machine_learning_u_port_rethink.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "id": "88ad6dfe",
   "metadata": {},
   "source": [
    "<center>\n",
    "<img src=\"university-of-portsmouth-logo.jpg\",width=\"50\", height=\"30\" alt= \"University of Portsmouth\">\n",
    "    </center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f0f5509",
   "metadata": {},
   "source": [
    "Submission for **Machine Learning Developer** position<br>\n",
    "By: Konark Karna"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fdf4a5c",
   "metadata": {},
   "source": [
    "### Task 1 <br>\n",
    "AI/ML approaches to cluster consumers based on the given data<br>\n",
    "Propose any approach to collect consumer satisfaction data and use it to classify tasks and roles within each store.<br>\n",
    "Are there any other possible measures to evaluate clients with respect to efficiency, roles, and activities?<br>\n",
    "\n",
    "Please include the answer to the following test questions in your presentation:\n",
    "Q1: For each client (or store):\n",
    "\n",
    "    Q1.1: estimate (a) time spent, (b) infectiveness time i.e. time with no value added  (NVA), (c) pace (i.e. rate of task), and (d) efficiency for each task\n",
    "    Q1.2: estimate the activity time, i.e., how long does a process/activity take?\n",
    "    Q1.3: estimate the time spent on each category \n",
    "\n",
    "\n",
    "Q2: As in Q1 but considering all the clients together\n",
    "\n",
    "Q3: Propose some solutions to reduce NVA and improve the efficiency of clients \n",
    "\n",
    "#### Key things:   \n",
    "``Clients``: are retailers that ask advice from ReThink    \n",
    "``Customers``: are people that buy goods from the retailers    \n",
    "``Stores``: retail locations for the clients \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "596f0b59",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing essential Libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d1ccfb2",
   "metadata": {},
   "source": [
    "# Part 1. Reading Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "281c68ed",
   "metadata": {},
   "source": [
    "1. Data-Front End efficiency <br>\n",
    "2. Data-Till Activity Study"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b79cab7",
   "metadata": {},
   "source": [
    "Reading first dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6b0ec82a",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data Summary</th>\n",
       "      <th>Unnamed: 1</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Study</td>\n",
       "      <td>Location</td>\n",
       "      <td>Tags</td>\n",
       "      <td>Date</td>\n",
       "      <td>Day</td>\n",
       "      <td>Round</td>\n",
       "      <td>Role</td>\n",
       "      <td>El Code</td>\n",
       "      <td>Elements</td>\n",
       "      <td>Rating</td>\n",
       "      <td>BMS</td>\n",
       "      <td>Qty</td>\n",
       "      <td>Area</td>\n",
       "      <td>Main Category</td>\n",
       "      <td>Category</td>\n",
       "      <td>Notes</td>\n",
       "      <td>Timeslot</td>\n",
       "    </tr>\n",
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       "      <td>StGW0710ras3</td>\n",
       "      <td>Stamford</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10:47</td>\n",
       "      <td>Team Member 1</td>\n",
       "      <td>2534.0</td>\n",
       "      <td>Serve Customer at Till</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tills</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Team Member 2</td>\n",
       "      <td>5206.0</td>\n",
       "      <td>Serve Customer at Collections/Click&amp;Collect</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Morning</td>\n",
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       "      <th>8</th>\n",
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       "      <td>Stamford</td>\n",
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       "      <td>Monday</td>\n",
       "      <td>10:47</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>Not Rated</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>NVA</td>\n",
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       "      <th>9</th>\n",
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       "      <td>Stamford</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10:47</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>Not Rated</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tills</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   Data Summary Unnamed: 1 Unnamed: 2  Unnamed: 3 Unnamed: 4 Unnamed: 5  \\\n",
       "0      Customer        NaN        NaN    Homebase        NaN        NaN   \n",
       "1           NaN        NaN        NaN         NaN        NaN        NaN   \n",
       "2           NaN        NaN        NaN         NaN        NaN        NaN   \n",
       "3           NaN        NaN        NaN         NaN        NaN        NaN   \n",
       "4           NaN        NaN        NaN         NaN        NaN        NaN   \n",
       "5         Study   Location       Tags        Date        Day      Round   \n",
       "6  StGW0710ras3   Stamford        NaN  07/10/2019     Monday      10:47   \n",
       "7  StGW0710ras3   Stamford        NaN  07/10/2019     Monday      10:47   \n",
       "8  StGW0710ras3   Stamford        NaN  07/10/2019     Monday      10:47   \n",
       "9  StGW0710ras3   Stamford        NaN  07/10/2019     Monday      10:47   \n",
       "\n",
       "       Unnamed: 6 Unnamed: 7                                   Unnamed: 8  \\\n",
       "0             NaN        NaN                                          NaN   \n",
       "1             NaN        NaN                                          NaN   \n",
       "2             NaN        NaN                                          NaN   \n",
       "3             NaN        NaN                                          NaN   \n",
       "4             NaN        NaN                                          NaN   \n",
       "5            Role    El Code                                     Elements   \n",
       "6   Team Member 1     2534.0                       Serve Customer at Till   \n",
       "7   Team Member 2     5206.0  Serve Customer at Collections/Click&Collect   \n",
       "8  Customer Count        5.0                               Customer Count   \n",
       "9  Customer Count        5.0                               Customer Count   \n",
       "\n",
       "  Unnamed: 9 Unnamed: 10 Unnamed: 11       Unnamed: 12    Unnamed: 13  \\\n",
       "0        NaN         NaN         NaN               NaN            NaN   \n",
       "1        NaN         NaN         NaN               NaN            NaN   \n",
       "2        NaN         NaN         NaN               NaN            NaN   \n",
       "3        NaN         NaN         NaN               NaN            NaN   \n",
       "4        NaN         NaN         NaN               NaN            NaN   \n",
       "5     Rating         BMS         Qty              Area  Main Category   \n",
       "6      100.0           5         1.0             Tills       Customer   \n",
       "7      100.0           5         1.0  Collections Desk       Customer   \n",
       "8  Not Rated           0         1.0  Collections Desk            NVA   \n",
       "9  Not Rated           0         1.0             Tills            NVA   \n",
       "\n",
       "      Unnamed: 14 Unnamed: 15 Unnamed: 16  \n",
       "0             NaN         NaN         NaN  \n",
       "1             NaN         NaN         NaN  \n",
       "2             NaN         NaN         NaN  \n",
       "3             NaN         NaN         NaN  \n",
       "4             NaN         NaN         NaN  \n",
       "5        Category       Notes    Timeslot  \n",
       "6             NaN         NaN     Morning  \n",
       "7             NaN         NaN     Morning  \n",
       "8  Customer Count         NaN     Morning  \n",
       "9  Customer Count         NaN     Morning  "
      ]
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     "execution_count": 2,
     "metadata": {},
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   ],
   "source": [
    "df1 = pd.read_excel(\"Data Summary.xlsx\",sheet_name=\"Data-Front End efficiency\")\n",
    "df1.head(10)"
   ]
  },
  {
   "cell_type": "code",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3706 entries, 0 to 3705\n",
      "Data columns (total 17 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Data Summary  3702 non-null   object\n",
      " 1   Unnamed: 1    3701 non-null   object\n",
      " 2   Unnamed: 2    1 non-null      object\n",
      " 3   Unnamed: 3    3702 non-null   object\n",
      " 4   Unnamed: 4    3701 non-null   object\n",
      " 5   Unnamed: 5    3701 non-null   object\n",
      " 6   Unnamed: 6    3701 non-null   object\n",
      " 7   Unnamed: 7    3701 non-null   object\n",
      " 8   Unnamed: 8    3701 non-null   object\n",
      " 9   Unnamed: 9    3701 non-null   object\n",
      " 10  Unnamed: 10   3701 non-null   object\n",
      " 11  Unnamed: 11   3701 non-null   object\n",
      " 12  Unnamed: 12   3701 non-null   object\n",
      " 13  Unnamed: 13   3701 non-null   object\n",
      " 14  Unnamed: 14   1068 non-null   object\n",
      " 15  Unnamed: 15   275 non-null    object\n",
      " 16  Unnamed: 16   3684 non-null   object\n",
      "dtypes: object(17)\n",
      "memory usage: 492.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c836d084",
   "metadata": {},
   "source": [
    "- As we can see, we have almost 5 ``NaN`` rows and each column has same ``object`` **datatype**. <br>\n",
    "So first lets change **datatype** and skip ``NaN`` rows <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9dc2a249",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating ``dtype`` separately so that, if we would be required to make change, it can be done smoothly over here.\n",
    "dtypes = {\n",
    "    'Study':object,\n",
    "    'Location': object,\n",
    "    'Tags': object,\n",
    "    'Date': object,\n",
    "    'Day':object,\n",
    "    'Round':object,\n",
    "    'Role': object,\n",
    "    'El Code':float,\n",
    "    'Elements':object,\n",
    "    'Rating':object,\n",
    "    'BMS':float,\n",
    "    'Qty':float,\n",
    "    'Area':object,\n",
    "    'Main Category':object,\n",
    "    'Category':object,\n",
    "    'Notes':object,\n",
    "    'Timeslot':object\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ae15ae8f",
   "metadata": {},
   "outputs": [
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       "      <th>1</th>\n",
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       "      <td>Team Member 2</td>\n",
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       "      <td>100.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Morning</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>StGW0710ras3</td>\n",
       "      <td>Stamford</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10:47</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>StGW0710ras3</td>\n",
       "      <td>Stamford</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10:47</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tills</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>StGW0710ras3</td>\n",
       "      <td>Stamford</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10:52</td>\n",
       "      <td>Team Member 1</td>\n",
       "      <td>5174.0</td>\n",
       "      <td>Deal with Store Visit RM / AM etc</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Task &amp; Process</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Study  Location Tags        Date     Day  Round            Role  \\\n",
       "0  StGW0710ras3  Stamford  NaN  07/10/2019  Monday  10:47   Team Member 1   \n",
       "1  StGW0710ras3  Stamford  NaN  07/10/2019  Monday  10:47   Team Member 2   \n",
       "2  StGW0710ras3  Stamford  NaN  07/10/2019  Monday  10:47  Customer Count   \n",
       "3  StGW0710ras3  Stamford  NaN  07/10/2019  Monday  10:47  Customer Count   \n",
       "4  StGW0710ras3  Stamford  NaN  07/10/2019  Monday  10:52   Team Member 1   \n",
       "\n",
       "   El Code                                     Elements     Rating  BMS  Qty  \\\n",
       "0   2534.0                       Serve Customer at Till      100.0  5.0  1.0   \n",
       "1   5206.0  Serve Customer at Collections/Click&Collect      100.0  5.0  1.0   \n",
       "2      5.0                               Customer Count  Not Rated  0.0  1.0   \n",
       "3      5.0                               Customer Count  Not Rated  0.0  1.0   \n",
       "4   5174.0            Deal with Store Visit RM / AM etc      100.0  5.0  1.0   \n",
       "\n",
       "               Area   Main Category        Category Notes Timeslot  \n",
       "0             Tills        Customer             NaN   NaN  Morning  \n",
       "1  Collections Desk        Customer             NaN   NaN  Morning  \n",
       "2  Collections Desk             NVA  Customer Count   NaN  Morning  \n",
       "3             Tills             NVA  Customer Count   NaN  Morning  \n",
       "4  Collections Desk  Task & Process             NaN   NaN  Morning  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_excel(\"Data Summary.xlsx\",sheet_name=\"Data-Front End efficiency\", dtype=dtypes,skiprows=range(0,6))\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f4f1ff76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('07/10/2019', '27/10/2019')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Date'].iloc[0],df1['Date'].iloc[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55df3502",
   "metadata": {},
   "source": [
    "Reading second dataset i.e, Data-Till Activity Study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dd8edd7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9940 entries, 0 to 9939\n",
      "Data columns (total 19 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Data Summary  9939 non-null   object\n",
      " 1   Unnamed: 1    9939 non-null   object\n",
      " 2   Unnamed: 2    7708 non-null   object\n",
      " 3   Unnamed: 3    9939 non-null   object\n",
      " 4   Unnamed: 4    9939 non-null   object\n",
      " 5   Unnamed: 5    9939 non-null   object\n",
      " 6   Unnamed: 6    9939 non-null   object\n",
      " 7   Unnamed: 7    9939 non-null   object\n",
      " 8   Unnamed: 8    9939 non-null   object\n",
      " 9   Unnamed: 9    3206 non-null   object\n",
      " 10  Unnamed: 10   9939 non-null   object\n",
      " 11  Unnamed: 11   9939 non-null   object\n",
      " 12  Unnamed: 12   9939 non-null   object\n",
      " 13  Unnamed: 13   9939 non-null   object\n",
      " 14  Unnamed: 14   9939 non-null   object\n",
      " 15  Unnamed: 15   9939 non-null   object\n",
      " 16  Unnamed: 16   1 non-null      object\n",
      " 17  Unnamed: 17   627 non-null    object\n",
      " 18  Unnamed: 18   9939 non-null   object\n",
      "dtypes: object(19)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df2 = pd.read_excel(\"Data Summary.xlsx\",sheet_name=\"Data-Till Activity Study\")\n",
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a7764ec9",
   "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>Data Summary</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "      <th>Unnamed: 14</th>\n",
       "      <th>Unnamed: 15</th>\n",
       "      <th>Unnamed: 16</th>\n",
       "      <th>Unnamed: 17</th>\n",
       "      <th>Unnamed: 18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Study</td>\n",
       "      <td>Location</td>\n",
       "      <td>Tags</td>\n",
       "      <td>Date</td>\n",
       "      <td>Day</td>\n",
       "      <td>Time</td>\n",
       "      <td>Area</td>\n",
       "      <td>Task</td>\n",
       "      <td>Element</td>\n",
       "      <td>UOM</td>\n",
       "      <td>Rating</td>\n",
       "      <td>Frequency</td>\n",
       "      <td>Obs Time</td>\n",
       "      <td>BMS</td>\n",
       "      <td>BMs per UOM</td>\n",
       "      <td>Main Category</td>\n",
       "      <td>Category</td>\n",
       "      <td>Notes</td>\n",
       "      <td>Timeslot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BrJC1708dots1</td>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>SAG D</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>10:03:26</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Wait No Customers</td>\n",
       "      <td>Per Occasion</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1239</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NVA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BrJC1708dots1</td>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>SAG D</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>10:03:33</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Call Up Next Customer &amp; Start Transaction</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.062</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BrJC1708dots1</td>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>SAG D</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>10:03:37</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Determine Customer Requirements</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Data Summary             Unnamed: 1 Unnamed: 2  Unnamed: 3 Unnamed: 4  \\\n",
       "0            NaN                    NaN        NaN         NaN        NaN   \n",
       "1          Study               Location       Tags        Date        Day   \n",
       "2  BrJC1708dots1  Bristol Emerson Green      SAG D  17/08/2021    Tuesday   \n",
       "3  BrJC1708dots1  Bristol Emerson Green      SAG D  17/08/2021    Tuesday   \n",
       "4  BrJC1708dots1  Bristol Emerson Green      SAG D  17/08/2021    Tuesday   \n",
       "\n",
       "  Unnamed: 5      Unnamed: 6                         Unnamed: 7  \\\n",
       "0        NaN             NaN                                NaN   \n",
       "1       Time            Area                               Task   \n",
       "2   10:03:26  Main Till Bank  Till Transaction - Main Till bank   \n",
       "3   10:03:33  Main Till Bank  Till Transaction - Main Till bank   \n",
       "4   10:03:37  Main Till Bank  Till Transaction - Main Till bank   \n",
       "\n",
       "                                  Unnamed: 8    Unnamed: 9 Unnamed: 10  \\\n",
       "0                                        NaN           NaN         NaN   \n",
       "1                                    Element           UOM      Rating   \n",
       "2                          Wait No Customers  Per Occasion   Not Rated   \n",
       "3  Call Up Next Customer & Start Transaction           NaN       100.0   \n",
       "4            Determine Customer Requirements           NaN       100.0   \n",
       "\n",
       "  Unnamed: 11 Unnamed: 12 Unnamed: 13  Unnamed: 14    Unnamed: 15 Unnamed: 16  \\\n",
       "0         NaN         NaN         NaN          NaN            NaN         NaN   \n",
       "1   Frequency    Obs Time         BMS  BMs per UOM  Main Category    Category   \n",
       "2         1.0      0.1239         0.0          0.0            NVA         NaN   \n",
       "3         1.0       0.062       0.062        0.062       Customer         NaN   \n",
       "4         1.0      0.0553      0.0553       0.0553       Customer         NaN   \n",
       "\n",
       "  Unnamed: 17 Unnamed: 18  \n",
       "0         NaN         NaN  \n",
       "1       Notes    Timeslot  \n",
       "2         NaN     Morning  \n",
       "3         NaN     Morning  \n",
       "4         NaN     Morning  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f109943",
   "metadata": {},
   "source": [
    "As we can see, each column again has ``object`` datatype and first row is ``NaN`` and column names are in row below, so processing it better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "33f4c44a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtypes = {\n",
