Customer Purchasing Behavior Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3e519a7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4f52141f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"C:/Users/ABHILASH/Downloads/QVI_transaction_data.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "416bbe78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </tbody>\n",
       "</table>\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",
       "                                  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  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "30472167",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(264836, 8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "820fb031",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE                int64\n",
       "STORE_NBR           int64\n",
       "LYLTY_CARD_NBR      int64\n",
       "TXN_ID              int64\n",
       "PROD_NBR            int64\n",
       "PROD_NAME          object\n",
       "PROD_QTY            int64\n",
       "TOT_SALES         float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ef6b03b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE              0\n",
       "STORE_NBR         0\n",
       "LYLTY_CARD_NBR    0\n",
       "TXN_ID            0\n",
       "PROD_NBR          0\n",
       "PROD_NAME         0\n",
       "PROD_QTY          0\n",
       "TOT_SALES         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data=df.isnull().sum()\n",
    "missing_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "63b66108",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta\n",
    "start_date = datetime(1899, 12, 30)  # Change this to your desired starting date\n",
    "df['DATE'] = start_date + pd.to_timedelta(df['DATE'], unit='D')\n",
    "\n",
    "# Format the date column as a string in 'YYYY-MM-DD' format\n",
    "df['DATE'] = df['DATE'].dt.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bd87c041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         2018-10-17\n",
       "1         2019-05-14\n",
       "2         2019-05-20\n",
       "3         2018-08-17\n",
       "4         2018-08-18\n",
       "             ...    \n",
       "264831    2019-03-09\n",
       "264832    2018-08-13\n",
       "264833    2018-11-06\n",
       "264834    2018-12-27\n",
       "264835    2018-09-22\n",
       "Name: DATE, Length: 264836, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df['DATE'] = pd.to_datetime(df['DATE'], origin='2019-12-30', unit='D')\n",
    "#df['DATE']=pd.to_datetime(df['DATE']).dt.date\n",
    "df['DATE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d8c54932",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-07-01\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'2019-06-30'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.DATE.min())\n",
    "df.DATE.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f64f2ffd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Kettle Mozzarella   Basil & Pesto 175g      3304\n",
       "Kettle Tortilla ChpsHny&Jlpno Chili 150g    3296\n",
       "Cobs Popd Swt/Chlli &Sr/Cream Chips 110g    3269\n",
       "Tyrrells Crisps     Ched & Chives 165g      3268\n",
       "Cobs Popd Sea Salt  Chips 110g              3265\n",
       "                                            ... \n",
       "RRD Pc Sea Salt     165g                    1431\n",
       "Woolworths Medium   Salsa 300g              1430\n",
       "NCC Sour Cream &    Garden Chives 175g      1419\n",
       "French Fries Potato Chips 175g              1418\n",
       "WW Crinkle Cut      Original 175g           1410\n",
       "Name: PROD_NAME, Length: 114, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.PROD_NAME.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "344989d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_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>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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "0  2018-10-17          1            1000       1         5   \n",
       "1  2019-05-14          1            1307     348        66   \n",
       "2  2019-05-20          1            1343     383        61   \n",
       "3  2018-08-17          2            2373     974        69   \n",
       "4  2018-08-18          2            2426    1038       108   \n",
       "\n",
       "                                  PROD_NAME  PROD_QTY  TOT_SALES  \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  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8ba892e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                                     264836\n",
       "unique                                       114\n",
       "top       Kettle Mozzarella   Basil & Pesto 175g\n",
       "freq                                        3304\n",
       "Name: PROD_NAME, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['PROD_NAME'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b094bd2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "product_words = df['PROD_NAME'].str.split(expand=True).stack().reset_index(level=0).rename(columns={0: 'words'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7c3aeb8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>level_0</th>\n",
       "      <th>words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Natural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Chip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>Compny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>SeaSalt175g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CCs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>264835</td>\n",
       "      <td>Tostitos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>264835</td>\n",
       "      <td>Splash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>264835</td>\n",
       "      <td>Of</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>264835</td>\n",
       "      <td>Lime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>264835</td>\n",
       "      <td>175g</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1359084 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    level_0        words\n",
       "0         0      Natural\n",
       "1         0         Chip\n",
       "2         0       Compny\n",
       "3         0  SeaSalt175g\n",
       "0         1          CCs\n",
       "..      ...          ...\n",
       "0    264835     Tostitos\n",
       "1    264835       Splash\n",
       "2    264835           Of\n",
       "3    264835         Lime\n",
       "4    264835         175g\n",
       "\n",
       "[1359084 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5d034b05",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ABHILASH\\AppData\\Local\\Temp\\ipykernel_18988\\4194265506.py:1: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  product_words['words'] = product_words['words'].str.replace(r'[^a-zA-Z]', '')\n"
     ]
    }
   ],
   "source": [
    "product_words['words'] = product_words['words'].str.replace(r'[^a-zA-Z]', '')\n",
    "\n",
    "# Filter out empty strings\n",
    "product_words = product_words[product_words['words'] != '']\n",
    "\n",
    "# Sort words by frequency of occurrence\n",
    "word_counts = product_words['words'].value_counts().reset_index()\n",
    "word_counts.columns = ['words', 'frequency']\n",
    "word_counts = word_counts.sort_values(by='frequency', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bfc7d93b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>words</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>g</td>\n",
       "      <td>246628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chips</td>\n",
       "      <td>49770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kettle</td>\n",
       "      <td>41288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Smiths</td>\n",
       "      <td>28860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Salt</td>\n",
       "      <td>27976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Sunbites</td>\n",
       "      <td>1432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Pc</td>\n",
       "      <td>1431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Garden</td>\n",
       "      <td>1419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>NCC</td>\n",
       "      <td>1419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>Fries</td>\n",
       "      <td>1418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>196 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        words  frequency\n",
