Quantium Data Analytics Virtual Experience Program (Task #2).ipynb

{
 "cells": [
  {
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1><center>Quantium Data Analytics Virtual Experience Program</center></h1>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2\n",
    "\n",
    "## Experimentation and uplift testing"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extend your analysis from Task 1 to help you identify benchmark stores that allow you to test the impact of the trial store layouts on customer sales."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here is the background information on your task**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You are part of Quantium’s retail analytics team and have been approached by your client, the Category Manager for Chips, has asked us to test the impact of the new trial layouts with a data driven recommendation to whether or not the trial layout should be rolled out to all their stores."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here is your task**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Julia has asked us to evaluate the performance of a store trial which was performed in stores 77, 86 and 88.\n",
    "\n",
    "To get started use the QVI_data dataset below or your output from task 1 and consider the monthly sales experience of each store. \n",
    "\n",
    "This can be broken down by:\n",
    "- total sales revenue\n",
    "- total number of customers\n",
    "- average number of transactions per customer\n",
    "\n",
    "Create a measure to compare different control stores to each of the trial stores to do this write a function to reduce having to re-do the analysis for each trial store. Consider using Pearson correlations or a metric such as a magnitude distance e.g. 1- (Observed distance – minimum distance)/(Maximum distance – minimum distance) as a measure.\n",
    "\n",
    "Once you have selected your control stores, compare each trial and control pair during the trial period. You want to test if total sales are significantly different in the trial period and if so, check if the driver of change is more purchasing customers or more purchases per customers etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#   Importing the necessary libraries/modules.\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.style as style\n",
    "import seaborn as sns\n",
    "import datetime\n",
    "import xlrd\n",
    "%matplotlib inline\n",
    "\n",
    "#   Ignoring any warnings.\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action=\"ignore\", category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
       "      <th>PROD_NBR</th>\n",
       "      <th>PROD_NAME</th>\n",
       "      <th>PROD_QTY</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>PACK_SIZE</th>\n",
       "      <th>BRAND</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>2018-10-17</td>\n",
       "      <td>1</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>1002</td>\n",
       "      <td>2018-09-16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>58</td>\n",
       "      <td>Red Rock Deli Chikn&amp;Garlic Aioli 150g</td>\n",
       "      <td>1</td>\n",
       "      <td>2.7</td>\n",
       "      <td>150</td>\n",
       "      <td>RRD</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>2019-03-07</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>52</td>\n",
       "      <td>Grain Waves Sour    Cream&amp;Chives 210G</td>\n",
       "      <td>1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>210</td>\n",
       "      <td>GRNWVES</td>\n",
       "      <td>YOUNG FAMILIES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003</td>\n",
       "      <td>2019-03-08</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>106</td>\n",
       "      <td>Natural ChipCo      Hony Soy Chckn175g</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>175</td>\n",
       "      <td>NATURAL</td>\n",
       "      <td>YOUNG FAMILIES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1004</td>\n",
       "      <td>2018-11-02</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>96</td>\n",
       "      <td>WW Original Stacked Chips 160g</td>\n",
       "      <td>1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>160</td>\n",
       "      <td>WOOLWORTHS</td>\n",
       "      <td>OLDER SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</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>264829</th>\n",
       "      <td>2370701</td>\n",
       "      <td>2018-12-08</td>\n",
       "      <td>88</td>\n",
       "      <td>240378</td>\n",
       "      <td>24</td>\n",
       "      <td>Grain Waves         Sweet Chilli 210g</td>\n",
       "      <td>2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>210</td>\n",
       "      <td>GRNWVES</td>\n",
       "      <td>YOUNG FAMILIES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264830</th>\n",
       "      <td>2370751</td>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>88</td>\n",
       "      <td>240394</td>\n",
       "      <td>60</td>\n",
       "      <td>Kettle Tortilla ChpsFeta&amp;Garlic 150g</td>\n",
       "      <td>2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>150</td>\n",
       "      <td>KETTLE</td>\n",
       "      <td>YOUNG FAMILIES</td>\n",
       "      <td>Premium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264831</th>\n",
       "      <td>2370961</td>\n",
       "      <td>2018-10-24</td>\n",
       "      <td>88</td>\n",
       "      <td>240480</td>\n",
       "      <td>70</td>\n",
       "      <td>Tyrrells Crisps     Lightly Salted 165g</td>\n",
       "      <td>2</td>\n",
       "      <td>8.4</td>\n",
       "      <td>165</td>\n",
       "      <td>TYRRELLS</td>\n",
       "      <td>OLDER FAMILIES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264832</th>\n",
       "      <td>2370961</td>\n",
       "      <td>2018-10-27</td>\n",
       "      <td>88</td>\n",
       "      <td>240481</td>\n",
       "      <td>65</td>\n",
       "      <td>Old El Paso Salsa   Dip Chnky Tom Ht300g</td>\n",
       "      <td>2</td>\n",
       "      <td>10.2</td>\n",
       "      <td>300</td>\n",
       "      <td>OLD</td>\n",
       "      <td>OLDER FAMILIES</td>\n",
       "      <td>Budget</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264833</th>\n",
       "      <td>2373711</td>\n",
       "      <td>2018-12-14</td>\n",
       "      <td>88</td>\n",
       "      <td>241815</td>\n",
       "      <td>16</td>\n",
       "      <td>Smiths Crinkle Chips Salt &amp; Vinegar 330g</td>\n",
       "      <td>2</td>\n",
       "      <td>11.4</td>\n",
       "      <td>330</td>\n",
       "      <td>SMITHS</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>264834 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        LYLTY_CARD_NBR        DATE  STORE_NBR  TXN_ID  PROD_NBR  \\\n",
       "0                 1000  2018-10-17          1       1         5   \n",
       "1                 1002  2018-09-16          1       2        58   \n",
       "2                 1003  2019-03-07          1       3        52   \n",
       "3                 1003  2019-03-08          1       4       106   \n",
       "4                 1004  2018-11-02          1       5        96   \n",
       "...                ...         ...        ...     ...       ...   \n",
       "264829         2370701  2018-12-08         88  240378        24   \n",
       "264830         2370751  2018-10-01         88  240394        60   \n",
       "264831         2370961  2018-10-24         88  240480        70   \n",
       "264832         2370961  2018-10-27         88  240481        65   \n",
       "264833         2373711  2018-12-14         88  241815        16   \n",
       "\n",
       "                                       PROD_NAME  PROD_QTY  TOT_SALES  \\\n",
       "0         Natural Chip        Compny SeaSalt175g         2        6.0   \n",
       "1          Red Rock Deli Chikn&Garlic Aioli 150g         1        2.7   \n",
       "2          Grain Waves Sour    Cream&Chives 210G         1        3.6   \n",
       "3         Natural ChipCo      Hony Soy Chckn175g         1        3.0   \n",
       "4                 WW Original Stacked Chips 160g         1        1.9   \n",
       "...                                          ...       ...        ...   \n",
       "264829     Grain Waves         Sweet Chilli 210g         2        7.2   \n",
       "264830      Kettle Tortilla ChpsFeta&Garlic 150g         2        9.2   \n",
       "264831   Tyrrells Crisps     Lightly Salted 165g         2        8.4   \n",
       "264832  Old El Paso Salsa   Dip Chnky Tom Ht300g         2       10.2   \n",
       "264833  Smiths Crinkle Chips Salt & Vinegar 330g         2       11.4   \n",
       "\n",
       "        PACK_SIZE       BRAND              LIFESTAGE PREMIUM_CUSTOMER  \n",
       "0             175     NATURAL  YOUNG SINGLES/COUPLES          Premium  \n",
       "1             150         RRD  YOUNG SINGLES/COUPLES       Mainstream  \n",
       "2             210     GRNWVES         YOUNG FAMILIES           Budget  \n",
       "3             175     NATURAL         YOUNG FAMILIES           Budget  \n",
       "4             160  WOOLWORTHS  OLDER SINGLES/COUPLES       Mainstream  \n",
       "...           ...         ...                    ...              ...  \n",
       "264829        210     GRNWVES         YOUNG FAMILIES       Mainstream  \n",
       "264830        150      KETTLE         YOUNG FAMILIES          Premium  \n",
       "264831        165    TYRRELLS         OLDER FAMILIES           Budget  \n",
       "264832        300         OLD         OLDER FAMILIES           Budget  \n",
       "264833        330      SMITHS  YOUNG SINGLES/COUPLES       Mainstream  \n",
       "\n",
       "[264834 rows x 12 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qvi_data=pd.read_csv(\"QVI_data.csv\")    #   Reading the CSV file into a pandas.DataFrame.\n",
    "qvi_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 264834 entries, 0 to 264833\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count   Dtype  \n",
      "---  ------            --------------   -----  \n",
      " 0   LYLTY_CARD_NBR    264834 non-null  int64  \n",
      " 1   DATE              264834 non-null  object \n",
      " 2   STORE_NBR         264834 non-null  int64  \n",
      " 3   TXN_ID            264834 non-null  int64  \n",
      " 4   PROD_NBR          264834 non-null  int64  \n",
      " 5   PROD_NAME         264834 non-null  object \n",
      " 6   PROD_QTY          264834 non-null  int64  \n",