    "    'Study':object,\n",
    "    'Location': object,\n",
    "    'Tags': object,\n",
    "    'Date': object,\n",
    "    'Day':object,\n",
    "    'Time':object,\n",
    "    'Area': object,\n",
    "    'Task':object,\n",
    "    'Element':object,\n",
    "    'UOM':object,\n",
    "    'Rating':object,\n",
    "    'Frquency':float,\n",
    "    'Obs Time':float,\n",
    "    'BMS':float,\n",
    "    'BMs per UOM':float,\n",
    "    'Main Category':object,\n",
    "    'Category':object,\n",
    "    'Notes': object,\n",
    "    'Timeslot':object\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8dafbd19",
   "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>Study</th>\n",
       "      <th>Location</th>\n",
       "      <th>Tags</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>Area</th>\n",
       "      <th>Task</th>\n",
       "      <th>Element</th>\n",
       "      <th>UOM</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Obs Time</th>\n",
       "      <th>BMS</th>\n",
       "      <th>BMs per UOM</th>\n",
       "      <th>Main Category</th>\n",
       "      <th>Category</th>\n",
       "      <th>Notes</th>\n",
       "      <th>Timeslot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BrJC1708dots1</td>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>SAG D</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>10:03:26</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Wait No Customers</td>\n",
       "      <td>Per Occasion</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1239</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NVA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BrJC1708dots1</td>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>SAG D</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>10:03:33</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Call Up Next Customer &amp; Start Transaction</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BrJC1708dots1</td>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>SAG D</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>10:03:37</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Determine Customer Requirements</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BrJC1708dots1</td>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>SAG D</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>10:03:40</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Request &amp; Process Advantage Card (Chip)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BrJC1708dots1</td>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>SAG D</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>10:03:48</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Scan &amp; Bag Item(s) - Number of Items</td>\n",
       "      <td>Number of Items</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.2318</td>\n",
       "      <td>0.2318</td>\n",
       "      <td>0.1159</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Study               Location   Tags        Date      Day      Time  \\\n",
       "0  BrJC1708dots1  Bristol Emerson Green  SAG D  17/08/2021  Tuesday  10:03:26   \n",
       "1  BrJC1708dots1  Bristol Emerson Green  SAG D  17/08/2021  Tuesday  10:03:33   \n",
       "2  BrJC1708dots1  Bristol Emerson Green  SAG D  17/08/2021  Tuesday  10:03:37   \n",
       "3  BrJC1708dots1  Bristol Emerson Green  SAG D  17/08/2021  Tuesday  10:03:40   \n",
       "4  BrJC1708dots1  Bristol Emerson Green  SAG D  17/08/2021  Tuesday  10:03:48   \n",
       "\n",
       "             Area                               Task  \\\n",
       "0  Main Till Bank  Till Transaction - Main Till bank   \n",
       "1  Main Till Bank  Till Transaction - Main Till bank   \n",
       "2  Main Till Bank  Till Transaction - Main Till bank   \n",
       "3  Main Till Bank  Till Transaction - Main Till bank   \n",
       "4  Main Till Bank  Till Transaction - Main Till bank   \n",
       "\n",
       "                                     Element              UOM     Rating  \\\n",
       "0                          Wait No Customers     Per Occasion  Not Rated   \n",
       "1  Call Up Next Customer & Start Transaction              NaN      100.0   \n",
       "2            Determine Customer Requirements              NaN      100.0   \n",
       "3    Request & Process Advantage Card (Chip)              NaN      100.0   \n",
       "4       Scan & Bag Item(s) - Number of Items  Number of Items      100.0   \n",
       "\n",
       "   Frequency  Obs Time     BMS  BMs per UOM Main Category Category Notes  \\\n",
       "0        1.0    0.1239  0.0000       0.0000           NVA      NaN   NaN   \n",
       "1        1.0    0.0620  0.0620       0.0620      Customer      NaN   NaN   \n",
       "2        1.0    0.0553  0.0553       0.0553      Customer      NaN   NaN   \n",
       "3        1.0    0.1262  0.1262       0.1262      Customer      NaN   NaN   \n",
       "4        2.0    0.2318  0.2318       0.1159      Customer      NaN     2   \n",
       "\n",
       "  Timeslot  \n",
       "0  Morning  \n",
       "1  Morning  \n",
       "2  Morning  \n",
       "3  Morning  \n",
       "4  Morning  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_excel(\"Data Summary.xlsx\",sheet_name=\"Data-Till Activity Study\",skiprows=range(0,2),dtype=dtypes)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91a48034",
   "metadata": {},
   "source": [
    "# Part 2: Early Stage of Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcd7f644",
   "metadata": {},
   "source": [
    "#### Exploring columns of the first dataset `df1`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "85aa657b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Study</th>\n",
       "      <th>Location</th>\n",
       "      <th>Tags</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Round</th>\n",
       "      <th>Role</th>\n",
       "      <th>El Code</th>\n",
       "      <th>Elements</th>\n",
       "      <th>Rating</th>\n",
       "      <th>BMS</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Area</th>\n",
       "      <th>Main Category</th>\n",
       "      <th>Category</th>\n",
       "      <th>Notes</th>\n",
       "      <th>Timeslot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>StGW0710ras3</td>\n",
       "      <td>Stamford</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10:47</td>\n",
       "      <td>Team Member 1</td>\n",
       "      <td>2534.0</td>\n",
       "      <td>Serve Customer at Till</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tills</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>StGW0710ras3</td>\n",
       "      <td>Stamford</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10:47</td>\n",
       "      <td>Team Member 2</td>\n",
       "      <td>5206.0</td>\n",
       "      <td>Serve Customer at Collections/Click&amp;Collect</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>StGW0710ras3</td>\n",
       "      <td>Stamford</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10:47</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>StGW0710ras3</td>\n",
       "      <td>Stamford</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10:47</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tills</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>StGW0710ras3</td>\n",
       "      <td>Stamford</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10:52</td>\n",
       "      <td>Team Member 1</td>\n",
       "      <td>5174.0</td>\n",
       "      <td>Deal with Store Visit RM / AM etc</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Task &amp; Process</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Study  Location Tags        Date     Day  Round            Role  \\\n",
       "0  StGW0710ras3  Stamford  NaN  07/10/2019  Monday  10:47   Team Member 1   \n",
       "1  StGW0710ras3  Stamford  NaN  07/10/2019  Monday  10:47   Team Member 2   \n",
       "2  StGW0710ras3  Stamford  NaN  07/10/2019  Monday  10:47  Customer Count   \n",
       "3  StGW0710ras3  Stamford  NaN  07/10/2019  Monday  10:47  Customer Count   \n",
       "4  StGW0710ras3  Stamford  NaN  07/10/2019  Monday  10:52   Team Member 1   \n",
       "\n",
       "   El Code                                     Elements     Rating  BMS  Qty  \\\n",
       "0   2534.0                       Serve Customer at Till      100.0  5.0  1.0   \n",
       "1   5206.0  Serve Customer at Collections/Click&Collect      100.0  5.0  1.0   \n",
       "2      5.0                               Customer Count  Not Rated  0.0  1.0   \n",
       "3      5.0                               Customer Count  Not Rated  0.0  1.0   \n",
       "4   5174.0            Deal with Store Visit RM / AM etc      100.0  5.0  1.0   \n",
       "\n",
       "               Area   Main Category        Category Notes Timeslot  \n",
       "0             Tills        Customer             NaN   NaN  Morning  \n",
       "1  Collections Desk        Customer             NaN   NaN  Morning  \n",
       "2  Collections Desk             NVA  Customer Count   NaN  Morning  \n",
       "3             Tills             NVA  Customer Count   NaN  Morning  \n",
       "4  Collections Desk  Task & Process             NaN   NaN  Morning  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd5ad41d",
   "metadata": {},
   "source": [
    "- ``Tags`` contains null values, and `Notes` are quite varied with highest as ``8`` occurence for **fetch**. So, dropping them.\n",
    "- Also, ``Study`` has alphanumeric characters in it (for anonymization). Therefore, not relevant - dropping it.\n",
    "- In addition, ``Category`` column has 1067 customer-count and remaining as ``null``. So dropping it too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "185c8225",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.drop(['Study','Tags','Category','Notes'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1497de9f",
   "metadata": {},
   "source": [
    "Replacing name of column ``Round`` with ``Time``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ca2fa49f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.rename(columns={\"Round\":\"Time\"},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c071465f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>Role</th>\n",
       "      <th>El Code</th>\n",
       "      <th>Elements</th>\n",
       "      <th>Rating</th>\n",
       "      <th>BMS</th>\n",
       "      <th>Qty</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10:47</td>\n",
       "      <td>Team Member 1</td>\n",
       "      <td>2534.0</td>\n",
       "      <td>Serve Customer at Till</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tills</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10:47</td>\n",
       "      <td>Team Member 2</td>\n",
       "      <td>5206.0</td>\n",
       "      <td>Serve Customer at Collections/Click&amp;Collect</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10:47</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10:47</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tills</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10:52</td>\n",
       "      <td>Team Member 1</td>\n",
       "      <td>5174.0</td>\n",
       "      <td>Deal with Store Visit RM / AM etc</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Task &amp; Process</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location        Date     Day   Time            Role  El Code  \\\n",
       "0  Stamford  07/10/2019  Monday  10:47   Team Member 1   2534.0   \n",
       "1  Stamford  07/10/2019  Monday  10:47   Team Member 2   5206.0   \n",
       "2  Stamford  07/10/2019  Monday  10:47  Customer Count      5.0   \n",
       "3  Stamford  07/10/2019  Monday  10:47  Customer Count      5.0   \n",
       "4  Stamford  07/10/2019  Monday  10:52   Team Member 1   5174.0   \n",
       "\n",
       "                                      Elements     Rating  BMS  Qty  \\\n",
       "0                       Serve Customer at Till      100.0  5.0  1.0   \n",
       "1  Serve Customer at Collections/Click&Collect      100.0  5.0  1.0   \n",
       "2                               Customer Count  Not Rated  0.0  1.0   \n",
       "3                               Customer Count  Not Rated  0.0  1.0   \n",
       "4            Deal with Store Visit RM / AM etc      100.0  5.0  1.0   \n",
       "\n",
       "               Area   Main Category Timeslot  \n",
       "0             Tills        Customer  Morning  \n",
       "1  Collections Desk        Customer  Morning  \n",
       "2  Collections Desk             NVA  Morning  \n",
       "3             Tills             NVA  Morning  \n",
       "4  Collections Desk  Task & Process  Morning  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82bc7e9e",
   "metadata": {},
   "source": [
    "#### Now, lets see what can be dropped easily from ``df2``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d0829af4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>BrJC1708dots1</td>\n",
       "      <td>Bristol Emerson Green</td>\n",
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       "      <th>1</th>\n",
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       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>Customer</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Morning</td>\n",
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       "      <th>2</th>\n",
       "      <td>BrJC1708dots1</td>\n",
       "      <td>Bristol Emerson Green</td>\n",
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       "      <td>17/08/2021</td>\n",
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       "      <td>10:03:37</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BrJC1708dots1</td>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>SAG D</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>10:03:40</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Request &amp; Process Advantage Card (Chip)</td>\n",
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       "      <td>0.1262</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BrJC1708dots1</td>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>SAG D</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>10:03:48</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Scan &amp; Bag Item(s) - Number of Items</td>\n",
       "      <td>Number of Items</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.2318</td>\n",
       "      <td>0.2318</td>\n",
       "      <td>0.1159</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Study               Location   Tags        Date      Day      Time  \\\n",
       "0  BrJC1708dots1  Bristol Emerson Green  SAG D  17/08/2021  Tuesday  10:03:26   \n",
       "1  BrJC1708dots1  Bristol Emerson Green  SAG D  17/08/2021  Tuesday  10:03:33   \n",
       "2  BrJC1708dots1  Bristol Emerson Green  SAG D  17/08/2021  Tuesday  10:03:37   \n",
       "3  BrJC1708dots1  Bristol Emerson Green  SAG D  17/08/2021  Tuesday  10:03:40   \n",
       "4  BrJC1708dots1  Bristol Emerson Green  SAG D  17/08/2021  Tuesday  10:03:48   \n",
       "\n",
       "             Area                               Task  \\\n",
       "0  Main Till Bank  Till Transaction - Main Till bank   \n",
       "1  Main Till Bank  Till Transaction - Main Till bank   \n",
       "2  Main Till Bank  Till Transaction - Main Till bank   \n",
       "3  Main Till Bank  Till Transaction - Main Till bank   \n",
       "4  Main Till Bank  Till Transaction - Main Till bank   \n",
       "\n",
       "                                     Element              UOM     Rating  \\\n",
       "0                          Wait No Customers     Per Occasion  Not Rated   \n",
       "1  Call Up Next Customer & Start Transaction              NaN      100.0   \n",
       "2            Determine Customer Requirements              NaN      100.0   \n",
       "3    Request & Process Advantage Card (Chip)              NaN      100.0   \n",
       "4       Scan & Bag Item(s) - Number of Items  Number of Items      100.0   \n",
       "\n",
       "   Frequency  Obs Time     BMS  BMs per UOM Main Category Category Notes  \\\n",
       "0        1.0    0.1239  0.0000       0.0000           NVA      NaN   NaN   \n",
       "1        1.0    0.0620  0.0620       0.0620      Customer      NaN   NaN   \n",
       "2        1.0    0.0553  0.0553       0.0553      Customer      NaN   NaN   \n",
       "3        1.0    0.1262  0.1262       0.1262      Customer      NaN   NaN   \n",
       "4        2.0    0.2318  0.2318       0.1159      Customer      NaN     2   \n",
       "\n",
       "  Timeslot  \n",
       "0  Morning  \n",
       "1  Morning  \n",
       "2  Morning  \n",
       "3  Morning  \n",
       "4  Morning  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d737c88e",
   "metadata": {},
   "source": [
    "From exploration, we get to know ``Tags`` corresponds to ``LOCATION``, we can drop ``Tags``<br>\n",
    "``Study`` has alphanumeric charaters, ``Notes`` are varied n trivial, ``Category`` has null values<br>\n",
    "So, ``Study``, ``Tags``, ``Notes`` and ``Category`` can be dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6bc12ac5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>Area</th>\n",
       "      <th>Task</th>\n",
       "      <th>Element</th>\n",
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       "      <th>Main Category</th>\n",
       "      <th>Timeslot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>10:03:26</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Wait No Customers</td>\n",
       "      <td>Per Occasion</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1239</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>10:03:33</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Call Up Next Customer &amp; Start Transaction</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>10:03:37</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Determine Customer Requirements</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>10:03:40</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Request &amp; Process Advantage Card (Chip)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>10:03:48</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Scan &amp; Bag Item(s) - Number of Items</td>\n",
       "      <td>Number of Items</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.2318</td>\n",
       "      <td>0.2318</td>\n",
       "      <td>0.1159</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Location        Date      Day      Time            Area  \\\n",
       "0  Bristol Emerson Green  17/08/2021  Tuesday  10:03:26  Main Till Bank   \n",
       "1  Bristol Emerson Green  17/08/2021  Tuesday  10:03:33  Main Till Bank   \n",
       "2  Bristol Emerson Green  17/08/2021  Tuesday  10:03:37  Main Till Bank   \n",
       "3  Bristol Emerson Green  17/08/2021  Tuesday  10:03:40  Main Till Bank   \n",
       "4  Bristol Emerson Green  17/08/2021  Tuesday  10:03:48  Main Till Bank   \n",
       "\n",
       "                                Task  \\\n",
       "0  Till Transaction - Main Till bank   \n",
       "1  Till Transaction - Main Till bank   \n",
       "2  Till Transaction - Main Till bank   \n",
       "3  Till Transaction - Main Till bank   \n",
       "4  Till Transaction - Main Till bank   \n",
       "\n",
       "                                     Element              UOM     Rating  \\\n",
       "0                          Wait No Customers     Per Occasion  Not Rated   \n",
       "1  Call Up Next Customer & Start Transaction              NaN      100.0   \n",
       "2            Determine Customer Requirements              NaN      100.0   \n",
       "3    Request & Process Advantage Card (Chip)              NaN      100.0   \n",
       "4       Scan & Bag Item(s) - Number of Items  Number of Items      100.0   \n",
       "\n",