       "0           g     246628\n",
       "1       Chips      49770\n",
       "2      Kettle      41288\n",
       "3      Smiths      28860\n",
       "4        Salt      27976\n",
       "..        ...        ...\n",
       "191  Sunbites       1432\n",
       "192        Pc       1431\n",
       "193    Garden       1419\n",
       "194       NCC       1419\n",
       "195     Fries       1418\n",
       "\n",
       "[196 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4a8c39b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[~df['PROD_NAME'].str.lower().str.contains('salsa', na=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "077d7bc5",
   "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",
       "       '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",
       "       '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",
       "       '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",
       "       'Kettle 135g Swt Pot Sea Salt', 'Pringles SourCream  Onion 134g',\n",
       "       'Doritos Corn Chips  Original 170g',\n",
       "       'Twisties Cheese     Burger 250g',\n",
       "       'Cobs Popd Swt/Chlli &Sr/Cream Chips 110g',\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",
       "       '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',\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'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['PROD_NAME'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3142f7d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STORE_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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e43adc64",
   "metadata": {},
   "outputs": [],
   "source": [
    "outlier_transaction = df[df['PROD_QTY'] == 200]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e8a614a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </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",
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      "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier_transaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "595ce9d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>69762</th>\n",
       "      <td>2018-08-19</td>\n",
       "      <td>226</td>\n",
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       "      <td>226201</td>\n",
       "      <td>4</td>\n",
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       "    </tr>\n",
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       "      <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",
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      "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other_transaction=df[df['LYLTY_CARD_NBR']==226000]\n",
    "other_transaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b11d5611",
   "metadata": {},
   "outputs": [
    {
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       "      <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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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>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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "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",
       "[246740 rows x 8 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['LYLTY_CARD_NBR'] != 226000]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "79ed0b86",
   "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>Transaction_Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-07-02</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-07-03</td>\n",
       "      <td>674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-07-04</td>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-07-05</td>\n",
       "      <td>660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>2019-06-26</td>\n",
       "      <td>657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>2019-06-27</td>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>2019-06-28</td>\n",
       "      <td>673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>2019-06-29</td>\n",
       "      <td>703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>364 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           DATE  Transaction_Count\n",
       "0    2018-07-01                663\n",
       "1    2018-07-02                650\n",
       "2    2018-07-03                674\n",
       "3    2018-07-04                669\n",
       "4    2018-07-05                660\n",
       "..          ...                ...\n",
       "359  2019-06-26                657\n",
       "360  2019-06-27                669\n",
       "361  2019-06-28                673\n",
       "362  2019-06-29                703\n",
       "363  2019-06-30                704\n",
       "\n",
       "[364 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction_count_by_date = df.groupby('DATE').size().reset_index(name='Transaction_Count')\n",
    "transaction_count_by_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "05a3b5cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate a sequence of dates from 1 Jul 2018 to 30 Jun 2019\n",
    "start_date = pd.to_datetime('2018-07-01')\n",
    "end_date = pd.to_datetime('2019-06-30')\n",
    "date_sequence = pd.date_range(start_date, end_date, freq='D')\n",
    "\n",
    "# Create a DataFrame with the sequence of dates\n",
    "date_df = pd.DataFrame({'DATE': date_sequence})\n",
    "\n",
    "# Convert the 'DATE' column in transaction_count_by_date to datetime\n",
    "transaction_count_by_date['DATE'] = pd.to_datetime(transaction_count_by_date['DATE'])\n",
    "\n",
    "# Merge the date sequence DataFrame with the transaction count by date\n",
    "merged_df = pd.merge(date_df, transaction_count_by_date, how='left', on='DATE')\n",
    "\n",
    "# Fill missing transaction counts with 0\n",
    "merged_df['Transaction_Count'] = merged_df['Transaction_Count'].fillna(0)\n",
    "\n",
    "# Plot the number of transactions over time\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(merged_df['DATE'], merged_df['Transaction_Count'], marker='o')\n",
    "plt.title('Number of Transactions Over Time')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Transaction Count')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d9563698",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          DATE  Transaction_Count\n",
      "153 2018-12-01              675.0\n",
      "154 2018-12-02              655.0\n",
      "155 2018-12-03              677.0\n",
      "156 2018-12-04              666.0\n",
      "157 2018-12-05              660.0\n",
      "158 2018-12-06              645.0\n",
      "159 2018-12-07              672.0\n",
      "160 2018-12-08              622.0\n",
      "161 2018-12-09              659.0\n",
      "162 2018-12-10              664.0\n",
      "163 2018-12-11              686.0\n",
      "164 2018-12-12              624.0\n",
      "165 2018-12-13              668.0\n",
      "166 2018-12-14              697.0\n",
      "167 2018-12-15              671.0\n",
      "168 2018-12-16              709.0\n",
      "169 2018-12-17              729.0\n",
      "170 2018-12-18              799.0\n",
      "171 2018-12-19              839.0\n",
      "172 2018-12-20              808.0\n",
      "173 2018-12-21              781.0\n",
      "174 2018-12-22              840.0\n",
      "175 2018-12-23              853.0\n",
      "176 2018-12-24              865.0\n",
      "177 2018-12-25                0.0\n",
      "178 2018-12-26              700.0\n",
      "179 2018-12-27              690.0\n",
      "180 2018-12-28              669.0\n",
      "181 2018-12-29              666.0\n",
      "182 2018-12-30              686.0\n",
      "183 2018-12-31              650.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Filter data for December\n",
    "december_data = merged_df[(merged_df['DATE'].dt.month == 12)]\n",
    "\n",
    "# Plot the number of transactions for December\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(december_data['DATE'], december_data['Transaction_Count'], marker='o')\n",
    "plt.title('Number of Transactions in December')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Transaction Count')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "print(december_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e4b17cba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70.0      1507\n",
      "90.0      3008\n",
      "110.0    22387\n",
      "125.0     1454\n",
      "134.0    25102\n",
      "135.0     3257\n",
      "150.0    40203\n",
      "160.0     2970\n",