      " 7   TOT_SALES         264834 non-null  float64\n",
      " 8   PACK_SIZE         264834 non-null  int64  \n",
      " 9   BRAND             264834 non-null  object \n",
      " 10  LIFESTAGE         264834 non-null  object \n",
      " 11  PREMIUM_CUSTOMER  264834 non-null  object \n",
      "dtypes: float64(1), int64(6), object(5)\n",
      "memory usage: 24.2+ MB\n"
     ]
    }
   ],
   "source": [
    "qvi_data.info() #    Getting a concise summary of the pandas.DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LYLTY_CARD_NBR      0\n",
       "DATE                0\n",
       "STORE_NBR           0\n",
       "TXN_ID              0\n",
       "PROD_NBR            0\n",
       "PROD_NAME           0\n",
       "PROD_QTY            0\n",
       "TOT_SALES           0\n",
       "PACK_SIZE           0\n",
       "BRAND               0\n",
       "LIFESTAGE           0\n",
       "PREMIUM_CUSTOMER    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qvi_data.isnull().sum() #   Checking for any null values in the pandas.DataFrame."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll start by assigning a control store to every trial store, which were stores 77, 86 and 88, because we want to find stores that similar attributes to that of the trial stores prior to the entire duration of recorded sales. This way, we can accurately deduce if a particular store has increased its sales during the recorded duration or not."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly, we need to aggregate some statistics about the stores for each recorded month over the entire duration of sales. We can start by finding the total sales for each month during the entire recorded duration, for each store."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>LYLTY_CARD_NBR</th>\n",
       "      <th>DATE</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>TXN_ID</th>\n",
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       "      <th>PREMIUM_CUSTOMER</th>\n",
       "      <th>YEAR_MONTH</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>2018-10-17</td>\n",
       "      <td>1</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>2018-10</td>\n",
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       "      <th>1</th>\n",
       "      <td>1002</td>\n",
       "      <td>2018-09-16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>58</td>\n",
       "      <td>Red Rock Deli Chikn&amp;Garlic Aioli 150g</td>\n",
       "      <td>1</td>\n",
       "      <td>2.7</td>\n",
       "      <td>150</td>\n",
       "      <td>RRD</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "      <td>2018-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003</td>\n",
       "      <td>2019-03-07</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>52</td>\n",
       "      <td>Grain Waves Sour    Cream&amp;Chives 210G</td>\n",
       "      <td>1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>210</td>\n",
       "      <td>GRNWVES</td>\n",
       "      <td>YOUNG FAMILIES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>2019-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003</td>\n",
       "      <td>2019-03-08</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>106</td>\n",
       "      <td>Natural ChipCo      Hony Soy Chckn175g</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>175</td>\n",
       "      <td>NATURAL</td>\n",
       "      <td>YOUNG FAMILIES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>2019-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1004</td>\n",
       "      <td>2018-11-02</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>96</td>\n",
       "      <td>WW Original Stacked Chips 160g</td>\n",
       "      <td>1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>160</td>\n",
       "      <td>WOOLWORTHS</td>\n",
       "      <td>OLDER SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "      <td>2018-11</td>\n",
       "    </tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264829</th>\n",
       "      <td>2370701</td>\n",
       "      <td>2018-12-08</td>\n",
       "      <td>88</td>\n",
       "      <td>240378</td>\n",
       "      <td>24</td>\n",
       "      <td>Grain Waves         Sweet Chilli 210g</td>\n",
       "      <td>2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>210</td>\n",
       "      <td>GRNWVES</td>\n",
       "      <td>YOUNG FAMILIES</td>\n",
       "      <td>Mainstream</td>\n",
       "      <td>2018-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264830</th>\n",
       "      <td>2370751</td>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>88</td>\n",
       "      <td>240394</td>\n",
       "      <td>60</td>\n",
       "      <td>Kettle Tortilla ChpsFeta&amp;Garlic 150g</td>\n",
       "      <td>2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>150</td>\n",
       "      <td>KETTLE</td>\n",
       "      <td>YOUNG FAMILIES</td>\n",
       "      <td>Premium</td>\n",
       "      <td>2018-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264831</th>\n",
       "      <td>2370961</td>\n",
       "      <td>2018-10-24</td>\n",
       "      <td>88</td>\n",
       "      <td>240480</td>\n",
       "      <td>70</td>\n",
       "      <td>Tyrrells Crisps     Lightly Salted 165g</td>\n",
       "      <td>2</td>\n",
       "      <td>8.4</td>\n",
       "      <td>165</td>\n",
       "      <td>TYRRELLS</td>\n",
       "      <td>OLDER FAMILIES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>2018-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264832</th>\n",
       "      <td>2370961</td>\n",
       "      <td>2018-10-27</td>\n",
       "      <td>88</td>\n",
       "      <td>240481</td>\n",
       "      <td>65</td>\n",
       "      <td>Old El Paso Salsa   Dip Chnky Tom Ht300g</td>\n",
       "      <td>2</td>\n",
       "      <td>10.2</td>\n",
       "      <td>300</td>\n",
       "      <td>OLD</td>\n",
       "      <td>OLDER FAMILIES</td>\n",
       "      <td>Budget</td>\n",
       "      <td>2018-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264833</th>\n",
       "      <td>2373711</td>\n",
       "      <td>2018-12-14</td>\n",
       "      <td>88</td>\n",
       "      <td>241815</td>\n",
       "      <td>16</td>\n",
       "      <td>Smiths Crinkle Chips Salt &amp; Vinegar 330g</td>\n",
       "      <td>2</td>\n",
       "      <td>11.4</td>\n",
       "      <td>330</td>\n",
       "      <td>SMITHS</td>\n",
       "      <td>YOUNG SINGLES/COUPLES</td>\n",
       "      <td>Mainstream</td>\n",
       "      <td>2018-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>264834 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        LYLTY_CARD_NBR        DATE  STORE_NBR  TXN_ID  PROD_NBR  \\\n",
       "0                 1000  2018-10-17          1       1         5   \n",
       "1                 1002  2018-09-16          1       2        58   \n",
       "2                 1003  2019-03-07          1       3        52   \n",
       "3                 1003  2019-03-08          1       4       106   \n",
       "4                 1004  2018-11-02          1       5        96   \n",
       "...                ...         ...        ...     ...       ...   \n",
       "264829         2370701  2018-12-08         88  240378        24   \n",
       "264830         2370751  2018-10-01         88  240394        60   \n",
       "264831         2370961  2018-10-24         88  240480        70   \n",
       "264832         2370961  2018-10-27         88  240481        65   \n",
       "264833         2373711  2018-12-14         88  241815        16   \n",
       "\n",
       "                                       PROD_NAME  PROD_QTY  TOT_SALES  \\\n",
       "0         Natural Chip        Compny SeaSalt175g         2        6.0   \n",
       "1          Red Rock Deli Chikn&Garlic Aioli 150g         1        2.7   \n",
       "2          Grain Waves Sour    Cream&Chives 210G         1        3.6   \n",
       "3         Natural ChipCo      Hony Soy Chckn175g         1        3.0   \n",
       "4                 WW Original Stacked Chips 160g         1        1.9   \n",
       "...                                          ...       ...        ...   \n",
       "264829     Grain Waves         Sweet Chilli 210g         2        7.2   \n",
       "264830      Kettle Tortilla ChpsFeta&Garlic 150g         2        9.2   \n",
       "264831   Tyrrells Crisps     Lightly Salted 165g         2        8.4   \n",
       "264832  Old El Paso Salsa   Dip Chnky Tom Ht300g         2       10.2   \n",
       "264833  Smiths Crinkle Chips Salt & Vinegar 330g         2       11.4   \n",
       "\n",
       "        PACK_SIZE       BRAND              LIFESTAGE PREMIUM_CUSTOMER  \\\n",
       "0             175     NATURAL  YOUNG SINGLES/COUPLES          Premium   \n",
       "1             150         RRD  YOUNG SINGLES/COUPLES       Mainstream   \n",
       "2             210     GRNWVES         YOUNG FAMILIES           Budget   \n",
       "3             175     NATURAL         YOUNG FAMILIES           Budget   \n",
       "4             160  WOOLWORTHS  OLDER SINGLES/COUPLES       Mainstream   \n",
       "...           ...         ...                    ...              ...   \n",
       "264829        210     GRNWVES         YOUNG FAMILIES       Mainstream   \n",
       "264830        150      KETTLE         YOUNG FAMILIES          Premium   \n",
       "264831        165    TYRRELLS         OLDER FAMILIES           Budget   \n",
       "264832        300         OLD         OLDER FAMILIES           Budget   \n",
       "264833        330      SMITHS  YOUNG SINGLES/COUPLES       Mainstream   \n",
       "\n",
       "       YEAR_MONTH  \n",
       "0         2018-10  \n",
       "1         2018-09  \n",
       "2         2019-03  \n",
       "3         2019-03  \n",
       "4         2018-11  \n",
       "...           ...  \n",
       "264829    2018-12  \n",
       "264830    2018-10  \n",
       "264831    2018-10  \n",
       "264832    2018-10  \n",
       "264833    2018-12  \n",
       "\n",
       "[264834 rows x 13 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qvi_data[\"YEAR_MONTH\"]=pd.to_datetime(qvi_data[\"DATE\"]).dt.to_period(\"M\")   #   Storing the year and month of the recorded duration in a separate column.\n",
    "qvi_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>TOT_SALES</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>YEAR_MONTH</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>2018-07</th>\n",
       "      <td>206.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08</th>\n",
