Chips-Store-Data-Analysis-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\mehra\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "# Data analysis and wrangling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Data visualisation\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import missingno\n",
    "\n",
    "# Dates\n",
    "import datetime\n",
    "from matplotlib.dates import DateFormatter\n",
    "\n",
    "# Text analysis\n",
    "import nltk\n",
    "nltk.download('punkt')\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.probability import FreqDist as fdist\n",
    "import re\n",
    "\n",
    "# Statistical analysis\n",
    "from scipy.stats import ttest_ind\n",
    "\n",
    "# Remove warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import and read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Customer Data\n",
    "Cdata = pd.read_csv(\"C:\\\\Users\\\\mehra\\\\OneDrive\\\\Desktop\\\\Quantium\\\\Task 1\\\\QVI_purchase_behaviour.csv\")\n",
    "\n",
    "#Transaction Data\n",
    "Tdata = pd.read_csv(\"C:\\\\Users\\\\mehra\\\\OneDrive\\\\Desktop\\\\Quantium\\\\Task 1\\\\QVI_transaction_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transaction data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>PACK_SIZE</th>\n",
       "      <th>BRAND</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-10-17</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Natural Chip        Compny Seasalt</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>175</td>\n",
       "      <td>Natural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-14</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>348</td>\n",
       "      <td>66</td>\n",
       "      <td>Ccs Nacho Cheese</td>\n",
       "      <td>3</td>\n",
       "      <td>6.3</td>\n",
       "      <td>175</td>\n",
       "      <td>CCS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1343</td>\n",
       "      <td>383</td>\n",
       "      <td>61</td>\n",
       "      <td>Smiths Crinkle Cut  Chips Chicken</td>\n",
       "      <td>2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>170</td>\n",
       "      <td>Smiths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-08-17</td>\n",
       "      <td>2</td>\n",
       "      <td>2373</td>\n",
       "      <td>974</td>\n",
       "      <td>69</td>\n",
       "      <td>Smiths Chip Thinly  S Cream Onion</td>\n",
       "      <td>5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>175</td>\n",
       "      <td>Smiths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-08-18</td>\n",
       "      <td>2</td>\n",
       "      <td>2426</td>\n",
       "      <td>1038</td>\n",
       "      <td>108</td>\n",
       "      <td>Kettle Tortilla Chpshny Jlpno Chili</td>\n",
       "      <td>3</td>\n",
       "      <td>13.8</td>\n",
       "      <td>150</td>\n",
       "      <td>Kettle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-05-16</td>\n",
       "      <td>4</td>\n",
       "      <td>4149</td>\n",
       "      <td>3333</td>\n",
       "      <td>16</td>\n",
       "      <td>Smiths Crinkle Chips Salt   Vinegar</td>\n",
       "      <td>1</td>\n",
       "      <td>5.7</td>\n",
       "      <td>330</td>\n",
       "      <td>Smiths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-05-16</td>\n",
       "      <td>4</td>\n",
       "      <td>4196</td>\n",
       "      <td>3539</td>\n",
       "      <td>24</td>\n",
       "      <td>Grain Waves         Sweet Chilli</td>\n",
       "      <td>1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>210</td>\n",
       "      <td>Grainwaves</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-08-20</td>\n",
       "      <td>5</td>\n",
       "      <td>5026</td>\n",
       "      <td>4525</td>\n",
       "      <td>42</td>\n",
       "      <td>Doritos Corn Chip Mexican Jalapeno</td>\n",
       "      <td>1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>150</td>\n",
       "      <td>Doritos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-08-18</td>\n",
       "      <td>7</td>\n",
       "      <td>7150</td>\n",
       "      <td>6900</td>\n",
       "      <td>52</td>\n",
       "      <td>Grain Waves Sour    Cream Chives</td>\n",
       "      <td>2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>210</td>\n",
       "      <td>Grainwaves</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-05-17</td>\n",
       "      <td>7</td>\n",
       "      <td>7215</td>\n",
       "      <td>7176</td>\n",
       "      <td>16</td>\n",
       "      <td>Smiths Crinkle Chips Salt   Vinegar</td>\n",
       "      <td>1</td>\n",
       "      <td>5.7</td>\n",
       "      <td>330</td>\n",
       "      <td>Smiths</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "0  2018-10-17          1            1000       1         5   \n",
       "1  2019-05-14          1            1307     348        66   \n",
       "2  2019-05-20          1            1343     383        61   \n",
       "3  2018-08-17          2            2373     974        69   \n",
       "4  2018-08-18          2            2426    1038       108   \n",
       "6  2019-05-16          4            4149    3333        16   \n",
       "7  2019-05-16          4            4196    3539        24   \n",
       "8  2018-08-20          5            5026    4525        42   \n",
       "9  2018-08-18          7            7150    6900        52   \n",
       "10 2019-05-17          7            7215    7176        16   \n",
       "\n",
       "                                PROD_NAME  PROD_QTY  TOT_SALES  PACK_SIZE  \\\n",
       "0     Natural Chip        Compny Seasalt          2        6.0        175   \n",
       "1                   Ccs Nacho Cheese              3        6.3        175   \n",
       "2     Smiths Crinkle Cut  Chips Chicken           2        2.9        170   \n",
       "3     Smiths Chip Thinly  S Cream Onion           5       15.0        175   \n",
       "4   Kettle Tortilla Chpshny Jlpno Chili           3       13.8        150   \n",
       "6   Smiths Crinkle Chips Salt   Vinegar           1        5.7        330   \n",
       "7      Grain Waves         Sweet Chilli           1        3.6        210   \n",
       "8    Doritos Corn Chip Mexican Jalapeno           1        3.9        150   \n",
       "9      Grain Waves Sour    Cream Chives           2        7.2        210   \n",
       "10  Smiths Crinkle Chips Salt   Vinegar           1        5.7        330   \n",
       "\n",
       "         BRAND  \n",
       "0      Natural  \n",
       "1          CCS  \n",
       "2       Smiths  \n",
       "3       Smiths  \n",
       "4       Kettle  \n",
       "6       Smiths  \n",
       "7   Grainwaves  \n",
       "8      Doritos  \n",
       "9   Grainwaves  \n",
       "10      Smiths  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tdata.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "246740"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Length of Transaction Data\n",
    "len(Tdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "245255"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tdata['TXN_ID'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TXN_ID is not unique to each row. This is because there can be sales of chips with different brands in a single Tdata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>PACK_SIZE</th>\n",
       "      <th>BRAND</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>55</td>\n",
       "      <td>55073</td>\n",
       "      <td>48887</td>\n",
       "      <td>113</td>\n",
       "      <td>Twisties Chicken</td>\n",
       "      <td>1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>270</td>\n",
       "      <td>Twisties</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>2019-01-10</td>\n",
       "      <td>7</td>\n",
       "      <td>7364</td>\n",
       "      <td>7739</td>\n",
       "      <td>20</td>\n",
       "      <td>Doritos Cheese      Supreme</td>\n",
       "      <td>2</td>\n",
       "      <td>11.4</td>\n",
       "      <td>330</td>\n",
       "      <td>Doritos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>2018-10-18</td>\n",
       "      <td>12</td>\n",
       "      <td>12301</td>\n",
       "      <td>10982</td>\n",
       "      <td>93</td>\n",
       "      <td>Doritos Corn Chip Southern Chicken</td>\n",
       "      <td>2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>150</td>\n",
       "      <td>Doritos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>2018-09-08</td>\n",
       "      <td>16</td>\n",
       "      <td>16427</td>\n",
       "      <td>14546</td>\n",
       "      <td>81</td>\n",
       "      <td>Pringles Original   Crisps</td>\n",
       "      <td>1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>134</td>\n",
       "      <td>Pringles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>2018-08-03</td>\n",
       "      <td>19</td>\n",
       "      <td>19272</td>\n",
       "      <td>16683</td>\n",
       "      <td>31</td>\n",
       "      <td>Infzns Crn Crnchers Tangy Gcamole</td>\n",
       "      <td>2</td>\n",
       "      <td>7.6</td>\n",
       "      <td>110</td>\n",
       "      <td>Infuzions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>2018-10-28</td>\n",
       "      <td>47</td>\n",
       "      <td>47204</td>\n",
       "      <td>42616</td>\n",
       "      <td>45</td>\n",
       "      <td>Smiths Thinly Cut   Roast Chicken</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>175</td>\n",
       "      <td>Smiths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>2019-05-24</td>\n",
       "      <td>48</td>\n",
       "      <td>48179</td>\n",
       "      <td>44177</td>\n",
       "      <td>56</td>\n",
       "      <td>Cheezels Cheese Box</td>\n",
       "      <td>2</td>\n",
       "      <td>4.2</td>\n",
       "      <td>125</td>\n",
       "      <td>Cheezels</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048</th>\n",
       "      <td>2019-04-04</td>\n",
       "      <td>55</td>\n",
       "      <td>55036</td>\n",
       "      <td>48663</td>\n",
       "      <td>91</td>\n",
       "      <td>Ccs Tasty Cheese</td>\n",
       "      <td>2</td>\n",
       "      <td>4.2</td>\n",
       "      <td>175</td>\n",
       "      <td>CCS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>55</td>\n",
       "      <td>55073</td>\n",
       "      <td>48884</td>\n",
       "      <td>91</td>\n",
       "      <td>Ccs Tasty Cheese</td>\n",
       "      <td>2</td>\n",
       "      <td>4.2</td>\n",
       "      <td>175</td>\n",
       "      <td>CCS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1143</th>\n",
       "      <td>2019-01-24</td>\n",
       "      <td>58</td>\n",
       "      <td>58121</td>\n",
       "      <td>53351</td>\n",
       "      <td>42</td>\n",
       "      <td>Doritos Corn Chip Mexican Jalapeno</td>\n",
       "      <td>2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>150</td>\n",
       "      <td>Doritos</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "42   2019-05-20         55           55073   48887       113   \n",
       "377  2019-01-10          7            7364    7739        20   \n",
       "419  2018-10-18         12           12301   10982        93   \n",
       "476  2018-09-08         16           16427   14546        81   \n",
       "511  2018-08-03         19           19272   16683        31   \n",
       "922  2018-10-28         47           47204   42616        45   \n",
       "953  2019-05-24         48           48179   44177        56   \n",
       "1048 2019-04-04         55           55036   48663        91   \n",
       "1055 2018-07-01         55           55073   48884        91   \n",
       "1143 2019-01-24         58           58121   53351        42   \n",
       "\n",
       "                                 PROD_NAME  PROD_QTY  TOT_SALES  PACK_SIZE  \\\n",
       "42                       Twisties Chicken          1        4.6        270   \n",
       "377          Doritos Cheese      Supreme           2       11.4        330   \n",
       "419   Doritos Corn Chip Southern Chicken           2        7.8        150   \n",
       "476           Pringles Original   Crisps           1        3.7        134   \n",
       "511    Infzns Crn Crnchers Tangy Gcamole           2        7.6        110   \n",
       "922    Smiths Thinly Cut   Roast Chicken           2        6.0        175   \n",
       "953                  Cheezels Cheese Box           2        4.2        125   \n",
       "1048                 Ccs Tasty Cheese              2        4.2        175   \n",
       "1055                 Ccs Tasty Cheese              2        4.2        175   \n",
       "1143  Doritos Corn Chip Mexican Jalapeno           2        7.8        150   \n",
       "\n",
       "          BRAND  \n",
       "42     Twisties  \n",
       "377     Doritos  \n",
       "419     Doritos  \n",
       "476    Pringles  \n",
       "511   Infuzions  \n",
       "922      Smiths  \n",
       "953    Cheezels  \n",
       "1048        CCS  \n",
       "1055        CCS  \n",
       "1143    Doritos  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look for duplicated TXN_ID\n",
    "\n",
    "Tdata[Tdata.duplicated(['TXN_ID'])].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
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       "      <th>BRAND</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>55</td>\n",
       "      <td>55073</td>\n",
       "      <td>48887</td>\n",
       "      <td>4</td>\n",
       "      <td>Dorito Corn Chp     Supreme</td>\n",
       "      <td>1</td>\n",
       "      <td>3.25</td>\n",
       "      <td>380</td>\n",
       "      <td>Doritos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>55</td>\n",
       "      <td>55073</td>\n",
       "      <td>48887</td>\n",
       "      <td>113</td>\n",
       "      <td>Twisties Chicken</td>\n",
       "      <td>1</td>\n",
       "      <td>4.60</td>\n",
       "      <td>270</td>\n",
       "      <td>Twisties</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "41 2019-05-20         55           55073   48887         4   \n",
       "42 2019-05-20         55           55073   48887       113   \n",
       "\n",
       "                        PROD_NAME  PROD_QTY  TOT_SALES  PACK_SIZE     BRAND  \n",
       "41  Dorito Corn Chp     Supreme           1       3.25        380   Doritos  \n",
       "42              Twisties Chicken          1       4.60        270  Twisties  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the first duplicated TXN_ID\n",
    "\n",
    "Tdata.loc[Tdata['TXN_ID'] == 48887, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 246740 entries, 0 to 264835\n",
      "Data columns (total 10 columns):\n",
      " #   Column          Non-Null Count   Dtype         \n",
      "---  ------          --------------   -----         \n",
      " 0   DATE            246740 non-null  datetime64[ns]\n",
      " 1   STORE_NBR       246740 non-null  int64         \n",
      " 2   LYLTY_CARD_NBR  246740 non-null  int64         \n",
      " 3   TXN_ID          246740 non-null  int64         \n",
      " 4   PROD_NBR        246740 non-null  int64         \n",
      " 5   PROD_NAME       246740 non-null  object        \n",
      " 6   PROD_QTY        246740 non-null  int64         \n",
      " 7   TOT_SALES       246740 non-null  float64       \n",
      " 8   PACK_SIZE       0 non-null       float64       \n",
      " 9   BRAND           246740 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(5), object(2)\n",
      "memory usage: 28.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# For summarised information of Transaction Dataset\n",
    "Tdata.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Missing values in Transaction Data(Tdata)\n",
    "\n",
    "missingno.matrix(Tdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DATE',\n",
       " 'STORE_NBR',\n",
       " 'LYLTY_CARD_NBR',\n",
       " 'TXN_ID',\n",
       " 'PROD_NBR',\n",
       " 'PROD_NAME',\n",
       " 'PROD_QTY',\n",
       " 'TOT_SALES',\n",
       " 'PACK_SIZE',\n",
       " 'BRAND']"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now let's explore the features in both dataset starting with 'Tdata'\n",
    "\n",
    "list(Tdata.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2018-10-17\n",
       "1   2019-05-14\n",
       "2   2019-05-20\n",