      "165.0    15297\n",
      "170.0    19983\n",
      "175.0    66390\n",
      "180.0     1468\n",
      "190.0     2995\n",
      "200.0     4473\n",
      "210.0     6272\n",
      "220.0     1564\n",
      "250.0     3169\n",
      "270.0     6285\n",
      "330.0    12540\n",
      "380.0     6416\n",
      "Name: PACK_SIZE, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df['PACK_SIZE']=df['PROD_NAME'].str.extract('(\\d+)',expand=False).astype(float)\n",
    "pack_size_counts = df['PACK_SIZE'].value_counts().sort_index()\n",
    "print((pack_size_counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "041c46cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IZoo04NzWbTXweM+w6Rabba7xkiRJp7SBC7AkrwQ+yczLtZ8+Udc+saFfOJlkS5Juku6xZ44OO40kSdKyMVABluRlzBRfN1XVbS18OMmqdnwVcKTFp4G1PcPXAE/2mXau8d+iqrZVVaeqOivOWDlIupIkScvavAVYkgCfAB6sqo/3HNoFbG7tzcDtPfFNSU5Psg6YBO7tM/Vc4yVJkk5pg5wB+0Hgp4C3J7mv/b0T2ApclmQ/cFnbp6r2ATuBB4A/Aa6pqmMASbYn6bR5+46XJEk61aVq6NuzRq7T6VS3213qNCRJkuaVZE9Vdfod80n4kiRJI2YBJkmSNGIWYJIkSSNmASZJkjRiFmCSJEkjZgEmSZI0YhZgkiRJI3baUiewEHufOMrE1B0v2vwHt17xos0tSZJ03CCvIrohyZEk9/fEvjfJnyfZm+T/TvKqnmPXJjmQ5OEkl88x51lJ7kqyv23PPDlfR5Ikafkb5BLkjcCGWbHtwFRVvRH4I+DfAiS5ENgEXNTGXJdkRZ85p4DdVTUJ7G77kiRJLwnzFmBVdTfw1KzwBcDdrX0X8OOtvRG4paqerapHgQPAxX2m3QjsaO0dwJULS1uSJGl8DXsT/v3Au1v7KmBta68GHu/pN91is51XVYcA2vbcuT4oyZYk3STdY88cHTJdSZKk5WPYAuyngWuS7AG+G/j7Fk+fvot623dVbauqTlV1VpyxcjFTSZIkLQtD/Qqyqh4C/hlAktcBx38+OM0LZ8MA1gBP9pnicJJVVXUoySrgyDB5SJIkjaOhzoAlObdtvwP434Hr26FdwKYkpydZB0wC9/aZYhewubU3A7cPk4ckSdI4mvcMWJKbgbcBZyeZBj4CvDLJNa3LbcB/AaiqfUl2Ag8AzwHXVNWxNs924Pqq6gJbgZ1JrgYeY+Y+snm9cfVKuj6rS5IkjblULeoWrZHqdDrV7XaXOg1JkqR5JdlTVZ1+x3wVkSRJ0ohZgEmSJI2YBZgkSdKIWYBJkiSNmAWYJEnSiFmASZIkjdhQT8JfKnufOMrE1B0D9z/oM8MkSdIyNO8ZsCQ3JDmS5P6e2Pokn09yX3tR9sU9x65NciDJw0kun2POs5LclWR/2555cr6OJEnS8jfIJcgbgQ2zYh8FfrWq1gMfbvskuRDYBFzUxlyXZEWfOaeA3VU1Cexu+5IkSS8J8xZgVXU38NTsMPCq1l7JCy/c3gjcUlXPVtWjwAHgYr7dRmBHa+8ArlxY2pIkSeNr2HvAPgjcmeQ3mCni/kmLrwY+39NvusVmO6+qDgFU1aHjL/fuJ8kWYAvAiledM2S6kiRJy8ewv4L8WeAXqmot8AvAJ1o8ffou6mWTVbWtqjpV1VlxxsrFTCVJkrQsDFuAbQZua+0/4IXLjNPA2p5+a3jh8mSvw0lWAbTtkSHzkCRJGjvDFmBPAv+0td8O7G/tXcCmJKcnWQdMAvf2Gb+LmSKOtr19yDwkSZLGzrz3gCW5GXgbcHaSaeAjwM8Av5XkNODvaPdoVdW+JDuBB4DngGuq6libZztwfVV1ga3AziRXA48BVw2S7BtXr6Trs70kSdKYS9WibtEaqU6nU91ud6nTkCRJmleSPVXV6XfMVxFJkiSNmAWYJEnSiFmASZIkjZgFmCRJ0ohZgEmSJI2YBZgkSdKIDfsuyCWx94mjTEzdsdRpSJKkMXVwmTxPdN4zYEluSHIkyf09sVuT3Nf+Dia5r8Unkvxtz7Hr55jzrCR3JdnftmeetG8kSZK0zA1yCfJGYENvoKp+oqrWV9V64JO88F5IgEeOH6uq988x5xSwu6omgd1tX5Ik6SVh3gKsqu4Gnup3LEmA9wI3L/BzNwI7WnsHcOUCx0uSJI2txd6EfylwuKr298TWJfliks8luXSOcedV1SGAtj13kXlIkiSNjcXehP8+vvXs1yHgtVX11SRvAT6V5KKqenrYD0iyhfay7xWvOmdRyUqSJC0HQ58BS3Ia8B7g1uOxqnq2qr7a2nuAR4DX9Rl+OMmqNs8q4Mhcn1NV26qqU1WdFWesHDZdSZKkZWMxlyDfATxUVdPHA0nOSbKitc8HJoGv9Bm7C9jc2puB2xeRhyRJ0lgZ5DEUNwN/DlyQZDrJ1e3QJr795vu3Al9O8iXgD4H3V9VTbZ7tSTqt31bgsiT7gcvaviRJ0ktCqmqpcxhYp9Opbre71GlIkiTNK8mequr0O+ariCRJkkbMAkySJGnELMAkSZJGzAJMkiRpxCzAJEmSRswCTJIkacQswCRJkkZs3ndBJrkBeBdwpKre0GK3Ahe0Lq8Gvl5V69uxa4GrgWPAz1XVnX3mPIuZVxhNAAeB91bV1+bLZe8TR5mYumO+btKL4uDWK5Y6BUnSKWKQM2A3Aht6A1X1E1W1vhVdnwRuA0hyITNPyL+ojbnu+KuJZpkCdlfVJLC77UuSJL0kzFuAVdXdwFP9jiUJ8F5eeCXRRuCW9lLuR4EDwMV9hm4EdrT2DuDKhaUtSZI0vhZ7D9ilwOGq2t/2VwOP9xyfbrHZzquqQwBte+4i85AkSRobiy3A3se3vpA7ffos6mWTSbYk6SbpHnvm6GKmkiRJWhaGLsCSnAa8h5mb6Y+bBtb27K8Bnuwz/HCSVW2eVcCRuT6nqrZVVaeqOivOWDlsupIkScvGYs6AvQN4qKqme2K7gE1JTk+yDpgE7u0zdhewubU3A7cvIg9JkqSxMm8BluRm4M+BC5JMJ7m6HdrEt15+pKr2ATuBB4A/Aa6pqmNtnu1JOq3rVuCyJPuBy9q+JEnSS0KqFnWL1kh1Op3qdrtLnYYkSdK8kuypqk6/Yz4JX5IkacQswCRJkkbMAkySJGnELMAkSZJGzAJMkiRpxCzAJEmSRswCTJIkacROW+oEFmLvE0eZmLpjqdPQi+zg1iuWOgVJkl5UgzwJ/4YkR5LcPyv+r5M8nGRfko+22ESSv01yX/u7fo45z0pyV5L9bXvmyfk6kiRJy98glyBvBDb0BpL8MLAReFNVXQT8Rs/hR6pqfft7/xxzTgG7q2oS2N32JUmSXhLmLcCq6m7gqVnhnwW2VtWzrc+RBX7uRmBHa+8ArlzgeEmSpLE17E34rwMuTXJPks8l+f6eY+uSfLHFL51j/HlVdQigbc+d64OSbEnSTdI99szRIdOVJElaPoa9Cf804EzgEuD7gZ1JzgcOAa+tqq8meQvwqSQXVdXTwyZYVduAbQCnr5ocnzeHS5IkzWHYM2DTwG01417geeDsqnq2qr4KUFV7gEeYOVs22+EkqwDadqGXMCVJksbWsAXYp4C3AyR5HfBy4G+SnJNkRYufD0wCX+kzfhewubU3A7cPmYckSdLYmfcSZJKbgbcBZyeZBj4C3ADc0B5N8ffA5qqqJG8F/l2S54BjwPur6qk2z3bg+qrqAluZuWx5NfAYcNUgyb5x9Uq6PiNKkiSNuVSNz21VnU6nut3uUqchSZI0ryR7qqrT75ivIpIkSRoxCzBJkqQRswCTJEkaMQswSZKkEbMAkyRJGjELMEmSpBEb9lVES2LvE0eZmLrjRZv/oM8YkyRJIzDvGbAkNyQ50h662hv/10keTrIvyUd74tcmOdCOXT7HnGcluSvJ/rY9c/FfRZIkaTwMcgnyRmBDbyDJDwMbgTdV1UXAb7T4hcAm4KI25rrjryaaZQrYXVWTwO62L0mS9JIwbwFWVXcDT80K/yywtaqebX2Ov0x7I3BLeyn3o8AB4OI+024EdrT2DuDKhacuSZI0noa9Cf91wKVJ7knyuSTf3+Krgcd7+k232GznVdUhgLY9d64PSrIlSTdJ99gzR4dMV5IkafkYtgA7DTgTuAT4t8y8WDtA+vRd1Msmq2pbVXWqqrPijJWLmUqSJGlZGLYAmwZuqxn3As8DZ7f42p5+a4An+4w/nGQVQNse6dNHkiTplDRsAfYp4O0ASV4HvBz4G2AXsCnJ6UnWAZPAvX3G7wI2t/Zm4PYh85AkSRo7gzyG4mbgz4ELkkwnuRq4ATi/PZriFmBzOxu2D9gJPAD8CXBNVR1r82xP0mnTbgUuS7IfuKztS5IkvSSkalG3aI1Up9Opbre71GlIkiTNK8mequr0O+ariCRJkkbMAkySJGnELMAkSZJGzAJMkiRpxCzAJEmSRswCTJIkacQswCRJkkbstPk6JLkBeBdwpKre0GK/AvwM8Net2y9X1R+3Y9cCVwPHgJ+rqjv7zHkWcCswARwE3ltVX5svl71PHGVi6o55v5QknaoObr1iqVOQdBIMcgbsRmBDn/hvVtX69ne8+LoQ2ARc1MZcl2RFn7FTwO6qmgR2t31JkqSXhHkLsKq6G3hqwPk2ArdU1bNV9ShwALh4jn47WnsHcOWA80uSJI29xdwD9oEkX05yQ5IzW2w18HhPn+kWm+28qjoE0LbnLiIPSZKksTJsAfa7wD8C1gOHgP/Q4unTd1Evm0yyJUk3SffYM0cXM5UkSdKyMFQBVlWHq+pYVT0P/GdeuMw4Dazt6boGeLLPFIeTrAJo2yMn+KxtVdWpqs6KM1YOk64kSdKyMlQBdrx4an4MuL+1dwGbkpyeZB0wCdzbZ4pdwObW3gzcPkwekiRJ42iQx1DcDLwNODvJNPAR4G1J1jNzefEg8C8Bqmpfkp3AA8BzwDVVdazNsx24vqq6wFZgZ5KrgceAq07u15IkSVq+UrWoW7RGqtPpVLfbXeo0JEmS5pVkT1V1+h3zSfiSJEkjZgEmSZI0YhZgkiRJI2YBJkmSNGIWYJIkSSNmASZJkjRiFmCSJEkjNu+DWJeTvU8cZWLqjhf9cw5uveJF/wxJkvTSNe8ZsCQ3JDmS5P6e2K8l+XKS+5J8Nsn3tPhEkr9t8fuSXD/HnGcluSvJ/rY98+R9JUmSpOVtkEuQNwIbZsU+VlVvqqr1wKeBD/cce6Sq1re/988x5xSwu6omgd1tX5Ik6SVh3gKsqu4GnpoVe7pn9xXMvBNyITYCO1p7B3DlAsdLkiSNraFvwk/y60keB36Sbz0Dti7JF5N8Lsmlcww/r6oOAbTtuSf4nC1Jukm6x545Omy6kiRJy8bQBVhVfaiq1gI3AR9o4UPAa6vqzcC/AX4/yasWk2BVbauqTlV1VpyxcjFTSZIkLQsn4zEUvw/8OEBVPVtVX23tPcAjwOv6jDmcZBVA2x45CXlIkiSNhaEKsCSTPbvvBh5q8XOSrGjt84FJ4Ct9ptgFbG7tzcDtw+QhSZI0juZ9DliSm4G3AWcnmQY+ArwzyQXA88BfAcd/7fhW4N8leQ44Bry/qp5q82wHrq+qLrAV2JnkauAx4KpBkn3j6pV0fUaXJEkac6la6A8Yl06n06lut7vUaUiSJM0ryZ6q6vQ75quIJEmSRswCTJIkacQswCRJkkbMAkySJGnELMAkSZJGzAJMkiRpxOZ9DthysveJo0xM3bHUaUjL1kGfkydJY2HeM2BJbkhyJMn9PbFfS/LlJPcl+WyS75k15rVJvpnkf51jzrOS3JVkf9ueufivIkmSNB4GuQR5I7BhVuxjVfWmqloPfBr48Kzjvwl85gRzTgG7q2oS2N32JUmSXhLmLcCq6m7gqVmxp3t2XwH8/4/TT3IlM+9/3HeCaTcCO1p7B3DlQNlKkiSdAoa+ByzJrwP/AjgK/HCLvQL4JeAyoO/lx+a8qjoEUFWHkpx7gs/ZAmwBWPGqc4ZNV5IkadkY+leQVfWhqloL3AR8oIV/FfjNqvrmyUiufc62qupUVWfFGStP1rSSJElL5mT8CvL3gTuAjwD/GPjnST4KvBp4PsnfVdVvzxpzOMmqdvZrFXDkJOQhSZI0FoYqwJJMVtX+tvtu4CGAqrq0p8+vAN/sU3wB7AI2A1vb9vZh8pAkSRpH8xZgSW4G3gacnWSamTNd70xyAfA88FfA+weYZztwfVV1mSm8dia5GngMuGqQZN+4eiVdn3MkSZLGXKpq/l7LRKfTqW63u9RpSJIkzSvJnqrq9Dvmq4gkSZJGzAJMkiRpxCzAJEmSRswCTJIkacQswCRJkkbMAkySJGnELMAkSZJGbJAHsd4AvAs4UlVvaLGPAf8j8PfAI8D/XFVfTzIBPAg83IZ/vqq+7SGtSc4CbgUmgIPAe6vqa/PlsveJo0xM3TH/t2oO+tBWSZK0DA1yBuxGYMOs2F3AG6rqTcBfAtf2HHukqta3v7mekD8F7K6qSWB325ckSXpJmLcAq6q7gadmxT5bVc+13c8Daxb4uRuBHa29A7hygeMlSZLG1sm4B+yngc/07K9L8sUkn0ty6RxjzquqQwBte+5JyEOSJGkszHsP2Ikk+RDwHHBTCx0CXltVX03yFuBTSS6qqqcX8RlbgC0AK151zmLSlSRJWhaGPgOWZDMzN+f/ZLU3elfVs1X11dbew8wN+q/rM/xwklVtnlXAkbk+p6q2VVWnqjorzlg5bLqSJEnLxlAFWJINwC8B766qZ3ri5yRZ0drnA5PAV/pMsQvY3NqbgduHyUOSJGkczVuAJbkZ+HPggiTTSa4Gfhv4buCuJPclub51fyvw5SRfAv4QeH9VPdXm2Z6k0/ptBS5Lsh+4rO1LkiS9JKRdPRwLnU6nut3uUqchSZI0ryR7qqrT75hPwpckSRoxCzBJkqQRswCTJEkaMQswSZKkEbMAkyRJGjELMEmSpBGzAJMkSRqxRb0LctT2PnGUiak7ljoNadEObr1iqVOQJC2hQZ6Ef0OSI0nu74ldlWRfkud7nm5//Ni1SQ4keTjJ5XPMeVaSu5Lsb9szF/9VJEmSxsMglyBvBDbMit0PvAe4uzeY5EJgE3BRG3Pd8XdDzjIF7K6qSWB325ckSXpJmLcAq6q7gadmxR6sqof7dN8I3FJVz1bVo8AB4OI5+u1o7R3AlQtJWpIkaZyd7JvwVwOP9+xPt9hs51XVIYC2PXeuCZNsSdJN0j32zNGTmqwkSdJSONkFWPrEFvW276raVlWdquqsOGPlYqaSJElaFk52ATYNrO3ZXwM82aff4SSrANr2yEnOQ5Ikadk62QXYLmBTktOTrAMmgXvn6Le5tTcDt5/kPCRJkpatVJ34CmGSm4G3AWcDh4GPMHNT/n8CzgG+DtxXVZe3/h8Cfhp4DvhgVX2mxbcD11dVN8lrgJ3Aa4HHgKuq6ltu9O+n0+lUt9td+LeUJEkasSR7qqrT99h8BdhyYgEmSZLGxYkKMF9FJEmSNGIWYJIkSSNmASZJkjRiFmCSJEkjZgEmSZI0YhZgkiRJI3baUiewEHufOMrE1B0v2vwHt17xos0tSZJ03KLOgCX5+ST3J9mX5IMtdlaSu5Lsb9sz5xi7IcnDSQ4kmVpMHpIkSeNk6AIsyRuAnwEuBr4XeFeSSWAK2F1Vk8Dutj977Argd4AfBS4E3pfkwmFzkSRJGieLOQP2euDzVfVMVT0HfA74MWAjsKP12QFc2WfsxcCBqvpKVf09cEsbJ0mSdMpbTAF2P/DWJK9JcgbwTmAtcF5VHQJo23P7jF0NPN6zP91i3ybJliTdJN1jzxxdRLqSJEnLw9A34VfVg0n+PXAX8E3gS8y8gHsQ6TflHJ+zDdgGcPqqyfF5caUkSdIcFnUTflV9oqq+r6reCjwF7AcOJ1kF0LZH+gydZuZs2XFrgCcXk4skSdK4WOyvIM9t29cC7wFuBnYBm1uXzcDtfYb+BTCZZF2SlwOb2jhJkqRT3mKfA/bJJK8B/jtwTVV9LclWYGeSq4HHgKsAknwPsL2q3llVzyX5AHAnsAK4oar2zfdhb1y9kq7P6pIkSWNuUQVYVV3aJ/ZV4Ef6xJ9k5kb94/t/DPzxYj5fkiRpHPkqIkmSpBGzAJMkSRoxCzBJkqQRswCTJEkaMQswSZKkEbMAkyRJGjELMEmSpBEb+jlgSS4Abu0JnQ98GPi9Fp8ADgLvraqv9Rm/AfgtZh7Eur2qts73mXufOMrE1B0D53jQh7ZKkqRlaOgzYFX1cFWtr6r1wFuAZ4A/