       "   Frequency  Obs Time     BMS  BMs per UOM Main Category Timeslot  \n",
       "0        1.0    0.1239  0.0000       0.0000           NVA  Morning  \n",
       "1        1.0    0.0620  0.0620       0.0620      Customer  Morning  \n",
       "2        1.0    0.0553  0.0553       0.0553      Customer  Morning  \n",
       "3        1.0    0.1262  0.1262       0.1262      Customer  Morning  \n",
       "4        2.0    0.2318  0.2318       0.1159      Customer  Morning  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.drop(['Study','Tags','Notes','Category'], axis=1,inplace=True)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2254b8b",
   "metadata": {},
   "source": [
    "### We need to create a column named ``Time Taken`` to understand how long it takes for a specific task<br>\n",
    "#### To do that we following data pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "272a19b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>Role</th>\n",
       "      <th>El Code</th>\n",
       "      <th>Elements</th>\n",
       "      <th>Rating</th>\n",
       "      <th>BMS</th>\n",
       "      <th>Qty</th>\n",
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       "      <th>Timeslot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Team Member 1</td>\n",
       "      <td>2534.0</td>\n",
       "      <td>Serve Customer at Till</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tills</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Team Member 2</td>\n",
       "      <td>5206.0</td>\n",
       "      <td>Serve Customer at Collections/Click&amp;Collect</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tills</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:52:00</td>\n",
       "      <td>Team Member 1</td>\n",
       "      <td>5174.0</td>\n",
       "      <td>Deal with Store Visit RM / AM etc</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Task &amp; Process</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location        Date     Day                Time            Role  El Code  \\\n",
       "0  Stamford  07/10/2019  Monday 2019-07-10 10:47:00   Team Member 1   2534.0   \n",
       "1  Stamford  07/10/2019  Monday 2019-07-10 10:47:00   Team Member 2   5206.0   \n",
       "2  Stamford  07/10/2019  Monday 2019-07-10 10:47:00  Customer Count      5.0   \n",
       "3  Stamford  07/10/2019  Monday 2019-07-10 10:47:00  Customer Count      5.0   \n",
       "4  Stamford  07/10/2019  Monday 2019-07-10 10:52:00   Team Member 1   5174.0   \n",
       "\n",
       "                                      Elements     Rating  BMS  Qty  \\\n",
       "0                       Serve Customer at Till      100.0  5.0  1.0   \n",
       "1  Serve Customer at Collections/Click&Collect      100.0  5.0  1.0   \n",
       "2                               Customer Count  Not Rated  0.0  1.0   \n",
       "3                               Customer Count  Not Rated  0.0  1.0   \n",
       "4            Deal with Store Visit RM / AM etc      100.0  5.0  1.0   \n",
       "\n",
       "               Area   Main Category Timeslot  \n",
       "0             Tills        Customer  Morning  \n",
       "1  Collections Desk        Customer  Morning  \n",
       "2  Collections Desk             NVA  Morning  \n",
       "3             Tills             NVA  Morning  \n",
       "4  Collections Desk  Task & Process  Morning  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import timedelta\n",
    "df1['Time'] = pd.to_datetime(df1['Date'] + ' ' + df1['Time'])\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "06825884",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating new column 'Time Taken'\n",
    "df1['Time Taken'] = df1.groupby('Date')['Time'].diff().dt.total_seconds() / 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5c0a39da",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>Stamford</td>\n",
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       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tills</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:52:00</td>\n",
       "      <td>Team Member 1</td>\n",
       "      <td>5174.0</td>\n",
       "      <td>Deal with Store Visit RM / AM etc</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Task &amp; Process</td>\n",
       "      <td>Morning</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location        Date     Day                Time            Role  El Code  \\\n",
       "0  Stamford  07/10/2019  Monday 2019-07-10 10:47:00   Team Member 1   2534.0   \n",
       "1  Stamford  07/10/2019  Monday 2019-07-10 10:47:00   Team Member 2   5206.0   \n",
       "2  Stamford  07/10/2019  Monday 2019-07-10 10:47:00  Customer Count      5.0   \n",
       "3  Stamford  07/10/2019  Monday 2019-07-10 10:47:00  Customer Count      5.0   \n",
       "4  Stamford  07/10/2019  Monday 2019-07-10 10:52:00   Team Member 1   5174.0   \n",
       "\n",
       "                                      Elements     Rating  BMS  Qty  \\\n",
       "0                       Serve Customer at Till      100.0  5.0  1.0   \n",
       "1  Serve Customer at Collections/Click&Collect      100.0  5.0  1.0   \n",
       "2                               Customer Count  Not Rated  0.0  1.0   \n",
       "3                               Customer Count  Not Rated  0.0  1.0   \n",
       "4            Deal with Store Visit RM / AM etc      100.0  5.0  1.0   \n",
       "\n",
       "               Area   Main Category Timeslot  Time Taken  \n",
       "0             Tills        Customer  Morning         NaN  \n",
       "1  Collections Desk        Customer  Morning         0.0  \n",
       "2  Collections Desk             NVA  Morning         0.0  \n",
       "3             Tills             NVA  Morning         0.0  \n",
       "4  Collections Desk  Task & Process  Morning         5.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fa07c1f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0.0      2718\n",
       " 5.0       960\n",
       " 20.0        2\n",
       " 10.0        2\n",
       " 15.0        2\n",
       "-365.0       1\n",
       " 35.0        1\n",
       "-355.0       1\n",
       "-360.0       1\n",
       " 6.0         1\n",
       "-499.0       1\n",
       "Name: Time Taken, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Time Taken'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8f77b597",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     2718\n",
       "5.0      960\n",
       "20.0       2\n",
       "10.0       2\n",
       "15.0       2\n",
       "35.0       1\n",
       "6.0        1\n",
       "Name: Time Taken, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.drop(df1.index[df1['Time Taken'] < 0], inplace=True)\n",
    "df1['Time Taken'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e83c6e7e",
   "metadata": {},
   "source": [
    "### Again, creating ``Time Taken`` column, for ``df2`` now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "606b9a89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>Area</th>\n",
       "      <th>Task</th>\n",
       "      <th>Element</th>\n",
       "      <th>UOM</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Obs Time</th>\n",
       "      <th>BMS</th>\n",
       "      <th>BMs per UOM</th>\n",
       "      <th>Main Category</th>\n",
       "      <th>Timeslot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:26</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Wait No Customers</td>\n",
       "      <td>Per Occasion</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1239</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:33</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Call Up Next Customer &amp; Start Transaction</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:37</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Determine Customer Requirements</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:40</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Request &amp; Process Advantage Card (Chip)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:48</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Scan &amp; Bag Item(s) - Number of Items</td>\n",
       "      <td>Number of Items</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.2318</td>\n",
       "      <td>0.2318</td>\n",
       "      <td>0.1159</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Location        Date      Day                Time  \\\n",
       "0  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:26   \n",
       "1  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:33   \n",
       "2  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:37   \n",
       "3  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:40   \n",
       "4  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:48   \n",
       "\n",
       "             Area                               Task  \\\n",
       "0  Main Till Bank  Till Transaction - Main Till bank   \n",
       "1  Main Till Bank  Till Transaction - Main Till bank   \n",
       "2  Main Till Bank  Till Transaction - Main Till bank   \n",
       "3  Main Till Bank  Till Transaction - Main Till bank   \n",
       "4  Main Till Bank  Till Transaction - Main Till bank   \n",
       "\n",
       "                                     Element              UOM     Rating  \\\n",
       "0                          Wait No Customers     Per Occasion  Not Rated   \n",
       "1  Call Up Next Customer & Start Transaction              NaN      100.0   \n",
       "2            Determine Customer Requirements              NaN      100.0   \n",
       "3    Request & Process Advantage Card (Chip)              NaN      100.0   \n",
       "4       Scan & Bag Item(s) - Number of Items  Number of Items      100.0   \n",
       "\n",
       "   Frequency  Obs Time     BMS  BMs per UOM Main Category Timeslot  \n",
       "0        1.0    0.1239  0.0000       0.0000           NVA  Morning  \n",
       "1        1.0    0.0620  0.0620       0.0620      Customer  Morning  \n",
       "2        1.0    0.0553  0.0553       0.0553      Customer  Morning  \n",
       "3        1.0    0.1262  0.1262       0.1262      Customer  Morning  \n",
       "4        2.0    0.2318  0.2318       0.1159      Customer  Morning  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import timedelta\n",
    "df2['Time'] = pd.to_datetime(df2['Date'] + ' ' + df2['Time'])\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "90f57bf8",
   "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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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>Area</th>\n",
       "      <th>Task</th>\n",
       "      <th>Element</th>\n",
       "      <th>UOM</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Obs Time</th>\n",
       "      <th>BMS</th>\n",
       "      <th>BMs per UOM</th>\n",
       "      <th>Main Category</th>\n",
       "      <th>Timeslot</th>\n",
       "      <th>Time Taken</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:26</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Wait No Customers</td>\n",
       "      <td>Per Occasion</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1239</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:33</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Call Up Next Customer &amp; Start Transaction</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.116667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:37</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Determine Customer Requirements</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:40</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Request &amp; Process Advantage Card (Chip)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:48</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Scan &amp; Bag Item(s) - Number of Items</td>\n",
       "      <td>Number of Items</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.2318</td>\n",
       "      <td>0.2318</td>\n",
       "      <td>0.1159</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.133333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Location        Date      Day                Time  \\\n",
       "0  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:26   \n",
       "1  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:33   \n",
       "2  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:37   \n",
       "3  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:40   \n",
       "4  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:48   \n",
       "\n",
       "             Area                               Task  \\\n",
       "0  Main Till Bank  Till Transaction - Main Till bank   \n",
       "1  Main Till Bank  Till Transaction - Main Till bank   \n",
       "2  Main Till Bank  Till Transaction - Main Till bank   \n",
       "3  Main Till Bank  Till Transaction - Main Till bank   \n",
       "4  Main Till Bank  Till Transaction - Main Till bank   \n",
       "\n",
       "                                     Element              UOM     Rating  \\\n",
       "0                          Wait No Customers     Per Occasion  Not Rated   \n",
       "1  Call Up Next Customer & Start Transaction              NaN      100.0   \n",
       "2            Determine Customer Requirements              NaN      100.0   \n",
       "3    Request & Process Advantage Card (Chip)              NaN      100.0   \n",
       "4       Scan & Bag Item(s) - Number of Items  Number of Items      100.0   \n",
       "\n",
       "   Frequency  Obs Time     BMS  BMs per UOM Main Category Timeslot  Time Taken  \n",
       "0        1.0    0.1239  0.0000       0.0000           NVA  Morning         NaN  \n",
       "1        1.0    0.0620  0.0620       0.0620      Customer  Morning    0.116667  \n",
       "2        1.0    0.0553  0.0553       0.0553      Customer  Morning    0.066667  \n",
       "3        1.0    0.1262  0.1262       0.1262      Customer  Morning    0.050000  \n",
       "4        2.0    0.2318  0.2318       0.1159      Customer  Morning    0.133333  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#here, we have time in seconds, so coding accordingly\n",
    "df2['Time Taken'] = df2.groupby('Date')['Time'].diff()//timedelta(seconds=1)\n",
    "df2['Time Taken'] = df2['Time Taken'].apply(lambda x: x/60) #as BMS is in minutes time taken should be minutes\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a4305b0",
   "metadata": {},
   "source": [
    "   # Solution to question 1 (a)\n",
    "   ## Estimation of Time spent at each store\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10d2be6e",
   "metadata": {},
   "source": [
    "###### Estimating Time at each store from the data coming from first dataset - Front End efficiency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3c8adcc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "Time_at_location_from_df1 = df1[['Location','Time Taken']]\n",
    "Time_at_location_from_df1 = Time_at_location_from_df1.groupby(['Location']).sum()\n",
    "Time_at_location_from_df1['Time Taken (in HH:MM)'] = pd.to_datetime(Time_at_location_from_df1['Time Taken'],unit='m').dt.strftime('%H:%M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "30174bc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time Taken</th>\n",
       "      <th>Time Taken (in HH:MM)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ashbourne</th>\n",
       "      <td>470.0</td>\n",
       "      <td>07:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bracknell</th>\n",
       "      <td>1315.0</td>\n",
       "      <td>21:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chichester</th>\n",
       "      <td>751.0</td>\n",
       "      <td>12:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kidderminster</th>\n",
       "      <td>840.0</td>\n",
       "      <td>14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leicester</th>\n",
       "      <td>885.0</td>\n",
       "      <td>14:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stamford</th>\n",
       "      <td>670.0</td>\n",
       "      <td>11:10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Time Taken Time Taken (in HH:MM)\n",
       "Location                                       \n",
       "Ashbourne           470.0                 07:50\n",
       "Bracknell          1315.0                 21:55\n",
       "Chichester          751.0                 12:31\n",
       "Kidderminster       840.0                 14:00\n",
       "Leicester           885.0                 14:45\n",
       "Stamford            670.0                 11:10"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Time_at_location_from_df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6260994b",
   "metadata": {},
   "source": [
    "##### Estimating Time at each stores from the data coming from second dataset - Data-Till Activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2c3f909e",
   "metadata": {},
   "outputs": [],
   "source": [
    "Time_at_location_from_df2 = df2[['Location','Time Taken']]\n",
    "Time_at_location_from_df2 = Time_at_location_from_df2.groupby(['Location']).sum()\n",
    "Time_at_location_from_df2['Time Taken (in HH:MM)'] = pd.to_datetime(Time_at_location_from_df2['Time Taken'],unit='m').dt.strftime('%H:%M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f27a6690",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time Taken</th>\n",
       "      <th>Time Taken (in HH:MM)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bristol Bradley Stoke</th>\n",
       "      <td>1049.733333</td>\n",
       "      <td>17:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Broadmead</th>\n",
       "      <td>941.016667</td>\n",
       "      <td>15:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Clifton Queen Rd</th>\n",
       "      <td>875.433333</td>\n",
       "      <td>14:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Cribbs Causeway</th>\n",
       "      <td>639.500000</td>\n",
       "      <td>10:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Emerson Green</th>\n",
       "      <td>1198.700000</td>\n",
       "      <td>19:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Henleaze</th>\n",
       "      <td>466.383333</td>\n",
       "      <td>07:46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Time Taken Time Taken (in HH:MM)\n",
       "Location                                                   \n",
       "Bristol Bradley Stoke     1049.733333                 17:29\n",
       "Bristol Broadmead          941.016667                 15:41\n",
       "Bristol Clifton Queen Rd   875.433333                 14:35\n",
       "Bristol Cribbs Causeway    639.500000                 10:39\n",
       "Bristol Emerson Green     1198.700000                 19:58\n",
       "Bristol Henleaze           466.383333                 07:46"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Time_at_location_from_df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b78779d",
   "metadata": {},
   "source": [
    "## Question 1 (b) : For Each Store\n",
    "### infectiveness time i.e. time with no value added  (NVA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "aa335b0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>Role</th>\n",
       "      <th>El Code</th>\n",
       "      <th>Elements</th>\n",
       "      <th>Rating</th>\n",
       "      <th>BMS</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Area</th>\n",
       "      <th>Main Category</th>\n",
       "      <th>Timeslot</th>\n",
       "      <th>Time Taken</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Team Member 1</td>\n",
       "      <td>2534.0</td>\n",
       "      <td>Serve Customer at Till</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tills</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Team Member 2</td>\n",
       "      <td>5206.0</td>\n",