       "3   2018-08-17\n",
       "4   2018-08-18\n",
       "Name: DATE, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tdata['DATE'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Date is not in the right format\n",
    "\n",
    "# Function that converts Excel integer into yyyy-mm-dd format\n",
    "def xlseriesdate_to_datetime(xlserialdate):\n",
    "    excel_anchor = datetime.datetime(1900, 1, 1)\n",
    "    if(xlserialdate < 60):\n",
    "        delta_in_days = datetime.timedelta(days = (xlserialdate - 1))\n",
    "    else:\n",
    "        delta_in_days = datetime.timedelta(days = (xlserialdate - 2))\n",
    "    converted_date = excel_anchor + delta_in_days\n",
    "    return converted_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2018-10-17\n",
       "1   2019-05-14\n",
       "2   2019-05-20\n",
       "3   2018-08-17\n",
       "4   2018-08-18\n",
       "Name: DATE, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the first 5 rows of the new date feature\n",
    "\n",
    "Tdata['DATE'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>PACK_SIZE</th>\n",
       "      <th>BRAND</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-10-17</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Natural Chip        Compny Seasalt</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>175</td>\n",
       "      <td>Natural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-14</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>348</td>\n",
       "      <td>66</td>\n",
       "      <td>Ccs Nacho Cheese</td>\n",
       "      <td>3</td>\n",
       "      <td>6.3</td>\n",
       "      <td>175</td>\n",
       "      <td>CCS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1343</td>\n",
       "      <td>383</td>\n",
       "      <td>61</td>\n",
       "      <td>Smiths Crinkle Cut  Chips Chicken</td>\n",
       "      <td>2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>170</td>\n",
       "      <td>Smiths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-08-17</td>\n",
       "      <td>2</td>\n",
       "      <td>2373</td>\n",
       "      <td>974</td>\n",
       "      <td>69</td>\n",
       "      <td>Smiths Chip Thinly  S Cream Onion</td>\n",
       "      <td>5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>175</td>\n",
       "      <td>Smiths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-08-18</td>\n",
       "      <td>2</td>\n",
       "      <td>2426</td>\n",
       "      <td>1038</td>\n",
       "      <td>108</td>\n",
       "      <td>Kettle Tortilla Chpshny Jlpno Chili</td>\n",
       "      <td>3</td>\n",
       "      <td>13.8</td>\n",
       "      <td>150</td>\n",
       "      <td>Kettle</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "0 2018-10-17          1            1000       1         5   \n",
       "1 2019-05-14          1            1307     348        66   \n",
       "2 2019-05-20          1            1343     383        61   \n",
       "3 2018-08-17          2            2373     974        69   \n",
       "4 2018-08-18          2            2426    1038       108   \n",
       "\n",
       "                               PROD_NAME  PROD_QTY  TOT_SALES  PACK_SIZE  \\\n",
       "0    Natural Chip        Compny Seasalt          2        6.0        175   \n",
       "1                  Ccs Nacho Cheese              3        6.3        175   \n",
       "2    Smiths Crinkle Cut  Chips Chicken           2        2.9        170   \n",
       "3    Smiths Chip Thinly  S Cream Onion           5       15.0        175   \n",
       "4  Kettle Tortilla Chpshny Jlpno Chili           3       13.8        150   \n",
       "\n",
       "     BRAND  \n",
       "0  Natural  \n",
       "1      CCS  \n",
       "2   Smiths  \n",
       "3   Smiths  \n",
       "4   Kettle  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Date is now in the right format\n",
    "\n",
    "Tdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Natural Chip        Compny Seasalt \n",
       "1                    Ccs Nacho Cheese     \n",
       "2      Smiths Crinkle Cut  Chips Chicken  \n",
       "3      Smiths Chip Thinly  S Cream Onion  \n",
       "4    Kettle Tortilla Chpshny Jlpno Chili  \n",
       "Name: PROD_NAME, dtype: object"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First 5 rows of PROD_NAME\n",
    "\n",
    "Tdata['PROD_NAME'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>PACK_SIZE</th>\n",
       "      <th>BRAND</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-10-17</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Natural Chip        Compny Seasalt</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Natural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-14</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>348</td>\n",
       "      <td>66</td>\n",
       "      <td>Ccs Nacho Cheese</td>\n",
       "      <td>3</td>\n",
       "      <td>6.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CCS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1343</td>\n",
       "      <td>383</td>\n",
       "      <td>61</td>\n",
       "      <td>Smiths Crinkle Cut  Chips Chicken</td>\n",
       "      <td>2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Smiths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-08-17</td>\n",
       "      <td>2</td>\n",
       "      <td>2373</td>\n",
       "      <td>974</td>\n",
       "      <td>69</td>\n",
       "      <td>Smiths Chip Thinly  S Cream Onion</td>\n",
       "      <td>5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Smiths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-08-18</td>\n",
       "      <td>2</td>\n",
       "      <td>2426</td>\n",
       "      <td>1038</td>\n",
       "      <td>108</td>\n",
       "      <td>Kettle Tortilla Chpshny Jlpno Chili</td>\n",
       "      <td>3</td>\n",
       "      <td>13.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kettle</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "0 2018-10-17          1            1000       1         5   \n",
       "1 2019-05-14          1            1307     348        66   \n",
       "2 2019-05-20          1            1343     383        61   \n",
       "3 2018-08-17          2            2373     974        69   \n",
       "4 2018-08-18          2            2426    1038       108   \n",
       "\n",
       "                               PROD_NAME  PROD_QTY  TOT_SALES  PACK_SIZE  \\\n",
       "0    Natural Chip        Compny Seasalt          2        6.0        NaN   \n",
       "1                  Ccs Nacho Cheese              3        6.3        NaN   \n",
       "2    Smiths Crinkle Cut  Chips Chicken           2        2.9        NaN   \n",
       "3    Smiths Chip Thinly  S Cream Onion           5       15.0        NaN   \n",
       "4  Kettle Tortilla Chpshny Jlpno Chili           3       13.8        NaN   \n",
       "\n",
       "     BRAND  \n",
       "0  Natural  \n",
       "1      CCS  \n",
       "2   Smiths  \n",
       "3   Smiths  \n",
       "4   Kettle  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract weights out of 'PROD_NAME'\n",
    "\n",
    "Tdata['PACK_SIZE'] = Tdata['PROD_NAME'].str.extract(\"(\\d+)\")\n",
    "Tdata['PACK_SIZE'] = pd.to_numeric(Tdata['PACK_SIZE'])\n",
    "Tdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create text cleaning function for PROD_NAME feature\n",
    "def clean_text(text):\n",
    "    text = re.sub('[&/]', ' ', text) # remove special characters '&' and '/'\n",
    "    text = re.sub('\\d\\w*', ' ', text) # remove product weights\n",
    "    return text\n",
    "\n",
    "# Apply text cleaning function to PROD_NAME column\n",
    "Tdata['PROD_NAME'] = Tdata['PROD_NAME'].apply(clean_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create one giant string and apply 'word_tokenize' to separate the words\n",
    "\n",
    "cleanProdName = Tdata['PROD_NAME']\n",
    "string = ''.join(cleanProdName)\n",
    "prodWord = word_tokenize(string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply 'fdist' function which computes the frequency of each token and put it into a dataframe\n",
    "\n",
    "wordFrequency = fdist(prodWord)\n",
    "freq_df = pd.DataFrame(list(wordFrequency.items()), columns = [\"Word\", \"Frequency\"]).sort_values(by = 'Frequency', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Word</th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Chips</td>\n",
       "      <td>49770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kettle</td>\n",
       "      <td>40704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cheese</td>\n",
       "      <td>27890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Smiths</td>\n",
       "      <td>27083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Pringles</td>\n",
       "      <td>24720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Word  Frequency\n",
       "10     Chips      49770\n",
       "16    Kettle      40704\n",
       "6     Cheese      27890\n",
       "7     Smiths      27083\n",
       "59  Pringles      24720"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see the top 5 most frequent words\n",
    "\n",
    "freq_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop rows with salsa word in PROD_NAME\n",
    "\n",
    "Tdata['PROD_NAME'] = Tdata['PROD_NAME'].apply(lambda x: x.lower())\n",
    "Tdata = Tdata[~Tdata['PROD_NAME'].str.contains(\"salsa\")]\n",
    "Tdata['PROD_NAME'] = Tdata['PROD_NAME'].apply(lambda x: x.title())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>PACK_SIZE</th>\n",
       "      <th>BRAND</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-10-17</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Natural Chip        Compny Seasalt</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Natural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-14</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>348</td>\n",
       "      <td>66</td>\n",
       "      <td>Ccs Nacho Cheese</td>\n",
       "      <td>3</td>\n",
       "      <td>6.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CCS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1343</td>\n",
       "      <td>383</td>\n",
       "      <td>61</td>\n",
       "      <td>Smiths Crinkle Cut  Chips Chicken</td>\n",
       "      <td>2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Smiths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-08-17</td>\n",
       "      <td>2</td>\n",
       "      <td>2373</td>\n",
       "      <td>974</td>\n",
       "      <td>69</td>\n",
       "      <td>Smiths Chip Thinly  S Cream Onion</td>\n",
       "      <td>5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Smiths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-08-18</td>\n",
       "      <td>2</td>\n",
       "      <td>2426</td>\n",
       "      <td>1038</td>\n",
       "      <td>108</td>\n",
       "      <td>Kettle Tortilla Chpshny Jlpno Chili</td>\n",
       "      <td>3</td>\n",
       "      <td>13.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kettle</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "0 2018-10-17          1            1000       1         5   \n",
       "1 2019-05-14          1            1307     348        66   \n",
       "2 2019-05-20          1            1343     383        61   \n",
       "3 2018-08-17          2            2373     974        69   \n",
       "4 2018-08-18          2            2426    1038       108   \n",
       "\n",
       "                               PROD_NAME  PROD_QTY  TOT_SALES  PACK_SIZE  \\\n",
       "0    Natural Chip        Compny Seasalt          2        6.0        NaN   \n",
       "1                  Ccs Nacho Cheese              3        6.3        NaN   \n",
       "2    Smiths Crinkle Cut  Chips Chicken           2        2.9        NaN   \n",
       "3    Smiths Chip Thinly  S Cream Onion           5       15.0        NaN   \n",
       "4  Kettle Tortilla Chpshny Jlpno Chili           3       13.8        NaN   \n",
       "\n",
       "     BRAND  \n",
       "0  Natural  \n",
       "1      CCS  \n",
       "2   Smiths  \n",
       "3   Smiths  \n",
       "4   Kettle  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    220070\n",
       "1     25476\n",
       "5       415\n",
       "3       408\n",
       "4       371\n",
       "Name: PROD_QTY, dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Value counts of PROD_QTY\n",
    "\n",
    "Tdata['PROD_QTY'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have two occurences of 200 in the dataset. This seems odd so let's explore further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>PACK_SIZE</th>\n",
       "      <th>BRAND</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [DATE, STORE_NBR, LYLTY_CARD_NBR, TXN_ID, PROD_NBR, PROD_NAME, PROD_QTY, TOT_SALES, PACK_SIZE, BRAND]\n",
       "Index: []"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tdata.loc[Tdata['PROD_QTY'] == 200, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both these Tdatas have been made by the same person at the same store. Let's see all the Tdatas this person has made by tracking his loyalty card number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>PACK_SIZE</th>\n",
       "      <th>BRAND</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [DATE, STORE_NBR, LYLTY_CARD_NBR, TXN_ID, PROD_NBR, PROD_NAME, PROD_QTY, TOT_SALES, PACK_SIZE, BRAND]\n",
       "Index: []"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tdata.loc[Tdata['LYLTY_CARD_NBR'] == 226000, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This customer has only made two Tdatas over the entire year so unlikely to be a retail customer. He/she is most likely purchasing for commercial purposes so it is safe for us to drop these this customer from both 'Tdata' and 'Cdata' dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "Tdata.drop(Tdata.index[Tdata['LYLTY_CARD_NBR'] == 226000], inplace = True)\n",
    "Cdata.drop(Cdata.index[Cdata['LYLTY_CARD_NBR'] == 226000], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>PACK_SIZE</th>\n",
       "      <th>BRAND</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [DATE, STORE_NBR, LYLTY_CARD_NBR, TXN_ID, PROD_NBR, PROD_NAME, PROD_QTY, TOT_SALES, PACK_SIZE, BRAND]\n",
       "Index: []"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make sure it has been dropped \n",
    "\n",
    "Tdata.loc[Tdata['LYLTY_CARD_NBR'] == 226000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "364"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now let's examine the number of Tdatas over time to see if there are any obvious data issues e.g. missing data\n",
    "\n",
    "Tdata['DATE'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-12-25'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look for the missing date \n",
    "\n",
    "pd.date_range(start = '2018-07-01', end = '2019-06-30').difference(Tdata['DATE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a missing date on Christmas Day. This makes sense because most retail stores are closed that day. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TOT_SALES</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>4920.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-02</th>\n",
       "      <td>4877.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-03</th>\n",
       "      <td>4954.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-04</th>\n",
       "      <td>4968.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-05</th>\n",
       "      <td>4682.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            TOT_SALES\n",
       "DATE                 \n",
       "2018-07-01     4920.1\n",
       "2018-07-02     4877.0\n",
       "2018-07-03     4954.7\n",
       "2018-07-04     4968.1\n",
       "2018-07-05     4682.0"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new dataframe which contains the total sale for each date\n",
    "\n",
    "a = pd.pivot_table(Tdata, values = 'TOT_SALES', index = 'DATE', aggfunc = 'sum')\n",
    "a.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "365"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = pd.DataFrame(index = pd.date_range(start = '2018-07-01', end = '2019-06-30'))\n",
    "b['TOT_SALES'] = 0\n",
    "len(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = a + b\n",
    "c.fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TOT_SALES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>4920.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-02</th>\n",