AqaA3VU1Cexu+98iyQrgd4AfBS4E3pfkwmFzkSRJGicn6xLkjwCPVNVfARuBHS2+A7iyT/+LgQNV9ZWq+nvgljZOkiTplHeyCrBNzLyIG+C8qjoE0Lbn9um/Gni8Z3+6xSRJkk55iy7AkrwceDfwBwsZ1idWc8y/JUk3SffYM0eHSVGSJGlZORlnwH4U+EJVHW77h5OsAmjbI33GTANre/bXAE/2m7yqtlVVp6o6K85YeRLSlSRJWlonowB7Hy9cfgTYBWxu7c3A7X3G/AUwmWRdO4O2qY2TJEk65S2qAEtyBnAZcFtPeCtwWZL97djW1vd7kvwxQFU9B3wAuBN4ENhZVfsWk4skSdK4SFXfW6+WpU6nU91ud6nTkCRJmleSPVXV6XfMJ+FLkiSNmAWYJEnSiFmASZIkjdhY3QOW5BvAw0udxxg7G/ibpU5ijLl+i+P6LY7rtziu3+K4fsP5h1V1Tr8DQ7+Me4k8PNfNbJpfkq7rNzzXb3Fcv8Vx/RbH9Vsc1+/k8xKkJEnSiFmASZIkjdi4FWDbljqBMef6LY7rtziu3+K4fovj+i2O63eSjdVN+JIkSaeCcTsDJkmSNPbGogBLsiHJw0kOJJla6nyWUpIbkhxJcn9P7KwkdyXZ37Zn9hy7tq3bw0ku74m/Jcneduw/JkmLn57k1ha/J8nESL/giyzJ2iR/muTBJPuS/HyLu4YDSPKdSe5N8qW2fr/a4q7fAiRZkeSLST7d9l2/ASU52L73fUm6Leb6DSjJq5P8YZKH2r8Hf8D1WyJVtaz/gBXAI8D5wMuBLwEXLnVeS7gebwW+D7i/J/ZRYKq1p4B/39oXtvU6HVjX1nFFO3Yv8ANAgM8AP9ri/wq4vrU3Abcu9Xc+yeu3Cvi+1v5u4C/bOrmGg61fgFe29suAe4BLXL8Fr+O/AX4f+HTbd/0GX7uDwNmzYq7f4Ou3A/hfWvvlwKtdvyX6z2KpExjgH5YfAO7s2b8WuHap81riNZngWwuwh4FVrb2KmeelfdtaAXe29VwFPNQTfx/wf/b2ae3TmHnwXpb6O7+Ia3k7cJlrONTanQF8AfjHrt+C1m0NsBt4Oy8UYK7f4Ot3kG8vwFy/wdbuVcCjs7+P67c0f+NwCXI18HjP/nSL6QXnVdUhgLY9t8XnWrvVrT07/i1jquo54Cjwmhct8yXUTo2/mZmzOK7hgNrls/uAI8BdVeX6Lcz/AfxvwPM9MddvcAV8NsmeJFtazPUbzPnAXwP/pV0C357kFbh+S2IcCrD0ifnTzcHMtXYnWtOXxHoneSXwSeCDVfX0ibr2ib2k17CqjlXVembO5Fyc5A0n6O769UjyLuBIVe0ZdEif2Et2/ZofrKrvA34UuCbJW0/Q1/X7VqcxcwvL71bVm4H/l5lLjnNx/V5E41CATQNre/bXAE8uUS7L1eEkqwDa9kiLz7V20609O/4tY5KcBqwEnnrRMl8CSV7GTPF1U1Xd1sKu4QJV1deBPwM24PoN6geBdyc5CNwCvD3J/4XrN7CqerJtjwB/BFyM6zeoaWC6nbUG+ENmCjLXbwmMQwH2F8BkknVJXs7MTX27ljin5WYXsLm1NzNzX9Px+Kb2q5R1wCRwbzvF/I0kl7RfrvyLWWOOz/XPgf9a7WL+qaB9308AD1bVx3sOuYYDSHJOkle39ncB7wAewvUbSFVdW1VrqmqCmX+X/deq+p9w/QaS5BVJvvt4G/hnwP24fgOpqv8HeDzJBS30I8ADuH5LY6lvQhvkD3gnM79WewT40FLns8RrcTNwCPjvzPw/jauZub6+G9jftmf19P9QW7eHab9SafEOM//iegT4bV54KO93An8AHGDmVy7nL/V3Psnr90PMnA7/MnBf+3unazjw+r0J+GJbv/uBD7e467fwtXwbL9yE7/oNtmbnM/OrvC8B+47/74Hrt6A1XA9023+HPwWc6fotzZ9PwpckSRqxcbgEKUmSdEqxAJMkSRoxCzBJkqQRswCTJEkaMQswSZKkEbMAkyRJGjELMEmSpBGzAJMkSRqx/w8XEhMPGPOCbQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pack_size_counts.plot.barh(figsize =(10,6),rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "541393e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist(df['PACK_SIZE'],bins=30,color='skyblue', edgecolor='black')\n",
    "plt.title('Histogram of Pack Size')\n",
    "plt.xlabel('Pack Size (g)')\n",
    "plt.ylabel('Number of Transactions')\n",
    "plt.grid(axis='y', alpha=0.75)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "510fcc65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Natural' 'CCs' 'Smiths' 'Kettle' 'Grain' 'Doritos' 'Twisties' 'WW'\n",
      " 'Thins' 'Burger' 'NCC' 'Cheezels' 'Infzns' 'Red' 'Pringles' 'Dorito'\n",
      " 'Infuzions' 'Smith' 'GrnWves' 'Tyrrells' 'Cobs' 'French' 'RRD' 'Tostitos'\n",
      " 'Cheetos' 'Woolworths' 'Snbts' 'Sunbites']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df['BRAND'] = df['PROD_NAME'].str.split().str[0]\n",
    "\n",
    "# Checking brands\n",
    "# Display unique values to inspect the extracted brand names\n",
    "print(df['BRAND'].unique())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d33468ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['NCC' 'CCs' 'Smith' 'Kettle' 'GrnWves' 'Dorito' 'Twisties' 'Woolworths'\n",
      " 'Thins' 'Burger' 'Cheezels' 'Infuzions' 'RRD' 'Pringles' 'Tyrrells'\n",
      " 'Cobs' 'French' 'Tostitos' 'Cheetos' 'Sunbites']\n"
     ]
    }
   ],
   "source": [
    "# Clean brand names\n",
    "df['BRAND'] = df['BRAND'].replace({'Red': 'RRD','WW':'Woolworths','Grain':'GrnWves','Smiths' : 'Smith','Infzns':'Infuzions','Snbts':'Sunbites','Doritos':'Dorito','Natural':'NCC'})\n",
    "\n",
    "# Display unique values to verify the cleaned brand names\n",
    "print(df['BRAND'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bb406718",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df['BRAND'].value_counts(ascending=True).plot.barh(figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "12f46af6",
   "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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72632</th>\n",
       "      <td>2370651</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72633</th>\n",
       "      <td>2370701</td>\n",
       "      <td>YOUNG FAMILIES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72634</th>\n",
       "      <td>2370751</td>\n",
       "      <td>YOUNG FAMILIES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72635</th>\n",
       "      <td>2370961</td>\n",
       "      <td>OLDER FAMILIES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72636</th>\n",
       "      <td>2373711</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72637 rows × 3 columns</p>\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\n",
       "...               ...                     ...              ...\n",
       "72632         2370651  MIDAGE SINGLES/COUPLES       Mainstream\n",
       "72633         2370701          YOUNG FAMILIES       Mainstream\n",
       "72634         2370751          YOUNG FAMILIES          Premium\n",
       "72635         2370961          OLDER FAMILIES           Budget\n",
       "72636         2373711   YOUNG SINGLES/COUPLES       Mainstream\n",
       "\n",
       "[72637 rows x 3 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv(\"C:/Users/ABHILASH/Downloads/QVI_purchase_behaviour.csv\")\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9cdf3896",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "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": [
    "x=df2['LIFESTAGE'].value_counts().plot(kind='bar',color='darkblue')\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b0d5e6c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LIFESTAGE               PREMIUM_CUSTOMER\n",
       "YOUNG SINGLES/COUPLES   Mainstream          8088\n",
       "RETIREES                Mainstream          6479\n",
       "OLDER SINGLES/COUPLES   Mainstream          4930\n",
       "                        Budget              4929\n",
       "                        Premium             4750\n",
       "OLDER FAMILIES          Budget              4675\n",
       "RETIREES                Budget              4454\n",
       "YOUNG FAMILIES          Budget              4017\n",
       "RETIREES                Premium             3872\n",
       "YOUNG SINGLES/COUPLES   Budget              3779\n",
       "MIDAGE SINGLES/COUPLES  Mainstream          3340\n",
       "OLDER FAMILIES          Mainstream          2831\n",
       "YOUNG FAMILIES          Mainstream          2728\n",
       "YOUNG SINGLES/COUPLES   Premium             2574\n",
       "YOUNG FAMILIES          Premium             2433\n",
       "MIDAGE SINGLES/COUPLES  Premium             2431\n",
       "OLDER FAMILIES          Premium             2274\n",
       "MIDAGE SINGLES/COUPLES  Budget              1504\n",
       "NEW FAMILIES            Budget              1112\n",
       "                        Mainstream           849\n",
       "                        Premium              588\n",
       "Name: LYLTY_CARD_NBR, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby(['LIFESTAGE','PREMIUM_CUSTOMER'])['LYLTY_CARD_NBR'].count().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f458ff66",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df= pd.merge(df,df2, how='left',on='LYLTY_CARD_NBR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1c38b42a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>PACK_SIZE</th>\n",
       "      <th>BRAND</th>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "    </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>NCC</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</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",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</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>Smith</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</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>Smith</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</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",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</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>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",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246736</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",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246737</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>Dorito</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246738</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>Dorito</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246739</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",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>246740 rows × 12 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",
