QVI_task1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantium Virtual Internship - Retail Strategy and Analytics - Task 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading required libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import datetime \n",
    "import xlrd\n",
    "import re\n",
    "from mlxtend.frequent_patterns import apriori\n",
    "from mlxtend.frequent_patterns import association_rules\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read data files into data frames \n",
    "customerdata = pd.read_csv('QVI_purchase_behaviour.csv')\n",
    "transactiondata = pd.read_excel('QVI_transaction_data.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "\n",
    "First, we want to examine the data and make sure that it is in a usable form for our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>43390</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Natural Chip        Compny SeaSalt175g</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43599</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>348</td>\n",
       "      <td>66</td>\n",
       "      <td>CCs Nacho Cheese    175g</td>\n",
       "      <td>3</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43605</td>\n",
       "      <td>1</td>\n",
       "      <td>1343</td>\n",
       "      <td>383</td>\n",
       "      <td>61</td>\n",
       "      <td>Smiths Crinkle Cut  Chips Chicken 170g</td>\n",
       "      <td>2</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43329</td>\n",
       "      <td>2</td>\n",
       "      <td>2373</td>\n",
       "      <td>974</td>\n",
       "      <td>69</td>\n",
       "      <td>Smiths Chip Thinly  S/Cream&amp;Onion 175g</td>\n",
       "      <td>5</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43330</td>\n",
       "      <td>2</td>\n",
       "      <td>2426</td>\n",
       "      <td>1038</td>\n",
       "      <td>108</td>\n",
       "      <td>Kettle Tortilla ChpsHny&amp;Jlpno Chili 150g</td>\n",
       "      <td>3</td>\n",
       "      <td>13.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264831</th>\n",
       "      <td>43533</td>\n",
       "      <td>272</td>\n",
       "      <td>272319</td>\n",
       "      <td>270088</td>\n",
       "      <td>89</td>\n",
       "      <td>Kettle Sweet Chilli And Sour Cream 175g</td>\n",
       "      <td>2</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264832</th>\n",
       "      <td>43325</td>\n",
       "      <td>272</td>\n",
       "      <td>272358</td>\n",
       "      <td>270154</td>\n",
       "      <td>74</td>\n",
       "      <td>Tostitos Splash Of  Lime 175g</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264833</th>\n",
       "      <td>43410</td>\n",
       "      <td>272</td>\n",
       "      <td>272379</td>\n",
       "      <td>270187</td>\n",
       "      <td>51</td>\n",
       "      <td>Doritos Mexicana    170g</td>\n",
       "      <td>2</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264834</th>\n",
       "      <td>43461</td>\n",
       "      <td>272</td>\n",
       "      <td>272379</td>\n",
       "      <td>270188</td>\n",
       "      <td>42</td>\n",
       "      <td>Doritos Corn Chip Mexican Jalapeno 150g</td>\n",
       "      <td>2</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264835</th>\n",
       "      <td>43365</td>\n",
       "      <td>272</td>\n",
       "      <td>272380</td>\n",
       "      <td>270189</td>\n",
       "      <td>74</td>\n",
       "      <td>Tostitos Splash Of  Lime 175g</td>\n",
       "      <td>2</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>264836 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "0       43390          1            1000       1         5   \n",
       "1       43599          1            1307     348        66   \n",
       "2       43605          1            1343     383        61   \n",
       "3       43329          2            2373     974        69   \n",
       "4       43330          2            2426    1038       108   \n",
       "...       ...        ...             ...     ...       ...   \n",
       "264831  43533        272          272319  270088        89   \n",
       "264832  43325        272          272358  270154        74   \n",
       "264833  43410        272          272379  270187        51   \n",
       "264834  43461        272          272379  270188        42   \n",
       "264835  43365        272          272380  270189        74   \n",
       "\n",
       "                                       PROD_NAME  PROD_QTY  TOT_SALES  \n",
       "0         Natural Chip        Compny SeaSalt175g         2        6.0  \n",
       "1                       CCs Nacho Cheese    175g         3        6.3  \n",
       "2         Smiths Crinkle Cut  Chips Chicken 170g         2        2.9  \n",
       "3         Smiths Chip Thinly  S/Cream&Onion 175g         5       15.0  \n",
       "4       Kettle Tortilla ChpsHny&Jlpno Chili 150g         3       13.8  \n",
       "...                                          ...       ...        ...  \n",
       "264831   Kettle Sweet Chilli And Sour Cream 175g         2       10.8  \n",
       "264832             Tostitos Splash Of  Lime 175g         1        4.4  \n",
       "264833                  Doritos Mexicana    170g         2        8.8  \n",
       "264834   Doritos Corn Chip Mexican Jalapeno 150g         2        7.8  \n",
       "264835             Tostitos Splash Of  Lime 175g         2        8.8  \n",
       "\n",
       "[264836 rows x 8 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examining the transaction data - view a summary of the table\n",
    "trans_df = transactiondata.copy() # Keep a copy for a quick reset \n",
    "trans_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the date is in an integer format; change to DD/MM/YYYY format. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datetime64[ns]\n"
     ]
    }
   ],
   "source": [
    "# Change date from xls integer dates to date format in customer data\n",
    "trans_df['DATE'] = pd.to_datetime(trans_df['DATE'], unit='D', origin='1899-12-30')\n",
    "print(trans_df['DATE'].dtype) # check format of replacement date column "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we want to ensure that we are only examining chip purchases. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Natural Chip        Compny SeaSalt175g',\n",
       "       'CCs Nacho Cheese    175g',\n",
       "       'Smiths Crinkle Cut  Chips Chicken 170g',\n",
       "       'Smiths Chip Thinly  S/Cream&Onion 175g',\n",
       "       'Kettle Tortilla ChpsHny&Jlpno Chili 150g',\n",
       "       'Old El Paso Salsa   Dip Tomato Mild 300g',\n",
       "       'Smiths Crinkle Chips Salt & Vinegar 330g',\n",
       "       'Grain Waves         Sweet Chilli 210g',\n",
       "       'Doritos Corn Chip Mexican Jalapeno 150g',\n",
       "       'Grain Waves Sour    Cream&Chives 210G',\n",
       "       'Kettle Sensations   Siracha Lime 150g',\n",
       "       'Twisties Cheese     270g', 'WW Crinkle Cut      Chicken 175g',\n",
       "       'Thins Chips Light&  Tangy 175g', 'CCs Original 175g',\n",
       "       'Burger Rings 220g', 'NCC Sour Cream &    Garden Chives 175g',\n",
       "       'Doritos Corn Chip Southern Chicken 150g',\n",
       "       'Cheezels Cheese Box 125g', 'Smiths Crinkle      Original 330g',\n",
       "       'Infzns Crn Crnchers Tangy Gcamole 110g',\n",
       "       'Kettle Sea Salt     And Vinegar 175g',\n",
       "       'Smiths Chip Thinly  Cut Original 175g', 'Kettle Original 175g',\n",
       "       'Red Rock Deli Thai  Chilli&Lime 150g',\n",
       "       'Pringles Sthrn FriedChicken 134g', 'Pringles Sweet&Spcy BBQ 134g',\n",
       "       'Red Rock Deli SR    Salsa & Mzzrlla 150g',\n",
       "       'Thins Chips         Originl saltd 175g',\n",
       "       'Red Rock Deli Sp    Salt & Truffle 150G',\n",
       "       'Smiths Thinly       Swt Chli&S/Cream175G', 'Kettle Chilli 175g',\n",
       "       'Doritos Mexicana    170g',\n",
       "       'Smiths Crinkle Cut  French OnionDip 150g',\n",
       "       'Natural ChipCo      Hony Soy Chckn175g',\n",
       "       'Dorito Corn Chp     Supreme 380g', 'Twisties Chicken270g',\n",
       "       'Smiths Thinly Cut   Roast Chicken 175g',\n",
       "       'Smiths Crinkle Cut  Tomato Salsa 150g',\n",
       "       'Kettle Mozzarella   Basil & Pesto 175g',\n",
       "       'Infuzions Thai SweetChili PotatoMix 110g',\n",
       "       'Kettle Sensations   Camembert & Fig 150g',\n",
       "       'Smith Crinkle Cut   Mac N Cheese 150g',\n",
       "       'Kettle Honey Soy    Chicken 175g',\n",
       "       'Thins Chips Seasonedchicken 175g',\n",
       "       'Smiths Crinkle Cut  Salt & Vinegar 170g',\n",
       "       'Infuzions BBQ Rib   Prawn Crackers 110g',\n",
       "       'GrnWves Plus Btroot & Chilli Jam 180g',\n",
       "       'Tyrrells Crisps     Lightly Salted 165g',\n",
       "       'Kettle Sweet Chilli And Sour Cream 175g',\n",
       "       'Doritos Salsa       Medium 300g', 'Kettle 135g Swt Pot Sea Salt',\n",
       "       'Pringles SourCream  Onion 134g',\n",
       "       'Doritos Corn Chips  Original 170g',\n",
       "       'Twisties Cheese     Burger 250g',\n",
       "       'Old El Paso Salsa   Dip Chnky Tom Ht300g',\n",
       "       'Cobs Popd Swt/Chlli &Sr/Cream Chips 110g',\n",
       "       'Woolworths Mild     Salsa 300g',\n",
       "       'Natural Chip Co     Tmato Hrb&Spce 175g',\n",
       "       'Smiths Crinkle Cut  Chips Original 170g',\n",
       "       'Cobs Popd Sea Salt  Chips 110g',\n",
       "       'Smiths Crinkle Cut  Chips Chs&Onion170g',\n",
       "       'French Fries Potato Chips 175g',\n",
       "       'Old El Paso Salsa   Dip Tomato Med 300g',\n",
       "       'Doritos Corn Chips  Cheese Supreme 170g',\n",
       "       'Pringles Original   Crisps 134g',\n",
       "       'RRD Chilli&         Coconut 150g',\n",
       "       'WW Original Corn    Chips 200g',\n",
       "       'Thins Potato Chips  Hot & Spicy 175g',\n",
       "       'Cobs Popd Sour Crm  &Chives Chips 110g',\n",
       "       'Smiths Crnkle Chip  Orgnl Big Bag 380g',\n",
       "       'Doritos Corn Chips  Nacho Cheese 170g',\n",
       "       'Kettle Sensations   BBQ&Maple 150g',\n",
       "       'WW D/Style Chip     Sea Salt 200g',\n",
       "       'Pringles Chicken    Salt Crips 134g',\n",
       "       'WW Original Stacked Chips 160g',\n",
       "       'Smiths Chip Thinly  CutSalt/Vinegr175g', 'Cheezels Cheese 330g',\n",
       "       'Tostitos Lightly    Salted 175g',\n",
       "       'Thins Chips Salt &  Vinegar 175g',\n",
       "       'Smiths Crinkle Cut  Chips Barbecue 170g', 'Cheetos Puffs 165g',\n",
       "       'RRD Sweet Chilli &  Sour Cream 165g',\n",
       "       'WW Crinkle Cut      Original 175g',\n",
       "       'Tostitos Splash Of  Lime 175g', 'Woolworths Medium   Salsa 300g',\n",
       "       'Kettle Tortilla ChpsBtroot&Ricotta 150g',\n",
       "       'CCs Tasty Cheese    175g', 'Woolworths Cheese   Rings 190g',\n",
       "       'Tostitos Smoked     Chipotle 175g', 'Pringles Barbeque   134g',\n",
       "       'WW Supreme Cheese   Corn Chips 200g',\n",
       "       'Pringles Mystery    Flavour 134g',\n",
       "       'Tyrrells Crisps     Ched & Chives 165g',\n",
       "       'Snbts Whlgrn Crisps Cheddr&Mstrd 90g',\n",
       "       'Cheetos Chs & Bacon Balls 190g', 'Pringles Slt Vingar 134g',\n",
       "       'Infuzions SourCream&Herbs Veg Strws 110g',\n",
       "       'Kettle Tortilla ChpsFeta&Garlic 150g',\n",
       "       'Infuzions Mango     Chutny Papadums 70g',\n",
       "       'RRD Steak &         Chimuchurri 150g',\n",
       "       'RRD Honey Soy       Chicken 165g',\n",
       "       'Sunbites Whlegrn    Crisps Frch/Onin 90g',\n",
       "       'RRD Salt & Vinegar  165g', 'Doritos Cheese      Supreme 330g',\n",
       "       'Smiths Crinkle Cut  Snag&Sauce 150g',\n",
       "       'WW Sour Cream &OnionStacked Chips 160g',\n",
       "       'RRD Lime & Pepper   165g',\n",
       "       'Natural ChipCo Sea  Salt & Vinegr 175g',\n",
       "       'Red Rock Deli Chikn&Garlic Aioli 150g',\n",
       "       'RRD SR Slow Rst     Pork Belly 150g', 'RRD Pc Sea Salt     165g',\n",
       "       'Smith Crinkle Cut   Bolognese 150g', 'Doritos Salsa Mild  300g'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View all unique entries in the product name column \n",
    "trans_df['PROD_NAME'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While it looks like we have chips, we want to check that the products are only chips by counting the word frequencies in the product names. To make this process clearer, we can remove the digits and symbols from the names. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove digits from the product names\n",
    "prod_name = trans_df['PROD_NAME'].str.replace(r'[0-9]+[gG]','');\n",
    "\n",
    "# Remove & characters from the product names and replace with a space to separate flavours\n",
    "prod_name = prod_name.str.replace(r'&',' ');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chips              49770\n",
       "Kettle             41288\n",
       "Smiths             28860\n",
       "Salt               27976\n",
       "Cheese             27890\n",
       "Pringles           25102\n",
       "Doritos            24962\n",
       "Crinkle            23960\n",
       "Corn               22063\n",
       "Original           21560\n",
       "Cut                20754\n",
       "Chip               18645\n",
       "Chicken            18577\n",
       "Salsa              18094\n",
       "Chilli             15390\n",
       "Sea                14145\n",
       "Thins              14075\n",
       "Sour               13882\n",
       "Crisps             12607\n",
       "Vinegar            12402\n",
       "RRD                11894\n",
       "Sweet              11060\n",
       "Infuzions          11057\n",
       "Supreme            10963\n",
       "Chives             10951\n",
       "Cream              10723\n",
       "WW                 10320\n",
       "Cobs                9693\n",
       "Popd                9693\n",
       "Tortilla            9580\n",
       "Tostitos            9471\n",
       "Twisties            9454\n",
       "BBQ                 9434\n",
       "Sensations          9429\n",
       "Lime                9347\n",
       "Dip                 9324\n",
       "Paso                9324\n",
       "Old                 9324\n",
       "El                  9324\n",
       "Tomato              7669\n",
       "Thinly              7507\n",
       "Tyrrells            6442\n",
       "And                 6373\n",
       "Tangy               6332\n",
       "SourCream           6296\n",
       "Waves               6272\n",
       "Grain               6272\n",
       "Salted              6248\n",
       "Lightly             6248\n",
       "Soy                 6121\n",
       "Onion               6116\n",
       "Natural             6050\n",
       "Mild                6048\n",
       "Rock                5885\n",
       "Red                 5885\n",
       "Deli                5885\n",
       "Thai                4737\n",
       "Burger              4733\n",
       "Swt                 4718\n",
       "Honey               4661\n",
       "Nacho               4658\n",
       "Potato              4647\n",
       "Cheezels            4603\n",
       "Garlic              4572\n",
       "CCs                 4551\n",
       "Woolworths          4437\n",
       "Pesto               3304\n",
       "Mozzarella          3304\n",
       "Basil               3304\n",
       "ChpsHny             3296\n",
       "Jlpno               3296\n",
       "Chili               3296\n",
       "Swt/Chlli           3269\n",
       "Sr/Cream            3269\n",
       "Ched                3268\n",
       "Pot                 3257\n",
       "Of                  3252\n",
       "Splash              3252\n",
       "SweetChili          3242\n",
       "PotatoMix           3242\n",
       "Bag                 3233\n",
       "Crnkle              3233\n",
       "Big                 3233\n",
       "Orgnl               3233\n",
       "Hot                 3229\n",
       "Spicy               3229\n",
       "Camembert           3219\n",
       "Fig                 3219\n",
       "Barbeque            3210\n",
       "Jalapeno            3204\n",
       "Mexican             3204\n",
       "Light               3188\n",
       "Chp                 3185\n",
       "Dorito              3185\n",
       "Spcy                3177\n",
       "Rib                 3174\n",
       "Crackers            3174\n",
       "Prawn               3174\n",
       "Southern            3172\n",
       "Crm                 3159\n",
       "Ricotta             3146\n",
       "ChpsBtroot          3146\n",
       "Chipotle            3145\n",
       "Smoked              3145\n",
       "Crnchers            3144\n",
       "Gcamole             3144\n",
       "Crn                 3144\n",
       "Infzns              3144\n",
       "ChpsFeta            3138\n",
       "Herbs               3134\n",
       "Strws               3134\n",
       "Veg                 3134\n",
       "Siracha             3127\n",
       "Chnky               3125\n",
       "Ht                  3125\n",
       "Tom                 3125\n",
       "Mexicana            3115\n",
       "Mystery             3114\n",
       "Seasonedchicken     3114\n",
       "Med                 3114\n",
       "Flavour             3114\n",
       "Crips               3104\n",
       "Vingar              3095\n",
       "Slt                 3095\n",
       "Sthrn               3083\n",
       "FriedChicken        3083\n",
       "Maple               3083\n",
       "Rings               3080\n",
       "ChipCo              3010\n",
       "SR                  2984\n",
       "Smith               2963\n",
       "Chs                 2960\n",
       "S/Cream             2934\n",
       "Cheetos             2927\n",
       "Medium              2879\n",
       "French              2856\n",
       "Cheddr              1576\n",
       "Snbts               1576\n",
       "Whlgrn              1576\n",
       "Mstrd               1576\n",
       "Hrb                 1572\n",
       "Tmato               1572\n",
       "Co                  1572\n",
       "Spce                1572\n",
       "Vinegr              1550\n",
       "Tasty               1539\n",
       "Slow                1526\n",
       "Belly               1526\n",
       "Rst                 1526\n",
       "Pork                1526\n",
       "Roast               1519\n",
       "Mac                 1512\n",
       "N                   1512\n",
       "Mango               1507\n",
       "Papadums            1507\n",
       "Chutny              1507\n",
       "Coconut             1506\n",
       "Sauce               1503\n",
       "Snag                1503\n",
       "Truffle             1498\n",
       "Sp                  1498\n",
       "Barbecue            1489\n",
       "Stacked             1487\n",
       "OnionStacked        1483\n",
       "Balls               1479\n",
       "Bacon               1479\n",
       "Pepper              1473\n",
       "D/Style             1469\n",
       "SeaSalt             1468\n",
       "Btroot              1468\n",
       "Jam                 1468\n",
       "Plus                1468\n",
       "Compny              1468\n",
       "GrnWves             1468\n",
       "Chli                1461\n",
       "Hony                1460\n",
       "Chckn               1460\n",
       "Mzzrlla             1458\n",
       "Chimuchurri         1455\n",
       "Steak               1455\n",
       "Box                 1454\n",
       "Bolognese           1451\n",
       "Puffs               1448\n",
       "saltd               1441\n",
       "Originl             1441\n",
       "CutSalt/Vinegr      1440\n",
       "OnionDip            1438\n",
       "Aioli               1434\n",
       "Chikn               1434\n",
       "Frch/Onin           1432\n",
       "Sunbites            1432\n",
       "Whlegrn             1432\n",
       "Pc                  1431\n",
       "NCC                 1419\n",
       "Garden              1419\n",
       "Fries               1418\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the frequencies of words in product names and display counts in descending order\n",
    "word_counts = pd.Series(' '.join(prod_name).split()).value_counts()\n",
    "\n",
    "with pd.option_context('display.max_rows', None): # show all rows\n",
    "  display(word_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some entries in our data are salsas; we want to remove these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(246742, 8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove salsas from the dataset\n",
    "trans_df = trans_df[trans_df['PROD_NAME'].str.contains(r\"[Ss]alsa\") == False]\n",
    "trans_df.shape # check for a reduction in no of rows "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can create summaries of the data (eg min, max, mean) to see if there are any obvious outliers in the data and if there are any nulls in any of the columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>246742.000000</td>\n",