       "      <td>4877.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-03</th>\n",
       "      <td>4954.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-04</th>\n",
       "      <td>4968.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-05</th>\n",
       "      <td>4682.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            TOT_SALES\n",
       "2018-07-01     4920.1\n",
       "2018-07-02     4877.0\n",
       "2018-07-03     4954.7\n",
       "2018-07-04     4968.1\n",
       "2018-07-05     4682.0"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>4920.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-02</th>\n",
       "      <td>4877.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-03</th>\n",
       "      <td>4954.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-04</th>\n",
       "      <td>4968.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-05</th>\n",
       "      <td>4682.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total Sales\n",
       "Date                   \n",
       "2018-07-01       4920.1\n",
       "2018-07-02       4877.0\n",
       "2018-07-03       4954.7\n",
       "2018-07-04       4968.1\n",
       "2018-07-05       4682.0"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.index.name = 'Date'\n",
    "c.rename(columns = {'TOT_SALES': 'Total Sales'}, inplace = True)\n",
    "c.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ddg3wV3v7DuAqEQmLyEKcQQPrbPNol4istaM93+V7jlJKpcVt9jxjXgnhYICX6js51NbHnBJnjU/R6TmUUhkoOMmv/1HgtyKSDewB3oMTHN4qIu8DDgBvBTDGbBGRW3GCuRhwnTFmyL7Oh4BfAjnA3fZHKaXS5s6hVlkY5rS5xazb28rhzv7hjNp0Fk4ppUYxqYGaMWYDsCbFny4Z5fE3ADek2L4eWD2hhVNKnVCau5051Cryw5y1oJTvPbQLgJpiJ1DTCW+VUplIVyZQSp0QmrucjFp5fpgzF3jzbPuaPp37GqcppTKJBmpKqRNCc/cAwYBQlBPijHklXmCWPJhAM2pKqUyigZpS6oTQ3D1AWX42ARusLa8qAGBWcQQYHkygYZpSKpNM9mACpZTKCM3dUcrzw979V55UhYgQDjor1bmDCXQJKaVUJtFATSl1QmjpiVLmC9Q+dekyPnnpMu9+wFvrc8qLppRSo9KmT6XUCaGzb5CinJB3PxAQsgLDk3KIrvWplMpAGqgppU4ITqA2eiOCNz1HXEM1pVTm0EBNKXXcM8bQ0TdIYSQ06mO8PmpTUySllEqLBmpKqeNeb3SIWNwkNH0m0yWklFKZSAM1pdRxr7N/EIDCMQM1e0PjNKUyRl90iO2Hu6a7GNNKAzWl1HGvo88J1MbMqNnf2kVNqczx22f287rvPU5fdGj8Bx+nNFBTSh33OvtiwNiBWiDgTnirkZpSmaK+o59oLE5rb3S6izJtNFBTSh333IxaOoMJNKOmVOZoswFaW48GakplvM7+Qb79j53EhuLTXRQ1w6TV9OlNeKuRmlKZwg3Q2nsHp7kk00cDNTVjPLC1gW/+Ywcv1XdOd1HUDNPpZtTGmEdNJ7xVKvO02QBNmz6VmgHqO/oB6O6PTcrr72zo4pEdTZPy2mp6uRm1gjGaPgOaUVMq47T3RhN+j+bGu7fxyT9smIISTT0N1NSMcdgGal0DkxOo/fCRPXz2jxsn5bVPFA9vb2T9vtbpLsYInf2DFISDCUtGJRtelH1qyqSUGl9rj9tHbeymzxcOtPHXDYdo6R6YimJNKQ3U1Iwx2Rm1noEYzd0DDMUNn/njRn7w8K5JeZ+ZZLyrWL943PDpWzfynQczb7919A2OOYca+JaQ0kBNTbPNtR1c/q1HvUzwiSo2FKfT1vdt49RFfYNDxA3c91LDVBRtSmmgpmaMhk4bqE1SRs090Nt6ozy0rZF1eycmMxSNxXloe+OMa1J7eHsja/77H9R39KX1+BfrOmjpidLdn3knl840AjWvj9oM+z+pYYNDcTqOg07nG2rb2Xa4ixcPdUx3UaZVuy9QHS9Q67XzrN394uFJLdN00EBNzRheRm0SAzWA+vZ+WnqiXgf0Y3XfS4d5zy+e5ak9LRPyeskaO/vZ2TDxM3ev29tKLG6obUsvUHt4u9O/b7L+P8eisy825oLsfppRm7l+/vheLv3mI+MG21+/Zxvfe3DnMb3Xg9saJi0odOue3U3dk/L6M4U/o982zr52J8R9clfzmP+X6+/YwmM7Z1ZfZA3U1IwQjcVptn0Puiap6bPfBmpbDzujSieq2WF/Sy8A922ZnJT8/967nQ/85rkJf92tdnRtuvMXPby9EYCegcybQbyjb3DMqTlguOlTx33OHLVtvQnBzIHWXhq7BuiNDtHQ2e/1a012/0sNPLCt8Zje972/XM837t9+1K8xFnfJs92Nx1+g1tU/yFCaV0NucJadFRi3G0ZvNMaCslxicePV4cn6okP88sl93D/Dmkc1UJsmg0Nx/utvL/FsBna8zkSNXf1eJ+/ugaMPoB7Z0cR3Hkh9Je2mzl+qcw7yzgkKCN2M1P0vNUxKs1pz9wAt3RM/dH1rvZOlS2f+ovbeKBsOthMQpyLONJ39g2NOdgv+ps8pKJCaEP92+4u89juP8+SuZmD4mG3tifK5P23is39KPTiorXfwmCZQfWaPU2//5YVD3gXeRHJX0tjd1DPhrz2dYkNxXv6/D/O7dQfSerw7kGBBeW5aTZ/LqwsARm0FqG1zLpon62J/smigNg2MMfzb7Zv52eN7uXNjHc3dA1zwtQfZUjc9/REyuU9OX3SIV33zEW577pC37VgGE9z+fC3fe2hXys/sps7d/8NENX0eau/zfm+pm/g54Lr6Y3QPxCb0/9jWE+Ww7RM4XgUJ8Nz+NuIGzpxfMuFlmQhHklE7UZs+jTEZs/j1nRvrOP/GBxkcZ3LrPc3d9A0O8Z5fPktjV793kdDWG6W2rdfr1+pnjKG9N0rLsQRqe1sIiBMY3jMJfaK8jNpx1vTZ3B2ltSfKnjQ/l5tFW1ieR1vPIL9+ah/fuG9kFnMobhiIxVlSmY8IHGztTfl6B+z2iarbp4oGapPg0R1N3Ldl9IP3ga2N3Lq+FhEnG7KzoZvatj6enaDO60fip4/t4bJvPZpxJ1ZXbVsvOxq6+dGjuwHIDgaOqQ9US0+UaCyeMkvkNX3aTNJALJ7yavnLd27hj+sPpv2eh9p6OXthKQGBPz1Xe5QlH11Xf4yhuPH62E0Ef9PBeH1DADbVdiAC5y4qI26gfzBzVo8YHIrTGx1KfzDBCdr0+ejOZi771qMZEaxtPNjOofY+r7tDKoNDcera+zltbjEDsTj7W3q9E3BrT5Tm7qiXmfLrGogRixu6+mPjBoKjWbe3lVesqGReaS5/fC79uiBd7ueo7+jPyD6fR8sNnNNdZaDVTsmxsDyf7oEYP3lsb0Ideuv6g5x/44P0RofX8q0qiIyaUXMDOM2oKX7w8C5uvGfbqH/fWNtOVkA4dU4xzd1RrzJKt9P2RGnuHuCb9+9gR0M3PdGxT/L3bTnMg9umvl3fTX27zZKLyvMSDrL4EaY/3CbCwymutN1Ax18xdiY14w3FDb975gD3jhGID8WNV3EYYzjU3scpNUX801lz+eWT+7y+XOPp6B3k1vUHxw2i3SzCRE5b4gar4eD4fUMANtW2s6Qin4rCiFMm2zx9qL2PDQfbJ6xcR2pwKM7OBufqffyMmvP7RM2o7bVZjh2TMDDlSDV0OXViU9fogVpdex9DccNpc4sB5+TvNn02dg7Q0Tc44vgFaO9JfyRhyrJ19rOvpZe1i8o4b3EZ2w9PfNarsz/mfR/TzT7NBG6g1ppmNrO9N0p2MEBNsVOvuH0Q3T5uOxu6ONTe59XrOdlB5pbmeE2cyQ7ac2yq70Um00BtEnT0xTjY2jvqmpQ7G7qZX5ZLTXEOLd0DXqB2cJQvVzp2N3Xzzft3HFFm7HsP7vICtPEmCfzOgzv54SN7jrp8R8t/QOeEsphdnOMFUi3dA6y+/l6esP1TjuT1kgM1Y1JnpJKvyOva+xiIxWkc4wTym6f3c+HXHyYeN7T2ROkfjFNTksOXXreKFdUF/Ottm9Iq69831/O5P21i5zgdit3AdSInAt5a30l5fpj5ZbnjVqrGGDbVdnDKnGIKws7ISjdo/O4DO/nQJAx0SNd3HtjJq7/zGDD28lGOE3tlAndU9YEUzUaNXf38z11bicbGzkBtre/krxsOjfmYdLgn9LEyau4gnZNrigDnpN6V1GTYGx0akTXzL0U03iSqd26sGzEg4Rnb8nHOwjIqCyO09AyMWtcbY44q8O3qG2RZldPfaqY2f3YPxEY0QboBeLoBcltvlNLcbErysr1tQ3Hjna/cc4H7fckNZTGnJPeIM2r9g0M8uTv988hU00BtEnT2DTI4ZLy+Scl2NnaxtDKfsvxsWnqi3lXjwdY+BmJDRzXA4C8vHOLbD+z0Tqqd/YN8+c4tY6bN/7LhEFWFYWDsChGguSvKwDiVtF//4BA3/P0lvnrX1mOaC8itVHOzs5hVFKEgEqTHfqbtDV30Roe80YnJ7/+pWzckzAFmjKGlx/mcDUmV70AsnrITefKVl1tppur74trV2E1z9wBd/TGvwqgpziESyuJNZ9TQ0DmQ1hWd+5ixKvp43NBts3cTmc7fcLCd1TWFFOdmj9lMsbOhi52N3bT0RDl1bhF5NlBzR3629Trf76kMfho7+/nKnS8RjcXZUtdJdWGEay9cxEXLKsd8XsA3mCAai/Pk7mZuXX/wqJvHMoUxhlvXH6RxjO8s+AK1lpGB2q3PHuTHj+5h8zjH8k8e3cNn/7Qp7VF9o3HLOlZGzQ0oT53rBGodfYPehZX/mEk+LvxBglsfuPwZ+obOfj76+xdGTHztnuyXVuVTWRDGGKfvVSpP7W7hVd989Iinz+nsH+TkmiKyAsLuxuEBBTc/uY/fPL3/iF5rNC3dA3z61o2TMvinobOfK7/3OG/70VMJ2xuPoN8rOE2fxbkhSnKzE7a7F9ru/9YNAHOzs5hTkkN9R1/K43a0jNpdm+v55588M+Kc/bV7tvH9h6Z/Am8N1CaB+yXY2+wcYE1dA97kqdFYnH0tvSytLKAsL0x776B3xXawrZdb1h3krT986oivouraE1PKT+5q5hdP7OPuzfUpH98/OER77yBnzi8BRq9oYDjAGTiCPlDP7W/jJ4/t5UeP7uFb/zj6+Ypabbm+/PpVXHvhIvLDQS/4rG11DqqmFEHmtsNd/Pn5Qzy1e3jusq6BGINDTkXsHui7Grv5wp83jwhoy/OdiiF5io49dhRWc3d01JORG1y29Ua9A7+mJAeA6iLn92jTBvi5AemOhtG/C93R2PBo2DQDtbaeKJd/69GEfePX3D3ArsZuzllYRkluaNRK9fGdzVz+7cd4801PAnDKnGLybaDmNn32DAwRi5uUfYUmy9831/PzJ/aysbadfS09nD6vmC+++qSEq/JUxF3rE8PX7tnGP//kGT73p008sn1mzbmU7IldLXzuT5v4/J83j/k49zu5v3XkSMNHdzrZhtGalFwHWnuJxuIcOoZuHMYYGjqdY3qseulAay/ZwQALy/PJCghNXQNeVtyfhU7uON6elFHb2dBFbzTG7qZuVn7pHjbapvr1+9oA53vu19k/SHYwQCSURZVt6h/twq3WHv/1aRzvLmOc46XMZrT954LfPrOfr9z5UspgOh2xoTif/MMGNtW289D2Jm57vnZSZh645ufr2N3Uw+HO/oRso7ufxstkutp7o5TmZXuBmlu/uN9Vt450A8Cc7CzmluQSNyPrWGMMtTbI7h6IJQTl7nmzLilQ+8sLh/jm/TuOen9PFA3UJtiQ7aQKsM8Gaj95bA/v+vkzGGPY19LDUNywtMrJqIGTGQLn6sCd28cd/p2uw53OF8wdyeReiY62yLj795OqC53njVEhdvY7Ac6RDEN3D5Ka4hxae45+7bWWnigF4SBvXTOXq86eR34k6O1f94q6uWtk2d3gwl9J+z+jW2HcubGO36874AXVboC2pDJ/xPNhOKM2ZJs1U3FPBK29Ue+ENac4F4DZRU7FnlwhpOJ+zrGuxv3ZgnSvjG9df5Bth7t44WBbyr+73721i0opzctOOZjgYGsv1/3ueeaU5BCNxQllCSfNKqAgkphRcwPg5mP4DhypXfYkve1wFwdbe5lflpfW89xZ1OJxJ5gttJ9lT/PMbHpyff+hXYjAg9saE/pHPrStkXf/Yp13wqq3dUjySal7IMbz+53vynj9aN1j0j1O+geHjnjt166BmBdwufWUMWZE5vxASy/zSnPJCgjFOaGEriP+ciZnT1p9QUJDZz+v/94TfP2e7dzz4mH6B+Nen8r1+51y72nuSci0dPbFvKleKgucFonRukK4U4AcyZyM/YNxokNxCnOCLK7I977PTtmjRIfi/MdfX+TW9QfHbQlJdrCtj9tfOMRfXqjzso4HWye2b/RAbIhth7uosNlGf1Ozu5+6B2LjNqPHhuLUtfdRkptNSZ6zvy9dWQWMXKXGa/rMDjLHXhQndyVq7x2kayBGdWEEY/BaIlK9Djj9Wxs6+4nFDd96YMcR7oWJpYHaBPNnNdyTf31HP/2DzpplbsfmJZX5XlCws7Hba3Zx+1sd6VVOfVJGza3gHtvZnDLz09jlPH7FLCdQa+4eYH9LT8pMnlsZHEnTp5uxOmlWQVqjBkfT1htNyIQUhIMMxOJEY3HvQExVWbnBkj+Q8QeMbiC5x/6PGu0V/JwSJ6BaWun0D0meS22Pb14jdx8mc08EbT1ORi0/HPT6R1XbQG20jFpvNMb1d2yhqWvAl1EbK1Ab3rej9VE70NLL5d96lPoOp/P1r23TSesowfkze1vIzc5idU2RbfqMjmi6/P5Du+gfHOLX7z2H373/HP7vracSDmZ5TZ/dXkZteF6rybLtcCe3+OZlck9sj2xvYnDIsKAsN63XCXgZNYgbQ0leNqV52extnt6rab9fPLGXXY3pN6M9t7+Np/a08NnLlrOgLJd//8uLXvbhjo11PLy9idq2PuJxw+GOfrICQn1nP3/fVM91v32eeNzw9O4WYrYOGW3aA3CCMvdEvLupm3jc8LHfv8BbfvgU+1t6qGvv87JVY3GPRRjOlj+xq4Urvv2YN8chwP5WJ1ADKMoNpexbByP7mbb3Rr0RvhsOttM3OMTfNtXzwFZnsJTb9+25/W1U24zZ476Z7Lv6B70gfryMmlv3HUmg5gaWRTkhFlfks6+lh9hQnHjc0NY7SEluiEd2NPG5P20adU7I0exvceqvLXUd3uje0fbb0XLr3MUVzgVS4gXy8P92vEFKP39iL3Ud/bz65FlUF0b4yMVL+NglS8kKyIimz8aEpk/nO1GbFIC654tVswsTngv+zFziOSJunGTD7S8c8s7n02FSAzUR2Scim0Vkg4ist9tKReR+Edlpf5f4Hv8FEdklIttF5DLf9jPt6+wSke+IeFOIZxz/1dtee8A3dQ0HUTsbuwgILK7IpzzfuRqLxuJex9GhuCErIEe0zqQxhrqOpIyareA6+gbZWNs+4jnuF7KmOIeinBAt3QP8622b+NyfRnZ0dw+0I8moNXb2UxAJMrs455hO0q09Turble/1gRruqJoqUHNT6/7/h9uMUlUY5rD9/HuS+pzNtRX/0qrRM2qLyvPsZxz7Krqtd5Datj5qinO8ZrWqwggiozeF3LGhjl8+uY8ndzd7V3n7WnoZiKXe94kZtdSB2vMH2th2uIun97Tw8PZGL9sw2jxSz+xpZc2CUkJZAUpyQ8Tihu6BGD0DMT782+e47bla/vz8Id66Zg7zynI5c34pV55WAwz/f9wLlh5v4MfkBGq90RjX/uo5vnj7Zi8z5F5sPL7LObmmnVHzrfUZN07gtqAsl71pZNTiccP/3LXVy6InS75YGrQn3vEc7ujnm/fv4IGtDfRFh/jynS/xh2fTmw6iLzrE52/bREVBmGvOXcC3rjqd1p4o7/r5OnqjMS9o2t7QRUtPlMEhw+qaIoyB6+/cwt831/PYrmYe3dlETiiLVbMLxxzw5G8W3d3Uw48f2+MtkL1ubytfvnMLb/vRU9R39PHojqZRRwO7gWTQNmeCE4zD8MnWGMNBX6BWnBPyMkOhrMTTQ3KQ1NoTpTgnREEk6GX7mrsHeP6AU579LT30RmNsqevkzWfWUFUY5jFf82dXf8zLHJfnZyNy5Bm1R3c0jTo7vlvnFEZCLK7IY3DIcLCtj047o/+HL1rCnR+5gJctLefBbcNrCA8OxcftG+jWmS/VdXqB2ljB99Fwy7+w3KlD/cd+Y2c/xblOdmysC/j6jj6+cd8OLl1ZxatPrkZE+Mxly1lYnkdlQZjDHakHE+RkZzGrOEJARmbU3IB0pReoDb+/9zq+i2+3nvz8FSu46R1npn3BNxmmIqN2sTHmNGPMGnv/88ADxpilwAP2PiKyErgKWAVcDvxARLLsc24CrgWW2p/Lp6DcR8U9IHOzs7xK261sWnsG2NnYzbzSXCKhLMpsoAZw+jwvXuW1p8ziUHuf1zxmjKGtJzpqxd7eO+jNW+VmSZq6BqgpziEgeFeKfu4Xu7IwTFl+Ns09UXY1dqfsg+IGQkcyN9bhzn6qCyOU5GbT0Tc46qioVPyPHRGo2SaH7oGY1zE0/Yyas23lrEIaOvuJx42XIXMr2oU2CFtQlkc4GPAqnb9uOMSnb91IY9cAaxeX2eeknkzT66PWE+VwZx+z7NBygFBWgIr88KgLnf/5BWfEXGffoFd5DMXNqFdzCZXNKIGae/W5rb6LB7c1UhAOsmp2YcpAra0nyvaGLs5ZWApAse0b0t47yIaD7dy1+TCf/uNGBuNx3nfBohHP9wK1pKbPycqofeO+HRxo7SVunIxie68zf1ZAhr+vC8rTq2D9KxPEjUFwTjb70sioHWzr5ceP7km5IHRL9wCnffm+hNGQl3/rUW56ZPeYr3movY9XfONhvv3ATn70yB7ve354lAuEZDfevZWdjd18462nkhcOctrcYr591elsO9zFH5496GWTdzR0eRnetfb/7tZZ33lgJ39cX8srTqpkUUX+mM1k7okwEgqw7XAnP3hoF5esqKQoJ8RTu1t4YlcLA7E4H/rN87z7F+t46w+f9PbJrc8e9Dq2uyfLpVUF3md2s1zu/daeKN0DseFAzdYzMHyx5QZs7sXazx7fy9U/e4b23kEvW1pnP3dOyDnVVBdG2N/ay8aDHQzFDWvml7J2URnP7R/uJtDZP+jNyRfMClCWF/YuxpOl6oIB8I37tvN/96Zefsotb2FOyOuCsdsO2AGoKAhz8pwiLltVTW1bH7ubnJaQi//vYf75J0+PelEHw/+jroGYVy8cnOBpoTqTM2q2JSMai9PSE2W5TUqMVSdsru1gIBbnuouXkJyXqSqMjGj6dC+ac7OzCGUFqCnJYV9SE/6Wuk6CAfGmc/FnWt36qsl3bLnN3atrirh8dfWIckyl6Wj6vBK42d6+GXiDb/stxpgBY8xeYBdwtojMAgqNMU8Z59LhV77nZBz3gFxdU0Rtm9Ox1q30WrqjHGjpZYENBtw+auB8qQvCQbKDAa45bwHgNH/Wtfdx+n/dz+n/dT9Xfv+JlF/uOt9J323ea+oaYHFlPpecVMVPHxvZXNLYNUAwIJTmZlOeF2ZvUw/N3c4IvdhQnPf+8ll+9dQ+W24bqMWG0h6919A5QHVRxAuy2tNM/T9/oI1VX7qXp+0C5iMzalle+Zu6BghlCS3dI4NY92rNn1FzP8fK2YW09kQ50Nrr9YVxg66XLS3nR1efyQVLyinMCXnP//Pzh7jteWeixbWLbKCW4oTZGx3y+l609kY53NHPrKJIwmNmFUVSZtQOtvZ6mdQOG6i5WdffP3OAOzbWeYHZi4c6+OQfNiR0yh2tj5p7Et52uIsNB9s5ZW4RlQXhlH0HX7SrMpxuKzO3E2+rDeTBmdT2HefM84Jav0goQFZA6B4YxBjjTf+Sbj/FI5kXLzYU5zdP7/eaoTr7Br1s2lkLnIAjHAxQVRAZ9TX8/IMJDE7gtrA8l8Od/fRGY/QPDnHFtx9LOYee26STqgnsuf1tdA3E+H/37yA2FGcgNsTupp6EZjxw/qdvvulJ7//12I4mZ1kcG7C4J+rkEcupDMSG+MP6g7z1zDlcuKzC2/7KkyqZVRRJGMm2/XCXV4ecs6jU7gt4w2mzeW5/GyLwxVefxNySHG/uslTcvm3nLy7nhQPtdPbHeOe581kzv4S/baqneyDGiuoCNhxsZ0V1IafPK+FTt26kvqOPmx7ZzW3P1/LWHz7ldQ9ZNbvQqzv3J/VHde/PL3MDteE58ubbQM0N4jr7Bmns6ucb923nsZ3NbD3cSUlutlevVBWGec0psyjPd34faO3lmb0tiMAZ80qYX5bH4c5+77j2Z9TA6afW0DnAv92+mT88m7g0khuo+TNqxhivo7273xLmbrQBRGEkyKIKG6g1dXvZObcryEXLnf/rTQ/v5q0/fIrOvkGe2dvKF8YYNLK/pZfsrOHT/tzSHGpbeyd0VLZ7DlxgM9luS4bbynOS7W7T7ts3yf3V/K0fyaoLIxzu7McY412cek2fIef/srSyYETf3hcPdbC8usBLkPjry54UGTW3f3Fy/T0dJjtQM8B9IvKciFxrt1UZY+oB7G93zHwN4M/p19ptNfZ28vaM5J7YT51TRNw4U3H415+r7+j3+j0UhIPeQVNREGZBeR6n1BRx6pxickJZbDjYzgsH2mnvHeSac+ezo6GLq378VELnUhjunwbDzVmNXQNU5Ie54Y2ryQsH+eQfNiYcjI1dA5TnhwkEhLL8bK9pIW6cprYHtzXyP3dtpa69jyZ70BiDN2pyPA2d/VQWRLxKxa1kHtjawFfv2jrq6LF7XzzMQCzOF2/fzEBsKEWg5lTIbnlXzS4iFjcjAkH3vj+j5g5McCtwNxiE4aArJ5TFZauqCQSEwkjQqzSbugZYUV3ANefO5xU2S5CqucMfSDd09tPcHaW6MCfhMdVFkRF91B7e3sinb3XWJcwKiBOo9cc4dU4RudlZ3PzUfj72+xc4538eYPvhLm5df5DbXzjkLUnlBEepM2pu8LD5UAfbDndx2txiSvPCXvY1Govz1w2H2Nc8HDy4lWmJ10zhNNsXRoL87v3n8N9vODnle4mIMzK3P8ZAbLgpxp+9M8bww0d2s7e5h/0tPVz0vw/x3P5Wbnp4N+fe+MCYGQG/Pc09DMTinLe4HHAqfPfYuGJ1NeCcLAKB9K6E/RPeGmMIiHjNN/uae9lwsJ2t9Z0J0yP0DzrzdLknXTfgj8cNr/72Y/y/+3d4TXz7W3q5Y2OddxJKnsvvwW2NPLe/jX//y4sYY1i3r5WyvGzOXVxGU9eAd6GRarLmZOv3tdE/GOdyux9cIsJlq6q9MqyZX5KQUVtd43zfzltcxqcuXU44GOALV6ygpjiHuaXOgtduNnj9vtaE7PeB1j5yQlmcZbNyJbkhLlhSzpoFpUSH4gQDws/ffRbXXbyYn7/7LG5808kMxQ03Pex8F15z8iy2He7i5if3kR8OsqAsl65+J0B2+1Z5c07aQG246XO4jnCbut2BBp39g3z3gV3epNl7mnqcQM1ehCwqz+fLr1/F3z56AQvL84jG4vzlhUOsnFVIUW6IOcU5GDN8HHX1D1IQHg4MqwrDbD/cxe/WORdTfqn6qDV1DdA9EKOjb5C+6BBv/METCdk1f0atKCdERUGY3U3DGbUyWx/OKcllaWU+tz1fS252Fn/9yAVce+Ei/vz8oVGnYjnQ2ss5i0oJ2i/7JSuq6LJlORb7W3q8+sct/5zSHIIB8b637v5z1+Pc3tDFFd9+jFO/fB+f+MMLCa/nBuhleSkCtaIIDR39DMTiXt9J971zsp0L+aWV+exp7vG+n+5cjyfXFHkXdv5zgxvw+fvQ1bX3UVEQJhLKYrpNdqB2vjHmDOAK4DoRuXCMx6aqTc0Y20e+gMi1IrJeRNY3NU3dkPrdTd18+c4tfPH2zd4X/pQ5xQAJKfOGzgFaega8Dqgi4g0oqMgP8423ncrX33IKWQFhSaUz2sfNEHz+ipP4xbvPoqlrgNd85zGe8QUZbsU5r9SZnDQeNzR3D1BZGKayIMInX7mUzYc62N3Uw08f28P/3ruNxi7n7+Bk9vwXyW6/jf7BOF+9e1vCZLj9aZxEh+KGxq4BqovCXmXoluv6O7fwo0f3cMk3HvEqX79HdzZTnh9mT1MPP3hoNwOxeFLTp3OQuSPATp/n7Ofk5k/3aq0zqemzND/b2/9P+KancE+wudnDB2VRTsj7fzZ1D3Da3GK+fOVq8sNBKgvCCU2fB1p6uebn69jn+0zb7Oz+IzNqOQkZtdhQnGt//Rx7mnv4t1efRHl+Np19Tp+wkrxs7vrYy/jHpy7kjx88lwEbVLmZN3c/VBdGRh1M4L5Xa0/UzuReQrmdw6+xs59Lv/kIH79lAzfctZWX6juZXTQcYPubPnc2dLO0qmDcJgBnCpWhhMDR30/lUHsfN969jT88e5Bn97Wxr6WXD/7mef7vvu00dA6M2szbMxDjDd9/wjum3KDyvCVOhtMN1LKDAV6xwhkdNv8I+pWIN+GtM/IzIOI1m+5r6fGWeHtyd4t34fFPP3qKr961zTsxuhX947uaeam+k9ueq+WFA86cdMuq8vn9ugPeY5ODdffi4x9bG7hr82Ge3dfKmgUlVBSE6RoYnpPPzSaM5dGdTYSyxMv++rnB26LyPNYsKGVPUw8HW3sJZQnleWG++U+ncf3rVjGvLJcN//kqrj53AQBzbSftg619vHiog7f88Clufmo4aD1g+4wttlmg15wyi1BWgLMWON06zphXwuziHD572QqqiyIsqshnRXWBN7jlXy9fwdpFpfREh6gsDHvZ5IbOfu+zJzeFzi0dmVFz+xKV54cpjARpsyt8vPH0Gu/CuCQ35H3HF1fmkRcOUl0U8bJA+1p6Odfuu9nFzoWW2xTW2ZecUYtwqL0PYxIHG4G/HhoOhHb5Bmy9VN9JS0+UdXtbGYobbn5yn/dZ3ZGliyvy2NU4MqMG8K7zFnD+kjL+/KHzWFie52WSUwXzxhgOtPaypDKfZVUF5GVnsdZmUA+29vHMnhYu+t+HOOk/7uFTt24Y8fzR1LX3cdH/Pcwp19/L/967zbu4LcoJOd1q7P/MvRh2A7XbXzjE1vpO1swv4Z4XDydcvDd3D1CcGyI7ODJEqbJ1XXL2OpQl3uOXVObbAWfOvqxt66Ojb5CT5xRRYPdrZ4o+av4A91C70784E0xqoGaMqbO/G4HbgbOBBtucif3tjhevBeb6nj4HqLPb56TYnur9fmyMWWOMWVNRUZHqIRMmHjd09Q/S1T/Im37wJL94Yh+/e+aA14fDbQf3DwrY3tCJMcMj/wAvDVteEGZZVYGX6l7qC9RqinPIyc7ivCXl3PuJC8kKiNcXJh431HX0E8oSllUV0NoTpcNOuFthX/v8JeVeWX786B5+8cQ+6tr7vKHl5fmJVy3r9rkzb5fy9011XqUI6Q0oaOlxlvioLox4w6rbeqM8ubuFg619vPf8hQzE4iM6Ezd29bO1vpP3nL+AM+YV89tnnGaE0lx/Rs0N1JwgyO3b15yU3XKbHLr6Btl2uJP3/fJZ9jb3UJqXzYrqQrKDAe7aXE9edhbZwYB3gs3xBWpu06c7E3ZFwfB+qiwMJ1x93br+II/saPKaxcLBADttc3N1iqbP7oGYl3qva3eaVT532XLef+EiL0DsGog5mYXyPJZUFnDWglLOnFfC3zfXs812BN562Ol3UZ6fPWJpre89uJMDLb02uzlc9lPnFlGal81ALM6dm+rZ39LLqXOKeGp3C5tqO1g5u8h7rD+jtrupmyX2+zkWJ1Ab9JoTIDHT6I5idb/fWQGhvTdKrr1y3dnQTW80RkdSZ+ONB9vZcLCdZ/Y6AfbW+k6ygwHOsN+Bjr5Bdjf1sKg8jzklOVQWhFldU0S6hjNqxumjJsPNN3ube1i3r5WCSJChuOG+lw7bKSO62FTb7gVd7snj93YU6qH2Pp7Z28Lpc0s4bW4x+1t6vWxBY1d/QlPvtvouXnlSFStnFXL9nVs42NrHWQtKve+d+z+PxuLeBcQf1x/kA79ez2f/uDEhu/XYjmbOnF/ijcL1O2tBKVWFYdYsKGF5dT7RoTiP7GiiqjBCIOBk3JbafkT+42FuqXPSqm3r9bLRv316vxc0HmztZW5pLmfMK2bV7ELecc58AE6eU0RFQZjXnjprRFlec/IsjHGCkXlluVy9dgEAVQUR73NvONjuZWbdk/6B1l6qCoezHW6gJjIcvJUXhCnMCfFSXScDsTjnLirzOpKX5g03fS4qH/5O+wN79wLAnQPxUJsziWrfYOK6sf7mufqO/oTl41Jl1PzBnHtRvL2hi39sbeBLd2zhJ485K8C4I8UXV+Szu6nHy6j568Or187nt/+ylkp78emWpSFFt4yWnii90SHmlebyljPn8Laz5nr76seP7eEdP32GgDhJgn+81JB2c2hz94A3l+OjO5qHM4KREGV5Ye8izZ3mZmFZHrnZWexv6aU4N8S3rjoNwKvv3ddMPi+5qouc7cktSzm+zJf7/XWbP92Jmk+uKfKC7IRRn1E3Gxjjjo11fPWurSdGoCYieSJS4N4GXgW8CNwBXGMfdg3wV3v7DuAqEQmLyEKcQQPrbPNol4istaM93+V7zrT5+B828L5frudXT+2no2+QD758MeBcIQXEGU1ZnBtKCNTcDIDb9AnD/dQqkr6US6ryqe/oZ+PBdhZXDlcklYURe+B2s7Ohi5Ovv5e/b6qnqjBCRYFd6cBWZm5Ft7A8j/L8bH799H4auwbojQ6xq7HbO7jdYNFNCbtlfufa+cRNYhPhQBoDChrsiJzKwuE+aq09g9zy7AGKckJ85BVLAEbMAu1OTXLh0gpetrTCq5T9GTX3INt8qIPCSJAV9uosedLb4VGfMR7f2cwD2xrZVNtBWV6Y6qII//bqkxiKGxZX5lMYGc6c+Q/2wkiIzr5BWnoGiBsSgp2qgkjCrOn32ADt2b1OtmdheZ7XTJycUXMDt4e3N7G/pcebYHSePUm45emxgZrfy5dXJATO7b2DFESCFERCdPuuELfUdfJ/9+3gN884/3O3P0tNcQ6VBcP/l+f2t5KdFeCDL19M90CMvc093skMnKtiESdQae6OeqNhx5IfCdLjy6iJJDZ9ugHH7qZu9jR1s6Aslz984Fxuv+48AuJMV/PZP23i1d95jB472jQai7PJVrbulflL9Z0sryrwjqGOPmfy6DklOQQCwv2fejkfumjxuOX1eIuyO82fIuJkWgojrN/XyvP727jytNnMLc3hrs2HvTmt9rf2ejOjN3YO0NjVz/0vNfCmM2oIiPNap84tpqY4l8auAe97Pzg0PPCkLzrE3pYeVtcU8sVXn+R9txIDteE+bW7G5HsP7eKhbU388bla72TU1DXAS/WdvGxp6ovVrIDwl+vO5z9eu9Ibbb6zsZvLV1WnfLxrth2ctL/F6Usp4jQ/P7W7xRmF2dbL3NIcyvLD/P1jL/Oaz8PBLJ76/Cu4eu38Ea95xclO8HbxcqcHzKtWVTG7KMKC8jzvRO1OPFtZEPaabA+09DK/dLiPpLuOa3446D2vLC+bwkjIq3cXVeR