Quantium Task 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d4e08209",
   "metadata": {},
   "source": [
    "## Load required libraries and datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bf97b1ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Wrangling\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Date Functionality\n",
    "import matplotlib.dates as mdates\n",
    "from matplotlib.dates import DateFormatter\n",
    "\n",
    "# Statistical analysis\n",
    "from scipy.stats import ttest_ind\n",
    "\n",
    "\n",
    "# Remove Warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a4a0766",
   "metadata": {},
   "source": [
    "### Examining Transaction Data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d60a5869",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>43390</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Natural Chip        Compny SeaSalt175g</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43599</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>348</td>\n",
       "      <td>66</td>\n",
       "      <td>CCs Nacho Cheese    175g</td>\n",
       "      <td>3</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43605</td>\n",
       "      <td>1</td>\n",
       "      <td>1343</td>\n",
       "      <td>383</td>\n",
       "      <td>61</td>\n",
       "      <td>Smiths Crinkle Cut  Chips Chicken 170g</td>\n",
       "      <td>2</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43329</td>\n",
       "      <td>2</td>\n",
       "      <td>2373</td>\n",
       "      <td>974</td>\n",
       "      <td>69</td>\n",
       "      <td>Smiths Chip Thinly  S/Cream&amp;Onion 175g</td>\n",
       "      <td>5</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43330</td>\n",
       "      <td>2</td>\n",
       "      <td>2426</td>\n",
       "      <td>1038</td>\n",
       "      <td>108</td>\n",
       "      <td>Kettle Tortilla ChpsHny&amp;Jlpno Chili 150g</td>\n",
       "      <td>3</td>\n",
       "      <td>13.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264831</th>\n",
       "      <td>43533</td>\n",
       "      <td>272</td>\n",
       "      <td>272319</td>\n",
       "      <td>270088</td>\n",
       "      <td>89</td>\n",
       "      <td>Kettle Sweet Chilli And Sour Cream 175g</td>\n",
       "      <td>2</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264832</th>\n",
       "      <td>43325</td>\n",
       "      <td>272</td>\n",
       "      <td>272358</td>\n",
       "      <td>270154</td>\n",
       "      <td>74</td>\n",
       "      <td>Tostitos Splash Of  Lime 175g</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264833</th>\n",
       "      <td>43410</td>\n",
       "      <td>272</td>\n",
       "      <td>272379</td>\n",
       "      <td>270187</td>\n",
       "      <td>51</td>\n",
       "      <td>Doritos Mexicana    170g</td>\n",
       "      <td>2</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264834</th>\n",
       "      <td>43461</td>\n",
       "      <td>272</td>\n",
       "      <td>272379</td>\n",
       "      <td>270188</td>\n",
       "      <td>42</td>\n",
       "      <td>Doritos Corn Chip Mexican Jalapeno 150g</td>\n",
       "      <td>2</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264835</th>\n",
       "      <td>43365</td>\n",
       "      <td>272</td>\n",
       "      <td>272380</td>\n",
       "      <td>270189</td>\n",
       "      <td>74</td>\n",
       "      <td>Tostitos Splash Of  Lime 175g</td>\n",
       "      <td>2</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>264836 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "0       43390          1            1000       1         5   \n",
       "1       43599          1            1307     348        66   \n",
       "2       43605          1            1343     383        61   \n",
       "3       43329          2            2373     974        69   \n",
       "4       43330          2            2426    1038       108   \n",
       "...       ...        ...             ...     ...       ...   \n",
       "264831  43533        272          272319  270088        89   \n",
       "264832  43325        272          272358  270154        74   \n",
       "264833  43410        272          272379  270187        51   \n",
       "264834  43461        272          272379  270188        42   \n",
       "264835  43365        272          272380  270189        74   \n",
       "\n",
       "                                       PROD_NAME  PROD_QTY  TOT_SALES  \n",
       "0         Natural Chip        Compny SeaSalt175g         2        6.0  \n",
       "1                       CCs Nacho Cheese    175g         3        6.3  \n",
       "2         Smiths Crinkle Cut  Chips Chicken 170g         2        2.9  \n",
       "3         Smiths Chip Thinly  S/Cream&Onion 175g         5       15.0  \n",
       "4       Kettle Tortilla ChpsHny&Jlpno Chili 150g         3       13.8  \n",
       "...                                          ...       ...        ...  \n",
       "264831   Kettle Sweet Chilli And Sour Cream 175g         2       10.8  \n",
       "264832             Tostitos Splash Of  Lime 175g         1        4.4  \n",
       "264833                  Doritos Mexicana    170g         2        8.8  \n",
       "264834   Doritos Corn Chip Mexican Jalapeno 150g         2        7.8  \n",
       "264835             Tostitos Splash Of  Lime 175g         2        8.8  \n",
       "\n",
       "[264836 rows x 8 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data_transaction = pd.read_excel(\"E:\\Deakin\\QVI_transaction_data.xlsx\")\n",
    "Data_transaction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1c3aa79",
   "metadata": {},
   "source": [
    "###  To Check Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cc9b8a1f",
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data_transaction.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "705b0372",
   "metadata": {},
   "source": [
    "-  No Missing Data in the datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9757e666",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>42</th>\n",
       "      <td>43605</td>\n",
       "      <td>55</td>\n",
       "      <td>55073</td>\n",
       "      <td>48887</td>\n",
       "      <td>113</td>\n",
       "      <td>Twisties Chicken270g</td>\n",
       "      <td>1</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>43475</td>\n",
       "      <td>7</td>\n",
       "      <td>7364</td>\n",
       "      <td>7739</td>\n",
       "      <td>20</td>\n",
       "      <td>Doritos Cheese      Supreme 330g</td>\n",
       "      <td>2</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>43391</td>\n",
       "      <td>12</td>\n",
       "      <td>12301</td>\n",
       "      <td>10982</td>\n",
       "      <td>93</td>\n",
       "      <td>Doritos Corn Chip Southern Chicken 150g</td>\n",
       "      <td>2</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>43351</td>\n",
       "      <td>16</td>\n",
       "      <td>16427</td>\n",
       "      <td>14546</td>\n",
       "      <td>81</td>\n",
       "      <td>Pringles Original   Crisps 134g</td>\n",
       "      <td>1</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>43315</td>\n",
       "      <td>19</td>\n",
       "      <td>19272</td>\n",
       "      <td>16683</td>\n",
       "      <td>31</td>\n",
       "      <td>Infzns Crn Crnchers Tangy Gcamole 110g</td>\n",
       "      <td>2</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "42   43605         55           55073   48887       113   \n",
       "377  43475          7            7364    7739        20   \n",
       "419  43391         12           12301   10982        93   \n",
       "476  43351         16           16427   14546        81   \n",
       "511  43315         19           19272   16683        31   \n",
       "\n",
       "                                   PROD_NAME  PROD_QTY  TOT_SALES  \n",
       "42                      Twisties Chicken270g         1        4.6  \n",
       "377         Doritos Cheese      Supreme 330g         2       11.4  \n",
       "419  Doritos Corn Chip Southern Chicken 150g         2        7.8  \n",
       "476          Pringles Original   Crisps 134g         1        3.7  \n",
       "511   Infzns Crn Crnchers Tangy Gcamole 110g         2        7.6  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look for duplicated TXN_ID\n",
    "\n",
    "Data_transaction[Data_transaction.duplicated(['TXN_ID'])].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "84bbea2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>41</th>\n",
       "      <td>43605</td>\n",
       "      <td>55</td>\n",
       "      <td>55073</td>\n",
       "      <td>48887</td>\n",
       "      <td>4</td>\n",
       "      <td>Dorito Corn Chp     Supreme 380g</td>\n",
       "      <td>1</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43605</td>\n",
       "      <td>55</td>\n",
       "      <td>55073</td>\n",
       "      <td>48887</td>\n",
       "      <td>113</td>\n",
       "      <td>Twisties Chicken270g</td>\n",
       "      <td>1</td>\n",
       "      <td>4.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "41  43605         55           55073   48887         4   \n",
       "42  43605         55           55073   48887       113   \n",
       "\n",
       "                           PROD_NAME  PROD_QTY  TOT_SALES  \n",
       "41  Dorito Corn Chp     Supreme 380g         1       3.25  \n",
       "42              Twisties Chicken270g         1       4.60  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the first duplicated TXN_ID\n",
    "\n",
    "Data_transaction.loc[Data_transaction['TXN_ID'] == 48887, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d53fc1fc",
   "metadata": {},
   "source": [
    "### Categorise Numeric and Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4108bb19",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data_transaction_numerics_only = Data_transaction.select_dtypes(include=np.number)\n",
    "Data_transaction_cat = set(Data_transaction.columns) - set(Data_transaction_numerics_only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "403ff615",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numeric Columns:\n",
      " ['DATE', 'STORE_NBR', 'LYLTY_CARD_NBR', 'TXN_ID', 'PROD_NBR', 'PROD_QTY', 'TOT_SALES']\n",
      "Categorical Columns:\n",
      " ['PROD_NAME']\n"
     ]
    }
   ],
   "source": [
    "print(\"Numeric Columns:\\n\",list(Data_transaction_numerics_only))\n",
    "print(\"Categorical Columns:\\n\",list(Data_transaction_cat))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "380bc74b",
   "metadata": {},
   "source": [
    "### To Check Outliers and Treat them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4e26cffa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_outlier_IQR(df):\n",
    "    Q1=df.quantile(0.25)\n",
    "    Q3=df.quantile(0.75)\n",
    "    IQR=Q3-Q1\n",
    "    df_final=df[~((df<(Q1-1.5*IQR)) | (df>(Q3+1.5*IQR)))]\n",
    "    return df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "11c7fc3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Total Sales)')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df_outlier_removed=remove_outlier_IQR(Data_transaction.TOT_SALES)\n",
    "df_outlier_removed=pd.DataFrame(df_outlier_removed)\n",
    "ind_diff=Data_transaction.index.difference(df_outlier_removed.index)\n",
    "\n",
    "for i in range(0, len(ind_diff),1):\n",
    "    df_final=Data_transaction.drop([ind_diff[i]])\n",
    "    Data_transaction =df_final\n",
    "    \n",
    "sns.boxplot(y='TOT_SALES', x='PROD_QTY',data=Data_transaction)\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('Total Sales)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "34dcb849",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of dataset after treating outliers: (264258, 8)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of dataset after treating outliers:\",Data_transaction.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d43b0d5",
   "metadata": {},
   "source": [
    "- Comparing the two boxplots, it can be seen that the outliers were removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6caa4ea2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>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>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>5</th>\n",
       "      <td>2019-05-19</td>\n",
       "      <td>4</td>\n",
       "      <td>4074</td>\n",
       "      <td>2982</td>\n",
       "      <td>57</td>\n",
       "      <td>Old El Paso Salsa   Dip Tomato Mild 300g</td>\n",
       "      <td>1</td>\n",
       "      <td>5.1</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>264258 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",