       "      <td>Serve Customer at Collections/Click&amp;Collect</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tills</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:52:00</td>\n",
       "      <td>Team Member 1</td>\n",
       "      <td>5174.0</td>\n",
       "      <td>Deal with Store Visit RM / AM etc</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Task &amp; Process</td>\n",
       "      <td>Morning</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location        Date     Day                Time            Role  El Code  \\\n",
       "0  Stamford  07/10/2019  Monday 2019-07-10 10:47:00   Team Member 1   2534.0   \n",
       "1  Stamford  07/10/2019  Monday 2019-07-10 10:47:00   Team Member 2   5206.0   \n",
       "2  Stamford  07/10/2019  Monday 2019-07-10 10:47:00  Customer Count      5.0   \n",
       "3  Stamford  07/10/2019  Monday 2019-07-10 10:47:00  Customer Count      5.0   \n",
       "4  Stamford  07/10/2019  Monday 2019-07-10 10:52:00   Team Member 1   5174.0   \n",
       "\n",
       "                                      Elements     Rating  BMS  Qty  \\\n",
       "0                       Serve Customer at Till      100.0  5.0  1.0   \n",
       "1  Serve Customer at Collections/Click&Collect      100.0  5.0  1.0   \n",
       "2                               Customer Count  Not Rated  0.0  1.0   \n",
       "3                               Customer Count  Not Rated  0.0  1.0   \n",
       "4            Deal with Store Visit RM / AM etc      100.0  5.0  1.0   \n",
       "\n",
       "               Area   Main Category Timeslot  Time Taken  \n",
       "0             Tills        Customer  Morning         NaN  \n",
       "1  Collections Desk        Customer  Morning         0.0  \n",
       "2  Collections Desk             NVA  Morning         0.0  \n",
       "3             Tills             NVA  Morning         0.0  \n",
       "4  Collections Desk  Task & Process  Morning         5.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0824cd7b",
   "metadata": {},
   "source": [
    "Time for no value added is ``NVA`` under column ``Main Category``. So to get total time for each store as ``NVA``, we need to do following :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "74116eeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First selecting only those rows that have NVA as Main Category\n",
    "nva_at_location_from_df1 = df1[df1[\"Main Category\"] == \"NVA\"]\n",
    "#nva_at_location_from_df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0892aae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#second creating a df from clearer groupby\n",
    "nva_at_location_from_df1 = nva_at_location_from_df1[['Location','Main Category','Time Taken']]\n",
    "\n",
    "#groupbying location\n",
    "nva_at_location_from_df1 = nva_at_location_from_df1.groupby(['Location']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5b6e06a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time Taken</th>\n",
       "      <th>Time Taken (in HH:MM)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ashbourne</th>\n",
       "      <td>105.0</td>\n",
       "      <td>01:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bracknell</th>\n",
       "      <td>115.0</td>\n",
       "      <td>01:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chichester</th>\n",
       "      <td>135.0</td>\n",
       "      <td>02:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kidderminster</th>\n",
       "      <td>710.0</td>\n",
       "      <td>11:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leicester</th>\n",
       "      <td>100.0</td>\n",
       "      <td>01:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stamford</th>\n",
       "      <td>55.0</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Time Taken Time Taken (in HH:MM)\n",
       "Location                                       \n",
       "Ashbourne           105.0                 01:45\n",
       "Bracknell           115.0                 01:55\n",
       "Chichester          135.0                 02:15\n",
       "Kidderminster       710.0                 11:50\n",
       "Leicester           100.0                 01:40\n",
       "Stamford             55.0                 00:55"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As we have 'Time Taken in DF1' in minutes, lets convert it to HH:MM for clearer understanding\n",
    "nva_at_location_from_df1['Time Taken (in HH:MM)'] = pd.to_datetime(nva_at_location_from_df1['Time Taken'], unit='m').dt.strftime('%H:%M')\n",
    "\n",
    "nva_at_location_from_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0630267f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>Area</th>\n",
       "      <th>Task</th>\n",
       "      <th>Element</th>\n",
       "      <th>UOM</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Obs Time</th>\n",
       "      <th>BMS</th>\n",
       "      <th>BMs per UOM</th>\n",
       "      <th>Main Category</th>\n",
       "      <th>Timeslot</th>\n",
       "      <th>Time Taken</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:26</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Wait No Customers</td>\n",
       "      <td>Per Occasion</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1239</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:33</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Call Up Next Customer &amp; Start Transaction</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.116667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:37</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Determine Customer Requirements</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:40</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Request &amp; Process Advantage Card (Chip)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:48</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Scan &amp; Bag Item(s) - Number of Items</td>\n",
       "      <td>Number of Items</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.2318</td>\n",
       "      <td>0.2318</td>\n",
       "      <td>0.1159</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.133333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Location        Date      Day                Time  \\\n",
       "0  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:26   \n",
       "1  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:33   \n",
       "2  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:37   \n",
       "3  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:40   \n",
       "4  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:48   \n",
       "\n",
       "             Area                               Task  \\\n",
       "0  Main Till Bank  Till Transaction - Main Till bank   \n",
       "1  Main Till Bank  Till Transaction - Main Till bank   \n",
       "2  Main Till Bank  Till Transaction - Main Till bank   \n",
       "3  Main Till Bank  Till Transaction - Main Till bank   \n",
       "4  Main Till Bank  Till Transaction - Main Till bank   \n",
       "\n",
       "                                     Element              UOM     Rating  \\\n",
       "0                          Wait No Customers     Per Occasion  Not Rated   \n",
       "1  Call Up Next Customer & Start Transaction              NaN      100.0   \n",
       "2            Determine Customer Requirements              NaN      100.0   \n",
       "3    Request & Process Advantage Card (Chip)              NaN      100.0   \n",
       "4       Scan & Bag Item(s) - Number of Items  Number of Items      100.0   \n",
       "\n",
       "   Frequency  Obs Time     BMS  BMs per UOM Main Category Timeslot  Time Taken  \n",
       "0        1.0    0.1239  0.0000       0.0000           NVA  Morning         NaN  \n",
       "1        1.0    0.0620  0.0620       0.0620      Customer  Morning    0.116667  \n",
       "2        1.0    0.0553  0.0553       0.0553      Customer  Morning    0.066667  \n",
       "3        1.0    0.1262  0.1262       0.1262      Customer  Morning    0.050000  \n",
       "4        2.0    0.2318  0.2318       0.1159      Customer  Morning    0.133333  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5ddd466e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First selecting only those rows that have NVA as Main Category\n",
    "nva_at_location_from_df2 = df2[df2[\"Main Category\"] == \"NVA\"]\n",
    "#nva_at_location_from_df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "244b0d2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#second creating a df from clearer groupby\n",
    "nva_at_location_from_df2 = nva_at_location_from_df2[['Location','Main Category','Time Taken']]\n",
    "\n",
    "#groupbying location\n",
    "nva_at_location_from_df2 = nva_at_location_from_df2.groupby(['Location']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0fe4ef03",
   "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>Time Taken</th>\n",
       "      <th>Time Taken (in HH:MM)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bristol Bradley Stoke</th>\n",
       "      <td>3.833333</td>\n",
       "      <td>00:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Broadmead</th>\n",
       "      <td>257.033333</td>\n",
       "      <td>04:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Clifton Queen Rd</th>\n",
       "      <td>91.250000</td>\n",
       "      <td>01:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Cribbs Causeway</th>\n",
       "      <td>233.000000</td>\n",
       "      <td>03:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Emerson Green</th>\n",
       "      <td>176.366667</td>\n",
       "      <td>02:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Henleaze</th>\n",
       "      <td>254.183333</td>\n",
       "      <td>04:14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Time Taken Time Taken (in HH:MM)\n",
       "Location                                                  \n",
       "Bristol Bradley Stoke       3.833333                 00:03\n",
       "Bristol Broadmead         257.033333                 04:17\n",
       "Bristol Clifton Queen Rd   91.250000                 01:31\n",
       "Bristol Cribbs Causeway   233.000000                 03:53\n",
       "Bristol Emerson Green     176.366667                 02:56\n",
       "Bristol Henleaze          254.183333                 04:14"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#as we have 'Time Taken' in df2 in seconds, lets convert it into HH:MM for clearer understanding\n",
    "nva_at_location_from_df2['Time Taken (in HH:MM)'] = pd.to_datetime(nva_at_location_from_df2['Time Taken'], unit='m').dt.strftime('%H:%M')\n",
    "\n",
    "nva_at_location_from_df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43a1085b",
   "metadata": {},
   "source": [
    "## Question 1 (c) : For Each Store\n",
    "### estimate pace (i.e. rate of task)\n",
    "### This is same as \n",
    "## Question 1.2 : For each Store\n",
    "### estimate the activity time, i.e., how long does a process/activity take?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8a2f3dc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>Role</th>\n",
       "      <th>El Code</th>\n",
       "      <th>Elements</th>\n",
       "      <th>Rating</th>\n",
       "      <th>BMS</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Area</th>\n",
       "      <th>Main Category</th>\n",
       "      <th>Timeslot</th>\n",
       "      <th>Time Taken</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Team Member 1</td>\n",
       "      <td>2534.0</td>\n",
       "      <td>Serve Customer at Till</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tills</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Team Member 2</td>\n",
       "      <td>5206.0</td>\n",
       "      <td>Serve Customer at Collections/Click&amp;Collect</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tills</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:52:00</td>\n",
       "      <td>Team Member 1</td>\n",
       "      <td>5174.0</td>\n",
       "      <td>Deal with Store Visit RM / AM etc</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Task &amp; Process</td>\n",
       "      <td>Morning</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location        Date     Day                Time            Role  El Code  \\\n",
       "0  Stamford  07/10/2019  Monday 2019-07-10 10:47:00   Team Member 1   2534.0   \n",
       "1  Stamford  07/10/2019  Monday 2019-07-10 10:47:00   Team Member 2   5206.0   \n",
       "2  Stamford  07/10/2019  Monday 2019-07-10 10:47:00  Customer Count      5.0   \n",
       "3  Stamford  07/10/2019  Monday 2019-07-10 10:47:00  Customer Count      5.0   \n",
       "4  Stamford  07/10/2019  Monday 2019-07-10 10:52:00   Team Member 1   5174.0   \n",
       "\n",
       "                                      Elements     Rating  BMS  Qty  \\\n",
       "0                       Serve Customer at Till      100.0  5.0  1.0   \n",
       "1  Serve Customer at Collections/Click&Collect      100.0  5.0  1.0   \n",
       "2                               Customer Count  Not Rated  0.0  1.0   \n",
       "3                               Customer Count  Not Rated  0.0  1.0   \n",
       "4            Deal with Store Visit RM / AM etc      100.0  5.0  1.0   \n",
       "\n",
       "               Area   Main Category Timeslot  Time Taken  \n",
       "0             Tills        Customer  Morning         NaN  \n",
       "1  Collections Desk        Customer  Morning         0.0  \n",
       "2  Collections Desk             NVA  Morning         0.0  \n",
       "3             Tills             NVA  Morning         0.0  \n",
       "4  Collections Desk  Task & Process  Morning         5.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09cc2ab8",
   "metadata": {},
   "source": [
    "Here, ``El Code`` is the 'code of activity' and <br>\n",
    "``Elements`` is 'description of the activity based on the observation by the Rethink staff' <br>\n",
    "- We can use any of them to estimate time taken on average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "61782c4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Serve Customer at Till                         1234\n",
       "Customer Count                                 1067\n",
       "Admin at Till                                   316\n",
       "Wait No Customer                                263\n",
       "Serve Customer at Collections/Click&Collect     175\n",
       "Break                                           137\n",
       "Assist Customer in Store                        115\n",
       "Return Product to Sale                           84\n",
       "Tidy till area                                   50\n",
       "Get Product for Customer                         44\n",
       "Talk Work NOTE who and topic                     41\n",
       "Returns/ Exchanges/ Repairs at Till              33\n",
       "Facing Up - away from till area                  32\n",
       "Telephone                                        18\n",
       "Talk Not Work                                    16\n",
       "Personal Needs                                   14\n",
       "Product Carry & Collect from car                 13\n",
       "Not Working                                      11\n",
       "Serve Customer with DPD parcel                    9\n",
       "Team Briefing 121                                 6\n",
       "Rug Doctor Rental                                 5\n",
       "Training                                          4\n",
       "COSHH - deal with spillage                        2\n",
       "Sell to Customer from Display                     2\n",
       "Replenish Brochures/Catalogues                    2\n",
       "System Delay                                      2\n",
       "Deal with Store Visit RM / AM etc                 1\n",
       "Name: Elements, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts= df1['Elements'].value_counts()\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5d6c014a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Serve Customer at Till                         1234\n",
       "Customer Count                                 1067\n",
       "Admin at Till                                   316\n",
       "Wait No Customer                                263\n",
       "Serve Customer at Collections/Click&Collect     175\n",
       "Break                                           137\n",
       "Assist Customer in Store                        115\n",
       "Return Product to Sale                           84\n",
       "Tidy till area                                   50\n",
       "Get Product for Customer                         44\n",
       "Talk Work NOTE who and topic                     41\n",
       "Returns/ Exchanges/ Repairs at Till              33\n",
       "Facing Up - away from till area                  32\n",
       "Telephone                                        18\n",
       "Talk Not Work                                    16\n",
       "Personal Needs                                   14\n",
       "Product Carry & Collect from car                 13\n",
       "Not Working                                      11\n",
       "Name: Elements, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#as we want to keep dataset as original as possible for the modelling purposes later\n",
    "\n",
    "df1_1 = df1[~df1['Elements'].isin(counts[counts < 10].index)]\n",
    "df1_1['Elements'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d7ec37ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Elements</th>\n",
       "      <th>Time Taken</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Serve Customer at Till</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Serve Customer at Collections/Click&amp;Collect</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Serve Customer at Collections/Click&amp;Collect</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3695</th>\n",
       "      <td>Leicester</td>\n",
       "      <td>Facing Up - away from till area</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3696</th>\n",
       "      <td>Leicester</td>\n",
       "      <td>Admin at Till</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3697</th>\n",
       "      <td>Leicester</td>\n",
       "      <td>Return Product to Sale</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3698</th>\n",
       "      <td>Leicester</td>\n",
       "      <td>Admin at Till</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3699</th>\n",
       "      <td>Leicester</td>\n",
       "      <td>Return Product to Sale</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3663 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Location                                     Elements  Time Taken\n",
       "0      Stamford                       Serve Customer at Till         NaN\n",
       "1      Stamford  Serve Customer at Collections/Click&Collect         0.0\n",
       "2      Stamford                               Customer Count         0.0\n",
       "3      Stamford                               Customer Count         0.0\n",
       "5      Stamford  Serve Customer at Collections/Click&Collect         0.0\n",
       "...         ...                                          ...         ...\n",
       "3695  Leicester              Facing Up - away from till area         0.0\n",
       "3696  Leicester                                Admin at Till         5.0\n",
       "3697  Leicester                       Return Product to Sale         0.0\n",
       "3698  Leicester                                Admin at Till         5.0\n",
       "3699  Leicester                       Return Product to Sale         0.0\n",
       "\n",
       "[3663 rows x 3 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pace_from_df1 = df1_1[['Location', 'Elements','Time Taken']]\n",
    "pace_from_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3beaed4c",