       "      <td>176.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09</th>\n",
       "      <td>278.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10</th>\n",
       "      <td>188.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11</th>\n",
       "      <td>192.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">272</th>\n",
       "      <th>2019-02</th>\n",
       "      <td>395.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03</th>\n",
       "      <td>442.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04</th>\n",
       "      <td>445.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05</th>\n",
       "      <td>314.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06</th>\n",
       "      <td>312.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3169 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      TOT_SALES\n",
       "STORE_NBR YEAR_MONTH           \n",
       "1         2018-07         206.9\n",
       "          2018-08         176.1\n",
       "          2018-09         278.8\n",
       "          2018-10         188.1\n",
       "          2018-11         192.6\n",
       "...                         ...\n",
       "272       2019-02         395.5\n",
       "          2019-03         442.3\n",
       "          2019-04         445.1\n",
       "          2019-05         314.6\n",
       "          2019-06         312.1\n",
       "\n",
       "[3169 rows x 1 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_sales=qvi_data.groupby([\"STORE_NBR\", \"YEAR_MONTH\"])[\"TOT_SALES\"].sum()    #   Grouping the pandas.DataFrame by the STORE_NBR and YEAR_MONTH column, and summing up the total sales for them.\n",
    "total_sales=total_sales.to_frame()  #   Converting the pandas.Series to a pandas.DataFrame.\n",
    "total_sales"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Likewise, we can also count the number of unique customers for for each month during the entire recorded duration, for each store."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>LYLTY_CARD_NBR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>YEAR_MONTH</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>2018-07</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09</th>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11</th>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">272</th>\n",
       "      <th>2019-02</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3169 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      LYLTY_CARD_NBR\n",
       "STORE_NBR YEAR_MONTH                \n",
       "1         2018-07                 49\n",
       "          2018-08                 42\n",
       "          2018-09                 59\n",
       "          2018-10                 44\n",
       "          2018-11                 46\n",
       "...                              ...\n",
       "272       2019-02                 45\n",
       "          2019-03                 50\n",
       "          2019-04                 54\n",
       "          2019-05                 34\n",
       "          2019-06                 34\n",
       "\n",
       "[3169 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_customers=qvi_data.groupby([\"STORE_NBR\", \"YEAR_MONTH\"])[\"LYLTY_CARD_NBR\"].nunique()   #   Grouping the pandas.DataFrame by the STORE_NBR and YEAR_MONTH column, and counting the unique number of customers for them.\n",
    "total_customers=total_customers.to_frame()  #   Converting the pandas.Series to a pandas.DataFrame.\n",
    "total_customers"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, we can also find the number of transactions per customer for each recorded month by diving the count of the number of unique transactions and the count of the number of unique customers for each store."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>YEAR_MONTH</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>2018-07</th>\n",
       "      <td>1.061224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08</th>\n",
       "      <td>1.023810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09</th>\n",
       "      <td>1.050847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10</th>\n",
       "      <td>1.022727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11</th>\n",
       "      <td>1.021739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">272</th>\n",
       "      <th>2019-02</th>\n",
       "      <td>1.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03</th>\n",
       "      <td>1.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04</th>\n",
       "      <td>1.018519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05</th>\n",
       "      <td>1.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06</th>\n",
       "      <td>1.088235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3169 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             0\n",
       "STORE_NBR YEAR_MONTH          \n",
       "1         2018-07     1.061224\n",
       "          2018-08     1.023810\n",
       "          2018-09     1.050847\n",
       "          2018-10     1.022727\n",
       "          2018-11     1.021739\n",
       "...                        ...\n",
       "272       2019-02     1.066667\n",
       "          2019-03     1.060000\n",
       "          2019-04     1.018519\n",
       "          2019-05     1.176471\n",
       "          2019-06     1.088235\n",
       "\n",
       "[3169 rows x 1 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions_per_customer=qvi_data.groupby([\"STORE_NBR\", \"YEAR_MONTH\"])[\"TXN_ID\"].nunique()/qvi_data.groupby([\"STORE_NBR\", \"YEAR_MONTH\"])[\"LYLTY_CARD_NBR\"].nunique()   #   Grouping the pandas.DataFrame by the STORE_NBR and YEAR_MONTH column, and counting the unique number of transactions per customer for them.\n",
    "transactions_per_customer=transactions_per_customer.to_frame()  #   Converting the pandas.Series to a pandas.DataFrame.\n",
    "transactions_per_customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>TOT_CUST</th>\n",
       "      <th>TXN_PER_CUST</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>YEAR_MONTH</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>2018-07</th>\n",
       "      <td>206.9</td>\n",
       "      <td>49</td>\n",
       "      <td>1.061224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08</th>\n",
       "      <td>176.1</td>\n",
       "      <td>42</td>\n",
       "      <td>1.023810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09</th>\n",
       "      <td>278.8</td>\n",
       "      <td>59</td>\n",
       "      <td>1.050847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10</th>\n",
       "      <td>188.1</td>\n",
       "      <td>44</td>\n",
       "      <td>1.022727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11</th>\n",
       "      <td>192.6</td>\n",
       "      <td>46</td>\n",
       "      <td>1.021739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">272</th>\n",
       "      <th>2019-02</th>\n",
       "      <td>395.5</td>\n",
       "      <td>45</td>\n",
       "      <td>1.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03</th>\n",
       "      <td>442.3</td>\n",
       "      <td>50</td>\n",
       "      <td>1.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04</th>\n",
       "      <td>445.1</td>\n",
       "      <td>54</td>\n",
       "      <td>1.018519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05</th>\n",
       "      <td>314.6</td>\n",
       "      <td>34</td>\n",
       "      <td>1.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06</th>\n",
       "      <td>312.1</td>\n",
       "      <td>34</td>\n",
       "      <td>1.088235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3169 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      TOT_SALES  TOT_CUST  TXN_PER_CUST\n",
       "STORE_NBR YEAR_MONTH                                   \n",
       "1         2018-07         206.9        49      1.061224\n",
       "          2018-08         176.1        42      1.023810\n",
       "          2018-09         278.8        59      1.050847\n",
       "          2018-10         188.1        44      1.022727\n",
       "          2018-11         192.6        46      1.021739\n",
       "...                         ...       ...           ...\n",
       "272       2019-02         395.5        45      1.066667\n",
       "          2019-03         442.3        50      1.060000\n",
       "          2019-04         445.1        54      1.018519\n",
       "          2019-05         314.6        34      1.176471\n",
       "          2019-06         312.1        34      1.088235\n",
       "\n",
       "[3169 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe_list=[total_sales, total_customers, transactions_per_customer]    #   Creating a list of all the pandas.DataFrames.\n",
    "dataframe=pd.concat(dataframe_list, axis=1) #   Concatenating all the pandas.DataFrames into one.\n",
    "dataframe.columns=[\"TOT_SALES\", \"TOT_CUST\", \"TXN_PER_CUST\"] #   Renaming the columns of the pandas.DataFrame.\n",
    "dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TOT_SALES       0\n",
       "TOT_CUST        0\n",
       "TXN_PER_CUST    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.isnull().sum() #   Checking for any null values in the pandas.DataFrame."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the dataset contains transaction details of many stores, there may be some stores that did not record sales for the entire duration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>YEAR_MONTH</th>\n",
       "      <th>2018-07</th>\n",
       "      <th>2018-08</th>\n",
       "      <th>2018-09</th>\n",
       "      <th>2018-10</th>\n",
       "      <th>2018-11</th>\n",
       "      <th>2018-12</th>\n",
       "      <th>2019-01</th>\n",
       "      <th>2019-02</th>\n",
       "      <th>2019-03</th>\n",
       "      <th>2019-04</th>\n",
       "      <th>2019-05</th>\n",
       "      <th>2019-06</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>43.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>138.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>160.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>134.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>120.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>127.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>52.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>139.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>127.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>139.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>127.0</td>\n",