       "4      2018-08-18          2            2426    1038       108   \n",
       "5      2019-05-19          4            4074    2982        57   \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",
       "4       Kettle Tortilla ChpsHny&Jlpno Chili 150g         3       13.8  \n",
       "5       Old El Paso Salsa   Dip Tomato Mild 300g         1        5.1  \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",
       "[264258 rows x 8 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert Date column into DATE format\n",
    "origin = pd.Timestamp(\"30/12/1899\")\n",
    "Data_transaction[\"DATE\"] = Data_transaction[\"DATE\"].apply(lambda x: origin + pd.Timedelta(days=x))\n",
    "Data_transaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3d5505a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Kettle Tortilla ChpsHny&Jlpno Chili 150g    3285\n",
       "Kettle Mozzarella   Basil & Pesto 175g      3280\n",
       "Tyrrells Crisps     Ched & Chives 165g      3264\n",
       "Cobs Popd Swt/Chlli &Sr/Cream Chips 110g    3260\n",
       "Cobs Popd Sea Salt  Chips 110g              3259\n",
       "                                            ... \n",
       "Woolworths Medium   Salsa 300g              1430\n",
       "RRD Pc Sea Salt     165g                    1429\n",
       "French Fries Potato Chips 175g              1418\n",
       "NCC Sour Cream &    Garden Chives 175g      1416\n",
       "WW Crinkle Cut      Original 175g           1410\n",
       "Name: PROD_NAME, Length: 114, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data_transaction['PROD_NAME'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ee1c566a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>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>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>6</th>\n",
       "      <td>2019-05-16</td>\n",
       "      <td>4</td>\n",
       "      <td>4149</td>\n",
       "      <td>3333</td>\n",
       "      <td>16</td>\n",
       "      <td>Smiths Crinkle Chips Salt &amp; Vinegar 330g</td>\n",
       "      <td>1</td>\n",
       "      <td>5.7</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>246204 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",
       "4      2018-08-18          2            2426    1038       108   \n",
       "6      2019-05-16          4            4149    3333        16   \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",
       "4       Kettle Tortilla ChpsHny&Jlpno Chili 150g         3       13.8  \n",
       "6       Smiths Crinkle Chips Salt & Vinegar 330g         1        5.7  \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",
       "[246204 rows x 8 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The customer only wants insights on chips category, hence, we eliminate all the other products.\n",
    "Data_transaction = Data_transaction[Data_transaction[\"PROD_NAME\"].str.contains(\"Salsa\")==False]\n",
    "Data_transaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2a2ec1f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Kettle Tortilla ChpsHny&Jlpno Chili 150g    3285\n",
       "Kettle Mozzarella   Basil & Pesto 175g      3280\n",
       "Tyrrells Crisps     Ched & Chives 165g      3264\n",
       "Cobs Popd Swt/Chlli &Sr/Cream Chips 110g    3260\n",
       "Cobs Popd Sea Salt  Chips 110g              3259\n",
       "                                            ... \n",
       "Sunbites Whlegrn    Crisps Frch/Onin 90g    1432\n",
       "RRD Pc Sea Salt     165g                    1429\n",
       "French Fries Potato Chips 175g              1418\n",
       "NCC Sour Cream &    Garden Chives 175g      1416\n",
       "WW Crinkle Cut      Original 175g           1410\n",
       "Name: PROD_NAME, Length: 105, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data_transaction['PROD_NAME'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "77575052",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>pack_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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</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</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</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-05-16</td>\n",
       "      <td>4</td>\n",
       "      <td>4149</td>\n",
       "      <td>3333</td>\n",
       "      <td>16</td>\n",
       "      <td>Smiths Crinkle Chips Salt &amp; Vinegar 330g</td>\n",
       "      <td>1</td>\n",
       "      <td>5.7</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264831</th>\n",
       "      <td>2019-03-09</td>\n",
       "      <td>272</td>\n",
       "      <td>272319</td>\n",
       "      <td>270088</td>\n",
       "      <td>89</td>\n",
       "      <td>Kettle Sweet Chilli And Sour Cream 175g</td>\n",
       "      <td>2</td>\n",
       "      <td>10.8</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264832</th>\n",
       "      <td>2018-08-13</td>\n",
       "      <td>272</td>\n",
       "      <td>272358</td>\n",
       "      <td>270154</td>\n",
       "      <td>74</td>\n",
       "      <td>Tostitos Splash Of  Lime 175g</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264833</th>\n",
       "      <td>2018-11-06</td>\n",
       "      <td>272</td>\n",
       "      <td>272379</td>\n",
       "      <td>270187</td>\n",
       "      <td>51</td>\n",
       "      <td>Doritos Mexicana    170g</td>\n",
       "      <td>2</td>\n",
       "      <td>8.8</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264834</th>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>272</td>\n",
       "      <td>272379</td>\n",
       "      <td>270188</td>\n",
       "      <td>42</td>\n",
       "      <td>Doritos Corn Chip Mexican Jalapeno 150g</td>\n",
       "      <td>2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264835</th>\n",
       "      <td>2018-09-22</td>\n",
       "      <td>272</td>\n",
       "      <td>272380</td>\n",
       "      <td>270189</td>\n",
       "      <td>74</td>\n",
       "      <td>Tostitos Splash Of  Lime 175g</td>\n",
       "      <td>2</td>\n",
       "      <td>8.8</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>246204 rows × 9 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",
       "4      2018-08-18          2            2426    1038       108   \n",
       "6      2019-05-16          4            4149    3333        16   \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",
       "4       Kettle Tortilla ChpsHny&Jlpno Chili 150g         3       13.8   \n",
       "6       Smiths Crinkle Chips Salt & Vinegar 330g         1        5.7   \n",
       "...                                          ...       ...        ...   \n",
       "264831   Kettle Sweet Chilli And Sour Cream 175g         2       10.8   \n",
       "264832             Tostitos Splash Of  Lime 175g         1        4.4   \n",
       "264833                  Doritos Mexicana    170g         2        8.8   \n",
       "264834   Doritos Corn Chip Mexican Jalapeno 150g         2        7.8   \n",
       "264835             Tostitos Splash Of  Lime 175g         2        8.8   \n",
       "\n",
       "       pack_size  \n",
       "0            175  \n",
       "1            175  \n",
       "2            170  \n",
       "4            150  \n",
       "6            330  \n",
       "...          ...  \n",
       "264831       175  \n",
       "264832       175  \n",
       "264833       170  \n",
       "264834       150  \n",
       "264835       175  \n",
       "\n",
       "[246204 rows x 9 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting pack size from the Product\n",
    "import re\n",
    "def find_number(text):\n",
    "    num = re.findall(r'[0-9]+',text)\n",
    "    return \" \".join(num)\n",
    "Data_transaction['pack_size']=Data_transaction['PROD_NAME'].apply(lambda x: find_number(x))\n",
    "Data_transaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "afe80915",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram showing the number of transactions by pack size \n",
    "\n",
    "fig = plt.figure(figsize = (15,4), linewidth=5, edgecolor=\"#04253a\", facecolor = '#e1ddbf')\n",
    "\n",
    "sns.set(rc = {'figure.figsize':(15,4)})\n",
    "ax = sns.histplot(data=Data_transaction, x=\"pack_size\",  edgecolor=\"#04253a\")\n",
    "\n",
    "ax.set_facecolor(\"#ffffff\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbad67fe",
   "metadata": {},
   "source": [
    "- 175g is the highest selling pack size for the chips followed by 150g."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "75626e48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create column for brand names\n",
    "Data_transaction['Brand Name'] = Data_transaction['PROD_NAME'].str.split(' ').str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8cb12dcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Kettle        41141\n",
       "Smiths        27340\n",
       "Pringles      25052\n",
       "Doritos       21975\n",
       "Thins         14049\n",
       "RRD           11880\n",
       "Infuzions     11035\n",
       "WW            10320\n",
       "Cobs           9669\n",
       "Tostitos       9443\n",
       "Twisties       9420\n",
       "Tyrrells       6428\n",
       "Grain          6265\n",
       "Natural        6037\n",
       "Cheezels       4583\n",
       "CCs            4551\n",
       "Red            4427\n",
       "Dorito         3175\n",
       "Infzns         3138\n",
       "Smith          2963\n",
       "Cheetos        2926\n",
       "Snbts          1576\n",
       "Burger         1564\n",
       "Woolworths     1516\n",
       "GrnWves        1465\n",
       "Sunbites       1432\n",
       "French         1418\n",
       "NCC            1416\n",
       "Name: Brand Name, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for any duplication or similar brands\n",
    "Data_transaction['Brand Name'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "481d7831",
   "metadata": {},
   "source": [
    "- Some Brands are similar like RRD and Red Rock Deli , WW and Woolworths,NCC and Natural Chip Company etc.Let us combine them together as they are a single unit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9eda4193",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data_transaction['Brand Name'] = Data_transaction['Brand Name'].str.replace('Red','RRD')\n",
    "Data_transaction['Brand Name'] = Data_transaction['Brand Name'].str.replace('Woolworths','WW')\n",
    "Data_transaction['Brand Name'] = Data_transaction['Brand Name'].str.replace('INFUZIONS','INFZNS')  \n",
    "Data_transaction['Brand Name'] = Data_transaction['Brand Name'].str.replace('SMITHS','SMITH')\n",
    "Data_transaction['Brand Name'] = Data_transaction['Brand Name'].str.replace('SUNBITES','SNBTS') \n",
    "Data_transaction['Brand Name'] = Data_transaction['Brand Name'].str.replace('DORITOS','DORITO')\n",
    "Data_transaction['Brand Name'] = Data_transaction['Brand Name'].str.replace('GRNWVES','GRAIN')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7a12fcb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Kettle       41141\n",
       "Smiths       27340\n",
       "Pringles     25052\n",
       "Doritos      21975\n",
       "RRD          16307\n",
       "Thins        14049\n",
       "WW           11836\n",
       "Infuzions    11035\n",
       "Cobs          9669\n",
       "Tostitos      9443\n",
       "Twisties      9420\n",
       "Tyrrells      6428\n",
       "Grain         6265\n",
       "Natural       6037\n",
       "Cheezels      4583\n",
       "CCs           4551\n",
       "Dorito        3175\n",
       "Infzns        3138\n",
       "Smith         2963\n",
       "Cheetos       2926\n",
       "Snbts         1576\n",
       "Burger        1564\n",
       "GrnWves       1465\n",
       "Sunbites      1432\n",
       "French        1418\n",
       "NCC           1416\n",
       "Name: Brand Name, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data_transaction['Brand Name'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f258ea9e",
   "metadata": {},
   "source": [
    "- Combined the similar brands."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "729189ba",
   "metadata": {},
   "source": [
    "### Examining customer data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "da668725",
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data_customer = pd.read_csv(\"E:\\Deakin\\QVI_purchase_behaviour.csv\")\n",
    "Data_customer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcc93503",
   "metadata": {},
   "source": [
    "### To check for null Values in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "06a585ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LYLTY_CARD_NBR      0\n",