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Time Taken</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th>Elements</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Ashbourne</th>\n",
       "      <th>Admin at Till</th>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assist Customer in Store</th>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer Count</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facing Up - away from till area</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Get Product for Customer</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Stamford</th>\n",
       "      <th>Return Product to Sale</th>\n",
       "      <td>1.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serve Customer at Collections/Click&amp;Collect</th>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serve Customer at Till</th>\n",
       "      <td>2.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tidy till area</th>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wait No Customer</th>\n",
       "      <td>2.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                       Time Taken\n",
       "Location  Elements                                               \n",
       "Ashbourne Admin at Till                                      1.09\n",
       "          Assist Customer in Store                           3.00\n",
       "          Customer Count                                     0.00\n",
       "          Facing Up - away from till area                    0.00\n",
       "          Get Product for Customer                           0.00\n",
       "...                                                           ...\n",
       "Stamford  Return Product to Sale                             1.18\n",
       "          Serve Customer at Collections/Click&Collect        1.33\n",
       "          Serve Customer at Till                             2.49\n",
       "          Tidy till area                                     5.00\n",
       "          Wait No Customer                                   2.12\n",
       "\n",
       "[85 rows x 1 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_pace_from_df1 = pace_from_df1.groupby(['Location','Elements']).mean().round(2)\n",
    "avg_pace_from_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "287b5dfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>Area</th>\n",
       "      <th>Task</th>\n",
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       "      <th>Obs Time</th>\n",
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       "      <th>BMs per UOM</th>\n",
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       "      <th>Timeslot</th>\n",
       "      <th>Time Taken</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:26</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Wait No Customers</td>\n",
       "      <td>Per Occasion</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1239</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:33</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Call Up Next Customer &amp; Start Transaction</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.116667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:37</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Determine Customer Requirements</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:40</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Request &amp; Process Advantage Card (Chip)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:48</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Scan &amp; Bag Item(s) - Number of Items</td>\n",
       "      <td>Number of Items</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.2318</td>\n",
       "      <td>0.2318</td>\n",
       "      <td>0.1159</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.133333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Location        Date      Day                Time  \\\n",
       "0  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:26   \n",
       "1  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:33   \n",
       "2  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:37   \n",
       "3  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:40   \n",
       "4  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:48   \n",
       "\n",
       "             Area                               Task  \\\n",
       "0  Main Till Bank  Till Transaction - Main Till bank   \n",
       "1  Main Till Bank  Till Transaction - Main Till bank   \n",
       "2  Main Till Bank  Till Transaction - Main Till bank   \n",
       "3  Main Till Bank  Till Transaction - Main Till bank   \n",
       "4  Main Till Bank  Till Transaction - Main Till bank   \n",
       "\n",
       "                                     Element              UOM     Rating  \\\n",
       "0                          Wait No Customers     Per Occasion  Not Rated   \n",
       "1  Call Up Next Customer & Start Transaction              NaN      100.0   \n",
       "2            Determine Customer Requirements              NaN      100.0   \n",
       "3    Request & Process Advantage Card (Chip)              NaN      100.0   \n",
       "4       Scan & Bag Item(s) - Number of Items  Number of Items      100.0   \n",
       "\n",
       "   Frequency  Obs Time     BMS  BMs per UOM Main Category Timeslot  Time Taken  \n",
       "0        1.0    0.1239  0.0000       0.0000           NVA  Morning         NaN  \n",
       "1        1.0    0.0620  0.0620       0.0620      Customer  Morning    0.116667  \n",
       "2        1.0    0.0553  0.0553       0.0553      Customer  Morning    0.066667  \n",
       "3        1.0    0.1262  0.1262       0.1262      Customer  Morning    0.050000  \n",
       "4        2.0    0.2318  0.2318       0.1159      Customer  Morning    0.133333  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "87860b34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wait No Customers                                 1716\n",
       "Call Up Next Customer & Start Transaction         1376\n",
       "Ask re Receipt / Change to Customer               1012\n",
       "Scan items (without bagging) - Number of Items     996\n",
       "Take a payment (Contactless Card)                  855\n",
       "                                                  ... \n",
       "Apply an App based offer (e.g. double points)        1\n",
       "Price overrides                                      1\n",
       "After payment till prompts e.g. coupons              1\n",
       "Selling a gift voucher                               1\n",
       "Staff Discount Card                                  1\n",
       "Name: Element, Length: 87, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = df2['Element'].value_counts()\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2570e479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wait No Customers                                                          1716\n",
       "Call Up Next Customer & Start Transaction                                  1376\n",
       "Ask re Receipt / Change to Customer                                        1012\n",
       "Scan items (without bagging) - Number of Items                              996\n",
       "Take a payment (Contactless Card)                                           855\n",
       "Waiting for Customer to put change /recepits away at end of transaction     827\n",
       "Determine Customer Requirements                                             503\n",
       "Scan & Bag Item(s) - Number of Items                                        384\n",
       "Request & Process Advantage Card (Chip)                                     358\n",
       "Take Payment (Card)                                                         205\n",
       "Take Payment (Cash)                                                         170\n",
       "Request & Process Advantage Card (Phone scan)                               138\n",
       "Determine Customer Requirements / make recommendation                       111\n",
       "Assist SCO Customer - Scanning                                               93\n",
       "Assist SCO Customer - Approval                                               87\n",
       "Assist SCO Customer - Advantage Card                                         77\n",
       "Issue pre-printed paper Voupon                                               77\n",
       "Assist SCO Customer - Payment                                                74\n",
       "Sign On or Off Till                                                          69\n",
       "Click & collect - get parcel                                                 67\n",
       "Take a payment (Contactless Other - e.g. Apple pay)                          67\n",
       "Click & collect - get customer details                                       61\n",
       "Click & collect - get Order Number, check location on till                   61\n",
       "Click & collect hand out parcel and scan through till                        57\n",
       "Assist SCO Customer - Education                                              55\n",
       "Assist SCO Customer - Other - please note                                    50\n",
       "Tidy In Queue merchandiser                                                   35\n",
       "Take Payment (Advantage Card)                                                34\n",
       "Bag items (after scanning)                                                   32\n",
       "Customer Interactions                                                        30\n",
       "Get Product for Customer                                                     22\n",
       "Bag Items For Customer                                                       18\n",
       "Remove hard tag or spider wrap                                               18\n",
       "Bag levy - ask customer if they require a bag                                17\n",
       "Invite to student discount 10% off                                           17\n",
       "Pleasantries with Customer During Till Transaction                           11\n",
       "Process / redeem a paper based voupon (till receipt or pre-printed)          11\n",
       "Get item for customer - empty box                                            10\n",
       "Name: Element, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_2 = df2[~df2['Element'].isin(counts[counts < 10].index)]\n",
    "df2_2['Element'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d1c033be",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Bristol Emerson Green</td>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <th>9933</th>\n",
       "      <td>Bristol Clifton Queen Rd</td>\n",
       "      <td>Scan items (without bagging) - Number of Items</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9934</th>\n",
       "      <td>Bristol Clifton Queen Rd</td>\n",
       "      <td>Take a payment (Contactless Card)</td>\n",
       "      <td>0.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9935</th>\n",
       "      <td>Bristol Clifton Queen Rd</td>\n",
       "      <td>Ask re Receipt / Change to Customer</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9936</th>\n",
       "      <td>Bristol Clifton Queen Rd</td>\n",
       "      <td>Waiting for Customer to put change /recepits a...</td>\n",
       "      <td>0.116667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9937</th>\n",
       "      <td>Bristol Clifton Queen Rd</td>\n",
       "      <td>Wait No Customers</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9801 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Location  \\\n",
       "0        Bristol Emerson Green   \n",
       "1        Bristol Emerson Green   \n",
       "2        Bristol Emerson Green   \n",
       "3        Bristol Emerson Green   \n",
       "4        Bristol Emerson Green   \n",
       "...                        ...   \n",
       "9933  Bristol Clifton Queen Rd   \n",
       "9934  Bristol Clifton Queen Rd   \n",
       "9935  Bristol Clifton Queen Rd   \n",
       "9936  Bristol Clifton Queen Rd   \n",
       "9937  Bristol Clifton Queen Rd   \n",
       "\n",
       "                                                Element  Time Taken  \n",
       "0                                     Wait No Customers         NaN  \n",
       "1             Call Up Next Customer & Start Transaction    0.116667  \n",
       "2                       Determine Customer Requirements    0.066667  \n",
       "3               Request & Process Advantage Card (Chip)    0.050000  \n",
       "4                  Scan & Bag Item(s) - Number of Items    0.133333  \n",
       "...                                                 ...         ...  \n",
       "9933     Scan items (without bagging) - Number of Items    0.100000  \n",
       "9934                  Take a payment (Contactless Card)    0.133333  \n",
       "9935                Ask re Receipt / Change to Customer    0.150000  \n",
       "9936  Waiting for Customer to put change /recepits a...    0.116667  \n",
       "9937                                  Wait No Customers    0.083333  \n",
       "\n",
       "[9801 rows x 3 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pace_from_df2 = df2_2[['Location', 'Element','Time Taken']]\n",
    "pace_from_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8ca8ef4a",
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Bristol Bradley Stoke</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Bristol Henleaze</th>\n",
       "      <th>Take Payment (Card)</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Take Payment (Cash)</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Take a payment (Contactless Card)</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wait No Customers</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Waiting for Customer to put change /recepits away at end of transaction</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>148 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                          Time Taken\n",
       "Location              Element                                                       \n",
       "Bristol Bradley Stoke Ask re Receipt / Change to Customer                        0.0\n",
       "                      Assist SCO Customer - Advantage Card                       1.0\n",
       "                      Assist SCO Customer - Approval                             1.0\n",
       "                      Assist SCO Customer - Other - please note                  1.0\n",
       "                      Assist SCO Customer - Payment                              0.0\n",
       "...                                                                              ...\n",
       "Bristol Henleaze      Take Payment (Card)                                        0.0\n",
       "                      Take Payment (Cash)                                        0.0\n",
       "                      Take a payment (Contactless Card)                          0.0\n",
       "                      Wait No Customers                                          6.0\n",
       "                      Waiting for Customer to put change /recepits aw...         0.0\n",
       "\n",
       "[148 rows x 1 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#AS df2 is in seconds, we are getting it in seconds,\n",
    "#so, we will round it up in seconds later\n",
    "avg_pace_from_df2 = pace_from_df2.groupby(['Location','Element']).mean().round()\n",
    "avg_pace_from_df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b30fe60",
   "metadata": {},
   "source": [
    "## Question 1 (d) : For Each Store\n",
    "### efficiency for each task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f702231f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>Role</th>\n",
       "      <th>El Code</th>\n",
       "      <th>Elements</th>\n",
       "      <th>Rating</th>\n",
       "      <th>BMS</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Area</th>\n",
       "      <th>Main Category</th>\n",
       "      <th>Timeslot</th>\n",
       "      <th>Time Taken</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Team Member 1</td>\n",
       "      <td>2534.0</td>\n",
       "      <td>Serve Customer at Till</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tills</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Team Member 2</td>\n",
       "      <td>5206.0</td>\n",
       "      <td>Serve Customer at Collections/Click&amp;Collect</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tills</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:52:00</td>\n",
       "      <td>Team Member 1</td>\n",
       "      <td>5174.0</td>\n",
       "      <td>Deal with Store Visit RM / AM etc</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Task &amp; Process</td>\n",
       "      <td>Morning</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location        Date     Day                Time            Role  El Code  \\\n",
       "0  Stamford  07/10/2019  Monday 2019-07-10 10:47:00   Team Member 1   2534.0   \n",
       "1  Stamford  07/10/2019  Monday 2019-07-10 10:47:00   Team Member 2   5206.0   \n",
       "2  Stamford  07/10/2019  Monday 2019-07-10 10:47:00  Customer Count      5.0   \n",
       "3  Stamford  07/10/2019  Monday 2019-07-10 10:47:00  Customer Count      5.0   \n",
       "4  Stamford  07/10/2019  Monday 2019-07-10 10:52:00   Team Member 1   5174.0   \n",
       "\n",
       "                                      Elements     Rating  BMS  Qty  \\\n",
       "0                       Serve Customer at Till      100.0  5.0  1.0   \n",
       "1  Serve Customer at Collections/Click&Collect      100.0  5.0  1.0   \n",
       "2                               Customer Count  Not Rated  0.0  1.0   \n",
       "3                               Customer Count  Not Rated  0.0  1.0   \n",
       "4            Deal with Store Visit RM / AM etc      100.0  5.0  1.0   \n",
       "\n",
       "               Area   Main Category Timeslot  Time Taken  \n",
       "0             Tills        Customer  Morning         0.0  \n",
       "1  Collections Desk        Customer  Morning         0.0  \n",
       "2  Collections Desk             NVA  Morning         0.0  \n",
       "3             Tills             NVA  Morning         0.0  \n",
       "4  Collections Desk  Task & Process  Morning         5.0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df1.fillna(0) #It will fill NaN in 'Time Taken' with 0\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e6ac6a5",
   "metadata": {},
   "source": [
    "As **BMS** - Benchmark minutes which is a kind average time for activities<br>\n",
    "we can substract, ``Time Taken`` from ``BMS`` to get efficient for that row (need to create new column). <br>\n",
    "Eventually, <br>\n",
    "we can groupby ``Location``, ``Elements`` and get **mean** of efficiency to see if that store is on average efficient at task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c3eecd4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>Role</th>\n",
       "      <th>El Code</th>\n",
       "      <th>Elements</th>\n",
       "      <th>Rating</th>\n",
       "      <th>BMS</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Area</th>\n",
       "      <th>Main Category</th>\n",
       "      <th>Timeslot</th>\n",
       "      <th>Time Taken</th>\n",
       "      <th>Efficiency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Team Member 1</td>\n",
       "      <td>2534.0</td>\n",
       "      <td>Serve Customer at Till</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tills</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Team Member 2</td>\n",
       "      <td>5206.0</td>\n",
       "      <td>Serve Customer at Collections/Click&amp;Collect</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tills</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:52:00</td>\n",