5F9DFucOTqPrr11lFEYIBISsgXnZqlm/uQ7eJzJ9Rc9e39ebds4FYZ3/MCzA7+pzBODfevS0hwHCnYRqKG75tJwZv7x0kEgoQDjp10ZLKfC9bnBPKSgiekyVPFzI4FOf5A20YY7x6Y35ZLu+9YCFfet0qL1C7c2Mdp84t5i8fOZ+3nDmHzv7YmMvk+blzJs4uzqG5e4DOvkGyAkJudpY3UA2ciYNXVBdQ4pvAds38EuaU5HLpyipuWXfAy6o5gVrqCardz5gcqOVmD/9P3EEY7sTHm2o7CGUJy6sLiISci/POpIFYbt3/Fdvqs7e5xwvSp9tkZtSqgMdFZCOwDvi7MeYe4EbgUhHZCVxq72OM2QLcCrwE3ANcZ4xx0zcfAn6KM8BgN3D3JJY7La88qZJ1+1r533u3c97iMl57ilPhvFTXSUEkRCAgLCjL877sVYVhbxRKlT9QywsTDMiIBaPdebz2tfR6o2dciyvy2NPUw9N7WuiJDnGgtZfZRTnO5KQ9Ue9E5lbwIsLZC0tHTBjpZdTsSfvUucVkBwPUtjlzf11yUiXBgBCzU4YAafUfck8i7qhPcCq5+7Y08MbTayjNy6Y4NzRi9vJ7XjxMSW6IVbMLuWBpube9JMX0HNFYnDedMccLcJNnMPc3OfgDKrd/x7vOnc+7zp3PG0+v8SpYIKE/gpvZcp/vz6hVFDpNn90DMXY1dnmVhjt5sb/yr0oK1NymlI/+/gU+8YcNCRWo+74NXc4cPvmRpEDNdgzPDga8q+eCiDPVgP8K8ZEdTqL6oW2NDMUNJ9c4WY0z7EoU/pPgvLJcLlha7vVbWTlrOFALZgVYUJbH7c8fGvG5RpMXDtI1EBuuwIsSJz3eYQO1/S09bK3vYnFFPmfMK2FJZQHzy/LYfriTx3Y0cai9j4/+/gXW/s8DfPnOLV4g0mCb/l6q63T6Edljp713kCbflXhRTohQVvpVnHiRmgGMl2G76uy5PLS9iZ7oEGcvLOOCJeVsPtThNSk3dQ1407xEh+LcubGeWNzwvgsWeit/nDa32Kv0N9UOL8PkZuJ2NHRhDKyodr77Fy6roCDijM51v+PudAr+5zV3DfDqk50A62k7UfFDdtLsly8bvVVhVlEOBZEQy6ucfpff/+cz+LfXnDTm/gllOdnLOzfV8ey+Vl53ymyKc0P8Yf1BeqJD9EaHEi5C/YJZgZRN5ksq8/nx1Wfy4YuXeO9xx0cv4N9fc5J3vD1uL+DWLCjxMucHbPbO5TbRF0ZCzCnJIRIKsLy6gMKcIFGbaVxUke91lSjNCzGvNJdgQLxRiG4555XmsrpmuIksEsqiPD/MofY+7+Tu/g2gypbz7WfPA4bnZnSbKgsiQTp6B/nz87X88JHd3LGxzpvi59l9w11jXqrv9EYeF/pe321KXre3NeGiNZWyvGwC4jThNXb1846fPMObfvAkz+xtHdGvz32f4twQpXnZfP+fz6AwEvKCd//3bSx9gzHvdVu6o86o2EgQEaEiP0xL9wC90Rjr97V5A1vclhY3GP74JcsYihvefNOT7G9xLgorRhkEVJ0UqEVCzjHu77aSHw4yuyjiPcYdSOAGv/7+x+A0fbqDo5q7h+fZO+4zasaYPcaYU+3PKmPMDXZ7izHmEmPMUvu71fecG4wxi40xy40xd/u2rzfGrLZ/+4jJgFWTrzytho9fshQRuO7iJd5syIc7+72U9SLfqLhlvsrA3xR2+epq3rl2/ogOz0t9J8TFSc1NiyvyOdTex7p9bRRGgtQU57CsOp/SvDCxuPH6tfkDC/eAWDmr0LvaqCxIzKgtrsj3DoK5pbnkZge9piN3ezpTdLhXc9VFESKhLHKzs3hqTwvRoTjn2qktaopzbL8OZ46ux3c2c++WBq62++K0ucXk2QOvzFc55WZneQfR1efOpygnRDAgCX3UorE4PdEhckJZ3sS4BeEgWQHx9r2I8JUrV/Oe8xd6V8ciTpOlqzAnmHBl6d+fFy2rJG7gHT95mm/aK+FlNtsUkOGRZ3nZWd5C5a6llfksKMtlXmkuW+o62d3k9KtyRycW5oS8qVmSm65WzS6koiDMaXOLvSabgkgwYWktGF6Rwr2irCqM8Ov3nc1/2JOxW+E3dg2woCyPgkjIC+JW+TJq4Iz4c/u/LU0jUCsIB+nuH276nFvqzKXnHrbbDneRFRDixjnhLvJ9v5dU5vPIjiY6+2PMLc3hwW2N9ESd5ogXbN+0w539NHY5oyBXzi4kJ5RFKEto642OaKI+Ev7BBO48agAffcVSXra0HBE4e0Ep80rzEkbBgtMM6353Ht7eSHYwwPKqAt65dj5nLyxlUXkes+00Lf4mf/dYcbNlJ81y6onvXnU6f/7QeQSzAt7n6Y0OeRdXDZ39TrYxOsTy6kKWVuZ7me+7X6xnTknOiP9jKsGsAF++cjWvOWVWWtMPvOu8Bexv6aWtd5ALlpZzxrwSZ21bt/P3KM1VY3nVquqEAKQ8P0xeOMisogjnLipjb3MPOaEsTqoudDq+9w5yuLM/oZmy2AbrBZEgZflhNvznq3jZ0gov4HGDkXMXl3mB2BWrq3n4sxeN6Jrw1TedzA1vWJ2wrabY6YfmXgz5L+4uWFrOxy5ZygdfvhgRZyoNGO5+sbA8j66BmHeh3toTtYFgkI6+Qcrysr3P8k9rnN4//qZV9+LoUHvfuIFaMCtAeb7TLePGu7d5q4/sauz2+s+6E8K6vvbmU/jVe8/29oNbj6W7mLw7SGNeaS7RoTi1bX1e+cvys2npjvLM3laiQ3FeZi/AvYyae16aXcgfP3geLd1Rbnn2IE1do2fU3HK6ff1m22X5kjONS6oKvO4n2w53JlyAFkRC7Gzo4vwbH+T5A20MxOIs9CVE/uM1K53XSKO+mwq6MsEx+OSly3jmi5dw/pJyinJC3gnZvcJ3p+EoyQ15QVF2MOD1+wFnSYzrX79qxGvPLc31OkaOCNTsl+eBrQ2cOreYf3zq5Xz59au9gMZtWvI31Z1tR2JdtLzCmyPL/bv7e3FFnncQzLP9UdznudmAdPqoNXT2E5DhAKskN9s7ObkHS01xDofa+vjdugOs/tK9vO/mZ1lQlptwZX2O7czrz6iJONnH85eUsbgi3xu12mwX/r7x7m3c/WK9/QxOhbS7sYelVfnc8ZHzed/LFo4or3t1nBPKSjhZFedkMxQ3XsVbkT9coZ+7uIwfvvNMth3u4u+b6nnZ0nJv9GFJbrZ3wqouiow4ARbnZvPwZy/m069aRjQW54GtjcwrzfWC9aKckBcQJwd5IsKPrj6TG96w2rs4cJs+3ZNIR98gzx9o95arAieDsqK60Gvu9lf4C22H+beeOYdT5xZ7r+u6dGUVl62qoiQ35FWKY8kLZyU0fc4vdZqBnbVW4+xu6ua8xcOd3Bf5KsillfneZ//Fu8/iv9+wmm9fdTpd/TFvzqvGzoGE0anOdyKbfc09xM3IPpfp8qbnsH3U3MAtKyDc9M4zueX9a6kuingnVbevHDiBnRsYPbOnleVVBQSzAlx5Wg23fuBcAgHxlhHb29zj9alys89b67vItesUgjPTvtvPpjQv27s4cZulD3cMeBcnFQVh1i4qY/2+Vtp6ojyxq4XLV03OvE9XrK726otzFpZSkR+msWu4LKOdXI+GiPDDq89kRXUBK2YVeAGrG3zMS8ioOfvTDRDczLgbqLnZksqCCI9+7mJWzS5y/idJQQvAOYvKRvRtnG0vLFNl1HKzg3zq0mWU5GUzpyRnOKNmA7X5ZXkYAy/VdXgtE8kXxe50OB+7ZCnl+eGEptVZhRGvWW68QA2ci7LDnf28VNfJ+UvKyQ4GONjay77mHmbbi2e/y1ZVJ3zesnxnIEe6GTUvULPHxZ6mHm+/l+WH6Rsc4t4XDxMOBryEQWleNuFggJN977u8uoBFFXlsru1ImJooWW52kIJI0KuX3Xkqc5MCtaWV+exs6Ka+o4/m7igrqocDtcJIkPX72zjU3uetebzYfkdWzirkvRcs5NHPXpxQT00nDdSOkRuAiYgXzLhfUjdQqygIe/1oqgrDaVWgWQHxArTFlclNn8723ugQq2YXkZOdRVZAvIN4++FOIqFAQv+mlbMK+a8rV/HeCxZ6VzXugbWgPI9vX3Uabzpjjld5uJXg2fbAmmNTwOksI9XUNUBZfpigbXYqzXMCnoLI8DpsNSVOxffErmZKckO8bGk533jbqQmVyNvWzOG8xWUJV68A33376dz4plO8++X5YZq6B9hwsJ0fPrKbb96/I+Hz7W3uobIgwqrZRQlNCi43A5p8oLtXU+6s5MmZmktXVvH8f1zKxi+9ipvfc7b3eGcyTed9Zo0R2KyynfYPtPZ6GThIvFpP1Rn8jHklLK0q8E4yBZEQ+ZGgt9DwE7ucZcM+6OufVVWUWHb/HH7u9/Sta+by1+vOT/n9/PZVp/O3j70sraku8sMhbyUDGP4/tHRH2dfcw+CQ4YrVs7zgw38h4vaBm1eay5JKJyN12apq7+LnlDlFNHb1e1f7brNVUU7Qu8KeqIwavv2QHw56Fw7usfHMnlb8u8Md6R0diidcvbucoN25vXJWISLOnGj9g0M8uqOJFdUFKfdvKCvgdSCfZbs4NHT1JzTJn2NHS3793m1Eh+JccfLY/c2OVigrwMcuWcr5S5zMVGWh07TlZp2PNkgeTVFOiL9cdz6/fM/Z3mu7KwjMS8iouU2ficdLkQ3g/AMGjkZNsTOHnNtcVhAZeVy67+M2g7v9ZN1RqM3dUd5yxhzWzC/h5csrvIviuaW5XHvhYv7rylXMLs7hK1eu8vo8AwQC4p0D0gvUwtS197GnuYflVQXMLcnhQGsve1t6E7JGY1lenZ92Rq3Pl1EDZ/Jnt051L9b/uqGOcxeXefX7e89fyNffcsqIUZ3Lqgq8fntjHcfVhRFvRL87/VFOduL/5PR5xQzE4tz2nDO714pZw61a/kDb7VNYU5LDovI83n62k9WcV5Y7rZPc+mmgNoHcPhNuMLDIF6i5B9hofThSWVFd4Myjk1T5zS/L9U4Q/uYN9z221jsL4vq/ZCLC1ecuoDw/zGWrqvn7xy5IaI698rQab4g6DB905y0p442n1/BKuxhuOhm1nuhQQibIzYittNkPcCq+3ugQT+xq4fwl5fz0mrM4c35pwutcvnoWv3v/2hEHy8uWViT0TzllThFP7mrh6/c4cxG5TQzuZ4gOxVNOnOhy50RKvtJ0rzKf2tNCQTiYshNvXjhIUY7TJ9EL1HJDXmo/uVnFb1F5nte861/myN/skTyYwM+fUXNPUN3RGL95ej+VBWHetmYu4WDAGRWaNB9RbnbQu0pPNXFtskgoK+3+Gm7Q2O01fTr/h9aeAS/be9rcYu/1Fidk1JzvpJv1BScLfdmqKkSceZ8GhwzP7mulPD/snYiLckJeX7+jzqi503PgzqOW+nFe5qC5h1lFOV4WyZ8dOMl3UvB/Djcb5TwvzOHOfr7yt5fY09zDxy5ZOmrZ3JNWuZ1epqHDF6jlhzlnYRkBgd+vO0h1YYTT55aM+lrH6p12pKGIUFEQJm6Gm8mONkgeSySURVFOiHL72s/ZxdL9GbWCSBARRlyIucfFojQDlNHMLs6hfzDOATvwJ7lPsWtRRR5b6zs59cv3eUt9LfAd2yfPKeJPHzqP0+YWD2fUSnJYObvQmwbl1SfP4rKkgR3uxUy6GbVdTd1EY3GWVOYzrzSX/S297G3qTijLWJZWFrCjoTutCajdEZPu/8OY4f+DeywODsX57GXLveecOrfYW3bOb3lVgZcMSD7v+VUntBbYjFpS/e0mGX5nR5P6M2r+QNtdIi4/HOLBz1zEO1MMepluo58F1BFzT5zJTZ8V+cOBWtURBGqfunQZ/3zOvBGBSiSUxVx78CWmrJ336Bsc4gMXjj7aTUS8bE4yfzoenBP6N//pNC8Nnk4ftb5oLCHoKbUnU/9oQndfdfQNeiOxjtYnL13G3zbW89SeFioKwt4JzN+HpXKM/e4etDlJB3pFQdibBXtOGkHKcKCWnVZgHggIK2cX8uy+toSyHmmgVhgJeY97YmczT+5u4YuvXkHErs94uKM/ZaamNC+bQ+19aQVqR8K/egQMZxSauga8ZdUWVeSxpDKf3uiQ1xEcnH145vwS3nh6YiX+2ctWcPnqam/U8TN7WllVkzg61R1hd7TBgtjLVmMMxtdHLVlhJERJboi23kFmFUUwONmSeWW53vaVoxxfs4tzaOh05jCcVRThwW2NNHdH+cCFi7hoeWXK54B7wuuiPD9s16rtT2j6rCgI87v3r6Wjb5CVswrTnuT3WLknU7cpOp1A4mi5AfGTu1uYV5qb0HfVHbzlv4CD4WMpeUDWkXJbS9y5EUfLqL3x9BoOd/SzqbaDdftayQpIwshBf/mSL4rHcqSBmtuLe1lVAS8e6uCJXU4f4XSP9eXVBfQNDlHb1peQuUylLzqESGLfNzdQcz/7dRcvGfWc47esevgCZ6wLLvc86k5NBCNbRCoLnW4K+1t6qfQlS2D4/5ebneXN25jv9VXOjCyan2bUJpD7RXXTvvnhIGsXlXLmglKvUjmSjNrc0lyvTT/Z4op88sPBhCazyoIIr1pZxbevOu2orwpW1xQRDgYSrj5geGRNOhm13uhQwkHjZtRO8jUH1RQPl/vUYwzUKgsifO6KFQQDwv+8cXjGfH8FONbJ2+ujliJjttoGA+VpnPzL8rKpKgwz206BAcNzT43Grbz8laH/aj151KffHC+DG/Qe97/3bacwEvRGoH30kqV84tJlqcubn00klP4SS+lyy1/b1ks4GPCOi7r2fuo6+inLyyYSyuKTr1zGjW9KXOEgEsritg+dx3lLyhO2VxSEecWKKi/g7hqIJTSZ+vfZ0faTcqtnd63PsWId97vl77NWVRDxTiArUmTUYHgUWUV+mKrCCM3dURaU5fKpV6X+H7nc729ZfjazinKobeujsWuAgAyfvNcuKuOyVdUjgpXJ5E6avfVwJ8W5RzbK9ki5J25jsB33E/9Bf7nufD58ceIFalWh09y8vHr8gRVjcVtH1tsBLaNdQJ0yp5ib3nkmn7b/z5LcUMJkvP46qSrpongsRxaoDddViyvzmWs7+UN62XMYDiKb01j6rTc6RG4oi+KckNcHzw2EllUVcMdHzufjY2SL/fwjcMsLRv+s7nk03/bPhdT1t3v+XJHUFWHN/FLOX1LGmgWl3lx37gVmJtKM2gSam9RHDeCWa88F4IUDzgE+VlPYkfjwRYt5/amzE66cswLCj9+15phe9+yFpWz58mVe/zKXmyFLp49a3+BQQkXm9q/x99txr7SyApLW6LTxXL12Pq89eRbFuSEvCzYvIYgdPdByA+tUS4WsriniH1sb08rSiAh/+uB53tIvv3//Ws6YXzzmc85eWMqvn96f0AztDzryxqg8qgsjXLS8grMXljFkL6H3NPXwlStXeZXXxWNkaeaU5CAw4dmXeXZ+qy11neSHg5TkhoiEAtS191Hf0edNT3I0Abr/JOQfkeVm5ZL7Zh4JN4M2POHt6PtlXlkeG2s7mFUUobIgQk7oMJWFzhx9PdFYyr6QMPy9dzNqAF96/Spv2oDReIFaXpjlVfn8ft0gW+o6Kc0LeyfH6eAOsDnY2jfpI+QioSyvC8KbzxzZbJaqOfKVJ1Vx7ycuPOas8eKKfAoiQQ609pKbnTWifkz2ulNn8437dpAXzvK+CyKJ0z2sXVTG6fOKWZ1GpunkmiKCvn7LY3EvZmqKc8j3LZkHw60843GPIf+k1aPpjQ6Rkx0kYLNbDZ0DCa0Cbt/NdMwtzSUSCtA/GE+5fJTLnfIoPxxMyI4lO3tBKX96rpaTqhMvnN521lzedtZcPvPHjd62/HDqYzYTaKA2geb4RmwlW1DmTDp7rM18rjULSlmzYEJeaoRUlZA79UA6GbW+6FBC/4ILljrzTvmDkZLcEDmhLBaU5yVMVHgs3Mzd2QtLuf+lhsSVBMbIGvlHfSZz+x2NFej5+a+Oz01jxNAVq6t59HMXJ1Tg/pN8wRiVR1ZA+OV7zgackbZnzi/hQy9f7PUnHM9XrlzN4ND4gfeRcpuZ9rc4SwqJCLPtsll17X1p95NJxf9/9AcG7okhuW/mkXCf5k14O8Zj53sZtRzeuXYel6+uJhLK4vNXrBjz5ObPqL1z7XyWVRWMGUy73OOpoiAbEefC5uk9LQl9G6eD/xibyBGfo/nAyxexrKpg3MDWlRWQhHrnaAUCwunzSnh0R9OoQbhfKCvAD95xBr3RIS+AnF2Uk9B5fkllPrd/+Py03n9eWS4v/OelCZ3gR+NmyN3jw83WB2R4GbDx5GUfSaAW84Ikd2qQ5EEd6coKCEsrCzjY1pty+SiXl1ELD7cmJA8mAKcOzs4KePMZJvPX62NdFE83DdQm0NKqfN5xzryUE02W5GWz/t8vnYZSTQw325TOWp99g0MJaejT55WMyPSJCK9YUZnQb22ifOrSZbzmlFnkZQe9meHHHEwwxhXZyXOKEBm5usBEEZERnfTdQD8gw03O46kqjHDbh847ovee6BF6roqCMHnZWfREh7xRq7OKI9R19FHf3u9NY3I0soMByvKcFTj8gZp7MjyWz+Rf69MYQ1Zg9H3vZilmFUUIB7O8AD25y0CycxaWsaK6gOXVBRTnZqcdRLxsWTmv2lfF3NJcL4vVGx2alM77RyLHzhPYNcZ0ChPpI69IrwltMpwxr5hHdzSN2j8tmZsxNsYQDMi43SDGk06QBsN1nTvnoRuc+ad8Go8btLiTVo/F39XF/Q4UjjLYIh0XLiv3lggcjRuoOQOpnPdKVX/PLc1l3b9dMurgD3+gdrSZ+KmQuSWbgUJZAW5448njP3AGcjNq6SwhldxHbTTff8cZx1yuVBaU53kp/vxwkN7okDcKM5XRBhOAk8H5/fvXTkjzbLrcq9H8cDAjO7aOR0RYWJHHi4c6vX4fs4tyuOfFw3QNxI456K0sjDAQiyf093Qr4rFGio1fbud33B1MMMY57Yz5xd4qGkdieXUB93ziwiMu24rqQu9iJxzMYnZRhLqO/mP6vBOlojBMV9PUBGrTyV1TNt1AzSUiVBVGpmzy1NK8bD52yVJeY5fnygsHKc/PPqJMtnuB5Y7oHEtfqkAtzaAylc9etmLcx7jTDY3X9AkkDFZK5h9klmoqpEyRuSVTGUVEyA4G0sqo9UeHUvb3mg6FOSHywsEx+2G5lUpklAN97aKpnfQwP+xkAtO9gs5EC8vzefFQpy+jluOtbjDrGJdlWVqZT2leKCGI9TJqx5Bh8sfEzmCC0b8zSyoLeOE/X3XU73WsTppVSF1H/5gdrqdKRX6YPU09U9L0OZ1Om1fsTAFyFNmiX7/v7DEvFieSiPCppAFE//HalUc0kG246TOdjFrM677ifh+PJaOWjnK79GJ+JMScklzec/6CtLoQJHMzauFgYFIHwhwrDdRU2iLBwLgZNWMMvYPpZdSmQkEkRHbW2FmpwjH6qE0HEbEBZmaU52i4o+TcQK2mePgkMfsYM2pfe/MpxJNWkXNH1h1LhskbTBA33qLsmWrFrAIe2NaYERk1NytxvGfUCiMhzllYmtYyaskWpTEIYDKlmrNsLJFQgICkP5jAXYmlwmv6nNzQImAHoS2uyCMrIHzpdSNX90mH2+f1SLOkUy2zS6cySiSUNe5ggsEhw1DcZEzQ87pTZ5E9zpXSWE2f06UoJ5TRfSbG404w6k587F+hYfYxZtRSDcOfkIya/e1OeJu5YdpwX7jp7qMGwyfn4z1QA/jdv6ydsjnqppOIkJcdTKvp09/V5Yz5JSyqyJuSxcxv//D5HOu1lDu9TCY3e4IGauoIREJZ407P4S4nkmoEznT48EVLxn1MQSRIRUE4YdLZ6VaRHx6zb0WmW5iUUXMXJA9I+iNoj8Si8jyuvXARr0pzxGsqidNzMOY8atPtvMVlzhyNo4xmm0ruya7sOG/6hImfyiaT5YWDaWfUvEBtXgkPfvqiSS6ZYyL+F5FQFgWRoNfUm6kyu3Qqo4SDgXEzan3275mUnRpPMCvAU59/xbTOR5XsG287ddy5mjLZwvK8hCV93IxaVWFkUj5XMCvAF1990jG9hjc9hwHD2H3UpltZftibo3G6raguIBIKpDXDvpo5csPOyO3x9Pn6qM1EFQXhMScWzwSZXTqVUdJp+uy1qfJM6aOWrkwLiqZ7fqxjVRAJ8fN3n+VN5umuiTpZ05xMBLdPmjGGeDyz+6hlkpcvq+CF/3hVyiZpNXPlp5FRy7Q+yUfjzWfMyfjya6Cm0hYJBcZv+rSBXKaM+lTTJ3kU1ilzitKaWX06ibgT3o69hJQaJiIapB2HcrOz6B1n1Gf/YBxjUvcbnSmuu3j87jHTTQM1lbZIKGvcKyy3j1qmX6Goqfer956d8VkqwZ3wlmPuqKzUTJYfDlLX3j/mY7wWFL0wn1SZ1d6jMprTRy29jNpMvsJSkyPTgzRwBhTEjcn4PmpKTbbc7KAXiI2m17sw15zPZNJATaUtHMoad8Jb98CdSYMJlHINN32igZo6oeWFg3SP0/TpXpjnzuA5H2cCDdRU2iLBrHEnvO3XjJqawcRm1OLGaNOnOqHlZWcdQUZN6/vJpIGaSls4FGAgNsSD2xqo7+hL+Rg9cNVMJgC2j5pm1NSJLM+ukxyPm1Ef02v7LOeEtOlzMmmgptIWCWbR0TfIv9y8nh88tDvlY/q06VPNYAHNqCkF4K2MMtbqBHphPjU0DFZpi4QCDA45V1cv1nWkfIwOJlAzmcjwqE/NqKkTmdvvrDc6REFk5CLrb7npyeHHan0/qTRQU2nzz422tb6TobgZMZt/X3SIgDDu+ppKZaKAiDePmsZp6kTmZtS6B2IkL8wWjxueO9CGsa2iemE+ufRsqtIWDjpfFxFnosM9Td0jHuOs+xacEVMxKJVMcII0zaipE5075UaqSW+7BmJekAZk/FqZM50GaiptbkbNXfg6VfNn3+CQrkqgZi7b9KkrE6gTXZ5t+uxOMcl5Z99gwn3NqE0uDdRU2iIh5+vy9rPnEQ4GePFQ54jHOAv06kGrZqaAiLPWpzEIGqmpE5ebJUs1RUdn/3CgFpDh1hY1OTRfqdJ2/pJy3rl2HucuLuOkWYVsGSWjpiM+1UzlTnhrDAT03KNOYHm+PmrJOmxGbe2iUg539GtXl0mmgZpK25ySXP77DScDcOqcIm5dX0tX/2DCiKDe6JCmwdWMNTw9x8xY8kqpyZLnG/WZrLPPCd7+/TUrWV5dMKXlOhFN+jWjiGSJyAsi8jd7v1RE7heRnfZ3ie+xXxCRXSKyXUQu820/U0Q22799R7QGnXZvPGMOfYND/HVDXcL2fs2oqRlseFF27aOmTmxuRq0nVR812/RZlBMipCP8J91U7OGPA1t99z8PPGCMWQo8YO8jIiuBq4BVwOXAD0TEPePfBFwLLLU/l09BudUYTp1TxKrZhfz2mQMYY9hc28ENf3+Jzj7to6ZmLmcJKXcwgUZq6sSVay+4e1KM+nQHExTmjJxfTU28SQ3URGQO8Brgp77NVwI329s3A2/wbb/FGDNgjNkL7ALOFpFZQKEx5iljjAF+5XuOmiYiwjvOmc/W+k5uvGcb1/xiHT95bC/bG7qIaKCmZiixa0jFDTqUQJ3QglkBIqFAypUJOvsGEYGCsPaemgqTnVH7FvA5wL+Sd5Uxph7A/q6022uAg77H1dptNfZ28nY1zd50Rg2Xr6rmR4/swRjjNXnmatOnmqECMtz0qT0s1ImuKCdES3cUYwx/fr6WQ+3OGs+d/TEKwkEC2j9gSkxaOCwirwUajTHPichF6TwlxTYzxvZU73ktThMp8+bNS6+g6qhFQln88Ooz2XiwnYJIkK/fs517thzWwQRqxhJEJ7xVylpaWcDOxi621HXyqVs3Upwb4qZ3nEln36A2e06hycyonQ+8XkT2AbcArxCR3wANtjkT+7vRPr4WmOt7/hygzm6fk2L7CMaYHxtj1hhj1lRUVEzkZ1FjOHVuMYsq8rnUToSrgZqaqUQnvFXKs7y6gB0NXWyqdaZiCgaEr969lc7+QYo0UJsykxaoGWO+YIyZY4xZgDNI4EFjzDuBO4Br7MOuAf5qb98BXCUiYRFZiDNoYJ1tHu0SkbV2tOe7fM9RGeQVKyrJDgYoy8ue7qIodVQC3mACtFlHnfCWVxXQPxjn7hfrKQgHuXx1NXube+joG6QwxULtanJMR0/AG4FbReR9wAHgrQDGmC0icivwEhADrjPGuMNNPgT8EsgB7rY/KsOU5GVzz8dfxqyinOkuilJHzeCuTKDUic2dI+3xXc2cvaCUBWV5dPXHONDay2lzi6e3cCeQKQnUjDEPAw/b2y3AJaM87gbghhTb1wOrJ6+EaqIsqsif7iIoddQCATuYAJ3wVqmlVfled4DVNUXMK80FoKFzQDNqU0hnqlNKKUtw1vrUCW+VgtzsoBecra4pZF5Zrvc37aM2dcYN1EQkT0QC9vYyEXm9iOh/SCl13AkIw33UNKOmFMuqnObP1bOHM2qgk91OpXQyao8CERGpwVlJ4D04/cWUUuq4IiIYdNSnUq5zFpZSVRhmUUU+udlByvPDABRGdLLbqZJOoCbGmF7gTcB3jTFvBFZObrGUUmrqOf1xnHnU0IyaUrz3/IU8+rmLybJXLvNt82dRrmbUpkpagZqInAu8A/i73aahtFLquCM42TRAM2pK4UxTEw4Oz43pNn/qYIKpk06g9gngC8DtdgqNRcBDk1oqpZSaBiLCUNwN1DRSUyqZF6hpH7UpM25mzBjzCPCIiOTZ+3uAj012wZRSaqoFBF+gNs2FUSoDLal0pmCqsH3V1ORLZ9TnuSLyErDV3j9VRH4w6SVTSqkpJgxn1HQeNaVGumJ1NX+4di0LyvOmuygnjHSaPr8FXAa0ABhjNgIXTmKZlFJqWojAkBm+rZRKFMwKcM6isukuxgklrQlvjTEHkzYNpXygUkrNYCJCXPuoKaUySDqjNw+KyHmAEZFsnP5pWye3WEopNfW0j5pSKtOkk1H7IHAdUAPUAqfZ+0opdVyRhEBNIzWl1PRLZ9RnM84cakopdVwLiDBk51FTSqlMMGqgJiLfBUatsYwxOkWHUuq4ImhGTSmVWcbKqK2fslIopVQmENGVCZRSGWXUQM0Yc/NUFkQppaZbQGAgZgM1jdSUUhlg3D5qIlIB/CvOQuwRd7sx5hWTWC6llJpy/rU+dcJbpVQmSGfU529xpuNYCHwZ2Ac8O4llUkqpaRHwrfWpYZpSKhOkE6iVGWN+BgwaYx4xxrwXWDvJ5VJKqSnnrEyggwmUUpkjnQlvB+3vehF5DVAHzJm8Iiml1PRIXJlgmgujlFKkF6j9t4gUAZ8GvgsUAp+c1FIppdQ0ECCm03MopTJIOhPe/s3e7AAuntziKKXU9An4MmoapymlMsGofdRE5P0istTeFhH5hYh0iMgmETl96oqolFJTw99HTUd9KqUywViDCT6OM8IT4O3AKcAi4FPAdya3WEopNfWctT6d29pHTSmVCcYK1GLGGHcgwWuBXxljWowx/wDyJr9oSik1tZzpOeLebaWUmm5jBWpxEZklIhHgEuAfvr/lTG6xlFJqegxpHzWlVAYZazDBf+Ks95kF3GGM2QIgIi8H9kxB2ZRSakr5J7zVjJpSKhOMtdbn30RkPlBgjGnz/Wk98E+TXjKllJpiImDjNM2oKaUywpjTcxhjYkBb0raeSS2RUkpNk4CIrkyglMoo6SwhpZRSJwQBX9Pn9JZFKaVgEgM1EYmIyDoR2SgiW0Tky3Z7qYjcLyI77e8S33O+ICK7RGS7iFzm236miGy2f/uO6ARHSqlJ4EzPofOoKaUyx6hNnyJyxlhPNMY8P85rDwCvMMZ0i0gIeFxE7gbeBDxgjLlRRD4PfB74VxFZCVwFrAJmA/8QkWXGmCHgJuBa4GngLuBy4O60PqFSSqXJH5xp06dSKhOM1UftG2P8zQCvGOuFjTEG6LZ3Q/bHAFcCF9ntNwMPA/9qt99ijBkA9orILuBsEdkHFBpjngIQkV8Bb0ADNaXUBJNRbiul1HQZa9TnMa/rKSJZwHPAEuD7xphnRKTKGFNv36NeRCrtw2twMmauWrtt0N5O3q6UUhPKn0ULaA9epVQGGHdRdgARWQ2sBCLuNmPMr8Z7nm22PE1EioHb7euM+japXmKM7anKeS1OEynz5s0br3hKKZXA39qpfdSUUplg3GtGEfkS8F37czHwdeD1R/Imxph2nCbOy4EGEZllX3sW0GgfVgvM9T1tDlBnt89JsT3V+/zYGLPGGLOmoqLiSIqolFKJGTUN1JRSGSCd5P5bcJaQOmyMeQ9wKhAe70kiUmEzaYhIDvBKYBtwB3CNfdg1wF/t7TuAq0QkLCILgaXAOttM2iUia+1oz3f5nqOUUhPHF5vp9BxKqUyQTtNnnzEmLiIxESnEyYAtSuN5s4CbbT+1AHCrXe3gKeBWEXkfcAB4K4AxZouI3Aq8BMSA62zTKcCHgF/irDF6NzqQQCk1CfxZNNHhBEqpDJBOoLbeZsZ+gjMwoBtYN96TjDGbgNNTbG/BydCles4NwA0ptq8HxurfppRSx8wfmmlGTSmVCcYN1IwxH7Y3fygi9+BMlbFpcoullFJTTwcTKKUyTTqDCR5wbxtj9hljNvm3KaXU8SJxMME0FkQppayxViaIALlAuV3mya22CnFWDlBKqeNKQtOnRmpKqQwwVtPnB4BP4ARl/uWiOoHvT2KZlFJqWkjCYAKllJp+Y61M8G3g2yLyUWPMd6ewTEopNS20j5pSKtOkM+rzRyLyMeBCe/9h4EfGmMFJK5VSSk2DgM6jppTKMOkEaj/AWVD9B/b+1cBNwL9MVqGUUmo6+OdO05UJlFKZYKzBBEFjTAw4yxhzqu9PD4rIxskvmlJKTS3/QuwaqCmlMsFY03O4k9oOichid6OILAKGUj9FKaVmMt9gAo3TlFIZYKymT7ea+gzwkIjssfcXAO+ZzEIppdR0SBxMMH3lUEop11iBWoWIfMre/hGQBfQAEZyloR6a5LIppdSUShxMoJGaUmr6jRWoZQH5JE4nlG9/F0xaiZRSaproYAKlVKYZK1CrN8Z8ZcpKopRS00yn51BKZZqxBhNoNaWUOqEkrEygNaBSKgOMFahdMmWlUEqpDKArEyilMs2ogZoxpnUqC6KUUtNN+6gppTLNWBk1pZQ6oWgfNaVUptFATSmlrISmT+2mq5TKABqoKaWUpYMJlFKZRgM1pZSy/MFZQNs+lVIZQAM1pZSyEgcTTGNBlFLK0kBNKaUsXUJKKZVpNFBTSikrcTCBUkpNPw3UlFLKCiQMJtBQTSk1/TRQU0opyx+aaR81pVQm0EBNKaUsfxZN+6gppTKBBmpKKWWJDiZQSmUYDdSUUspKWI1A4zSlVAbQQE0ppSxd61MplWk0UFNKKUubPpVSmWbSAjURmSsiD4nIVhHZIiIft9tLReR+Edlpf5f4nvMFEdklIttF5DLf9jNFZLP923dEx80rpSaBDiZQSmWaycyoxYBPG2NOAtYC14nISuDzwAPGmKXAA/Y+9m9XAauAy4EfiEiWfa2bgGuBpfbn8kkst1LqBJUw4a3GaUqpDDBpgZoxpt4Y87y93QVsBWqAK4Gb7cNuBt5gb18J3GKMGTDG7AV2AWeLyCyg0BjzlDHGAL/yPUcppSZM4oS301gQpZSypqSPmogsAE4HngGqjDH14ARzQKV9WA1w0Pe0Wrutxt5O3p7qfa4VkfUisr6pqWlCP4NS6viXOOGtRmpKqek36YGaiOQDtwGfMMZ0jvXQFNvMGNtHbjTmx8aYNcaYNRUVFUdeWKXUCS2gfdSUUhlmUgM1EQnhBGm/Ncb82W5usM2Z2N+NdnstMNf39DlAnd0+J8V2pZSaUKLTcyilMsxkjvoU4GfAVmPM//P96Q7gGnv7GuCvvu1XiUhYRBbiDBpYZ5tHu0RkrX3Nd/meo5RSk0IHlyulMkFwEl/7fOBqYLOIbLDbvgjcCNwqIu8DDgBvBTDGbBGRW4GXcEaMXmeMGbLP+xDwSyAHuNv+KKXUhHKbOzVGU0plikkL1IwxjzP6IiyXjPKcG4AbUmxfD6yeuNIppdRIboCm/dOUUplCVyZQSinLDdC0f5pSKlNooKaUUpabSNP+aUqpTKGBmlJKWaIZNaVUhtFATSmlLPF+a6SmlMoMGqgppZSlfdSUUplGAzWllLJ01KdSKtNooKaUUpbX9KlxmlIqQ2igppRSltf0qW2fSqkMoYGaUkq5JOGXUkpNOw3UlFLKGh5MoKGaUiozaKCmlFLWcB81DdSUUplBAzWllLICtkbULmpKqUyhgZpSSlnuRLfa9KmUyhQaqCmllDW81uf0lkMppVwaqCmllCU6mEAplWE0UFNKKUsnvFVKZRoN1JRSytLpOZRSmUYDNaWUsobX+pzeciillEsDNaWUsgLeYAKN1JRSmUEDNaWU8jgBmsZpSqlMoYGaUkpZAa/pUyM1pVRm0