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       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>129.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>129.0</td>\n",
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       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>52.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>272 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "YEAR_MONTH  2018-07  2018-08  2018-09  2018-10  2018-11  2018-12  2019-01  \\\n",
       "STORE_NBR                                                                   \n",
       "1              52.0     43.0     62.0     45.0     47.0     47.0     36.0   \n",
       "2              41.0     43.0     37.0     43.0     40.0     38.0     45.0   \n",
       "3             138.0    134.0    119.0    119.0    118.0    129.0    121.0   \n",
       "4             160.0    151.0    138.0    155.0    139.0    133.0    168.0   \n",
       "5             120.0    112.0    125.0    107.0    111.0    125.0    118.0   \n",
       "...             ...      ...      ...      ...      ...      ...      ...   \n",
       "268            52.0     54.0     34.0     48.0     51.0     43.0     38.0   \n",
       "269           139.0    132.0    124.0    148.0    136.0    133.0    144.0   \n",
       "270           139.0    154.0    126.0    119.0    133.0    149.0    155.0   \n",
       "271           129.0    101.0    114.0    114.0    122.0    117.0    120.0   \n",
       "272            52.0     48.0     36.0     51.0     45.0     47.0     50.0   \n",
       "\n",
       "YEAR_MONTH  2019-02  2019-03  2019-04  2019-05  2019-06  \n",
       "STORE_NBR                                                \n",
       "1              55.0     49.0     43.0     51.0     43.0  \n",
       "2              32.0     46.0     49.0     50.0     42.0  \n",
       "3             139.0    130.0    110.0    123.0    122.0  \n",
       "4             102.0    135.0    137.0    126.0    134.0  \n",
       "5             106.0     97.0    109.0    104.0    127.0  \n",
       "...             ...      ...      ...      ...      ...  \n",
       "268            37.0     47.0     50.0     52.0     40.0  \n",
       "269           133.0    122.0    139.0    130.0    127.0  \n",
       "270           125.0    143.0    132.0    128.0    127.0  \n",
       "271           102.0    101.0    109.0    127.0    129.0  \n",
       "272            48.0     53.0     56.0     40.0     37.0  \n",
       "\n",
       "[272 rows x 12 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recorded_stores=pd.pivot_table(qvi_data, index=\"STORE_NBR\", columns=\"YEAR_MONTH\", values=\"TXN_ID\", aggfunc=\"count\") #   Pivoting the pandas.DataFrame to get all the recorded transactions for each store during the entire duration.\n",
    "recorded_stores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YEAR_MONTH\n",
       "2018-07    6\n",
       "2018-08    9\n",
       "2018-09    8\n",
       "2018-10    7\n",
       "2018-11    8\n",
       "2018-12    9\n",
       "2019-01    9\n",
       "2019-02    8\n",
       "2019-03    7\n",
       "2019-04    7\n",
       "2019-05    9\n",
       "2019-06    8\n",
       "Freq: M, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recorded_stores.isnull().sum()  #   Checking for any null values in the pandas.DataFrame."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, there are null values for a few months, which means that some of these are unrecorded for certain stores, so we need to find them and remove these stores from the dataset, since they can't be control stores anyway."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[11, 31, 44, 76, 85, 92, 117, 193, 206, 211, 218, 252]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unrecorded_stores=[]\n",
    "for i in recorded_stores.index:\n",
    "    if recorded_stores.loc[i].isnull().any():\n",
    "        unrecorded_stores.append(i)\n",
    "unrecorded_stores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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></th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>TOT_CUST</th>\n",
       "      <th>TXN_PER_CUST</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>YEAR_MONTH</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>2018-07</th>\n",
       "      <td>206.9</td>\n",
       "      <td>49</td>\n",
       "      <td>1.061224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08</th>\n",
       "      <td>176.1</td>\n",
       "      <td>42</td>\n",
       "      <td>1.023810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09</th>\n",
       "      <td>278.8</td>\n",
       "      <td>59</td>\n",
       "      <td>1.050847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10</th>\n",
       "      <td>188.1</td>\n",
       "      <td>44</td>\n",
       "      <td>1.022727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11</th>\n",
       "      <td>192.6</td>\n",
       "      <td>46</td>\n",
       "      <td>1.021739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">272</th>\n",
       "      <th>2019-02</th>\n",
       "      <td>395.5</td>\n",
       "      <td>45</td>\n",
       "      <td>1.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03</th>\n",
       "      <td>442.3</td>\n",
       "      <td>50</td>\n",
       "      <td>1.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04</th>\n",
       "      <td>445.1</td>\n",
       "      <td>54</td>\n",
       "      <td>1.018519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05</th>\n",
       "      <td>314.6</td>\n",
       "      <td>34</td>\n",
       "      <td>1.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06</th>\n",
       "      <td>312.1</td>\n",
       "      <td>34</td>\n",
       "      <td>1.088235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3120 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      TOT_SALES  TOT_CUST  TXN_PER_CUST\n",
       "STORE_NBR YEAR_MONTH                                   \n",
       "1         2018-07         206.9        49      1.061224\n",
       "          2018-08         176.1        42      1.023810\n",
       "          2018-09         278.8        59      1.050847\n",
       "          2018-10         188.1        44      1.022727\n",
       "          2018-11         192.6        46      1.021739\n",
       "...                         ...       ...           ...\n",
       "272       2019-02         395.5        45      1.066667\n",
       "          2019-03         442.3        50      1.060000\n",
       "          2019-04         445.1        54      1.018519\n",
       "          2019-05         314.6        34      1.176471\n",
       "          2019-06         312.1        34      1.088235\n",
       "\n",
       "[3120 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe=dataframe.drop(unrecorded_stores, axis=0)\n",
    "dataframe"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can filter the dataset to the pre-trial duration — that is, before February 2022."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>YEAR_MONTH</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>TOT_CUST</th>\n",
       "      <th>TXN_PER_CUST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2018-07</td>\n",
       "      <td>206.9</td>\n",
       "      <td>49</td>\n",
       "      <td>1.061224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2018-08</td>\n",
       "      <td>176.1</td>\n",
       "      <td>42</td>\n",
       "      <td>1.023810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2018-09</td>\n",
       "      <td>278.8</td>\n",
       "      <td>59</td>\n",
       "      <td>1.050847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>188.1</td>\n",
       "      <td>44</td>\n",
       "      <td>1.022727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>192.6</td>\n",
       "      <td>46</td>\n",
       "      <td>1.021739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1815</th>\n",
       "      <td>272</td>\n",
       "      <td>2018-09</td>\n",
       "      <td>304.7</td>\n",
       "      <td>32</td>\n",
       "      <td>1.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1816</th>\n",
       "      <td>272</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>430.6</td>\n",
       "      <td>44</td>\n",
       "      <td>1.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1817</th>\n",
       "      <td>272</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>376.2</td>\n",
       "      <td>41</td>\n",
       "      <td>1.097561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1818</th>\n",
       "      <td>272</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>403.9</td>\n",
       "      <td>47</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1819</th>\n",
       "      <td>272</td>\n",
       "      <td>2019-01</td>\n",
       "      <td>423.0</td>\n",
       "      <td>46</td>\n",
       "      <td>1.086957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1820 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      STORE_NBR YEAR_MONTH  TOT_SALES  TOT_CUST  TXN_PER_CUST\n",
       "0             1    2018-07      206.9        49      1.061224\n",
       "1             1    2018-08      176.1        42      1.023810\n",
       "2             1    2018-09      278.8        59      1.050847\n",
       "3             1    2018-10      188.1        44      1.022727\n",
       "4             1    2018-11      192.6        46      1.021739\n",
       "...         ...        ...        ...       ...           ...\n",
       "1815        272    2018-09      304.7        32      1.125000\n",
       "1816        272    2018-10      430.6        44      1.136364\n",
       "1817        272    2018-11      376.2        41      1.097561\n",
       "1818        272    2018-12      403.9        47      1.000000\n",
       "1819        272    2019-01      423.0        46      1.086957\n",
       "\n",
       "[1820 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_trial_data=dataframe.loc[dataframe.index.get_level_values(\"YEAR_MONTH\")<\"2019-02\"]  #   Extracting the pandas.DataFrame for the pre-trial duration.\n",
    "pre_trial_data=pre_trial_data.reset_index()\n",
    "pre_trial_data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the pre-trial dataset, we can now filter the control stores, which are the ones that do *not* include STORE_NBR 77, 88, and 89."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>TOT_CUST</th>\n",
       "      <th>TXN_PER_CUST</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1386.90</td>\n",
