "                   'volume_sold_lt': [np.sum, np.mean],\n",
    "                   'net_profit': [np.sum, np.mean],\n",
    "                   'state_bottle_cost': [np.sum, np.mean],\n",
    "                   'state_bottle_retail': [np.sum, np.mean],\n",
    "                   'bottles_sold': [np.sum, np.mean]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>store_number</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sale_dollars</th>\n",
       "      <th colspan=\"2\" halign=\"left\">volume_sold_lt</th>\n",
       "      <th colspan=\"2\" halign=\"left\">net_profit</th>\n",
       "      <th colspan=\"2\" halign=\"left\">state_bottle_cost</th>\n",
       "      <th colspan=\"2\" halign=\"left\">state_bottle_retail</th>\n",
       "      <th colspan=\"2\" halign=\"left\">bottles_sold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2106</td>\n",
       "      <td>174332.60</td>\n",
       "      <td>270.702795</td>\n",
       "      <td>11825.60</td>\n",
       "      <td>18.362733</td>\n",
       "      <td>58188.57</td>\n",
       "      <td>90.354922</td>\n",
       "      <td>6230.99</td>\n",
       "      <td>9.675450</td>\n",
       "      <td>9354.41</td>\n",
       "      <td>14.525481</td>\n",
       "      <td>12555</td>\n",
       "      <td>19.495342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2113</td>\n",
       "      <td>11325.72</td>\n",
       "      <td>61.889180</td>\n",
       "      <td>836.10</td>\n",
       "      <td>4.568852</td>\n",
       "      <td>3785.73</td>\n",
       "      <td>20.687049</td>\n",
       "      <td>1960.62</td>\n",
       "      <td>10.713770</td>\n",
       "      <td>2942.86</td>\n",
       "      <td>16.081202</td>\n",
       "      <td>829</td>\n",
       "      <td>4.530055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2130</td>\n",
       "      <td>139727.54</td>\n",
       "      <td>274.513831</td>\n",
       "      <td>8448.27</td>\n",
       "      <td>16.597780</td>\n",
       "      <td>46613.49</td>\n",
       "      <td>91.578566</td>\n",
       "      <td>5099.22</td>\n",
       "      <td>10.018114</td>\n",
       "      <td>7651.68</td>\n",
       "      <td>15.032770</td>\n",
       "      <td>9156</td>\n",
       "      <td>17.988212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2152</td>\n",
       "      <td>9097.51</td>\n",
       "      <td>53.201813</td>\n",
       "      <td>773.50</td>\n",
       "      <td>4.523392</td>\n",
       "      <td>3048.97</td>\n",
       "      <td>17.830234</td>\n",
       "      <td>1461.46</td>\n",
       "      <td>8.546550</td>\n",
       "      <td>2198.51</td>\n",
       "      <td>12.856784</td>\n",
       "      <td>709</td>\n",
       "      <td>4.146199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2178</td>\n",
       "      <td>29860.72</td>\n",
       "      <td>101.567075</td>\n",
       "      <td>2436.17</td>\n",
       "      <td>8.286293</td>\n",
       "      <td>10017.14</td>\n",
       "      <td>34.071905</td>\n",
       "      <td>2799.30</td>\n",
       "      <td>9.521429</td>\n",
       "      <td>4205.54</td>\n",
       "      <td>14.304558</td>\n",
       "      <td>2407</td>\n",
       "      <td>8.187075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  store_number sale_dollars             volume_sold_lt            net_profit  \\\n",
       "                        sum        mean            sum       mean        sum   \n",
       "0         2106    174332.60  270.702795       11825.60  18.362733   58188.57   \n",