EBNKaWs4ek5prkgSillaaCmlFKW10cNjdSUUplBAzWllLJ0ZQKlVKbRQE0ppSxd61MplWk0UFNKKcubR01rRqVUhtDqSCmlLF3rUymVaTRQU0opa3gwgVJKZQYN1JRSyhJvwlsN1ZRSmWHSAjUR+bmINIrIi75tpSJyv4jstL9LfH/7gojsEpHtInKZb/uZIrLZ/u07ojWoUmqSBHStT6VUhpnMjNovgcuTtn0eeMAYsxR4wN5HRFYCVwGr7HN+ICJZ9jk3AdcCS+1P8msqpdTE0JUJlFIZZtICNWPMo0Br0uYrgZvt7ZuBN/i232KMGTDG7AV2AWeLyCyg0BjzlDHGAL/yPUcppSZUQAcTKKUyzFT3UasyxtQD2N+VdnsNcND3uFq7rcbeTt6ulFITTkbcUEqp6ZUpgwlSVYtmjO2pX0TkWhFZLyLrm5qaJqxwSqkTg671qZTKNFMdqDXY5kzs70a7vRaY63vcHKDObp+TYntKxpgfG2PWGGPWVFRUTGjBlVLHv4D2UVNKZZipDtTuAK6xt68B/urbfpWIhEVkIc6ggXW2ebRLRNba0Z7v8j1HKaUmlGigppTKMMHJemER+T1wEVAuIrXAl4AbgVtF5H3AAeCtAMaYLSJyK/ASEAOuM8YM2Zf6EM4I0hzgbvujlFITzm361DhNKZUpJi1QM8a8fZQ/XTLK428AbkixfT2wegKLppRSKbnxmU7XqJTKFJkymEAppaZdQAcTKKUyjAZqSillaR81pVSm0UBNKaUszagppTKNBmpKKZVE+6gppTKFBmpKKWW58ZmGaUqpTKGBmlJKWbrWp1Iq02igppRSljeYQGtGpVSG0OpIKaWsgDfhrWbUlFKZQQM1pZSy3PBMmz6VUplCAzWllLK8JaSmuRxKKeXSQE0ppazhCW+ntxxKKeXSQE0ppSwd9amUyjQaqCmllKWLsiulMo0GakopZWnTp1Iq02igppRS1vD0HNNcEKWUsjRQU0qpJNpHTSmVKTRQU0opKxDQCW+VUplFAzWllLKGJ7yd1mIopZRHAzWllLJ0eg6lVKbRQE0ppSw3PtM4TSmVKTRQU0opa3h6Do3UlFKZQQM1pZSyBJ2eQymVWTRQU0opSzNqSqlMo4GaUkpZw4MJprkgSillaaCmlFKWrvWplMo0GqgppZSlS0gppTKNBmpKKeXSPmpKqQyjgZpSSlkBSfytlFLTTQM1pZSyRFcmUEplGA3UlFLKcjNpGqYppTLFjAnURORyEdkuIrtE5PPTXR6l1PFneMJbDdWUUplhRgRqIpIFfB+4AlgJvF1EVk5vqZRSxxud8FYplWlmRKAGnA3sMsbsMcZEgVuAK6e5TEqp44zoYAKlVIaZKYFaDXDQd7/WbksgIteKyHoRWd/U1DRlhVNKHR+CgQAluSHKC8LTXRSllAIgON0FSFOq61szYoMxPwZ+DLBmzZoRf1dKqbFkBYSHP3MxeeGs6S6KUkoBMydQqwXm+u7PAeqmqSxKqeNYUW5ououglFKemdL0+SywVEQWikg2cBVwxzSXSSmllFJqUs2IjJoxJiYiHwHuBbKAnxtjtkxzsZRSSimlJtWMCNQAjDF3AXdNdzmUUkoppabKTGn6VEoppZQ64WigppRSSimVoTRQU0oppZTKUBqoKaWUUkplKA3UlFJKKaUylAZqSimllFIZSgM1pZRSSqkMJcYcn0tiikgTsH+S36YcaJ7k9zgR6X6deLpPJ57u04mj+3Li6T6deJO9T+cbYyqSNx63gdpUEJH1xpg1012O443u14mn+3Ti6T6dOLovJ57u04k3XftUmz6VUkoppTKUBmpKKaWUUhlKA7Vj8+PpLsBxSvfrxNN9OvF0n04c3ZcTT/fpxJuWfap91JRSSimlMpRm1JRSSimlMtQJFaiJyFwReUhEtorIFhH5uN1eKiL3i8hO+7vEbi+zj+8Wke8lvdbbRWSziGwSkXtEpHyU9zzTPm6XiHxHRMRu/6aIbLA/O0SkfZI//qTJsP06X0QesM9/WETmTPbnnwzTtE9vEJGDItKdtP1CEXleRGIi8pbJ+syTbYL36T/Z/blFRL4+xnuO9j2d0fs0w/blB+32DSLyuIisnMzPPlkybJ++W0SaZPgc9S+T+dknS4bt06M/5xtjTpgfYBZwhr1dAOwAVgJfBz5vt38e+Jq9nQdcAHwQ+J7vdYJAI1Bu738duH6U91wHnAsIcDdwRYrHfBT4+XTvn+NhvwJ/BK6xt18B/Hq6988M2qdr7ft2J21fAJwC/Ap4y3TvmwzYp2XAAaDC3r8ZuOQIv6czep9m2L4s9D3m9cA9071/joN9+m7/a87Un0zap0mPOaJz/gmVUTPG1Btjnre3u4CtQA1wJc6Ox/5+g31MjzHmcaA/6aXE/uTZaLkQqEt+PxGZhVOJPGWc/86v3NdO8nbg98f04aZRhu3XlcAD9vZDtgwzzlTvU/saTxtj6lNs32eM2QTEj/VzTacJ3KeLgB3GmCZ7/x/Am5Pfb6zv6Uzfpxm2Lzt9D80DZmTH60zap8eLDN6nR3TOP6ECNT8RWQCcDjwDVLknKPu7cqznGmMGgQ8Bm3FOeiuBn6V4aA1Q67tfa7f5yzEfWAg8eDSfI9NkwH7dyPAB9EagQETKjuazZIop2qcnlGPZp8AuYIWILBCRIE5FPDfF48Y9/o8HmbAvReQ6EdmNkyn52NF9ksyRCfsUeLNt6vuTiKR6/oySIfv0qM75J2SgJiL5wG3AJ5KuxtJ9fgjn5Hc6MBvYBHwh1UNTbEu+2rsK+JMxZuhIy5FpMmS/fgZ4uYi8ALwcOATEjrQsmWIK9+kJ41j3qTGmDWef/gF4DNhH6u9YOsf/jJYp+9IY831jzGLgX4F/P9JyZJIM2ad3AguMMafgZI9uTvHYGSND9qnriM/5J1ygZk9ctwG/Ncb82W5usClLN3XZOM7LnAZgjNlt05u3AueJSJavs+BXcKJpf2f2OYxsdrqKGdzs6cqU/WqMqTPGvMkYczrwb3Zbx4R8yCk2xfv0hDBB+xRjzJ3GmHOMMecC24GdR3n8z1gZui9vYQY332XKPjXGtBhjBuz2nwBnTsTnmw6Zsk99jvicf0IFaraPzs+ArcaY/+f70x3ANfb2NcBfx3mpQ8BKEXEXT73UvuaQMeY0+/OfNqXaJSJr7Xu/y//aIrIcKAGeOuYPN40yab+KSLmIuN/rLwA/P+YPOA2mep9OZNkz1QTuU0Sk0v4uAT4M/PRIj/+ZLJP2pYgs9b3ca4Cdx/jxpkWG7dNZvpd7PU7frhknk/apfe7RnfNNBozMmKofnNEcBqf5Z4P9eTXOiI4HcA7wB4BS33P2Aa1AN060vNJu/yDOl3cTTpq4bJT3XAO8COwGvoedZNj+7XrgxuneL8fTfgXeYt9vB/BTIDzd+2cG7dOv2+fF7e/r7faz7P0eoAXYMt37JwP26e+Bl+zPVWO852jf0xm9TzNsX34b2GLL8BCwarr3z3GwT79q9+lGu09XTPf+men71P7teo7inK8rEyillFJKZagTqulTKaWUUmom0UBNKaWUUipDaaCmlFJKKZWhNFBTSimllMpQGqgppZRSSmUoDdSUUicsESnzTVh5WEQO2dvdIvKD6S6fUkrp9BxKKQWIyPVAtzHm/6a7LEop5dKMmlJKJRGRi0Tkb/b29SJys4jcJyL7RORNIvJ1EdksIvfYJWoQkTNF5BEReU5E7k2a3V0ppY6KBmpKKTW+xTjLE10J/AZ4yBhzMtAHvMYGa98F3mKMORNn6bIbpquwSqnjR3C6C6CUUjPA3caYQRHZDGQB99jtm4EFwHJgNXC/s8QfWUD9NJRTKXWc0UBNKaXGNwBgjImLyKAZ7twbx6lHBWe9znOnq4BKqeOTNn0qpdSx2w5UiMi5ACISEpFV01wmpdRxQAM1pZQ6RsaYKPAW4GsishHYAJw3rYVSSh0XdHoOpZRSSqkMpRk1pZRSSqkMpYGaUkoppVSG0kBNKaWUUipDaaCmlFJKKZWhNFBTSimllMpQGqgppZRSSmUoDdSUUkoppTKUBmpKKaWUUhnq/wM5e43s53CwCQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "timeline = c.index\n",
    "graph = c['Total Sales']\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (10, 5))\n",
    "ax.plot(timeline, graph)\n",
    "\n",
    "date_form = DateFormatter(\"%Y-%m\")\n",
    "ax.xaxis.set_major_formatter(date_form)\n",
    "plt.title('Total Sales from July 2018 to June 2019')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Total Sales')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We can see that sales spike up during the December month and zero sale on Christmas Day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-12-25</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total Sales\n",
       "Date                   \n",
       "2018-12-25          0.0"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirm the date where sales count equals to zero\n",
    "\n",
    "c[c['Total Sales'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>5000.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-02</th>\n",
       "      <td>4781.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-03</th>\n",
       "      <td>4927.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-04</th>\n",
       "      <td>4869.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-05</th>\n",
       "      <td>4900.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total Sales\n",
       "Date                   \n",
       "2018-12-01       5000.9\n",
       "2018-12-02       4781.1\n",
       "2018-12-03       4927.0\n",
       "2018-12-04       4869.4\n",
       "2018-12-05       4900.5"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's look at the December month only\n",
    "\n",
    "c_december = c[(c.index < \"2019-01-01\") & (c.index > \"2018-11-30\")]\n",
    "c_december.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Total Sales in December')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 5))\n",
    "plt.plot(c_december)\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Total Sales')\n",
    "plt.title('Total Sales in December')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5000.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4781.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4927.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4869.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4900.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Sales\n",
       "0       5000.9\n",
       "1       4781.1\n",
       "2       4927.0\n",
       "3       4869.4\n",
       "4       4900.5"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reset index\n",
    "\n",
    "c_december.reset_index(drop = True, inplace = True)\n",
    "c_december.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Sales</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5000.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4781.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4927.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4869.4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4900.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Sales  Date\n",
       "0       5000.9     1\n",
       "1       4781.1     2\n",
       "2       4927.0     3\n",
       "3       4869.4     4\n",
       "4       4900.5     5"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Relabel Date\n",
    "\n",
    "c_december['Date'] = c_december.index + 1\n",
    "c_december.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date', ylabel='Total Sales'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,5))\n",
    "sns.barplot(x = 'Date', y ='Total Sales', data = c_december)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we move on to PACK_SIZE that we created at the beginning by extracting the weight from the PROD_NAME column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    175\n",
       "1    175\n",
       "2    170\n",
       "3    175\n",
       "4    150\n",
       "Name: PACK_SIZE, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tdata['PACK_SIZE'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([175, 170, 150, 330, 210, 270, 220, 125, 110, 134, 380, 180, 165,\n",
       "       135, 250, 200, 160, 190,  90,  70], dtype=int64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tdata['PACK_SIZE'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Pack Size Histogram')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check the distribution of PACK_SIZE\n",
    "\n",
    "plt.figure(figsize = (10, 5))\n",
    "plt.hist(Tdata['PACK_SIZE'])     \n",
    "plt.xlabel('Pack Size')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Pack Size Histogram')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>PACK_SIZE</th>\n",
       "      <th>BRAND</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-10-17</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Natural Chip        Compny Seasalt</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>175</td>\n",
       "      <td>Natural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-14</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>348</td>\n",
       "      <td>66</td>\n",
       "      <td>Ccs Nacho Cheese</td>\n",
       "      <td>3</td>\n",
       "      <td>6.3</td>\n",
       "      <td>175</td>\n",
       "      <td>Ccs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1343</td>\n",
       "      <td>383</td>\n",
       "      <td>61</td>\n",
       "      <td>Smiths Crinkle Cut  Chips Chicken</td>\n",
       "      <td>2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>170</td>\n",
       "      <td>Smiths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-08-17</td>\n",
       "      <td>2</td>\n",
       "      <td>2373</td>\n",
       "      <td>974</td>\n",
       "      <td>69</td>\n",
       "      <td>Smiths Chip Thinly  S Cream Onion</td>\n",
       "      <td>5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>175</td>\n",
       "      <td>Smiths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-08-18</td>\n",
       "      <td>2</td>\n",
       "      <td>2426</td>\n",
       "      <td>1038</td>\n",
       "      <td>108</td>\n",
       "      <td>Kettle Tortilla Chpshny Jlpno Chili</td>\n",
       "      <td>3</td>\n",
       "      <td>13.8</td>\n",
       "      <td>150</td>\n",
       "      <td>Kettle</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "0 2018-10-17          1            1000       1         5   \n",
       "1 2019-05-14          1            1307     348        66   \n",
       "2 2019-05-20          1            1343     383        61   \n",
       "3 2018-08-17          2            2373     974        69   \n",
       "4 2018-08-18          2            2426    1038       108   \n",
       "\n",
       "                               PROD_NAME  PROD_QTY  TOT_SALES  PACK_SIZE  \\\n",
       "0    Natural Chip        Compny Seasalt          2        6.0        175   \n",
       "1                  Ccs Nacho Cheese              3        6.3        175   \n",
       "2    Smiths Crinkle Cut  Chips Chicken           2        2.9        170   \n",
       "3    Smiths Chip Thinly  S Cream Onion           5       15.0        175   \n",
       "4  Kettle Tortilla Chpshny Jlpno Chili           3       13.8        150   \n",
       "\n",
       "     BRAND  \n",
       "0  Natural  \n",
       "1      Ccs  \n",
       "2   Smiths  \n",
       "3   Smiths  \n",
       "4   Kettle  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract brand name from PROD_NAME and create new column called BRAND\n",
    "\n",
    "part = Tdata['PROD_NAME'].str.partition()\n",
    "Tdata['BRAND'] = part[0]\n",
    "Tdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Natural', 'Ccs', 'Smiths', 'Kettle', 'Grain', 'Doritos',\n",
       "       'Twisties', 'Ww', 'Thins', 'Burger', 'Ncc', 'Cheezels', 'Infzns',\n",
       "       'Red', 'Pringles', 'Dorito', 'Infuzions', 'Smith', 'Grnwves',\n",
       "       'Tyrrells', 'Cobs', 'French', 'Rrd', 'Tostitos', 'Cheetos',\n",
       "       'Woolworths', 'Snbts', 'Sunbites'], dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tdata['BRAND'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Natural', 'CCS', 'Smiths', 'Kettle', 'Grainwaves', 'Doritos',\n",
       "       'Twisties', 'Woolworths', 'Thins', 'Burger', 'Cheezels',\n",
       "       'Infuzions', 'Red Rock Deli', 'Pringles', 'Tyrrells', 'Cobs',\n",
       "       'French', 'Tostitos', 'Cheetos', 'Sunbites'], dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename brand names for consistency\n",
    "\n",
    "Tdata['BRAND'].replace('Ncc', 'Natural', inplace = True)\n",
    "Tdata['BRAND'].replace('Ccs', 'CCS', inplace = True)\n",
    "Tdata['BRAND'].replace('Smith', 'Smiths', inplace = True)\n",
    "Tdata['BRAND'].replace(['Grain', 'Grnwves'], 'Grainwaves', inplace = True)\n",
    "Tdata['BRAND'].replace('Dorito', 'Doritos', inplace = True)\n",
    "Tdata['BRAND'].replace('Ww', 'Woolworths', inplace = True)\n",
    "Tdata['BRAND'].replace('Infzns', 'Infuzions', inplace = True)\n",
    "Tdata['BRAND'].replace(['Red', 'Rrd'], 'Red Rock Deli', inplace = True)\n",
    "Tdata['BRAND'].replace('Snbts', 'Sunbites', inplace = True)\n",
    "\n",
    "Tdata['BRAND'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BRAND\n",
       "Kettle           390239.8\n",
       "Doritos          226329.9\n",
       "Smiths           217492.0\n",
       "Pringles         177655.5\n",
       "Infuzions         99047.6\n",
       "Thins             88852.5\n",
       "Red Rock Deli     87607.5\n",
       "Twisties          81522.1\n",
       "Tostitos          79789.6\n",
       "Cobs              70569.8\n",
       "Tyrrells          51647.4\n",
       "Grainwaves        51617.2\n",
       "Natural           42318.0\n",
       "Woolworths        41059.1\n",
       "Cheezels          40029.9\n",
       "CCS               18078.9\n",
       "Cheetos           16884.5\n",
       "Sunbites           9676.4\n",
       "French             7929.0\n",
       "Burger             6831.0\n",
       "Name: TOT_SALES, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Which brand had the most sales?\n",
    "\n",
    "Tdata.groupby('BRAND').TOT_SALES.sum().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Customer Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LYLTY_CARD_NBR', 'LIFESTAGE', 'PREMIUM_CUSTOMER']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(Cdata.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003</td>\n",
       "      <td>YOUNG FAMILIES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1004</td>\n",
       "      <td>OLDER SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LYLTY_CARD_NBR               LIFESTAGE PREMIUM_CUSTOMER\n",
       "0            1000   YOUNG SINGLES/COUPLES          Premium\n",
       "1            1002   YOUNG SINGLES/COUPLES       Mainstream\n",
       "2            1003          YOUNG FAMILIES           Budget\n",
       "3            1004   OLDER SINGLES/COUPLES       Mainstream\n",
       "4            1005  MIDAGE SINGLES/COUPLES       Mainstream"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Missing values in Cdata\n",
    "\n",
    "missingno.matrix(Cdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72636"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Cdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72636"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cdata['LYLTY_CARD_NBR'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the number of rows in Cdata is equal to number of unique loyalty card number, we conclude that loyalty card numbers are unique to each row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many unique lifestages?\n",
    "\n",
    "Cdata['LIFESTAGE'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['YOUNG SINGLES/COUPLES', 'YOUNG FAMILIES', 'OLDER SINGLES/COUPLES',\n",
       "       'MIDAGE SINGLES/COUPLES', 'NEW FAMILIES', 'OLDER FAMILIES',\n",
       "       'RETIREES'], dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What are those lifestages?\n",
    "\n",
    "Cdata['LIFESTAGE'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RETIREES                  14805\n",
       "OLDER SINGLES/COUPLES     14609\n",
       "YOUNG SINGLES/COUPLES     14441\n",
       "OLDER FAMILIES             9779\n",
       "YOUNG FAMILIES             9178\n",
       "MIDAGE SINGLES/COUPLES     7275\n",