       "LIFESTAGE           0\n",
       "PREMIUM_CUSTOMER    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data_customer.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca416e33",
   "metadata": {},
   "source": [
    "### Categorise Numeric and Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "58f87238",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data_customer_numerics_only = Data_customer.select_dtypes(include=np.number)\n",
    "Data_customer_cat = set(Data_customer.columns) - set(Data_customer_numerics_only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3a948d6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numeric Columns:\n",
      " ['LYLTY_CARD_NBR']\n",
      "Categorical Columns:\n",
      " {'LIFESTAGE', 'PREMIUM_CUSTOMER'}\n"
     ]
    }
   ],
   "source": [
    "print(\"Numeric Columns:\\n\",list(Data_customer_numerics_only))\n",
    "print(\"Categorical Columns:\\n\",Data_customer_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ffb2e4a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>pack_size</th>\n",
       "      <th>Brand Name</th>\n",
       "      <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</td>\n",
       "      <td>Natural</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</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>2018-11-10</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>346</td>\n",
       "      <td>96</td>\n",
       "      <td>WW Original Stacked Chips 160g</td>\n",
       "      <td>2</td>\n",
       "      <td>3.8</td>\n",
       "      <td>160</td>\n",
       "      <td>WW</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-03-09</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>347</td>\n",
       "      <td>54</td>\n",
       "      <td>CCs Original 175g</td>\n",
       "      <td>1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>175</td>\n",
       "      <td>CCs</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1343</td>\n",
       "      <td>383</td>\n",
       "      <td>61</td>\n",
       "      <td>Smiths Crinkle Cut  Chips Chicken 170g</td>\n",
       "      <td>2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>170</td>\n",
       "      <td>Smiths</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "    </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>246199</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</td>\n",
       "      <td>Kettle</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246200</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</td>\n",
       "      <td>Tostitos</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246201</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</td>\n",
       "      <td>Doritos</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246202</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</td>\n",
       "      <td>Doritos</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246203</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</td>\n",
       "      <td>Tostitos</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>246204 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      2018-11-10          1            1307     346        96   \n",
       "3      2019-03-09          1            1307     347        54   \n",
       "4      2019-05-20          1            1343     383        61   \n",
       "...           ...        ...             ...     ...       ...   \n",
       "246199 2019-03-09        272          272319  270088        89   \n",
       "246200 2018-08-13        272          272358  270154        74   \n",
       "246201 2018-11-06        272          272379  270187        51   \n",
       "246202 2018-12-27        272          272379  270188        42   \n",
       "246203 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                WW Original Stacked Chips 160g         2        3.8   \n",
       "3                             CCs Original 175g         1        2.1   \n",
       "4        Smiths Crinkle Cut  Chips Chicken 170g         2        2.9   \n",
       "...                                         ...       ...        ...   \n",
       "246199  Kettle Sweet Chilli And Sour Cream 175g         2       10.8   \n",
       "246200            Tostitos Splash Of  Lime 175g         1        4.4   \n",
       "246201                 Doritos Mexicana    170g         2        8.8   \n",
       "246202  Doritos Corn Chip Mexican Jalapeno 150g         2        7.8   \n",
       "246203            Tostitos Splash Of  Lime 175g         2        8.8   \n",
       "\n",
       "       pack_size Brand Name               LIFESTAGE PREMIUM_CUSTOMER  \n",
       "0            175    Natural   YOUNG SINGLES/COUPLES          Premium  \n",
       "1            175        CCs  MIDAGE SINGLES/COUPLES           Budget  \n",
       "2            160         WW  MIDAGE SINGLES/COUPLES           Budget  \n",
       "3            175        CCs  MIDAGE SINGLES/COUPLES           Budget  \n",
       "4            170     Smiths  MIDAGE SINGLES/COUPLES           Budget  \n",
       "...          ...        ...                     ...              ...  \n",
       "246199       175     Kettle   YOUNG SINGLES/COUPLES          Premium  \n",
       "246200       175   Tostitos   YOUNG SINGLES/COUPLES          Premium  \n",
       "246201       170    Doritos   YOUNG SINGLES/COUPLES          Premium  \n",
       "246202       150    Doritos   YOUNG SINGLES/COUPLES          Premium  \n",
       "246203       175   Tostitos   YOUNG SINGLES/COUPLES          Premium  \n",
       "\n",
       "[246204 rows x 12 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging the two dataframes\n",
    "df4 = pd.merge(Data_transaction,Data_customer)\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8532f928",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE                0\n",
       "STORE_NBR           0\n",
       "LYLTY_CARD_NBR      0\n",
       "TXN_ID              0\n",
       "PROD_NBR            0\n",
       "PROD_NAME           0\n",
       "PROD_QTY            0\n",
       "TOT_SALES           0\n",
       "pack_size           0\n",
       "Brand Name          0\n",
       "LIFESTAGE           0\n",
       "PREMIUM_CUSTOMER    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To check if some customers were not matched on by checking for nulls.\n",
    "df4.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "69885f06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-12-25'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start = '2018-07-01', end = '2019-06-30').difference(df4['DATE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26681e3d",
   "metadata": {},
   "source": [
    "#### We have a missing date on Christmas Day. This makes sense because most retail stores are closed that day.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "34ec51c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>TOT_SALES</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>4920.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-02</th>\n",
       "      <td>4877.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-03</th>\n",
       "      <td>4954.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-04</th>\n",
       "      <td>4968.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-05</th>\n",
       "      <td>4682.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            TOT_SALES\n",
       "DATE                 \n",
       "2018-07-01     4920.1\n",
       "2018-07-02     4877.0\n",
       "2018-07-03     4954.7\n",
       "2018-07-04     4968.1\n",
       "2018-07-05     4682.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new dataframe which contains the total sale for each date\n",
    "\n",
    "df5 = pd.pivot_table(df4, values = 'TOT_SALES', index = 'DATE', aggfunc = 'sum')\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e7d4a56d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "365"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6 = pd.DataFrame(index = pd.date_range(start = '2018-07-01', end = '2019-06-30'))\n",
    "df6['TOT_SALES'] = 0\n",
    "len(df6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e59cb119",
   "metadata": {},
   "outputs": [],
   "source": [
    "z = df5 + df6\n",
    "z.fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f0e746ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>4920.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-02</th>\n",
       "      <td>4877.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-03</th>\n",
       "      <td>4954.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-04</th>\n",
       "      <td>4968.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-05</th>\n",
       "      <td>4682.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total Sales\n",
       "Date                   \n",
       "2018-07-01       4920.1\n",
       "2018-07-02       4877.0\n",
       "2018-07-03       4954.7\n",
       "2018-07-04       4968.1\n",
       "2018-07-05       4682.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.index.name = 'Date'\n",
    "z.rename(columns = {'TOT_SALES': 'Total Sales'}, inplace = True)\n",
    "z.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "42b5d77b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Total Sales')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "timeline = z.index\n",
    "graph = z['Total Sales']\n",
    "\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (15, 5), linewidth=5, edgecolor=\"#04253a\", facecolor = '#e1ddbf')\n",
    "\n",
    "ax.plot(timeline, graph)\n",
    "\n",
    "ax.set_facecolor(\"#ffffff\")\n",
    "\n",
    "date_form = DateFormatter(\"%Y-%m\")\n",
    "ax.xaxis.set_major_formatter(date_form)\n",
    "plt.title('Total Sales from July 2018 to June 2019')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Total Sales')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64db2ea6",
   "metadata": {},
   "source": [
    "#### We can see that sales spike up during the December month and zero sale on Christmas Day.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8081a779",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>5000.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-02</th>\n",
       "      <td>4781.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-03</th>\n",
       "      <td>4927.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-04</th>\n",
       "      <td>4869.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-05</th>\n",
       "      <td>4900.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total Sales\n",
       "Date                   \n",
       "2018-12-01       5000.9\n",
       "2018-12-02       4781.1\n",
       "2018-12-03       4927.0\n",
       "2018-12-04       4869.4\n",
       "2018-12-05       4900.5"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's look at the December month only\n",
    "\n",
    "z_december = z[(z.index < \"2019-01-01\") & (z.index > \"2018-11-30\")]\n",