       "1         2113     11325.72   61.889180         836.10   4.568852    3785.73   \n",
       "2         2130    139727.54  274.513831        8448.27  16.597780   46613.49   \n",
       "3         2152      9097.51   53.201813         773.50   4.523392    3048.97   \n",
       "4         2178     29860.72  101.567075        2436.17   8.286293   10017.14   \n",
       "\n",
       "             state_bottle_cost            state_bottle_retail             \\\n",
       "        mean               sum       mean                 sum       mean   \n",
       "0  90.354922           6230.99   9.675450             9354.41  14.525481   \n",
       "1  20.687049           1960.62  10.713770             2942.86  16.081202   \n",
       "2  91.578566           5099.22  10.018114             7651.68  15.032770   \n",
       "3  17.830234           1461.46   8.546550             2198.51  12.856784   \n",
       "4  34.071905           2799.30   9.521429             4205.54  14.304558   \n",
       "\n",
       "  bottles_sold             \n",
       "           sum       mean  \n",
       "0        12555  19.495342  \n",
       "1          829   4.530055  \n",
       "2         9156  17.988212  \n",
       "3          709   4.146199  \n",
       "4         2407   8.187075  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Bottles Sold x Sales $')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VAm4Wsl7V9BGMA4YDz6Tvd02XDWQa8D5ghaTem5nPJAmAayWdCjxBUsMwswHUGgAXnTiF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111604be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df_2015['state_bottle_retail'], df_2015['state_bottle_cost'])\n",
    "\n",
    "plt.xlabel('Sales $');\n",
    "plt.ylabel('Bottles Sold');\n",
    "plt.title('Bottles Sold x Sales $')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12292168.080000965"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = df_2015.sales_jan_mar.sum()\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34097063.51999809"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = df_2016.sale_dollars.sum()\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21804895.43999712"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff= (b-a)\n",
    "\n",
    "diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3605051817084351"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent = (a/b)\n",
    "percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_2015['sales_jan_mar']=[y if ((x.month >=1) & (x.month <=3)) else 0.0 for x, y in zip(df['Date'],df['sale_dollars'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Find the top selling types of liquor. \n",
    "\n",
    "df_top_categories_2015 = df.groupby(df_2015.category_name,as_index=False).agg({\n",
    "        'sale_dollars': np.sum,\n",
    "        'category_name': lambda x: x.iloc[0]\n",
    "                }).sort_values(by='sale_dollars', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0.5,'Type of Liquor'),\n",
       " Text(0.5,0,'Sales UDS ($)'),\n",
       " Text(0.5,1,'Top Selling Liquors 2015')]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iIiKVYEJKREREREREKsGElIiIiIiIiFSC3yElomrlfmyQqkMgIiIi+miZjIxR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\nVfwv7NQGRKb5bnkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11176cd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "plt.style.use('fivethirtyeight')\n",
    "ax = sns.barplot(x = 'sale_dollars', y = 'category_name', data = df_top_categories_2015.head(10))\n",