       "      <td>2.467420e+05</td>\n",
       "      <td>2.467420e+05</td>\n",
       "      <td>246742.000000</td>\n",
       "      <td>246742.000000</td>\n",
       "      <td>246742.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>135.051098</td>\n",
       "      <td>1.355310e+05</td>\n",
       "      <td>1.351311e+05</td>\n",
       "      <td>56.351789</td>\n",
       "      <td>1.908062</td>\n",
       "      <td>7.321322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>76.787096</td>\n",
       "      <td>8.071528e+04</td>\n",
       "      <td>7.814772e+04</td>\n",
       "      <td>33.695428</td>\n",
       "      <td>0.659831</td>\n",
       "      <td>3.077828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>7.001500e+04</td>\n",
       "      <td>6.756925e+04</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>130.000000</td>\n",
       "      <td>1.303670e+05</td>\n",
       "      <td>1.351830e+05</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>203.000000</td>\n",
       "      <td>2.030840e+05</td>\n",
       "      <td>2.026538e+05</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>272.000000</td>\n",
       "      <td>2.373711e+06</td>\n",
       "      <td>2.415841e+06</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>650.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           STORE_NBR  LYLTY_CARD_NBR        TXN_ID       PROD_NBR  \\\n",
       "count  246742.000000    2.467420e+05  2.467420e+05  246742.000000   \n",
       "mean      135.051098    1.355310e+05  1.351311e+05      56.351789   \n",
       "std        76.787096    8.071528e+04  7.814772e+04      33.695428   \n",
       "min         1.000000    1.000000e+03  1.000000e+00       1.000000   \n",
       "25%        70.000000    7.001500e+04  6.756925e+04      26.000000   \n",
       "50%       130.000000    1.303670e+05  1.351830e+05      53.000000   \n",
       "75%       203.000000    2.030840e+05  2.026538e+05      87.000000   \n",
       "max       272.000000    2.373711e+06  2.415841e+06     114.000000   \n",
       "\n",
       "            PROD_QTY      TOT_SALES  \n",
       "count  246742.000000  246742.000000  \n",
       "mean        1.908062       7.321322  \n",
       "std         0.659831       3.077828  \n",
       "min         1.000000       1.700000  \n",
       "25%         2.000000       5.800000  \n",
       "50%         2.000000       7.400000  \n",
       "75%         2.000000       8.800000  \n",
       "max       200.000000     650.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create summaries of the transaction data\n",
    "trans_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if there are any nans in the dataset \n",
    "trans_df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the summary, there is at least one transaction with 200 packets. Let's investigate this purchase further. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69762</th>\n",
       "      <td>2018-08-19</td>\n",
       "      <td>226</td>\n",
       "      <td>226000</td>\n",
       "      <td>226201</td>\n",
       "      <td>4</td>\n",
       "      <td>Dorito Corn Chp     Supreme 380g</td>\n",
       "      <td>200</td>\n",
       "      <td>650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69763</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>226</td>\n",
       "      <td>226000</td>\n",
       "      <td>226210</td>\n",
       "      <td>4</td>\n",
       "      <td>Dorito Corn Chp     Supreme 380g</td>\n",
       "      <td>200</td>\n",
       "      <td>650.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "69762 2018-08-19        226          226000  226201         4   \n",
       "69763 2019-05-20        226          226000  226210         4   \n",
       "\n",
       "                              PROD_NAME  PROD_QTY  TOT_SALES  \n",
       "69762  Dorito Corn Chp     Supreme 380g       200      650.0  \n",
       "69763  Dorito Corn Chp     Supreme 380g       200      650.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter the entries that have 200 packets. \n",
    "trans_df.loc[trans_df['PROD_QTY'] == 200.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same customer has made these transactions. They could have been for commercial purposes so we can check to see if they made any other purchases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69762</th>\n",
       "      <td>2018-08-19</td>\n",
       "      <td>226</td>\n",
       "      <td>226000</td>\n",
       "      <td>226201</td>\n",
       "      <td>4</td>\n",
       "      <td>Dorito Corn Chp     Supreme 380g</td>\n",
       "      <td>200</td>\n",
       "      <td>650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69763</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>226</td>\n",
       "      <td>226000</td>\n",
       "      <td>226210</td>\n",
       "      <td>4</td>\n",
       "      <td>Dorito Corn Chp     Supreme 380g</td>\n",
       "      <td>200</td>\n",
       "      <td>650.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "69762 2018-08-19        226          226000  226201         4   \n",
       "69763 2019-05-20        226          226000  226210         4   \n",
       "\n",
       "                              PROD_NAME  PROD_QTY  TOT_SALES  \n",
       "69762  Dorito Corn Chp     Supreme 380g       200      650.0  \n",
       "69763  Dorito Corn Chp     Supreme 380g       200      650.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter the entires by the customer\n",
    "trans_df.loc[trans_df['LYLTY_CARD_NBR'] == 226000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like this is the only purchase they have made so we will remove these transactions from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(246740, 8)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the transactions\n",
    "trans_df = trans_df[trans_df['LYLTY_CARD_NBR'] != 226000]\n",
    "trans_df.shape # check for a reduction of 2 rows (i.e. 246740 rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>246740.000000</td>\n",
       "      <td>2.467400e+05</td>\n",
       "      <td>2.467400e+05</td>\n",
       "      <td>246740.000000</td>\n",
       "      <td>246740.000000</td>\n",
       "      <td>246740.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>135.050361</td>\n",
       "      <td>1.355303e+05</td>\n",
       "      <td>1.351304e+05</td>\n",
       "      <td>56.352213</td>\n",
       "      <td>1.906456</td>\n",
       "      <td>7.316113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>76.786971</td>\n",
       "      <td>8.071520e+04</td>\n",
       "      <td>7.814760e+04</td>\n",
       "      <td>33.695235</td>\n",
       "      <td>0.342499</td>\n",
       "      <td>2.474897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>7.001500e+04</td>\n",
       "      <td>6.756875e+04</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>130.000000</td>\n",
       "      <td>1.303670e+05</td>\n",
       "      <td>1.351815e+05</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>203.000000</td>\n",
       "      <td>2.030832e+05</td>\n",
       "      <td>2.026522e+05</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>272.000000</td>\n",
       "      <td>2.373711e+06</td>\n",
       "      <td>2.415841e+06</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>29.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           STORE_NBR  LYLTY_CARD_NBR        TXN_ID       PROD_NBR  \\\n",
       "count  246740.000000    2.467400e+05  2.467400e+05  246740.000000   \n",
       "mean      135.050361    1.355303e+05  1.351304e+05      56.352213   \n",
       "std        76.786971    8.071520e+04  7.814760e+04      33.695235   \n",
       "min         1.000000    1.000000e+03  1.000000e+00       1.000000   \n",
       "25%        70.000000    7.001500e+04  6.756875e+04      26.000000   \n",
       "50%       130.000000    1.303670e+05  1.351815e+05      53.000000   \n",
       "75%       203.000000    2.030832e+05  2.026522e+05      87.000000   \n",
       "max       272.000000    2.373711e+06  2.415841e+06     114.000000   \n",
       "\n",
       "            PROD_QTY      TOT_SALES  \n",
       "count  246740.000000  246740.000000  \n",
       "mean        1.906456       7.316113  \n",
       "std         0.342499       2.474897  \n",
       "min         1.000000       1.700000  \n",
       "25%         2.000000       5.800000  \n",
       "50%         2.000000       7.400000  \n",
       "75%         2.000000       8.800000  \n",
       "max         5.000000      29.500000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recheck the data summary \n",
    "trans_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The summaries now look reasonable. Now look at the number of transaction lines over time to see if there are any obvious data issues such as missing data from particular days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(364, 2)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count transactions by date to see if there are any missing days \n",
    "count = trans_df.groupby(trans_df['DATE'].dt.date).size().reset_index(name = 'COUNT')\n",
    "count.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9161</th>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>88</td>\n",
       "      <td>88140</td>\n",
       "      <td>86914</td>\n",
       "      <td>25</td>\n",
       "      <td>Pringles SourCream  Onion 134g</td>\n",
       "      <td>2</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155442</th>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>60</td>\n",
       "      <td>60276</td>\n",
       "      <td>57330</td>\n",
       "      <td>3</td>\n",
       "      <td>Kettle Sensations   Camembert &amp; Fig 150g</td>\n",
       "      <td>2</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181349</th>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>199</td>\n",
       "      <td>199014</td>\n",
       "      <td>197623</td>\n",
       "      <td>104</td>\n",
       "      <td>Infuzions Thai SweetChili PotatoMix 110g</td>\n",
       "      <td>2</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229948</th>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>35</td>\n",
       "      <td>35052</td>\n",
       "      <td>31630</td>\n",
       "      <td>11</td>\n",
       "      <td>RRD Pc Sea Salt     165g</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104647</th>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>72</td>\n",
       "      <td>72104</td>\n",
       "      <td>71038</td>\n",
       "      <td>20</td>\n",
       "      <td>Doritos Cheese      Supreme 330g</td>\n",
       "      <td>2</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10254</th>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>112</td>\n",
       "      <td>112141</td>\n",
       "      <td>114611</td>\n",
       "      <td>98</td>\n",
       "      <td>NCC Sour Cream &amp;    Garden Chives 175g</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113220</th>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>207</td>\n",
       "      <td>207155</td>\n",
       "      <td>205513</td>\n",
       "      <td>99</td>\n",
       "      <td>Pringles Sthrn FriedChicken 134g</td>\n",
       "      <td>2</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229182</th>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>10</td>\n",
       "      <td>10140</td>\n",
       "      <td>9882</td>\n",
       "      <td>12</td>\n",
       "      <td>Natural Chip Co     Tmato Hrb&amp;Spce 175g</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229015</th>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>6</td>\n",
       "      <td>6258</td>\n",
       "      <td>6047</td>\n",
       "      <td>29</td>\n",
       "      <td>French Fries Potato Chips 175g</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262768</th>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>183</td>\n",
       "      <td>183196</td>\n",
       "      <td>185975</td>\n",
       "      <td>22</td>\n",
       "      <td>Thins Chips         Originl saltd 175g</td>\n",
       "      <td>2</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>246740 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "9161   2018-07-01         88           88140   86914        25   \n",
       "155442 2018-07-01         60           60276   57330         3   \n",
       "181349 2018-07-01        199          199014  197623       104   \n",
       "229948 2018-07-01         35           35052   31630        11   \n",
       "104647 2018-07-01         72           72104   71038        20   \n",
       "...           ...        ...             ...     ...       ...   \n",
       "10254  2019-06-30        112          112141  114611        98   \n",
       "113220 2019-06-30        207          207155  205513        99   \n",
       "229182 2019-06-30         10           10140    9882        12   \n",
       "229015 2019-06-30          6            6258    6047        29   \n",
       "262768 2019-06-30        183          183196  185975        22   \n",
       "\n",
       "                                       PROD_NAME  PROD_QTY  TOT_SALES  \n",
       "9161              Pringles SourCream  Onion 134g         2        7.4  \n",
       "155442  Kettle Sensations   Camembert & Fig 150g         2        9.2  \n",
       "181349  Infuzions Thai SweetChili PotatoMix 110g         2        7.6  \n",
       "229948                  RRD Pc Sea Salt     165g         1        3.0  \n",
       "104647          Doritos Cheese      Supreme 330g         2       11.4  \n",
       "...                                          ...       ...        ...  \n",
       "10254     NCC Sour Cream &    Garden Chives 175g         2        6.0  \n",
       "113220          Pringles Sthrn FriedChicken 134g         2        7.4  \n",
       "229182   Natural Chip Co     Tmato Hrb&Spce 175g         2        6.0  \n",
       "229015            French Fries Potato Chips 175g         1        3.0  \n",
       "262768    Thins Chips         Originl saltd 175g         2        6.6  \n",
       "\n",
       "[246740 rows x 8 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There is one day of data missing. First check the range of dates by sorting in time order. \n",
    "trans_df.sort_values(by='DATE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the dates range from 1 Jul 2018 to 30 Jun 2019. Now we want to check through the year of dates to see which day the data is missing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-12-25'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a list of dates with transactions in ascending order \n",
    "date_counts = trans_df.groupby('DATE').size()\n",
    "\n",
    "# Then compare to a full list of dates within the same range to find differences between them \n",
    "pd.date_range(start = '2018-07-01', end = '2019-06-30' ).difference(date_counts.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The missing date is Christmas day, a public holiday, so it is expected that there are no sales on this day. Now we move onto creating other features such as the pack size, and checking this for any outliers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40783</th>\n",
       "      <td>2018-09-25</td>\n",
       "      <td>97</td>\n",
       "      <td>97067</td>\n",
       "      <td>96696</td>\n",
       "      <td>38</td>\n",
       "      <td>Infuzions Mango     Chutny Papadums 70g</td>\n",
       "      <td>2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42461</th>\n",
       "      <td>2019-05-05</td>\n",
       "      <td>110</td>\n",
       "      <td>110030</td>\n",
       "      <td>111890</td>\n",
       "      <td>38</td>\n",
       "      <td>Infuzions Mango     Chutny Papadums 70g</td>\n",
       "      <td>2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176183</th>\n",
       "      <td>2018-12-30</td>\n",
       "      <td>82</td>\n",
       "      <td>82183</td>\n",
       "      <td>81660</td>\n",
       "      <td>38</td>\n",
       "      <td>Infuzions Mango     Chutny Papadums 70g</td>\n",
       "      <td>2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227309</th>\n",
       "      <td>2018-12-03</td>\n",
       "      <td>236</td>\n",
       "      <td>236091</td>\n",
       "      <td>239098</td>\n",
       "      <td>38</td>\n",
       "      <td>Infuzions Mango     Chutny Papadums 70g</td>\n",
       "      <td>2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42418</th>\n",
       "      <td>2018-11-05</td>\n",
       "      <td>109</td>\n",
       "      <td>109217</td>\n",
       "      <td>111470</td>\n",
       "      <td>38</td>\n",
       "      <td>Infuzions Mango     Chutny Papadums 70g</td>\n",
       "      <td>2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192034</th>\n",
       "      <td>2019-03-12</td>\n",
       "      <td>100</td>\n",
       "      <td>100121</td>\n",
       "      <td>99145</td>\n",
       "      <td>4</td>\n",
       "      <td>Dorito Corn Chp     Supreme 380g</td>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255797</th>\n",
       "      <td>2019-01-19</td>\n",
       "      <td>235</td>\n",
       "      <td>235098</td>\n",
       "      <td>238018</td>\n",
       "      <td>4</td>\n",
       "      <td>Dorito Corn Chp     Supreme 380g</td>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233814</th>\n",
       "      <td>2019-01-24</td>\n",
       "      <td>151</td>\n",
       "      <td>151102</td>\n",
       "      <td>149810</td>\n",
       "      <td>4</td>\n",
       "      <td>Dorito Corn Chp     Supreme 380g</td>\n",
       "      <td>1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131573</th>\n",
       "      <td>2018-07-09</td>\n",
       "      <td>213</td>\n",
       "      <td>213087</td>\n",
       "      <td>212416</td>\n",
       "      <td>4</td>\n",
       "      <td>Dorito Corn Chp     Supreme 380g</td>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102409</th>\n",
       "      <td>2019-05-08</td>\n",
       "      <td>43</td>\n",
       "      <td>43184</td>\n",
       "      <td>39874</td>\n",
       "      <td>4</td>\n",
       "      <td>Dorito Corn Chp     Supreme 380g</td>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>380.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>246740 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "40783  2018-09-25         97           97067   96696        38   \n",
       "42461  2019-05-05        110          110030  111890        38   \n",
       "176183 2018-12-30         82           82183   81660        38   \n",
       "227309 2018-12-03        236          236091  239098        38   \n",
       "42418  2018-11-05        109          109217  111470        38   \n",
       "...           ...        ...             ...     ...       ...   \n",
       "192034 2019-03-12        100          100121   99145         4   \n",
       "255797 2019-01-19        235          235098  238018         4   \n",
       "233814 2019-01-24        151          151102  149810         4   \n",
       "131573 2018-07-09        213          213087  212416         4   \n",
       "102409 2019-05-08         43           43184   39874         4   \n",
       "\n",
       "                                      PROD_NAME  PROD_QTY  TOT_SALES  \\\n",
       "40783   Infuzions Mango     Chutny Papadums 70g         2        4.8   \n",
       "42461   Infuzions Mango     Chutny Papadums 70g         2        4.8   \n",
       "176183  Infuzions Mango     Chutny Papadums 70g         2        4.8   \n",
       "227309  Infuzions Mango     Chutny Papadums 70g         2        4.8   \n",
       "42418   Infuzions Mango     Chutny Papadums 70g         2        4.8   \n",
       "...                                         ...       ...        ...   \n",
       "192034         Dorito Corn Chp     Supreme 380g         2       13.0   \n",
       "255797         Dorito Corn Chp     Supreme 380g         2       13.0   \n",
       "233814         Dorito Corn Chp     Supreme 380g         1        6.5   \n",
       "131573         Dorito Corn Chp     Supreme 380g         2       13.0   \n",
       "102409         Dorito Corn Chp     Supreme 380g         2       13.0   \n",
       "\n",
       "        PACK_SIZE  \n",
       "40783        70.0  \n",
       "42461        70.0  \n",
       "176183       70.0  \n",
       "227309       70.0  \n",
       "42418        70.0  \n",
       "...           ...  \n",
       "192034      380.0  \n",
       "255797      380.0  \n",
       "233814      380.0  \n",
       "131573      380.0  \n",
       "102409      380.0  \n",
       "\n",
       "[246740 rows x 9 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a new column to data with packet sizes and extract sizes from product name column\n",
    "trans_df.insert(8, \"PACK_SIZE\", trans_df['PROD_NAME'].str.extract('(\\d+)').astype(float), True)\n",
    "\n",
    "# Sort by packet sizes to check for outliers \n",