    "z_december.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "262cd757",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Total Sales in December')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zUvY5bTU1ycox47I5Zlw2Pp/B5u3VLGsNlp9+tYUP5m8izGJiUB87I/qnMKJ/Cn2zEoPu+r/O4PH6WLrWydwlhXy7qphml5eUpGh+dURfpo7uRZ/0+ECXeNActhiclQqSIqGiy4Lkwq+WMXXKaFJSkgC45eaLKSpykpmRSnpaMgBHTRvH/AWLye6dTnOzi8GDcgE49pjxvPTKhxx/3CRWrFjPHbdd5t9+/cwHFCRFRERCREOTm4+/3My/522kpt7F8LxkZp57GEP7JndoP2azib5ZifTNSuTUaXk0u72s2lTeGiydvPzpGl7+dA1x1nCG9UvxB8s0e+BaWhwswzBYs6WCuUsKWbhsO7UNLuKs4Rw5uhdTR2UxqI+tR7XKcNisrN5SEegyRKSduixIbt/uJCw8jFtvfwxnaQWHjx1KdnYGdluC/zZ2WwKlZZWUV1Rh22W7rXV7dU0dVmsUFovFf/uyssquKllEREQ6SV2jmw8XbOKD+Rupa3QzamAqZx49gEE5tk7Zf2S4hVEDUhk1IBUYQmVtE8vXl7FsnZNl60r58oftAKTbY/yhclheCrHR4Z1y/K60tbiGeUsKmbe0CGdFAxHhFsYNSWPqqCxGDkglPMwc6BK7RKrNyvxlRXi9PiyWnvkYRXqSLguSXp+PFUvXcP891xMdHcltdzxOREQE7DJ9xTBams76fMZu01oMw8BsMmH4fHtMd9GqbSIiIsGrpt7F+/M38tHCTTQ0eRg3JI3fHN2f/r2TuvS4SXFRTB2VxdRRWRiGQaGzjqWtoXLukgI+/XoLZhPk9UpiaL9kcjMTyM1MIN0eExSjeqWVjSxYVsjcJYVs3l6D2QQj+qdyzvSBHJ6fhjUq+APwwXLYrPh8BqVVjSE9kixyqOiyIGlLimfkyEEkJsYBMHHCCOYvWIzZ/NMnTBWVNdhtiaQkJ1FRUe3fXllZg92eSGJiPPX1jf5PpsorqrHbE7uqZBERETlAVbXN/HveBj75ajONzV4mDEvnjKMHkJuZ0PadO5nJZKKXI45ejjhOntwXt8fHum2V/mD5r7kb8PoMAKIiLORkJJCTEU9uZgI5GQlkp8d3S6uMugYXX/6wnblLClm1qRzDgAG9k/j9KUOZNCKDpLhDa3HBXVduVZAUCX5dFiTHjR3GfQ88T11dA9HRUXz33UomTxrFm2/9h6LtTtIcyXwxdxHTj52Iw2EnIiKclas2kD+kH3M+/4Yxhw0hLMxCfn4/5s3/nmlHjmX2nJbtIiIiEhwqapp474sNfPr1FtweL5NHZPKbo/uTnRY8i7+Eh5kZkmtnSK6d3x43CLfHy7biWjZvr2bT9ho2FVXzxeJCPvlqCwBmE2SmxpGbkUBuZjw5GS2jlwmxkQddS7Pby3eri5m7uJDFP5bg8RpkpsRy9vSBTBmZSUZy7EEfI1SpBYhIaOmyIDloYA6/OW06195wHx6Pl1GjBnHSiUfQKyuNO//6FC6Xm7Fj8pkyaRQAN828kIdmvUJDQyP9+vXmlJOnAXDV5Wdz7wMv8Orrn5CamsSfbvxdV5UsIiIi7VRa2ci7X6znv99uxeszmDoqi9OPyiMrNS7QpbUpPMziX7hnJ5/PwFnZwKaiajZtr2ZzUQ2rNpczb2mh/zb2hCh/qMzNSCAnM540W9tTY70+gx/WlzJvaSFf/bCDxmYPtvhITpyYy9RRWfTNStClO0BKYjRms0lBUiREmMLyJhg7v9n42d2BrKVTWcKiyOx9eKDLEBER6VGKy+t553/r+fy7bRgGHDWmN6dNyyM9uWdORaypd7F5ezWbt1ezsaiazUXVFDjr8LVOjY2OtNAnPcE/LTY3M57stHjCw8ysL6hi3tJCFiwtorK2GWtUGBOGZjB1VBb5/ZJ7ZJuSg3XR3bMZ3MfG9eeMDnQpIj1K0bZv8HqaOm1/fY+7petGJEVERKTn2F5ax1ufr+OLxYWYTSaOGZfNaUfmkdo6HbGnio+JYHheCsPzUvzbXO6WqbEtI5ctI5j/+76AxubNQEurkviYCKpqmwmzmBkz2MERo7IYM8hBRDdcexnKHElWjUiKhAgFSREREdknl9vLa//5kX/N3UCYxcwvJubw6yP7YU+IDnRpARMRbqFfr0T69Ur0b/P5DEoqGvzhsri8gaH9kpk4LJ1Ya0Tgig0xDpuVJWtLAl2GiLSDgqSIiIjs1YaCKh56Ywnbims5dlw2vz1uIEnxh9ZKou1lNptIT44hPTmGicMyAl1OyHLYrVTUNNPs9nbLyrkicuAUJEVERGQ3Hq+Pt+as480560iMjeT2iw9n9EBHoMuSQ8DOlVudFQ30cgT/wk0ihzIFSREREfHbuqOGB19fwqaiao4cncXvTxmqqZnSbXZtAaIgKRLcFCRFREQEr8/gX3M38OpnPxITHcafzh/D+KGaoindyz8iWakFd0SCnYKkiIjIIa6otI5/vL6EH7dWMmFYOn84dTgJsZGBLksOQUlxUYSHmSkpV5AUCXYKkiIiIocon8/goy838eLHa4gIM3PDOaOZMjITk0n9DSUwzGYTqUnRagEiEgIUJEVERA5BJRUNzHpjKSs2lnHYIAdXnD78kG7pIcHDYYuhpKI+0GWISBsUJEVERA4hhmHw32+38uwHKwETV/1mBEeP7a1RSAkaDpuV9QWVgS5DRNqgICkiInKIKK9u5OG3lrHkRyfD+iVz9RkjSW1d3EQkWDhsVmob3DQ0ubFGhQe6HBHZBwVJERGRHs4wDOYuKeSpf63A4/Vx6a+GcvyEHMxmjUJK8HHYf2oBkpOREOBqRGRfFCRFRER6sKraZh5/dzlfr9jBoD42rjlrJBnJsYEuS2Sfdu0lqSApErwUJEVERHqoL5dv5/F3l9PY7OGCXwzhl0f0xaJRSAlyDlsMgFZuFQlyCpIiIiI9TG2Diyff+4H5S4vo1yuRa88cSe+0+ECXJdIucdZwoiMtCpIiQU5BUkREpAf5bnUxj769jOo6F+ccN5DTpuURZjEHuiyRdjOZTC0tQMoVJEWCmYKkiIhID9DQ5Oaf769k9qJt9EmP57aLDqdvVmKgyxI5IA6bleJy9ZIUCWYKkiIiIl3E6/XhMwzMJhMmkwmTiS7p17h8XSmz3lpKeVUjpx+Vx1nHDiA8zNLpxxHpLg6bleXrSzEMQz1ORYKUgqSIiEgnqqpt5rvVxXyzsphl65y4PL7dfm5uDZMmkwmz2eT/fn9fm0wt35v99/vpa4AtO2rITInh3isnMyDbFoiHLdKpHDYrTS4vNfUuEmIjA12OiOyFgqSIiAScYRhs3l7D0rVOlq0rxWSC4XkpDM9LISczIehXGt1eVse3K4v5ZuUO1mypwDAgNSmaYw/PxhYfhc9n4DNaHmfL1waGwe5fGwZG6/d7vW3r97t/3fL92CFpnH5UHlERelmXnmHXFiChFCTrG90sWFaE2+Nr/d019vL7v+//C3Z+b/gMvDu/3sttAfpmJTB2cBoZKWrnI4GhVxwREQmIytomlq0rZUlreKyqbQYgOy0OA3jh49VAywqOw/qlMDwvmeH9U0i3xwR8qpvPZ7ChsIpvVu7g21XFbCuuBSA3I4GzjhnAuPx0cjLiA16nSKhK3SVI9u+dFOBq2m/2om08+8HKdt12b7MTdpuB8PPZCK3fW0wmPD4fc5cU8uwHq8hMiWXskDTGDnYwqI8NixbXkm6iICki0ol8PoOGJjd1jW7qGtzUNbqob/SQ1yvR/8boUOX2eFm9uYKla50sXVvKpu3VAMTHRDCifwqjBqQyon8K9oRoACpqmvhhfSnL1peyfF0pX/6wHYCUpGhG5KUwLK8lXCbFRXVT/T5WbCxrCY8ri6moacJsNpGfa2f6KdmMG5LuH0URkYOz64hkKNlWXENibCSP3zhtjxC46/T0zrheuqSigUWrivludTEfLtjIv+ZuIDY6nNEDHYwd4mDUQAex0eGd9MhE9tSlQfKGGx+gqqoWS+sF/9dc+VsaG5t48um3aXa5mTplNBfMOAWADRsLeHDWSzQ0NDE0P49rrjwHi8WC01nB/933LFVVtWRlpXHzzAuJju6eNw0icmjy7gyDrUGw5e+d4dBFfePuQfGnr900NLkxjD33GRFu4dzjB3HS5Nygn6bZWQzDoNBZ1xIc15WyYmMZzS4vFrOJQTk2zjthECP7p5KbmYB5L8+JLT6KqaN7MXV0LwzDYHtZPcvWlbJ8fSlfrdjB7EXbgJYRzOH9UxiRl8KQXDvWqM5749TQ5GbxGiffrNzB9z+W0NDkITLCwqgBqRyen86YwQ7irBGddjwRaWGNCifOGhFyQbLQWUeWI7Zb/l9w2KycNDmXkybn0tDkZum6UhatKub7NSXMW1qIxWxiSK6dMYMdmgIrXaLLgqRhGBQWOXn1xb9hsbQEyeZmFxdcfBsP3HsDqSlJ3HLboyz6biVjx+Rzz33Pce3V5zJ4UC4PPPQSn3y6kJN+cQQPP/YaJ504lSOnjuGV1z7mldc+5uKLTu2qskXkEOD2+NhUVMXqzRWsL6iiuq7ZHxTrG1w0NHv2GgZ3CrOYibOGE2sNJzY6gqS4KHo54oiNbvm+ZXvrH2sE4WFm3pi9lmc/WMnC5UVcfcZIejniuu8Bd6O6BhfL1peydG0pS9c5Ka1sBCAjOYZjxvRm5IBU8vt2POyZTCYyU2LJTInlxIk5eH0GGwurWL6+JVh++tUWPpi/CYvZRP/eSQzPS2FE/xT6904iPKxj07zKqxv5dlUx364s5ocNpXi8BomxkUwclsHhQ9MZnpdCZLhWRBXpag67lZIQagFiGAYFJbVMHpHZ7ce2RoUzcVgGE4dl4PUZrNtayaLVLaOVz36w6pCeAtvY7GFbcQ2REWE4bFaiIzUhs7N02TNZUFgCwE23zKKmpp7jj5tETp9MMjNSSU9LBuCoaeOYv2Ax2b3TaW52MXhQLgDHHjOel175kOOPm8SKFeu547bL/Nuvn/lASAdJZ2UDX6/YwYDeSeT1SjxkfolFAqmhyc2PWypZvbmc1ZsrWLutEpfbC7Rch2OPj8IWH0XvtH2HwZa/w4mJDicy3NLhKUm3XjiOeUuLePpfK7jqgbmcdewAfn1kv5BvFO/1+li7rbIlOK51sr6gEp8BMVFhDMtL4fSj+jOyfwpp9phOPe7OwNi/dxKnH9WfZreXHzdXsHxDKcvWlfLmnLW8MXstUREWhuTaGdG/ZeGe7LT4PUY/d775+2ZlMd+u2sG6bVUApCfHcPLkvozLT2NAtu2QGUkWCRYOm5XNRdWBLqPdqlo/lMxyBHbkb+esj0E5NmacOJji8nq+W13Coh4+BdYwDEorG9m8vZrNO2pa/t5ew46y3T+MSIyNxGG3km6PwWG3kmaLIc1uJc0egy0+aq8zZGTvuixI1tXVM3LEQK647Ew8Hi833PgAvzl9OnZbgv82dlsCpWWVlFdUYdtlu611e3VNHVZrlH9E025LoKyssqtK7hZbd9Tw7AcrMQywRoUxtG8yI/q3fHKemRKrhRnkgDS7vWwsrGLt1kqclQ1MGp7J4BzbIfvvqby6kdWbKvzBccuOanxGy8IFuZkJHDc+m8E5dgb3sZEU3z1T5U0mE1NHZTEiL4Un//UDL3+6hi9/2M7VZ4wkNzOh7R0EkeLyepauawmOy9eX0tDkwWyCvN5J/OboAYwakEr/3t37QVlkuIXh/VMY3j+F805oGRldsbGsdSpsGc9+sAqAhNiI1oV7UkizWVm81sm3K3ewvfWNRv/eiZx3wiDGDUmjlyPukP0dEgkGaTYr364sxuczQuLNfaGzDoBeqcE14yTNHrP7FNi1pSxavbcpsC2jlaEwBdbl9rKtuJbN26vZ1BoYt+yoob7RDYDJ1PK4czLimXZYL/qkx+P2+Cgur6ekooEdZfWs3lLB/KWF+HaZgRRmMeOwWf3BMs1uxWHb+be1Uy+d6Am6LEgOHtSXwYP6+r8/bvpEXnr5A4YM6effZhgt/zH4fLs3mzVamzcbPt8eL+Kh/qI+ZnAaL99+nP8NzrJ1pXy7qhiA5MSWBSSG9+/eBSQOhNvjpdBZR7PbS9zO0ZrocI2wdgPDMNhRVs+PWytZt62StVsr2Ly9Bm/r/4ThYWY+WriZ3IwETpqcw5SRWUT04Gl4Pp9BgbOW1Zt/Co7O1mtqoiIsDMy2ceYxAxicY6d/dlLAp7QkxkVy03lj+OqH7Tzx3g9c9495nDYtjzOO6R/UDeRdbi+ff7eNDxZs8r9ZSk6MZtLwTEYNSGVYXnJQXSsYa41g/NAMxg/NAKC0srFlGuyGloV7FiwrAiDMYmJYvxROOaIvY4ek+Rf6EZHAc9iseLw+KmubQuJ3s7CkZfXmrCALkruyRoUzcXgGE4fvPgV20epinv1gJc9+sHK3KbADsm0dvjygMxmGQWVts390ceffRaV1/jYoUREW+qTHM2VEJjkZ8eRkJJCdHt+u13uP10dpZSPF5fUUVzRQUl5PcXkDxRX1/Lilgvomz263T4iNIM0W4x/RTLNbcdhjSLPFYEuIOuRmrnTZO6qVKzfgcrsZNXIQ0PIPweGwU1Hx0xSFisoa7LZEUpKTdtteWVmD3Z5IYmI89fWNeL0+LBYz5RXV2O2JXVVyt0mIjWTS8EwmDW+ZQ7/z0/3l60r5ZuUO5nzXsoBEn/R4/2jlkBw7UQF4A7xzmsCW4hq2bK9h644aNu/Y/Rd4VzFRYcRaI1qvH2sJmHHWlmmCcbts//m2rngD7fX6aHZ7W/64vLh2+9pHs9tDs8tLs9uHy+31T3Vw2KzEx0QEzYcWdQ0u1m2rYm1raFy3rZLahpZP3KIjLeT1SuLXR/ZjQO8kf1Cat6SQDxdsYtaby3j+o9VMPzybEybkkJwY/C/EbXF7vKwvqPIHxzWbK6hr/QQyMS6SITl2fjk5l8E5dnIy4oP2w40JwzIY2i+Zf76/kjfnrOOrFTu4+owRQddMvrHZw2dfb+Hf8zZQUdNM/96JXHxKPiP7p5KVGjqzKFKSojl6bG+OHtvbP5W1uKKBITl2YnrAlC6RnmjnStfF5Q0hESQLnHVER1pITgzegYBd7W0K7KLVxXy3qsQ/BRZaFouLiQrDGhWGNSqcmKhwoqPCiIkK92/z/yw6DGtkONboMKyRYcREhxMdGUZ0ZFibrxcer49CZ93PQmM11XUu/21SkqLJSU9gwtB0cjISyMmIJ80ec8Aj1mEWM+nJMaQn7/3yi9oGV0vILG/wj2YWl9ezdmslC5dv3+29cJjFzFFjenHF6SMOqJZQ1HVTW+sbePHlD5j14I14PF5mz/mGq688m7/+7RmKtjtJcyTzxdxFTD92Ig6HnYiIcFau2kD+kH7M+fwbxhw2hLAwC/n5/Zg3/3umHTmW2XNatvc0afYYjh8fw/Hj++D1GWwqqvKPVn60cDP/nreRMIuJgX1sLcEyL4V+WZ0/bay+0c2WHTVsLW6ZHrBle8vXDbt8GpOaFE12ejyH56fRJz0ea1Q4dQ0uahtaVrOsbXRT29CyymVtg4vSygb/z/aSO/0iIyzERe8eMneG0KjIMNyen0Lg3oPhnl97vPs5YBuiIy04bDGt0xti/NMcHDYrqTZrlzX99np9bC2uZe3WCv+I487RH5MJejviODw/nQHZNgZmJ5HliNvrp1/TD+/DseOyWbGxjA8XbOLd/63n3S82MGFoOidNzmVQn9CZ9lrX4GLNlgp/cFxfUIXb4wMgMyWW8UPTGZxjZ0iunTS7NWQeF0CcNYJrzxrF5BGZPPb2MmY+soCTp/TlnOMGBryxfG2Di48WbOKDBZuoa3QzPC+Z684ezbB+ySH1HO+NyWSid1o8vdPiA12KiOzHri1AhuTaA1xN2wpKaslMDd0p8Wn2luvCT57c1z8FtsBZS0OTh4YmN/WNbhqaPTQ0uqmobaKh0U19k4fGZk+b+zabILo1cMZEtYTLmOhwrJFhmM0mthXXsq2kFo+35fU9PMxM77Q4xgxK848y9smI7/aZL3Gt70vzeu3Zy9Tj9VFW1TKauaO8ZTSzd1rwjkZ3BVNY3gT/u+2Nn93dqTt/4aX3mb9wCT6fj5N/MZVfn3IUS5au4aln3sHlcjN2TD6X/v50TCYTGzcV8NCsV2hoaKRfv97ccO0MIiLCKSkp594HXqCqqpbU1CT+dOPviItre9EGS1gUmb0P79THEwhNLg9rNle0BMv1pWxqveh850IWw/NSGNk/hfTk9jfo9nh9FJXWsXVHa2Bs/bNzdcWd+89Ojyc7PZ6c1r+z0+IP+JN7n8+gsdlDnT9o7hI+G3YPnz+/jdvjw2I2ERlhITLcQkS4Za9f+/9u/Tpi16/DWn8eYSFyl6933iY8zExlbXPLlIaKBv8nTi1/N/gXZtkpKS7SHzBbLtRumdrgsFmxJ0S3e2pDeXUja7dWtvzZVsmGwiqaXS3HSoiNYEBvGwOyk1oWZ+qdeMBz84vL6/nkqy3899ut1De66ZuVwEmTcpk8IjPopr02NntYubGMJWudrNhQxtbWRu8Ws4l+WYkMyrG1XN+YYyMhNjLA1XaehiY3z3+0ms++3kJ6cgxX/mYEQ/smd3sdFTVN/HveRj77ejONzV7GDUnj9KPygm6kVER6Ppfby6k3fcTZ0wdy1rEDAl1Omy648z8M7dfyoduhZOd7vJ2Bs6HJQ32T2//17ts8u22vb/Lg8frISoltGWHMbBllzEqJDdoZRaGqaNs3eD1Nnba/vsfd0rVBMpB6SpD8ueq6Zn5YX8ay9aUsW+fE2Rr+djbo3rkyYUJsJIZhUFHT1DLK2DoldeuOGgpK6vyf+FjMJrJSY8lOj6dP65/s9HhSEqOD5hO1nVObA8UwDKrqmikpb/DPn98ZMEsq6imravzZhdomUpJ2D5c7RzNdbl/rdY0t01TLqpv89+mbmUj/1tA4IDsJh63zR9eamj180TrttaCkloTYCKYf3ocTJvQJ2LQhn89g0/Zqlq51smxdKas3l+PxGkSEWxiSY2NIrp3BOXbyeicGfJSuO/ywoZRH3lpGcXkDJ0zow4wTB3fLxf3F5fW8N3cDcxZtw+v1MXlEFqcdlUefdI3aiUjgzLjjP4wckMI1Z44KdCn71dDk5oxbPuG8EwZx+lH9A12OyB66Ikj2/HdlPUxCbCSTR2YyeWRmy6Ir5fX+abC7NujOSo2luq7Zfy0dgD0hij7p8YwakOoPjFmpcQG9iLo9Av2JlMlkIikuiqS4KAb22XNUZueF2iUV9a3h8qfRzA3Lt1Pb4NrjPqk2K4Nz7AzIbrmuMTcjoVtGBqMiwzh+fB+OOzybH9aX8eHCTbz9+Tre/d96JgzL4KRJuQzsk9TlHyJU1DSxbJ2TJT+Wsmy903/9Q05GPCdP7suoAakMyrEF3WhpdxjWL4VHrj+SVz77kQ8WbOS7NSVccdoIRg1M7ZLjbSuu4Z3/rWfe0iLMJhNHjenFqUfm7fN6ERGR7uSwWSlpXUAtmBWVtlyGkpUa/CueinQWBckQZjKZyEiOJSM5lhMm/NSge9m6Un7cWsGQXPtuo4zBtKJiT9LWhdoNTW5/uDSbTPTPTgr4irwmk8nfKqG4vJ6Pv9zM7G+3smBZEf2yEjhpcsu0185aBMnl9rJ6czlLWnsNbtlRA7RM3x3ZP5WRA1IZ0T8FWze14gh2UZFh/O6X+UwansGsN5fyl2e+5ugxvbno5CHEdtLv8fqCSt7+fD1fr9hBZISFkybl8qupfUNiQQsROXQ47FZWbyoPdBltKijZGSQPrWvk5NCmINmD7NqgW4KHNSq8dWWx4OwVmGaP4aKT8zl7+kDmLi7gw4WbeOj1pTz/4Wqmj8/m+PEdn/ZqGAbbSmr9TepXbizD5fERZjExOMfOjBMHM7J/CjkZCSHRGyxQBvaxMeu6qbwxey3vfrGBxT+W8IfThnN4fvoB7c8wDFZuKuftOetYuq6UmOhwzjimPydNyu1R15uKSM/hsFmZv6QQj9dHWBBfM1forMViNmk2hxxSFCRFBIDoyDCOn5DDceP7sHx9KR8u2Mxbc9bxzufrmTg8g5Mm5zKg976nvdbUu1i+rpQla50sXeekvPX6z8yUWKaP78PI/ikM7ZsckDY2oSwi3MJ5JwxmwrAMZr2xlLufX8TkEZlc8quh7Q5/hmGw+Ecnb81Zx5otFSTGRjLjxMGcMKGPmiuLSFBzJFnxGVBW1UiaPXhDWkFJLRkpMUEddkU6m97RichuTCYTI/qnMqJ/KjvKWqe9LtrK/KVF5PVK5KTJuUwanoHJZOLHLRUsbQ2PGwurMAyIiQ5nRF4KIwekMLJ/qr8PmBycflmJPHjNEbz7xXrenL2W5etLueRXQ5k8InOf4d7rM/jqh+28/fk6Nm+vISUpmkt/NZSjx2UTeQhefyoiocdhb20BUt4Q5EGy7pBr/SCiICki+5SeHMPvfpnP2dMH8MXiltVeH3xtCc9+sBKX20djswezCQZk2zjrmAGMHJhKXhf0OJUW4WFmzjxmAOPz05n15lLue2Ux85cWcdmpw3abfuz2+Ji3pIB3/reeotJ6MlNiufqMkUwdnaVPy0UkpDhsLeGxuKKB4QGuZV88Xh/F5fVMGHZglx2IhCoFSRFpkzUqnBMn5nD8+D4sW1/K7G+3EhMdzqgBqQzLSyH2APuLyoHJTo/nvisn8/78Tbz62Rouv/d/LYvzjMhk9rfbeG/uBsqqGsnNTOCm88Zw+ND0dvc2FREJJskJUZjNJkoq6gNdyj7tKKvH6zPo5dCIpBxaFCRFpN3MZhOjBqQyakDXtKKQ9rNYzPz6yH4cnp/Gw28tY9aby3jivRW43F4G59i44vThjBqQGjT9YEVEDoTFYiYlMTqoW4AUlNQC0EsrtsohRkFSRCSEZaTE8rfLJvLZN1tYu7WSY8dlMyTXHuiyREQ6jcNmxRnMQdLZEiTVQ1IONQqSIiIhzmw2ccKEHE6YkBPoUkREOp3DZuX7NSWBLmOfCkvqSEmK1qrkcsjRqgsiIiIiErQcNiuVtc00u72BLmWvCpy1mtYqhyQFSREREREJWo7WNlLBOL3V5zModNZpWqsckhQkRURERCRo7WwBEowL7pRVN9Ls8pKlFVvlEKQgKSIiIiJBy2FvGZEsKQ++FiCFJXUA9NKIpByCFCRFREREJGglxUUSEWamOAhHJHeu2KoeknIoUpAUERERkaBlMplItVlxVgZhkCypJc4aQUJsZKBLEel2CpIiIiIiEtRSbdagvEZSC+3IoUxBUkRERESCmsNmpaQ8GINkraa1yiFLQVJEREREglqazUpdo5v6RnegS/GrqXdRXeeil0MjknJoUpAUERERkaAWjC1ACkpaFtrJStWIpByaFCRFREREJKg5bK0tQCqCpwVIoVZslUNcWEfv4PF4CQuztPv2Tz3zDtU1dcy8/nyWLF3Dk0+/TbPLzdQpo7lgxikAbNhYwIOzXqKhoYmh+Xlcc+U5WCwWnM4K/u++Z6mqqiUrK42bZ15IdHRUR0sWERERkRDm7yUZVCOSdUSEW0hJjA50KSIB0eaI5MqVG3j19Y9xuz1cec3fOeW0q5k777t27XzJ0jXMnvM1AM3NLu5/6EXu+MsfeO7p21m7biuLvlsJwD33PccVl53FC/+8Cwz45NOFADz82GucdOJUnnvmTvrnZfPKax8f6OMUERERkRAVGx2ONSosqIJkobOWrJRYzGZToEsRCYg2g+TTz77DoIG5fPX1MuLiYvnnU7fzzntz2txxTW09z7/4PmedeTwAP67dQmZGKulpyVgsFo6aNo75CxZTUlJOc7OLwYNyATj2mPHMX7gYj8fLihXrmTJ51C7blxzMYxURERGREGQymUhNCq4WIAXOOrK00I4cwtoMkj6fwaiRg1i8dA0Txw8nzZGMz+drc8f/ePgVLpjxS2JjW6YilFdUYbcl+H9utyVQWlZJeUUVtl2221q3V9fUYbVGYbFY/LcvK6vs8AMUERERkdDnCKJekk0uD6WVDbo+Ug5p7QiSPn5cu5lvF61g1KjBbN5ShMfj3e99PvlsIakpSYwaOci/zfAZYPpp6N8wDMxmEz6fgenn200mDJ9vt+3AHt+LiIiIyKHBYW8JkoZhBLoUipx1GAb00oqtcghrc7Gds848nr/f8yzHT59Ieloy557/Jy679Iz93mfe/O+pqKjmksvvora2nqbGZpzOcszmn3JrRWUNdlsiKclJVFRU+7dXVtZgtyeSmBhPfX0jXq8Pi8VMeUU1dnvigT9SEREREQlZDpuVZpeX6joXiXGRAa2lwFkHoKmtckhrM0hOnjiKyRNH+b9/4dm/YrHsfyDznr9d4//6P7O/YvkP67jmynM4/6JbKdruJM2RzBdzFzH92Ik4HHYiIsJZuWoD+UP6Mefzbxhz2BDCwizk5/dj3vzvmXbkWGbPadkuIiIiIoeeNH8vyfqAB8lCZy1mE2QkxwS0DpFAanNqa0VFNbfc9gjn/+5WKitruOW2RyjfZQSxvSIiwrnhuhnc+den+N0lt9MrK40pk1oC6k0zL+TJp9/mwotvo7GpmVNOngbAVZefzcefLuCi39/OylXrueC8X3b4uCIiIiIS+n7qJRn46yQLS+pIs8cQ3oGWeCI9TZsjkg8/9hoTx4/g/Q/nEhcXQ9/cLB78x0vcfeeV7TrA9GMmMP2YCQCMGjmIpx6/dY/b9M3txaOzbt5ju8Nh54F7r2/XcURERESk50oNoiBZ4KzVQjtyyGtzRLKkpJwTjp+MyWQiLMzCxRedirO0ojtqExEREREBIDoyjPiYiIAHSa/Xx/bSOrJSdX2kHNraDJIms2m3dh8NDU0tK7CKiIiIiHSjYGgBUlzRgMdraERSDnltTm2dNGEkf7/3OeobGvnok/l8+tlCjpgyujtqExERERHxc9isbCzq+FodnamgpBZAI5JyyGszSJ595gnMnvM1huFjyZI1nHj8ZI4/blJ31CYiIiIi4uewWflm5Q68PgOLOTD9xQt3tv5QD0k5xLUZJAGOOXo8xxw9vqtrERERERHZJ4c9Bo/XoKK6iZSk6IDUUFBSiy0+ipjo8IAcXyRY7DNInvzrqzCx5yc9BgYmTLz/3qwuLUxEREREZFc7W4A4KxsCFiQLnbX0cmhaq8g+g+QzT/ylO+sQEREREdmvn3pJ1jMk197txzcMg4KSOo46rFe3H1sk2OwzSDocP/1ybtiwjcamZgzDwOcz2L7dyQnHT+6WAkVEREREAFKTojGZoKQ8MCu3VtQ00djs0UI7IrTjGskHZ73M118vx+VyY7cnULS9lPwh/RQkRURERKRbhYdZsMVHURygFiCFJa0L7aj1h0jbQXLJkjW89MLdPPLoa/z27F9QWlbBm2//tztqExERERHZTSB7SRY4W1p/qIekCJjbuoHNFk90VCS9eqWxeUsRw4cNoKyssjtqExERERHZTUCDZEktMVFhJMVFBuT4IsGkzSAZFhbGDyvWkd07ne8Wr6K+vpHGpubuqE1EREREZDcOWwzl1Y24Pb5uP3ahs44sRxwmU2B6WIoEkzaD5O8u/DUff7KAsWOGsnFjAaeecR1HHTmuO2oTEREREdmNw2bFMKCsqrHbj11QUkuvVE1rFYF2XCM5eFAugwflAvDIP26ivr6RmJjA9O0RERERkUPbri1A0pNjuu24dY1uKmubtWKrSKv9jkh+8tlCtm7dDsCHH8/j95fdyeNPvamprSIiIiISED8Fye69TrJQC+2I7GafQfL1Nz/lo0/mYTKb2bCxgKeeeZszTp+OLSmBZ559tztrFBEREREBwJ4YjcVs6v4gWdISJLMcGpEUgf1MbZ07/3seuu+PWK1RPP/ivxk7ZihHTRuHYRhcdMnt3ViiiIiIiEgLi9lESlI0JeXdGyQLSuoIDzPjsHXfdFqRYLbfqa1WaxQAq1ZvZOTwgQCYTCYsljbX6BERERER6RKBaAFS4KwlMyUWi1krtorA/oKkAYZh0NTkYu3aLQwfPgCA5mYXLpenu+oTEREREdmNwxYTgKmtdWRqoR0Rv31ObT1s9GD++vdn8Hq89O6dTu9eaRQWlfDiyx8yftzQ7qxRRERERMTPYbNSVddMk8tDVESbTQgOmsvtpaSiniNGZXX5sURCxT5HJC88/xQG9u9DWnoyd9x2GQCffraQiIgwLjz/V91WoIiIiIjIrlJbV251dtOo5PayenwG9NJCOyJ++/wIx2KxcPppx+627eKLTu3ygkRERERE9idtlxYgvdPiu/x4BSVq/SHyc106F+CFlz5gwcIlmExw3PSJnPbrY1iydA1PPv02zS43U6eM5oIZpwCwYWMBD856iYaGJobm53HNledgsVhwOiv4v/uepaqqlqysNG6eeSHR0VFdWbaIiIiIBLHu7iVZWFKLyQQZKRqRFNmpy5ZfXf7DOpYt/5Gnn7iVxx7+E+9/8AUbNxVw/0Mvcsdf/sBzT9/O2nVbWfTdSgDuue85rrjsLF74511gwCefLgTg4cde46QTp/LcM3fSPy+bV177uKtKFhEREZEQkBgXSUS4pduCZIGzjtQkK5Hhlm45nkgo6LIgOXxYf+6/53osFgtVVbV4vT7q6hrJzEglPS0Zi8XCUdPGMX/BYkpKymludjF4UC4Axx4znvkLF+PxeFmxYj1TJo/aZfuSripZREREREKAyWTCYYvuviBZUqtprSI/s8+prQu+3H9gmzxxVNs7D7Pw4ssf8M67s5k8eTTlFVXYbQn+n9ttCZSWVVJeUYVtl+221u3VNXVYrVFYLBb/7cvKKts8roiIiIj0bA5bDCXlXR8kvT6D7aV1jOif0uXHEgkl+wyS73/wxT7vZDKZ2hUkAWacezJnnH4ct97+KEVFJWD6qYmrYRiYzSZ8PgPTz7ebTBg+327bdx5bRERERA5tqUnRrNlS0eXHKa1swOXxaURS5Gf2GSTvv+f6g9rxtoJiXC43/fr2IioqgkkTR7Jg4RLM5p9m01ZU1mC3JZKSnERFRbV/e2VlDXZ7IomJ8dTXN+L1+rBYzJRXVGO3Jx5UXSIiIiIS+hy2GOob3dQ1uomNDu+y4/hXbE1VkBTZVZurthYWlfD+h3NpamzGMAx8Ph9FO0qZ9cDM/d5vx45SXnrlQx66/4+YTCa++no5Jx4/hWeefZei7U7SHMl8MXcR04+diMNhJyIinJWrNpA/pB9zPv+GMYcNISzMQn5+P+bN/55pR45l9pyW7SIiIiJyaHPYW1duLa8nNiuxy45TUFIHQJZ6SIrsps0g+fd7nqV/Xjar1mzkyCPG8M23P5CXl93mjseNHcradVu47Iq7MVtapsIeOXUMCQmx3PnXp3C53Iwdk8+USS1TZG+aeSEPzXqFhoZG+vXrzSknTwPgqsvP5t4HXuDV1z8hNTWJP934u4N8yCIiIiIS6nZtAdK3C4NkobOWxNhI4qwRXXYMkVDUZpBsbGzi6ivP4fEn32TsYfn86pfTuH7mA+3a+Xm/PYnzfnvSbttGjRzEU4/fusdt++b24tFZN++x3eGw88C9BzfNVkRERER6lrRu6iVZ6KzTaKTIXrTZ/iMuvuUXJyMjlc1bi4iNtYLWuxERERGRAIq1RhATFdalQdIwjJbWH7o+UmQPbY5IZqan8PiTb3Ls0eN54B8v0dTYjNfj7Y7aRERERET2yWGL6dIgWVXXTF2jWyOSInvR5ojkVVecw9D8PPr1680Jx01m2fK1XHv1ud1Rm4iIiIjIPqXaors0SBa2LrSjEUmRPbUZJN9461Mmty6Ic9IvjuD22y5j7vzvu7wwEREREZH9cdhicFY2YBhGl+y/wNnS+iNLQVJkD/uc2vriyx9QV9fA3PnfU1/f6N/u8Xj5fslqLr/0jG4pUERERERkbxw2K80uL1V1zSTFRXX6/gtKaomOtJCc2Pn7Fgl1+wySAwfksG79FswmE/HxMf7tZrOFm2de2C3FiYiIiIjsi7+XZEVDlwTJQmcdmalxmExaaVLk5/YZJMeNHcq4sUMZc1g+Awfk4PV6MQwIC7N0Z30iIiIiInvl7yVZ3sDAbFun77+wpJah/ZI7fb8iPUGbq7Y6Uu386daHWbZsLV6fl2FD+3PjHy8k2Z7YDeWJiIiIiOydI6nrekk2NLkpq26il0PXR4rsTZuL7Tz6xBsMGpjDW6/fx9uv30/+kH48/Ohr3VGbiIiIiMg+RUWGkRgbibOy84NkobNlxdasVLX+ENmbNoNkYVEJ555zErGxVuLjY5lx7sls3+7sjtpERERERPYr1RZNSXlXBEmt2CqyP20GSa/Hi8vl9n/f1OTSBcciIiIiEhQctpgumdpa6KzDYjaRnhzT9o1FDkH7vEbS5XITERHO1CPGMPPmh5h+7ARMmPjsv1/6+0qKiIiIiASSw2bl6xXb8foMLObOG+woKKklIyWGMEub4y4ih6R9/mZcfd09APz27BM5bvpEFi9ZzaLvVnLsMRM495xfdFuBIiIiIiL74rBZ8XgNyqsb275xBxSU1Glaq8h+7HNE0jB++vq4Yydy3LETu6MeEREREZF287cAqWggtXUV14Pl9vjYUV7PxOEZnbI/kZ5o31Nb3W42bNiGsY+f5/Xr3UUliYiIiIi0j8P+Uy/JoX07Z587yurw+Qyt2CqyH/sMksU7yrjj7qd2H5rcyWTi5efv7sq6RERERETalJIYjclEp7YA2dn6o5emtors0z6DZO/e6Tz52J+7sxYRERERkQ4JD7Ngj4/q1JVbC/ytPzQiKbIvWoZKREREREKaw965LUAKS+pISYomKnKfYy4ih7x9BsmhQ/O6sw4RERERkQPisFkpKa/vtP0VOGs1rVWkDfsMkpdfekZ31iEiIiIickAcNivlNU24Pd6D3pfPZ1DorNO0VpE2aGqriIiIiIQ0h82KYUBp5cH3kiyraqTZ5SXLoRFJkf1RkBQRERGRkLazl2RxJ1wn+dOKrRqRFNmfLr2C+OVXP2Te/MUAjBs7lIsvOpUlS9fw5NNv0+xyM3XKaC6YcQoAGzYW8OCsl2hoaGJofh7XXHkOFosFp7OC/7vvWaqqasnKSuPmmRcSHR3VlWWLiIiISAhJbQ2Szk4IkjtXbO2lEUmR/eqyEcklS9fw/ZI1PPHon3nysVtZt34r/5u7iPsfepE7/vIHnnv6dtau28qi71YCcM99z3HFZWfxwj/vAgM++XQhAA8/9honnTiV5565k/552bzy2sddVbKIiIiIhCB7QjRhFlOnrNxaUFJLnDWChNjITqhMpOfqsiBpsyVwye9OIzw8jLAwC717p1NUVEJmRirpaclYLBaOmjaO+QsWU1JSTnOzi8GDcgE49pjxzF+4GI/Hy4oV65kyedQu25d0VckiIiIiEoIsZhMpidZOCZJaaEekfbosSPbJzvAHw8KiEubPX4zJZMZuS/Dfxm5LoLSskvKKKmy7bLe1bq+uqcNqjcJisfhvX1ZW2VUli4iIiEiIctislFQcfAuQgpJaTWsVaYcuX2xny9bt3PSnWVx80amkpyWDyeT/mWEYmM0mfD4D08+3m0wYPt9u24E9vhcRERERcdgPfkSyuq6ZmnoXvRwakRRpS5cGyZWrNjDz5oe46IJfcewx40lOTqKiotr/84rKGuy2RFJ+tr2ysga7PZHExHjq6xvxen0AlFdUY7cndmXJIiIiIhKCHDYr1XUuGps9B7yPnSu2ZqVqRFKkLV0WJJ2lFdx+5xPcPPMijpw6BoBBA3MoLCyhaLsTr9fHF3MXMWZMPg6HnYiIcFau2gDAnM+/YcxhQwgLs5Cf3495878HYPaclu0iIiIiIrva2QLEWXngo5KFWrFVpN26rP3H2+/OxuV28+Qzb/u3/eKEKdxw3Qzu/OtTuFxuxo7JZ8qkloV0bpp5IQ/NeoWGhkb69evNKSdPA+Cqy8/m3gde4NXXPyE1NYk/3fi7ripZRERERELUzhYgJRUNZKfFH9A+CkrqiAi3kJIY3ZmlifRIXRYkL7/0DC6/9Iy9/uypx2/dY1vf3F48OuvmPbY7HHYeuPf6Tq9PRERERHqOnSOSJeUHPiJZ4KwlKyUWs1lrcoi0pcsX2xERERER6WqJsZFERlgOasGdwpJasrTQjki7KEiKiIiISMgzmUykJh14C5AmlwdnZaOujxRpJwVJEREREekRWnpJHtiIZFHriq29tGKrSLsoSIqIiIhIj5DWGiQNw+jwfQt2tv7Q1FaRdlGQFBEREZEeIdVmpaHJQ32ju8P3LSypxWyCjOSYLqhMpOdRkBQRERGRHmHnyq3FBzC9tcBZS5o9hvAwS2eXJdIjKUiKiIiISI/g2KWXZEcVOuu00I5IByhIioiIiEiP4LC3TEvtaC9Jr9fH9tI6slJ1faRIeylIioiIiEiPEBsdTkx0eIdbgBRXNODxGhqRFOkABUkRERER6TEOpAVIQUktgEYkRTpAQVJEREREeoyDC5IakRRpLwVJEREREekxHDYrzg72kix01mGLjyImOrwLKxPpWRQkRURERKTHcNisuDw+qmqb232fQmctvRya1irSEQqSIiIiItJjdLQFiGEYFJTU0UvTWkU6REFSRERERHqMnUGyuJ1BsqKmicZmD1lasVWkQxQkRURERKTHSPWPSLavBYhWbBU5MAqSIiIiItJjREWEkRgXSUl5+0YkC0rqANRDUqSDFCRFREREpEfpSAuQQmctMVFhJMVFdnFVIj2LgqSIiIiI9CiOJCvOyvYGyTqyHHGYTKYurkqkZ1GQFBEREZEexWG3UlrZiNfXdi/JgpJardgqcgAUJEVERESkR3HYrHh9BuVVjfu9XV2jm8raZi20I3IAFCRFREREpEdpby/JwtYVW7XQjkjHhXXlzuvrG7nm+nu5647LSXMks2TpGp58+m2aXW6mThnNBTNOAWDDxgIenPUSDQ1NDM3P45orz8FiseB0VvB/9z1LVVUtWVlp3DzzQqKjo7qyZBEREREJcQ5bDNDSAmQoyfu8nb/1h0MjkiId1WUjkmt+3My1N9xHYVEJAM3NLu5/6EXu+MsfeO7p21m7biuLvlsJwD33PccVl53FC/+8Cwz45NOFADz82GucdOJUnnvmTvrnZfPKax93VbkiIiIi0kMkJ0ZjNkFxWyOSzjrCw8z+4Cki7ddlQfKTzxZw5eVnYbclAvDj2i1kZqSSnpaMxWLhqGnjmL9gMSUl5TQ3uxg8KBeAY48Zz/yFi/F4vKxYsZ4pk0ftsn1JV5UrIiIiIj1EeJgZW0I0zjaCZIGzlsyUWCxmrdgq0lFdNrX1+mvO2+378ooq7LYE//d2WwKlZZWUV1Rh22W7rXV7dU0dVmsUFovFf/uyssquKldEREREepD29JIsLKkjNythv7cRkb3rtsV2DJ8Bu/TnMQwDs9mEz2fs1rfHMAzMJhOGz7dHPx/19xERERGR9mgrSLrcXkoq6tX6Q+QAdVuQTE5OoqKi2v99RWUNdlsiKT/bXllZg92eSGJiPPX1jXi9PgDKK6qx2xO7q1wRERERCWFpNisVNU24Pd69/ryotA6fAb200I7IAem2IDloYA6FhSUUbXfi9fr4Yu4ixozJx+GwExERzspVGwCY8/k3jDlsCGFhFvLz+zFv/vcAzJ7Tsl1EREREpC0OuxXDAGfl3ntJFjrrALX+EDlQXdr+Y1cREeHccN0M7vzrU7hcbsaOyWfKpJaFdG6aeSEPzXqFhoZG+vXrzSknTwPgqsvP5t4HXuDV1z8hNTWJP934u+4qV0RERERCmL8FSHkDmSl7jjoWltRiMkHGXn4mIm3r8iD5yot/8389auQgnnr81j1u0ze3F4/OunmP7Q6HnQfuvb5L6xMRERGRnsdhswItvST3psBZh8NmJTLc0p1lifQY3Ta1VURERESkuyTFRxFmMe1zwZ2CklqytNCOyAFTkBQRERGRHsdiNpGStPeVW70+g6LSOrJSNa1V5EApSIqIiIhIj7SvFiDOigbcHp8W2hE5CAqSIiIiItIj7StIFjprAdRDUuQgKEiKiIiISI/ksFmpqXfR2OzZbXtBSUvrjyz1kBQ5YAqSIiIiItIjpe1sAfKzUclCZy2JsZHEWSMCUZZIj6AgKSIiIiI9ksPe2gKkfPcWIAUltRqNFDlICpIiIiIi0iOlJrUGycqfRiQNw6DAWafrI0UOkoKkiIiIiPRICbERREZYdpvaWlXXTH2jWyOSIgdJQVJEREREeiSTydSycmv5T0GysHWhHY1IihwcBUkRERER6bF+3gKkoLX1R5aCpMhBUZAUERERkR5rZ5A0DANoWWgnOtJCcmJUgCsTCW0KkiIiIiLSYzlsMTQ2e6htcAMtU1szU+MwmUwBrkwktClIioiIiEiP5bBFA1BS0dICpMBZS69ULbQjcrAUJEVERESkx3LYYgBwVjTS0OSmvLqJXg5dHylysMICXYCIiIiISFdx2Fp7SVbUU+hsGZ3UQjsiB09BUkRERER6rJjocGKjwymuaCAxbueKrZraKnKwFCRFREREpEdz2FtWbo2JCsdiNpGeHBPokkRCnoKkiIiIiPRoDpuVrTtqCbeYyUiJIcyiZUJEDpZ+i0RERESkR3PYYnBWNlBQUqvrI0U6iYKkiIiIiPRojqRo3B4f28vqtWKrSCdRkBQRERGRHs1h/+maSC20I9I5QuIayf99sYhXX/8Ej9fLr0+Zxi9POjLQJYmIiIhIiNjZAgSgl6a2inSKoA+SZWWVPP/iv3nskVsIDw/jmuvuZcSwAWRnZwS6NBEREREJAam7BEmNSIp0jqCf2rpk6Y+MGD6Q+LgYoqMimTxpFPMXLgl0WSIiIiISIiLDLSTFRZKSFE1UZNCPo4iEhKD/TSqvqMJmi/d/b7MlsHbt5gBWJCIiIiKhpm9WIlERlkCXIdJjBH2Q9PkMTCbTTxsMA5M56AdSRURERCSI3DRjDKa2byYi7RT0iSwlOYnyimr/9xWVNdhtCQGsSERERERCTWS4hYhwjUiKdJagD5KjRg5k2bIfqaqqpanJxYIvlzDmsCGBLktEREREROSQFfRTW5OTk7hgxin88aYHcXs8HD99EgMH5AS6LBERERERkUNW0AdJgGlHjmXakWMDXYaIiIiIiIgQAlNbRUREREREJLgoSIqIiIiIiEiHKEiKiIiIiIhIhyhIioiIiIiISIf02CBpsUQGugQREREREZGA64psFBKrth4IV3MNxUVL8XqbA12KiIiIiIhIQFgskbiaazp9vz02SIKBq7k60EWIiIiIiIgEjNfT1CX77bFTW0VERERERKRrKEiKiIiIiIhIhyhIioiIiIiISIcoSIqIiIiIiEiHKEiKiIiIiIhIhyhIioiIiIiISIcoSIqIiIiIiEiH9OA+kh3z8qsfMm/+YgDGjR3KxRedypKla3jy6bdpdrmZOmU0F8w4Zbf73HP/84wYPoDpx0wAoLikjHvvf576hiZiY6zMvP58HA77Po/58/uvXLWBJ59+G7fHQ3xcDDdcO2Ov99+wsYAHZ71EQ0MTQ/PzuObKc7BYLP6fv/DS+5jNZs777UkH+7QEXE84L/+d/TXPPv8eiUnxLY9jzFAuPP+UPe4fKnrCOflx7WYeeex13G4Pqak2rrv6XGy2hE56hgIjlM7Lvu6/YuV6nnjqLdweD2mOZGZefz5xcTEH87QEVKifk8qqGm66ZZb/Zw31jVRV1/Hhvx4+0KckKITSefnq62W8+MqHYEBamp0brp2x2+9ET3m97wnnRK/1wXdOuvu1vjOes/bWfLBZoLOyhEYkgSVL1/D9kjU88eifefKxW1m3fiv/m7uI+x96kTv+8geee/p21q7byqLvVgJQVl7FrX95lAULF++2nxde+oAjjxjLU4/dyuSJI3n+xX/v9Xj7uv//3fsc111zLk89ditHHTmOx558c6/3v+e+57jisrN44Z93gQGffLoQgPr6Rh546CXeeW/2QT4jwaGnnJd167dwye9P56nHbuWpx24N6ReWnnBODMPgzruf4uKLTuXpJ27jmKMO56GHXzn4JyeAQu287Ov+9z/4IjfecAHPPPEXsnun89a7/z3AZyTwesI5SUqM9/+/9cQjt+Bw2Ln2qt8exLMSeKF0XurrG5n16GvcfccVPPX4reT0yeKlVz/y/6ynvN73lHOi1/rgOifd/VrfGc9ZR2o+2CzQWVlCQRKw2RK45HenER4eRliYhd690ykqKiEzI5X0tGQsFgtHTRvH/AUtJ/t/X3zL+PEjmDL5sN324/P5qG9oBKCp2UVEZMRej7e3+7tcbs6fcTK5OVkA5ORk4nRW7HHfkpJymptdDB6UC8Cxx4xnfus/wq++XkZmZiqn/fqYg3xGgkNPOS9r121l9pyvufiyO/i/e5+ltrb+IJ+ZwOkJ56S6ug5Xs5sRwwcAMG7sML5fvAqXy32Qz07ghNJ52d/xn336DrKzM/B4vJSVVxEXaz2wJyQI9JRzstN/Zn9FZGQE044c27EnIsiE0nnxer1c+YezSE5OAiA3J5PS1tv1pNf7nnJO9FofXOeku1/rO+M5a2/NB5sFOjNLKEgCfbIz/E9mYVEJ8+cvxmQyY99lKNluS6C0rBKA35w2nROOm7THfs4/95e8+685nPnbG3nn3dmccfr0vR5vb/ePiAjn6GmHAy2/eC+/+hETJwzf477lFVW7DXHbdqnrmKPHc+ZvjsNs7hmntaecF7stgXPOOpGnH7+NlBQbjz7+RkeehqDSE85JQkIsUVGRfL94NQBz532Hx+OlJoRf9EPpvOzv+GFhFjZvLuKsc29k+fK1TD1iTDsefXDqKecEwOv18dobn/K7C37dxqMOfqF0XuLjY5k0cSQAzc0u3nj7MyaMHwH0rNf7nnJO9FofXOeku1/rO+M5a2/NB5sFOjNLhP7/QJ1oy9bt3PSnWVx80amkpyWDyeT/mWEYmM2m/dwb7n3gea696re88co9XH3lOdx+1xMYhtGhGtxuD3+/9zm8Xi9nnXHCHj/3+QxMP6/LtP+6Ql2on5fbb7uM/CH9MJlM/Oa0Y/nu+5UdOnYwCuVzYjKZuO3Pl/D6m59y6eV/pa6+gfj4GMLDQv+S8VA4L23Jycnk7dfv55yzT+Tuvz/T4fsHm55wTr5fvIrMjFRycjI7fN9gFUrnpb6+kT//5VH65vTi2GPGd+gYoSTUz4le6/cUyHMSqNf6g3nO2lvzwWaBzswSof/OqZOsXLWBO+9+ist+/xuOnDqG5T+so6Ki2v/zisoa7LbEfd6/qqqWgsJi/ydTkyeNYtYjr+J0VnDbnY8DYLcl8re7rtznPhobm7jtjseJi4vhjtv+QFiYhbLyKm657RH//a++4uzd6qqsrMFu33ddoS7Uz0t9fSOf/fdLTv3V0QAYsNvFzKEo1M8JtIx8PXDv9S3bq2p49fVPiIsL3WmUEDrnZV/3d7ncfPf9KiZOaDn+UdPG8dQz73TgGQg+oX5Odvry62VMPWLvU15DUSidl/KKam6+ZRYjhg/gskt+c/APPkiF+jnRa/2eAn1OoPtf6w/2OdtXzW6Ph0suvws4sCzQlVlCQRJwllZw+51PcMvNFzNyxEAABg3MobCwhKLtTtIcyXwxdxHTj524z30kJMQSER7OipXrGZqfx8pVG4i2RuFw2HnqsVvbVcf/3fccGekpXH3lOf4h5WR74h73j4gIZ+WqDeQP6cecz79hzGFDDvCRB7eecF6ioiJ56+3/MHhQXwYNzOH9D77wv1EORT3hnEDLoi5XX3EOAwb04d335jBl0qiQniIWaudlbywWC488/jopKUn0z8tm3vzvyR/St13HDUY94ZzstGbNpn1OSQs1oXRevF4ft97+KEdMGc05Z514EI86uPWEc6LX+j0F+pxA977Wd8Zztq+aDzYLdGWWUJAE3n53Ni63myefedu/7RcnTOGG62Zw51+fwuVyM3ZMPlMmjdrnPkwmE3+59VIee+INmpvdRFuj+Mstl7S7hg0btvHV18vJ7p3OZVfeDez7k5qbZl7IQ7NeoaGhkX79enPKydM68GhDR084LxaLmT//6fc8/OirNLvcZGU6uPGGCzrwLASXnnBOAK664mz+8cgrNDe7yMnJ4vprzmv38YNRqJ2XvbFYzPz5pov5x8Ov4PX5SLYnct3VoXteesI52WlHcSkprQtZhLpQOi9ff7OcDRsK8Hp9zF+4BID+edkh///Vz/WUc6LX+t0Fwznpztf6znjOoP3vTw42C3RWljCF5U3wT1be+NndB7QTEREREREROTT0Pe4WLbYjIiIiIiIiHaMgKSIiIiIiIh2iICkiIiIiIiIdoiApIiIiIiIiHaIgKSIiIiIiIh2i9h8iIiIdUFxSxowL/0yfPpkAGD6D6OhIfn3KURwx5bD93vflVz+ib26Wv0m3iIhIqFKQFBER6aCIiIjdGjyXlJQz8+aHMJvNTN5Pn7Bly9eS3Tu9O0oUERHpUgqSIiIiB8nhsDPj3JN5653/ktMnk0cef52GhiYqKqvpm9uLP998MZ/+ZyHr1m/lmWffxWw2M27sUP753Hv8sGIdPp9B3769uPzSM4iJiQ70wxEREWmTrpEUERHpBLm5WWzZUsQnny3gmKMP55F/3MSLz/6V4uIyvl20gl+edCT987K5+KJTmTRxJG+89RkWi5nHH7mFpx6/Fbs9gWef/1egH4aIiEi7aERSRESkE5iAyMgIfnfhr1m8dA1vvv0fCotKKK+oorGxeY/bf7toBXX1DSxeugYAj9tLYmJcN1ctIiJyYBQkRUREOsHadVvJycnkb/f8E6/XxxGTRzNu7FCczgoMjD1u7/P5+MMlZzB2TD4AjY1NuFye7i5bRETkgGhqq4iIyEEqLCzh1dc/5rRfH8P3i1fz27NPZOoRYwD4ce1mfD4fABaLGY/XC8Do0YN5/8MvcLs9+Hw+Hpz1Cs++oKmtIiISGjQiKSIi0kEul4tLLr8LALPJREREOBde8CvGjR3Kheefwu13PUlUVAQx1miGDe3P9u2lAIw/fDjPPf9vPG4vvz3rRJ765ztcdsVf8fl89M3txSW/Oy2QD0tERKTdTGF5E/zzbTZ+dncgaxEREREREZEg1/e4WzS1VURERERERDpGQVJEREREREQ6REFSREREREREOkRBUkRERERERDpEQVJEREREREQ6REFSREREREREOmS39h8iIiIiIiIibdGIpIiIiIiIiHTI/wOm7Whwxp1QKQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 5))\n",
    "fig = plt.figure(linewidth=5, edgecolor=\"#04253a\", facecolor = '#e1ddbf')\n",
    "\n",
    "ax = sns.lineplot(data= z_december, x= 'Date', y = 'Total Sales')\n",
    "ax.set_facecolor(\"#ffffff\")\n",
    "\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Total Sales')\n",
    "plt.title('Total Sales in December')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8eee7bde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reset index\n",
    "\n",
    "z_december.reset_index(drop = True, inplace = True)\n",
    "z_december.head()\n",
    "\n",
    "z_december['Date'] = z_december.index + 1\n",
    "z_december.head()\n",
    "\n",
    "fig = plt.figure(linewidth=5, edgecolor=\"#04253a\", facecolor = '#e1ddbf')\n",
    "\n",
    "ax = sns.barplot(x = 'Date', y ='Total Sales', data = z_december,  edgecolor=\"#04253a\")\n",
    "\n",
    "ax.set_facecolor(\"#ffffff\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92e41543",
   "metadata": {},
   "source": [
    "- We can see that the Sales have increased till the day before Christmas i.e. 2018-12-24 and there are no transaction records on 25th of December because of the Holiday and also the sales went down after Christmas."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1451e5d7",
   "metadata": {},
   "source": [
    "## Data analysis on customer segments"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7174af15",
   "metadata": {},
   "source": [
    "### 1. Who spends the most on chips i.e. describing customers by lifestage and premimum category?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0cbced8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>pack_size</th>\n",
       "      <th>Brand Name</th>\n",
       "      <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</td>\n",
       "      <td>Natural</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</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>2018-11-10</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>346</td>\n",
       "      <td>96</td>\n",
       "      <td>WW Original Stacked Chips 160g</td>\n",
       "      <td>2</td>\n",