    "ax.set(ylabel='Type of Liquor', \n",
    "       xlabel='Sales UDS ($)', \n",
    "       title='Top Selling Liquors 2015')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# find highest profit margin by bottle size.\n",
    "\n",
    "df_top_profit_2015 = df.groupby(df_2015.category_name,as_index=False).agg({\n",
    "        'net_profit': np.sum,\n",
    "        'consolidated_bottle_ml': lambda x: x.iloc[0]\n",
    "                }).sort_values(by='net_profit', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0.5,'Bottle Size (mL)'),\n",
       " Text(0.5,0,'Net Profit ($)'),\n",
       " Text(0.5,1,'Top Profit Margins 2015')]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SUnknfSJ18HQkUTmmo6ODVatWoXfv3hgwYAB8fX1hb2+PpKQkREVFoXLlyli5cqXKNyIf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\nOsKOHTuETz75RBg2bFiFnItJkyYJHh4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RTqcDAJSXl8Pe3vTfmq3tzJ9BCg8Ph6en56Rx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1114fb9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.style.use('fivethirtyeight')\n",
    "ax = sns.barplot(x = 'net_profit', y = 'consolidated_bottle_ml', data = df_top_profit_2015.head(10))\n",
    "ax.set(ylabel='Bottle Size (mL)', \n",
    "       xlabel='Net Profit ($)', \n",
    "       title='Top Profit Margins 2015')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GpXZlEWvFL8NIZJqWaI5TPbnMzEzeffddxo0bh9ls5sUXX2Ts2LEsXLiw0df8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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111e18ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(color_codes=True)\n",
    "for column in ['net_profit']:\n",
    "    plt.figure(figsize=(4,4))\n",
    "    sns.regplot(x=column, y='sale_dollars', data=df_2015)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RUhSFHQfO8uneU6p3dUCvRGaOH0BcjGfHQVPngjbCnumVgleRcyXlb9u2DYvF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\nEDmBQBDRCJETCAQRjRA5gUAQ0fw/InJVeqHrHpgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1127dd780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GXV0d1q5diz179mDLli3Iy8vD5MmTUVhYiEOHDiE5OVmy72Cj58gnbbgON6cP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\nIqIhkSMIIqL5f44s5Oenrug5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1127ba748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(color_codes=True)\n",
    "for column in ['bottles_sold','volume_sold_lt']:\n",
    "    plt.figure(figsize=(4,4))\n",
    "    sns.regplot(x=column, y='sale_dollars', data=df_2015)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Some Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn import datasets, linear_model, metrics\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score, cross_val_predict\n",
    "from sklearn.linear_model import Ridge, Lasso, ElasticNet, LinearRegression, RidgeCV, LassoCV, ElasticNetCV\n",
    "lm = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validated scores: 0.9999885976555467\n"
     ]
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "scores = cross_val_score(lr, X, y, cv=5)\n",
    "print('Cross-validated scores:', scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999946764030134"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train, y_train)\n",
    "lr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60.480774070258555\n"
     ]
    }
   ],
   "source": [
    "# calculate RMSE\n",
    "\n",
    "print(np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60.48077406548601\n"