    "trans_df.sort_values(by='PACK_SIZE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Minimum packet size is 70g while max is 380g - this is reasonable. \n",
    "# Plot a histogram to visualise distribution of pack sizes. \n",
    "plt.hist(trans_df['PACK_SIZE'], weights=trans_df['PROD_QTY']);\n",
    "plt.xlabel('Packet size (g)');\n",
    "plt.ylabel('Quantity');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the pack size looks reasonable, we can create the brand names using the first word of each product name. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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_NAME</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 SeaSalt175g</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>175.0</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    175g</td>\n",
       "      <td>3</td>\n",
       "      <td>6.3</td>\n",
       "      <td>175.0</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 170g</td>\n",
       "      <td>2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>170.0</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&amp;Onion 175g</td>\n",
       "      <td>5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>175.0</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&amp;Jlpno Chili 150g</td>\n",
       "      <td>3</td>\n",
       "      <td>13.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>Kettle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264831</th>\n",
       "      <td>2019-03-09</td>\n",
       "      <td>272</td>\n",
       "      <td>272319</td>\n",
       "      <td>270088</td>\n",
       "      <td>89</td>\n",
       "      <td>Kettle Sweet Chilli And Sour Cream 175g</td>\n",
       "      <td>2</td>\n",
       "      <td>10.8</td>\n",
       "      <td>175.0</td>\n",
       "      <td>Kettle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264832</th>\n",
       "      <td>2018-08-13</td>\n",
       "      <td>272</td>\n",
       "      <td>272358</td>\n",
       "      <td>270154</td>\n",
       "      <td>74</td>\n",
       "      <td>Tostitos Splash Of  Lime 175g</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>175.0</td>\n",
       "      <td>Tostitos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264833</th>\n",
       "      <td>2018-11-06</td>\n",
       "      <td>272</td>\n",
       "      <td>272379</td>\n",
       "      <td>270187</td>\n",
       "      <td>51</td>\n",
       "      <td>Doritos Mexicana    170g</td>\n",
       "      <td>2</td>\n",
       "      <td>8.8</td>\n",
       "      <td>170.0</td>\n",
       "      <td>Doritos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264834</th>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>272</td>\n",
       "      <td>272379</td>\n",
       "      <td>270188</td>\n",
       "      <td>42</td>\n",
       "      <td>Doritos Corn Chip Mexican Jalapeno 150g</td>\n",
       "      <td>2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>Doritos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264835</th>\n",
       "      <td>2018-09-22</td>\n",
       "      <td>272</td>\n",
       "      <td>272380</td>\n",
       "      <td>270189</td>\n",
       "      <td>74</td>\n",
       "      <td>Tostitos Splash Of  Lime 175g</td>\n",
       "      <td>2</td>\n",
       "      <td>8.8</td>\n",
       "      <td>175.0</td>\n",
       "      <td>Tostitos</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>246740 rows × 10 columns</p>\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",
       "264831 2019-03-09        272          272319  270088        89   \n",
       "264832 2018-08-13        272          272358  270154        74   \n",
       "264833 2018-11-06        272          272379  270187        51   \n",
       "264834 2018-12-27        272          272379  270188        42   \n",
       "264835 2018-09-22        272          272380  270189        74   \n",
       "\n",
       "                                       PROD_NAME  PROD_QTY  TOT_SALES  \\\n",
       "0         Natural Chip        Compny SeaSalt175g         2        6.0   \n",
       "1                       CCs Nacho Cheese    175g         3        6.3   \n",
       "2         Smiths Crinkle Cut  Chips Chicken 170g         2        2.9   \n",
       "3         Smiths Chip Thinly  S/Cream&Onion 175g         5       15.0   \n",
       "4       Kettle Tortilla ChpsHny&Jlpno Chili 150g         3       13.8   \n",
       "...                                          ...       ...        ...   \n",
       "264831   Kettle Sweet Chilli And Sour Cream 175g         2       10.8   \n",
       "264832             Tostitos Splash Of  Lime 175g         1        4.4   \n",
       "264833                  Doritos Mexicana    170g         2        8.8   \n",
       "264834   Doritos Corn Chip Mexican Jalapeno 150g         2        7.8   \n",
       "264835             Tostitos Splash Of  Lime 175g         2        8.8   \n",
       "\n",
       "        PACK_SIZE BRAND_NAME  \n",
       "0           175.0    Natural  \n",
       "1           175.0        CCs  \n",
       "2           170.0     Smiths  \n",
       "3           175.0     Smiths  \n",
       "4           150.0     Kettle  \n",
       "...           ...        ...  \n",
       "264831      175.0     Kettle  \n",
       "264832      175.0   Tostitos  \n",
       "264833      170.0    Doritos  \n",
       "264834      150.0    Doritos  \n",
       "264835      175.0   Tostitos  \n",
       "\n",
       "[246740 rows x 10 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a column to extract the first word of each product name to. \n",
    "trans_df.insert(9, \"BRAND_NAME\",trans_df['PROD_NAME'].str.split().str.get(0), True)\n",
    "trans_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Then print all unique entries to check the brand names created\n",
    "trans_df[\"BRAND_NAME\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some brand names have been doubled up. Replace all contractions and double ups with their full name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Natural Chip Co', 'CCs', 'Smiths', 'Kettle', 'Grain Waves',\n",
       "       'Doritos', 'Twisties', 'Woolworths', 'Thins', 'Burger', 'Cheezels',\n",
       "       'Infuzions', 'Red Rock Deli', 'Pringles', 'Tyrrells', 'Cobs',\n",
       "       'French', 'Tostitos', 'Cheetos', 'Sunbites'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a function to identify the string replacements needed.\n",
    "def replace_brandname(line):\n",
    "    name = line['BRAND_NAME']\n",
    "    if name == \"Infzns\":\n",
    "        return \"Infuzions\"\n",
    "    elif name == \"Red\":\n",
    "        return \"Red Rock Deli\"\n",
    "    elif name == \"RRD\":\n",
    "        return \"Red Rock Deli\"\n",
    "    elif name == \"Grain\":\n",
    "        return \"Grain Waves\"\n",
    "    elif name == \"GrnWves\":\n",
    "        return \"Grain Waves\"\n",
    "    elif name == \"Snbts\":\n",
    "        return \"Sunbites\"\n",
    "    elif name == \"Natural\":\n",
    "        return \"Natural Chip Co\"\n",
    "    elif name == \"NCC\":\n",
    "        return \"Natural Chip Co\"\n",
    "    elif name == \"WW\":\n",
    "        return \"Woolworths\"\n",
    "    elif name == \"Smith\":\n",
    "        return \"Smiths\"\n",
    "    elif name == \"Dorito\":\n",
    "        return \"Doritos\"\n",
    "    else:\n",
    "        return name \n",
    "\n",
    "# Then apply the function to clean the brand names\n",
    "trans_df[\"BRAND_NAME\"] = trans_df.apply(lambda line: replace_brandname(line), axis=1)\n",
    "\n",
    "# Check that there are no duplicate brands \n",
    "trans_df[\"BRAND_NAME\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The brand names seme reasonable, without duplicates. \n",
    "\n",
    "Now we want to examine the customer data. We can generate summaries and check the categories in this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now examine customer data\n",
    "cust_df = customerdata.copy()\n",
    "cust_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename \"PREMIUM_CUSTOMER\" to \"MEMBER_TYPE\" for easier identification of the column data\n",
    "cust_df = cust_df.rename(columns={'PREMIUM_CUSTOMER': 'MEMBER_TYPE'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>LYLTY_CARD_NBR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.263700e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.361859e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.989293e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.620200e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.340400e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.033750e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.373711e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       LYLTY_CARD_NBR\n",
       "count    7.263700e+04\n",
       "mean     1.361859e+05\n",
       "std      8.989293e+04\n",
       "min      1.000000e+03\n",
       "25%      6.620200e+04\n",
       "50%      1.340400e+05\n",
       "75%      2.033750e+05\n",
       "max      2.373711e+06"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the summary of the customer data\n",
    "cust_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Premium', 'Mainstream', 'Budget'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the entries in the member type and lifestage columns\n",
    "cust_df[\"MEMBER_TYPE\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_df[\"LIFESTAGE\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the customer dataset looks fine, we want to add this information to the transactions dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>LYLTY_CARD_NBR</th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_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_NAME</th>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th>MEMBER_TYPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21037</td>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>21</td>\n",
       "      <td>17576</td>\n",
       "      <td>62</td>\n",
       "      <td>Pringles Mystery    Flavour 134g</td>\n",
       "      <td>2</td>\n",
       "      <td>7.4</td>\n",
       "      <td>134.0</td>\n",
       "      <td>Pringles</td>\n",
       "      <td>RETIREES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25040</td>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>25</td>\n",
       "      <td>21704</td>\n",
       "      <td>87</td>\n",
       "      <td>Infuzions BBQ Rib   Prawn Crackers 110g</td>\n",
       "      <td>2</td>\n",
       "      <td>7.6</td>\n",
       "      <td>110.0</td>\n",
       "      <td>Infuzions</td>\n",
       "      <td>OLDER FAMILIES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>59236</td>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>59</td>\n",
       "      <td>55555</td>\n",
       "      <td>42</td>\n",
       "      <td>Doritos Corn Chip Mexican Jalapeno 150g</td>\n",
       "      <td>2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>Doritos</td>\n",
       "      <td>OLDER SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>271083</td>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>271</td>\n",
       "      <td>268688</td>\n",
       "      <td>97</td>\n",
       "      <td>RRD Salt &amp; Vinegar  165g</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>Red Rock Deli</td>\n",
       "      <td>YOUNG FAMILIES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65015</td>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>65</td>\n",
       "      <td>61737</td>\n",
       "      <td>17</td>\n",
       "      <td>Kettle Sensations   BBQ&amp;Maple 150g</td>\n",
       "      <td>2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>150.0</td>\n",
       "      <td>Kettle</td>\n",
       "      <td>YOUNG FAMILIES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246735</th>\n",
       "      <td>48160</td>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>48</td>\n",
       "      <td>44051</td>\n",
       "      <td>11</td>\n",
       "      <td>RRD Pc Sea Salt     165g</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>Red Rock Deli</td>\n",
       "      <td>RETIREES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246736</th>\n",
       "      <td>175371</td>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>175</td>\n",
       "      <td>176890</td>\n",
       "      <td>40</td>\n",
       "      <td>Thins Chips Seasonedchicken 175g</td>\n",
       "      <td>2</td>\n",
       "      <td>6.6</td>\n",
       "      <td>175.0</td>\n",
       "      <td>Thins</td>\n",
       "      <td>OLDER SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246737</th>\n",
       "      <td>203312</td>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>203</td>\n",
       "      <td>203610</td>\n",
       "      <td>68</td>\n",
       "      <td>Pringles Chicken    Salt Crips 134g</td>\n",
       "      <td>2</td>\n",
       "      <td>7.4</td>\n",
       "      <td>134.0</td>\n",
       "      <td>Pringles</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246738</th>\n",
       "      <td>222003</td>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>222</td>\n",
       "      <td>221524</td>\n",
       "      <td>17</td>\n",
       "      <td>Kettle Sensations   BBQ&amp;Maple 150g</td>\n",
       "      <td>2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>150.0</td>\n",
       "      <td>Kettle</td>\n",
       "      <td>RETIREES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246739</th>\n",
       "      <td>55142</td>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>55</td>\n",
       "      <td>49322</td>\n",
       "      <td>78</td>\n",
       "      <td>Thins Chips Salt &amp;  Vinegar 175g</td>\n",
       "      <td>2</td>\n",
       "      <td>6.6</td>\n",
       "      <td>175.0</td>\n",
       "      <td>Thins</td>\n",
       "      <td>RETIREES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>246740 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        LYLTY_CARD_NBR       DATE  STORE_NBR  TXN_ID  PROD_NBR  \\\n",
       "0                21037 2018-07-01         21   17576        62   \n",
       "1                25040 2018-07-01         25   21704        87   \n",
       "2                59236 2018-07-01         59   55555        42   \n",
       "3               271083 2018-07-01        271  268688        97   \n",
       "4                65015 2018-07-01         65   61737        17   \n",
       "...                ...        ...        ...     ...       ...   \n",
       "246735           48160 2019-06-30         48   44051        11   \n",
       "246736          175371 2019-06-30        175  176890        40   \n",
       "246737          203312 2019-06-30        203  203610        68   \n",
       "246738          222003 2019-06-30        222  221524        17   \n",
       "246739           55142 2019-06-30         55   49322        78   \n",
       "\n",
       "                                      PROD_NAME  PROD_QTY  TOT_SALES  \\\n",
       "0              Pringles Mystery    Flavour 134g         2        7.4   \n",
       "1       Infuzions BBQ Rib   Prawn Crackers 110g         2        7.6   \n",
       "2       Doritos Corn Chip Mexican Jalapeno 150g         2        7.8   \n",
       "3                      RRD Salt & Vinegar  165g         2        6.0   \n",
       "4            Kettle Sensations   BBQ&Maple 150g         2        9.2   \n",
       "...                                         ...       ...        ...   \n",
       "246735                 RRD Pc Sea Salt     165g         2        6.0   \n",
       "246736         Thins Chips Seasonedchicken 175g         2        6.6   \n",
       "246737      Pringles Chicken    Salt Crips 134g         2        7.4   \n",
       "246738       Kettle Sensations   BBQ&Maple 150g         2        9.2   \n",
       "246739         Thins Chips Salt &  Vinegar 175g         2        6.6   \n",
       "\n",
       "        PACK_SIZE     BRAND_NAME               LIFESTAGE MEMBER_TYPE  \n",
       "0           134.0       Pringles                RETIREES  Mainstream  \n",
       "1           110.0      Infuzions          OLDER FAMILIES      Budget  \n",
       "2           150.0        Doritos   OLDER SINGLES/COUPLES      Budget  \n",
       "3           165.0  Red Rock Deli          YOUNG FAMILIES      Budget  \n",
       "4           150.0         Kettle          YOUNG FAMILIES     Premium  \n",
       "...           ...            ...                     ...         ...  \n",
       "246735      165.0  Red Rock Deli                RETIREES  Mainstream  \n",
       "246736      175.0          Thins   OLDER SINGLES/COUPLES      Budget  \n",
       "246737      134.0       Pringles  MIDAGE SINGLES/COUPLES  Mainstream  \n",
       "246738      150.0         Kettle                RETIREES  Mainstream  \n",
       "246739      175.0          Thins                RETIREES  Mainstream  \n",
       "\n",
       "[246740 rows x 12 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join the customer and transaction datasets, and sort transactons by date \n",
    "full_df = trans_df.set_index('LYLTY_CARD_NBR').join(cust_df.set_index('LYLTY_CARD_NBR'))\n",
    "full_df = full_df.reset_index()\n",
    "full_df = full_df.sort_values(by='DATE').reset_index(drop=True)\n",
    "full_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for nulls in the full dataset\n",
    "full_df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# looks like all the data is reasonable so export to CSV\n",
    "full_df.to_csv('QVI_fulldata.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data analysis on customer segments \n",
    "\n",
    "Now that the data has been cleaned, we want to look for interesting insights in the chip market to help recommend a business strategy. \n",
    "\n",
    "To do so, some metrics we want to consider are:\n",
    "- Who spends the most on chips (total sales), 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's the average chip price by customer segment\n",
    "\n",
    "Some more information from the data team that we could ask for, to analyse with the chip information for more insight includes \n",
    "- The customer’s total spend over the period and total spend for each transaction to understand what proportion of their grocery spend is on chips. \n",
    "- Spending on other snacks, such as crackers and biscuits, to determine the preference and the purchase frequency of chips compared to other snacks\n",
    "- Proportion of customers in each customer segment overall to compare against the mix of customers who purchase chips\n",
    "\n",
    "Firstly, we want to take a look at the split of the total sales by LIFESTAGE and MEMBER_TYPE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>sum_tot_sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th>MEMBER_TYPE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OLDER FAMILIES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>156863.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUNG SINGLES/COUPLES</th>\n",
       "      <th>Mainstream</th>\n",
       "      <td>147582.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETIREES</th>\n",
       "      <th>Mainstream</th>\n",
       "      <td>145168.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUNG FAMILIES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>129717.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">OLDER SINGLES/COUPLES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>127833.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>124648.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>123537.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETIREES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>105916.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OLDER FAMILIES</th>\n",
       "      <th>Mainstream</th>\n",
       "      <td>96413.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETIREES</th>\n",
       "      <th>Premium</th>\n",
       "      <td>91296.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUNG FAMILIES</th>\n",
       "      <th>Mainstream</th>\n",
       "      <td>86338.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIDAGE SINGLES/COUPLES</th>\n",
       "      <th>Mainstream</th>\n",
       "      <td>84734.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUNG FAMILIES</th>\n",
       "      <th>Premium</th>\n",
       "      <td>78571.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OLDER FAMILIES</th>\n",
       "      <th>Premium</th>\n",
       "      <td>75242.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUNG SINGLES/COUPLES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>57122.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIDAGE SINGLES/COUPLES</th>\n",
       "      <th>Premium</th>\n",
       "      <td>54443.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUNG SINGLES/COUPLES</th>\n",
       "      <th>Premium</th>\n",