       "      <td>Team Member 1</td>\n",
       "      <td>5174.0</td>\n",
       "      <td>Deal with Store Visit RM / AM etc</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Task &amp; Process</td>\n",
       "      <td>Morning</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location        Date     Day                Time            Role  El Code  \\\n",
       "0  Stamford  07/10/2019  Monday 2019-07-10 10:47:00   Team Member 1   2534.0   \n",
       "1  Stamford  07/10/2019  Monday 2019-07-10 10:47:00   Team Member 2   5206.0   \n",
       "2  Stamford  07/10/2019  Monday 2019-07-10 10:47:00  Customer Count      5.0   \n",
       "3  Stamford  07/10/2019  Monday 2019-07-10 10:47:00  Customer Count      5.0   \n",
       "4  Stamford  07/10/2019  Monday 2019-07-10 10:52:00   Team Member 1   5174.0   \n",
       "\n",
       "                                      Elements     Rating  BMS  Qty  \\\n",
       "0                       Serve Customer at Till      100.0  5.0  1.0   \n",
       "1  Serve Customer at Collections/Click&Collect      100.0  5.0  1.0   \n",
       "2                               Customer Count  Not Rated  0.0  1.0   \n",
       "3                               Customer Count  Not Rated  0.0  1.0   \n",
       "4            Deal with Store Visit RM / AM etc      100.0  5.0  1.0   \n",
       "\n",
       "               Area   Main Category Timeslot  Time Taken  Efficiency  \n",
       "0             Tills        Customer  Morning         0.0         5.0  \n",
       "1  Collections Desk        Customer  Morning         0.0         5.0  \n",
       "2  Collections Desk             NVA  Morning         0.0         0.0  \n",
       "3             Tills             NVA  Morning         0.0         0.0  \n",
       "4  Collections Desk  Task & Process  Morning         5.0         0.0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Efficiency'] = df1['BMS'] - df1['Time Taken']\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9bd7e445",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Elements</th>\n",
       "      <th>Efficiency</th>\n",
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       "      <td>Stamford</td>\n",
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       "      <th>2</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stamford</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3695</th>\n",
       "      <td>Leicester</td>\n",
       "      <td>Facing Up - away from till area</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3696</th>\n",
       "      <td>Leicester</td>\n",
       "      <td>Admin at Till</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3697</th>\n",
       "      <td>Leicester</td>\n",
       "      <td>Return Product to Sale</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3698</th>\n",
       "      <td>Leicester</td>\n",
       "      <td>Admin at Till</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3699</th>\n",
       "      <td>Leicester</td>\n",
       "      <td>Return Product to Sale</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3696 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Location                                     Elements  Efficiency\n",
       "0      Stamford                       Serve Customer at Till        5.00\n",
       "1      Stamford  Serve Customer at Collections/Click&Collect        5.00\n",
       "2      Stamford                               Customer Count        0.00\n",
       "3      Stamford                               Customer Count        0.00\n",
       "4      Stamford            Deal with Store Visit RM / AM etc        0.00\n",
       "...         ...                                          ...         ...\n",
       "3695  Leicester              Facing Up - away from till area        3.75\n",
       "3696  Leicester                                Admin at Till        0.00\n",
       "3697  Leicester                       Return Product to Sale        3.75\n",
       "3698  Leicester                                Admin at Till        0.00\n",
       "3699  Leicester                       Return Product to Sale        3.75\n",
       "\n",
       "[3696 rows x 3 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eff_from_df1 = df1[['Location','Elements','Efficiency']]\n",
    "eff_from_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "828e2024",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Elements</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Ashbourne</th>\n",
       "      <th>Admin at Till</th>\n",
       "      <td>3.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assist Customer in Store</th>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer Count</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facing Up - away from till area</th>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Get Product for Customer</th>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Stamford</th>\n",
       "      <th>Serve Customer at Till</th>\n",
       "      <td>2.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serve Customer with DPD parcel</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tidy till area</th>\n",
       "      <td>-0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Training</th>\n",
       "      <td>1.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wait No Customer</th>\n",
       "      <td>-2.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Efficiency\n",
       "Location  Elements                                   \n",
       "Ashbourne Admin at Till                          3.41\n",
       "          Assist Customer in Store               2.00\n",
       "          Customer Count                         0.00\n",
       "          Facing Up - away from till area        4.50\n",
       "          Get Product for Customer               5.00\n",
       "...                                               ...\n",
       "Stamford  Serve Customer at Till                 2.54\n",
       "          Serve Customer with DPD parcel         0.00\n",
       "          Tidy till area                        -0.88\n",
       "          Training                               1.67\n",
       "          Wait No Customer                      -2.12\n",
       "\n",
       "[100 rows x 1 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This reveals stores at those locations are efficient by x minutes (in case of positive number)\n",
    "#This reveals stores at those locations are inefficient by x minutes (in case of negative number)\n",
    "\n",
    "eff_from_df1 = eff_from_df1.groupby(['Location','Elements']).mean().round(2)\n",
    "eff_from_df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e691a7de",
   "metadata": {},
   "source": [
    "#### Now, getting efficiency from df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "de82fb83",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>Area</th>\n",
       "      <th>Task</th>\n",
       "      <th>Element</th>\n",
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       "      <th>BMs per UOM</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:26</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Wait No Customers</td>\n",
       "      <td>Per Occasion</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1239</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:33</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Call Up Next Customer &amp; Start Transaction</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.116667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:37</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Determine Customer Requirements</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:40</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Request &amp; Process Advantage Card (Chip)</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:48</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Scan &amp; Bag Item(s) - Number of Items</td>\n",
       "      <td>Number of Items</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.2318</td>\n",
       "      <td>0.2318</td>\n",
       "      <td>0.1159</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.133333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Location        Date      Day                Time  \\\n",
       "0  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:26   \n",
       "1  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:33   \n",
       "2  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:37   \n",
       "3  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:40   \n",
       "4  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:48   \n",
       "\n",
       "             Area                               Task  \\\n",
       "0  Main Till Bank  Till Transaction - Main Till bank   \n",
       "1  Main Till Bank  Till Transaction - Main Till bank   \n",
       "2  Main Till Bank  Till Transaction - Main Till bank   \n",
       "3  Main Till Bank  Till Transaction - Main Till bank   \n",
       "4  Main Till Bank  Till Transaction - Main Till bank   \n",
       "\n",
       "                                     Element              UOM     Rating  \\\n",
       "0                          Wait No Customers     Per Occasion  Not Rated   \n",
       "1  Call Up Next Customer & Start Transaction                0      100.0   \n",
       "2            Determine Customer Requirements                0      100.0   \n",
       "3    Request & Process Advantage Card (Chip)                0      100.0   \n",
       "4       Scan & Bag Item(s) - Number of Items  Number of Items      100.0   \n",
       "\n",
       "   Frequency  Obs Time     BMS  BMs per UOM Main Category Timeslot  Time Taken  \n",
       "0        1.0    0.1239  0.0000       0.0000           NVA  Morning    0.000000  \n",
       "1        1.0    0.0620  0.0620       0.0620      Customer  Morning    0.116667  \n",
       "2        1.0    0.0553  0.0553       0.0553      Customer  Morning    0.066667  \n",
       "3        1.0    0.1262  0.1262       0.1262      Customer  Morning    0.050000  \n",
       "4        2.0    0.2318  0.2318       0.1159      Customer  Morning    0.133333  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 =df2.fillna(0)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae61ff91",
   "metadata": {},
   "source": [
    "Again, <br>\n",
    "As **BMS** - Benchmark minutes which is a kind average time for activities<br>\n",
    "we can substract, ``Time Taken`` from ``BMS`` to get efficient for that row (need to create new column). <br>\n",
    "Eventually, <br>\n",
    "we can groupby ``Location``, ``Elements`` and get **mean** of efficiency to see if that store is on average efficient at task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d5d7f97d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:26</td>\n",
       "      <td>Main Till Bank</td>\n",
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       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:33</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Call Up Next Customer &amp; Start Transaction</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.116667</td>\n",
       "      <td>-0.054667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:37</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Determine Customer Requirements</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>-0.011367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:40</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Request &amp; Process Advantage Card (Chip)</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.076200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:48</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Scan &amp; Bag Item(s) - Number of Items</td>\n",
       "      <td>Number of Items</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.2318</td>\n",
       "      <td>0.2318</td>\n",
       "      <td>0.1159</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.098467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Location        Date      Day                Time  \\\n",
       "0  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:26   \n",
       "1  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:33   \n",
       "2  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:37   \n",
       "3  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:40   \n",
       "4  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:48   \n",
       "\n",
       "             Area                               Task  \\\n",
       "0  Main Till Bank  Till Transaction - Main Till bank   \n",
       "1  Main Till Bank  Till Transaction - Main Till bank   \n",
       "2  Main Till Bank  Till Transaction - Main Till bank   \n",
       "3  Main Till Bank  Till Transaction - Main Till bank   \n",
       "4  Main Till Bank  Till Transaction - Main Till bank   \n",
       "\n",
       "                                     Element              UOM     Rating  \\\n",
       "0                          Wait No Customers     Per Occasion  Not Rated   \n",
       "1  Call Up Next Customer & Start Transaction                0      100.0   \n",
       "2            Determine Customer Requirements                0      100.0   \n",
       "3    Request & Process Advantage Card (Chip)                0      100.0   \n",
       "4       Scan & Bag Item(s) - Number of Items  Number of Items      100.0   \n",
       "\n",
       "   Frequency  Obs Time     BMS  BMs per UOM Main Category Timeslot  \\\n",
       "0        1.0    0.1239  0.0000       0.0000           NVA  Morning   \n",
       "1        1.0    0.0620  0.0620       0.0620      Customer  Morning   \n",
       "2        1.0    0.0553  0.0553       0.0553      Customer  Morning   \n",
       "3        1.0    0.1262  0.1262       0.1262      Customer  Morning   \n",
       "4        2.0    0.2318  0.2318       0.1159      Customer  Morning   \n",
       "\n",
       "   Time Taken  Efficiency  \n",
       "0    0.000000    0.000000  \n",
       "1    0.116667   -0.054667  \n",
       "2    0.066667   -0.011367  \n",
       "3    0.050000    0.076200  \n",
       "4    0.133333    0.098467  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Efficiency'] = df2['BMS'] - df2['Time Taken']\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d4c5c0cb",
   "metadata": {},
   "outputs": [
    {
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9933</th>\n",
       "      <td>Bristol Clifton Queen Rd</td>\n",
       "      <td>Scan items (without bagging) - Number of Items</td>\n",
       "      <td>0.027600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9934</th>\n",
       "      <td>Bristol Clifton Queen Rd</td>\n",
       "      <td>Take a payment (Contactless Card)</td>\n",
       "      <td>0.018967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9935</th>\n",
       "      <td>Bristol Clifton Queen Rd</td>\n",
       "      <td>Ask re Receipt / Change to Customer</td>\n",
       "      <td>-0.031200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9936</th>\n",
       "      <td>Bristol Clifton Queen Rd</td>\n",
       "      <td>Waiting for Customer to put change /recepits a...</td>\n",
       "      <td>-0.038567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9937</th>\n",
       "      <td>Bristol Clifton Queen Rd</td>\n",
       "      <td>Wait No Customers</td>\n",
       "      <td>-0.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9938 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Location  \\\n",
       "0        Bristol Emerson Green   \n",
       "1        Bristol Emerson Green   \n",
       "2        Bristol Emerson Green   \n",
       "3        Bristol Emerson Green   \n",
       "4        Bristol Emerson Green   \n",
       "...                        ...   \n",
       "9933  Bristol Clifton Queen Rd   \n",
       "9934  Bristol Clifton Queen Rd   \n",
       "9935  Bristol Clifton Queen Rd   \n",
       "9936  Bristol Clifton Queen Rd   \n",
       "9937  Bristol Clifton Queen Rd   \n",
       "\n",
       "                                                Element  Efficiency  \n",
       "0                                     Wait No Customers    0.000000  \n",
       "1             Call Up Next Customer & Start Transaction   -0.054667  \n",
       "2                       Determine Customer Requirements   -0.011367  \n",
       "3               Request & Process Advantage Card (Chip)    0.076200  \n",
       "4                  Scan & Bag Item(s) - Number of Items    0.098467  \n",
       "...                                                 ...         ...  \n",
       "9933     Scan items (without bagging) - Number of Items    0.027600  \n",
       "9934                  Take a payment (Contactless Card)    0.018967  \n",
       "9935                Ask re Receipt / Change to Customer   -0.031200  \n",
       "9936  Waiting for Customer to put change /recepits a...   -0.038567  \n",
       "9937                                  Wait No Customers   -0.083333  \n",
       "\n",
       "[9938 rows x 3 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eff_from_df2 = df2[['Location','Element','Efficiency']]\n",
    "eff_from_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3c1b628a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Efficiency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th>Element</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Bristol Bradley Stoke</th>\n",
       "      <th>Add points to Advantage Card manually</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>After Sales Chat</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alert - New order on till</th>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apply an App based offer (e.g. double points)</th>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ask re Receipt / Change to Customer</th>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Bristol Henleaze</th>\n",
       "      <th>Take a payment (Contactless Card)</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Talk Work NOTE who and topic</th>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wait No Customers</th>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wait for Customer to Get Product</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Waiting for Customer to put change /recepits away at end of transaction</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>236 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                          Efficiency\n",
       "Location              Element                                                       \n",
       "Bristol Bradley Stoke Add points to Advantage Card manually                      0.0\n",
       "                      After Sales Chat                                           0.0\n",
       "                      Alert - New order on till                                 -1.0\n",
       "                      Apply an App based offer (e.g. double points)             -0.0\n",
       "                      Ask re Receipt / Change to Customer                       -0.0\n",
       "...                                                                              ...\n",
       "Bristol Henleaze      Take a payment (Contactless Card)                          0.0\n",
       "                      Talk Work NOTE who and topic                              49.0\n",
       "                      Wait No Customers                                         -6.0\n",
       "                      Wait for Customer to Get Product                           1.0\n",
       "                      Waiting for Customer to put change /recepits aw...         0.0\n",
       "\n",
       "[236 rows x 1 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eff_from_df2 = eff_from_df2.groupby(['Location','Element']).mean().round(0)\n",
    "eff_from_df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1e875b1",