     ]
    }
   ],
   "source": [
    "ridgereg = Ridge(alpha=0, normalize=True)\n",
    "ridgereg.fit(X_train, y_train)\n",
    "y_pred = ridgereg.predict(X_test)\n",
    "print(np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2815.543487430163\n"
     ]
    }
   ],
   "source": [
    "# try alpha=0.1\n",
    "ridgereg = Ridge(alpha=0.1, normalize=True)\n",
    "ridgereg.fit(X_train, y_train)\n",
    "y_pred = ridgereg.predict(X_test)\n",
    "print(np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create an array of alpha values\n",
    "alpha_range = 10.**np.arange(-2, 3)\n",
    "alpha_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridgeregcv = RidgeCV(alphas=alpha_range, normalize=True, scoring='neg_mean_squared_error')\n",
    "ridgeregcv.fit(X_train, y_train)\n",
    "ridgeregcv.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1633.946726644581\n"
     ]
    }
   ],
   "source": [
    "y_pred = ridgeregcv.predict(X_test)\n",
    "print(np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-2.05468098e-04  1.11529427e-05 -0.00000000e+00 -0.00000000e+00\n",
      "   1.31296355e-04 -0.00000000e+00  9.99914483e-01]\n",
      " [-9.22063150e-02  3.01402961e+00  4.67086179e-02 -2.45312504e-02\n",
      "  -3.60561792e-03 -2.37005443e-03  2.39685141e-02]]\n"
     ]
    }
   ],
   "source": [
    "# try alpha=0.001 and examine coefficients\n",
    "from sklearn.linear_model import Lasso\n",
    "lassoreg = Lasso(alpha=0.001, normalize=True)\n",
    "lassoreg.fit(X_train, y_train)\n",
    "print(lassoreg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61.03812850940097\n"
     ]
    }
   ],
   "source": [
    "# calculate RMSE (for alpha=0.001)\n",
    "y_pred = lassoreg.predict(X_test)\n",
    "print(np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.00000000e+00 -0.00000000e+00 -0.00000000e+00 -0.00000000e+00\n",
      "  -0.00000000e+00 -0.00000000e+00  9.99605196e-01]\n",
      " [-8.17995251e-02  3.01123328e+00  8.32230070e-03  0.00000000e+00\n",
      "   9.20321294e-04 -2.15972729e-03  2.52094501e-02]]\n"
     ]
    }
   ],
   "source": [
    "# try alpha=0.01 and examine coefficients\n",
    "lassoreg = Lasso(alpha=0.01, normalize=True)\n",
    "lassoreg.fit(X_train, y_train)\n",
    "print(lassoreg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61.32500161319103\n"
     ]
    }
   ],
   "source": [
    "# calculate RMSE (for alpha=0.01)\n",
    "y_pred = lassoreg.predict(X_test)\n",
    "print(np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.72200000e+03, 4.30045016e+04],\n",
       "       [2.53800000e+03, 6.81295683e+04],\n",
       "       [4.84400000e+03, 2.20923822e+03],\n",
       "       [5.19100000e+03, 4.57377967e+02],\n",
       "       [5.23400000e+03, 5.14826583e+02],\n",
       "       [4.32600000e+03, 5.42564899e+03],\n",
       "       [5.08600000e+03, 8.30257116e+03],\n",
       "       [4.24700000e+03, 1.41129548e+04],\n",
       "       [3.96800000e+03, 5.58301962e+03],\n",
       "       [4.63400000e+03, 4.99323070e+03],\n",
       "       [4.39600000e+03, 3.56318608e+03],\n",
       "       [2.50900000e+03, 5.91999405e+04],\n",
       "       [3.91200000e+03, 1.76847577e+04],\n",
       "       [2.55800000e+03, 9.78644182e+04],\n",
       "       [4.79100000e+03, 2.78337971e+03],\n",
       "       [4.99300000e+03, 1.19466000e+04],\n",
       "       [4.21400000e+03, 2.87946073e+04],\n",
       "       [2.62300000e+03, 1.17416625e+05],\n",
       "       [4.69200000e+03, 2.47060860e+03],\n",