       "      <td>39052.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIDAGE SINGLES/COUPLES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>33345.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">NEW FAMILIES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>20607.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>15979.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>10760.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    sum_tot_sales\n",
       "LIFESTAGE              MEMBER_TYPE               \n",
       "OLDER FAMILIES         Budget           156863.75\n",
       "YOUNG SINGLES/COUPLES  Mainstream       147582.20\n",
       "RETIREES               Mainstream       145168.95\n",
       "YOUNG FAMILIES         Budget           129717.95\n",
       "OLDER SINGLES/COUPLES  Budget           127833.60\n",
       "                       Mainstream       124648.50\n",
       "                       Premium          123537.55\n",
       "RETIREES               Budget           105916.30\n",
       "OLDER FAMILIES         Mainstream        96413.55\n",
       "RETIREES               Premium           91296.65\n",
       "YOUNG FAMILIES         Mainstream        86338.25\n",
       "MIDAGE SINGLES/COUPLES Mainstream        84734.25\n",
       "YOUNG FAMILIES         Premium           78571.70\n",
       "OLDER FAMILIES         Premium           75242.60\n",
       "YOUNG SINGLES/COUPLES  Budget            57122.10\n",
       "MIDAGE SINGLES/COUPLES Premium           54443.85\n",
       "YOUNG SINGLES/COUPLES  Premium           39052.30\n",
       "MIDAGE SINGLES/COUPLES Budget            33345.70\n",
       "NEW FAMILIES           Budget            20607.45\n",
       "                       Mainstream        15979.70\n",
       "                       Premium           10760.80"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate total sales by lifestage and member type and generate a list  \n",
    "total_sales_cust = full_df.groupby(['LIFESTAGE','MEMBER_TYPE'], as_index = False)['TOT_SALES'].agg(['sum'])\n",
    "total_sales_cust = total_sales_cust.rename(columns={'sum': 'sum_tot_sales'})\n",
    "total_sales_cust.sort_values(by = \"sum_tot_sales\", ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the total sales \n",
    "total_sales = full_df['TOT_SALES'].agg(['sum'])['sum']\n",
    "\n",
    "# Plot a breakdown of the total sales by lifestage and member type\n",
    "total_sales_breakdown = full_df.groupby(['LIFESTAGE','MEMBER_TYPE'], as_index = False)['TOT_SALES'].agg(['sum', 'mean']).unstack('MEMBER_TYPE').fillna(0)\n",
    "ax = total_sales_breakdown['sum'].plot(kind='barh', stacked=True, figsize=(15, 5))\n",
    "\n",
    "# Add percentages of the summed total sales as labels to each bar \n",
    "# .patches is everything inside of the chart\n",
    "for rect in ax.patches:\n",
    "    # Find where everything is located\n",
    "    height = rect.get_height()\n",
    "    width = rect.get_width()\n",
    "    label = width / total_sales * 100\n",
    "    x = rect.get_x()\n",
    "    y = rect.get_y()\n",
    "    \n",
    "    label_text = f'{(label):.1f}%' \n",
    "    \n",
    "    # Set label positions\n",
    "    label_x = x + width / 2\n",
    "    label_y = y + height / 2\n",
    "    \n",
    "    # only plot labels greater than given width\n",
    "    if width > 0:\n",
    "        ax.text(label_x, label_y, label_text, ha='center', va='center', fontsize=8)\n",
    "        \n",
    "ax.set_xlabel(\"Total Sales\")\n",
    "ax.set_title('Total Sales Distributions By Customer Lifestage and Affluence')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we can see the most sales are from Older families - Budget, Young singles/couples - Mainstream and Retirees - Mainstream. We can see if this is because of the customer numbers in each segment. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check all rows are unique in customer information\n",
    "len(cust_df['LYLTY_CARD_NBR'].unique()) == cust_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if all customers made chip purchases. \n",
    "len(cust_df['LYLTY_CARD_NBR'].unique()) == len(full_df['LYLTY_CARD_NBR'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the numbers of customers in each segment by counting the unique LYLTY_CARD_NBR entries\n",
    "sum_customers= full_df.groupby(['LIFESTAGE','MEMBER_TYPE'])['LYLTY_CARD_NBR'].agg('nunique').unstack('MEMBER_TYPE').fillna(0)\n",
    "ax = sum_customers.plot(kind='barh', stacked=True, figsize=(15, 5))\n",
    "\n",
    "# Add customer numbers as labels to each bar \n",
    "# .patches is everything inside of the chart\n",
    "for rect in ax.patches:\n",
    "    # Find where everything is located\n",
    "    height = rect.get_height()\n",
    "    width = rect.get_width()\n",
    "    x = rect.get_x()\n",
    "    y = rect.get_y()\n",
    "    \n",
    "    label_text = f'{(width):.0f}'\n",
    "    \n",
    "    # Set label positions\n",
    "    label_x = x + width / 2\n",
    "    label_y = y + height / 2\n",
    "    \n",
    "    # only plot labels greater than given width\n",
    "    if width > 0:\n",
    "        ax.text(label_x, label_y, label_text, ha='center', va='center', fontsize=8)\n",
    "        \n",
    "ax.set_xlabel(\"No of customers\")\n",
    "ax.set_title('Distribution of the Number of Customers By Customer Lifestage and Affluence')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are more Young singles/couples - mainstream and Retirees - mainstream who buy chips. This contributes to there being more sales to these customer segments but this is not a major driver for the Older families - budget segment.\n",
    "\n",
    "We can then take a look at the total and average units of chips bought per customer by LIFESTAGE and MEMBER_TYPE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAGpCAYAAADFpuEPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd5hU9dnG8e/NgjQRFTAWFLAjRVRQMRpbYgON72vBGgz2EkuE2BJFY4+xoMb+WqIiamISNbao2CMBRVGwi4oVUNEgKOV5//idXQfcMju7w+wO9+e65to9/Zkzc2ae+bWjiMDMzMzMlqwWpQ7AzMzMbGnkJMzMzMysBJyEmZmZmZWAkzAzMzOzEnASZmZmZlYCTsLMzMzMSsBJmJWMpJGSbq1l+auStlmCITUaSVMl/bTEMYyVdEgNy2o999Ywue9dJTdK+kLSuBKH1iRICklrF7jtepJelPS1pGMltZV0r6RZku6SdJCkpxs7ZrNicBJmRSVpP0njJf1X0seSHpC0ZT7bRkSviBjbyPHcJOm7LJ7PJT0iaf3GPMbSqCGvcy37bNKJYm3xLfbe3RL4GdA1IjYtxvHKUXatzpe06mKLfgOMjYgOETEK2BP4EdApIvZa4oGaNYCTMCsaSb8GLgXOJX1IrgH8Cfh5KeMCLoyIZYGuwGfATfXdgaSWjR1Uc9WEX+dG08DXuxswNSJmN1Y85U5Se2APYBaw/2KLuwGvLjb9RkTMX0LhmTUaJ2FWFJI6AmcBR0fEXyNidkTMi4h7I2JEzqrLSLolq1p4VVL/nH1UVellpQB3SxqTrfuCpA1z1j1J0ofZstclbV9XjBHxDXA70Hvx4+Uc89bs/+5ZFcrBkt4HHsvmHyppSnbcyZI2zjlEP0kvZ9UkYyS1ybZZQdJ9kqZnVVT3Seqac9yDJL2T7fNdSfvnLBuWHe8LSQ9J6paz7GeSXsuOdwWgOk5Bm+rOp6QRkv6Su6KkyyVduvgO8nmdsxKNs3O22UbStJzpH7x2knYCTgWGZKVrL2XrrirpH1kp5luSDl3s9bpL0q3ZviZJWlfSKZI+k/SBpB1yY5d0Q1Zy96GksyVV5LwGz0i6RNLnwMg6zuXi52WqpJ9KOhi4HhiYPY8zs+WDJU2U9KWkZyX1LfB8/DLn/feOpMMXi+M32fP7SNIhyqkGlNRa0kWS3pf0qaSrJbWt4fmsJekxSTMlzZB0m6TlF3u+w6t7v2fLR+TEMSyPU7gH8CXpvTU0Zz+PAdsCV2TnYTRwes55OXixuCuv25Y58xappq/jmgpJR0h6M1t+pSTlLK/2+s/ep39RusbflXRsHs/ZlkYR4Ycfjf4AdgLmAy1rWWckMBfYBagAzgP+nbN8KvDTnHXnkaoeWgHDgXez/9cDPgBWzdbtDqxVwzFvAs7O/l+WlIQ9tfjxco55a84+A7gFaA+0BfYCPgQGkBKetYFuOfsaB6wKrAhMAY7IlnUifcm0AzoAdwF/y5a1B74C1sumVwF6Zf/vDrwF9ARaAr8Fns2Wdc62qzw/J2Tn/5Bazn1N53MVYDawfLZuS1KJ4SYFvs5V5zyb3gaYlv1f42uXe/5ztn2CVMrWBugHTAe2X+z9tGMW8y3Zczote16HAu/m7OtvwDXZOV8pe70Oz5YdlD2vX2X7alvDOby1huc8le/fuwcBT+cs2zg7n5uR3vdDs/VbF3A+BgFrkd5/WwPfABvnvDafAL1I77U/k97Da2fLLwX+QXp/dgDuBc6r4fmsTapSbQ10AZ4ELl3s+db0ft8J+JT0Y6c96ZqriqOG4z0KXEgqWZ1f+ZyyZWPJeV8vfl5yzzffX7ctq9ueWq6pbHkA9wHLk0p4pwM7Zcuqvf5JhRsTSMnhMsCawDvAjkvq89eP5vNwSZgVSydgRtRdRfB0RPwzIhaQviQ2rGXdCRFxd0TMAy4mfRFvDiwgfTlsIKlVREyNiLdr2c9wSV+SPnyXJX1o52tkpNKeOcAhpKrN/0TyVkS8l7PuqIj4KCI+J33B9QOIiJkR8ZeI+CYivgbOIX2BVloI9JbUNiI+jojKqpfDSV+SU7Lzei6ptK0bKZGdnHN+LiV9Adem2vMZER+TvmQr29fsRHotJ1Szj3xf55rk/dpJWp3UvuqkiJgbERNJpUwH5qz2VEQ8lMVzFylhOD97jncA3SUtL+lHwM7A8dnr+RlwCbBPzr4+iojLI2J+9no3lkOBayLi+YhYEBE3A99SwHs5Iu6PiLez998TwMPAVtnivYEbI+LVSKW+Z1Zul5XmHAqcEBGfZ+/Dc1n0+ece562IeCQivo2I6aT3y9aLrVbt+z0njlciVcmOrO3kSFqDVNp1e0R8SkrIhta2TQPUdk1VOj8ivoyI94HH+f551XT9DwC6RMRZEfFdRLwDXEcN59aWbk7CrFhmAp1Vd1ua3EThG1IVWU3bfFD5T0QsBKaRSgzeAo4nfbh/JukO/bAxb66LImL5iFg5InarI2GrMQZgdaC2bRd/bssCSGon6RpJ70n6ipTwLC+pIvuSGgIcAXws6X5933GgG3BZVoX1JfA56Rf4aqQSiNzzE4vFWutzyT2f2aybgQOy/w8gJcjVyfd1rlY9X7tVgcqEodJ7pOdf6dOc/+eQEsQFOdOQXodupNKxj3PO5zWkErFKdZ2/QnUDTqw8bnbs1SngvSxpZ0n/Vqqe/ZKUjHfOFi/ynljs/y6k0rEJOTE8mM2v7jgrZbF8mL1nb805TqVq3+/VxJH7Q6U6BwJTsiQb4DZgP0mt6tiuELVdU5Vqel41Xf/dgFUXe31PJZXqmS3CSZgVy3OkqqHdG3Gfq1f+I6kFqWH9RwARcXtEbEn6AAzgggL2P5v0xVRp5WrWiZz/PyBVBdXXiaRqp80iYjngJ9l8AWQlOT8jVQu+RvoVXXm8w7MEsvLRNiKeBT5m0fOj3Oka1Hg+SVV1fSX1BgaTvgirk8/rXOt5reW1yz3XZLGtKKlDzrw1SFVC9fUBqfSpc865XC4ieuWGVsB+8z32OYu9ju0iYjTkfz4ktQb+AlwE/Cgilgf+yfdtAT8mvaaVct8PM0hJaa+cGDpG6rBSnfOy4/fN3rMH5BynLou8N0mvWW1+Aawp6RNJn5BK3TqTSi7rq7IzRE3vv9quqbrUdP1/QKr2zt1nh4jYpYD4rcw5CbOiiIhZpDYRV0raPSv9aZX9cr+wwN1uIul/s1KX40lfov9WGjdou+xLaS7py2VBbTuqwURgnyzO/qT2UrW5nlS1uYmStRerxqhJhyzGLyWtCJxRuUDSjyTtptQ77FvgvznP5WrgFEm9snU7SqqsMrwf6JVzfo6l+iQyV7XnEyAi5gJ3k9rvjMuqYn4gz9d5IrCLpBUlrZwdq/L51vbafUqqPmyRHesD4FngPEltlBqzH0zNCWKNsirXh4E/SlpOUgulxueLV7HVpUUWS+WjdR7bXAccIWmz7H3TXtIgSR3qcz5I7Y1ak9opzZe0M7BDznHuBH4pqaekdqTXqfL5L8ziuETSSgCSVpO0Yw0xdyC9F7+UtBowoob1qnMncJCkDbI4zqhpRUkDSYnNpqRqv36ktmS3U0CVZFZ1+iFwgKQKpU4BuYlTbddUXWq6/scBXyl1sGibHbe3pAH1jd/Kn5MwK5qIuBj4Namx63TSL8RjSKUshfg7qaruC1KVxf9mbX1aA+eTft1/QqpSOrWA/f+O9AH9Ban9zO21rRwRd5Hac90OfE16XivmcZxLSQ37Z5CSngdzlrUglZR9RKoa2Ro4KjvePaRSkTuyKqFXyEoHImIGqQ3X+aQqwnWAZ+qIo6bzWelmoA81V0WSHbuu1/nPwEukxtsPA2NyNq/ttbsr+ztT0gvZ//uSGlt/BNwDnBERj9TxPGvyC1IiM5l0Du4mlT7Wx76kRKnyUWfVdkSMJ7XHuiI77lt83y4x7/ORVcseS0pyvgD2IzW0rzzOA8AoUjumt0illpCSbYCTsvn/zt5P/yKV0FbnTFKHglmkhP+vdT3PxeK4lNSj+K3sb02GAn+PiEkR8UnlA7gMGJz9aKmvQ0lJ40xSJ4WqUq7arqm61HT9Z9Xfu5ISyHdJr+X1QMcCYrcyp9R0xKxpkzSS1JvqgLrWtcah1ED6NWDliPiq1PFYw0jqSUoyWofH1DJrElwSZmY/kFV5/Rq4wwlY8yXpfyQtI2kFUonPvU7AzJoOJ2FmtoisPdpXpHGhamy/Y83C4aQq4rdJbcuOLG04ZpbL1ZFmZmZmJeCSMDMzM7MScBJmZmZmVgIFjXJdLJ07d47u3buXOgwzMzOzOk2YMGFGRFR7p4l8NKkkrHv37owfP77UYZiZmZnVSVJdt+GqlasjzczMzErASZiZmZlZCTgJMzMzMyuBJtUmrDrz5s1j2rRpzJ07t9ShLDXatGlD165dadWqValDMTMzK1tNPgmbNm0aHTp0oHv37kgqdThlLyKYOXMm06ZNo0ePHqUOx8zMrGw1+erIuXPn0qlTJydgS4gkOnXq5JJHMzOzImvySRjgBGwJ8/k2MzMrvmaRhJmZmZmVm7JLwiRx4IEHVk3Pnz+fLl26MHjwYABuuukmunTpQr9+/aoekydPZurUqUjid7/7XdW2M2bMoFWrVhxzzDEAjBw5ktVWW41+/fqx/vrrc+SRR7Jw4UIADjroIHr06FG1zy222OIHx1t//fW55JJLaoz9nHPOqdq+oqKi6v9Ro0YxcOBAKm+2vmDBAvr168ezzz67SEy9e/fmH//4xw9irXx8+eWXjXimzczMrCHKLglr3749r7zyCnPmzAHgkUceYbXVVltknSFDhjBx4sSqxwYbbADAmmuuyX333Ve13l133UWvXr0W2faEE05g4sSJTJ48mUmTJvHEE09ULfvDH/5Qtc9nn332B8d75plnOOecc/jggw+qjf20006r2r5t27ZV/x977LF069aNG264AYDLL7+cAQMGVCV6lTHdddddDBs2rCoxrJxf+Vh++eULOqdmZmbW+MouCQPYeeeduf/++wEYPXo0++67b17btW3blp49e1bdOmnMmDHsvffe1a773XffMXfuXFZYYYW84+rUqRNrr702H3/8cd7bVLrkkks477zzePXVV7niiiu44IILfrBOz549admyJTNmzKj3/s3MzGzJavJDVBRin3324ayzzmLw4MG8/PLLDBs2jKeeeqpq+ZgxY3j66aerpp977rlFtr3jjjtYeeWVqaioYNVVV+Wjjz6qWn7JJZdw66238t5777HzzjvTr1+/qmUjRozg7LPPBqBXr17cdttti8T1/vvvM3fuXPr27Vvv57TKKqtw/PHHM3DgQEaNGsWKK674g3Wef/55WrRoQZcuXRaJFWCFFVbg8ccfr/dxbQka2bHA7WY1bhxmZrZElGVJWN++fZk6dSqjR49ml112+cHyxasj27ZtW7Vsp5124pFHHmH06NEMGTLkB9tWVvF99tlnzJ49mzvuuKNqWW51ZG4CNmbMGHr16sWaa67JcccdR5s2bQp6XkcffTQLFizgoIMOWmT+JZdcQr9+/Rg+fDhjxoyp6t2YWx3pBMzMzKxpKcskDGC33XZj+PDheVdFVlpmmWXYZJNN+OMf/8gee+xR43qtWrVip5124sknn6xzn0OGDOHVV1/lqaee4sQTT+STTz6pV0yVWrRoUe3wEZXJ1lNPPcVWW21V0L7NzMxsySrbJGzYsGGcfvrp9OnTp97bnnjiiVxwwQV06tSpxnUigmeffZa11lor7/0OHDiQAw88kMsuu6zeMZmZmVl5KdskrGvXrhx33HHVLhszZswiQzfk9mSE1J5r6NCh1W5bWfXXu3dv5s+fz1FHHVW1bMSIEYvs97vvvvvB9ieddBI33ngjX3/9dQOeXX4qY618TJ06tejHNDMzs/yocuyppqB///5R2TOx0pQpU+jZs2eJIlp6+bw3TPeT76/3NlPb7FfQsfr0WKPe20waOqmgY5mZ2fckTYiI/oVuX7YlYWZmZmZNWVkOUdEcnHPOOdx1112LzNtrr7047bTTShSRmZmZLUlOwkrktNNOc8JlZma2FHN1pJmZmVkJOAkzMzMzKwEnYWZmZmYl4CQsDxUVFfTr148NN9yQjTfe+AfjitVl7NixDB48uODjn3vuuQVva2ZmZk1Ts2uYX8j4S7WZev6gOtdp27YtEydOBOChhx7ilFNO4YknnmjUOGpz7rnncuqppy6x45mZmVnxNbskrNS++uorVlhhBSCVcF100UXcd999ABxzzDH079+fgw46iAcffJDjjz+ezp07s/HGG1dtP336dPbbbz9mzpzJgAEDePDBB5kwYQKdO3fm1ltvZdSoUXz33Xdsttlm/OlPf+K0005jzpw59OvXj169ei1yY3AzM2tcBQ20nMePebPqOAnLQ2USNHfuXD7++GMee+yxWtefO3cuhx56KI899hhrr702Q4YMqVp25plnst1223HKKafw4IMPcu211wJphPoxY8bwzDPP0KpVK4466ihuu+02zj//fK644oqqkjgzMysPfW6u/72NfbeL8uIkLA+51ZHPPfccv/jFL3jllVdqXP+1116jR48erLPOOgAccMABVcnW008/zT333APATjvtVFWq9uijjzJhwgQGDBgApMRvpZVWKtpzMjMzs9JyElZPAwcOZMaMGUyfPp2WLVuycOHCqmVz586t+l9StdvXdK/OiGDo0KGcd955jRuwmZkV18iOhW1XwH1frby4d2Q9vfbaayxYsIBOnTrRrVs3Jk+ezLfffsusWbN49NFHAVh//fV59913efvttwEYPXp01fZbbrkld955JwAPP/wwX3zxBQDbb789d999N5999hkAn3/+Oe+99x4ArVq1Yt68eUvsOZqZmVnxuSQsD5VtwiCVWN18881UVFSw+uqrs/fee9O3b1/WWWcdNtpoIwDatGnDtddey6BBg+jcuTNbbrllVfXlGWecwb777suYMWPYeuutWWWVVejQoQOdO3fm7LPPZocddmDhwoW0atWKK6+8km7dunHYYYfRt29fNt54YzfMNzMzKxOqqXqsFPr37x/jx49fZN6UKVPo2bNniSJqfN9++y0VFRW0bNmS5557jiOPPLJJNrovt/O+pBXUw6rNfgUdq08BVRpu3GtWPV+7Vh+SJkRE/0K3d0nYEvb++++z9957s3DhQpZZZhmuu+66UodkZmZmJeAkbAlbZ511ePHFF0sdhpmZmZWYG+abmZmZlYCTMDMzM7MScBJmZmZmVgJOwszMzMxKwElYHiRx4IEHVk3Pnz+fLl26MHjw4Fq3Gz9+PMcee2xBx7z00kv55ptvCtrWzMzMmr7m1zuy0NtD1Li/WXWu0r59e1555RXmzJlD27ZteeSRR1httdXq3K5///7071/Y8CGXXnopBxxwAO3atfvBsgULFlBRUVHQfs3MzKxpcElYnnbeeWfuvz8N4jd69Gj23XffqmXjxo1jiy22YKONNmKLLbbg9ddfB2Ds2LFVpWUjR45k2LBhbLPNNqy55pqMGjUKgNmzZzNo0CA23HBDevfuzZgxYxg1ahQfffQR2267Ldtuuy0Ayy67LKeffjqbbbYZzz33HLfeeiubbrop/fr14/DDD2fBggUAHHnkkfTv359evXpxxhlnVMXYvXt3Tj31VAYOHEj//v154YUX2HHHHVlrrbW4+uqri38CzczMbBFOwvK0zz77cMcddzB37lxefvllNttss6pl66+/Pk8++SQvvvgiZ511Fqeeemq1+3jttdd46KGHGDduHGeeeSbz5s3jwQcfZNVVV+Wll17ilVdeYaedduLYY49l1VVX5fHHH+fxxx8HUrLWu3dvnn/+eTp16sSYMWN45plnmDhxIhUVFVW3MzrnnHMYP348L7/8Mk888QQvv/xy1fFXX311nnvuObbaaisOOugg7r77bv79739z+umnF/HMmZmZWXWKWh0p6QTgECCAScAvI2JuMY9ZLH379mXq1KmMHj2aXXbZZZFls2bNYujQobz55ptIqvFm24MGDaJ169a0bt2alVZaiU8//ZQ+ffowfPhwTjrpJAYPHsxWW21V7bYVFRXsscceADz66KNMmDCBAQMGAOneliuttBIAd955J9deey3z58/n448/ZvLkyfTt2xeA3XbbDYA+ffrw3//+lw4dOtChQwfatGnDl19+yfLLL9/wE2VmZmZ5KVpJmKTVgGOB/hHRG6gA9inW8ZaE3XbbjeHDhy9SFQnwu9/9jm233ZZXXnmFe++9l7lzq88zW7duXfV/RUUF8+fPZ91112XChAn06dOHU045hbPOOqvabdu0aVPVDiwiGDp0KBMnTmTixIm8/vrrjBw5knfffZeLLrqIRx99lJdffplBgwYtEkvl8Vu0aLFILC1atGD+/PmFnRQzMzMrSLGrI1sCbSW1BNoBHxX5eEU1bNgwTj/9dPr06bPI/FmzZlU11L/pppvqtc+PPvqIdu3accABBzB8+HBeeOEFADp06MDXX39d7Tbbb789d999N5999hkAn3/+Oe+99x5fffUV7du3p2PHjnz66ac88MAD9XyGZmZmtqQUrToyIj6UdBHwPjAHeDgiHi7W8ZaErl27ctxxx/1g/m9+8xuGDh3KxRdfzHbbbVevfU6aNIkRI0bQokULWrVqxVVXXQXAYYcdxs4778wqq6xS1S6s0gYbbMDZZ5/NDjvswMKFC2nVqhVXXnklm2++ORtttBG9evVizTXX5Mc//nHhT9bMzMyKShFRnB1LKwB/AYYAXwJ3AXdHxK2LrXcYcBjAGmusscl77723yH6mTJlCz549ixKj1cznvWG6n3x/vbeZ2ma/go7Vp8ca9d5m0tBJBR3LrNz52rX6kDQhIgobi4riVkf+FHg3IqZHxDzgr8AWi68UEddGRP+I6N+lS5cihmNmZmbWdBQzCXsf2FxSO0kCtgemFPF4ZmZmZs1G0ZKwiHgeuBt4gTQ8RQvg2mIdz8zMzKw5Keo4YRFxBnBGnSuamZmZLWU8Yr6ZmZlZCTgJMzMzMysBJ2F5qKiooF+/fvTu3Zu99tqLb775ptGPcfXVV3PLLbc0+n7NzMysaSpqm7Bi6HNzn7pXqod8xlxp27YtEydOBGD//ffn6quv5te//nXV8gULFlTdUqhQRxxxRIO2NzMzs+bFJWH1tNVWW/HWW28xduxYtt12W/bbbz/69OnDggULGDFiBAMGDKBv375cc801AIwdO5att96avffem3XXXZeTTz6Z2267jU033ZQ+ffrw9ttvAzBy5EguuugiALbZZhvGjx8PwIwZM+jevTuQbom0++67s+uuu9KjRw+uuOIKLr74YjbaaCM233xzPv/88yV/QszMzKwgTsLqYf78+TzwwANV944cN24c55xzDpMnT+aGG26gY8eO/Oc//+E///kP1113He+++y4AL730EpdddhmTJk3iz3/+M2+88Qbjxo3jkEMO4fLLL69XDK+88gq3334748aN47TTTqNdu3a8+OKLDBw40NWZZmZmzYiTsDzMmTOHfv360b9/f9ZYYw0OPvhgADbddFN69OgBwMMPP8wtt9xCv3792GyzzZg5cyZvvvkmAAMGDGCVVVahdevWrLXWWuywww4A9OnTh6lTp9Yrlm233ZYOHTrQpUsXOnbsyK677lrwvszMzKx0ml2bsFLIbROWq3379lX/RwSXX345O+644yLrjB07ltatW1dNt2jRomq6RYsWzJ8//wf7bdmyJQsXLgRg7ty5iyyr777MzMysaXJJWCPZcccdueqqq5g3bx4Ab7zxBrNnzy5oX927d2fChAkA3H333Y0Wo5mZmTUdTsIaySGHHMIGG2zAxhtvTO/evTn88MMLLpkaPnw4V111FVtssQUzZsxo5EjNzMysKVBElDqGKv3794/KXoGVpkyZQs+ePUsU0dLL571hup98f723mdpmv4KO1afHGvXeJp+hWcyWRr52rT4kTYiI/oVu75IwMzMzsxJwEmZmZmZWAk7CzMzMzEqgWSRhTand2tLA59vMzKz4mnwS1qZNG2bOnOnEYAmJCGbOnEmbNm1KHYqZmVlZa/KDtXbt2pVp06Yxffr0Uoey1GjTpg1du3YtdRhmZmZlrcknYa1ataq6NZCZmZlZuWjy1ZFmZmZm5ajGkjBJK9a2YUR83vjhmJmZmS0daquOnAAEIGAN4Ivs/+WB9wHXEZqZmZkVqMbqyIjoERFrAg8Bu0ZE54joBAwG/rqkAjQzMzMrR/m0CRsQEf+snIiIB4CtixeSmZmZWfnLp3fkDEm/BW4lVU8eAMwsalRmZmZmZS6fkrB9gS7APdmjSzbPzMzMzApUZ0lY1gvyOEnLRsR/l0BMZmZmZmWvzpIwSVtImgxMzqY3lPSnokdmZmZmVsbyqY68BNiRrB1YRLwE/KSYQZmZmZmVu7xGzI+IDxabtaAIsZiZmZktNfLpHfmBpC2AkLQMcCwwpbhhmdnSrPvJ99d7m6nnD6r3Nn1u7lPvbQAmDZ1U0HZmZrnyKQk7AjgaWA2YBvTLps3MzMysQPn0jpwB7L8EYjEzK9zIjvXfpscajR+HmVme8ukdeaGk5SS1kvSopBmSDlgSwZmZmZmVq3yqI3eIiK9I94ycBqwLjChqVGZmZmZlLp8krFX2dxdgdDZ4q5mZmZk1QD69I++V9BowBzhKUhdgbnHDMjMzMytvdZaERcTJwECgf0TMA2YDPy92YGZmZmblLJ+SMEjDU/xMUpucebcUIR4zMzOzpUKdSZikM4BtgA2AfwI7A0/jJMzMzMysYPmUhO0JbAi8GBG/lPQj4PrihlVChYw1NHJW48dhZmZmZS2f3pFzImIhMF/ScsBnwJrFDcvMzMysvOVTEjZe0vLAdcAE4L/AuKJGZWZmZlbm8rlt0VHZv1dLehBYLiJeLm5YZmZmZuUtr96RklYDulWuL+knEfFkMQMzMzMzK2f59I68ABgCTAYWZLMDcBJmZmZmVqB8SsJ2B9aLiG+LHYyZmZnZ0iKf3pHv8P39I83MzMysEdRYEibpclK14zfAREmPAlWlYRFxbF07z3pVXg/0zvY1LCKea/yYEkUAACAASURBVGjQZmZWve4n31/QdlPPH9TIkZhZXWqrjhyf/Z0A/KPA/V8GPBgRe0paBmhX4H6atD4396n3NpOGTipCJGZmS44/+8wapsYkLCJuBpDUHpgbEQuy6QqgdV07zgZ2/QlwULa/74DvGh6ymZmZWfOXT8P8R4GfkgZpBWgLPAxsUcd2awLTgRslbUgqUTsuImYXGGu9FVIsP7VN3euYmZmZNVQ+DfPbRERlAkb2fz7Vii2BjYGrImIjYDZw8uIrSTpM0nhJ46dPn55n2GZmZmbNWz5J2GxJG1dOSNoEmJPHdtOAaRHxfDZ9NykpW0REXBsR/SOif5cuXfKJ2czMzKzZy6c68jjgLkkfZdOrkAZvrVVEfCLpA0nrRcTrwPakAV/NzKypGdmx/tv0WKPx4zBbitSahGWN8LcC1gfWAwS8FhHz8tz/r4Dbsp6R7wC/bECsZmZmZmWj1iQsIhZI+nlEXAK8Ut+dR8REoH+hwZmZmZmVq3yqI5+RdAUwhtS4HoCIeKFoUZmZmZmVuXySsMqhKM7KmRfAdo0fjpmZmdnSoc4kLCK2XRKBmJmZmS1N6kzCJJ1e3fyIOKu6+WZmZmZWt3yqI3NHuG8DDAamFCccMzMzs6VDPtWRf8ydlnQRhd/Q28zMzMzIb8T8xbUj3RfSzMzMzAqUT5uwSaTekAAVQBcW7SlpZmZmZvWUT5uwwTn/zwc+jYj5RYrHzMzMbKmQT5uw97IbeG9JKhF7Gnix2IGZmZmZlbM624RlQ1TcDHQCOgM3SfptsQMzMzMzK2f5VEfuC2wUEXMBJJ0PvACcXczAzMzMzMpZPr0jp5LGB6vUGni7KNGYmZmZLSXyKQn7FnhV0iOkNmE/A56WNAogIo4tYnxmZmZmZSmfJOye7FFpbHFCMTMzM1t65NM78uYlEYiZmZnZ0qSQEfPNzMzMrIGchJmZmZmVQL2SMEktJC1XrGDMzMzMlhb5DNZ6u6TlJLUHJgOvSxpR/NDMzMzMylc+JWEbRMRXwO7AP4E1gAOLGpWZmZlZmcsnCWslqRUpCft7RMwrckxmZmZmZS+fJOwa0qj57YEnJXUDZhUzKDMzM7Nyl08Sdm9ErBYRu0REAO8Dw4ocl5mZmVlZyycJ+0vuRJaI3VGccMzMzMyWDjWOmC9pfaAX0FHS/+YsWo5Fb+htZmZmZvVU222L1gMGA8sDu+bM/xo4tJhBWTM2smMB27iJoZmZLX1qTMIi4u/A3yUNjIjnlmBMZmZmZmWvzht4AzMlPQr8KCJ6S+oL7BYRZxc5Niuh7iffX9B2UwuoqO5zc596bzNp6KT6H8jMzKwJyadh/nXAKcA8gIh4GdinmEGZmZmZlbt8krB2ETFusXnzixGMmZmZ2dIinyRshqS1gACQtCfwcVGjMjMzMytz+bQJOxq4Flhf0ofAu8D+RY3KzMzMrMzlk4StEBE/ldQeaBERX0vaFXivyLGZmZmZla18krDrJA2NiEkAkvYBTgDuLWpkZmZmVv6W4vEl80nC9gTulrQ/sCXwC2CHokZlZmZmVubqTMIi4p2s9OtvwAfADhExp+iRmZmZmZWx2u4dOYmsR2RmRaACeF4SEdG32MGZmZmZlavaSsIGL7EozMzMzJYyNY4TFhHvRcR7wCrA5znTnwMrL6kAzczMzMpRPoO1XgX8N2d6djbPzMzMzAqUTxKmiKhqGxYRC8mvV6WZmZmZ1SCfJOwdScdKapU9jgPeKXZgZmZmZuUsnxKtI4BRwG9JvSUfBQ4rZlBmZmZmNelzc5+Ctps0dFIjR9Iw+YwT9hmwzxKIxczMzJqx7iffX+9tprYpQiDNRJ1JmKQ2wMFAL6DqVEXEsCLGZWZmZlbW8mkT9mfSkBQ7Ak8AXYGvixmUmZmZWbnLJwlbOyJ+B8yOiJuBQUDelbGSKiS9KOm+QoM0MzMzKzf5JGHzsr9fSuoNdAS61+MYxwFT6hmXmZmZWVnLJwm7VtIKwO+AfwCTgQvy2bmkrqSSs+sLjtDMzMysDOXTO7IygXoCWLOe+78U+A3QoZ7bmZmZmZW1OkvCJHWSdLmkFyRNkHSppE55bDcY+CwiJtSx3mGSxksaP3369HqEbmZmZtZ85VMdeQfwGbAHsCcwAxiTx3Y/BnaTNDXbx3aSbl18pYi4NiL6R0T/Ll265B24mZmZWXOWTxK2YkT8PiLezR5nA8vXtVFEnBIRXSOiO2mw18ci4oAGxmtmZmZWFvJJwh6XtI+kFtljb6D+Q+KamZmZWZV8krDDgduBb7PHHcCvJX0t6at8DhIRYyNicOFhmpmZmZWXfHpHumejmZmZWSPLpyTMzMzMzBqZkzAzMzOzEnASZmZmZlYCdbYJA5C0IbBVNvlURLxUvJDMzMzMyl8+I+YfB9wGrJQ9bpX0q2IHZmZmZlbO8ikJOxjYLCJmA0i6AHgOuLyYgZmZmZmVs3zahAlYkDO9IJtnZmZmZgXKpyTsRuB5Sfdk07sDNxQvJDMzM7Pyl89grRdLeoJ0Q24Bv4yIF4semZmZmVkZy6t3JDAR+LhyfUlrRMT7RYvKzMzMrMzVmYRlPSHPAD7l+/ZgAfQtbmhmZmZm5SufkrDjgPUiYmaxgzEzMzNbWuTTO/IDYFaxAzEzMzNbmuRTEvYOMFbS/cC3lTMj4uKiRWVmZmZW5vJJwt7PHstkDzMzMzNroHyGqDhzSQRiZmZmtjTJp02YmZmZmTUyJ2FmZmZmJeAkzMzMzKwE8hmsdVQ1s2cB4yPi740fkpmZmVn5y6ckrA3QD3gze/QFVgQOlnRpEWMzMzMzK1v5DFGxNrBdRMwHkHQV8DDwM2BSEWMzMzMzK1v5lIStBrTPmW4PrBoRC8gZvNXMzMzM8pdPSdiFwERJY0k37/4JcK6k9sC/ihibmZmZWdnKZ7DWGyT9E9iUlISdGhEfZYtHFDM4MzMzs3KVT+/IfwCjgX9ExOzih2RmZmZW/vJpE/ZHYCtgsqS7JO0pqU2R4zIzMzMra/lURz4BPCGpAtgOOBT4P2C5IsdmZmZmVrbyaZiPpLbArsAQYGPg5mIGZWZmZlbu8mkTNgbYDHgQuBIYGxELix2YmZmZWTnLpyTsRmC/bFwwJP1Y0n4RcXRxQzMzMzMrX/m0CXtQUj9J+5KqI98F/lr0yMzMzMzKWI1JmKR1gX2AfYGZwBhAEbHtEorNzMzMrGzVVhL2GvAUsGtEvAUg6YQlEpWZmZlZmattnLA9gE+AxyVdJ2l70oj5ZmZmZtZANSZhEXFPRAwB1gfGAicAP5J0laQdllB8ZmZmZmWpzhHzI2J2RNwWEYOBrsBE4OSiR2ZmZmZWxvK5bVGViPg8Iq6JiO2KFZCZmZnZ0qBeSZiZmZmZNQ4nYWZmZmYl4CTMzMzMrASchJmZmZmVgJMwMzMzsxJwEmZmZmZWAk7CzMzMzErASZiZmZlZCRQtCZO0uqTHJU2R9Kqk44p1LDMzM7PmpmUR9z0fODEiXpDUAZgg6ZGImFzEY5qZmZk1C0UrCYuIjyPihez/r4EpwGrFOp6ZmZlZc7JE2oRJ6g5sBDy/JI5nZmZm1tQVPQmTtCzwF+D4iPiqmuWHSRovafz06dOLHY6ZmZlZk1DUJExSK1ICdltE/LW6dSLi2ojoHxH9u3TpUsxwzMzMzJqMYvaOFHADMCUiLi7WcczMzMyao2KWhP0YOBDYTtLE7LFLEY9nZmZm1mwUbYiKiHgaULH2b2ZmZtacecR8MzMzsxJwEmZmZmZWAk7CzMzMzErASZiZmZlZCTgJMzMzMysBJ2FmZmZmJeAkzMzMzKwEnISZmZmZlYCTMDMzM7MScBJmZmZmVgJOwszMzMxKwEmYmZmZWQk4CTMzMzMrASdhZmZmZiXgJMzMzMysBJyEmZmZmZWAkzAzMzOzEnASZmZmZlYCTsLMzMzMSsBJmJmZmVkJOAkzMzMzKwEnYWZmZmYl4CTMzMzMrASchJmZmZmVgJMwMzMzsxJwEmZmZmZWAk7CzMzMzErASZiZmZlZCTgJMzMzMysBJ2FmZmZmJeAkzMzMzKwEnISZmZmZlYCTMDMzM7MScBJmZmZmVgJOwszMzMxKwEmYmZmZWQk4CTMzMzMrASdhZmZmZiXgJMzMzMysBJyEmZmZmZWAkzAzMzOzEnASZmZmZlYCTsLMzMzMSsBJmJmZmVkJOAkzMzMzKwEnYWZmZmYl4CTMzMzMrASKmoRJ2knS65LeknRyMY9lZmZm1pwULQmTVAFcCewMbADsK2mDYh3PzMzMrDkpZknYpsBbEfFORHwH3AH8vIjHMzMzM2s2FBHF2bG0J7BTRBySTR8IbBYRxyy23mHAYdnkesDrRQmo4ToDM0odRDPm89cwPn8N4/NXOJ+7hvH5a5imfv66RUSXQjdu2ZiRLEbVzPtBxhcR1wLXFjGORiFpfET0L3UczZXPX8P4/DWMz1/hfO4axuevYcr9/BWzOnIasHrOdFfgoyIez8zMzKzZKGYS9h9gHUk9JC0D7AP8o4jHMzMzM2s2ilYdGRHzJR0DPARUAP8XEa8W63hLQJOvMm3ifP4axuevYXz+Cudz1zA+fw1T1uevaA3zzczMzKxmHjHfzMzMrASchJmZmZmVgJOwMiapumFCzBYhqZOkVqWOw8zqT9I6ktqVOg4rjJOwMiRpX0kdokwb/EnaTtJepY6jHEgaDFwOrC/JnwclImmnUsfQWCRtIunHpY5jaSBpZ9KoA32zUQhsCZP0a0n9Ct3eH7plRtINwN7Aatl0WZWGZR86lwPLSmpT6niasywBOxv4c0RMioiFpY5paSTpWmC4pFVKHUtDZdfnzUBnSe1LHU85k7QdcCHwq4j4d3Z7QFuCJF0HbA18K6mikO/bYo6Yb0uYpMuB5SLifyrnlVNpmKT+wPnA0IgYX+p4mjNJywIHA0dHxDNZdUZ70gDL70dEU75NSNmQdBnpmv1pqWNpKEk/Af4AHBkRT5U6nnKVfdEL2B34U0T8S1JHYFVgC2AK8J+ImFfCMMuepPOALhHx85x5ywD1SoadhJWXNsCpAJI2A9Yi3TT9ijL5UFwWeDwixmcfOrsBuwAzgVsiYlxJo2smshKKOaSka2VJbYHfA32B5YCFkvaLiHdLGGbZy97DXYHTsumNge7AZsB9wPPNrHRjDeCaiHhKUmdgc1JSMCcifg8pgSinH4Yl0jkipkuaTKqG3Jb0g6oV0A94GtiEVGNgxfMtcCmApG2AjYF9JP0FuDIi/pvPTlwdWQayX6AAKwF7SjqD9KW6Kykrv17S5qWKr6EkbSlpJWAu6S4MRwOPAzuSboXVDjheUusShtksSNoBGBERC4ArgZOBN4EVgeuBQcC/SQmuFYmk7hExC/gA2F7SmaRrdgjpi/RgYEAJQ8ybpD6SlieVzpwoaWvgLmAoqVnEXpJGQ3mVzJdC1oTg9iyBfwX4GrgF+AoYRUoEJpCSeysCSQdlpZFtSc0ILgH+CKwDjAH2BX6V7/5cEtbMSbqKlIQ8SfpFfTypSulyYHJEvJ19wP+U9OXarGRJw1XAXhHxb0l3A+uRGqPeFBFTs/UeJ/0Sf7NUsTZ1kn5G+uV2VDbrfuBFYO2IeCxnvXmAG/kWiaRbSD+ODgGeJ31x7gycCYyLiPck3QysDzxTskDzkHUo+C1wUET8WdL6wHDS++qGiHg163l7v6TlI+LLUsbbnEnakXSuz8wS+KeBpyVdEREfVJYyZs2SVs9+lH7nxLfxSPoTsFJE3AScJOl4Uun1CcCbEfGxpHHA/pJaRsT8uvbpJKwZyxKwThGxdzZrWkQcIqnVYu0BNgT+teQjbJiske85pDZgL2S//u5avJhX0hBSIvpFCcJsFrJf0OcD+0XEREmrA9sDN0fE+znrDQG2BQ4oTaTlLfsQr4iIQ7JZd0fEaEmnR8ScnFWXJ93urcnKfiCdSypZfSur1v4tsExEfJuz6v6kpN5tlAokaQvgDmC3rLp3HdL1PAyYBqmUUdJhwKHA/ou9BtZA2fdt54jYM5vuA1xWTZJ7PPBGPgkYuDqy2ZJ0AdCnMgGT9Cvgj5LaRMQ8SctK6ibpr8BnEXFFSQOuJ0nrAZcA10XE05JWJZXc/CRnndWzD51TgYPdmLxWuwFtswRsWeBeoF3lB4ikzpJOAM4AfhkRr5cw1rIk6ffA1hGxfzb9G+D/JFVExBxJbSR1kPQ3YGZENNl75knalFT1clREPCqpO3A7sFHll3/2+XMoqWrm6IiYXap4y8CPSLUd7bLPwmtJje9nZclXJ6Vhew4CDoyIySWMtexIOon03XNANn0sqWd5h2y6naT+2bX7eUScks2vs7ekS8Kar8+AWZK6AnsA+wBDImJutlzAL0ilY8cCSGrRHIYhyHrqfQg8BbTJSsR+C9wWEf/MWbUz0IdUutOcbw5fNJI2jogXIuIwSVdIepbUgPfiiLglZ9XWwEPAPZVVvNZ4sqqhOcCDkjYhNVgfRKrGW5CttirpOv4oIo7Ktmty12z2XN4FZgAbSHobuI1UqvdCzqrdScPlDPX1WZiss0bPiLgt+0Lfj9QW9syIuCqno0M74AVg14iYWcKQy9UTpPO+s6S1gf8lfd9+BRAR32S1C5Mi4neQ/7XrG3g3M9mv5gXZ/78B/ofUy+3nub3ZJP0kIp7MmW5yH+bVyYrZDwFuJCWaZwLbAA9ExG9y1tsNeA740l2xaybp38A3EbFdNn01sFNEdM9ZZz9SY/AzFqsSs0ZQee1ljdeHAVsCG5FKxXKrgtcDvoiIz3K3K0nQNcjaJW0VEb+V1AP4O6lD0O8i4rqc9bYCxpO+Y74pTbTNm9LgyYOB40i9Tu+UtDvwS2BMRNyerXcIsCewd2VSYI0jS3yVXb8/JpV+rQrsHhFTctY7Frg8p2Yh72vX1ZHNTEQsUDYyckRcCPwf8CkpEQNA0h3AgTnTamof5rVoRSqhPZBU0nUK8BjwX0kbAkjaG7gMWN4JWPUqi8EjYnOgQtK92fQRwAOS/pWtNwg4idTJwQlYEWQf4BVZo/TrST17HyT1pgJA0v3AoTkJWJO7ZrOOHZeQtS/NfvTtQGqTtFzOegeQeot1dgJWuOz1/xfpnB+oNGzM30i9IbeRNETSPqTEfoQTsMYlaZUsqVKWVD1DaoD/EdC/8jNW0m3AJrltw+pz7bokrJmQdDAwsLJBr6RlIhtDKCsR25KUpZ9IqpM+smTB1lNOkXrl9PqkJKyC70vEzgI+ISVpu5CqOKZUszvLLPYeeYb0vtg1m76CVFX0Iak61+eykUm6Eng9IkZl0xXZj6jlSUNQrEyq5tiPVAJ2dOmirV3WseMC0nvlJUlrAD+LiBuy9mD3kRLMD0jDngx1u6TGkTXP2B44jNQk4w5J/wMcCWwA7OBz3biy9/sI4IiImCKpgtT3obJE7ExSm8idSW2uj8i2q/c4eE7CmomsbcCRwFcRcWI2L/dLdjipAexDEXFYNq/JVWdUR1K7rE69qlenpJ6kRpAVpNK+6aTRuPvhNiY1krQusHJlVXQdidjFwI0RMalkAZcxpfH7/gb8JiKuz+ZVJmLtgCNI1UhvR8SB2fImd81mv/gvAwZHxJpKg/0+Q+o0c2W2TndSiXUrYEcnBYWRNABYhdT8Yl7O/NxE7M9Z1eQgYEpEvFOaaMuX0mDDw0gDJ/82JxFrGRHfShpI6hzxTE4CVtC16ySsiZN0ILCQ1Eh9JVL349kR8evF1tuYNFzFI9l0k/swX1zW5qEbaXDBrSKNKVQ1toqkDUjd20Vq+PsusGxllY0tSlJLUu/GjsCdEfF0Nj83EXuKdN1vWbpIy5ukX5CqLMaT2o88TZaILfZDYwjwdWSdTZryNZtdqxeTRsFvSeqa/+fF1lmBdAum90oQYlnImloMJ5W0PFxDIvZr4OqIGFOaKMuXpBFAJ1JP/M+BnUi1TKdExGs56/0P8E5EvJRNF3ztOglrwiRdQxr5eFngDVLm/S2pXvrLiDghW+85Uq+M5lYC1jIi5ks6nVQqsH32i6MlsDAr+u0OHEu6NdEfonndxmWJkdQr+/cT4GjSe+beyG5XJal1fD90wBhSG5L3q92ZFSy7ZnuS2mnOBUaSkuLHSInYtdl6TwBPRMTp2XSTu52PUi/OjqTS04nZvFGkTkDdctbbjzS6/0m+PgujNObUWhHxN0l7AMeQkt4HF0vENifdnu7NiPiwNNGWJ6Ux/LqQfuyvS2p79ySp9HFT0jArH0p6jFQjtXu2XYOuXQ9R0UQpDQw3ICI2zqZvJVXDHSPpUuBoSRcCvUnVGYdVbttMErANgBsk7RoRZ0n6FnhSqVfnFKVRtheS2s1MBv7mD/jqZefq56Qv/3OBK0g9qnbNEvInsiL0EcC6ETGkhOGWLUl3AUTET7IfD8eQeqydJ2kX4N7sfb4j8EkTT8B2IjWufxeYLen+SEOaDCfdW/SxiNgue14nAfv6+myQTYFdJC2IiL9kJY+/Jr09HoyI75R6QR4E/K9rAxqXpJHAHhHxo2z6eFL76sq2jvOBUdl1PTmn+UCDr133jmy63gU+0vf3hbwcWCarl34F+BOpvvq7iKgcQK5ZvJ5Zu5jJpFsM/UVSp4i4ALiIlIhtEGnA2aOAO0nF8h6ItRrZL+iOwHXAJFIp6cqkNjxzgEGS1smqtQ8FrilVrEuBFmQ9HiONtfYa6fYxrSLiWdJwAzeSqtSHQFWpdVNLwAaREvntSfeyfJTvn9d3pORgkqQZpKR/H7cBK4ykrSUNiogbSLdiGyLp5xFxF+kz/gRgI0lHkG5Ld7QTsKJ4kvTdMzybvoVUmt0uO99/Jn0nv7FY+82GX7sR4UcTegCnA+tl/x9Kati7P6lb++E56wnomjPdotSx5/n8dgAeALpk01eTGvl2yqZPAt4n9fScDPQrdcxN9UEqBV0I/JU0ovaKpCE9riX1mloxez89TOrY0LfUMZfjgzS0ROX/o0ltwdbIrtlfZvNbZH9Xz1m3SV6zpFLUhTnX5OrAI9m1OyCbV9n+sE+p422uD1KTgY+BiaRSGEglXbeSqnwB9gKmAO/5+i3Ka3Am8Kvs/x+Tejz+ARgLHLnYuh1y/m+0a9dtwpoYSeeSbra9T0S8k/0COoNUHXdkts4iNwZtitUZ1clK8U4htZM5D7giIj5VGkC0D+m+aDMlnZqtMyCyho9WPaXxv9oCX5KKz2eR2tetSbpZ90ekXrX3RE7DUmsc2Xv6JeCFiPhFNu920sj3x0fEqKyEOiD1cc/WaXLtNpVuRfRGRHyZXYPHk+47ezjp7hsfkO4B+S2pvdJ9Te05NDfZeR5IupH725HuI3oQ6TtgTETcK2kb4IOIeLt0kZYnpbHvLgRGRcSNkrYkJWFfRcSO2TpVHZuy6Ub9vnUS1gRJOpl0W4TKROxI0kV5ZUQ8VtroGiZr7HsDqY79KeDciJieJWI9Sb8IZ0haPtLglrYYfT/MQQWp0egqpC/InUlfnF9l8zcCzg4P51FUWZu8B4EPcxKxm4F1ImKLbLrJJV25lIadGEUaAmb7LBH7Lamk4JlI7dyWIVW5HgvcHhHTShdx85X1cpwTEaF0Z4GrSLUDC0nJ/Jis+cCepJHy/1nL7qyBstdgFGnE+//LErGjgJcj4vxiH79ZtCEqd5L6Zh9wAGQv/D3AHZJ6RMRVpA/5c7MG7c2KpJ5KNyeuiIgJwPmkIvcuwIWSOkcaa2UacGtWcjCrhCE3WUq3jblA0qqRbl91Hyn5mkMaCuES0k1lryf9uv68VLGWM0mb6/s7V8wjdWVfPUu+iIihwAeSPsymm3ICthtwS0QcTKpKvVfSChFxNqlNUk9JK0fEdxExNyIudAJWGEk7kNrYnao0PuJTwO9JP6KmAttK2jPS8B93kKoqrRFJGiSpU+V09hocB/xK0tBIQ/v8Cfip0qCtxY3HJWGllWXdV5Kq6R6JRbsjjwD6k+6l2JLUJuCJkgRaIKVBV18l3Rz6RVJD3i1IY6/8nvTcIQ2I91n2Yf9JSYJtBpTG+foxcDupJPE14DvS++QvpAEGf0K6v9zHTfnLv7mStC0wlNRO6s74ftyvZUgj4E+JiGHZvCOzH1FNUpbUX0gaGfy5bN71wHqk5gFfZCXz55J61r5VumibP6XbDP2eVK37MKlB+DqksR8vULrl089IzU/uKV2k5UnSj0hjgP0VuCoivshZthVwKmmk/NeANSPijWLH5JKwEsuy7otI7Xl2yKo2Ktua3EX6gu0YEV9UJmBSumdVMzGX1FB8Oql6bBSpQeowUruZo0glYqdlVTZOwGoREVuRelFtRmqLNAI4h5QUfEXqJfkw6QeWE7DieJaUAG9O6s3WCqp6Du4CdFe6cwGVCVhT7LmcJWC3kEpgXqicH+nWaK8D/8iaBZz//+2dZ5Qe1bG1ny0BEiAyFllwTf4wOYPJYAMiZwHC5GhyEDnnjIgmRwswiHSNbaIvGGNyNDlHAwIRZRAS+/tR55WaYYSRPJp+Z6aetbSY7rd7ODPTp7u6zq5dhDVF0/0MHYEW9+vbCMnAvUQg9i6wEnBMybpcR3jKPdje4+wK2P6AeO4sA/xWYTDcmJ+PExnJyW2PaARg4/t5mz5hNVEm3FTAI0Ta+SOi5xqS7rb9NfBGidxnIJbqgNHi3o6A7dclnU2Uuj8BTAJ8RSw3zu0wa92UaPabQUMrtBSG2l5P0kNEVd5akvoRmbBZHV0HzqwWbiRtQwlaRgIfOXombkkEw0i6oczZ4cAXhHh9FM12bUtag8huHUW8FJ1Yrps3IQIxhfFsw7vv9BqH29HpQ1Q3Yvsrhbl2d6J/6KTA2oR9yaslq3pFXQPtrEj6LTAlYTPxGFFoclX57DzbHxN+eDMTleajGN/P21yOrAFJlxKB1ZfEzXo6wo5iLsKD549EWfJvCAHnjmP4Vk2Joq/WDLYHV/YtThiKjiBuMkOJH560JgAAIABJREFU9hCvN9sDqpkoS1/HERmL621/UvnsaeDJihi8Q1TJdkQkXQNMS2R2pySayvcnmqAvQgRe5xBVg583KpmbEUk/Jypob7H9gMKFfW2iyvZMVzopKBzyT3O2IhonJM1BdDs5E3jBozsm9CQCr+2IFkS3VM7JedyGlOX1GYG7iKrx+Yhn0M1EX+LXiSTHksDXtrdu1/Hl37p9UTjzLurRTZSnJaLyzYD1gf8hNAGzAB/YPqgc1/QTs6RtpwJuJ5YeDwGG2T6vfP4L4sE1AhhcRPrJj1A0g1cTovslgMOAtyr6nb8Tv+NV6xtl50bSKcA8jTlb9t1DtPPZSNIyhAZvUuBj27uXY5puziqMWA8EtqwGVpKWBNYhArEzbL9d0xA7FZJmJ/RH9xJZ1KWJ3//Ltt+XtAFRbXq+sxdkmyNpO6Lifs2y3YNYNTidkAHdRTgRzAh8Y/vkcly7zd0MwtqREqScDvzF9p8bZetF0Lsf0MP2EQ39SCNDpCYvb2+Jor3G3oQWbEFgJqIi8jEiDX804V91RlnCSVogaVZC4zUhkQU7hMgczk0E7VcTN+7hkm4Fds2KtfGDpLOAS2w/LWlS21+V/fcT9g0NGUEv21+Wr5tuzpbl1COBo8r9pxfQ3fZn5fMliOzMdMAxeT2NOyXz/5bDB3Fv4vf6a+JleyMim3oCocfrQxRz5O+7jZG0LzCV7UNVrH3K/r7AerZ3aOWcdp27KbRsJxStS0yYai5V/UMXvc+LwArlQvmuEoB1CIG1pDUlHVM2byasEx4qVWJfEGnfPxGu24OAizMAa51yg7gEmNTRMuMawkzwDqJqZzYiazpI0oG218kbeNujaCQPsYSxPozS9ExY9h8OTFt5aWoEYE03ZxXtrf4EHFICsNkJG5w+jWNsP0xUMb8NfNvqN0r+IyXYvYF4+YS4970G9CCWJhclMmPHEy9UD+f8bVskzVi+/CcxRydpBGCFN4jn7cwtz23vuZvC/HZA0gmE6O8iYhKuRyw3VnUWdxAtKnoAwxo7m205ozUU3jdHUwoLHGar3YCNJX0MLECk3IcDBwAbVUuDk9GUG/jRwP623ykP/GuAuctSdn+iWfJdJXPxXn2j7bxIOg54U9KfCTPNtSQtafshj7aRGUKI2iekIsRvpjlbWVZ5gwi6NpH0GpGl/rPtZ6rHF43YY/mCNG4oPNcOIJZ7Hy/VpZ9K+pKojJyRaJNzs6SrgM8amcikbSiFYD0lnQF8TKwerEjIZAAoBUzPEwa5tZKZsPFMEeHPQ7yFfmp7EGGseYakhSRNXQ69gvCKGTaGb9WUKBqMXw/sWAKDWYqO6XDCwPIpIqC4zfZfgL4ZgLVOEfFeQDg33yOpD9HweWJiafIwYtnxrnLKI/kG3fYoDFfnIzy//kVU9Q4H1ikvHA2OInSb3/zwuzQNDUPZL4getL2AVwkfqlMaWTxJy6gYQWcANvYomIDQGX1p+/6Sjbm9aGGPJKx4/lACsG62XytVeUkbIekSIgN5ONHq6RGiCvIYSZuWIhQk3Qh8Ybv2l9jMhI1HJO0ITGe7b3W/7a2LzmQvYInyZvqpw7G6KQW9rVE0blMQlZxTSJqcCMjOtf03RYn7rLZvKRmdEbQo3U9GY/sVSXcAC0t6g2hifmvRIJ0saWFGL3E0VcalsyBpH6C37TUqu98v1/ImwJ6S9iOW6z60vWc5r+nmbAkYd5H0FNGCZbCknYg+lksTwf53krYhDKE3qXG4HZrytx9RXkD/UYKBWYGrbT8LIOkcQtcJpZdo0naU6336xvO2IhO4VNIwwiD8UEmvEIFy/3JcrXM3hfnjkXJDx/bpRRP2rUrz7XKB9ALmIMpinyvnNJ2g98eQNDGR6t2dKNU/2Pal5bNFiHZL67pU8yWt00I0ejYh4r3W9uGVY/Ym3qaPavLsS4dF0gDgHdvXSJrY9r8bN2lJvQmPu6UI65i/l3Oabs5KWp3I1F0J9CaWwU62/bKkyYi2LCZkEDsTjvnP1jXezkDl3v4zwsx3qO2lK5+vQrQVW7ZkJpM2ROG9uZXtTSr7Rs1NRWVkL6Cb7Y9afl4XuRw5HpC0o0p1DLCopMkrOhKVrNB6wES2H68EYE0n6G0NVdy/bf8b+CtxU3+FSs9H248DhxLameRHcDTkbry57U7Rj0iaQ9E9AaIt0XkZgLU9kgaU5aReQF+Ia7tkexvX+0JAT9t3N3kANjWhfznW9rlEF4WJKFmYEgBsR/QY/R0hJcgA7L+g3LtHlEDsIyLrMpPC3gQA23cDv8wArG2RtIWkHYBnge6S5qx83OhAsynR9u/jSgDWFM/bDMLaGI2uqJqbqGQTsKykSSCa/ZaA7DeE79Momm05ozWKGLzhcTYBjArE7iJKrvsrvFkon11o++U6xtrsSOpWHvKjHuaVQGxfQod0KDBX+fyt1ICNN2YiWmhdTdyft4SYkyVA7k4s2c1TPakZbuItcRj6rk244E9erpmfle0zFWX7kxKB2ByNl8Bk7JC0qKQNyu/YAJVAbAjhR7W+pGrv0AzA2pBy/xxCyGKGEZnqNSXNAFB5Ye1HFIiNolmetxmEtTGOdjEvEcaHbxLByfbABpIWkDShpBsIDdjtP/KtmpX5gcskzVJZVm2Iee8h3qy3lLRVnYNsdspy0aXAQElLlQBMLQKxXYk5uifhr5aMPy4hApNhRGZ3eUl7S+pRljGuJSrZHqhxjD8Z238kvAcfK8vbEwHnEvrNzYBTgBG2369vlB0XRRXzZYS59hKV/dWM2IfAssRLeO+6tUedjSLhMJEBW4EopjkBWAXYTlJ/SdNLuokQ4V9S43DHSGrC2ghJmwOP236hbA8A5i0i/K2J4GUj4B9EFeS25bgOMTEb+pjy9QmE6HRX25+2OG4SIhX/XGZtWqcEYMcTy0TTEI7Nm9h+pXJMVSM2vbOxeZtTlij+Zvvdom0cCDxh+zxJ6xOZoimJN+1POtqcBZC0KqH7msHRvLghJ5i6ZGuSsaRou84HtrX9t8r+Xra/bMzdqv63GTOmHRlJ+wMPAw86DKuXIDSQ2wDTA8sDWwLPEJrr3cp5Tfe3yCCsDZB0ISF83YloW/J1Wb44HPhfR5kskmYiArBPy3bTXRCtIWll4q36ctvXK0qvDwXuclRcdZiHUt1Imge4E9jD9k1l37HAdS6eTRUh+IQVLWHShiiq1yYjbD9eLQ/L3oSQ/ZSi30HSvMScfatsd4g5W0XRrPtUYOVGIJaMPZV5eQrwku2LKp+dR7jiL2/7jRYvUXl/bEMkXUAYVvcn+rR+U/bvAgyx/Yey3R34rvG7b9a5m8uR/yWSTgamtb2W7XfLshxlAr5JZL8avFcJwJpCFPgT+ZBI9e5SAs4hhPljQ8BsVcT6SesUwfSrwEOENUnv8tEywHGSrpa0JqEnJAOw8YOkc4EpbW9i+8UWy0fnAfOWJUiIpsuNAKwjzdlR2P4TcDDwp5yn404lkJoIGNWeStKGQE/C6/GZkrke2cp5yX+JwnFgBtur2/7I9jdF4jMB0Ravn6IdFxRJZzmvaeduTsj/giIKnBI4pmz/UtKuki6TtL/DqmFuSXvA9ydjR5iYRQ/T3VE5dQjxgPqaWEp7Cfh1yeI0pUC5mShLkGcSTXy3IbQkB5U36AmA3xOp8yOAAZKmqmusXYDuRNYaSUsVucBVinZRjxFLGXO2PKkjzNkxYfsWIkuT83QckLSgpIbH1wfEC2nP8vt8HNjO9mFEs+5F6xpnF2BSwkQZSb+WdCix3H41UZ1/H3B2WUUYda0389zN5chxpGgt/goMANYlROkrET0gXyFu5LcSVgN7EMaIr9Uy2HFA4Z59GtHe5D5gWkIgfhThbTYnUeG5OLCBo69h0gqSViMat+9h+96yb2JCKL04sLrtd8v+GQgjwayiamMkrero6nAH8D4h6F2TaG3yDrAjsBqh09sX2Lhkx5IujqRBwFREUcM3hN/XMOCgynLYFsBviesm9bBtiKT+RF/TeYjir2eJ1ZnbiL6cs5Z/OxMSg8G2/1HPaMeOdMwfBxStiEaUG/pZREuZXxGtKZ60/b6kTYhg5VNiOa/DtKeQtCRhr/E3IpjcmMiE/ZVoAbGBo8fcJ0SfwxdqGmrTU7IrA4G1bD+vaEW0PeGG/1uiQnIPSRfafjWr1cYPCpuAbkS18jpEe6hfEi8az9l+TdJLwCq2j5Y0C03QVy5pDmz3U/R6vBjYhaiM3Ba4t+zvDWwOrJ8BWNui6Lk8he2rgA8VFkgbEi+2zzt6cy5JrDB8TSRBOowtUmbCxhJFufe0tvuV7dmKEPN7oj9JNwPP2D6spK07RD82ReuHy4HVHE1O5yJ0bbsSQsiNCD3YmY4OAKOqJpMfIml34Czb3RQmvQ8CV9g+u3zeCxhEWAccVNWSJG2Dol3MjLY3KNvTOprMd6/+viXdCtxn+1RJE9keXteYk/opVZCTA2/YfqLsu4jwpNqNePnerWwDDHKpjk/aBkW7sIlsb1O2FyNcCL5rcdy1RBuxPTpaIUQGYWOBpCOB/Wz3KtsDgIWBrUtFZA/g58DJRGPf7ctxHeKiKKLwo4C9Hb0fpyH8VYYX8enmhCh1dmBt2692lJ+tTopuYV/gPcLFfFDlsymIhu7TugmayXY2JA0ElrS9ZNneE5iXuMb/LWlSQp93JFHZvFNtg02aBoXVzp+IbOkbxIvS68A1RAXtZ8ABtofWNcbOTlnevQqYtMzV/QkT4rVsf15eauclKn/fcQe0kIEU5o8t1xPmh3somt72BQ6pZLkmIi6KVzpgAPZzwsBxcAnAZiR0bmsC2L4ROBC4hRA2f1v2N/3P1t5Iml/SipJ2VngHHQscQOjo7qgctwWxvDFRBmDjjUeBqSXNVpYxNgeOr2RvhxHX+IeNACwrCLs2kua0PYzIcp1G3Ac/IRzX/0C8TG0F3NAooClFWknbcgNwDlE0M4Dop7uN7c/L542WYndXArBuHe2ZlJmwn0gjmCrLc1cQE3L6qoBa0mbAnbY/LttN6UvSkvLWJ+AgovT6LUL3cI3tC1o5vsMsr7Y3RQN2AmFD8QsiYD3e9s2KdjEDCAuKxYCTgC2dbWPaHH2/ce92RKbrC6J58tDKcWu60rmio8zZZPygaG5+LpEV3btojdYGsH2owuevD5HZnpPwXnujrvF2VirP24mIQHg3YH7b/6wcsxdwm+1Xy3aHnLv5xvcfUHg7jcr42H6JqAp8nKh6bBx3E7BYIwArxzb9BSFpDkJcPwshdJyQCBSerQZgktaW1PCvygCsFRStTI4lytV3sL00kT3dQtJatk8jbCo+JsT6W2QA1vZImsTR/qnR2/QSorK3J2Hy2DhuELB6ZbtpvYSSdmMYcBYwmaQTbD9E2E5MLukYwgz0DkIbO38GYG2LpNnge8/b4UTAew5wisKAFUk3AvM0ArBybIecu5kJ+xEUndmnITJCb5d91YxYw75hRuBb27tUj6lr3GODpEWATQET/fPeA/YBehBp3ntLpefxwK+rF30yGkn/Q1TeHWv7MpWWJeWzA4H1gRWKdnBb4GGH/1rShihaES0LHGF7aIu/w7bEC8a2RIXq8NSAJfCDzGk3YEEicH/f9kGSFiXsKXoAR1VftpO2QVI/wubpQtuPln2N520PQi4zNxEov+fRrYg6zPO2NTIT9uO8SniRrKsoWW+4w09YMmI7Em9EU1cCsA6xJi1pSgDbjxNC0xHEzzMz8dbxDbCipDOIoGztDMDGjO3XCc+a5SXNT5iyNj47kZhr65btSzMAG28MIa7lfSRN5eKGX4KxS4ETgfuJdiapAUuQtCDwxyLLaGRUniay1r0VxtuPAbcTlkN5vYwf7iDkMBuXKsjq8/YbonXeS8TcrfaCbPrn7Y+RF1MrSNpU0kK27yH8hJYE1qsEYo12MssTLtSN0vcOsZyhMJp9WNJZkhYnRKfnEBNgK2A6QhcxEdFSZ3vbz9c13man6BawvRex1Hgo8P8ULU0agt3nCf+aZDwgaQ1J8zl6Pt5ELD3u2wjEGtkw4BFgYdvblfM6pI4kaTtsP0V4wl1XCcRGEoagVwPzlevoXuBE2x/VN9rORyl0W594eTqGmLubSlqsvDw1nrenEL//Dct5nWLuZhDWAkVz0M2BOSRNbfsBYtlxSWBDhaUAkm4HfmV7SNnuSCnRIYS4dBvi57qPaD47GeF9sz0wCXAc0DezNq1TliBxWHg0ArH9CPf1w4F5y5vcxoRIP41YxwMKH7AdgAUkTWH7fuB/iRYnBzQyXZLuJ5YpnyrbneImnowbknpJmhbAdl/gI2BwJRAbQbTImRaYoewbVtNwOyWKXsQrAsOBnqVo5kiioGkLwvAcSf8LzOzRnUU6RMLjp5CasAoKJ/wpbG+oYuRYqmWGEdWQexNVb1sAb3q0YWtHCsCAUW2J/o8QPd5PrMX3J4wHFyKE4/s6zUNbpSznXkQUMBxV9o0y+JR0KqEnfIXQg21j+5m6xttZkXQZEWxt2piDjb9DWdLoR2R4VwTest2/tsEmTUOpYt4XmJhY4rrf9sUlKJiJyP6/L2kNIrO9ge0P6htx56MkPGawvW5l3zRALyIgPqrsXg14wfZm5ZgO97z9MTIIK0iaCRjYSHWWfX2IptVPE2WysxDGcG97tINvh32bLkuRdwF72r68VJ4sSLRguiWXIMdMEYouTfQqe7Lovr5n36HorrAM0D+rINueUq17EvGAbIiqZyJayjxp+4AiqD4aGGZ743JMh52zyX+PpNUJucWORK/flcq/j2wPKDrYPoSJ8hzADvkC1bYoTJJ/B+ziYvNUnreDiJZDFxEOBKcBI23vXo7pdHM3g7CCwv/lFqJdz1sKN97rieW5N4BujhZEs9p+s5zT4S+IEojdQZjOnlf3eJodSTMT/ckmLG/KyxE9IJ+0fULluL7AUOClxpJ10rZIWgY4xfayZXtS4Ebi5j0D8LntPSX93PZr5ZgOP2eTcUdhgdCPyKzcVPZ1I7L/uwF/tX1VCd67Af9yqYxP2o4yV+8DBti+q+w7imgT9Tzx99ifCMAaL7Wdcu52eU2YpE0kTebo+fV3wm6iIb7fxfZvgHuBGYrepBGAdYo1aduPAKsC5yhK+JMxIGldwjH7YuAWRTuip4mihoUlHVSO60+80X2cAVjbo2ivhe2/Ax9LWq9sfwUcaPtgImPdXVKPSgDWKeZsMm5I+hlwPrAoYXVAkZ18BzwJPExkxLD9mO1HMgBrWyRtL2m5MlfvBWarVCcfb3tvogq1NzBTJQDrtHO3SwdhCqPS1YFDFcaOzwHHSJq7TM5/lUN/Q/SC/Kxxbmdaky7l14sCD9Q9lmZF0kpEdc5vga0J/dzahH3HY4SGbj5J9xCi/F/afrGe0XZeynLjFZIOKbvuA1Yofx9sP1n2Hwh8WUrbKZ91mjmbjBM9gakJ66GeEFWQpQLvO+BOYGZJPWscY6dFUm8iybF9kRJcT9xPNy2JkMZcPQ543WEDBXTuudullyNL4LU4UQ35ie0jFK7ICwNPEWnRTYm+co2S9k4lCkx+GuWh/5ntcxq6L0mzAjcD19k+UdLKhIh3n0owkLQxCh+2a4msxgVEl4KpiabKtxIB2PvuYP1bk/FPub9/Rmg5f2/78Mpn2wIbA+tVg/ek7SjLwRsThW4HAz8nbCleJPoRz0KsIGxdju/0c7dLBmGSdib+6Pc7zBwXIuwaPieyGCsRgdg0RHB2ajmv018Qyfdp/M0lnU9kQ4+UJEIjOFJh9DiQqID8Euju0c2hkzaizNlbic4UH0lagPBwusj22ZJWJDQ9rwAjbB9Wzss524WRtDxhkmyiYOMAYhnsNuAfhJXJl0Q13q7ARk5LnjZF0pGE+feVhE52AoqlDGFH8TVRADE/0Rbq+nJel5i7XS4IK4Lp24B/EbqdSYiH6LyEO/4EwAlu0R+xs4oCk5+GpFWIBucDbD9WdAzdgZ8RmrCtbH9Z5xg7K5I2JLR4jxKGjucRWeoPgD8TVc0XtnJeztkuTln2+gVhc/Ap0eHkfcJm6CtCUjAfUQl5nSsNopP/HkmzA88Qy7+nERXlJxLea92AFQgD1udbnNdl5m6XCsIU5qufSNoH2IuYiA1LipUJD7DliDfuQzzaqTfp4pRqnv2JoP26oqND0VdzF8ImYWiNQ+yUFA3YJ8ARwJTAm8TS4wLAa4w2dTzM9ul1jTPpGJQMal9gKmCQ7SfK/i7z0G8vJC1g+2mFX9+1hNZrKLAIERQPJQLgF4BtXYxYuxpdRpgvaSvgKkl9ys16EOEfdIKj3cwBxFLGhMDEGYAlVUo1z0XE2/Ppkk6UdDSRTt8rA7C2R9IGhDxgduIG/i0g4obel6hmfpn4m8xZ0zCTJqfIBxrLW08DNxAG3NtKWroc1nWyEe2ApF8CV0ta09GMeyfgMEJOcDiwJrE8fC/wRlcNwKCLZMI02kH7fCKTcajttyWdRqRD12+UIpeqyJHl6y6xJp38dCRNDCwG/Jpo//SnrIJsexRmuDMRWs0JgSuA94gXp5HAhcVWBkn/42ignnM2+UkofCHXBy529oJsU0qi4y1JWxNZ6jNs3y5pNcKg9XDbV7dyXpecu50+CFO0oXgeOLuI8C8kWlUcXAKxEwmfrI1sv1E5r0teEElSN5JOB16xfZ6kOYmm8j2IN+d3iXYm3wI32X6wcl7O2eQnI2nCXPFoWxRtxD4Hji0FNNsT7gOnlkBsZcJn8Vjbl1bO67Jzt1MvRyp6QfYGLmnss70j8AVwkqSZbR9ICH63qZ7bVS+IJKkTSb8jlhavKhnsl4kqyOHAdoTP0BHEvO5TPTfnbDI2ZADWtpTn7WTAQY3sou2LgcuBA8rS5D2EN9h81XO78tzttJkwRX+wPWyvWbanAKYHvrD9nqRTiOWOg6sZsCRJ6kHSWkSXir5le0pgIuJl8StCt9mTKHV/uWUFc5Ik9VBsnk6wvUbZngWYlchgP0CsNu1LrEgNrm2gTcgEdQ9gPPI5MJmkSYiG1KsS5cjPSnrc9v6SriW0Pb+Drp0STZImoAfQaMS9PrAiodmckjBgPQk4Hvh/Lg2Vc84mSVPwPjCppDWIytO+RP/H14A9CdPzPsBctY2wSenMmbCpCV+nlYmS9rOItkRDgQHA1unrlCTNQ6lie4IwdpwZOBN4hBDiX0yUtk9u+/3aBpkkyQ+QNCHR3m9zwgX/eGLuvkv0cT3N9lP1jbB56bSZsOIHdjxwC/A28E5xOJ+EiNRnBf4J+TadJHXTqEqWtCSwFtEY/Q3b35Y5+zzQqxGA5ZxNkvppzMMyT68ABhPJnY8rx8xCSH8yCGuFTpMJk7QbcIPtD37kmO6EnuQL2zu32+CSJBkjldZQrQZWkq4BhtneoYbhJUnSAkm/IJpsf1XZN8rwtnQUmZTwVvwi5+6Y6RTVkZLOA9atBmANg77y9QSS5iVM+oY3ArDqMUmStB+StpO0UXHV/kEApmAxSXcRBo87NPbXNugkSZB0AaHR3FZS9xJw0SIAmxM4A/h3zt0fp8NnwoovSTfbvynbv7J9RyvHTQfMb/uusp1tKpKkBopp8vzAY8AawKaN9jEtjpsUWMf2oLKdczZJaqQsOfYA+hH6zM8qmewJbI8ox00CzOrSEzLn7pjp0JowRTPu1YgLAkm7A7tLWrq6Jg1QsmQflOOUF0SStD+SzgFmsf3Lsv02sCwhyK8uTXYvSx0ZgCVJE6BoIzaD7V+VXZ9J6gMMlLRHccmfwPYI28MIHWc+b/8DHToIAx4nOrOvI2kLovx1lRaiwB/oTFLQmyTtT1mmmA54TdKUtj8F/gWsKekj4FVHnzlcWoc1yJt4ktTOcOBZiFaARAvA84BpgSckLdqa52Y+b3+cDqkJk7SzpHUJA8cbiRv5qsBFLj0gy3GHAbPVMsgkSUYhaX9gDqIzxc+Aw4rB44GED9jqwK2Sri2tTZIkaQIk9SpfTgysXLLSw4EpgLNsL0UEY7s19GHJT6fD/cIknUsYrH5FlKy/BfweOB9YtpS4I+n3wNwujX2TJKmH0opoKdsvFW++7YmM2PXAINtr2t6GkBacW1qbJElSM5J2BG4rmes/EC3+zpI0ue03bd9ZDp0FeDMz1mNPhxLml6qM6W2vV9k3FTAhMAzYAZgHWBR4yPZu5Zj0FEqSGpB0MYDt7cv2moQg/3PgAiKLfRbwYUPUW47LOZskTUBJaEwE7ESsLG1D+GweQhgrnwB8YHuXusbYkekwQZiknoTnyB62h5Z9MxPC3ZFEU9DXCJf8qSsBWAp6k6QGJK0I3APMYfs1SfsCGxMVjx+WZY6LgRHA7o15nSRJvUiaExhSedZeTvh+7UZoyfcDfgF8CHyWCY9xp0MEYWWduTfwN2Aj209KmgA4F3gTeA/YCliXsKv4rHFeBmBJUh9Fl7k9IRlYlGjQ/Wrl84mBrWz/rqYhJklSQdJBwHHA/cBtwJ9tPyvpImLVaZ/SkWZKwkR5eDkvn7fjQIcIwhpIOgH4DLjA9qeSprU9pFwMZwMDbL9Xjs2IPElqQFJvYErbL5Xt3Yklx1Vs31s57nTgCpeecjlnk6R+ihv+AGAyYpXpXULmcyVwInA3EYj9qAtB8tNoamG+pGXKDb3Bo8DswPpFKDik7B8IfNwIwCDLYpOkDkr3it8Bv5f0oKSVbJ8N7AUMKr5CSLqeKKwZ1U8u52yS1EfpYnGe7WeJl6ZHCInPgcScnohIgvQHvqf/yrk77jRtJkzSlkTkfRtwv+1Ty/5+wMpEQ9C7CWuKj2xvVT7PiDxJaqA44U8L7Ey8Qe9NOONfZ/s6SQcSwdhzwHO2f1vOyzmbJDVSWgotTgRX79s+WNKihMznU+BM20MlTQ/MZPuxGofbqWjmIKwPcCZwBbA/Ycz6LnAqMD2wHKETG2b74nJO3syTpAbK0uICtldtsX9PYENgc9vvSDpAAh9fAAAII0lEQVQEWND2JuXz1JEkSRMgqTvx0rQXEYgdJGkRYAuiCnKg7X9Vjs+52wY07XJk8f8aCSxZWpxcQrxZ30lkwp6zPbASgHXLACxJamWIpD6laAYA22cBHxNVy9g+LgOwJGkOJO1VerQ2ulQ8QyQ/ppd0lO3HgRuIDPdy1XNz7rYNTReElXYIDXYB5pI0O1Fh9QRwNbAQsE71vLwgkqT9KW/P2N4HeIfIVM9VPmvM5QeAV1qcl/3kkqR+1gauVzTcrgZiA4GZJS1u+0Hg9GLWmrQxTROESTq+NPd9QtJOkhYrwvsXgZsIQX5f25cCR9o+ts7xJkkSN+1GsGV7P+Bt4AhJCzRK14EVgH+3OC+z1klSM7ZXAYYAgxuBGPAd8CTQndL2z/YLMEo7lrQhTaEJK21NZgQOB5Yl3qQnB04Hvgb+Cmxo+0FJ3Uu0nhqwJKkJSbsAM9s+pGxPVPELaug2jwGOBr62/ZvaBpskySgkHQX0JDy/brT9QPEAm4V4zn5VjhsMDLZ9dX2j7fzUHoSViqo5q4JeSfMBmwBTEzqwfQnPkpNsf1HLQJMkAUYtMy5HVEE+avukxv4WgdgmwF9s71D2pQYsSWpE0pXEc/UMwty8F9Fy6KBiLzMj8CCwFPBFw3UgGX/UuhxZyl2nA+6s+oHZ/idhTbEgMCfwKnGhZACWJDVje7jtuwkvoUWLwzaV5UeIquY9MwBLkuZA0krAtLbXKvN3L+A8oLekA2zvCgwGPgD+r2L71DSypc5IbZkwSRsSlY5zECWwnwCDSo+5bra/k3QbcLHtWyrn5RJkktSApOOBnwEiMmAXSFqO6Cf3jO3jynEPEd5++5XtDMCSpEbKi9IcwGS2N2lkrUthzWpAv9YkAzl3xz+1RLjFA6wv0RrhReAa4ua+qaS5Kn/0kYRR3CgyAEuS9kfSZcB8wHXA7cB+RVvyItHDdX5J+0u6GXilEYBBVi4nSZ1I6gHMCrxAFM5QArBuRV/9d2BJSYu1PDfn7vin3YMwSWsUD7CBhDjwYOJGfiUh5u0rabaydv2Z7f9r7zEmSTIaSasRLtnr2r7L9mBgFWB5YDfb9wPnAFsT2fUtynm5jJEkNWP7G+B1whF/DUnHlP3flf9+DrwEfF7bILsw7XqTLH/8P0o6xPaTROA1CaMDsSuAPsAdwLeN9GiWxSZJ7bwDIGlCSRPYfpPoIde/tDd5EFjf9rrluFzGSJIakbSupLXK8/NlIuO1ArCVpGMlrSJpckk3AkNtv1TrgLso7aoJk7Qw4cb7BfCC7f0kLUi8QX8FnECsWy9t+4JyTt7Mk6RGJM0DXAvsbPsfZd+ktr+S9Huinck/KsenbjNJakTSzMBJhKvAV4Tj/eLAksCXwCGEJcVw4FPb25Xzcu62M+0dhPUGDgUeBeYFetjeR9ICwDaEb8l+tr8ux2cAliQ1U96kDyDkAleULHbjs7uAU2z/pa7xJUkyZiT9nAi4NiA01tfb/meZ11PaHlqOy+dtDYz35UhJp0o6sJisfkgYr+4I/BmYVNLJtp8mxPkPNgIwSFFgkjQD5c34SuKN+hBJ20qaX9JNwJAMwJKk+ajIeF4v2uqBRGVzf0krO2gEYNlGrCbGayZM0izAm2Xz4PLfwYTR46vAR8B+wHDbO1XOy5RokjQZkqYGfgXsATwFfGN7r/JZztkkaXIkzUlYyjyQvSCbg/G+HClpXkJofyeR7ToVmAD4q+3dJS0CLGz7kvE6kCRJ2oSqM37ZzmWMJOkgSJrO9gd1jyMJ2kUTJmkh4B5ge+BuoB9hT3EBkQX7rhyXb9NJ0uRU52nO2STpmOTcbQ7aTZgvaQkiI7aX7curjbiTJEmSJEm6GhO01//I9sOSVgXukDSN7dPa6/+dJEmSJEnSbLSrWavtR4G1iKbcSZIkSZIkXZbaGnhDrkknSZIkSdJ1qTUIS5IkSZIk6apkg90kSZIkSZIayCAsSZIkSZKkBjIIS5IkSZIkqYEMwpIkqQ1JX7ay70hJ+5WvL5f0uqQny789yv43JD1T2T+w7F9K0kNl3/Ple21TOW545bwTyzl7S/pa0hQtxrG6pIclvVCOv05SnzGM6+/j+3eVJEnno918wpIkScaR/W3f0Mr+lWwPabHvCmAT209J6g7Mbfs54DKI4K2V8/oBjwDrA5eX434BnA2sY/v5sm8dYDbgrf8wriRJkp9EZsKSJOlM9AbeB7A9sgRgY0TS7EAv4FAiGGswADi+EYCV73er7fvafshJknRVMghLkqTZOaWy7Dd/Zf+9lf17l31nAC9KuknSTpJ6/ofv3Q8YBNwPzC2pd9k/H/D4WIzrmrH9oZIkSXI5MkmSZucnL0faProERL8CNieCrBV/5HtvBqxv+ztJg4GNgXOrB0iaBrgbmAS40Pap/2FcSZIkP4nMhCVJ0qmw/art84FVgAVLEPUDJC1AtFC7s2jFNmP0kuQ/gUXK9/vY9kLAhcTSZZIkSZuQQViSJJ0GSX0lqWzOCYwEPh3D4f2AI23PVv7NCMwkaVbgZOAQSfNWjp9kvA08SZIuSS5HJklSJ5NIeqeyffpYnHuvpJHl66dtbwX0B86QNAwYAWxhe+QYzt8MWKPFvpuAzWyfJGlP4EpJkwEfE1WRR1SOPUXSoZXtJWwPH4vxJ0nSxcnekUmSJEmSJDWQy5FJkiRJkiQ1kEFYkiRJkiRJDWQQliRJkiRJUgMZhCVJkiRJktRABmFJkiRJkiQ1kEFYkiRJkiRJDWQQliRJkiRJUgMZhCVJkiRJktTA/we240jkW6mf/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the average no of chip packets bought per customer by LIFESTAGE and MEMBER_TYPE.\n",
    "no_packets_data = full_df.groupby(['LIFESTAGE','MEMBER_TYPE'])['PROD_QTY'].sum()/full_df.groupby(['LIFESTAGE','MEMBER_TYPE'])['LYLTY_CARD_NBR'].nunique(0)\n",
    "ax = no_packets_data.unstack('MEMBER_TYPE').fillna(0).plot.bar(stacked = False,figsize=(10, 5))\n",
    "ax.set_ylabel(\"Avg no packets purchased\")\n",
    "ax.set_title('Chips Purchased by Customer Lifestage and Affluence')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Older families and young families in general buy more chips per customer. We can also investigate the average price per unit sold by LIFESTAGE and MEMBER_TYPE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a column for the unit price of chips purchased per transaction \n",
    "full_df['UNIT_PRICE'] = full_df['TOT_SALES']/full_df['PROD_QTY']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution of the average unit price per transaction by LIFESTAGE and MEMBER_TYPE.\n",
    "avg_priceperunit = full_df.groupby(['LIFESTAGE', 'MEMBER_TYPE'], as_index = False)['UNIT_PRICE'].agg(['mean']).unstack('MEMBER_TYPE').fillna(0)\n",
    "ax = avg_priceperunit['mean'].plot.bar(stacked=False, figsize=(10, 5))\n",