       "      <td>317</td>\n",
       "      <td>7.327967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1128.50</td>\n",
       "      <td>272</td>\n",
       "      <td>7.359700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7526.15</td>\n",
       "      <td>744</td>\n",
       "      <td>8.209829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9127.00</td>\n",
       "      <td>849</td>\n",
       "      <td>8.535253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5739.70</td>\n",
       "      <td>651</td>\n",
       "      <td>8.791906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>1549.05</td>\n",
       "      <td>304</td>\n",
       "      <td>7.373037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>6664.50</td>\n",
       "      <td>746</td>\n",
       "      <td>8.921035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>6697.95</td>\n",
       "      <td>734</td>\n",
       "      <td>9.147187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>5765.10</td>\n",
       "      <td>652</td>\n",
       "      <td>8.671966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>2744.35</td>\n",
       "      <td>302</td>\n",
       "      <td>7.620124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>257 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           TOT_SALES  TOT_CUST  TXN_PER_CUST\n",
       "STORE_NBR                                   \n",
       "1            1386.90       317      7.327967\n",
       "2            1128.50       272      7.359700\n",
       "3            7526.15       744      8.209829\n",
       "4            9127.00       849      8.535253\n",
       "5            5739.70       651      8.791906\n",
       "...              ...       ...           ...\n",
       "268          1549.05       304      7.373037\n",
       "269          6664.50       746      8.921035\n",
       "270          6697.95       734      9.147187\n",
       "271          5765.10       652      8.671966\n",
       "272          2744.35       302      7.620124\n",
       "\n",
       "[257 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "control_stores=pre_trial_data[(pre_trial_data.STORE_NBR!=77 ) & (pre_trial_data.STORE_NBR!=86) & (pre_trial_data.STORE_NBR!=88)][[\"TOT_SALES\", \"TOT_CUST\", \"TXN_PER_CUST\"]].groupby(pre_trial_data.STORE_NBR).sum()\n",
    "control_stores"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Likewise, we can also filter the trial stores from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>TOT_CUST</th>\n",
       "      <th>TXN_PER_CUST</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>1699.00</td>\n",
       "      <td>299</td>\n",
       "      <td>7.405289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>6119.85</td>\n",
       "      <td>697</td>\n",
       "      <td>8.798544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>9383.60</td>\n",
       "      <td>880</td>\n",
       "      <td>8.523817</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           TOT_SALES  TOT_CUST  TXN_PER_CUST\n",
       "STORE_NBR                                   \n",
       "77           1699.00       299      7.405289\n",
       "86           6119.85       697      8.798544\n",
       "88           9383.60       880      8.523817"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trial_stores=pre_trial_data[(pre_trial_data.STORE_NBR==77 ) | (pre_trial_data.STORE_NBR==86) | (pre_trial_data.STORE_NBR==88)][[\"TOT_SALES\", \"TOT_CUST\", \"TXN_PER_CUST\"]].groupby(pre_trial_data.STORE_NBR).sum()\n",
    "trial_stores"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With both the control stores and the trial stores filtered, we can find the Pearson correlation coefficient of the attributes between the two to find the control stores for each of the trial stores."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### • STORE_NBR 77:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>TOT_CUST</th>\n",
       "      <th>TXN_PER_CUST</th>\n",
       "      <th>DIFFERENCE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1493.2</td>\n",
       "      <td>257.0</td>\n",
       "      <td>0.405289</td>\n",
       "      <td>609.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>1486.9</td>\n",
       "      <td>256.0</td>\n",
       "      <td>0.012432</td>\n",
       "      <td>603.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>1459.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>0.405289</td>\n",
       "      <td>575.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>39.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.115969</td>\n",
       "      <td>-844.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>-59.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>0.094215</td>\n",
       "      <td>-942.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           TOT_SALES  TOT_CUST  TXN_PER_CUST  DIFFERENCE\n",
       "STORE_NBR                                               \n",
       "139           1493.2     257.0      0.405289      609.34\n",
       "135           1486.9     256.0      0.012432      603.04\n",
       "161           1459.0     252.0      0.405289      575.14\n",
       "233             39.2       1.0      0.115969     -844.66\n",
       "46             -59.0      -3.0      0.094215     -942.86"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "difference=control_stores.loc[control_stores.corrwith(trial_stores.loc[77], method=\"pearson\", axis=1).nlargest(5).index]    #   Getting the pandas.DataFrame for the top five stores with the highest correlation with the trial store.\n",
    "\n",
    "#   Getting the difference between the trial store and the top five stores with the highest correlation with the trial store.\n",
    "\n",
    "difference=(trial_stores.loc[77]-difference).sort_values(by=\"TOT_SALES\", ascending=False)\n",
    "difference[\"DIFFERENCE\"]=difference[\"TOT_SALES\"]-difference[\"TOT_SALES\"].mean()\n",
    "difference.sort_values(by=\"DIFFERENCE\", ascending=False)    #   Sorting the pandas.DataFrame by the DIFFERENCE column."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For STORE_NBR 77, we can see that STORE_NBR 46 would be the most suitable control store."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### • STORE_NBR 86:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>TOT_CUST</th>\n",
       "      <th>TXN_PER_CUST</th>\n",
       "      <th>DIFFERENCE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>5934.85</td>\n",
       "      <td>670.0</td>\n",
       "      <td>1.798544</td>\n",
       "      <td>4066.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>3411.85</td>\n",
       "      <td>386.0</td>\n",
       "      <td>1.486773</td>\n",
       "      <td>1543.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>29.25</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.023669</td>\n",
       "      <td>-1839.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>-6.45</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.040716</td>\n",
       "      <td>-1874.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>-27.55</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.031815</td>\n",
       "      <td>-1895.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           TOT_SALES  TOT_CUST  TXN_PER_CUST  DIFFERENCE\n",
       "STORE_NBR                                               \n",
       "258          5934.85     670.0      1.798544     4066.46\n",
       "215          3411.85     386.0      1.486773     1543.46\n",
       "225            29.25       3.0      0.023669    -1839.14\n",
       "196            -6.45       1.0      0.040716    -1874.84\n",
       "57            -27.55      -2.0      0.031815    -1895.94"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "difference=control_stores.loc[control_stores.corrwith(trial_stores.loc[86], axis=1).nlargest(5).index]  #   Getting the pandas.DataFrame for the top five stores with the highest correlation with the trial store.\n",
    "\n",
    "#   Getting the difference between the trial store and the top five stores with the highest correlation with the trial store.\n",
    "\n",
    "difference=(trial_stores.loc[86]-difference).sort_values(by=\"TOT_SALES\", ascending=False)\n",
    "difference[\"DIFFERENCE\"]=difference[\"TOT_SALES\"]-difference[\"TOT_SALES\"].mean()\n",
    "difference.sort_values(by=\"DIFFERENCE\", ascending=False)    #   Sorting the pandas.DataFrame by the DIFFERENCE column."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For STORE_NBR 86, we can see that STORE_NBR 57 would be the most suitable control store."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### • STORE_NBR 88:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>TOT_CUST</th>\n",
       "      <th>TXN_PER_CUST</th>\n",
       "      <th>DIFFERENCE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1697.1</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0.052504</td>\n",
       "      <td>783.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1420.1</td>\n",
       "      <td>129.0</td>\n",
       "      <td>0.078986</td>\n",
       "      <td>506.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>865.1</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.085871</td>\n",
       "      <td>-48.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>439.9</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.135139</td>\n",
       "      <td>-473.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>145.8</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.093389</td>\n",
       "      <td>-767.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           TOT_SALES  TOT_CUST  TXN_PER_CUST  DIFFERENCE\n",
       "STORE_NBR                                               \n",
       "60            1697.1     154.0      0.052504       783.5\n",
       "75            1420.1     129.0      0.078986       506.5\n",
       "72             865.1      79.0      0.085871       -48.5\n",
       "203            439.9      38.0      0.135139      -473.7\n",
       "165            145.8      18.0      0.093389      -767.8"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "difference=control_stores.loc[control_stores.corrwith(trial_stores.loc[88], axis=1).nlargest(5).index]  #   Getting the pandas.DataFrame for the top five stores with the highest correlation with the trial store.\n",
    "\n",
    "#   Getting the difference between the trial store and the top five stores with the highest correlation with the trial store.\n",
    "\n",
    "difference=(trial_stores.loc[88]-difference).sort_values(by=\"TOT_SALES\", ascending=False)\n",