       "       [4.76500000e+03, 2.59063718e+03],\n",
       "       [5.07300000e+03, 9.22195348e+03],\n",
       "       [3.67800000e+03, 1.93106899e+04],\n",
       "       [4.50800000e+03, 2.87401169e+03],\n",
       "       [3.44700000e+03, 2.51568910e+05],\n",
       "       [3.94300000e+03, 1.80273563e+04],\n",
       "       [2.52900000e+03, 4.23359694e+04],\n",
       "       [2.60200000e+03, 5.98504744e+04],\n",
       "       [4.67200000e+03, 5.87053277e+02],\n",
       "       [2.50600000e+03, 1.71924184e+05],\n",
       "       [5.15600000e+03, 6.46098508e+02],\n",
       "       [3.43700000e+03, 2.03767199e+04],\n",
       "       [5.24000000e+03, 4.67694660e+02],\n",
       "       [4.87000000e+03, 7.15474845e+02],\n",
       "       [4.11600000e+03, 6.33302892e+03],\n",
       "       [4.50000000e+03, 4.04152809e+03],\n",
       "       [2.60700000e+03, 5.93771244e+04],\n",
       "       [5.12000000e+03, 9.11916798e+02],\n",
       "       [2.84000000e+03, 9.32197716e+03],\n",
       "       [2.57100000e+03, 1.03027069e+05],\n",
       "       [4.71400000e+03, 1.96147229e+03],\n",
       "       [4.79800000e+03, 6.00393167e+03],\n",
       "       [5.05500000e+03, 1.35839529e+03],\n",
       "       [5.20700000e+03, 8.47989580e+03],\n",
       "       [4.27400000e+03, 3.54146266e+03],\n",
       "       [4.97300000e+03, 2.44776322e+04],\n",
       "       [4.06200000e+03, 3.72306686e+04],\n",
       "       [5.06300000e+03, 2.88056958e+04],\n",
       "       [4.85800000e+03, 9.46264207e+02],\n",
       "       [3.59100000e+03, 4.16563536e+03],\n",
       "       [3.62600000e+03, 3.70060199e+04],\n",
       "       [4.65800000e+03, 1.93361561e+03],\n",
       "       [4.29900000e+03, 9.50529712e+03],\n",
       "       [4.09200000e+03, 4.07593821e+04],\n",
       "       [4.71800000e+03, 6.30361655e+03],\n",
       "       [2.63700000e+03, 1.01735869e+05],\n",
       "       [4.97000000e+03, 7.45289704e+03],\n",
       "       [4.77100000e+03, 6.93310306e+03],\n",
       "       [5.02400000e+03, 1.88562150e+03],\n",
       "       [4.99800000e+03, 1.40272531e+04],\n",
       "       [3.97300000e+03, 1.47956634e+04],\n",
       "       [4.29100000e+03, 3.27210023e+04],\n",
       "       [4.42600000e+03, 3.44245154e+04],\n",
       "       [4.94400000e+03, 2.24557448e+03],\n",
       "       [5.10900000e+03, 2.46510029e+03],\n",
       "       [4.13400000e+03, 8.43029706e+03],\n",
       "       [4.61700000e+03, 3.73650282e+04],\n",
       "       [3.76600000e+03, 8.70719883e+03],\n",
       "       [4.39700000e+03, 4.69466448e+03],\n",
       "       [2.85400000e+03, 1.91892987e+03],\n",
       "       [4.35900000e+03, 4.05745158e+03],\n",
       "       [4.52200000e+03, 2.88156266e+03],\n",
       "       [4.73000000e+03, 4.21570593e+03],\n",
       "       [3.87700000e+03, 1.50125787e+04],\n",
       "       [2.52400000e+03, 1.24327368e+05],\n",
       "       [4.23100000e+03, 1.45969959e+04],\n",
       "       [2.58800000e+03, 9.56861990e+04],\n",
       "       [4.93200000e+03, 1.14613243e+04],\n",
       "       [2.54300000e+03, 7.30335809e+04],\n",
       "       [2.50000000e+03, 1.78034394e+05],\n",
       "       [4.70100000e+03, 3.62229122e+03],\n",
       "       [5.14600000e+03, 1.74841472e+04],\n",
       "       [4.92600000e+03, 4.41974328e+03],\n",
       "       [2.52800000e+03, 1.36873332e+05],\n",
       "       [4.55800000e+03, 1.21855009e+04],\n",
       "       [4.74800000e+03, 2.76318897e+03],\n",
       "       [4.92700000e+03, 2.91073313e+03],\n",