       "246735  2019-03-09        272          272319  270088        89   \n",
       "246736  2018-08-13        272          272358  270154        74   \n",
       "246737  2018-11-06        272          272379  270187        51   \n",
       "246738  2018-12-27        272          272379  270188        42   \n",
       "246739  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",
       "246735   Kettle Sweet Chilli And Sour Cream 175g         2       10.8   \n",
       "246736             Tostitos Splash Of  Lime 175g         1        4.4   \n",
       "246737                  Doritos Mexicana    170g         2        8.8   \n",
       "246738   Doritos Corn Chip Mexican Jalapeno 150g         2        7.8   \n",
       "246739             Tostitos Splash Of  Lime 175g         2        8.8   \n",
       "\n",
       "        PACK_SIZE     BRAND               LIFESTAGE PREMIUM_CUSTOMER  \n",
       "0           175.0       NCC   YOUNG SINGLES/COUPLES          Premium  \n",
       "1           175.0       CCs  MIDAGE SINGLES/COUPLES           Budget  \n",
       "2           170.0     Smith  MIDAGE SINGLES/COUPLES           Budget  \n",
       "3           175.0     Smith  MIDAGE SINGLES/COUPLES           Budget  \n",
       "4           150.0    Kettle  MIDAGE SINGLES/COUPLES           Budget  \n",
       "...           ...       ...                     ...              ...  \n",
       "246735      175.0    Kettle   YOUNG SINGLES/COUPLES          Premium  \n",
       "246736      175.0  Tostitos   YOUNG SINGLES/COUPLES          Premium  \n",
       "246737      170.0    Dorito   YOUNG SINGLES/COUPLES          Premium  \n",
       "246738      150.0    Dorito   YOUNG SINGLES/COUPLES          Premium  \n",
       "246739      175.0  Tostitos   YOUNG SINGLES/COUPLES          Premium  \n",
       "\n",
       "[246740 rows x 12 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7baa611a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [DATE, STORE_NBR, LYLTY_CARD_NBR, TXN_ID, PROD_NBR, PROD_NAME, PROD_QTY, TOT_SALES, PACK_SIZE, BRAND, LIFESTAGE, PREMIUM_CUSTOMER]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "# checking if any transaction did not have a matched customer\n",
    "miss=final_df[final_df['LYLTY_CARD_NBR'].isnull()]\n",
    "print(miss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e5cc95d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.to_csv('C:/Users/ABHILASH/Downloads/QVI_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "382c9bb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LIFESTAGE               PREMIUM_CUSTOMER\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\n",
       "Name: TOT_SALES, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_sales= final_df.groupby(['LIFESTAGE','PREMIUM_CUSTOMER']).TOT_SALES.sum().sort_values(ascending=False)\n",
    "total_sales"
   ]
  },
  {
   "cell_type": "raw",
   "id": "79038d31",
   "metadata": {},
   "source": [
    "Top 3 customers by lifestage and purchasing behaviour\n",
    "\n",
    "OLDER FAMILIES          Budget              156863.75\n",
    "YOUNG SINGLES/COUPLES   Mainstream          147582.20\n",
    "RETIREES                Mainstream          145168.95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "27267364",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Spending by customers by lifestage and how premium their purchasing behaviour was -\n",
    "total_sales= final_df.groupby(['LIFESTAGE','PREMIUM_CUSTOMER']).TOT_SALES.sum().sort_values(ascending=False).reset_index()\n",
    "\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='TOT_SALES', y='LIFESTAGE', hue='PREMIUM_CUSTOMER', data=total_sales, ci=None, palette='viridis')\n",
    "plt.title('Total Sales by LIFESTAGE and PREMIUM_CUSTOMER')\n",
    "plt.xlabel('Total Sales')\n",
    "plt.ylabel('LIFESTAGE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a969413c",
   "metadata": {},
   "source": [
    "Older families spend more on budget friendly chips.\n",
    "Young singles/couples & Retirees spend more in mainstream chips than others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "87d026ff",
   "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>LYLTY_CARD_NBR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YOUNG SINGLES/COUPLES</th>\n",
       "      <th>Mainstream</th>\n",
       "      <td>7917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETIREES</th>\n",
       "      <th>Mainstream</th>\n",
       "      <td>6358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">OLDER SINGLES/COUPLES</th>\n",
       "      <th>Mainstream</th>\n",
       "      <td>4858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Budget</th>\n",
       "      <td>4849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>4682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OLDER FAMILIES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>4611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETIREES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>4385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUNG FAMILIES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>3953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETIREES</th>\n",
       "      <th>Premium</th>\n",
       "      <td>3812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUNG SINGLES/COUPLES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>3647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIDAGE SINGLES/COUPLES</th>\n",
       "      <th>Mainstream</th>\n",
       "      <td>3298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OLDER FAMILIES</th>\n",
       "      <th>Mainstream</th>\n",
       "      <td>2788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUNG FAMILIES</th>\n",
       "      <th>Mainstream</th>\n",
       "      <td>2685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUNG SINGLES/COUPLES</th>\n",
       "      <th>Premium</th>\n",
       "      <td>2480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUNG FAMILIES</th>\n",
       "      <th>Premium</th>\n",
       "      <td>2398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIDAGE SINGLES/COUPLES</th>\n",
       "      <th>Premium</th>\n",
       "      <td>2369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OLDER FAMILIES</th>\n",
       "      <th>Premium</th>\n",
       "      <td>2231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIDAGE SINGLES/COUPLES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>1474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">NEW FAMILIES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>1087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         LYLTY_CARD_NBR\n",
       "LIFESTAGE              PREMIUM_CUSTOMER                \n",
       "YOUNG SINGLES/COUPLES  Mainstream                  7917\n",
       "RETIREES               Mainstream                  6358\n",
       "OLDER SINGLES/COUPLES  Mainstream                  4858\n",
       "                       Budget                      4849\n",
       "                       Premium                     4682\n",
       "OLDER FAMILIES         Budget                      4611\n",
       "RETIREES               Budget                      4385\n",
       "YOUNG FAMILIES         Budget                      3953\n",
       "RETIREES               Premium                     3812\n",
       "YOUNG SINGLES/COUPLES  Budget                      3647\n",
       "MIDAGE SINGLES/COUPLES Mainstream                  3298\n",
       "OLDER FAMILIES         Mainstream                  2788\n",
       "YOUNG FAMILIES         Mainstream                  2685\n",
       "YOUNG SINGLES/COUPLES  Premium                     2480\n",
       "YOUNG FAMILIES         Premium                     2398\n",
       "MIDAGE SINGLES/COUPLES Premium                     2369\n",
       "OLDER FAMILIES         Premium                     2231\n",
       "MIDAGE SINGLES/COUPLES Budget                      1474\n",
       "NEW FAMILIES           Budget                      1087\n",
       "                       Mainstream                   830\n",
       "                       Premium                      575"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Customers in each segment\n",
    "customers_in_segment=final_df.groupby(['LIFESTAGE','PREMIUM_CUSTOMER']).LYLTY_CARD_NBR.nunique().sort_values(ascending=False).reset_index()\n",