       "      <td>3.8</td>\n",
       "      <td>160</td>\n",
       "      <td>WW</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-03-09</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>347</td>\n",
       "      <td>54</td>\n",
       "      <td>CCs Original 175g</td>\n",
       "      <td>1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>175</td>\n",
       "      <td>CCs</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1343</td>\n",
       "      <td>383</td>\n",
       "      <td>61</td>\n",
       "      <td>Smiths Crinkle Cut  Chips Chicken 170g</td>\n",
       "      <td>2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>170</td>\n",
       "      <td>Smiths</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "    </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>246199</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</td>\n",
       "      <td>Kettle</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246200</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</td>\n",
       "      <td>Tostitos</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246201</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</td>\n",
       "      <td>Doritos</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246202</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</td>\n",
       "      <td>Doritos</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246203</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</td>\n",
       "      <td>Tostitos</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>246204 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      2018-11-10          1            1307     346        96   \n",
       "3      2019-03-09          1            1307     347        54   \n",
       "4      2019-05-20          1            1343     383        61   \n",
       "...           ...        ...             ...     ...       ...   \n",
       "246199 2019-03-09        272          272319  270088        89   \n",
       "246200 2018-08-13        272          272358  270154        74   \n",
       "246201 2018-11-06        272          272379  270187        51   \n",
       "246202 2018-12-27        272          272379  270188        42   \n",
       "246203 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                WW Original Stacked Chips 160g         2        3.8   \n",
       "3                             CCs Original 175g         1        2.1   \n",
       "4        Smiths Crinkle Cut  Chips Chicken 170g         2        2.9   \n",
       "...                                         ...       ...        ...   \n",
       "246199  Kettle Sweet Chilli And Sour Cream 175g         2       10.8   \n",
       "246200            Tostitos Splash Of  Lime 175g         1        4.4   \n",
       "246201                 Doritos Mexicana    170g         2        8.8   \n",
       "246202  Doritos Corn Chip Mexican Jalapeno 150g         2        7.8   \n",
       "246203            Tostitos Splash Of  Lime 175g         2        8.8   \n",
       "\n",
       "       pack_size Brand Name               LIFESTAGE PREMIUM_CUSTOMER  \n",
       "0            175    Natural   YOUNG SINGLES/COUPLES          Premium  \n",
       "1            175        CCs  MIDAGE SINGLES/COUPLES           Budget  \n",
       "2            160         WW  MIDAGE SINGLES/COUPLES           Budget  \n",
       "3            175        CCs  MIDAGE SINGLES/COUPLES           Budget  \n",
       "4            170     Smiths  MIDAGE SINGLES/COUPLES           Budget  \n",
       "...          ...        ...                     ...              ...  \n",
       "246199       175     Kettle   YOUNG SINGLES/COUPLES          Premium  \n",
       "246200       175   Tostitos   YOUNG SINGLES/COUPLES          Premium  \n",
       "246201       170    Doritos   YOUNG SINGLES/COUPLES          Premium  \n",
       "246202       150    Doritos   YOUNG SINGLES/COUPLES          Premium  \n",
       "246203       175   Tostitos   YOUNG SINGLES/COUPLES          Premium  \n",
       "\n",
       "[246204 rows x 12 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2dea43c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OLDER SINGLES/COUPLES     50677\n",
       "RETIREES                  46342\n",
       "OLDER FAMILIES            45042\n",
       "YOUNG FAMILIES            40395\n",
       "YOUNG SINGLES/COUPLES     33917\n",
       "MIDAGE SINGLES/COUPLES    23342\n",
       "NEW FAMILIES               6489\n",
       "Name: LIFESTAGE, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4['LIFESTAGE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ad23564e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mainstream    94839\n",
       "Budget        86567\n",
       "Premium       64798\n",
       "Name: PREMIUM_CUSTOMER, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4['PREMIUM_CUSTOMER'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "27b3d3e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x143059724c0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df8 = pd.DataFrame(df4.groupby(['LIFESTAGE', 'PREMIUM_CUSTOMER']).TOT_SALES.sum())\n",
    "\n",
    "fig = plt.figure(linewidth=5, edgecolor=\"#04253a\", facecolor = '#e1ddbf')\n",
    "\n",
    "df8.unstack().plot(kind = 'bar', stacked = True, figsize = (12, 7), title = 'Total Sales by LIFESTAGE' , edgecolor=\"#04253a\")\n",
    "\n",
    "plt.ylabel('Total Sales')\n",
    "plt.legend(['Budget', 'Mainstream', 'Premium'], loc = 2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a32d3464",
   "metadata": {},
   "source": [
    "- *The sales are high for Budget-Older Families, Mainstream-young singles/couples, Mainstream - retirees and premium - Older Single/Couples.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02b28561",
   "metadata": {},
   "source": [
    "### 2. How many customers are there in each segment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d4634470",
   "metadata": {},
   "outputs": [],
   "source": [
    "df9 = pd.DataFrame(df4.groupby(['PREMIUM_CUSTOMER', 'LIFESTAGE']).LYLTY_CARD_NBR.nunique())\n",
    "df9.rename(columns = {'LYLTY_CARD_NBR': 'Number of Customers'}, inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "bcfc0bf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df9 = df9.sort_values(by = 'Number of Customers', ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9092ac02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Number of Customers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Mainstream</th>\n",
       "      <th>YOUNG SINGLES/COUPLES</th>\n",
       "      <td>7905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETIREES</th>\n",
       "      <td>6357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OLDER SINGLES/COUPLES</th>\n",
       "      <td>4853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Budget</th>\n",
       "      <th>OLDER SINGLES/COUPLES</th>\n",
       "      <td>4846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <th>OLDER SINGLES/COUPLES</th>\n",
       "      <td>4681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Budget</th>\n",
       "      <th>OLDER FAMILIES</th>\n",
       "      <td>4606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETIREES</th>\n",
       "      <td>4382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUNG FAMILIES</th>\n",
       "      <td>3951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <th>RETIREES</th>\n",
       "      <td>3811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Budget</th>\n",
       "      <th>YOUNG SINGLES/COUPLES</th>\n",
       "      <td>3644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Number of Customers\n",
       "PREMIUM_CUSTOMER LIFESTAGE                                 \n",
       "Mainstream       YOUNG SINGLES/COUPLES                 7905\n",
       "                 RETIREES                              6357\n",
       "                 OLDER SINGLES/COUPLES                 4853\n",
       "Budget           OLDER SINGLES/COUPLES                 4846\n",
       "Premium          OLDER SINGLES/COUPLES                 4681\n",
       "Budget           OLDER FAMILIES                        4606\n",
       "                 RETIREES                              4382\n",
       "                 YOUNG FAMILIES                        3951\n",
       "Premium          RETIREES                              3811\n",
       "Budget           YOUNG SINGLES/COUPLES                 3644"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4b4b1ce3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df9 = pd.DataFrame(df4.groupby(['LIFESTAGE', 'PREMIUM_CUSTOMER']).LYLTY_CARD_NBR.nunique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c5958405",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14305986910>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df9.unstack().plot(kind='bar', stacked = True , rot=0 , figsize = (16, 7), title = 'Number of Customers by Customer Segment',  edgecolor=\"#04253a\")\n",
    "plt.ylabel('Number of Customers')\n",
    "plt.legend(['Budget', 'Mainstream', 'Premium'], loc = 2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68f1f0a2",
   "metadata": {},
   "source": [
    "#### There are more mainstream young singles/couples and retirees. This contributes to to more chips sales in these segments however this is not the major driver for the budget older families segment."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca6634d4",
   "metadata": {},
   "source": [
    "### 3. How many chips are bought per customer by segment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b628e604",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Average units per customer by PREMIUM_CUSTOMER and LIFESTAGE\n",
    "\n",
    "df10 = df4.groupby(['PREMIUM_CUSTOMER', 'LIFESTAGE']).PROD_QTY.sum() / df4.groupby(['PREMIUM_CUSTOMER', 'LIFESTAGE']).LYLTY_CARD_NBR.nunique()\n",
    "\n",
    "df10 = pd.DataFrame(df10, columns = {'Average Unit per Customer'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "617f702a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Average Unit per Customer</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mainstream</th>\n",
       "      <th>OLDER FAMILIES</th>\n",
       "      <td>9.195839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Budget</th>\n",
       "      <th>OLDER FAMILIES</th>\n",
       "      <td>9.038645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <th>OLDER FAMILIES</th>\n",
       "      <td>9.013453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Budget</th>\n",
       "      <th>YOUNG FAMILIES</th>\n",
       "      <td>8.677803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <th>YOUNG FAMILIES</th>\n",
       "      <td>8.674593</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Average Unit per Customer\n",
       "PREMIUM_CUSTOMER LIFESTAGE                                \n",
       "Mainstream       OLDER FAMILIES                   9.195839\n",
       "Budget           OLDER FAMILIES                   9.038645\n",
       "Premium          OLDER FAMILIES                   9.013453\n",
       "Budget           YOUNG FAMILIES                   8.677803\n",
       "Premium          YOUNG FAMILIES                   8.674593"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10.sort_values(by = 'Average Unit per Customer', ascending = False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7ae81452",
   "metadata": {},
   "outputs": [],
   "source": [
    "df10 = pd.DataFrame(df4.groupby(['LIFESTAGE', 'PREMIUM_CUSTOMER']).PROD_QTY.sum() / df4.groupby(['LIFESTAGE', 'PREMIUM_CUSTOMER']).LYLTY_CARD_NBR.nunique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "48bc7d29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14311bace80>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df10.unstack().plot(kind = 'bar', figsize = (16, 7), rot = 0, title = 'Average Unit by Customer Segment',  edgecolor=\"#04253a\")\n",
    "plt.ylabel('Average Number of Units')\n",
    "plt.legend(['Budget', 'Mainstream', 'Premium'], loc = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0b8e2a6",
   "metadata": {},
   "source": [
    "- For all the three Lifestages, Older families and Young Families buy more chips per customer."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa943e1b",
   "metadata": {},
   "source": [
    "### 4. What's the average chip price by customer segment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "59ecae92",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Average price per unit by PREMIUM_CUSTOMER and LIFESTAGE\n",
    "\n",
    "df11 = df4.groupby(['PREMIUM_CUSTOMER', 'LIFESTAGE']).TOT_SALES.sum() / df4.groupby(['PREMIUM_CUSTOMER', 'LIFESTAGE']).PROD_QTY.sum()\n",
    "df11 = pd.DataFrame(df11, columns = {'Price per Unit'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5fe71815",