       "       [2.64200000e+03, 7.55111819e+04],\n",
       "       [3.39000000e+03, 1.50409502e+05],\n",
       "       [3.62400000e+03, 1.99535020e+04],\n",
       "       [4.31800000e+03, 1.05525137e+04],\n",
       "       [4.49500000e+03, 8.41711722e+03],\n",
       "       [5.11500000e+03, 3.00253886e+03],\n",
       "       [4.23200000e+03, 6.72188362e+03],\n",
       "       [4.01100000e+03, 1.90168252e+04],\n",
       "       [5.05700000e+03, 5.53710307e+03],\n",
       "       [4.78000000e+03, 3.58118218e+03],\n",
       "       [4.25100000e+03, 1.64424519e+04],\n",
       "       [4.72700000e+03, 3.58468269e+03],\n",
       "       [4.93300000e+03, 2.43055596e+03],\n",
       "       [4.36000000e+03, 4.96242746e+03],\n",
       "       [3.87900000e+03, 1.33491432e+04],\n",
       "       [4.15600000e+03, 4.01999415e+04],\n",
       "       [4.25800000e+03, 1.64434897e+04],\n",
       "       [3.90900000e+03, 1.60210116e+04],\n",
       "       [3.71300000e+03, 5.46681549e+04],\n",
       "       [4.54200000e+03, 1.64027888e+03],\n",
       "       [4.94700000e+03, 2.10898296e+04],\n",
       "       [5.19000000e+03, 2.87473041e+03],\n",
       "       [3.49500000e+03, 4.10614243e+04],\n",
       "       [4.75300000e+03, 1.62619735e+03],\n",
       "       [2.56900000e+03, 6.11346028e+04],\n",
       "       [4.01200000e+03, 1.95212641e+03],\n",
       "       [2.49800000e+03, 3.00025199e+03],\n",
       "       [4.53000000e+03, 4.71518746e+03],\n",
       "       [4.38500000e+03, 3.51741618e+03],\n",
       "       [2.64300000e+03, 8.54678452e+04],\n",
       "       [4.44000000e+03, 3.62659265e+03],\n",
       "       [4.62800000e+03, 9.64312667e+03],\n",
       "       [4.31900000e+03, 9.64045487e+03],\n",
       "       [2.46000000e+03, 4.02704540e+04],\n",
       "       [2.56700000e+03, 5.53238101e+04],\n",
       "       [4.71600000e+03, 1.40925875e+03],\n",
       "       [3.47700000e+03, 1.69681834e+05],\n",
       "       [4.74500000e+03, 5.62991638e+03],\n",
       "       [5.03500000e+03, 1.55204788e+03],\n",
       "       [2.65100000e+03, 9.45990388e+04],\n",
       "       [4.47500000e+03, 5.92397279e+03],\n",
       "       [3.72800000e+03, 4.04288968e+04],\n",
       "       [4.23500000e+03, 9.30932442e+03],\n",
       "       [5.18600000e+03, 6.30261702e+02],\n",
       "       [4.83100000e+03, 2.92770435e+03],\n",
       "       [3.62900000e+03, 5.51058215e+04],\n",
       "       [4.90700000e+03, 9.37568507e+03],\n",
       "       [5.22400000e+03, 2.59064583e+03],\n",
       "       [4.50600000e+03, 6.12868916e+03],\n",
       "       [4.26500000e+03, 3.18744828e+04],\n",
       "       [4.07000000e+03, 2.33270067e+04],\n",
       "       [4.58500000e+03, 2.56240812e+03],\n",
       "       [4.15800000e+03, 2.59225941e+04],\n",
       "       [5.09600000e+03, 7.49135140e+03],\n",
       "       [4.56700000e+03, 2.34743089e+01],\n",
       "       [4.14300000e+03, 9.62140151e+03],\n",
       "       [5.12800000e+03, 2.69004218e+04],\n",
       "       [2.41300000e+03, 1.12640372e+05],\n",
       "       [3.99700000e+03, 1.63116175e+04],\n",
       "       [5.01000000e+03, 9.09796948e+03],\n",
       "       [4.95900000e+03, 3.00264467e+04],\n",
       "       [4.97200000e+03, 7.07444294e+03],\n",
       "       [4.49800000e+03, 1.46264143e+03],\n",
       "       [2.56800000e+03, 3.32667497e+04],\n",
       "       [5.13200000e+03, 8.54009108e+03],\n",
       "       [4.97900000e+03, 3.29127006e+04],\n",