    "difference[\"DIFFERENCE\"]=difference[\"TOT_SALES\"]-difference[\"TOT_SALES\"].mean()\n",
    "difference.sort_values(by=\"DIFFERENCE\", ascending=False)    #   Sorting the pandas.DataFrame by the DIFFERENCE column."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For STORE_NBR 88, we can see that STORE_NBR 165 would be the most suitable control store."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the different control stores to each of the trial stores during the pre-trial duration to find any significant difference in the statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#   Extracting the pandas.DataFrames for each of the trial stores.\n",
    "\n",
    "trial_stores_one=pre_trial_data.loc[pre_trial_data.STORE_NBR.isin([77])].reset_index()\n",
    "trial_stores_two=pre_trial_data.loc[pre_trial_data.STORE_NBR.isin([86])].reset_index()\n",
    "trial_stores_three=pre_trial_data.loc[pre_trial_data.STORE_NBR.isin([88])].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#   Extracting the pandas.DataFrames for each of the control stores.\n",
    "\n",
    "control_stores_one=pre_trial_data.loc[pre_trial_data.STORE_NBR.isin([46])].reset_index()\n",
    "control_stores_two=pre_trial_data.loc[pre_trial_data.STORE_NBR.isin([57])].reset_index()\n",
    "control_stores_three=pre_trial_data.loc[pre_trial_data.STORE_NBR.isin([165])].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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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>index</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>YEAR_MONTH</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>TOT_CUST</th>\n",
       "      <th>TXN_PER_CUST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>504</td>\n",
       "      <td>77</td>\n",
       "      <td>2018-07</td>\n",
       "      <td>296.80</td>\n",
       "      <td>51</td>\n",
       "      <td>1.078431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>505</td>\n",
       "      <td>77</td>\n",
       "      <td>2018-08</td>\n",
       "      <td>255.50</td>\n",
       "      <td>47</td>\n",
       "      <td>1.021277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>506</td>\n",
       "      <td>77</td>\n",
       "      <td>2018-09</td>\n",
       "      <td>225.20</td>\n",
       "      <td>42</td>\n",
       "      <td>1.047619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>507</td>\n",
       "      <td>77</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>204.50</td>\n",
       "      <td>37</td>\n",
       "      <td>1.027027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>508</td>\n",
       "      <td>77</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>245.30</td>\n",
       "      <td>41</td>\n",
       "      <td>1.073171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>509</td>\n",
       "      <td>77</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>267.30</td>\n",
       "      <td>46</td>\n",
       "      <td>1.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>510</td>\n",
       "      <td>77</td>\n",
       "      <td>2019-01</td>\n",
       "      <td>204.40</td>\n",
       "      <td>35</td>\n",
       "      <td>1.114286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>560</td>\n",
       "      <td>86</td>\n",
       "      <td>2018-07</td>\n",
       "      <td>892.20</td>\n",
       "      <td>99</td>\n",
       "      <td>1.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>561</td>\n",
       "      <td>86</td>\n",
       "      <td>2018-08</td>\n",
       "      <td>764.05</td>\n",
       "      <td>94</td>\n",
       "      <td>1.170213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>562</td>\n",
       "      <td>86</td>\n",
       "      <td>2018-09</td>\n",
       "      <td>914.60</td>\n",
       "      <td>103</td>\n",
       "      <td>1.242718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>563</td>\n",
       "      <td>86</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>948.40</td>\n",
       "      <td>109</td>\n",
       "      <td>1.266055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>564</td>\n",
       "      <td>86</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>918.00</td>\n",
       "      <td>100</td>\n",
       "      <td>1.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>565</td>\n",
       "      <td>86</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>841.20</td>\n",
       "      <td>98</td>\n",
       "      <td>1.224490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>566</td>\n",
       "      <td>86</td>\n",
       "      <td>2019-01</td>\n",
       "      <td>841.40</td>\n",
       "      <td>94</td>\n",
       "      <td>1.372340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>574</td>\n",
       "      <td>88</td>\n",
       "      <td>2018-07</td>\n",
       "      <td>1310.00</td>\n",
       "      <td>129</td>\n",
       "      <td>1.186047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>575</td>\n",
       "      <td>88</td>\n",
       "      <td>2018-08</td>\n",
       "      <td>1323.80</td>\n",
       "      <td>131</td>\n",
       "      <td>1.206107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>576</td>\n",
       "      <td>88</td>\n",
       "      <td>2018-09</td>\n",
       "      <td>1423.00</td>\n",
       "      <td>124</td>\n",
       "      <td>1.266129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>577</td>\n",
       "      <td>88</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>1352.40</td>\n",
       "      <td>123</td>\n",
       "      <td>1.260163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>578</td>\n",
       "      <td>88</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>1382.80</td>\n",
       "      <td>130</td>\n",
       "      <td>1.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>579</td>\n",
       "      <td>88</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>1325.20</td>\n",
       "      <td>126</td>\n",
       "      <td>1.174603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>580</td>\n",
       "      <td>88</td>\n",
       "      <td>2019-01</td>\n",
       "      <td>1266.40</td>\n",
       "      <td>117</td>\n",
       "      <td>1.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>294</td>\n",
       "      <td>46</td>\n",
       "      <td>2018-07</td>\n",
       "      <td>253.00</td>\n",
       "      <td>45</td>\n",
       "      <td>1.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>295</td>\n",
       "      <td>46</td>\n",
       "      <td>2018-08</td>\n",
       "      <td>240.70</td>\n",
       "      <td>44</td>\n",
       "      <td>1.045455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>296</td>\n",
       "      <td>46</td>\n",
       "      <td>2018-09</td>\n",
       "      <td>233.00</td>\n",
       "      <td>41</td>\n",
       "      <td>1.048780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>297</td>\n",
       "      <td>46</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>275.10</td>\n",
       "      <td>47</td>\n",
       "      <td>1.042553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>298</td>\n",
       "      <td>46</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>273.10</td>\n",
       "      <td>42</td>\n",
       "      <td>1.047619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>299</td>\n",
       "      <td>46</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>306.90</td>\n",
       "      <td>50</td>\n",
       "      <td>1.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>300</td>\n",
       "      <td>46</td>\n",
       "      <td>2019-01</td>\n",
       "      <td>176.20</td>\n",
       "      <td>33</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>371</td>\n",
       "      <td>57</td>\n",
       "      <td>2018-07</td>\n",
       "      <td>839.60</td>\n",
       "      <td>103</td>\n",
       "      <td>1.203883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>372</td>\n",
       "      <td>57</td>\n",
       "      <td>2018-08</td>\n",
       "      <td>915.40</td>\n",
       "      <td>102</td>\n",
       "      <td>1.274510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>373</td>\n",
       "      <td>57</td>\n",
       "      <td>2018-09</td>\n",
       "      <td>792.80</td>\n",
       "      <td>99</td>\n",
       "      <td>1.171717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>374</td>\n",
       "      <td>57</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>965.80</td>\n",
       "      <td>104</td>\n",
       "      <td>1.307692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>375</td>\n",
       "      <td>57</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>830.00</td>\n",
       "      <td>100</td>\n",
       "      <td>1.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>376</td>\n",
       "      <td>57</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>951.00</td>\n",
       "      <td>104</td>\n",
       "      <td>1.259615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>377</td>\n",
       "      <td>57</td>\n",
       "      <td>2019-01</td>\n",
       "      <td>852.80</td>\n",
       "      <td>87</td>\n",
       "      <td>1.379310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1099</td>\n",
       "      <td>165</td>\n",
       "      <td>2018-07</td>\n",
       "      <td>1457.00</td>\n",
       "      <td>133</td>\n",
       "      <td>1.255639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1100</td>\n",
       "      <td>165</td>\n",
       "      <td>2018-08</td>\n",
       "      <td>1206.60</td>\n",
       "      <td>109</td>\n",
       "      <td>1.256881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1101</td>\n",
       "      <td>165</td>\n",
       "      <td>2018-09</td>\n",
       "      <td>1281.20</td>\n",
       "      <td>122</td>\n",
       "      <td>1.172131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1102</td>\n",
       "      <td>165</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>1234.40</td>\n",
       "      <td>118</td>\n",
       "      <td>1.169492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1103</td>\n",
       "      <td>165</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>1291.20</td>\n",
       "      <td>126</td>\n",
       "      <td>1.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1104</td>\n",
       "      <td>165</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>1345.40</td>\n",
       "      <td>121</td>\n",
       "      <td>1.206612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1105</td>\n",