    "customers_in_segment=final_df.groupby(['LIFESTAGE','PREMIUM_CUSTOMER']).LYLTY_CARD_NBR.nunique().sort_values(ascending=False)\n",
    "pd.DataFrame(customers_in_segment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "50e085cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "customers_in_segment.sort_values().plot.barh(figsize=(10, 6))\n",
    "\n",
    "plt.title('Number of Customers by LIFESTAGE and PREMIUM_CUSTOMER')\n",
    "plt.xlabel('Number of Customers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2ece85ae",
   "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>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">OLDER FAMILIES</th>\n",
       "      <th>Mainstream</th>\n",
       "      <td>9.255380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Budget</th>\n",
       "      <td>9.076773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>9.071717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">YOUNG FAMILIES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>8.722995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>8.716013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>8.638361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">OLDER SINGLES/COUPLES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>6.781398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>6.769543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <td>6.712021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIDAGE SINGLES/COUPLES</th>\n",
       "      <th>Mainstream</th>\n",
       "      <td>6.432080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">RETIREES</th>\n",
       "      <th>Budget</th>\n",
       "      <td>6.141847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>6.103358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MIDAGE SINGLES/COUPLES</th>\n",
       "      <th>Premium</th>\n",
       "      <td>6.078514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Budget</th>\n",
       "      <td>6.026459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETIREES</th>\n",
       "      <th>Mainstream</th>\n",
       "      <td>5.925920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">NEW FAMILIES</th>\n",
       "      <th>Mainstream</th>\n",
       "      <td>4.891566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Budget</th>\n",
       "      <td>4.821527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>4.815652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">YOUNG SINGLES/COUPLES</th>\n",
       "      <th>Mainstream</th>\n",
       "      <td>4.575597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>4.264113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Budget</th>\n",
       "      <td>4.250069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                0\n",
       "LIFESTAGE              PREMIUM_CUSTOMER          \n",
       "OLDER FAMILIES         Mainstream        9.255380\n",
       "                       Budget            9.076773\n",
       "                       Premium           9.071717\n",
       "YOUNG FAMILIES         Budget            8.722995\n",
       "                       Premium           8.716013\n",
       "                       Mainstream        8.638361\n",
       "OLDER SINGLES/COUPLES  Budget            6.781398\n",
       "                       Premium           6.769543\n",
       "                       Mainstream        6.712021\n",
       "MIDAGE SINGLES/COUPLES Mainstream        6.432080\n",
       "RETIREES               Budget            6.141847\n",
       "                       Premium           6.103358\n",
       "MIDAGE SINGLES/COUPLES Premium           6.078514\n",
       "                       Budget            6.026459\n",
       "RETIREES               Mainstream        5.925920\n",
       "NEW FAMILIES           Mainstream        4.891566\n",
       "                       Budget            4.821527\n",
       "                       Premium           4.815652\n",
       "YOUNG SINGLES/COUPLES  Mainstream        4.575597\n",
       "                       Premium           4.264113\n",
       "                       Budget            4.250069"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average number of chips brought by customer segment\n",
    "average_no_chips=final_df.groupby(['LIFESTAGE','PREMIUM_CUSTOMER']).PROD_QTY.sum().reset_index()\n",
    "average_no_chips=final_df.groupby(['LIFESTAGE','PREMIUM_CUSTOMER']).PROD_QTY.sum()\n",
    "no_chips_by_customers = pd.DataFrame((average_no_chips/final_df.groupby(['LIFESTAGE','PREMIUM_CUSTOMER']).LYLTY_CARD_NBR.nunique()).sort_values(ascending=False))\n",
    "no_chips_by_customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6fcbba51",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='LIFESTAGE'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "no_chips_by_customers.unstack().plot.barh(figsize=(10, 6),rot=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d85b561",
   "metadata": {},
   "source": [
    "# Average price per unit by LIFESTAGE and PREMIUM_CUSTOMER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b436e4e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LIFESTAGE               PREMIUM_CUSTOMER\n",
      "YOUNG SINGLES/COUPLES   Mainstream          4.074043\n",
      "MIDAGE SINGLES/COUPLES  Mainstream          3.994449\n",
      "NEW FAMILIES            Mainstream          3.935887\n",
      "RETIREES                Budget              3.932731\n",
      "NEW FAMILIES            Budget              3.931969\n",
      "RETIREES                Premium             3.924037\n",
      "OLDER SINGLES/COUPLES   Premium             3.897698\n",
      "                        Budget              3.887529\n",
      "NEW FAMILIES            Premium             3.886168\n",
      "RETIREES                Mainstream          3.852986\n",
      "OLDER SINGLES/COUPLES   Mainstream          3.822753\n",
      "MIDAGE SINGLES/COUPLES  Premium             3.780823\n",
      "YOUNG FAMILIES          Budget              3.761903\n",
      "                        Premium             3.759232\n",
      "MIDAGE SINGLES/COUPLES  Budget              3.753878\n",
      "OLDER FAMILIES          Budget              3.747969\n",
      "                        Mainstream          3.736380\n",
      "YOUNG FAMILIES          Mainstream          3.722439\n",
      "OLDER FAMILIES          Premium             3.717703\n",
      "YOUNG SINGLES/COUPLES   Premium             3.692889\n",
      "                        Budget              3.685297\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21142697640>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average price per unit by LIFESTAGE and PREMIUM_CUSTOMER\n",
    "avg_price_per_unit = final_df.groupby(['LIFESTAGE', 'PREMIUM_CUSTOMER'])['TOT_SALES'].sum()/final_df.groupby(['LIFESTAGE', 'PREMIUM_CUSTOMER'])['PROD_QTY'].sum()\n",
    "\n",
    "print(avg_price_per_unit.sort_values(ascending=False))\n",
    "\n",
    "avg_price_per_unit.unstack().plot.bar(figsize=(15, 6),rot=0)\n",
    "plt.legend(loc=\"center left\", bbox_to_anchor=(1,0.5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2af49acc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import ttest_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "60d46813",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-Statistic: 33.200521751400586\n",
      "P-Value: 1.9916804791117584e-239\n",
      "The unit price for mainstream, young and mid-age singles and couples is significantly higher than that of budget or premium.\n"
     ]
    }
   ],
   "source": [
    "final_df['CUSTOMER_SEGMENT'] = 'Mainstream'\n",
    "final_df.loc[final_df['PREMIUM_CUSTOMER'].isin(['Premium', 'Budget']), 'CUSTOMER_SEGMENT'] = 'Premium/Budget'\n",
    "\n",
    "# Extract data for the two groups\n",
    "mainstream_data = final_df[(final_df['CUSTOMER_SEGMENT'] == 'Mainstream') & (final_df['LIFESTAGE'].isin(['MIDAGE SINGLES/COUPLES', 'YOUNG SINGLES/COUPLES']))]\n",
    "premium_budget_data = final_df[(final_df['CUSTOMER_SEGMENT'] == 'Premium/Budget') & (final_df['LIFESTAGE'].isin(['MIDAGE SINGLES/COUPLES', 'YOUNG SINGLES/COUPLES']))]\n",
    "\n",
    "column_to_compare = 'TOT_SALES'\n",
    "\n",
    "# Perform an independent t-test\n",
    "t_statistic, p_value = ttest_ind(mainstream_data[column_to_compare], premium_budget_data[column_to_compare])\n",
    "\n",
    "# Display the results\n",
    "print(f\"t-Statistic: {t_statistic}\")\n",
    "print(f\"P-Value: {p_value}\")\n",
    "\n",
    "# Check for significance\n",
    "alpha = 0.05\n",
    "if p_value < alpha:\n",