   "metadata": {},
   "source": [
    "This reveals stores at those locations are efficient by x minutes (in case of positive number)<br>\n",
    "This reveals stores at those locations are inefficient by x minutes (in case of negative number)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f97e9e5",
   "metadata": {},
   "source": [
    "# Q1.3: For each store<br>\n",
    "### estimate the time spent on each category "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aca996a6",
   "metadata": {},
   "source": [
    "#### we have already created ``pace_from_df1`` & ``pace_from_df2`` when we were finding out average time rate of task <br>\n",
    "so using them, we can find time spend on each category for each store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f999496e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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></th>\n",
       "      <th>Time Taken</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th>Elements</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Ashbourne</th>\n",
       "      <th>Admin at Till</th>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assist Customer in Store</th>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer Count</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facing Up - away from till area</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Get Product for Customer</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Stamford</th>\n",
       "      <th>Return Product to Sale</th>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serve Customer at Collections/Click&amp;Collect</th>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serve Customer at Till</th>\n",
       "      <td>455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tidy till area</th>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wait No Customer</th>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                       Time Taken\n",
       "Location  Elements                                               \n",
       "Ashbourne Admin at Till                                      25.0\n",
       "          Assist Customer in Store                           30.0\n",
       "          Customer Count                                      0.0\n",
       "          Facing Up - away from till area                     0.0\n",
       "          Get Product for Customer                            0.0\n",
       "...                                                           ...\n",
       "Stamford  Return Product to Sale                             20.0\n",
       "          Serve Customer at Collections/Click&Collect        20.0\n",
       "          Serve Customer at Till                            455.0\n",
       "          Tidy till area                                     20.0\n",
       "          Wait No Customer                                   55.0\n",
       "\n",
       "[85 rows x 1 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est_time_from_df1 = pace_from_df1.groupby(['Location','Elements']).sum() \n",
    "#as 'Time Taken' is in minutes, we get estimated time spent on each category in minutes\n",
    "\n",
    "est_time_from_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "782666de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Time Taken</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th>Element</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Bristol Bradley Stoke</th>\n",
       "      <th>Ask re Receipt / Change to Customer</th>\n",
       "      <td>14.683333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assist SCO Customer - Advantage Card</th>\n",
       "      <td>13.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assist SCO Customer - Approval</th>\n",
       "      <td>38.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assist SCO Customer - Other - please note</th>\n",
       "      <td>41.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assist SCO Customer - Payment</th>\n",
       "      <td>12.933333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Bristol Henleaze</th>\n",
       "      <th>Take Payment (Card)</th>\n",
       "      <td>1.766667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Take Payment (Cash)</th>\n",
       "      <td>0.716667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Take a payment (Contactless Card)</th>\n",
       "      <td>6.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wait No Customers</th>\n",
       "      <td>252.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Waiting for Customer to put change /recepits away at end of transaction</th>\n",
       "      <td>1.183333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>148 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                          Time Taken\n",
       "Location              Element                                                       \n",
       "Bristol Bradley Stoke Ask re Receipt / Change to Customer                  14.683333\n",
       "                      Assist SCO Customer - Advantage Card                 13.400000\n",
       "                      Assist SCO Customer - Approval                       38.283333\n",
       "                      Assist SCO Customer - Other - please note            41.350000\n",
       "                      Assist SCO Customer - Payment                        12.933333\n",
       "...                                                                              ...\n",
       "Bristol Henleaze      Take Payment (Card)                                   1.766667\n",
       "                      Take Payment (Cash)                                   0.716667\n",
       "                      Take a payment (Contactless Card)                     6.250000\n",
       "                      Wait No Customers                                   252.750000\n",
       "                      Waiting for Customer to put change /recepits aw...    1.183333\n",
       "\n",
       "[148 rows x 1 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#similarly\n",
    "est_time_from_df2 = pace_from_df2.groupby(['Location','Element']).sum()\n",
    "#as 'Time Taken' is in minutes now, we get estimated time spent on each category in minutes\n",
    "est_time_from_df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dede27b2",
   "metadata": {},
   "source": [
    "# Q2: As in Q1 but considering all the clients together"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89dd2fbd",
   "metadata": {},
   "source": [
    "So, for all store, we need to find <br>\n",
    "(a) time spent <br>\n",
    "(b) infectiveness time i.e. time with no value added  (NVA)<br>\n",
    "(c) pace (i.e. rate of task), and (d) efficiency for each task **or** Q1.2: estimate the activity time, i.e., how long does a process/activity take? <br>\n",
    "Q1.3: estimate the time spent on each category "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62e6c3a1",
   "metadata": {},
   "source": [
    "### (a) Time spent <br>\n",
    "we have already calculated separately this coming frome each ``df1`` and ``df2`` as : <br>\n",
    "``Time_at_location_from_df1`` and ``Time_at_location_from_df2``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f15c94f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>Time Taken (in HH:MM)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ashbourne</th>\n",
       "      <td>07:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bracknell</th>\n",
       "      <td>21:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chichester</th>\n",
       "      <td>12:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kidderminster</th>\n",
       "      <td>14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leicester</th>\n",
       "      <td>14:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stamford</th>\n",
       "      <td>11:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Bradley Stoke</th>\n",
       "      <td>17:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Broadmead</th>\n",
       "      <td>15:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Clifton Queen Rd</th>\n",
       "      <td>14:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Cribbs Causeway</th>\n",
       "      <td>10:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Emerson Green</th>\n",
       "      <td>19:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Henleaze</th>\n",
       "      <td>07:46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Time Taken (in HH:MM)\n",
       "Location                                      \n",
       "Ashbourne                                07:50\n",
       "Bracknell                                21:55\n",
       "Chichester                               12:31\n",
       "Kidderminster                            14:00\n",
       "Leicester                                14:45\n",
       "Stamford                                 11:10\n",
       "Bristol Bradley Stoke                    17:29\n",
       "Bristol Broadmead                        15:41\n",
       "Bristol Clifton Queen Rd                 14:35\n",
       "Bristol Cribbs Causeway                  10:39\n",
       "Bristol Emerson Green                    19:58\n",
       "Bristol Henleaze                         07:46"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_time_at_location = pd.concat([Time_at_location_from_df1,Time_at_location_from_df2])\n",
    "\n",
    "#we can drop 'Time Taken' column as elements have minutes and seconds coming from df1 and df2, respectively\n",
    "total_time_at_location = total_time_at_location.drop(['Time Taken'], axis=1)\n",
    "total_time_at_location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3dc48c30",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_time_at_location.to_csv('total_time_at_location.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f4b479e",
   "metadata": {},
   "source": [
    "### (b) infective time i.e, time with no value added (NVA) <br>\n",
    "we have already calculated separately this coming frome each ``df1`` and ``df2`` as : <br>\n",
    "``nva_at_location_from_df1`` and ``nva_at_location_from_df2``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7eb7da71",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time Taken</th>\n",
       "      <th>Time Taken (in HH:MM)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ashbourne</th>\n",
       "      <td>105.0</td>\n",
       "      <td>01:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bracknell</th>\n",
       "      <td>115.0</td>\n",
       "      <td>01:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chichester</th>\n",
       "      <td>135.0</td>\n",
       "      <td>02:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kidderminster</th>\n",
       "      <td>710.0</td>\n",
       "      <td>11:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leicester</th>\n",
       "      <td>100.0</td>\n",
       "      <td>01:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stamford</th>\n",
       "      <td>55.0</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Time Taken Time Taken (in HH:MM)\n",
       "Location                                       \n",
       "Ashbourne           105.0                 01:45\n",
       "Bracknell           115.0                 01:55\n",
       "Chichester          135.0                 02:15\n",
       "Kidderminster       710.0                 11:50\n",
       "Leicester           100.0                 01:40\n",
       "Stamford             55.0                 00:55"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nva_at_location_from_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0a817f92",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Time Taken</th>\n",
       "      <th>Time Taken (in HH:MM)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bristol Bradley Stoke</th>\n",
       "      <td>3.833333</td>\n",
       "      <td>00:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Broadmead</th>\n",
       "      <td>257.033333</td>\n",
       "      <td>04:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Clifton Queen Rd</th>\n",
       "      <td>91.250000</td>\n",
       "      <td>01:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Cribbs Causeway</th>\n",
       "      <td>233.000000</td>\n",
       "      <td>03:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Emerson Green</th>\n",
       "      <td>176.366667</td>\n",
       "      <td>02:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Henleaze</th>\n",
       "      <td>254.183333</td>\n",
       "      <td>04:14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Time Taken Time Taken (in HH:MM)\n",
       "Location                                                  \n",
       "Bristol Bradley Stoke       3.833333                 00:03\n",
       "Bristol Broadmead         257.033333                 04:17\n",
       "Bristol Clifton Queen Rd   91.250000                 01:31\n",
       "Bristol Cribbs Causeway   233.000000                 03:53\n",
       "Bristol Emerson Green     176.366667                 02:56\n",
       "Bristol Henleaze          254.183333                 04:14"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nva_at_location_from_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a278ecff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time Taken (in HH:MM)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ashbourne</th>\n",
       "      <td>01:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bracknell</th>\n",
       "      <td>01:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chichester</th>\n",
       "      <td>02:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kidderminster</th>\n",
       "      <td>11:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leicester</th>\n",
       "      <td>01:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stamford</th>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Bradley Stoke</th>\n",
       "      <td>00:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Broadmead</th>\n",
       "      <td>04:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Clifton Queen Rd</th>\n",
       "      <td>01:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Cribbs Causeway</th>\n",
       "      <td>03:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Emerson Green</th>\n",
       "      <td>02:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bristol Henleaze</th>\n",
       "      <td>04:14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Time Taken (in HH:MM)\n",
       "Location                                      \n",
       "Ashbourne                                01:45\n",
       "Bracknell                                01:55\n",
       "Chichester                               02:15\n",
       "Kidderminster                            11:50\n",
       "Leicester                                01:40\n",
       "Stamford                                 00:55\n",
       "Bristol Bradley Stoke                    00:03\n",
       "Bristol Broadmead                        04:17\n",
       "Bristol Clifton Queen Rd                 01:31\n",
       "Bristol Cribbs Causeway                  03:53\n",
       "Bristol Emerson Green                    02:56\n",
       "Bristol Henleaze                         04:14"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_nva_at_location = pd.concat([nva_at_location_from_df1,nva_at_location_from_df2])\n",
    "\n",
    "#we can drop 'Time Taken' column as elements have minutes and seconds coming from df1 and df2, respectively\n",
    "total_nva_at_location = total_nva_at_location.drop(['Time Taken'], axis=1)\n",
    "total_nva_at_location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3e932d90",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_nva_at_location.to_csv('total_nva_at_location.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8197fb86",
   "metadata": {},
   "source": [
    "# (c) pace (i.e. rate of task)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d4d8d5cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Time_at_location_from_df1['Time Taken (in HH:MM)'] = pd.to_datetime(Time_at_location_from_df1['Time Taken'],unit='m').dt.strftime('%H:%M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6a0a65c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Time Taken (in MM:SS)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th>Elements</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Ashbourne</th>\n",
       "      <th>Admin at Till</th>\n",
       "      <td>01:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assist Customer in Store</th>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer Count</th>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facing Up - away from till area</th>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Get Product for Customer</th>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Stamford</th>\n",
       "      <th>Return Product to Sale</th>\n",
       "      <td>01:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serve Customer at Collections/Click&amp;Collect</th>\n",
       "      <td>01:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serve Customer at Till</th>\n",
       "      <td>02:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tidy till area</th>\n",
       "      <td>05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wait No Customer</th>\n",
       "      <td>02:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      Time Taken (in MM:SS)\n",
       "Location  Elements                                                         \n",
       "Ashbourne Admin at Till                                               01:05\n",
       "          Assist Customer in Store                                    03:00\n",
       "          Customer Count                                              00:00\n",
       "          Facing Up - away from till area                             00:00\n",
       "          Get Product for Customer                                    00:00\n",
       "...                                                                     ...\n",
       "Stamford  Return Product to Sale                                      01:10\n",
       "          Serve Customer at Collections/Click&Collect                 01:19\n",
       "          Serve Customer at Till                                      02:29\n",
       "          Tidy till area                                              05:00\n",
       "          Wait No Customer                                            02:07\n",
       "\n",
       "[85 rows x 1 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_pace_from_df1['Time Taken (in MM:SS)'] = pd.to_datetime(avg_pace_from_df1['Time Taken'],unit='m').dt.strftime('%M:%S')\n",
    "avg_pace_from_df1 = avg_pace_from_df1.drop(['Time Taken'],axis=1)\n",
    "avg_pace_from_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "12d07bc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Bristol Bradley Stoke</th>\n",
       "      <th>Ask re Receipt / Change to Customer</th>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assist SCO Customer - Advantage Card</th>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assist SCO Customer - Approval</th>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assist SCO Customer - Other - please note</th>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assist SCO Customer - Payment</th>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Bristol Henleaze</th>\n",
       "      <th>Take Payment (Card)</th>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Take Payment (Cash)</th>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Take a payment (Contactless Card)</th>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wait No Customers</th>\n",