    "ax.set_ylabel(\"Avg unit price per transaction\")\n",
    "ax.set_title('Average Unit Price Per Transaction by Customer Lifestage and Affluence')\n",
    "plt.legend(loc = \"upper left\",bbox_to_anchor=(1.0, 1.0))\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For young and midage singles/couples, the mainstream group are more willing to pay more for a packet of chips than their budget and premium counterpart. Given the total sales, as well as the number of customers buying chips, is higher in these groups compared to the non-mainstream groups, this suggests that chips may not be the choice of snack for these groups. Further information on shopping habits would be useful in this case. \n",
    "\n",
    "As the difference in average price per unit isn’t large, we can check if this difference is statistically different, with a t-test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.967354232991988e-306 37.6243885962296\n"
     ]
    }
   ],
   "source": [
    "# Check the difference in the average price unit between the mainstream and premium/budget groups for young/midage singles/couples\n",
    "from scipy.stats import ttest_ind\n",
    "\n",
    "# Identify the groups to test the hypthesis with \n",
    "mainstream = full_df[\"MEMBER_TYPE\"] == \"Mainstream\"\n",
    "young_midage = (full_df[\"LIFESTAGE\"] == \"MIDAGE SINGLES/COUPLES\") | (full_df[\"LIFESTAGE\"] == \"YOUNG SINGLES/COUPLES\")\n",
    "premium_budget = full_df[\"MEMBER_TYPE\"] != \"Mainstream\"\n",
    "\n",
    "group1 = full_df[mainstream & young_midage][\"UNIT_PRICE\"]\n",
    "group2 = full_df[premium_budget & young_midage][\"UNIT_PRICE\"]\n",
    "\n",
    "# Generate the t-test \n",
    "stat, pval = ttest_ind(group1.values, group2.values, equal_var=False)\n",
    "\n",
    "print(pval, stat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The t-test results in a p-value of 6.97e-306, being close to 0, indicates that the unit price for mainstream,\n",
    "young and mid-age singles and couples ARE significantly higher than\n",
    "that of budget or premium, young and midage singles and couples.\n",
    "\n",
    "### Deep dive into specific customer segments for insights\n",
    "We have found quite a few interesting insights that we can dive deeper into.\n",
    "We might want to target customer segments that contribute the most to sales to\n",
    "retain them or further increase sales. Let's look at Mainstream - young\n",
    "singles/couples. For instance, let's find out if they tend to buy a particular\n",
    "brand of chips.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a visual of what brands young singles/couples are purchasing the most for a general indication\n",
    "young_mainstream = full_df.loc[full_df['LIFESTAGE'] == \"YOUNG SINGLES/COUPLES\"]\n",
    "young_mainstream = young_mainstream.loc[young_mainstream['MEMBER_TYPE'] == \"Mainstream\"]\n",
    "ax = young_mainstream[\"BRAND_NAME\"].value_counts().sort_values(ascending = True).plot.barh(figsize=(10, 5))\n",
    "ax.set_xlabel(\"No packets purchased\")\n",
    "ax.set_ylabel(\"Brands\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = full_df.copy()\n",
    "temp[\"group\"] = temp[\"LIFESTAGE\"] + ' - ' + temp['MEMBER_TYPE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>MIDAGE SINGLES/COUPLES - Budget</th>\n",
       "      <th>MIDAGE SINGLES/COUPLES - Mainstream</th>\n",
       "      <th>MIDAGE SINGLES/COUPLES - Premium</th>\n",
       "      <th>NEW FAMILIES - Budget</th>\n",
       "      <th>NEW FAMILIES - Mainstream</th>\n",
       "      <th>NEW FAMILIES - Premium</th>\n",
       "      <th>OLDER FAMILIES - Budget</th>\n",
       "      <th>OLDER FAMILIES - Mainstream</th>\n",
       "      <th>OLDER FAMILIES - Premium</th>\n",
       "      <th>OLDER SINGLES/COUPLES - Budget</th>\n",
       "      <th>...</th>\n",
       "      <th>Natural Chip Co</th>\n",
       "      <th>Pringles</th>\n",
       "      <th>Red Rock Deli</th>\n",
       "      <th>Smiths</th>\n",
       "      <th>Sunbites</th>\n",
       "      <th>Thins</th>\n",
       "      <th>Tostitos</th>\n",
       "      <th>Twisties</th>\n",
       "      <th>Tyrrells</th>\n",
       "      <th>Woolworths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246735</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246736</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246737</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246738</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246739</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>246740 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        MIDAGE SINGLES/COUPLES - Budget  MIDAGE SINGLES/COUPLES - Mainstream  \\\n",
       "0                                     0                                    0   \n",
       "1                                     0                                    0   \n",
       "2                                     0                                    0   \n",
       "3                                     0                                    0   \n",
       "4                                     0                                    0   \n",
       "...                                 ...                                  ...   \n",
       "246735                                0                                    0   \n",
       "246736                                0                                    0   \n",
       "246737                                0                                    1   \n",
       "246738                                0                                    0   \n",
       "246739                                0                                    0   \n",
       "\n",
       "        MIDAGE SINGLES/COUPLES - Premium  NEW FAMILIES - Budget  \\\n",
       "0                                      0                      0   \n",
       "1                                      0                      0   \n",
       "2                                      0                      0   \n",
       "3                                      0                      0   \n",
       "4                                      0                      0   \n",
       "...                                  ...                    ...   \n",
       "246735                                 0                      0   \n",
       "246736                                 0                      0   \n",
       "246737                                 0                      0   \n",
       "246738                                 0                      0   \n",
       "246739                                 0                      0   \n",
       "\n",
       "        NEW FAMILIES - Mainstream  NEW FAMILIES - Premium  \\\n",
       "0                               0                       0   \n",
       "1                               0                       0   \n",
       "2                               0                       0   \n",
       "3                               0                       0   \n",
       "4                               0                       0   \n",
       "...                           ...                     ...   \n",
       "246735                          0                       0   \n",
       "246736                          0                       0   \n",
       "246737                          0                       0   \n",
       "246738                          0                       0   \n",
       "246739                          0                       0   \n",
       "\n",
       "        OLDER FAMILIES - Budget  OLDER FAMILIES - Mainstream  \\\n",
       "0                             0                            0   \n",
       "1                             1                            0   \n",
       "2                             0                            0   \n",
       "3                             0                            0   \n",
       "4                             0                            0   \n",
       "...                         ...                          ...   \n",
       "246735                        0                            0   \n",
       "246736                        0                            0   \n",
       "246737                        0                            0   \n",
       "246738                        0                            0   \n",
       "246739                        0                            0   \n",
       "\n",
       "        OLDER FAMILIES - Premium  OLDER SINGLES/COUPLES - Budget  ...  \\\n",
       "0                              0                               0  ...   \n",
       "1                              0                               0  ...   \n",
       "2                              0                               1  ...   \n",
       "3                              0                               0  ...   \n",
       "4                              0                               0  ...   \n",
       "...                          ...                             ...  ...   \n",
       "246735                         0                               0  ...   \n",
       "246736                         0                               1  ...   \n",
       "246737                         0                               0  ...   \n",
       "246738                         0                               0  ...   \n",
       "246739                         0                               0  ...   \n",
       "\n",
       "        Natural Chip Co  Pringles  Red Rock Deli  Smiths  Sunbites  Thins  \\\n",
       "0                     0         1              0       0         0      0   \n",
       "1                     0         0              0       0         0      0   \n",
       "2                     0         0              0       0         0      0   \n",
       "3                     0         0              1       0         0      0   \n",
       "4                     0         0              0       0         0      0   \n",
       "...                 ...       ...            ...     ...       ...    ...   \n",
       "246735                0         0              1       0         0      0   \n",
       "246736                0         0              0       0         0      1   \n",
       "246737                0         1              0       0         0      0   \n",
       "246738                0         0              0       0         0      0   \n",
       "246739                0         0              0       0         0      1   \n",
       "\n",
       "        Tostitos  Twisties  Tyrrells  Woolworths  \n",
       "0              0         0         0           0  \n",
       "1              0         0         0           0  \n",
       "2              0         0         0           0  \n",
       "3              0         0         0           0  \n",
       "4              0         0         0           0  \n",
       "...          ...       ...       ...         ...  \n",
       "246735         0         0         0           0  \n",
       "246736         0         0         0           0  \n",
       "246737         0         0         0           0  \n",
       "246738         0         0         0           0  \n",
       "246739         0         0         0           0  \n",
       "\n",
       "[246740 rows x 41 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups = pd.get_dummies(temp[\"group\"])\n",
    "brands = pd.get_dummies(temp[\"BRAND_NAME\"])\n",
    "groups_brands = groups.join(brands)\n",
    "groups_brands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq_groupsbrands = apriori(groups_brands, min_support=0.008, use_colnames=True)\n",
    "rules = association_rules(freq_groupsbrands, metric=\"lift\", min_threshold=0.5)\n",
    "rules.sort_values('confidence', ascending = False, inplace = True)"
   ]
  },
  {
   "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>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>(YOUNG SINGLES/COUPLES - Mainstream)</td>\n",
       "      <td>(Kettle)</td>\n",
       "      <td>0.079209</td>\n",
       "      <td>0.167334</td>\n",
       "      <td>0.015579</td>\n",
       "      <td>0.196684</td>\n",
       "      <td>1.175400</td>\n",
       "      <td>0.002325</td>\n",
       "      <td>1.036537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(MIDAGE SINGLES/COUPLES - Mainstream)</td>\n",
       "      <td>(Kettle)</td>\n",
       "      <td>0.044966</td>\n",
       "      <td>0.167334</td>\n",
       "      <td>0.008657</td>\n",
       "      <td>0.192519</td>\n",
       "      <td>1.150508</td>\n",
       "      <td>0.001132</td>\n",
       "      <td>1.031190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>(RETIREES - Budget)</td>\n",
       "      <td>(Kettle)</td>\n",
       "      <td>0.057652</td>\n",
       "      <td>0.167334</td>\n",
       "      <td>0.010505</td>\n",
       "      <td>0.182214</td>\n",
       "      <td>1.088926</td>\n",
       "      <td>0.000858</td>\n",
       "      <td>1.018196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>(RETIREES - Premium)</td>\n",
       "      <td>(Kettle)</td>\n",
       "      <td>0.049591</td>\n",
       "      <td>0.167334</td>\n",
       "      <td>0.008981</td>\n",
       "      <td>0.181105</td>\n",
       "      <td>1.082296</td>\n",
       "      <td>0.000683</td>\n",
       "      <td>1.016816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(OLDER SINGLES/COUPLES - Budget)</td>\n",
       "      <td>(Kettle)</td>\n",
       "      <td>0.069596</td>\n",
       "      <td>0.167334</td>\n",
       "      <td>0.012422</td>\n",
       "      <td>0.178488</td>\n",
       "      <td>1.066658</td>\n",
       "      <td>0.000776</td>\n",
       "      <td>1.013578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(OLDER SINGLES/COUPLES - Premium)</td>\n",
       "      <td>(Kettle)</td>\n",
       "      <td>0.067115</td>\n",
       "      <td>0.167334</td>\n",
       "      <td>0.011944</td>\n",
       "      <td>0.177959</td>\n",
       "      <td>1.063495</td>\n",
       "      <td>0.000713</td>\n",
       "      <td>1.012925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>(RETIREES - Mainstream)</td>\n",
       "      <td>(Kettle)</td>\n",
       "      <td>0.080935</td>\n",
       "      <td>0.167334</td>\n",
       "      <td>0.013723</td>\n",
       "      <td>0.169554</td>\n",
       "      <td>1.013269</td>\n",
       "      <td>0.000180</td>\n",
       "      <td>1.002674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(OLDER SINGLES/COUPLES - Mainstream)</td>\n",
       "      <td>(Kettle)</td>\n",
       "      <td>0.069146</td>\n",
       "      <td>0.167334</td>\n",
       "      <td>0.011490</td>\n",
       "      <td>0.166168</td>\n",
       "      <td>0.993034</td>\n",
       "      <td>-0.000081</td>\n",
       "      <td>0.998602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>(YOUNG FAMILIES - Budget)</td>\n",
       "      <td>(Kettle)</td>\n",
       "      <td>0.071991</td>\n",
       "      <td>0.167334</td>\n",
       "      <td>0.011117</td>\n",
       "      <td>0.154422</td>\n",
       "      <td>0.922837</td>\n",
       "      <td>-0.000930</td>\n",
       "      <td>0.984730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(OLDER FAMILIES - Budget)</td>\n",
       "      <td>(Kettle)</td>\n",
       "      <td>0.087193</td>\n",
       "      <td>0.167334</td>\n",
       "      <td>0.013455</td>\n",
       "      <td>0.154318</td>\n",
       "      <td>0.922216</td>\n",
       "      <td>-0.001135</td>\n",
       "      <td>0.984609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(OLDER FAMILIES - Mainstream)</td>\n",
       "      <td>(Kettle)</td>\n",
       "      <td>0.053664</td>\n",
       "      <td>0.167334</td>\n",
       "      <td>0.008183</td>\n",
       "      <td>0.152481</td>\n",
       "      <td>0.911237</td>\n",
       "      <td>-0.000797</td>\n",
       "      <td>0.982475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(OLDER FAMILIES - Budget)</td>\n",
       "      <td>(Smiths)</td>\n",
       "      <td>0.087193</td>\n",
       "      <td>0.123016</td>\n",
       "      <td>0.011948</td>\n",
       "      <td>0.137027</td>\n",
       "      <td>1.113895</td>\n",
       "      <td>0.001222</td>\n",
       "      <td>1.016236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>(YOUNG FAMILIES - Budget)</td>\n",
       "      <td>(Smiths)</td>\n",
       "      <td>0.071991</td>\n",
       "      <td>0.123016</td>\n",
       "      <td>0.009459</td>\n",
       "      <td>0.131397</td>\n",
       "      <td>1.068126</td>\n",
       "      <td>0.000603</td>\n",
       "      <td>1.009648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>(YOUNG SINGLES/COUPLES - Mainstream)</td>\n",
       "      <td>(Doritos)</td>\n",
       "      <td>0.079209</td>\n",
       "      <td>0.102229</td>\n",
       "      <td>0.009642</td>\n",
       "      <td>0.121725</td>\n",
       "      <td>1.190712</td>\n",
       "      <td>0.001544</td>\n",
       "      <td>1.022198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(OLDER SINGLES/COUPLES - Mainstream)</td>\n",
       "      <td>(Smiths)</td>\n",
       "      <td>0.069146</td>\n",
       "      <td>0.123016</td>\n",
       "      <td>0.008389</td>\n",
       "      <td>0.121329</td>\n",
       "      <td>0.986288</td>\n",
       "      <td>-0.000117</td>\n",
       "      <td>0.998080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>(RETIREES - Mainstream)</td>\n",
       "      <td>(Smiths)</td>\n",
       "      <td>0.080935</td>\n",
       "      <td>0.123016</td>\n",
       "      <td>0.009593</td>\n",
       "      <td>0.118528</td>\n",
       "      <td>0.963514</td>\n",
       "      <td>-0.000363</td>\n",
       "      <td>0.994908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>(YOUNG SINGLES/COUPLES - Mainstream)</td>\n",
       "      <td>(Pringles)</td>\n",
       "      <td>0.079209</td>\n",
       "      <td>0.101735</td>\n",
       "      <td>0.009382</td>\n",
       "      <td>0.118451</td>\n",
       "      <td>1.164310</td>\n",
       "      <td>0.001324</td>\n",
       "      <td>1.018962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(OLDER SINGLES/COUPLES - Budget)</td>\n",
       "      <td>(Smiths)</td>\n",
       "      <td>0.069596</td>\n",
       "      <td>0.123016</td>\n",
       "      <td>0.008146</td>\n",
       "      <td>0.117051</td>\n",
       "      <td>0.951509</td>\n",
       "      <td>-0.000415</td>\n",
       "      <td>0.993244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(RETIREES - Mainstream)</td>\n",
       "      <td>(Pringles)</td>\n",
       "      <td>0.080935</td>\n",
       "      <td>0.101735</td>\n",
       "      <td>0.008523</td>\n",
       "      <td>0.105308</td>\n",
       "      <td>1.035124</td>\n",
       "      <td>0.000289</td>\n",
       "      <td>1.003994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>(RETIREES - Mainstream)</td>\n",
       "      <td>(Doritos)</td>\n",
       "      <td>0.080935</td>\n",
       "      <td>0.102229</td>\n",
       "      <td>0.008466</td>\n",
       "      <td>0.104607</td>\n",
       "      <td>1.023260</td>\n",
       "      <td>0.000192</td>\n",
       "      <td>1.002656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(OLDER FAMILIES - Budget)</td>\n",
       "      <td>(Doritos)</td>\n",
       "      <td>0.087193</td>\n",
       "      <td>0.102229</td>\n",
       "      <td>0.008235</td>\n",
       "      <td>0.094450</td>\n",
       "      <td>0.923907</td>\n",
       "      <td>-0.000678</td>\n",
       "      <td>0.991410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(OLDER FAMILIES - Budget)</td>\n",
       "      <td>(Pringles)</td>\n",
       "      <td>0.087193</td>\n",
       "      <td>0.101735</td>\n",
       "      <td>0.008089</td>\n",
       "      <td>0.092777</td>\n",
       "      <td>0.911949</td>\n",
       "      <td>-0.000781</td>\n",
       "      <td>0.990126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              antecedents consequents  antecedent support  \\\n",
       "41   (YOUNG SINGLES/COUPLES - Mainstream)    (Kettle)            0.079209   \n",
       "1   (MIDAGE SINGLES/COUPLES - Mainstream)    (Kettle)            0.044966   \n",
       "23                    (RETIREES - Budget)    (Kettle)            0.057652   \n",
       "32                   (RETIREES - Premium)    (Kettle)            0.049591   \n",
       "13       (OLDER SINGLES/COUPLES - Budget)    (Kettle)            0.069596   \n",
       "21      (OLDER SINGLES/COUPLES - Premium)    (Kettle)            0.067115   \n",
       "27                (RETIREES - Mainstream)    (Kettle)            0.080935   \n",
       "17   (OLDER SINGLES/COUPLES - Mainstream)    (Kettle)            0.069146   \n",
       "35              (YOUNG FAMILIES - Budget)    (Kettle)            0.071991   \n",
       "5               (OLDER FAMILIES - Budget)    (Kettle)            0.087193   \n",
       "10          (OLDER FAMILIES - Mainstream)    (Kettle)            0.053664   \n",
       "9               (OLDER FAMILIES - Budget)    (Smiths)            0.087193   \n",