   "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>Price per Unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Mainstream</th>\n",
       "      <th>YOUNG SINGLES/COUPLES</th>\n",
       "      <td>4.072432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIDAGE SINGLES/COUPLES</th>\n",
       "      <td>3.991286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEW FAMILIES</th>\n",
       "      <td>3.936082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Budget</th>\n",
       "      <th>NEW FAMILIES</th>\n",
       "      <td>3.929084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETIREES</th>\n",
       "      <td>3.928989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Price per Unit\n",
       "PREMIUM_CUSTOMER LIFESTAGE                             \n",
       "Mainstream       YOUNG SINGLES/COUPLES         4.072432\n",
       "                 MIDAGE SINGLES/COUPLES        3.991286\n",
       "                 NEW FAMILIES                  3.936082\n",
       "Budget           NEW FAMILIES                  3.929084\n",
       "                 RETIREES                      3.928989"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df11.sort_values(by = 'Price per Unit', ascending = False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6de10bc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14311e31880>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualise \n",
    "\n",
    "df11 = pd.DataFrame(df4.groupby(['LIFESTAGE', 'PREMIUM_CUSTOMER']).TOT_SALES.sum() / df4.groupby(['LIFESTAGE', 'PREMIUM_CUSTOMER']).PROD_QTY.sum())\n",
    "df11.unstack().plot(kind = 'bar', figsize = (12, 7), title = 'Average Price by Customer Segment', ylim = (0, 5),  edgecolor=\"#04253a\")\n",
    "plt.ylabel('Average Price')\n",
    "plt.legend(['Budget', 'Mainstream', 'Premium'], loc = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3064ceb3",
   "metadata": {},
   "source": [
    "- Mainstream midage singles/couples and young singles/couples  pay more per packet of chips compared to other segments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5bdfe0d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>pack_size</th>\n",
       "      <th>Brand Name</th>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-10-17</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Natural Chip        Compny SeaSalt175g</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>175</td>\n",
       "      <td>Natural</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-14</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>348</td>\n",
       "      <td>66</td>\n",
       "      <td>CCs Nacho Cheese    175g</td>\n",
       "      <td>3</td>\n",
       "      <td>6.3</td>\n",
       "      <td>175</td>\n",
       "      <td>CCs</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-11-10</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>346</td>\n",
       "      <td>96</td>\n",
       "      <td>WW Original Stacked Chips 160g</td>\n",
       "      <td>2</td>\n",
       "      <td>3.8</td>\n",
       "      <td>160</td>\n",
       "      <td>WW</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-03-09</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>347</td>\n",
       "      <td>54</td>\n",
       "      <td>CCs Original 175g</td>\n",
       "      <td>1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>175</td>\n",
       "      <td>CCs</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1343</td>\n",
       "      <td>383</td>\n",
       "      <td>61</td>\n",
       "      <td>Smiths Crinkle Cut  Chips Chicken 170g</td>\n",
       "      <td>2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>170</td>\n",
       "      <td>Smiths</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>1.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE  STORE_NBR  LYLTY_CARD_NBR  TXN_ID  PROD_NBR  \\\n",
       "0 2018-10-17          1            1000       1         5   \n",
       "1 2019-05-14          1            1307     348        66   \n",
       "2 2018-11-10          1            1307     346        96   \n",
       "3 2019-03-09          1            1307     347        54   \n",
       "4 2019-05-20          1            1343     383        61   \n",
       "\n",
       "                                PROD_NAME  PROD_QTY  TOT_SALES pack_size  \\\n",
       "0  Natural Chip        Compny SeaSalt175g         2        6.0       175   \n",
       "1                CCs Nacho Cheese    175g         3        6.3       175   \n",
       "2          WW Original Stacked Chips 160g         2        3.8       160   \n",
       "3                       CCs Original 175g         1        2.1       175   \n",
       "4  Smiths Crinkle Cut  Chips Chicken 170g         2        2.9       170   \n",
       "\n",
       "  Brand Name               LIFESTAGE PREMIUM_CUSTOMER  PRICE  \n",
       "0    Natural   YOUNG SINGLES/COUPLES          Premium   3.00  \n",
       "1        CCs  MIDAGE SINGLES/COUPLES           Budget   2.10  \n",
       "2         WW  MIDAGE SINGLES/COUPLES           Budget   1.90  \n",
       "3        CCs  MIDAGE SINGLES/COUPLES           Budget   2.10  \n",
       "4     Smiths  MIDAGE SINGLES/COUPLES           Budget   1.45  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform an independent t-test between mainstream vs non-mainstream midage and young singles/couples to test this difference\n",
    "\n",
    "# Create a new dataframe pricePerUnit\n",
    "pricePerUnit = df4\n",
    "\n",
    "# Create a new column under pricePerUnit called PRICE\n",
    "pricePerUnit['PRICE'] = pricePerUnit['TOT_SALES'] / pricePerUnit['PROD_QTY']\n",
    "\n",
    "# Let's have a look\n",
    "pricePerUnit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2d8f627f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's group our data into mainstream and non-mainstream\n",
    "\n",
    "mainstream = pricePerUnit.loc[(pricePerUnit['PREMIUM_CUSTOMER'] == 'Mainstream') & ( (pricePerUnit['LIFESTAGE'] == 'YOUNG SINGLES/COUPLES') | (pricePerUnit['LIFESTAGE'] == 'MIDAGE SINGLES/COUPLES') ), 'PRICE']\n",
    "nonMainstream = pricePerUnit.loc[(pricePerUnit['PREMIUM_CUSTOMER'] != 'Mainstream') & ( (pricePerUnit['LIFESTAGE'] == 'YOUNG SINGLES/COUPLES') | (pricePerUnit['LIFESTAGE'] == 'MIDAGE SINGLES/COUPLES') ), 'PRICE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b56139d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=37.752225863415426, pvalue=4.307427069453844e-308)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform t-test \n",
    "\n",
    "ttest_ind(mainstream, nonMainstream)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "794a6f3a",
   "metadata": {},
   "source": [
    "### Let us further explore and target the segment Mainstream and young singles/couples that contributes most to the sales."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8215a61",
   "metadata": {},
   "source": [
    "#### Let's find out if they tend to buy a particular brand of chips."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1401ff61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "      <th>pack_size</th>\n",
       "      <th>Brand Name</th>\n",
       "      <th>LIFESTAGE</th>\n",
       "      <th>PREMIUM_CUSTOMER</th>\n",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-10-17</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Natural Chip        Compny SeaSalt175g</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>175</td>\n",
       "      <td>Natural</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-14</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>348</td>\n",
       "      <td>66</td>\n",
       "      <td>CCs Nacho Cheese    175g</td>\n",
       "      <td>3</td>\n",
       "      <td>6.3</td>\n",
       "      <td>175</td>\n",
       "      <td>CCs</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-11-10</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>346</td>\n",
       "      <td>96</td>\n",
       "      <td>WW Original Stacked Chips 160g</td>\n",
       "      <td>2</td>\n",
       "      <td>3.8</td>\n",
       "      <td>160</td>\n",
       "      <td>WW</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-03-09</td>\n",
       "      <td>1</td>\n",
       "      <td>1307</td>\n",
       "      <td>347</td>\n",
       "      <td>54</td>\n",
       "      <td>CCs Original 175g</td>\n",
       "      <td>1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>175</td>\n",
       "      <td>CCs</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1343</td>\n",
       "      <td>383</td>\n",
       "      <td>61</td>\n",
       "      <td>Smiths Crinkle Cut  Chips Chicken 170g</td>\n",
       "      <td>2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>170</td>\n",
       "      <td>Smiths</td>\n",
       "      <td>MIDAGE SINGLES/COUPLES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>1.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246199</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</td>\n",
       "      <td>Kettle</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "      <td>5.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246200</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</td>\n",
       "      <td>Tostitos</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "      <td>4.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246201</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</td>\n",
       "      <td>Doritos</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "      <td>4.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246202</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</td>\n",
       "      <td>Doritos</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "      <td>3.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246203</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</td>\n",
       "      <td>Tostitos</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Premium</td>\n",
       "      <td>4.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>246204 rows × 13 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      2018-11-10          1            1307     346        96   \n",
       "3      2019-03-09          1            1307     347        54   \n",
       "4      2019-05-20          1            1343     383        61   \n",
       "...           ...        ...             ...     ...       ...   \n",
       "246199 2019-03-09        272          272319  270088        89   \n",
       "246200 2018-08-13        272          272358  270154        74   \n",
       "246201 2018-11-06        272          272379  270187        51   \n",
       "246202 2018-12-27        272          272379  270188        42   \n",
       "246203 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                WW Original Stacked Chips 160g         2        3.8   \n",
       "3                             CCs Original 175g         1        2.1   \n",
       "4        Smiths Crinkle Cut  Chips Chicken 170g         2        2.9   \n",
       "...                                         ...       ...        ...   \n",
       "246199  Kettle Sweet Chilli And Sour Cream 175g         2       10.8   \n",
       "246200            Tostitos Splash Of  Lime 175g         1        4.4   \n",
       "246201                 Doritos Mexicana    170g         2        8.8   \n",
       "246202  Doritos Corn Chip Mexican Jalapeno 150g         2        7.8   \n",
       "246203            Tostitos Splash Of  Lime 175g         2        8.8   \n",
       "\n",
       "       pack_size Brand Name               LIFESTAGE PREMIUM_CUSTOMER  PRICE  \n",
       "0            175    Natural   YOUNG SINGLES/COUPLES          Premium   3.00  \n",
       "1            175        CCs  MIDAGE SINGLES/COUPLES           Budget   2.10  \n",
       "2            160         WW  MIDAGE SINGLES/COUPLES           Budget   1.90  \n",
       "3            175        CCs  MIDAGE SINGLES/COUPLES           Budget   2.10  \n",
       "4            170     Smiths  MIDAGE SINGLES/COUPLES           Budget   1.45  \n",
       "...          ...        ...                     ...              ...    ...  \n",
       "246199       175     Kettle   YOUNG SINGLES/COUPLES          Premium   5.40  \n",
       "246200       175   Tostitos   YOUNG SINGLES/COUPLES          Premium   4.40  \n",
       "246201       170    Doritos   YOUNG SINGLES/COUPLES          Premium   4.40  \n",
       "246202       150    Doritos   YOUNG SINGLES/COUPLES          Premium   3.90  \n",
       "246203       175   Tostitos   YOUNG SINGLES/COUPLES          Premium   4.40  \n",
       "\n",