       "      <td>165</td>\n",
       "      <td>2019-01</td>\n",
       "      <td>1422.00</td>\n",
       "      <td>133</td>\n",
       "      <td>1.203008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  STORE_NBR YEAR_MONTH  TOT_SALES  TOT_CUST  TXN_PER_CUST\n",
       "0    504         77    2018-07     296.80        51      1.078431\n",
       "1    505         77    2018-08     255.50        47      1.021277\n",
       "2    506         77    2018-09     225.20        42      1.047619\n",
       "3    507         77    2018-10     204.50        37      1.027027\n",
       "4    508         77    2018-11     245.30        41      1.073171\n",
       "5    509         77    2018-12     267.30        46      1.043478\n",
       "6    510         77    2019-01     204.40        35      1.114286\n",
       "0    560         86    2018-07     892.20        99      1.272727\n",
       "1    561         86    2018-08     764.05        94      1.170213\n",
       "2    562         86    2018-09     914.60       103      1.242718\n",
       "3    563         86    2018-10     948.40       109      1.266055\n",
       "4    564         86    2018-11     918.00       100      1.250000\n",
       "5    565         86    2018-12     841.20        98      1.224490\n",
       "6    566         86    2019-01     841.40        94      1.372340\n",
       "0    574         88    2018-07    1310.00       129      1.186047\n",
       "1    575         88    2018-08    1323.80       131      1.206107\n",
       "2    576         88    2018-09    1423.00       124      1.266129\n",
       "3    577         88    2018-10    1352.40       123      1.260163\n",
       "4    578         88    2018-11    1382.80       130      1.200000\n",
       "5    579         88    2018-12    1325.20       126      1.174603\n",
       "6    580         88    2019-01    1266.40       117      1.230769\n",
       "0    294         46    2018-07     253.00        45      1.066667\n",
       "1    295         46    2018-08     240.70        44      1.045455\n",
       "2    296         46    2018-09     233.00        41      1.048780\n",
       "3    297         46    2018-10     275.10        47      1.042553\n",
       "4    298         46    2018-11     273.10        42      1.047619\n",
       "5    299         46    2018-12     306.90        50      1.060000\n",
       "6    300         46    2019-01     176.20        33      1.000000\n",
       "0    371         57    2018-07     839.60       103      1.203883\n",
       "1    372         57    2018-08     915.40       102      1.274510\n",
       "2    373         57    2018-09     792.80        99      1.171717\n",
       "3    374         57    2018-10     965.80       104      1.307692\n",
       "4    375         57    2018-11     830.00       100      1.170000\n",
       "5    376         57    2018-12     951.00       104      1.259615\n",
       "6    377         57    2019-01     852.80        87      1.379310\n",
       "0   1099        165    2018-07    1457.00       133      1.255639\n",
       "1   1100        165    2018-08    1206.60       109      1.256881\n",
       "2   1101        165    2018-09    1281.20       122      1.172131\n",
       "3   1102        165    2018-10    1234.40       118      1.169492\n",
       "4   1103        165    2018-11    1291.20       126      1.166667\n",
       "5   1104        165    2018-12    1345.40       121      1.206612\n",
       "6   1105        165    2019-01    1422.00       133      1.203008"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stores=pd.concat([trial_stores_one, trial_stores_two, trial_stores_three, control_stores_one, control_stores_two, control_stores_three], axis=0)    #   Concatenating all the pandas.DataFrames into one.\n",
    "stores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x700 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#   Plotting the bar graphs for the total sales, total customers, and transactions per customer for each of the trial stores and the control stores.\n",
    "\n",
    "sns.set_style(\"darkgrid\")\n",
    "figure, axis=plt.subplots(1, 3, figsize=(20, 7))\n",
    "sns.barplot(x=\"STORE_NBR\", y=\"TOT_SALES\", data=stores, ax=axis[0], palette=\"pastel\")\n",
    "axis[0].set_title(\"Total Sales\")\n",
    "sns.barplot(x=\"STORE_NBR\", y=\"TOT_CUST\", data=stores, ax=axis[1], palette=\"pastel\")\n",
    "axis[1].set_title(\"Total Customers\")\n",
    "sns.barplot(x=\"STORE_NBR\", y=\"TXN_PER_CUST\", data=stores, ax=axis[2], palette=\"pastel\")\n",
    "axis[2].set_title(\"Transactions per Customer\")\n",
    "figure.suptitle(\"Comparison of the Total Sales, Total Customers, and Transactions per Customer for Each the Trial Stores and the Control Stores During the Pre-Trial Duration\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While the other trial stores performed the same as their corresponding control stores, we can see, however, that STORE_NBR 88 slightly out-performed its control store in all attributes. We can also notice that STORE_NBR 86 and 88 show a significant difference in terms of the total sales, but this isn't the case with STORE_NBR 77, whose sales are considerably less."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Likewise, we can also compare different control stores to each of the trial stores during the trial duration, as well, to find if the total sales are significantly different in the trial duration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>YEAR_MONTH</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>TOT_CUST</th>\n",
       "      <th>TXN_PER_CUST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-02</td>\n",
       "      <td>225.4</td>\n",
       "      <td>52</td>\n",
       "      <td>1.057692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-03</td>\n",
       "      <td>192.9</td>\n",
       "      <td>45</td>\n",
       "      <td>1.088889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-04</td>\n",
       "      <td>192.9</td>\n",
       "      <td>42</td>\n",
       "      <td>1.023810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-05</td>\n",
       "      <td>221.4</td>\n",
       "      <td>46</td>\n",
       "      <td>1.108696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-06</td>\n",
       "      <td>174.1</td>\n",
       "      <td>42</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>272</td>\n",
       "      <td>2019-02</td>\n",
       "      <td>395.5</td>\n",
       "      <td>45</td>\n",
       "      <td>1.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>272</td>\n",
       "      <td>2019-03</td>\n",
       "      <td>442.3</td>\n",
       "      <td>50</td>\n",
       "      <td>1.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1297</th>\n",
       "      <td>272</td>\n",
       "      <td>2019-04</td>\n",
       "      <td>445.1</td>\n",
       "      <td>54</td>\n",
       "      <td>1.018519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>272</td>\n",
       "      <td>2019-05</td>\n",
       "      <td>314.6</td>\n",
       "      <td>34</td>\n",
       "      <td>1.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>272</td>\n",
       "      <td>2019-06</td>\n",
       "      <td>312.1</td>\n",
       "      <td>34</td>\n",
       "      <td>1.088235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1300 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      STORE_NBR YEAR_MONTH  TOT_SALES  TOT_CUST  TXN_PER_CUST\n",
       "0             1    2019-02      225.4        52      1.057692\n",
       "1             1    2019-03      192.9        45      1.088889\n",
       "2             1    2019-04      192.9        42      1.023810\n",
       "3             1    2019-05      221.4        46      1.108696\n",
       "4             1    2019-06      174.1        42      1.000000\n",
       "...         ...        ...        ...       ...           ...\n",
       "1295        272    2019-02      395.5        45      1.066667\n",
       "1296        272    2019-03      442.3        50      1.060000\n",
       "1297        272    2019-04      445.1        54      1.018519\n",
       "1298        272    2019-05      314.6        34      1.176471\n",
       "1299        272    2019-06      312.1        34      1.088235\n",
       "\n",
       "[1300 rows x 5 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trial_data=dataframe.loc[dataframe.index.get_level_values(\"YEAR_MONTH\")>=\"2019-02\"]  #   Extracting the pandas.DataFrame for the trial duration.\n",
    "trial_data=trial_data.reset_index()\n",
    "trial_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#   Extracting the pandas.DataFrames for each of the trial stores.\n",
    "\n",
    "trial_stores_one=trial_data.loc[trial_data.STORE_NBR.isin([77])].reset_index()\n",
    "trial_stores_two=trial_data.loc[trial_data.STORE_NBR.isin([86])].reset_index()\n",
    "trial_stores_three=trial_data.loc[trial_data.STORE_NBR.isin([88])].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#   Extracting the pandas.DataFrames for each of the control stores.\n",
    "\n",
    "control_stores_one=trial_data.loc[trial_data.STORE_NBR.isin([46])].reset_index()\n",
    "control_stores_two=trial_data.loc[trial_data.STORE_NBR.isin([57])].reset_index()\n",
    "control_stores_three=trial_data.loc[trial_data.STORE_NBR.isin([165])].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>STORE_NBR</th>\n",
       "      <th>YEAR_MONTH</th>\n",
       "      <th>TOT_SALES</th>\n",
       "      <th>TOT_CUST</th>\n",
       "      <th>TXN_PER_CUST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>360</td>\n",
       "      <td>77</td>\n",
       "      <td>2019-02</td>\n",
       "      <td>235.00</td>\n",
       "      <td>45</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>361</td>\n",
       "      <td>77</td>\n",
       "      <td>2019-03</td>\n",
       "      <td>278.50</td>\n",
       "      <td>50</td>\n",
       "      <td>1.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>362</td>\n",
       "      <td>77</td>\n",
       "      <td>2019-04</td>\n",
       "      <td>263.50</td>\n",
       "      <td>47</td>\n",
       "      <td>1.021277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>363</td>\n",
       "      <td>77</td>\n",
       "      <td>2019-05</td>\n",
       "      <td>299.30</td>\n",
       "      <td>55</td>\n",
       "      <td>1.018182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>364</td>\n",
       "      <td>77</td>\n",
       "      <td>2019-06</td>\n",
       "      <td>264.70</td>\n",
       "      <td>41</td>\n",