       "37              (YOUNG FAMILIES - Budget)    (Smiths)            0.071991   \n",
       "39   (YOUNG SINGLES/COUPLES - Mainstream)   (Doritos)            0.079209   \n",
       "19   (OLDER SINGLES/COUPLES - Mainstream)    (Smiths)            0.069146   \n",
       "31                (RETIREES - Mainstream)    (Smiths)            0.080935   \n",
       "42   (YOUNG SINGLES/COUPLES - Mainstream)  (Pringles)            0.079209   \n",
       "15       (OLDER SINGLES/COUPLES - Budget)    (Smiths)            0.069596   \n",
       "28                (RETIREES - Mainstream)  (Pringles)            0.080935   \n",
       "25                (RETIREES - Mainstream)   (Doritos)            0.080935   \n",
       "3               (OLDER FAMILIES - Budget)   (Doritos)            0.087193   \n",
       "6               (OLDER FAMILIES - Budget)  (Pringles)            0.087193   \n",
       "\n",
       "    consequent support   support  confidence      lift  leverage  conviction  \n",
       "41            0.167334  0.015579    0.196684  1.175400  0.002325    1.036537  \n",
       "1             0.167334  0.008657    0.192519  1.150508  0.001132    1.031190  \n",
       "23            0.167334  0.010505    0.182214  1.088926  0.000858    1.018196  \n",
       "32            0.167334  0.008981    0.181105  1.082296  0.000683    1.016816  \n",
       "13            0.167334  0.012422    0.178488  1.066658  0.000776    1.013578  \n",
       "21            0.167334  0.011944    0.177959  1.063495  0.000713    1.012925  \n",
       "27            0.167334  0.013723    0.169554  1.013269  0.000180    1.002674  \n",
       "17            0.167334  0.011490    0.166168  0.993034 -0.000081    0.998602  \n",
       "35            0.167334  0.011117    0.154422  0.922837 -0.000930    0.984730  \n",
       "5             0.167334  0.013455    0.154318  0.922216 -0.001135    0.984609  \n",
       "10            0.167334  0.008183    0.152481  0.911237 -0.000797    0.982475  \n",
       "9             0.123016  0.011948    0.137027  1.113895  0.001222    1.016236  \n",
       "37            0.123016  0.009459    0.131397  1.068126  0.000603    1.009648  \n",
       "39            0.102229  0.009642    0.121725  1.190712  0.001544    1.022198  \n",
       "19            0.123016  0.008389    0.121329  0.986288 -0.000117    0.998080  \n",
       "31            0.123016  0.009593    0.118528  0.963514 -0.000363    0.994908  \n",
       "42            0.101735  0.009382    0.118451  1.164310  0.001324    1.018962  \n",
       "15            0.123016  0.008146    0.117051  0.951509 -0.000415    0.993244  \n",
       "28            0.101735  0.008523    0.105308  1.035124  0.000289    1.003994  \n",
       "25            0.102229  0.008466    0.104607  1.023260  0.000192    1.002656  \n",
       "3             0.102229  0.008235    0.094450  0.923907 -0.000678    0.991410  \n",
       "6             0.101735  0.008089    0.092777  0.911949 -0.000781    0.990126  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set_temp = temp[\"group\"].unique()\n",
    "rules[rules[\"antecedents\"].apply(lambda x: list(x)).apply(lambda x: x in set_temp)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>(YOUNG SINGLES/COUPLES - Mainstream)</td>\n",
       "      <td>(Kettle)</td>\n",
       "      <td>0.079209</td>\n",
       "      <td>0.167334</td>\n",
       "      <td>0.015579</td>\n",
       "      <td>0.196684</td>\n",
       "      <td>1.175400</td>\n",
       "      <td>0.002325</td>\n",
       "      <td>1.036537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>(YOUNG SINGLES/COUPLES - Mainstream)</td>\n",
       "      <td>(Doritos)</td>\n",
       "      <td>0.079209</td>\n",
       "      <td>0.102229</td>\n",
       "      <td>0.009642</td>\n",
       "      <td>0.121725</td>\n",
       "      <td>1.190712</td>\n",
       "      <td>0.001544</td>\n",
       "      <td>1.022198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>(YOUNG SINGLES/COUPLES - Mainstream)</td>\n",
       "      <td>(Pringles)</td>\n",
       "      <td>0.079209</td>\n",
       "      <td>0.101735</td>\n",
       "      <td>0.009382</td>\n",
       "      <td>0.118451</td>\n",
       "      <td>1.164310</td>\n",
       "      <td>0.001324</td>\n",
       "      <td>1.018962</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             antecedents consequents  antecedent support  \\\n",
       "41  (YOUNG SINGLES/COUPLES - Mainstream)    (Kettle)            0.079209   \n",
       "39  (YOUNG SINGLES/COUPLES - Mainstream)   (Doritos)            0.079209   \n",
       "42  (YOUNG SINGLES/COUPLES - Mainstream)  (Pringles)            0.079209   \n",
       "\n",
       "    consequent support   support  confidence      lift  leverage  conviction  \n",
       "41            0.167334  0.015579    0.196684  1.175400  0.002325    1.036537  \n",
       "39            0.102229  0.009642    0.121725  1.190712  0.001544    1.022198  \n",
       "42            0.101735  0.009382    0.118451  1.164310  0.001324    1.018962  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules[rules['antecedents'] == {'YOUNG SINGLES/COUPLES - Mainstream'}]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From apriori analysis, we can see that for Mainstream - young singles/couples, Kettle is the brand of choice. This is also true for most other segments. We can use the affinity index to see if there are brands this segment prefers more than the other segments to target. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>BRANDS</th>\n",
       "      <th>target</th>\n",
       "      <th>other</th>\n",
       "      <th>affinity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Tyrrells</td>\n",
       "      <td>0.017088</td>\n",
       "      <td>0.013368</td>\n",
       "      <td>1.278270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Twisties</td>\n",
       "      <td>0.024845</td>\n",
       "      <td>0.019632</td>\n",
       "      <td>1.265496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Doritos</td>\n",
       "      <td>0.065673</td>\n",
       "      <td>0.052511</td>\n",
       "      <td>1.250646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Tostitos</td>\n",
       "      <td>0.024569</td>\n",
       "      <td>0.019944</td>\n",
       "      <td>1.231911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Kettle</td>\n",
       "      <td>0.106115</td>\n",
       "      <td>0.086574</td>\n",
       "      <td>1.225712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Pringles</td>\n",
       "      <td>0.063906</td>\n",
       "      <td>0.052477</td>\n",
       "      <td>1.217793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Cobs</td>\n",
       "      <td>0.023851</td>\n",
       "      <td>0.020004</td>\n",
       "      <td>1.192293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Infuzions</td>\n",
       "      <td>0.034507</td>\n",
       "      <td>0.029930</td>\n",
       "      <td>1.152890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Grain Waves</td>\n",
       "      <td>0.017833</td>\n",
       "      <td>0.016214</td>\n",
       "      <td>1.099878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Thins</td>\n",
       "      <td>0.032188</td>\n",
       "      <td>0.029771</td>\n",
       "      <td>1.081172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cheezels</td>\n",
       "      <td>0.009551</td>\n",
       "      <td>0.009866</td>\n",
       "      <td>0.968161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Smiths</td>\n",
       "      <td>0.053030</td>\n",
       "      <td>0.064809</td>\n",
       "      <td>0.818247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cheetos</td>\n",
       "      <td>0.004582</td>\n",
       "      <td>0.006139</td>\n",
       "      <td>0.746405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>French</td>\n",
       "      <td>0.002153</td>\n",
       "      <td>0.003017</td>\n",
       "      <td>0.713793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Red Rock Deli</td>\n",
       "      <td>0.024155</td>\n",
       "      <td>0.035152</td>\n",
       "      <td>0.687154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Natural Chip Co</td>\n",
       "      <td>0.010876</td>\n",
       "      <td>0.016236</td>\n",
       "      <td>0.669883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CCs</td>\n",
       "      <td>0.006128</td>\n",
       "      <td>0.009668</td>\n",
       "      <td>0.633867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sunbites</td>\n",
       "      <td>0.003533</td>\n",
       "      <td>0.006576</td>\n",
       "      <td>0.537349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Woolworths</td>\n",
       "      <td>0.013223</td>\n",
       "      <td>0.025567</td>\n",
       "      <td>0.517189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Burger</td>\n",
       "      <td>0.001712</td>\n",
       "      <td>0.003415</td>\n",
       "      <td>0.501180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             BRANDS    target     other  affinity\n",
       "8          Tyrrells  0.017088  0.013368  1.278270\n",
       "13         Twisties  0.024845  0.019632  1.265496\n",
       "18          Doritos  0.065673  0.052511  1.250646\n",
       "12         Tostitos  0.024569  0.019944  1.231911\n",
       "19           Kettle  0.106115  0.086574  1.225712\n",
       "17         Pringles  0.063906  0.052477  1.217793\n",
       "10             Cobs  0.023851  0.020004  1.192293\n",
       "15        Infuzions  0.034507  0.029930  1.152890\n",
       "9       Grain Waves  0.017833  0.016214  1.099878\n",
       "14            Thins  0.032188  0.029771  1.081172\n",
       "5          Cheezels  0.009551  0.009866  0.968161\n",
       "16           Smiths  0.053030  0.064809  0.818247\n",
       "3           Cheetos  0.004582  0.006139  0.746405\n",
       "1            French  0.002153  0.003017  0.713793\n",
       "11    Red Rock Deli  0.024155  0.035152  0.687154\n",
       "6   Natural Chip Co  0.010876  0.016236  0.669883\n",
       "4               CCs  0.006128  0.009668  0.633867\n",
       "2          Sunbites  0.003533  0.006576  0.537349\n",
       "7        Woolworths  0.013223  0.025567  0.517189\n",
       "0            Burger  0.001712  0.003415  0.501180"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the target rating proportion\n",
    "target_segment = young_mainstream[\"BRAND_NAME\"].value_counts().sort_values(ascending = True).rename_axis('BRANDS').reset_index(name='target')\n",
    "target_segment.target /= young_mainstream[\"PROD_QTY\"].sum()\n",
    "\n",
    "# find the other rating proportion\n",
    "not_young_mainstream = full_df.loc[full_df['LIFESTAGE'] != \"YOUNG SINGLES/COUPLES\"]\n",
    "not_young_mainstream = not_young_mainstream.loc[not_young_mainstream['MEMBER_TYPE'] != \"Mainstream\"]\n",
    "other = not_young_mainstream[\"BRAND_NAME\"].value_counts().sort_values(ascending = True).rename_axis('BRANDS').reset_index(name='other')\n",
    "other.other /= not_young_mainstream[\"PROD_QTY\"].sum()\n",
    "\n",
    "# join the two dataframes \n",
    "brand_proportions = target_segment.set_index('BRANDS').join(other.set_index('BRANDS'))\n",
    "# full_df = trans_df.set_index('LYLTY_CARD_NBR').join(cust_df.set_index('LYLTY_CARD_NBR'))\n",
    "brand_proportions = brand_proportions.reset_index()\n",
    "brand_proportions['affinity'] = brand_proportions['target']/brand_proportions['other']\n",
    "brand_proportions.sort_values(by = 'affinity', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By using the affinity index, we can see that mainstream young singles/couples are 28% more likely to purcahse Tyrrells chips than the other segments. However, they are 50% less likely to purchase Burger Rings. \n",
    "\n",
    "We also want to find out if our target segment tends to buy larger packs of chips."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution of the packet sizes for a general indication of what it most popular. \n",
    "young_mainstream = full_df.loc[full_df['LIFESTAGE'] == \"YOUNG SINGLES/COUPLES\"]\n",
    "young_mainstream = young_mainstream.loc[young_mainstream['MEMBER_TYPE'] == \"Mainstream\"]\n",
    "ax = young_mainstream[\"PACK_SIZE\"].value_counts().sort_values(ascending = True).plot.barh()\n",
    "ax.set_ylabel(\"Packet size (g)\")\n",
    "ax.set_xlabel(\"Packets purchased\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Also want to check which brands correspond to what sized packets. \n",
    "brand_size = young_mainstream.groupby(['BRAND_NAME','PACK_SIZE'], as_index = False)['TOT_SALES'].agg(['sum'])\n",
    "ax = brand_size.sort_values(by = 'sum').plot.barh(y = \"sum\", figsize=(10,10))\n",
    "ax.set_ylabel(\"(Brand, packet size(g))\")\n",
    "ax.set_xlabel(\"Packets purchased\")\n",
    "plt.show()"
   ]
  },
  {
   "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>MIDAGE SINGLES/COUPLES - Budget</th>\n",
       "      <th>MIDAGE SINGLES/COUPLES - Mainstream</th>\n",
       "      <th>MIDAGE SINGLES/COUPLES - Premium</th>\n",
       "      <th>NEW FAMILIES - Budget</th>\n",
       "      <th>NEW FAMILIES - Mainstream</th>\n",
       "      <th>NEW FAMILIES - Premium</th>\n",
       "      <th>OLDER FAMILIES - Budget</th>\n",
       "      <th>OLDER FAMILIES - Mainstream</th>\n",
       "      <th>OLDER FAMILIES - Premium</th>\n",
       "      <th>OLDER SINGLES/COUPLES - Budget</th>\n",
       "      <th>...</th>\n",
       "      <th>175.0</th>\n",
       "      <th>180.0</th>\n",
       "      <th>190.0</th>\n",
       "      <th>200.0</th>\n",
       "      <th>210.0</th>\n",
       "      <th>220.0</th>\n",
       "      <th>250.0</th>\n",
       "      <th>270.0</th>\n",
       "      <th>330.0</th>\n",
       "      <th>380.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246735</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246736</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246737</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246738</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246739</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>246740 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        MIDAGE SINGLES/COUPLES - Budget  MIDAGE SINGLES/COUPLES - Mainstream  \\\n",
       "0                                     0                                    0   \n",
       "1                                     0                                    0   \n",
       "2                                     0                                    0   \n",
       "3                                     0                                    0   \n",
       "4                                     0                                    0   \n",
       "...                                 ...                                  ...   \n",
       "246735                                0                                    0   \n",
       "246736                                0                                    0   \n",
       "246737                                0                                    1   \n",
       "246738                                0                                    0   \n",
       "246739                                0                                    0   \n",
       "\n",
       "        MIDAGE SINGLES/COUPLES - Premium  NEW FAMILIES - Budget  \\\n",
       "0                                      0                      0   \n",
       "1                                      0                      0   \n",
       "2                                      0                      0   \n",
       "3                                      0                      0   \n",
       "4                                      0                      0   \n",
       "...                                  ...                    ...   \n",
       "246735                                 0                      0   \n",
       "246736                                 0                      0   \n",
       "246737                                 0                      0   \n",
       "246738                                 0                      0   \n",
       "246739                                 0                      0   \n",
       "\n",
       "        NEW FAMILIES - Mainstream  NEW FAMILIES - Premium  \\\n",
       "0                               0                       0   \n",
       "1                               0                       0   \n",
       "2                               0                       0   \n",
       "3                               0                       0   \n",
       "4                               0                       0   \n",
       "...                           ...                     ...   \n",
       "246735                          0                       0   \n",
       "246736                          0                       0   \n",
       "246737                          0                       0   \n",
       "246738                          0                       0   \n",
       "246739                          0                       0   \n",
       "\n",
       "        OLDER FAMILIES - Budget  OLDER FAMILIES - Mainstream  \\\n",
       "0                             0                            0   \n",
       "1                             1                            0   \n",
       "2                             0                            0   \n",
       "3                             0                            0   \n",
       "4                             0                            0   \n",
       "...                         ...                          ...   \n",
       "246735                        0                            0   \n",
       "246736                        0                            0   \n",
       "246737                        0                            0   \n",
       "246738                        0                            0   \n",
       "246739                        0                            0   \n",
       "\n",
       "        OLDER FAMILIES - Premium  OLDER SINGLES/COUPLES - Budget  ...  175.0  \\\n",
       "0                              0                               0  ...      0   \n",
       "1                              0                               0  ...      0   \n",
       "2                              0                               1  ...      0   \n",
       "3                              0                               0  ...      0   \n",
       "4                              0                               0  ...      0   \n",
       "...                          ...                             ...  ...    ...   \n",
       "246735                         0                               0  ...      0   \n",
       "246736                         0                               1  ...      1   \n",
       "246737                         0                               0  ...      0   \n",
       "246738                         0                               0  ...      0   \n",
       "246739                         0                               0  ...      1   \n",
       "\n",
       "        180.0  190.0  200.0  210.0  220.0  250.0  270.0  330.0  380.0  \n",
       "0           0      0      0      0      0      0      0      0      0  \n",
       "1           0      0      0      0      0      0      0      0      0  \n",
       "2           0      0      0      0      0      0      0      0      0  \n",
       "3           0      0      0      0      0      0      0      0      0  \n",
       "4           0      0      0      0      0      0      0      0      0  \n",
       "...       ...    ...    ...    ...    ...    ...    ...    ...    ...  \n",
       "246735      0      0      0      0      0      0      0      0      0  \n",
       "246736      0      0      0      0      0      0      0      0      0  \n",
       "246737      0      0      0      0      0      0      0      0      0  \n",
       "246738      0      0      0      0      0      0      0      0      0  \n",
       "246739      0      0      0      0      0      0      0      0      0  \n",
       "\n",
       "[246740 rows x 41 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups = pd.get_dummies(temp[\"group\"])\n",
    "brands = pd.get_dummies(temp[\"PACK_SIZE\"])\n",
    "groups_brands = groups.join(brands)\n",
    "groups_brands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>(YOUNG FAMILIES - Premium)</td>\n",
       "      <td>(175.0)</td>\n",
       "      <td>0.043706</td>\n",
       "      <td>0.269069</td>\n",
       "      <td>0.012150</td>\n",
       "      <td>0.278004</td>\n",
       "      <td>1.033210</td>\n",
       "      <td>0.000391</td>\n",
       "      <td>1.012377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>(YOUNG FAMILIES - Budget)</td>\n",
       "      <td>(175.0)</td>\n",
       "      <td>0.071991</td>\n",
       "      <td>0.269069</td>\n",
       "      <td>0.019944</td>\n",
       "      <td>0.277037</td>\n",
       "      <td>1.029613</td>\n",
       "      <td>0.000574</td>\n",