       "NEW FAMILIES               2549\n",
       "Name: LIFESTAGE, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Value counts for lifestages\n",
    "\n",
    "Cdata['LIFESTAGE'].value_counts().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='count', ylabel='LIFESTAGE'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y = Cdata['LIFESTAGE'], order = Cdata['LIFESTAGE'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many unique premium customer categories?\n",
    "\n",
    "Cdata['PREMIUM_CUSTOMER'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mainstream    29245\n",
       "Budget        24470\n",
       "Premium       18921\n",
       "Name: PREMIUM_CUSTOMER, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Value counts for each premium customer category\n",
    "\n",
    "Cdata['PREMIUM_CUSTOMER'].value_counts().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Premium Customer')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 7))\n",
    "sns.countplot(y = Cdata['PREMIUM_CUSTOMER'], order = Cdata['PREMIUM_CUSTOMER'].value_counts().index)\n",
    "plt.xlabel('Number of Customers')\n",
    "plt.ylabel('Premium Customer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge Tdata and Cdata together\n",
    "\n",
    "combineData = pd.merge(Tdata, Cdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tdata data shape:  (246740, 10)\n",
      "Customer data shape:  (72636, 3)\n",
      "Combined data shape:  (246740, 12)\n"
     ]
    }
   ],
   "source": [
    "print(\"Tdata data shape: \", Tdata.shape)\n",
    "print(\"Customer data shape: \", Cdata.shape)\n",
    "print(\"Combined data shape: \", combineData.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE                0\n",
       "STORE_NBR           0\n",
       "LYLTY_CARD_NBR      0\n",
       "TXN_ID              0\n",
       "PROD_NBR            0\n",
       "PROD_NAME           0\n",
       "PROD_QTY            0\n",
       "TOT_SALES           0\n",
       "PACK_SIZE           0\n",
       "BRAND               0\n",
       "LIFESTAGE           0\n",
       "PREMIUM_CUSTOMER    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for null values\n",
    "\n",
    "combineData.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data analysis on customer segments\n",
    "\n",
    "Now that our data is ready for analysis, we can define some metrics of interest to the client:\n",
    "\n",
    "- Who spends the most on chips, describing customers by lifestage and how premium their general purchasing behaviour is\n",
    "- How many customers are in each segment\n",
    "- How many chips are bought per customer by segment\n",
    "- What is the average chip price by customer segment\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Total Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Budget</th>\n",
       "      <th>OLDER FAMILIES</th>\n",
       "      <td>156863.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Mainstream</th>\n",
       "      <th>YOUNG SINGLES/COUPLES</th>\n",
       "      <td>147582.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETIREES</th>\n",
       "      <td>145168.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Budget</th>\n",
       "      <th>YOUNG FAMILIES</th>\n",
       "      <td>129717.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OLDER SINGLES/COUPLES</th>\n",
       "      <td>127833.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <th>OLDER SINGLES/COUPLES</th>\n",
       "      <td>124648.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <th>OLDER SINGLES/COUPLES</th>\n",
       "      <td>123537.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Budget</th>\n",
       "      <th>RETIREES</th>\n",
       "      <td>105916.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <th>OLDER FAMILIES</th>\n",
       "      <td>96413.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <th>RETIREES</th>\n",
       "      <td>91296.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Mainstream</th>\n",
       "      <th>YOUNG FAMILIES</th>\n",
       "      <td>86338.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIDAGE SINGLES/COUPLES</th>\n",
       "      <td>84734.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Premium</th>\n",
       "      <th>YOUNG FAMILIES</th>\n",
       "      <td>78571.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OLDER FAMILIES</th>\n",
       "      <td>75242.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Budget</th>\n",
       "      <th>YOUNG SINGLES/COUPLES</th>\n",
       "      <td>57122.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Premium</th>\n",
       "      <th>MIDAGE SINGLES/COUPLES</th>\n",
       "      <td>54443.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUNG SINGLES/COUPLES</th>\n",
       "      <td>39052.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Budget</th>\n",
       "      <th>MIDAGE SINGLES/COUPLES</th>\n",
       "      <td>33345.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEW FAMILIES</th>\n",
       "      <td>20607.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <th>NEW FAMILIES</th>\n",
       "      <td>15979.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <th>NEW FAMILIES</th>\n",
       "      <td>10760.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Total Sales\n",
       "PREMIUM_CUSTOMER LIFESTAGE                          \n",
       "Budget           OLDER FAMILIES            156863.75\n",
       "Mainstream       YOUNG SINGLES/COUPLES     147582.20\n",
       "                 RETIREES                  145168.95\n",
       "Budget           YOUNG FAMILIES            129717.95\n",
       "                 OLDER SINGLES/COUPLES     127833.60\n",
       "Mainstream       OLDER SINGLES/COUPLES     124648.50\n",
       "Premium          OLDER SINGLES/COUPLES     123537.55\n",
       "Budget           RETIREES                  105916.30\n",
       "Mainstream       OLDER FAMILIES             96413.55\n",
       "Premium          RETIREES                   91296.65\n",
       "Mainstream       YOUNG FAMILIES             86338.25\n",
       "                 MIDAGE SINGLES/COUPLES     84734.25\n",
       "Premium          YOUNG FAMILIES             78571.70\n",
       "                 OLDER FAMILIES             75242.60\n",
       "Budget           YOUNG SINGLES/COUPLES      57122.10\n",
       "Premium          MIDAGE SINGLES/COUPLES     54443.85\n",
       "                 YOUNG SINGLES/COUPLES      39052.30\n",
       "Budget           MIDAGE SINGLES/COUPLES     33345.70\n",
       "                 NEW FAMILIES               20607.45\n",
       "Mainstream       NEW FAMILIES               15979.70\n",
       "Premium          NEW FAMILIES               10760.80"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total sales by PREMIUM_CUSTOMER and LIFESTAGE\n",
    "\n",
    "sales = pd.DataFrame(combineData.groupby(['PREMIUM_CUSTOMER', 'LIFESTAGE']).TOT_SALES.sum())\n",
    "sales.rename(columns = {'TOT_SALES': 'Total Sales'}, inplace = True)\n",
    "sales.sort_values(by = 'Total Sales', ascending = False, inplace = True)\n",
    "sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2685745dee0>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualise\n",
    "\n",
    "salesPlot = pd.DataFrame(combineData.groupby(['LIFESTAGE', 'PREMIUM_CUSTOMER']).TOT_SALES.sum())\n",
    "salesPlot.unstack().plot(kind = 'bar', stacked = True, figsize = (12, 7), title = 'Total Sales by Customer Segment')\n",
    "plt.ylabel('Total Sales')\n",
    "plt.legend(['Budget', 'Mainstream', 'Premium'], loc = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Top 3 sales come from budget older families, mainstream young singles/couples and mainstream retirees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Number of Customers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Mainstream</th>\n",
       "      <th>YOUNG SINGLES/COUPLES</th>\n",
       "      <td>7917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETIREES</th>\n",
       "      <td>6358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OLDER SINGLES/COUPLES</th>\n",
       "      <td>4858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Budget</th>\n",
       "      <th>OLDER SINGLES/COUPLES</th>\n",
       "      <td>4849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <th>OLDER SINGLES/COUPLES</th>\n",
       "      <td>4682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Budget</th>\n",
       "      <th>OLDER FAMILIES</th>\n",
       "      <td>4611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETIREES</th>\n",
       "      <td>4385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUNG FAMILIES</th>\n",
       "      <td>3953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <th>RETIREES</th>\n",
       "      <td>3812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Budget</th>\n",
       "      <th>YOUNG SINGLES/COUPLES</th>\n",
       "      <td>3647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Number of Customers\n",
       "PREMIUM_CUSTOMER LIFESTAGE                                 \n",
       "Mainstream       YOUNG SINGLES/COUPLES                 7917\n",
       "                 RETIREES                              6358\n",
       "                 OLDER SINGLES/COUPLES                 4858\n",
       "Budget           OLDER SINGLES/COUPLES                 4849\n",
       "Premium          OLDER SINGLES/COUPLES                 4682\n",
       "Budget           OLDER FAMILIES                        4611\n",
       "                 RETIREES                              4385\n",
       "                 YOUNG FAMILIES                        3953\n",
       "Premium          RETIREES                              3812\n",
       "Budget           YOUNG SINGLES/COUPLES                 3647"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of customers by PREMIUM_CUSTOMER and LIFESTAGE\n",
    "\n",
    "customers = pd.DataFrame(combineData.groupby(['PREMIUM_CUSTOMER', 'LIFESTAGE']).LYLTY_CARD_NBR.nunique())\n",
    "customers.rename(columns = {'LYLTY_CARD_NBR': 'Number of Customers'}, inplace = True)\n",
    "customers.sort_values(by = 'Number of Customers', ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26857c9b8e0>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualise Number of Customers by Customer Segment\n",
    "\n",
    "customersPlot = pd.DataFrame(combineData.groupby(['LIFESTAGE', 'PREMIUM_CUSTOMER']).LYLTY_CARD_NBR.nunique())\n",
    "customersPlot.unstack().plot(kind = 'bar', stacked = True, figsize = (12, 7), title = 'Number of Customers by Customer Segment')\n",
    "plt.ylabel('Number of Customers')\n",
    "plt.legend(['Budget', 'Mainstream', 'Premium'], loc = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "There are more mainstream young singles/couples and retirees. This contributes to to more chips sales in these segments however this is not the major driver for the budget older families segment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Average Unit per Customer</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <th>OLDER FAMILIES</th>\n",
       "      <td>9.255380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Budget</th>\n",
       "      <th>OLDER FAMILIES</th>\n",
       "      <td>9.076773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <th>OLDER FAMILIES</th>\n",
       "      <td>9.071717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Budget</th>\n",
       "      <th>YOUNG FAMILIES</th>\n",
       "      <td>8.722995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <th>YOUNG FAMILIES</th>\n",
       "      <td>8.716013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Average Unit per Customer\n",
       "PREMIUM_CUSTOMER LIFESTAGE                                \n",
       "Mainstream       OLDER FAMILIES                   9.255380\n",
       "Budget           OLDER FAMILIES                   9.076773\n",
       "Premium          OLDER FAMILIES                   9.071717\n",
       "Budget           YOUNG FAMILIES                   8.722995\n",
       "Premium          YOUNG FAMILIES                   8.716013"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average units per customer by PREMIUM_CUSTOMER and LIFESTAGE\n",
    "\n",
    "avg_units = combineData.groupby(['PREMIUM_CUSTOMER', 'LIFESTAGE']).PROD_QTY.sum() / combineData.groupby(['PREMIUM_CUSTOMER', 'LIFESTAGE']).LYLTY_CARD_NBR.nunique()\n",
    "avg_units = pd.DataFrame(avg_units, columns = {'Average Unit per Customer'})\n",
    "avg_units.sort_values(by = 'Average Unit per Customer', ascending = False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26857ce1d00>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualise Average Units per Customer by Customer Segment\n",
    "\n",
    "avgUnitsPlot = pd.DataFrame(combineData.groupby(['LIFESTAGE', 'PREMIUM_CUSTOMER']).PROD_QTY.sum() / combineData.groupby(['LIFESTAGE', 'PREMIUM_CUSTOMER']).LYLTY_CARD_NBR.nunique())\n",
    "avgUnitsPlot.unstack().plot(kind = 'bar', figsize = (12, 7), title = 'Average Unit by Customer Segment')\n",
    "plt.ylabel('Average Number of Units')\n",
    "plt.legend(['Budget', 'Mainstream', 'Premium'], loc = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Older families and young families buy more chips per customer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price per Unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Mainstream</th>\n",
       "      <th>YOUNG SINGLES/COUPLES</th>\n",
       "      <td>4.074043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIDAGE SINGLES/COUPLES</th>\n",
       "      <td>3.994449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEW FAMILIES</th>\n",
       "      <td>3.935887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Budget</th>\n",
       "      <th>RETIREES</th>\n",
       "      <td>3.932731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEW FAMILIES</th>\n",
       "      <td>3.931969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Price per Unit\n",
       "PREMIUM_CUSTOMER LIFESTAGE                             \n",
       "Mainstream       YOUNG SINGLES/COUPLES         4.074043\n",
       "                 MIDAGE SINGLES/COUPLES        3.994449\n",
       "                 NEW FAMILIES                  3.935887\n",
       "Budget           RETIREES                      3.932731\n",
       "                 NEW FAMILIES                  3.931969"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average price per unit by PREMIUM_CUSTOMER and LIFESTAGE\n",
    "\n",
    "avg_price = combineData.groupby(['PREMIUM_CUSTOMER', 'LIFESTAGE']).TOT_SALES.sum() / combineData.groupby(['PREMIUM_CUSTOMER', 'LIFESTAGE']).PROD_QTY.sum()\n",
    "avg_price = pd.DataFrame(avg_price, columns = {'Price per Unit'})\n",
    "avg_price.sort_values(by = 'Price per Unit', ascending = False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26857d38760>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualise Average Price per Unit by Customer Segment\n",
    "\n",
    "avgPricePlot = pd.DataFrame(combineData.groupby(['LIFESTAGE', 'PREMIUM_CUSTOMER']).TOT_SALES.sum() / combineData.groupby(['LIFESTAGE', 'PREMIUM_CUSTOMER']).PROD_QTY.sum())\n",
    "avgPricePlot.unstack().plot(kind = 'bar', figsize = (12, 7), title = 'Average Price by Customer Segment', ylim = (0, 6))\n",
    "plt.ylabel('Average Price')\n",
    "plt.legend(['Budget', 'Mainstream', 'Premium'], loc = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Mainstream midage and young singles and couples are more willing to pay more per packet of chips compared to their budget and premium counterparts. This may be due to premium shoppers being more likely to buy healthy snacks and when they do buy chips, it is mainly for entertainment purposes rather than their own consumption. This is also supported by there being fewer premium midage and young singles and couples buying chips compared to their mainstream counterparts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>PACK_SIZE</th>\n",
       "      <th>BRAND</th>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-10-17</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Natural Chip        Compny Seasalt</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>175</td>\n",
       "      <td>Natural</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-14</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>348</td>\n",