    "    print(\"The unit price for mainstream, young and mid-age singles and couples is significantly higher than that of budget or premium.\")\n",
    "else:\n",
    "    print(\"There is no significant difference in unit price between the groups.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "838d4343",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ABHILASH\\anaconda3\\lib\\site-packages\\mlxtend\\frequent_patterns\\fpcommon.py:110: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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",
       "      <th>zhangs_metric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(OLDER FAMILIES - Budget)</td>\n",
       "      <td>(Smith)</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",
       "      <td>0.112016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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",
       "      <td>0.067167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</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",
       "      <td>0.064000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</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",
       "      <td>0.086660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</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",
       "      <td>0.014248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</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",
       "      <td>0.162062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             antecedents consequents  antecedent support  \\\n",
       "1              (OLDER FAMILIES - Budget)     (Smith)            0.087193   \n",
       "2       (OLDER SINGLES/COUPLES - Budget)    (Kettle)            0.069596   \n",
       "5      (OLDER SINGLES/COUPLES - Premium)    (Kettle)            0.067115   \n",
       "6                    (RETIREES - Budget)    (Kettle)            0.057652   \n",
       "9                (RETIREES - Mainstream)    (Kettle)            0.080935   \n",
       "10  (YOUNG SINGLES/COUPLES - Mainstream)    (Kettle)            0.079209   \n",
       "\n",
       "    consequent support   support  confidence      lift  leverage  conviction  \\\n",
       "1             0.123016  0.011948    0.137027  1.113895  0.001222    1.016236   \n",
       "2             0.167334  0.012422    0.178488  1.066658  0.000776    1.013578   \n",
       "5             0.167334  0.011944    0.177959  1.063495  0.000713    1.012925   \n",
       "6             0.167334  0.010505    0.182214  1.088926  0.000858    1.018196   \n",
       "9             0.167334  0.013723    0.169554  1.013269  0.000180    1.002674   \n",
       "10            0.167334  0.015579    0.196684  1.175400  0.002325    1.036537   \n",
       "\n",
       "    zhangs_metric  \n",
       "1        0.112016  \n",
       "2        0.067167  \n",
       "5        0.064000  \n",
       "6        0.086660  \n",
       "9        0.014248  \n",
       "10       0.162062  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from mlxtend.frequent_patterns import apriori, association_rules\n",
    "\n",
    "temp = final_df.reset_index().rename(columns = {\"index\" : \"transaction\"})\n",
    "temp[\"Segment\"] = temp[\"LIFESTAGE\"] + ' - ' + temp[\"PREMIUM_CUSTOMER\"]\n",
    "segment_brand_encode = pd.concat([pd.get_dummies(temp['Segment']), pd.get_dummies(temp['BRAND'])], axis=1)\n",
    "\n",
    "frequent_itemsets = apriori(segment_brand_encode, min_support=0.01, use_colnames=True)\n",
    "\n",
    "rules = association_rules(frequent_itemsets, metric=\"lift\", min_threshold=1)\n",
    "\n",
    "set_temp = temp[\"Segment\"].unique()\n",
    "rules[rules['antecedents'].apply(lambda x: list(x)).apply(lambda x: x in set_temp)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7bbc71fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== YOUNG SINGLES/COUPLES - Premium ==========\n",
      "134.0     537\n",
      "150.0     933\n",
      "175.0    1618\n",
      "Name: PACK_SIZE, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== YOUNG SINGLES/COUPLES - Budget ==========\n",
      "134.0     832\n",
      "150.0    1390\n",
      "175.0    2338\n",
      "Name: PACK_SIZE, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== YOUNG SINGLES/COUPLES - Mainstream ==========\n",
      "134.0    2315\n",
      "150.0    3080\n",
      "175.0    4997\n",
      "Name: PACK_SIZE, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== MIDAGE SINGLES/COUPLES - Premium ==========\n",
      "134.0     781\n",
      "150.0    1207\n",
      "175.0    2082\n",
      "Name: PACK_SIZE, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== MIDAGE SINGLES/COUPLES - Budget ==========\n",
      "134.0     449\n",
      "150.0     771\n",
      "175.0    1277\n",
      "Name: PACK_SIZE, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== MIDAGE SINGLES/COUPLES - Mainstream ==========\n",
      "134.0    1159\n",
      "150.0    1777\n",
      "175.0    2975\n",
      "Name: PACK_SIZE, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== NEW FAMILIES - Premium ==========\n",
      "134.0    165\n",
      "150.0    233\n",
      "175.0    376\n",
      "Name: PACK_SIZE, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== NEW FAMILIES - Budget ==========\n",
      "134.0    309\n",
      "150.0    440\n",
      "175.0    777\n",
      "Name: PACK_SIZE, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== NEW FAMILIES - Mainstream ==========\n",
      "134.0    224\n",
      "150.0    374\n",
      "175.0    589\n",
      "Name: PACK_SIZE, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUoAAABVCAYAAAAv3q3/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAIdUlEQVR4nO3dbYxcZRnG8f/lQqsUsOBWU2lxWlNJiiFL3VQbbFORSqkGUKPWxMgHTG1CE4kx2toEIcakYhQ/GCFYX0iEAikITdUAwbcvxrprX2iha1tY7NLaCsRi1Rgttx/OM+F0O7OnOzO7cw69fslkzzznZa5pp3fnzJm9H0UEZmbW3Bu6HcDMrOxcKM3MCrhQmpkVcKE0MyvgQmlmVuCsbgcYj97e3qjVat2OYWavM4ODgy9GxIxm6ytVKGu1GgMDA92OYWavM5KeH2u9T73NzAq4UJqZFXChNDMrUKnPKJ964Ri1tT/vdgwzK7HhDR/u+DEL31FK+pGko5J258YekLQj3YYl7UjjNUn/zq27q8kxL5T0hKR96ecFHXtGZmYddjqn3j8BlucHIuJTEdEXEX3AQ8DDudUH6usiYnWTY64FnoyIecCT6b6ZWSkVFsqI+B3wcqN1kgR8Etg0zse9DrgnLd8DXD/O/c3MJk27F3MWA0ciYl9ubI6k7ZJ+K2lxk/3eFhGHAdLPt7aZw8xswrR7MefTnPxu8jBwcUS8JOk9wCOSLo2IV1p9AEmrgFUAPec3/eK8mdmEafkdpaSzgI8BD9THIuI/EfFSWh4EDgDvarD7EUkz03FmAkebPU5E3B0R/RHR33POm1uNa2bWsnZOva8C9kbESH1A0gxJPWl5LjAPeLbBvluAG9LyDcCjbeQwM5tQp/P1oE3A74FLJI1IujGtWsmpF3GWALsk7QQ2A6sj4uV0nI2S+tN2G4BlkvYBy9J9M7NSUpXmzOnv7w83xTCzTpM0GBH9zdb7VxjNzAq4UJqZFXChNDMr4EJpZlbAhdLMrIALpZlZARdKM7MCLpRmZgXc4dysgyaiu7Z1X6sdzm+V9EKuk/mK3Lp1kvZLGpJ0dZNjusO5mVVGSx3Okztyncx/ASBpPtnvgF+a9vl+vUnGKO5wbmaV0VaH8wauA+5P7daeA/YDC5ts5w7nZlYJ7VzMWSNpVzo1r586XwQczG0zksZGc4dzM6uMVgvlncA7gT6yrubfTuNqsG1b7YkkrZI0IGngxL+OtXMoM7OWtFQoI+JIRJyIiFeBH/Da6fUIMDu36SzgUINDuMO5mVVGS4WyXuSSjwL1K+JbgJWSpkqaQ9bhfFuDQ7jDuZlVRuH3KFOH86VAr6QR4GvAUkl9ZKfVw8DnASJij6QHgaeB/wE3RcSJdJyNwF0RMUDW0fzB1C39L8AnOvu0zMw6xx3OzeyM5w7nZmZtcqE0MyvgQmlmVsCF0sysgAulmVkBF0ozswIulGZmBVwozcwKuMO5tcUdve1M0GqH86+nFms7JD0u6e2j9rlY0nFJX2pyTHc4N7PKaLXD+bci4rKI6AO2AreMWn8H8MsxjukO52ZWGS11OI+IV3J3p5HrOSnpeuBZYM8Yh3WHczOrjJY/o5T0DeCzwDHgA2lsGvAVYBnQ8LQ7OanDuSR3ODez0mr5qndErI+I2cC9wJo0fBvZpGPHOxEO3OHczLqvE18Pug/4eFp+L3C7pGHgZuCrktY02Mcdzs2sMlo69ZY0LyL2pbvXAnsBImJxbptbgeMR8b0Gh6h3ON+AO5ybWcm12uF8haRLgFeB54HVp3Ecdzg3s0pyh3MzO+O5w7mZWZtcKM3MCrhQmpkVcKE0MytQqYs5kv4BDHU7xzj1Ai92O8Q4OfPkqGJmqGbuoszviIgZzVZWqs0aMDTWlakykjTgzBPPmSdPFXO3m9mn3mZmBVwozcwKVK1Q3t3tAC1w5snhzJOnirnbylypizlmZt1QtXeUZmaTzoXSzKxAJQqlpOWShiTtl1Sq+XWaTL7WdPI0SevS8xiSdHUX8s6W9GtJz0jaI+kLFcj8RknbJO1MmW8re+Zcjh5J2yVtrVDmYUlPpckDB6qQW9J0SZsl7U2v7UUdzRwRpb4BPcABYC4wBdgJzO92rly+JcACYHdu7HZgbVpeC3wzLc9P+acCc9Lz6pnkvDOBBWn5PODPKVeZMws4Ny2fDfwBeF+ZM+eyf5GsufXWsr82cpmHgd5RY6XOTTb31ufS8hRgeiczT/pfQgt/AIuAx3L31wHrup1rVMbaqEI5BMxMyzPJvih/SnbgMWBRl7M/SjbHUSUyA+cAfyLrpl/qzMAssllGr8wVylJnTo/dqFCWNjdwPvAc6eL0RGSuwqn3RcDB3P2RNFZmJ02eBtQnTyvVc5FUAy4ne4dW6szpFHYH2bQhT0RE6TMD3wW+TNbguq7smSGbVfVxSYOSVqWxMueeC/wN+HH6mGNjmuiwY5mrUCjVYKyq32kqzXORdC7wEHBznDz98CmbNhib9MwRcSKyeeRnAQslvXuMzbueWdJHgKMRMXi6uzQY69br/IqIWABcA9wkackY25Yh91lkH3/dGRGXA/8kO9VuZtyZq1AoR4DZufuzgENdynK6mk2eVornIulssiJ5b0Q8nIZLnbkuIv4O/AZYTrkzXwFcmybaux+4UtJPKXdmACLiUPp5FPgZsJBy5x4BRtJZBsBmssLZscxVKJR/BOZJmiNpCrCSbHKyMqtPngYnT562BVgpaaqkOcA8YNtkBpMk4IfAMxHxndyqMmeeIWl6Wn4TcBXZhHalzRwR6yJiVkTUyF6zv4qIz5Q5M4CkaZLOqy8DHwJ2U+LcEfFX4KCyebwAPgg8TSczd+PD4hY+rF1BdnX2ALC+23lGZdsEHAb+S/Y/1Y3AW8g+xN+Xfl6Y2359eh5DwDVdyPt+stOMXcCOdFtR8syXAdtT5t3ALWm8tJlH5V/KaxdzSp2Z7PO+nem2p/7vrQK5+4CB9Bp5BLigk5n9K4xmZgWqcOptZtZVLpRmZgVcKM3MCrhQmpkVcKE0MyvgQmlmVsCF0syswP8ByfwYVkxkAHgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== OLDER FAMILIES - Premium ==========\n",