       "      <td>1.011021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>(YOUNG SINGLES/COUPLES - Budget)</td>\n",
       "      <td>(175.0)</td>\n",
       "      <td>0.034745</td>\n",
       "      <td>0.269069</td>\n",
       "      <td>0.009476</td>\n",
       "      <td>0.272717</td>\n",
       "      <td>1.013558</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>1.005016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(OLDER FAMILIES - Mainstream)</td>\n",
       "      <td>(175.0)</td>\n",
       "      <td>0.053664</td>\n",
       "      <td>0.269069</td>\n",
       "      <td>0.014542</td>\n",
       "      <td>0.270977</td>\n",
       "      <td>1.007091</td>\n",
       "      <td>0.000102</td>\n",
       "      <td>1.002617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(OLDER FAMILIES - Premium)</td>\n",
       "      <td>(175.0)</td>\n",
       "      <td>0.042162</td>\n",
       "      <td>0.269069</td>\n",
       "      <td>0.011413</td>\n",
       "      <td>0.270691</td>\n",
       "      <td>1.006030</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>1.002225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>(RETIREES - Budget)</td>\n",
       "      <td>(175.0)</td>\n",
       "      <td>0.057652</td>\n",
       "      <td>0.269069</td>\n",
       "      <td>0.015591</td>\n",
       "      <td>0.270439</td>\n",
       "      <td>1.005094</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>1.001879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>(RETIREES - Premium)</td>\n",
       "      <td>(175.0)</td>\n",
       "      <td>0.049591</td>\n",
       "      <td>0.269069</td>\n",
       "      <td>0.013399</td>\n",
       "      <td>0.270186</td>\n",
       "      <td>1.004154</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>1.001531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(OLDER FAMILIES - Budget)</td>\n",
       "      <td>(175.0)</td>\n",
       "      <td>0.087193</td>\n",
       "      <td>0.269069</td>\n",
       "      <td>0.023539</td>\n",
       "      <td>0.269964</td>\n",
       "      <td>1.003327</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>1.001226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(OLDER SINGLES/COUPLES - Budget)</td>\n",
       "      <td>(175.0)</td>\n",
       "      <td>0.069596</td>\n",
       "      <td>0.269069</td>\n",
       "      <td>0.018744</td>\n",
       "      <td>0.269334</td>\n",
       "      <td>1.000985</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>1.000363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(OLDER SINGLES/COUPLES - Premium)</td>\n",
       "      <td>(175.0)</td>\n",
       "      <td>0.067115</td>\n",
       "      <td>0.269069</td>\n",
       "      <td>0.018068</td>\n",
       "      <td>0.269203</td>\n",
       "      <td>1.000499</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>1.000184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(MIDAGE SINGLES/COUPLES - Mainstream)</td>\n",
       "      <td>(175.0)</td>\n",
       "      <td>0.044966</td>\n",
       "      <td>0.269069</td>\n",
       "      <td>0.012057</td>\n",
       "      <td>0.268139</td>\n",
       "      <td>0.996544</td>\n",
       "      <td>-0.000042</td>\n",
       "      <td>0.998729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>(YOUNG FAMILIES - Mainstream)</td>\n",
       "      <td>(175.0)</td>\n",
       "      <td>0.048419</td>\n",
       "      <td>0.269069</td>\n",
       "      <td>0.012864</td>\n",
       "      <td>0.265673</td>\n",
       "      <td>0.987381</td>\n",
       "      <td>-0.000164</td>\n",
       "      <td>0.995376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(OLDER SINGLES/COUPLES - Mainstream)</td>\n",
       "      <td>(175.0)</td>\n",
       "      <td>0.069146</td>\n",
       "      <td>0.269069</td>\n",
       "      <td>0.018339</td>\n",
       "      <td>0.265225</td>\n",
       "      <td>0.985714</td>\n",
       "      <td>-0.000266</td>\n",
       "      <td>0.994769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>(RETIREES - Mainstream)</td>\n",
       "      <td>(175.0)</td>\n",
       "      <td>0.080935</td>\n",
       "      <td>0.269069</td>\n",
       "      <td>0.021460</td>\n",
       "      <td>0.265148</td>\n",
       "      <td>0.985428</td>\n",
       "      <td>-0.000317</td>\n",
       "      <td>0.994664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>(YOUNG SINGLES/COUPLES - Mainstream)</td>\n",
       "      <td>(175.0)</td>\n",
       "      <td>0.079209</td>\n",
       "      <td>0.269069</td>\n",
       "      <td>0.020252</td>\n",
       "      <td>0.255679</td>\n",
       "      <td>0.950239</td>\n",
       "      <td>-0.001061</td>\n",
       "      <td>0.982012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(OLDER SINGLES/COUPLES - Premium)</td>\n",
       "      <td>(150.0)</td>\n",
       "      <td>0.067115</td>\n",
       "      <td>0.162937</td>\n",
       "      <td>0.011218</td>\n",
       "      <td>0.167150</td>\n",
       "      <td>1.025857</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>1.005059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(OLDER FAMILIES - Budget)</td>\n",
       "      <td>(150.0)</td>\n",
       "      <td>0.087193</td>\n",
       "      <td>0.162937</td>\n",
       "      <td>0.014542</td>\n",
       "      <td>0.166775</td>\n",
       "      <td>1.023558</td>\n",
       "      <td>0.000335</td>\n",
       "      <td>1.004607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>(RETIREES - Mainstream)</td>\n",
       "      <td>(150.0)</td>\n",
       "      <td>0.080935</td>\n",
       "      <td>0.162937</td>\n",
       "      <td>0.013334</td>\n",
       "      <td>0.164747</td>\n",
       "      <td>1.011111</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>1.002168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(OLDER SINGLES/COUPLES - Budget)</td>\n",
       "      <td>(150.0)</td>\n",
       "      <td>0.069596</td>\n",
       "      <td>0.162937</td>\n",
       "      <td>0.011393</td>\n",
       "      <td>0.163697</td>\n",
       "      <td>1.004665</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>1.000909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(RETIREES - Budget)</td>\n",
       "      <td>(150.0)</td>\n",
       "      <td>0.057652</td>\n",
       "      <td>0.162937</td>\n",
       "      <td>0.009399</td>\n",
       "      <td>0.163023</td>\n",
       "      <td>1.000529</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>1.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(OLDER SINGLES/COUPLES - Mainstream)</td>\n",
       "      <td>(150.0)</td>\n",
       "      <td>0.069146</td>\n",
       "      <td>0.162937</td>\n",
       "      <td>0.011239</td>\n",
       "      <td>0.162534</td>\n",
       "      <td>0.997531</td>\n",
       "      <td>-0.000028</td>\n",
       "      <td>0.999520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>(YOUNG FAMILIES - Budget)</td>\n",
       "      <td>(150.0)</td>\n",
       "      <td>0.071991</td>\n",
       "      <td>0.162937</td>\n",
       "      <td>0.011599</td>\n",
       "      <td>0.161121</td>\n",
       "      <td>0.988859</td>\n",
       "      <td>-0.000131</td>\n",
       "      <td>0.997836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>(YOUNG SINGLES/COUPLES - Mainstream)</td>\n",
       "      <td>(150.0)</td>\n",
       "      <td>0.079209</td>\n",
       "      <td>0.162937</td>\n",
       "      <td>0.012483</td>\n",
       "      <td>0.157593</td>\n",
       "      <td>0.967205</td>\n",
       "      <td>-0.000423</td>\n",
       "      <td>0.993657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>(YOUNG SINGLES/COUPLES - Mainstream)</td>\n",
       "      <td>(134.0)</td>\n",
       "      <td>0.079209</td>\n",
       "      <td>0.101735</td>\n",
       "      <td>0.009382</td>\n",
       "      <td>0.118451</td>\n",
       "      <td>1.164310</td>\n",
       "      <td>0.001324</td>\n",
       "      <td>1.018962</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              antecedents consequents  antecedent support  \\\n",
       "38             (YOUNG FAMILIES - Premium)     (175.0)            0.043706   \n",
       "34              (YOUNG FAMILIES - Budget)     (175.0)            0.071991   \n",
       "40       (YOUNG SINGLES/COUPLES - Budget)     (175.0)            0.034745   \n",
       "6           (OLDER FAMILIES - Mainstream)     (175.0)            0.053664   \n",
       "8              (OLDER FAMILIES - Premium)     (175.0)            0.042162   \n",
       "24                    (RETIREES - Budget)     (175.0)            0.057652   \n",
       "30                   (RETIREES - Premium)     (175.0)            0.049591   \n",
       "5               (OLDER FAMILIES - Budget)     (175.0)            0.087193   \n",
       "12       (OLDER SINGLES/COUPLES - Budget)     (175.0)            0.069596   \n",
       "21      (OLDER SINGLES/COUPLES - Premium)     (175.0)            0.067115   \n",
       "0   (MIDAGE SINGLES/COUPLES - Mainstream)     (175.0)            0.044966   \n",
       "36          (YOUNG FAMILIES - Mainstream)     (175.0)            0.048419   \n",
       "17   (OLDER SINGLES/COUPLES - Mainstream)     (175.0)            0.069146   \n",
       "29                (RETIREES - Mainstream)     (175.0)            0.080935   \n",
       "46   (YOUNG SINGLES/COUPLES - Mainstream)     (175.0)            0.079209   \n",
       "19      (OLDER SINGLES/COUPLES - Premium)     (150.0)            0.067115   \n",
       "3               (OLDER FAMILIES - Budget)     (150.0)            0.087193   \n",
       "27                (RETIREES - Mainstream)     (150.0)            0.080935   \n",
       "10       (OLDER SINGLES/COUPLES - Budget)     (150.0)            0.069596   \n",
       "22                    (RETIREES - Budget)     (150.0)            0.057652   \n",
       "15   (OLDER SINGLES/COUPLES - Mainstream)     (150.0)            0.069146   \n",
       "33              (YOUNG FAMILIES - Budget)     (150.0)            0.071991   \n",
       "44   (YOUNG SINGLES/COUPLES - Mainstream)     (150.0)            0.079209   \n",
       "42   (YOUNG SINGLES/COUPLES - Mainstream)     (134.0)            0.079209   \n",
       "\n",
       "    consequent support   support  confidence      lift  leverage  conviction  \n",
       "38            0.269069  0.012150    0.278004  1.033210  0.000391    1.012377  \n",
       "34            0.269069  0.019944    0.277037  1.029613  0.000574    1.011021  \n",
       "40            0.269069  0.009476    0.272717  1.013558  0.000127    1.005016  \n",
       "6             0.269069  0.014542    0.270977  1.007091  0.000102    1.002617  \n",
       "8             0.269069  0.011413    0.270691  1.006030  0.000068    1.002225  \n",
       "24            0.269069  0.015591    0.270439  1.005094  0.000079    1.001879  \n",
       "30            0.269069  0.013399    0.270186  1.004154  0.000055    1.001531  \n",
       "5             0.269069  0.023539    0.269964  1.003327  0.000078    1.001226  \n",
       "12            0.269069  0.018744    0.269334  1.000985  0.000018    1.000363  \n",
       "21            0.269069  0.018068    0.269203  1.000499  0.000009    1.000184  \n",
       "0             0.269069  0.012057    0.268139  0.996544 -0.000042    0.998729  \n",
       "36            0.269069  0.012864    0.265673  0.987381 -0.000164    0.995376  \n",
       "17            0.269069  0.018339    0.265225  0.985714 -0.000266    0.994769  \n",
       "29            0.269069  0.021460    0.265148  0.985428 -0.000317    0.994664  \n",
       "46            0.269069  0.020252    0.255679  0.950239 -0.001061    0.982012  \n",
       "19            0.162937  0.011218    0.167150  1.025857  0.000283    1.005059  \n",
       "3             0.162937  0.014542    0.166775  1.023558  0.000335    1.004607  \n",
       "27            0.162937  0.013334    0.164747  1.011111  0.000147    1.002168  \n",
       "10            0.162937  0.011393    0.163697  1.004665  0.000053    1.000909  \n",
       "22            0.162937  0.009399    0.163023  1.000529  0.000005    1.000103  \n",
       "15            0.162937  0.011239    0.162534  0.997531 -0.000028    0.999520  \n",
       "33            0.162937  0.011599    0.161121  0.988859 -0.000131    0.997836  \n",
       "44            0.162937  0.012483    0.157593  0.967205 -0.000423    0.993657  \n",
       "42            0.101735  0.009382    0.118451  1.164310  0.001324    1.018962  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_groupsbrands = apriori(groups_brands, min_support=0.009, use_colnames=True)\n",
    "rules = association_rules(freq_groupsbrands, metric=\"lift\", min_threshold=0.5)\n",
    "rules.sort_values('confidence', ascending = False, inplace = True)\n",
    "set_temp = temp[\"group\"].unique()\n",
    "rules[rules[\"antecedents\"].apply(lambda x: list(x)).apply(lambda x: x in set_temp)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While it appears that most segments purchase more chip packets that are 175g, which is also the size that most Kettles chips are purchased in, we can also determine whether mainstream young singles/couples have certain preferences over the other segments again using the affinity index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>SIZES</th>\n",
       "      <th>target</th>\n",
       "      <th>other</th>\n",
       "      <th>affinity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>270.0</td>\n",
       "      <td>0.017115</td>\n",
       "      <td>0.012958</td>\n",
       "      <td>1.320826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>380.0</td>\n",
       "      <td>0.017281</td>\n",
       "      <td>0.013375</td>\n",
       "      <td>1.291992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>330.0</td>\n",
       "      <td>0.032988</td>\n",
       "      <td>0.026455</td>\n",
       "      <td>1.246968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>210.0</td>\n",
       "      <td>0.015901</td>\n",
       "      <td>0.012973</td>\n",
       "      <td>1.225655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>134.0</td>\n",
       "      <td>0.063906</td>\n",
       "      <td>0.052477</td>\n",
       "      <td>1.217793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>110.0</td>\n",
       "      <td>0.056618</td>\n",
       "      <td>0.046653</td>\n",
       "      <td>1.213618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>135.0</td>\n",
       "      <td>0.008006</td>\n",
       "      <td>0.006750</td>\n",
       "      <td>1.185951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>250.0</td>\n",
       "      <td>0.007729</td>\n",
       "      <td>0.006674</td>\n",
       "      <td>1.158076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>170.0</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.041826</td>\n",
       "      <td>1.039502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>150.0</td>\n",
       "      <td>0.085024</td>\n",
       "      <td>0.084969</td>\n",
       "      <td>1.000652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>175.0</td>\n",
       "      <td>0.137943</td>\n",
       "      <td>0.141498</td>\n",
       "      <td>0.974878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>165.0</td>\n",
       "      <td>0.030421</td>\n",
       "      <td>0.032135</td>\n",
       "      <td>0.946660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>190.0</td>\n",
       "      <td>0.004086</td>\n",
       "      <td>0.006318</td>\n",
       "      <td>0.646684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>180.0</td>\n",
       "      <td>0.001932</td>\n",
       "      <td>0.003240</td>\n",
       "      <td>0.596328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>160.0</td>\n",
       "      <td>0.003533</td>\n",
       "      <td>0.006428</td>\n",
       "      <td>0.549720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90.0</td>\n",
       "      <td>0.003533</td>\n",
       "      <td>0.006576</td>\n",
       "      <td>0.537349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70.0</td>\n",
       "      <td>0.001739</td>\n",
       "      <td>0.003282</td>\n",
       "      <td>0.529870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>125.0</td>\n",
       "      <td>0.001629</td>\n",
       "      <td>0.003153</td>\n",
       "      <td>0.516530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>200.0</td>\n",
       "      <td>0.004941</td>\n",
       "      <td>0.009714</td>\n",
       "      <td>0.508695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>220.0</td>\n",
       "      <td>0.001712</td>\n",
       "      <td>0.003415</td>\n",
       "      <td>0.501180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SIZES    target     other  affinity\n",
       "11  270.0  0.017115  0.012958  1.320826\n",
       "12  380.0  0.017281  0.013375  1.291992\n",
       "14  330.0  0.032988  0.026455  1.246968\n",
       "10  210.0  0.015901  0.012973  1.225655\n",
       "17  134.0  0.063906  0.052477  1.217793\n",
       "16  110.0  0.056618  0.046653  1.213618\n",
       "9   135.0  0.008006  0.006750  1.185951\n",
       "8   250.0  0.007729  0.006674  1.158076\n",
       "15  170.0  0.043478  0.041826  1.039502\n",
       "18  150.0  0.085024  0.084969  1.000652\n",
       "19  175.0  0.137943  0.141498  0.974878\n",
       "13  165.0  0.030421  0.032135  0.946660\n",
       "6   190.0  0.004086  0.006318  0.646684\n",
       "3   180.0  0.001932  0.003240  0.596328\n",
       "5   160.0  0.003533  0.006428  0.549720\n",
       "4    90.0  0.003533  0.006576  0.537349\n",
       "2    70.0  0.001739  0.003282  0.529870\n",
       "0   125.0  0.001629  0.003153  0.516530\n",
       "7   200.0  0.004941  0.009714  0.508695\n",
       "1   220.0  0.001712  0.003415  0.501180"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the target rating proportion\n",
    "target_segment = young_mainstream[\"PACK_SIZE\"].value_counts().sort_values(ascending = True).rename_axis('SIZES').reset_index(name='target')\n",
    "target_segment.target /= young_mainstream[\"PROD_QTY\"].sum()\n",
    "\n",
    "# find the other rating proportion\n",
    "other = not_young_mainstream[\"PACK_SIZE\"].value_counts().sort_values(ascending = True).rename_axis('SIZES').reset_index(name='other')\n",
    "other.other /= not_young_mainstream[\"PROD_QTY\"].sum()\n",
    "\n",
    "# join the two dataframes \n",
    "brand_proportions = target_segment.set_index('SIZES').join(other.set_index('SIZES'))\n",
    "brand_proportions = brand_proportions.reset_index()\n",
    "brand_proportions['affinity'] = brand_proportions['target']/brand_proportions['other']\n",
    "brand_proportions.sort_values(by = 'affinity', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we can see that mainstream young singles/couples are 32% more likely to purcahse 270g chips than the other segments. However, they are 50% less likely to purchase 220g chips. The chips that come in 270g bags are Twisties while Burger Rings come in 220g bags, which is consistent with the affinity testing for the chip brands.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary of Insights\n",
    "\n",
    "The three highest contributing segments to the total sales are:\n",
    "1. Older families - Budget\n",
    "2. Young singles/couples - Mainstream\n",
    "3. Retirees - Mainstream\n",
    "\n",
    "The largest population group is mainstream young singles/couples, followed by mainstream retirees which explains their large total sales. While population is not a driving factor for budget older families, older families and young families in general buy more chips per customer. Furthermore, mainstream young singles/couples have the highest spend per purchase, which is statistically significant compared to the non-mainstream young singles/couples. Taking a further look at the mainstream yong singles/couples segment, we have found that they are 28% more likely to purchase Tyrells chips than the other segments. This segment does purchase the most Kettles chips, which is also consistent with most other segments. However, they are 50% less likely to purchase Burger Rings, which was also evident in the preferences for packet sizes given they are the only chips that come in 220g sizes. Mainstream young singles/couples are 32% more likely to purchase 270g chips, which is the size that Twisties come in, compare to the other segments. The packet size purchased most over many segments is 175g. \n",
    "\n",
    "Perhaps we can use the fact that Tyrells and (the packet size of) Twisties chips are more likely to be purchased by mainstream young singles/couples and place these products where they are more likely to be seen by this segment. Furthermore, given that Kettles chips are still the most popular, if the primary target segment are mainstream young singles/couples, Tyrells and Twisties could be placed closer to the Kettles chips. This strategy, with the brands they are more likely to purchase, could also be applied to other segments that purchase the most of Kettles to increase their total sales. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}