       "       [4.69000000e+03, 2.65677335e+03],\n",
       "       [4.96800000e+03, 1.04103486e+03],\n",
       "       [2.84100000e+03, 1.47411528e+04],\n",
       "       [4.26000000e+03, 2.68873956e+04],\n",
       "       [4.37600000e+03, 1.02659261e+04],\n",
       "       [3.46100000e+03, 1.53858065e+04],\n",
       "       [4.67500000e+03, 6.07007160e+03],\n",
       "       [3.58200000e+03, 2.92462554e+03],\n",
       "       [5.17400000e+03, 4.23341343e+03],\n",
       "       [3.71000000e+03, 7.56260555e+03],\n",
       "       [2.51700000e+03, 9.09678478e+04],\n",
       "       [4.79900000e+03, 3.71950521e+03],\n",
       "       [4.43400000e+03, 8.07620873e+03],\n",
       "       [4.24600000e+03, 7.68145712e+03],\n",
       "       [4.09700000e+03, 1.12342729e+04],\n",
       "       [4.58200000e+03, 5.05323880e+03],\n",
       "       [2.96100000e+03, 5.76508275e+02],\n",
       "       [5.00100000e+03, 1.82374628e+04],\n",
       "       [4.80600000e+03, 1.25873837e+04],\n",
       "       [4.77300000e+03, 5.74595944e+03],\n",
       "       [4.18200000e+03, 2.22283613e+04],\n",
       "       [3.85900000e+03, 2.98133264e+04],\n",
       "       [4.90000000e+03, 2.09632191e+04],\n",
       "       [4.85700000e+03, 3.00683439e+03],\n",
       "       [4.22800000e+03, 3.23763743e+04],\n",
       "       [2.84600000e+03, 8.52915170e+03],\n",
       "       [3.45600000e+03, 8.02809505e+04],\n",
       "       [2.60000000e+03, 2.53683514e+04],\n",
       "       [4.80200000e+03, 2.22864573e+04],\n",
       "       [4.81000000e+03, 1.11338249e+04],\n",
       "       [4.53600000e+03, 2.41892805e+03],\n",
       "       [5.19600000e+03, 9.69569108e+02],\n",
       "       [3.77600000e+03, 5.94672493e+04],\n",
       "       [4.60100000e+03, 5.37474690e+03],\n",
       "       [4.72200000e+03, 4.38338492e+03],\n",
       "       [4.86000000e+03, 2.07763624e+04],\n",
       "       [3.52100000e+03, 3.01396330e+04],\n",
       "       [4.12100000e+03, 3.60756212e+02],\n",
       "       [4.63000000e+03, 2.47988650e+04],\n",
       "       [2.84500000e+03, 4.18129427e+03],\n",
       "       [5.18100000e+03, 1.37045595e+03],\n",
       "       [4.49100000e+03, 5.72710349e+03],\n",
       "       [3.94200000e+03, 4.96019090e+04],\n",
       "       [5.03900000e+03, 4.38349908e+03],\n",
       "       [4.95200000e+03, 1.02188114e+04],\n",
       "       [4.41700000e+03, 4.19827049e+03],\n",
       "       [4.59100000e+03, 2.04279651e+03],\n",
       "       [4.45900000e+03, 8.50082850e+03],\n",
       "       [4.81100000e+03, 8.02673047e+02],\n",
       "       [4.69100000e+03, 1.36997812e+03],\n",
       "       [5.05100000e+03, 7.00024334e+03],\n",
       "       [4.58300000e+03, 2.01064711e+03],\n",
       "       [3.87200000e+03, 8.73866921e+03],\n",
       "       [4.52800000e+03, 2.49223188e+03],\n",
       "       [4.54100000e+03, 2.10552306e+03],\n",
       "       [4.42100000e+03, 3.63248501e+03],\n",
       "       [2.62600000e+03, 1.08735560e+05],\n",
       "       [5.03300000e+03, 8.19141026e+02],\n",
       "       [3.65600000e+03, 2.41436659e+04],\n",
       "       [3.71200000e+03, 1.57121318e+04],\n",
       "       [4.25400000e+03, 1.26800838e+04],\n",
       "       [4.45200000e+03, 1.44795639e+04],\n",
       "       [3.87100000e+03, 1.19906111e+04],\n",
       "       [4.96200000e+03, 4.14010088e+04],\n",
       "       [5.05600000e+03, 1.64709339e+02],\n",
       "       [4.37400000e+03, 4.88119099e+03],\n",
       "       [4.36500000e+03, 3.79335291e+03],\n",