      "134.0    1014\n",
      "150.0    1673\n",
      "175.0    2816\n",
      "Name: PACK_SIZE, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== OLDER FAMILIES - Budget ==========\n",
      "134.0    1996\n",
      "150.0    3588\n",
      "175.0    5808\n",
      "Name: PACK_SIZE, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== OLDER FAMILIES - Mainstream ==========\n",
      "134.0    1234\n",
      "150.0    2189\n",
      "175.0    3588\n",
      "Name: PACK_SIZE, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== OLDER SINGLES/COUPLES - Premium ==========\n",
      "134.0    1744\n",
      "150.0    2768\n",
      "175.0    4458\n",
      "Name: PACK_SIZE, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== OLDER SINGLES/COUPLES - Budget ==========\n",
      "134.0    1843\n",
      "150.0    2811\n",
      "175.0    4625\n",
      "Name: PACK_SIZE, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== OLDER SINGLES/COUPLES - Mainstream ==========\n",
      "134.0    1720\n",
      "150.0    2773\n",
      "175.0    4525\n",
      "Name: PACK_SIZE, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== RETIREES - Premium ==========\n",
      "134.0    1331\n",
      "150.0    1943\n",
      "175.0    3306\n",
      "Name: PACK_SIZE, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== RETIREES - Budget ==========\n",
      "134.0    1517\n",
      "150.0    2319\n",
      "175.0    3847\n",
      "Name: PACK_SIZE, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== RETIREES - Mainstream ==========\n",
      "134.0    2103\n",
      "150.0    3290\n",
      "175.0    5295\n",
      "Name: PACK_SIZE, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== YOUNG FAMILIES - Premium ==========\n",
      "134.0    1007\n",
      "150.0    1778\n",
      "175.0    2998\n",
      "Name: PACK_SIZE, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== YOUNG FAMILIES - Budget ==========\n",
      "134.0    1674\n",
      "150.0    2862\n",
      "175.0    4921\n",
      "Name: PACK_SIZE, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUwAAABVCAYAAAAiwN24AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAIXklEQVR4nO3dfYxcVRnH8e/PpVQtoOJWU9viFlNJiiErbCoGS6qhUouhqFFrYuAPktrEJhJjtLUJQoxJxSjGECVYjRixhQhoUzWhqW//EOuubEtru/aFRZY2bYDYl2hQy+Mf90y4XWa6tzM7nTnb3yeZzJ1z7z17nnT36b1zZp6jiMDMzCb2uk4PwMwsF06YZmYVOWGamVXkhGlmVpETpplZRRd0egBno7e3N/r6+jo9DDObYoaGhl6IiJkTHZdVwuzr62NwcLDTwzCzKUbSs1WO8y25mVlFTphmZhU5YZqZVZTVe5hPP3+MvjW/7vQwzKyLja6/qW19T3iFKenHko5K2lVqe1jScHqMShpO7X2S/l3ad3+DPi+VtFXSvvT8lkmLyMysTarckv8EWFpuiIhPR0R/RPQDjwKPlXYfqO2LiFUN+lwDbIuI+cC29NrMrKtNmDAj4k/AS/X2SRLwKWDjWf7c5cCDaftB4JazPN/M7JxrddJnEXAkIvaV2uZJekrSHyUtanDe2yPiMEB6fluL4zAza7tWJ30+w+lXl4eByyLiRUnXAL+UdGVEHG/2B0haCawE6Llkwg/im5m1TdNXmJIuAD4OPFxri4iXI+LFtD0EHADeXef0I5JmpX5mAUcb/ZyIeCAiBiJioOeNb2p2uGZmLWvllvwGYG9EjNUaJM2U1JO2LwfmAwfrnLsZuC1t3wb8qoVxmJmdE1U+VrQReBK4QtKYpNvTrhW8drLnemCnpB3AL4BVEfFS6meDpIF03HpgiaR9wJL02sysqymnNX0GBgbCxTfMbLJJGoqIgYmO81cjzcwqcsI0M6vICdPMrCInTDOzipwwzcwqcsI0M6vICdPMrCInTDOzilxx3Wycdlbstrw1W3H9LknPlyqrLyvtWytpv6QRSTc26NMV180sO01VXE/uLVVW/w2ApAUU3zG/Mp3z/VoxjnFccd3MstNSxfU6lgObUpm3Z4D9wMIGx7niupllpZVJn9WSdqZb9tot9WzgudIxY6ltPFdcN7PsNJswfwC8C+inqLL+7dSuOse2VA5J0kpJg5IGT/3rWCtdmZm1pKmEGRFHIuJURLwC/JBXb7vHgLmlQ+cAh+p04YrrZpadphJmLdklHwNqM+ibgRWSpkuaR1FxfXudLlxx3cyyM+HnMFPF9cVAr6Qx4GvAYkn9FLfbo8DnACJit6RHgL8B/wM+HxGnUj8bgPsjYpCiwvojqXr7P4BPTm5YZmaTzxXXzey854rrZmaTzAnTzKwiJ0wzs4qcMM3MKnLCNDOryAnTzKwiJ0wzs4qcMM3MKnLFdXsNVxw3q6/ZiutfT6XdhiU9Iekd4865TNJJSV9q0KcrrptZdpqtuP6tiLgqIvqBLcCd4/bfC/z2DH264rqZZaepiusRcbz0cgalmpeSbgEOArvP0K0rrptZdpp+D1PSN4BbgWPAB1PbDOArwBKg7u14clrFdUmuuG5mXa/pWfKIWBcRc4GHgNWp+W6KxdFOTsbgwBXXzax7TMbHin4OfCJtvw+4R9IocAfwVUmr65zjiutmlp2mbsklzY+IfenlzcBegIhYVDrmLuBkRNxXp4taxfX1uOK6mWWi2YrryyRdAbwCPAusqtCPK66bWdZccd3MznuuuG5mNsmcMM3MKnLCNDOryAnTzKyirCZ9JJ0ARjo9jjboBV7o9CDawHHl5XyO650RMXOijrIq7waMVJnJyo2kQceVD8eVl8mMy7fkZmYVOWGamVWUW8J8oNMDaBPHlRfHlZdJiyurSR8zs07K7QrTzKxjnDDNzCrKImFKWippRNJ+SV2//k+DheMaLvwmaW2KbUTSjaX2ayQ9nfZ9T5LOdSxlkuZK+r2kPZJ2S/pCas86Nkmvl7Rd0o4U192pPeu4SmPqkfSUpC3pdfZxSRpN4xmWNJja2h9XRHT1A+gBDgCXAxcCO4AFnR7XBGO+Hrga2FVquwdYk7bXAN9M2wtSTNOBeSnWnrRvO/B+QBSLyn2kw3HNAq5O2xcDf0/jzzq2NIaL0vY04M/AtbnHVYrvixSFvrdMod/FUaB3XFvb48rhCnMhsD8iDkbEf4BNFIuoda2os3AcjRd+Ww5sioiXI+IZYD+wMFWivyQinoziX/andHixuIg4HBF/TdsngD3AbDKPLQq1ZVWmpUeQeVwAkuYANwEbSs3Zx9VA2+PKIWHOBp4rvR5Lbbk5beE3oLbwW6P4Zqft8e1dQVIf8F6Kq7HsY0u3rcMUy6VsjYgpERfwXeDLFMW+a6ZCXAE8IWlI0srU1va4cvhqZL33FKbSZ6Eaxde1cUu6CHgUuCMijp/hbZ9sYouIU0C/pDcDj0t6zxkOzyIuSR8FjkbEkKTFVU6p09Z1cSXXRcQhFSvObpW09wzHTlpcOVxhjgFzS6/nAIc6NJZWNFr4rVF8Y2l7fHtHSZpGkSwfiojHUvOUiA0gIv4J/AFYSv5xXQfcrGJRwk3AhyT9jPzjIiIOpeejwOMUb921Pa4cEuZfgPmS5km6EFhBsYhabmoLv8HpC79tBlZImi5pHjAf2J5uKU5IujbN3N1KhxeLS+P4EbAnIr5T2pV1bJJmpitLJL0BuIFiYb+s44qItRExJyL6KP5ufhcRnyXzuCTNkHRxbRv4MLCLcxFXJ2e6zmJGbBnFjOwBYF2nx1NhvBuBw8B/Kf4Xux14K7AN2JeeLy0dvy7FNkJplg4YSL8IB4D7SN/M6mBcH6C4ZdkJDKfHstxjA64Cnkpx7QLuTO1ZxzUuxsW8OkuedVwUn5jZkR67aznhXMTlr0aamVWUwy25mVlXcMI0M6vICdPMrCInTDOzipwwzcwqcsI0M6vICdPMrKL/Ax88V+fkN7IXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== YOUNG FAMILIES - Mainstream ==========\n",
      "134.0    1148\n",
      "150.0    2004\n",
      "175.0    3174\n",
      "Name: PACK_SIZE, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "for stage in final_df[\"LIFESTAGE\"].unique():\n",
    "    for prem in final_df[\"PREMIUM_CUSTOMER\"].unique():\n",
    "        print('==========',stage, '-', prem,'==========')\n",
    "        summary = final_df[(final_df[\"LIFESTAGE\"] == stage) & (final_df[\"PREMIUM_CUSTOMER\"] == prem)][\"PACK_SIZE\"].value_counts().head(3).sort_index()\n",
    "        print(summary)\n",
    "        plt.figure()\n",
    "        summary.plot.barh(figsize=(5,1))\n",
    "        plt.show()\n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "263b2b16",
   "metadata": {},
   "source": [
    "Insights :\n",
    "\n",
    "1. Top 3 customers by lifestage and purchasing behaviour contributes to highest sales are \n",
    "\n",
    "•\tOLDER FAMILIES - Budget  $ 156863.75\n",
    "\n",
    "•\tYOUNG SINGLES/COUPLES - Mainstream   $ 147582.20\n",
    "\n",
    "•\tRETIREES - Mainstream  $ 145168.95.\n",
    "\n",
    "The sales contribution can be from number of customers in those segments or frequency of purchases than other segments or higher price per unit.\n",
    "\n",
    "2.Segments which have higher frequency of purchases are : OLDER FAMILIES and YOUNG FAMILIES. Older families has higher frequency   of purchases, Older Families contribute total higher sales.\n",
    "\n",
    "3.By analysing the results, Young singles/couples have higher number of customers than Retirees and Older Families which           contribute to higher sales.\n",
    "\n",
    "5.Kettle brand chips are most purchased brand chips by every segment.\n",
    "\n",
    "6.Every customer segment mostly preferred packsize of 175 g followed by 150 g.\n",
    "\n",
    "7.Older families and Young families have higher frequency of purchases.\n",
    "\n",
    "8.Mainstream Young singles/couples and Midage singles/couples spend more on each packet of chips than premium and budget. As there isn't large difference in average price per unit, t-test is conducted to determine if is there any statistical significance. After observing the p-value, the unit price for mainstream Young and Mid-age singles and couples is significantly higher than that of budget or premium.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "250ae72f",
   "metadata": {},
   "source": [
    "Business Recommendations :\n",
    "1. Focus on top customer segments by promoting and attracting customers from 'Older Families - Budget', 'Young Singles/Couples -    Mainstream' and 'Retirees - Mainstream'\n",
    "2. Targeting the high frequency of purchases customer segments (Older Families & Young Families) by developing marketing            strategy like personalised offers or discounts to encourage them repeat purchases.\n",
    "3. Higher number of customers are present in Young singles/couples, target these customers by marketing the chips are organic      who focus alot on health.\n",
    "4. Considering pricing strategy that allows customer preferences and willing to pay in Young singles/couples and Midage            singles/couples - Budget and Premium segements.\n",
    "5. Advertising more on Kettle brand can increase the sales. And optimizing inventory as most purchases are of 175g and 150g        sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4821606",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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