       "      <td>1.024390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>400</td>\n",
       "      <td>86</td>\n",
       "      <td>2019-02</td>\n",
       "      <td>913.20</td>\n",
       "      <td>107</td>\n",
       "      <td>1.289720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>401</td>\n",
       "      <td>86</td>\n",
       "      <td>2019-03</td>\n",
       "      <td>1026.80</td>\n",
       "      <td>115</td>\n",
       "      <td>1.217391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>402</td>\n",
       "      <td>86</td>\n",
       "      <td>2019-04</td>\n",
       "      <td>848.20</td>\n",
       "      <td>105</td>\n",
       "      <td>1.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>403</td>\n",
       "      <td>86</td>\n",
       "      <td>2019-05</td>\n",
       "      <td>889.30</td>\n",
       "      <td>104</td>\n",
       "      <td>1.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>404</td>\n",
       "      <td>86</td>\n",
       "      <td>2019-06</td>\n",
       "      <td>838.00</td>\n",
       "      <td>98</td>\n",
       "      <td>1.204082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>410</td>\n",
       "      <td>88</td>\n",
       "      <td>2019-02</td>\n",
       "      <td>1370.20</td>\n",
       "      <td>124</td>\n",
       "      <td>1.233871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>411</td>\n",
       "      <td>88</td>\n",
       "      <td>2019-03</td>\n",
       "      <td>1477.20</td>\n",
       "      <td>134</td>\n",
       "      <td>1.261194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>412</td>\n",
       "      <td>88</td>\n",
       "      <td>2019-04</td>\n",
       "      <td>1439.40</td>\n",
       "      <td>128</td>\n",
       "      <td>1.265625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>413</td>\n",
       "      <td>88</td>\n",
       "      <td>2019-05</td>\n",
       "      <td>1308.25</td>\n",
       "      <td>128</td>\n",
       "      <td>1.203125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>414</td>\n",
       "      <td>88</td>\n",
       "      <td>2019-06</td>\n",
       "      <td>1354.60</td>\n",
       "      <td>121</td>\n",
       "      <td>1.223140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>210</td>\n",
       "      <td>46</td>\n",
       "      <td>2019-02</td>\n",
       "      <td>222.40</td>\n",
       "      <td>38</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>211</td>\n",
       "      <td>46</td>\n",
       "      <td>2019-03</td>\n",
       "      <td>259.20</td>\n",
       "      <td>41</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>212</td>\n",
       "      <td>46</td>\n",
       "      <td>2019-04</td>\n",
       "      <td>260.00</td>\n",
       "      <td>47</td>\n",
       "      <td>1.042553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>213</td>\n",
       "      <td>46</td>\n",
       "      <td>2019-05</td>\n",
       "      <td>243.55</td>\n",
       "      <td>38</td>\n",
       "      <td>1.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>214</td>\n",
       "      <td>46</td>\n",
       "      <td>2019-06</td>\n",
       "      <td>280.30</td>\n",
       "      <td>47</td>\n",
       "      <td>1.042553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>265</td>\n",
       "      <td>57</td>\n",
       "      <td>2019-02</td>\n",
       "      <td>919.80</td>\n",
       "      <td>108</td>\n",
       "      <td>1.194444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>266</td>\n",
       "      <td>57</td>\n",
       "      <td>2019-03</td>\n",
       "      <td>807.40</td>\n",
       "      <td>99</td>\n",
       "      <td>1.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>267</td>\n",
       "      <td>57</td>\n",
       "      <td>2019-04</td>\n",
       "      <td>900.00</td>\n",
       "      <td>106</td>\n",
       "      <td>1.292453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>268</td>\n",
       "      <td>57</td>\n",
       "      <td>2019-05</td>\n",
       "      <td>846.70</td>\n",
       "      <td>109</td>\n",
       "      <td>1.192661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>269</td>\n",
       "      <td>57</td>\n",
       "      <td>2019-06</td>\n",
       "      <td>911.00</td>\n",
       "      <td>104</td>\n",
       "      <td>1.288462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>785</td>\n",
       "      <td>165</td>\n",
       "      <td>2019-02</td>\n",
       "      <td>1237.50</td>\n",
       "      <td>113</td>\n",
       "      <td>1.230088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>786</td>\n",
       "      <td>165</td>\n",
       "      <td>2019-03</td>\n",
       "      <td>1215.40</td>\n",
       "      <td>114</td>\n",
       "      <td>1.184211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>787</td>\n",
       "      <td>165</td>\n",
       "      <td>2019-04</td>\n",
       "      <td>1391.70</td>\n",
       "      <td>129</td>\n",
       "      <td>1.232558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>788</td>\n",
       "      <td>165</td>\n",
       "      <td>2019-05</td>\n",
       "      <td>1441.05</td>\n",
       "      <td>133</td>\n",
       "      <td>1.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>789</td>\n",
       "      <td>165</td>\n",
       "      <td>2019-06</td>\n",
       "      <td>1450.30</td>\n",
       "      <td>137</td>\n",
       "      <td>1.197080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  STORE_NBR YEAR_MONTH  TOT_SALES  TOT_CUST  TXN_PER_CUST\n",
       "0    360         77    2019-02     235.00        45      1.000000\n",
       "1    361         77    2019-03     278.50        50      1.100000\n",
       "2    362         77    2019-04     263.50        47      1.021277\n",
       "3    363         77    2019-05     299.30        55      1.018182\n",
       "4    364         77    2019-06     264.70        41      1.024390\n",
       "0    400         86    2019-02     913.20       107      1.289720\n",
       "1    401         86    2019-03    1026.80       115      1.217391\n",
       "2    402         86    2019-04     848.20       105      1.200000\n",
       "3    403         86    2019-05     889.30       104      1.230769\n",
       "4    404         86    2019-06     838.00        98      1.204082\n",
       "0    410         88    2019-02    1370.20       124      1.233871\n",
       "1    411         88    2019-03    1477.20       134      1.261194\n",
       "2    412         88    2019-04    1439.40       128      1.265625\n",
       "3    413         88    2019-05    1308.25       128      1.203125\n",
       "4    414         88    2019-06    1354.60       121      1.223140\n",
       "0    210         46    2019-02     222.40        38      1.000000\n",
       "1    211         46    2019-03     259.20        41      1.000000\n",
       "2    212         46    2019-04     260.00        47      1.042553\n",
       "3    213         46    2019-05     243.55        38      1.105263\n",
       "4    214         46    2019-06     280.30        47      1.042553\n",
       "0    265         57    2019-02     919.80       108      1.194444\n",
       "1    266         57    2019-03     807.40        99      1.222222\n",
       "2    267         57    2019-04     900.00       106      1.292453\n",
       "3    268         57    2019-05     846.70       109      1.192661\n",
       "4    269         57    2019-06     911.00       104      1.288462\n",
       "0    785        165    2019-02    1237.50       113      1.230088\n",
       "1    786        165    2019-03    1215.40       114      1.184211\n",
       "2    787        165    2019-04    1391.70       129      1.232558\n",
       "3    788        165    2019-05    1441.05       133      1.285714\n",
       "4    789        165    2019-06    1450.30       137      1.197080"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stores=pd.concat([trial_stores_one, trial_stores_two, trial_stores_three, control_stores_one, control_stores_two, control_stores_three], axis=0)    #   Concatenating all the pandas.DataFrames into one.\n",
    "stores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x700 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#   Plotting the bar graphs for the total sales, total customers, and transactions per customer for each of the trial stores and the control stores.\n",
    "\n",
    "sns.set_style(\"darkgrid\")\n",
    "figure, axis=plt.subplots(1, 3, figsize=(20, 7))\n",
    "sns.barplot(x=\"STORE_NBR\", y=\"TOT_SALES\", data=stores, ax=axis[0], palette=\"pastel\")\n",
    "axis[0].set_title(\"Total Sales\")\n",
    "sns.barplot(x=\"STORE_NBR\", y=\"TOT_CUST\", data=stores, ax=axis[1], palette=\"pastel\")\n",
    "axis[1].set_title(\"Total Customers\")\n",
    "sns.barplot(x=\"STORE_NBR\", y=\"TXN_PER_CUST\", data=stores, ax=axis[2], palette=\"pastel\")\n",
    "axis[2].set_title(\"Transactions per Customer\")\n",
    "figure.suptitle(\"Comparison of the Total Sales, Total Customers, and Transactions per Customer for Each the Trial Stores and the Control Stores During the Trial Duration\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can, once again, notice that STORE_NBR 88 slightly out-performs its control store, STORE_NBR 165, and still remains the best implementation of the trial of all the trial stores. The driver for this seems to be the purchasing customers rather than purchases per customer, as we can see that with the increase in the total customers, there's also an increase in the total sales almost identically, but the transactions per customer seem to be reasonably high for all the trial stores regardless of the total sales."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ‣ Conclusion:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- While the other trial stores performed the same as their corresponding control stores, we can see, however, that STORE_NBR 88 slightly out-performed its control store, STORE_NBR 165, in all attributes.\n",
    "\n",
    "- STORE_NBR 86 and 88 show a significant difference in terms of the total sales, but this isn't the case with STORE_NBR 77, which may be because of the way the trial was implemented for it.\n",
    "\n",
    "- Due to the maximum difference in the total sales of all the trial stores, STORE_NBR 88 remains the best implementation of the trial.\n",
    "\n",
    "- The driver for the increase in total sales seems to be the purchasing customers rather than purchases per customer — the more the customers, the higher the sales."
   ]
  }
 ],
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