       "[246204 rows x 13 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b41ec63b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfa = df4[(df4['LIFESTAGE'] == 'YOUNG SINGLES/COUPLES') & (df4['PREMIUM_CUSTOMER'] == 'Mainstream')]\n",
    "dfb = df4[(df4['LIFESTAGE'] != 'YOUNG SINGLES/COUPLES') & (df4['PREMIUM_CUSTOMER'] != 'Mainstream')]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4036fbf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36074"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfa_quantity = dfa['PROD_QTY'].sum()\n",
    "dfa_quantity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2841834d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "262133"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfb_quantity = dfb['PROD_QTY'].sum()\n",
    "dfb_quantity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "98577aba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brand Name\n",
       "Burger        106\n",
       "CCs           405\n",
       "Cheetos       291\n",
       "Cheezels      651\n",
       "Cobs         1609\n",
       "Dorito        569\n",
       "Doritos      3867\n",
       "French        143\n",
       "Grain        1055\n",
       "GrnWves       125\n",
       "Infuzions    1797\n",
       "Infzns        541\n",
       "Kettle       7106\n",
       "NCC           132\n",
       "Natural       578\n",
       "Pringles     4306\n",
       "RRD          1582\n",
       "Smith         239\n",
       "Smiths       3240\n",
       "Snbts         126\n",
       "Sunbites      104\n",
       "Thins        2182\n",
       "Tostitos     1640\n",
       "Twisties     1664\n",
       "Tyrrells     1143\n",
       "WW            873\n",
       "Name: PROD_QTY, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfa_quantity_brand = dfa.groupby(['Brand Name'])['PROD_QTY'].sum()\n",
    "dfa_quantity_brand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1d732aeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brand Name\n",
       "Burger        1723\n",
       "CCs           4861\n",
       "Cheetos       3094\n",
       "Cheezels      4926\n",
       "Cobs         10085\n",
       "Dorito        3379\n",
       "Doritos      23079\n",
       "French        1504\n",
       "Grain         6525\n",
       "GrnWves       1618\n",
       "Infuzions    11768\n",
       "Infzns        3290\n",
       "Kettle       43564\n",
       "NCC           1587\n",
       "Natural       6522\n",
       "Pringles     26502\n",
       "RRD          17671\n",
       "Smith         3259\n",
       "Smiths       29344\n",
       "Snbts         1780\n",
       "Sunbites      1544\n",
       "Thins        14999\n",
       "Tostitos     10032\n",
       "Twisties      9903\n",
       "Tyrrells      6727\n",
       "WW           12847\n",
       "Name: PROD_QTY, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfb_quantity_brand = dfb.groupby(['Brand Name'])['PROD_QTY'].sum()\n",
    "dfb_quantity_brand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5ab5b27e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand Name</th>\n",
       "      <th>Affinity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tyrrells</td>\n",
       "      <td>1.234674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dorito</td>\n",
       "      <td>1.223634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Twisties</td>\n",
       "      <td>1.220995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Doritos</td>\n",
       "      <td>1.217544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Infzns</td>\n",
       "      <td>1.194892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tostitos</td>\n",
       "      <td>1.187911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Kettle</td>\n",
       "      <td>1.185291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Pringles</td>\n",
       "      <td>1.180654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Grain</td>\n",
       "      <td>1.174896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Cobs</td>\n",
       "      <td>1.159331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Infuzions</td>\n",
       "      <td>1.109616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Thins</td>\n",
       "      <td>1.057109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Cheezels</td>\n",
       "      <td>0.960316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Smiths</td>\n",
       "      <td>0.802330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>French</td>\n",
       "      <td>0.690901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Cheetos</td>\n",
       "      <td>0.683440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>RRD</td>\n",
       "      <td>0.650538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Natural</td>\n",
       "      <td>0.643983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CCs</td>\n",
       "      <td>0.605420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NCC</td>\n",
       "      <td>0.604400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>GrnWves</td>\n",
       "      <td>0.561383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Smith</td>\n",
       "      <td>0.532894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Snbts</td>\n",
       "      <td>0.514373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>WW</td>\n",
       "      <td>0.493787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Sunbites</td>\n",
       "      <td>0.489456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Burger</td>\n",
       "      <td>0.447042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Brand Name  Affinity\n",
       "0    Tyrrells  1.234674\n",
       "1      Dorito  1.223634\n",
       "2    Twisties  1.220995\n",
       "3     Doritos  1.217544\n",
       "4      Infzns  1.194892\n",
       "5    Tostitos  1.187911\n",
       "6      Kettle  1.185291\n",
       "7    Pringles  1.180654\n",
       "8       Grain  1.174896\n",
       "9        Cobs  1.159331\n",
       "10  Infuzions  1.109616\n",
       "11      Thins  1.057109\n",
       "12   Cheezels  0.960316\n",
       "13     Smiths  0.802330\n",
       "14     French  0.690901\n",
       "15    Cheetos  0.683440\n",
       "16        RRD  0.650538\n",
       "17    Natural  0.643983\n",
       "18        CCs  0.605420\n",
       "19        NCC  0.604400\n",
       "20    GrnWves  0.561383\n",
       "21      Smith  0.532894\n",
       "22      Snbts  0.514373\n",
       "23         WW  0.493787\n",
       "24   Sunbites  0.489456\n",
       "25     Burger  0.447042"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To check the brand affinity\n",
    "\n",
    "dfa_affinity = dfa_quantity_brand/dfa_quantity\n",
    "dfb_affinity = dfb_quantity_brand/dfb_quantity\n",
    "\n",
    "affinity = (dfa_affinity/dfb_affinity).sort_values(ascending = False)\n",
    "\n",
    "df_affinity= pd.DataFrame({'Brand Name':affinity.index, 'Affinity':affinity.values})\n",
    "df_affinity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a1167d8",
   "metadata": {},
   "source": [
    "- Tyrell chips seems to be the most popular brand for Mainstream young singles/couples almost 23% more than the rest of the audience whereas Burger Rings were purchased less than 56% less as compared to rest of the people."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5585072c",
   "metadata": {},
   "source": [
    "### Let’s also find out if our target segment tends to buy larger packs of chips."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "937342c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfa_quantity_pack = dfa.groupby(['pack_size'])['PROD_QTY'].sum()\n",
    "dfb_quantity_pack = dfb.groupby(['pack_size'])['PROD_QTY'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "488142e4",
   "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>Pack Size</th>\n",
       "      <th>Affinity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>270</td>\n",
       "      <td>1.276508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>380</td>\n",
       "      <td>1.252702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>330</td>\n",
       "      <td>1.210177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>110</td>\n",
       "      <td>1.187112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>134</td>\n",
       "      <td>1.180654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>210</td>\n",
       "      <td>1.174896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>135</td>\n",
       "      <td>1.127825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>250</td>\n",
       "      <td>1.113282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>170</td>\n",
       "      <td>1.008014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>150</td>\n",
       "      <td>0.962471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>175</td>\n",
       "      <td>0.940317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>165</td>\n",
       "      <td>0.905054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>190</td>\n",
       "      <td>0.616927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>180</td>\n",
       "      <td>0.561383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>160</td>\n",
       "      <td>0.523389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>125</td>\n",
       "      <td>0.502890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>90</td>\n",
       "      <td>0.502799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>200</td>\n",
       "      <td>0.485132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>70</td>\n",
       "      <td>0.482680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>220</td>\n",
       "      <td>0.447042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pack Size  Affinity\n",
       "0        270  1.276508\n",
       "1        380  1.252702\n",
       "2        330  1.210177\n",
       "3        110  1.187112\n",
       "4        134  1.180654\n",
       "5        210  1.174896\n",
       "6        135  1.127825\n",
       "7        250  1.113282\n",
       "8        170  1.008014\n",
       "9        150  0.962471\n",
       "10       175  0.940317\n",
       "11       165  0.905054\n",
       "12       190  0.616927\n",
       "13       180  0.561383\n",
       "14       160  0.523389\n",
       "15       125  0.502890\n",
       "16        90  0.502799\n",
       "17       200  0.485132\n",
       "18        70  0.482680\n",
       "19       220  0.447042"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To Check Pack Affinity\n",
    "dfa_affinity_pack = dfa_quantity_pack/dfa_quantity\n",
    "dfb_affinity_pack = dfb_quantity_pack/dfb_quantity\n",
    "\n",
    "affinity_pack = (dfa_affinity_pack/dfb_affinity_pack).sort_values(ascending = False)\n",
    "\n",
    "df_affinity_pack = pd.DataFrame({'Pack Size':affinity_pack.index, 'Affinity':affinity_pack.values})\n",
    "df_affinity_pack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0cdca747",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Twisties Cheese     270g', 'Twisties Chicken270g'], dtype=object)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Product_name = df4[(df4['pack_size'] == '270')]['PROD_NAME']\n",
    "                   \n",
    "Product_name.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0edddd7c",
   "metadata": {},
   "source": [
    "- Mainstream young singles/couples are 27% more likely to purchase a 270g pack of Twisties Cheese chips which reflects the higher volume of sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3dce049f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Burger Rings 220g'], dtype=object)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Product_name_least = df4[(df4['pack_size'] == '220')]['PROD_NAME']\n",
    "                   \n",
    "Product_name_least.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2392bedb",
   "metadata": {},
   "source": [
    "- Mainstream young singles/couples are 56% less likely to purchase a 220g pack of Burger Rings which reflects the lower volume of sales."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef21b827",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af3f8c4a",
   "metadata": {},
   "source": [
    "- Budget – older families, Mainstream – young singles/couples, and Mainstream – retirees shoppers have accounted for the majority of sales.\n",
    "- Mainstream young singles/couples and retirees spend more money on chips than other purchasers mainly due to the fact that there are more customers in these segments.\n",
    "- Additionally, young, middle-aged, and mainstream individuals and couples are more inclined to pay more for each bag of chips. \n",
    "- They are also 23% more likely to purchase 'Tyrrells' and '270g' pack sizes than the rest of the population."
   ]
  }
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