       "      <td>66</td>\n",
       "      <td>Ccs Nacho Cheese</td>\n",
       "      <td>3</td>\n",
       "      <td>6.3</td>\n",
       "      <td>175</td>\n",
       "      <td>CCS</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-11-10</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>346</td>\n",
       "      <td>96</td>\n",
       "      <td>Ww Original Stacked Chips</td>\n",
       "      <td>2</td>\n",
       "      <td>3.8</td>\n",
       "      <td>160</td>\n",
       "      <td>Woolworths</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-03-09</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>347</td>\n",
       "      <td>54</td>\n",
       "      <td>Ccs Original</td>\n",
       "      <td>1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>175</td>\n",
       "      <td>CCS</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1343</td>\n",
       "      <td>383</td>\n",
       "      <td>61</td>\n",
       "      <td>Smiths Crinkle Cut  Chips Chicken</td>\n",
       "      <td>2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>170</td>\n",
       "      <td>Smiths</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>1.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "0 2018-10-17          1            1000       1         5   \n",
       "1 2019-05-14          1            1307     348        66   \n",
       "2 2018-11-10          1            1307     346        96   \n",
       "3 2019-03-09          1            1307     347        54   \n",
       "4 2019-05-20          1            1343     383        61   \n",
       "\n",
       "                             PROD_NAME  PROD_QTY  TOT_SALES  PACK_SIZE  \\\n",
       "0  Natural Chip        Compny Seasalt          2        6.0        175   \n",
       "1                Ccs Nacho Cheese              3        6.3        175   \n",
       "2          Ww Original Stacked Chips           2        3.8        160   \n",
       "3                       Ccs Original           1        2.1        175   \n",
       "4  Smiths Crinkle Cut  Chips Chicken           2        2.9        170   \n",
       "\n",
       "        BRAND               LIFESTAGE PREMIUM_CUSTOMER  PRICE  \n",
       "0     Natural   YOUNG SINGLES/COUPLES          Premium   3.00  \n",
       "1         CCS  MIDAGE SINGLES/COUPLES           Budget   2.10  \n",
       "2  Woolworths  MIDAGE SINGLES/COUPLES           Budget   1.90  \n",
       "3         CCS  MIDAGE SINGLES/COUPLES           Budget   2.10  \n",
       "4      Smiths  MIDAGE SINGLES/COUPLES           Budget   1.45  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform an independent t-test between mainstream vs non-mainstream midage and young singles/couples to test this difference\n",
    "\n",
    "# Create a new dataframe pricePerUnit\n",
    "pricePerUnit = combineData\n",
    "\n",
    "# Create a new column under pricePerUnit called PRICE\n",
    "pricePerUnit['PRICE'] = pricePerUnit['TOT_SALES'] / pricePerUnit['PROD_QTY']\n",
    "\n",
    "# Let's have a look\n",
    "pricePerUnit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's group our data into mainstream and non-mainstream\n",
    "\n",
    "mainstream = pricePerUnit.loc[(pricePerUnit['PREMIUM_CUSTOMER'] == 'Mainstream') & ( (pricePerUnit['LIFESTAGE'] == 'YOUNG SINGLES/COUPLES') | (pricePerUnit['LIFESTAGE'] == 'MIDAGE SINGLES/COUPLES') ), 'PRICE']\n",
    "nonMainstream = pricePerUnit.loc[(pricePerUnit['PREMIUM_CUSTOMER'] != 'Mainstream') & ( (pricePerUnit['LIFESTAGE'] == 'YOUNG SINGLES/COUPLES') | (pricePerUnit['LIFESTAGE'] == 'MIDAGE SINGLES/COUPLES') ), 'PRICE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Price per Unit')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare histograms of mainstream and non-mainstream customers\n",
    "\n",
    "plt.figure(figsize = (10, 5))\n",
    "plt.hist(mainstream, label = 'Mainstream')\n",
    "plt.hist(nonMainstream, label = 'Premium & Budget')\n",
    "plt.legend()\n",
    "plt.xlabel('Price per Unit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mainstream average price per unit: $4.04\n",
      "Non-mainstream average price per unit: $3.71\n",
      "Mainstream customers have higher average price per unit. \n"
     ]
    }
   ],
   "source": [
    "print(\"Mainstream average price per unit: ${:.2f}\".format(np.mean(mainstream)))\n",
    "print(\"Non-mainstream average price per unit: ${:.2f}\".format(np.mean(nonMainstream)))\n",
    "if np.mean(mainstream) > np.mean(nonMainstream):\n",
    "    print(\"Mainstream customers have higher average price per unit. \")\n",
    "else:\n",
    "    print(\"Non-mainstream customers have a higher average price per unit. \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=37.83196107667825, pvalue=2.235645611540966e-309)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform t-test \n",
    "\n",
    "ttest_ind(mainstream, nonMainstream)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mainstream customers have higher average price per unit than that of non-mainstream customers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have found quite a few interesting insights that we can dive deeper into. For example, we might want to target customers segments that contribute the most to sales to retain them to further increase sales. Let's examine mainstream young singles/couples against the rest of the cutomer segments to see if they prefer any particular brand of chips."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>PACK_SIZE</th>\n",
       "      <th>BRAND</th>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>221345</th>\n",
       "      <td>2018-08-16</td>\n",
       "      <td>1</td>\n",
       "      <td>1020</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>Smiths Crinkle Cut  Snag Sauce</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>150</td>\n",
       "      <td>Smiths</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221346</th>\n",
       "      <td>2018-10-02</td>\n",
       "      <td>1</td>\n",
       "      <td>1020</td>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>Smiths Crinkle      Original</td>\n",
       "      <td>1</td>\n",
       "      <td>5.7</td>\n",
       "      <td>330</td>\n",
       "      <td>Smiths</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221347</th>\n",
       "      <td>2019-05-02</td>\n",
       "      <td>1</td>\n",
       "      <td>1020</td>\n",
       "      <td>28</td>\n",
       "      <td>84</td>\n",
       "      <td>Grnwves Plus Btroot   Chilli Jam</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>180</td>\n",
       "      <td>Grainwaves</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221348</th>\n",
       "      <td>2018-08-17</td>\n",
       "      <td>1</td>\n",
       "      <td>1163</td>\n",
       "      <td>188</td>\n",
       "      <td>46</td>\n",
       "      <td>Kettle Original</td>\n",
       "      <td>1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>175</td>\n",
       "      <td>Kettle</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221349</th>\n",
       "      <td>2019-02-07</td>\n",
       "      <td>1</td>\n",
       "      <td>1163</td>\n",
       "      <td>189</td>\n",
       "      <td>12</td>\n",
       "      <td>Natural Chip Co     Tmato Hrb Spce</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>175</td>\n",
       "      <td>Natural</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "221345 2018-08-16          1            1020      26        19   \n",
       "221346 2018-10-02          1            1020      27         7   \n",
       "221347 2019-05-02          1            1020      28        84   \n",
       "221348 2018-08-17          1            1163     188        46   \n",
       "221349 2019-02-07          1            1163     189        12   \n",
       "\n",
       "                                   PROD_NAME  PROD_QTY  TOT_SALES  PACK_SIZE  \\\n",
       "221345      Smiths Crinkle Cut  Snag Sauce           1        2.6        150   \n",
       "221346        Smiths Crinkle      Original           1        5.7        330   \n",
       "221347    Grnwves Plus Btroot   Chilli Jam           1        3.1        180   \n",
       "221348                     Kettle Original           1        5.4        175   \n",
       "221349  Natural Chip Co     Tmato Hrb Spce           1        3.0        175   \n",
       "\n",
       "             BRAND              LIFESTAGE PREMIUM_CUSTOMER  PRICE  \n",
       "221345      Smiths  YOUNG SINGLES/COUPLES       Mainstream    2.6  \n",
       "221346      Smiths  YOUNG SINGLES/COUPLES       Mainstream    5.7  \n",
       "221347  Grainwaves  YOUNG SINGLES/COUPLES       Mainstream    3.1  \n",
       "221348      Kettle  YOUNG SINGLES/COUPLES       Mainstream    5.4  \n",
       "221349     Natural  YOUNG SINGLES/COUPLES       Mainstream    3.0  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = combineData.loc[(combineData['LIFESTAGE'] == 'YOUNG SINGLES/COUPLES') & (combineData['PREMIUM_CUSTOMER'] == 'Mainstream'), :]\n",
    "nonTarget = combineData.loc[(combineData['LIFESTAGE'] != 'YOUNG SINGLES/COUPLES' ) & (combineData['PREMIUM_CUSTOMER'] != 'Mainstream'), :]\n",
    "target.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Affinity to brand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Target Segment\n",
    "targetBrand = target.loc[:, ['BRAND', 'PROD_QTY']]\n",
    "targetSum = targetBrand['PROD_QTY'].sum()\n",
    "targetBrand['Target Brand Affinity'] = targetBrand['PROD_QTY'] / targetSum\n",
    "targetBrand = pd.DataFrame(targetBrand.groupby('BRAND')['Target Brand Affinity'].sum())\n",
    "\n",
    "# Non-target segment\n",
    "nonTargetBrand = nonTarget.loc[:, ['BRAND', 'PROD_QTY']]\n",
    "nonTargetSum = nonTargetBrand['PROD_QTY'].sum()\n",
    "nonTargetBrand['Non-Target Brand Affinity'] = nonTargetBrand['PROD_QTY'] / nonTargetSum\n",
    "nonTargetBrand = pd.DataFrame(nonTargetBrand.groupby('BRAND')['Non-Target Brand Affinity'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Target Brand Affinity</th>\n",
       "      <th>Non-Target Brand Affinity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRAND</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Burger</th>\n",
       "      <td>0.002926</td>\n",
       "      <td>0.006538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCS</th>\n",
       "      <td>0.011180</td>\n",
       "      <td>0.018445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cheetos</th>\n",
       "      <td>0.008033</td>\n",
       "      <td>0.011759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cheezels</th>\n",
       "      <td>0.017971</td>\n",
       "      <td>0.018904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cobs</th>\n",
       "      <td>0.044638</td>\n",
       "      <td>0.038449</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Target Brand Affinity  Non-Target Brand Affinity\n",
       "BRAND                                                     \n",
       "Burger                 0.002926                   0.006538\n",
       "CCS                    0.011180                   0.018445\n",
       "Cheetos                0.008033                   0.011759\n",
       "Cheezels               0.017971                   0.018904\n",
       "Cobs                   0.044638                   0.038449"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the two dataframes together\n",
    "\n",
    "brand_proportions = pd.merge(targetBrand, nonTargetBrand, left_index = True, right_index = True)\n",
    "brand_proportions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Target Brand Affinity</th>\n",
       "      <th>Non-Target Brand Affinity</th>\n",
       "      <th>Affinity to Brand</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRAND</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tyrrells</th>\n",
       "      <td>0.031553</td>\n",
       "      <td>0.025715</td>\n",
       "      <td>1.227025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Twisties</th>\n",
       "      <td>0.046184</td>\n",
       "      <td>0.037933</td>\n",
       "      <td>1.217506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Doritos</th>\n",
       "      <td>0.122761</td>\n",
       "      <td>0.101171</td>\n",
       "      <td>1.213397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kettle</th>\n",
       "      <td>0.197985</td>\n",
       "      <td>0.166560</td>\n",
       "      <td>1.188667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tostitos</th>\n",
       "      <td>0.045411</td>\n",
       "      <td>0.038350</td>\n",
       "      <td>1.184100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pringles</th>\n",
       "      <td>0.119420</td>\n",
       "      <td>0.101110</td>\n",
       "      <td>1.181090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cobs</th>\n",
       "      <td>0.044638</td>\n",
       "      <td>0.038449</td>\n",
       "      <td>1.160959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infuzions</th>\n",
       "      <td>0.064679</td>\n",
       "      <td>0.057410</td>\n",
       "      <td>1.126622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thins</th>\n",
       "      <td>0.060373</td>\n",
       "      <td>0.057159</td>\n",
       "      <td>1.056217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grainwaves</th>\n",
       "      <td>0.032712</td>\n",
       "      <td>0.031069</td>\n",
       "      <td>1.052895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cheezels</th>\n",
       "      <td>0.017971</td>\n",
       "      <td>0.018904</td>\n",
       "      <td>0.950653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Smiths</th>\n",
       "      <td>0.096370</td>\n",
       "      <td>0.124218</td>\n",
       "      <td>0.775811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>French</th>\n",
       "      <td>0.003948</td>\n",
       "      <td>0.005707</td>\n",
       "      <td>0.691724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cheetos</th>\n",
       "      <td>0.008033</td>\n",
       "      <td>0.011759</td>\n",
       "      <td>0.683150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Red Rock Deli</th>\n",
       "      <td>0.043810</td>\n",
       "      <td>0.067184</td>\n",
       "      <td>0.652080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Natural</th>\n",
       "      <td>0.019600</td>\n",
       "      <td>0.030959</td>\n",
       "      <td>0.633091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCS</th>\n",
       "      <td>0.011180</td>\n",
       "      <td>0.018445</td>\n",
       "      <td>0.606142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunbites</th>\n",
       "      <td>0.006349</td>\n",
       "      <td>0.012613</td>\n",
       "      <td>0.503398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woolworths</th>\n",
       "      <td>0.024099</td>\n",
       "      <td>0.048747</td>\n",
       "      <td>0.494376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burger</th>\n",
       "      <td>0.002926</td>\n",
       "      <td>0.006538</td>\n",
       "      <td>0.447574</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Target Brand Affinity  Non-Target Brand Affinity  \\\n",
       "BRAND                                                             \n",
       "Tyrrells                    0.031553                   0.025715   \n",
       "Twisties                    0.046184                   0.037933   \n",
       "Doritos                     0.122761                   0.101171   \n",
       "Kettle                      0.197985                   0.166560   \n",
       "Tostitos                    0.045411                   0.038350   \n",
       "Pringles                    0.119420                   0.101110   \n",
       "Cobs                        0.044638                   0.038449   \n",
       "Infuzions                   0.064679                   0.057410   \n",
       "Thins                       0.060373                   0.057159   \n",
       "Grainwaves                  0.032712                   0.031069   \n",
       "Cheezels                    0.017971                   0.018904   \n",
       "Smiths                      0.096370                   0.124218   \n",
       "French                      0.003948                   0.005707   \n",
       "Cheetos                     0.008033                   0.011759   \n",