       "       [2.50100000e+03, 1.72259048e+05],\n",
       "       [4.29500000e+03, 7.21339331e+03],\n",
       "       [4.08300000e+03, 3.45515404e+04],\n",
       "       [4.82000000e+03, 8.48737597e+03],\n",
       "       [4.89100000e+03, 2.40124331e+04],\n",
       "       [5.03600000e+03, 7.75236697e+03],\n",
       "       [2.57200000e+03, 1.16831731e+05],\n",
       "       [4.17700000e+03, 7.82112908e+03],\n",
       "       [2.56600000e+03, 3.69659565e+04],\n",
       "       [3.83000000e+03, 3.58988941e+04],\n",
       "       [4.88800000e+03, 1.11701717e+04],\n",
       "       [4.08100000e+03, 8.72592802e+03],\n",
       "       [5.10600000e+03, 3.30425718e+04],\n",
       "       [4.60700000e+03, 2.90133290e+03],\n",
       "       [2.59000000e+03, 1.42343880e+05],\n",
       "       [4.41600000e+03, 5.13531324e+03],\n",
       "       [4.79500000e+03, 1.39607364e+04],\n",
       "       [4.20300000e+03, 2.46310315e+04],\n",
       "       [2.62700000e+03, 1.02116419e+05],\n",
       "       [4.79000000e+03, 1.92763738e+03],\n",
       "       [4.31200000e+03, 1.73508777e+05],\n",
       "       [4.09800000e+03, 1.99093572e+04],\n",
       "       [5.14900000e+03, 4.54941277e+03],\n",
       "       [4.66900000e+03, 8.54043590e+03],\n",
       "       [2.66000000e+03, 2.70745381e+04],\n",
       "       [5.21400000e+03, 1.64773742e+03],\n",
       "       [3.88600000e+03, 3.67601160e+04],\n",
       "       [4.81300000e+03, 4.48505060e+03],\n",
       "       [4.75400000e+03, 2.35731745e+03],\n",
       "       [4.44500000e+03, 4.08860538e+03],\n",
       "       [4.95700000e+03, 2.24480196e+03],\n",
       "       [4.73700000e+03, 6.28996606e+02],\n",
       "       [4.13500000e+03, 2.39876195e+04],\n",
       "       [2.65200000e+03, 5.25642254e+04],\n",
       "       [4.54900000e+03, 3.77303909e+03],\n",
       "       [2.50500000e+03, 8.47032799e+04],\n",
       "       [4.11900000e+03, 8.97206045e+03],\n",
       "       [5.07400000e+03, 3.60057004e+03],\n",
       "       [4.81900000e+03, 1.61129620e+04],\n",
       "       [5.24700000e+03, 2.67122876e+02],\n",
       "       [5.20100000e+03, 2.79800245e+02],\n",
       "       [3.81600000e+03, 1.77871874e+04],\n",
       "       [5.20400000e+03, 2.58570483e+03],\n",
       "       [2.55100000e+03, 4.19132973e+04],\n",
       "       [9.00100000e+03, 2.74480965e+04],\n",
       "       [5.08200000e+03, 1.14429264e+03],\n",
       "       [4.59500000e+03, 5.36646466e+03],\n",
       "       [4.98000000e+03, 2.26021303e+03],\n",
       "       [4.53800000e+03, 3.88210668e+03],\n",
       "       [3.90700000e+03, 9.87410740e+03],\n",
       "       [4.73800000e+03, 4.86842237e+03],\n",
       "       [4.76000000e+03, 2.59207134e+03],\n",
       "       [4.16900000e+03, 9.69757270e+03],\n",
       "       [5.05200000e+03, 2.70188208e+03],\n",
       "       [2.85000000e+03, 4.75531577e+03],\n",
       "       [4.01300000e+03, 1.16679847e+04],\n",
       "       [4.38200000e+03, 7.99021919e+03],\n",
       "       [3.96300000e+03, 2.37549250e+04],\n",
       "       [3.87000000e+03, 1.11945868e+04],\n",
       "       [4.63600000e+03, 4.62011687e+03],\n",
       "       [4.67000000e+03, 1.97334379e+04]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Presentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Presentation:\n",
    "\n",
    "https://docs.google.com/presentation/d/1XHIZjRBZu-u2tOOpH9cECmVPLyFc7PT59l7CZbfzahQ/edit?usp=sharing"
   ]
  }
 ],
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