       "Red Rock Deli               0.043810                   0.067184   \n",
       "Natural                     0.019600                   0.030959   \n",
       "CCS                         0.011180                   0.018445   \n",
       "Sunbites                    0.006349                   0.012613   \n",
       "Woolworths                  0.024099                   0.048747   \n",
       "Burger                      0.002926                   0.006538   \n",
       "\n",
       "               Affinity to Brand  \n",
       "BRAND                             \n",
       "Tyrrells                1.227025  \n",
       "Twisties                1.217506  \n",
       "Doritos                 1.213397  \n",
       "Kettle                  1.188667  \n",
       "Tostitos                1.184100  \n",
       "Pringles                1.181090  \n",
       "Cobs                    1.160959  \n",
       "Infuzions               1.126622  \n",
       "Thins                   1.056217  \n",
       "Grainwaves              1.052895  \n",
       "Cheezels                0.950653  \n",
       "Smiths                  0.775811  \n",
       "French                  0.691724  \n",
       "Cheetos                 0.683150  \n",
       "Red Rock Deli           0.652080  \n",
       "Natural                 0.633091  \n",
       "CCS                     0.606142  \n",
       "Sunbites                0.503398  \n",
       "Woolworths              0.494376  \n",
       "Burger                  0.447574  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brand_proportions['Affinity to Brand'] = brand_proportions['Target Brand Affinity'] / brand_proportions['Non-Target Brand Affinity']\n",
    "brand_proportions.sort_values(by = 'Affinity to Brand', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mainstream young singles/couples are more likely to purchase Tyrrells chips compared to other brands."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Affinity to pack size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Target segment \n",
    "targetSize = target.loc[:, ['PACK_SIZE', 'PROD_QTY']]\n",
    "targetSum = targetSize['PROD_QTY'].sum()\n",
    "targetSize['Target Pack Affinity'] = targetSize['PROD_QTY'] / targetSum\n",
    "targetSize = pd.DataFrame(targetSize.groupby('PACK_SIZE')['Target Pack Affinity'].sum())\n",
    "\n",
    "# Non-target segment\n",
    "nonTargetSize = nonTarget.loc[:, ['PACK_SIZE', 'PROD_QTY']]\n",
    "nonTargetSum = nonTargetSize['PROD_QTY'].sum()\n",
    "nonTargetSize['Non-Target Pack Affinity'] = nonTargetSize['PROD_QTY'] / nonTargetSum\n",
    "nonTargetSize = pd.DataFrame(nonTargetSize.groupby('PACK_SIZE')['Non-Target Pack Affinity'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Target Pack Affinity</th>\n",
       "      <th>Non-Target Pack Affinity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PACK_SIZE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0.003037</td>\n",
       "      <td>0.006284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>0.006349</td>\n",
       "      <td>0.012613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>0.106280</td>\n",
       "      <td>0.089575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>0.003009</td>\n",
       "      <td>0.005976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>0.119420</td>\n",
       "      <td>0.101110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Target Pack Affinity  Non-Target Pack Affinity\n",
       "PACK_SIZE                                                \n",
       "70                     0.003037                  0.006284\n",
       "90                     0.006349                  0.012613\n",
       "110                    0.106280                  0.089575\n",
       "125                    0.003009                  0.005976\n",
       "134                    0.119420                  0.101110"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the two dataframes together\n",
    "\n",
    "pack_proportions = pd.merge(targetSize, nonTargetSize, left_index = True, right_index = True)\n",
    "pack_proportions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Target Pack Affinity</th>\n",
       "      <th>Non-Target Pack Affinity</th>\n",
       "      <th>Affinity to Pack</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PACK_SIZE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>0.031829</td>\n",
       "      <td>0.025070</td>\n",
       "      <td>1.269608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>0.032160</td>\n",
       "      <td>0.025711</td>\n",
       "      <td>1.250827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>0.061284</td>\n",
       "      <td>0.050974</td>\n",
       "      <td>1.202243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>0.106280</td>\n",
       "      <td>0.089575</td>\n",
       "      <td>1.186492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>0.119420</td>\n",
       "      <td>0.101110</td>\n",
       "      <td>1.181090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>0.029124</td>\n",
       "      <td>0.024891</td>\n",
       "      <td>1.170020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>0.014769</td>\n",
       "      <td>0.012931</td>\n",
       "      <td>1.142086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>0.014355</td>\n",
       "      <td>0.012863</td>\n",
       "      <td>1.115959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>0.080773</td>\n",
       "      <td>0.080347</td>\n",
       "      <td>1.005300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>0.157598</td>\n",
       "      <td>0.163070</td>\n",
       "      <td>0.966449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>0.254990</td>\n",
       "      <td>0.271458</td>\n",
       "      <td>0.939335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>0.055652</td>\n",
       "      <td>0.061587</td>\n",
       "      <td>0.903629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>0.007481</td>\n",
       "      <td>0.012131</td>\n",
       "      <td>0.616696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>0.003589</td>\n",
       "      <td>0.006177</td>\n",
       "      <td>0.580943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>0.006404</td>\n",
       "      <td>0.012222</td>\n",
       "      <td>0.524013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>0.003009</td>\n",
       "      <td>0.005976</td>\n",
       "      <td>0.503490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>0.006349</td>\n",
       "      <td>0.012613</td>\n",
       "      <td>0.503398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>0.008972</td>\n",
       "      <td>0.018471</td>\n",
       "      <td>0.485711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0.003037</td>\n",
       "      <td>0.006284</td>\n",
       "      <td>0.483256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>0.002926</td>\n",
       "      <td>0.006538</td>\n",
       "      <td>0.447574</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Target Pack Affinity  Non-Target Pack Affinity  Affinity to Pack\n",
       "PACK_SIZE                                                                  \n",
       "270                    0.031829                  0.025070          1.269608\n",
       "380                    0.032160                  0.025711          1.250827\n",
       "330                    0.061284                  0.050974          1.202243\n",
       "110                    0.106280                  0.089575          1.186492\n",
       "134                    0.119420                  0.101110          1.181090\n",
       "210                    0.029124                  0.024891          1.170020\n",
       "135                    0.014769                  0.012931          1.142086\n",
       "250                    0.014355                  0.012863          1.115959\n",
       "170                    0.080773                  0.080347          1.005300\n",
       "150                    0.157598                  0.163070          0.966449\n",
       "175                    0.254990                  0.271458          0.939335\n",
       "165                    0.055652                  0.061587          0.903629\n",
       "190                    0.007481                  0.012131          0.616696\n",
       "180                    0.003589                  0.006177          0.580943\n",
       "160                    0.006404                  0.012222          0.524013\n",
       "125                    0.003009                  0.005976          0.503490\n",
       "90                     0.006349                  0.012613          0.503398\n",
       "200                    0.008972                  0.018471          0.485711\n",
       "70                     0.003037                  0.006284          0.483256\n",
       "220                    0.002926                  0.006538          0.447574"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pack_proportions['Affinity to Pack'] = pack_proportions['Target Pack Affinity'] / pack_proportions['Non-Target Pack Affinity']\n",
    "pack_proportions.sort_values(by = 'Affinity to Pack', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like mainstream singles/couples are more likely to purchase a 270g pack size compared to other pack sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>PACK_SIZE</th>\n",
       "      <th>BRAND</th>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2019-05-18</td>\n",
       "      <td>9</td>\n",
       "      <td>9208</td>\n",
       "      <td>8634</td>\n",
       "      <td>15</td>\n",
       "      <td>Twisties Cheese</td>\n",
       "      <td>2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>270</td>\n",
       "      <td>Twisties</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>2018-08-24</td>\n",
       "      <td>39</td>\n",
       "      <td>39167</td>\n",
       "      <td>35639</td>\n",
       "      <td>113</td>\n",
       "      <td>Twisties Chicken</td>\n",
       "      <td>2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>270</td>\n",
       "      <td>Twisties</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>2019-05-06</td>\n",
       "      <td>54</td>\n",
       "      <td>54305</td>\n",
       "      <td>48304</td>\n",
       "      <td>15</td>\n",
       "      <td>Twisties Cheese</td>\n",
       "      <td>2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>270</td>\n",
       "      <td>Twisties</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>55</td>\n",
       "      <td>55073</td>\n",
       "      <td>48887</td>\n",
       "      <td>113</td>\n",
       "      <td>Twisties Chicken</td>\n",
       "      <td>1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>270</td>\n",
       "      <td>Twisties</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>80</td>\n",
       "      <td>80182</td>\n",
       "      <td>78980</td>\n",
       "      <td>15</td>\n",
       "      <td>Twisties Cheese</td>\n",
       "      <td>2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>270</td>\n",
       "      <td>Twisties</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>2018-11-18</td>\n",
       "      <td>84</td>\n",
       "      <td>84096</td>\n",
       "      <td>83672</td>\n",
       "      <td>113</td>\n",
       "      <td>Twisties Chicken</td>\n",
       "      <td>2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>270</td>\n",
       "      <td>Twisties</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>88</td>\n",
       "      <td>88320</td>\n",
       "      <td>87811</td>\n",
       "      <td>113</td>\n",
       "      <td>Twisties Chicken</td>\n",
       "      <td>2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>270</td>\n",
       "      <td>Twisties</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>2018-07-11</td>\n",
       "      <td>102</td>\n",
       "      <td>102115</td>\n",
       "      <td>102207</td>\n",
       "      <td>15</td>\n",
       "      <td>Twisties Cheese</td>\n",
       "      <td>2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>270</td>\n",
       "      <td>Twisties</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>2019-04-05</td>\n",
       "      <td>118</td>\n",
       "      <td>118007</td>\n",
       "      <td>120735</td>\n",
       "      <td>113</td>\n",
       "      <td>Twisties Chicken</td>\n",
       "      <td>2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>270</td>\n",
       "      <td>Twisties</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>119</td>\n",
       "      <td>119051</td>\n",
       "      <td>122360</td>\n",
       "      <td>113</td>\n",
       "      <td>Twisties Chicken</td>\n",
       "      <td>2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>270</td>\n",
       "      <td>Twisties</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "34  2019-05-18          9            9208    8634        15   \n",
       "79  2018-08-24         39           39167   35639       113   \n",
       "125 2019-05-06         54           54305   48304        15   \n",
       "132 2019-05-20         55           55073   48887       113   \n",
       "209 2019-01-01         80           80182   78980        15   \n",
       "252 2018-11-18         84           84096   83672       113   \n",
       "253 2019-05-20         88           88320   87811       113   \n",
       "278 2018-07-11        102          102115  102207        15   \n",
       "347 2019-04-05        118          118007  120735       113   \n",
       "350 2018-07-01        119          119051  122360       113   \n",
       "\n",
       "                 PROD_NAME  PROD_QTY  TOT_SALES  PACK_SIZE     BRAND  \\\n",
       "34   Twisties Cheese               2        9.2        270  Twisties   \n",
       "79       Twisties Chicken          2        9.2        270  Twisties   \n",
       "125  Twisties Cheese               2        9.2        270  Twisties   \n",
       "132      Twisties Chicken          1        4.6        270  Twisties   \n",
       "209  Twisties Cheese               2        9.2        270  Twisties   \n",
       "252      Twisties Chicken          2        9.2        270  Twisties   \n",
       "253      Twisties Chicken          2        9.2        270  Twisties   \n",
       "278  Twisties Cheese               2        9.2        270  Twisties   \n",
       "347      Twisties Chicken          2        9.2        270  Twisties   \n",
       "350      Twisties Chicken          2        9.2        270  Twisties   \n",
       "\n",
       "                  LIFESTAGE PREMIUM_CUSTOMER  PRICE  \n",
       "34   MIDAGE SINGLES/COUPLES           Budget    4.6  \n",
       "79   MIDAGE SINGLES/COUPLES           Budget    4.6  \n",
       "125  MIDAGE SINGLES/COUPLES           Budget    4.6  \n",
       "132  MIDAGE SINGLES/COUPLES           Budget    4.6  \n",
       "209  MIDAGE SINGLES/COUPLES           Budget    4.6  \n",
       "252  MIDAGE SINGLES/COUPLES           Budget    4.6  \n",
       "253  MIDAGE SINGLES/COUPLES           Budget    4.6  \n",
       "278  MIDAGE SINGLES/COUPLES           Budget    4.6  \n",
       "347  MIDAGE SINGLES/COUPLES           Budget    4.6  \n",
       "350  MIDAGE SINGLES/COUPLES           Budget    4.6  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Which brand offers 270g pack size?\n",
    "\n",
    "combineData.loc[combineData['PACK_SIZE'] == 270, :].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Twisties'], dtype=object)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Is Twisties the only brand who sells 270g pack size?\n",
    "\n",
    "combineData.loc[combineData['PACK_SIZE'] == 270, 'BRAND'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Twisties is the only brand that offers 270g pack size."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "- Sales are highest for (Budget, OLDER FAMILIES), (Mainstream, YOUNG SINGLES/COUPLES) and (Mainstream, RETIREES)\n",
    "- We found that (Mainstream, YOUNG SINGLES/COUPLES) and (Mainstream, RETIREES) are mainly due to the fact that there are more customers in these segments\n",
    "- (Mainstream, YOUNG SINGLES/COUPLES) are more likely to pay more per packet of chips than their premium and budget counterparts\n",
    "- They are also more likely to purchase 'Tyrrells' and '270g' pack sizes than the rest of the population"
   ]
  }
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