       "      <td>06:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Waiting for Customer to put change /recepits away at end of transaction</th>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>148 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                         Time Taken (in MM:SS)\n",
       "Location              Element                                                                 \n",
       "Bristol Bradley Stoke Ask re Receipt / Change to Customer                                00:00\n",
       "                      Assist SCO Customer - Advantage Card                               01:00\n",
       "                      Assist SCO Customer - Approval                                     01:00\n",
       "                      Assist SCO Customer - Other - please note                          01:00\n",
       "                      Assist SCO Customer - Payment                                      00:00\n",
       "...                                                                                        ...\n",
       "Bristol Henleaze      Take Payment (Card)                                                00:00\n",
       "                      Take Payment (Cash)                                                00:00\n",
       "                      Take a payment (Contactless Card)                                  00:00\n",
       "                      Wait No Customers                                                  06:00\n",
       "                      Waiting for Customer to put change /recepits aw...                 00:00\n",
       "\n",
       "[148 rows x 1 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_pace_from_df2['Time Taken (in MM:SS)'] = pd.to_datetime(avg_pace_from_df2['Time Taken'],unit='m').dt.strftime('%M:%S')\n",
    "avg_pace_from_df2 = avg_pace_from_df2.drop(['Time Taken'],axis=1)\n",
    "avg_pace_from_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3833ec81",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Ashbourne</th>\n",
       "      <th>Admin at Till</th>\n",
       "      <td>01:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assist Customer in Store</th>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer Count</th>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facing Up - away from till area</th>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Get Product for Customer</th>\n",
       "      <td>00:00</td>\n",
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       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Bristol Henleaze</th>\n",
       "      <th>Take Payment (Card)</th>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Take Payment (Cash)</th>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Take a payment (Contactless Card)</th>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wait No Customers</th>\n",
       "      <td>06:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Waiting for Customer to put change /recepits away at end of transaction</th>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>233 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                    Time Taken (in MM:SS)\n",
       "Location         Elements                                                                \n",
       "Ashbourne        Admin at Till                                                      01:05\n",
       "                 Assist Customer in Store                                           03:00\n",
       "                 Customer Count                                                     00:00\n",
       "                 Facing Up - away from till area                                    00:00\n",
       "                 Get Product for Customer                                           00:00\n",
       "...                                                                                   ...\n",
       "Bristol Henleaze Take Payment (Card)                                                00:00\n",
       "                 Take Payment (Cash)                                                00:00\n",
       "                 Take a payment (Contactless Card)                                  00:00\n",
       "                 Wait No Customers                                                  06:00\n",
       "                 Waiting for Customer to put change /recepits aw...                 00:00\n",
       "\n",
       "[233 rows x 1 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.concat([avg_pace_from_df1,avg_pace_from_df2])\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "47efb0a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "result.to_csv('result.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a54b7541",
   "metadata": {},
   "source": [
    "# Q3: Propose some solutions to reduce NVA and improve the efficiency of clients \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cdb0b96",
   "metadata": {},
   "source": [
    "First, lets see what it is special in the cases of ``NVA``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "95669711",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>Role</th>\n",
       "      <th>El Code</th>\n",
       "      <th>Elements</th>\n",
       "      <th>Rating</th>\n",
       "      <th>BMS</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Area</th>\n",
       "      <th>Main Category</th>\n",
       "      <th>Timeslot</th>\n",
       "      <th>Time Taken</th>\n",
       "      <th>Efficiency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tills</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:52:00</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:57:00</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tills</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:57:00</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Location        Date     Day                Time            Role  El Code  \\\n",
       "2   Stamford  07/10/2019  Monday 2019-07-10 10:47:00  Customer Count      5.0   \n",
       "3   Stamford  07/10/2019  Monday 2019-07-10 10:47:00  Customer Count      5.0   \n",
       "6   Stamford  07/10/2019  Monday 2019-07-10 10:52:00  Customer Count      5.0   \n",
       "9   Stamford  07/10/2019  Monday 2019-07-10 10:57:00  Customer Count      5.0   \n",
       "10  Stamford  07/10/2019  Monday 2019-07-10 10:57:00  Customer Count      5.0   \n",
       "\n",
       "          Elements     Rating  BMS  Qty              Area Main Category  \\\n",
       "2   Customer Count  Not Rated  0.0  1.0  Collections Desk           NVA   \n",
       "3   Customer Count  Not Rated  0.0  1.0             Tills           NVA   \n",
       "6   Customer Count  Not Rated  0.0  1.0  Collections Desk           NVA   \n",
       "9   Customer Count  Not Rated  0.0  1.0             Tills           NVA   \n",
       "10  Customer Count  Not Rated  0.0  1.0  Collections Desk           NVA   \n",
       "\n",
       "   Timeslot  Time Taken  Efficiency  \n",
       "2   Morning         0.0         0.0  \n",
       "3   Morning         0.0         0.0  \n",
       "6   Morning         0.0         0.0  \n",
       "9   Morning         0.0         0.0  \n",
       "10  Morning         0.0         0.0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nva_from_df1 = df1[df1[\"Main Category\"] == \"NVA\"]\n",
    "nva_from_df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6350a2b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer Count      1067\n",
       "Wait No Customer     263\n",
       "Break                137\n",
       "Talk Not Work         16\n",
       "Personal Needs        14\n",
       "Not Working           11\n",
       "System Delay           2\n",
       "Name: Elements, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nva_from_df1['Elements'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2fb15c1a",
   "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>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>Area</th>\n",
       "      <th>Task</th>\n",
       "      <th>Element</th>\n",
       "      <th>UOM</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Obs Time</th>\n",
       "      <th>BMS</th>\n",
       "      <th>BMs per UOM</th>\n",
       "      <th>Main Category</th>\n",
       "      <th>Timeslot</th>\n",
       "      <th>Time Taken</th>\n",
       "      <th>Efficiency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:26</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Wait No Customers</td>\n",
       "      <td>Per Occasion</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1239</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:04:06</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Wait No Customers</td>\n",
       "      <td>Per Occasion</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.3283</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>-0.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:06:26</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Wait No Customers</td>\n",
       "      <td>Per Occasion</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.4030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>-2.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:08:53</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Wait No Customers</td>\n",
       "      <td>Per Occasion</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6904</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.116667</td>\n",
       "      <td>-0.116667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:09:57</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Wait No Customers</td>\n",
       "      <td>Per Occasion</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0262</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>-0.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Location        Date      Day                Time  \\\n",
       "0   Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:26   \n",
       "6   Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:04:06   \n",
       "7   Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:06:26   \n",
       "15  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:08:53   \n",
       "18  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:09:57   \n",
       "\n",
       "              Area                               Task            Element  \\\n",
       "0   Main Till Bank  Till Transaction - Main Till bank  Wait No Customers   \n",
       "6   Main Till Bank  Till Transaction - Main Till bank  Wait No Customers   \n",
       "7   Main Till Bank  Till Transaction - Main Till bank  Wait No Customers   \n",
       "15  Main Till Bank  Till Transaction - Main Till bank  Wait No Customers   \n",
       "18  Main Till Bank  Till Transaction - Main Till bank  Wait No Customers   \n",
       "\n",
       "             UOM     Rating  Frequency  Obs Time  BMS  BMs per UOM  \\\n",
       "0   Per Occasion  Not Rated        1.0    0.1239  0.0          0.0   \n",
       "6   Per Occasion  Not Rated        1.0    2.3283  0.0          0.0   \n",
       "7   Per Occasion  Not Rated        1.0    1.4030  0.0          0.0   \n",
       "15  Per Occasion  Not Rated        1.0    0.6904  0.0          0.0   \n",
       "18  Per Occasion  Not Rated        1.0    1.0262  0.0          0.0   \n",
       "\n",
       "   Main Category Timeslot  Time Taken  Efficiency  \n",
       "0            NVA  Morning    0.000000    0.000000  \n",
       "6            NVA  Morning    0.066667   -0.066667  \n",
       "7            NVA  Morning    2.333333   -2.333333  \n",
       "15           NVA  Morning    0.116667   -0.116667  \n",
       "18           NVA  Morning    0.333333   -0.333333  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nva_from_df2 = df2[df2[\"Main Category\"] == \"NVA\"]\n",
    "nva_from_df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "5d8f8e4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wait No Customers    1716\n",
       "Break                   8\n",
       "Not Working             4\n",
       "Personal Needs          2\n",
       "Talk Not Work           2\n",
       "Name: Element, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nva_from_df2['Element'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "78b466a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Serve Customer at Till                         1234\n",
       "Customer Count                                 1067\n",
       "Admin at Till                                   316\n",
       "Wait No Customer                                263\n",
       "Serve Customer at Collections/Click&Collect     175\n",
       "Break                                           137\n",
       "Assist Customer in Store                        115\n",
       "Return Product to Sale                           84\n",
       "Tidy till area                                   50\n",
       "Get Product for Customer                         44\n",
       "Talk Work NOTE who and topic                     41\n",
       "Returns/ Exchanges/ Repairs at Till              33\n",
       "Facing Up - away from till area                  32\n",
       "Telephone                                        18\n",
       "Talk Not Work                                    16\n",
       "Personal Needs                                   14\n",
       "Product Carry & Collect from car                 13\n",
       "Not Working                                      11\n",
       "Serve Customer with DPD parcel                    9\n",
       "Team Briefing 121                                 6\n",
       "Rug Doctor Rental                                 5\n",
       "Training                                          4\n",
       "COSHH - deal with spillage                        2\n",
       "Sell to Customer from Display                     2\n",
       "Replenish Brochures/Catalogues                    2\n",
       "System Delay                                      2\n",
       "Deal with Store Visit RM / AM etc                 1\n",
       "Name: Elements, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Elements'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "761e1671",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wait No Customers                                 1716\n",
       "Call Up Next Customer & Start Transaction         1376\n",
       "Ask re Receipt / Change to Customer               1012\n",
       "Scan items (without bagging) - Number of Items     996\n",
       "Take a payment (Contactless Card)                  855\n",
       "                                                  ... \n",
       "Apply an App based offer (e.g. double points)        1\n",
       "Price overrides                                      1\n",
       "After payment till prompts e.g. coupons              1\n",
       "Selling a gift voucher                               1\n",
       "Staff Discount Card                                  1\n",
       "Name: Element, Length: 87, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Element'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39315172",
   "metadata": {},
   "source": [
    "## Proposal <br>\n",
    "1. Well, most of the ``NVA`` in 2019 dataset from stores of Ashbourne, Stamford, Bracknell, etc, are caused by **Customer Count**, followed by **Wait No Customer**, then **break** and least by **talking** and **personal needs** <br>\n",
    "And, ``NVA`` caused by **break** and **personal needs** can easily be avoided by replacing with other available staff at the moment. <br>\n",
    "Secondly, staffs must be heavily discouraged from calling or talking with other staff while on duty <br>\n",
    "Thirdly, **Wait No Customer** ``NVA`` perhaps, can be used with other trivial tasks at hand, like ``Tidy Till Area`` as it is  mentioned as one of the activity in the dataset from 2019<br>\n",
    "2. However, each of the ``NVA`` in 2021 dataset from the stores of Bristol are caused by **Wait No Customers** only. As to reduce ``NVA``, we need to replace it with other task, and looking at ``Element`` in this dataset, there isn't any other suitable task to replace it. No other tasks are standalone i.e, all appears to have something to do with assisting customers. One can **Sign off/on** but that would be stupid.<br>\n",
    "In this scenario, we need to attract more and more customers, and it would require higher rating with the **customer satisfaction**. Staffs needs to be as helpful, well-informed about products and/or offer, polite and quick with transaction and bagging etc., so that customers would want to come back more often than not\n",
    "3. **Price override** that has been done separately - can be done at the time of **Wait No Customer** to reduce ``NVA``. \n",
    "4. In general, any changes in price or information in the system shall be encouraged to be done when there are no customers at till<br>\n",
    "5. **Staff Discount Card** has been worked on separetely - can again could be done at the time of **Wait No Customer**. Generally speaking, staff must be encouraged if they need to work on their discount card or shop for themselves or family to do when they are no customers around, and swiftly ofcourse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16c7deb0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a17572c0",
   "metadata": {},
   "source": [
    "<center><img src=\"modelling.png\", width=400, height=400, alt=modelling><center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7925cd34",
   "metadata": {},
   "source": [
    "### To cluster clients, it would be better if we merge both dataset, so that client similar to each other in different dataset can be clustered and understood better.<br>\n",
    "#### Therefore, we need too look for ways to merge two dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f3f2f8cc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>Role</th>\n",
       "      <th>El Code</th>\n",
       "      <th>Elements</th>\n",
       "      <th>Rating</th>\n",
       "      <th>BMS</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Area</th>\n",
       "      <th>Main Category</th>\n",
       "      <th>Timeslot</th>\n",
       "      <th>Time Taken</th>\n",
       "      <th>Efficiency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Team Member 1</td>\n",
       "      <td>2534.0</td>\n",
       "      <td>Serve Customer at Till</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tills</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Team Member 2</td>\n",
       "      <td>5206.0</td>\n",
       "      <td>Serve Customer at Collections/Click&amp;Collect</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Tills</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>07/10/2019</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:52:00</td>\n",
       "      <td>Team Member 1</td>\n",
       "      <td>5174.0</td>\n",
       "      <td>Deal with Store Visit RM / AM etc</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Task &amp; Process</td>\n",
       "      <td>Morning</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   Location        Date     Day                Time            Role  El Code  \\\n",
       "0  Stamford  07/10/2019  Monday 2019-07-10 10:47:00   Team Member 1   2534.0   \n",
       "1  Stamford  07/10/2019  Monday 2019-07-10 10:47:00   Team Member 2   5206.0   \n",
       "2  Stamford  07/10/2019  Monday 2019-07-10 10:47:00  Customer Count      5.0   \n",
       "3  Stamford  07/10/2019  Monday 2019-07-10 10:47:00  Customer Count      5.0   \n",
       "4  Stamford  07/10/2019  Monday 2019-07-10 10:52:00   Team Member 1   5174.0   \n",
       "\n",
       "                                      Elements     Rating  BMS  Qty  \\\n",
       "0                       Serve Customer at Till      100.0  5.0  1.0   \n",
       "1  Serve Customer at Collections/Click&Collect      100.0  5.0  1.0   \n",
       "2                               Customer Count  Not Rated  0.0  1.0   \n",
       "3                               Customer Count  Not Rated  0.0  1.0   \n",
       "4            Deal with Store Visit RM / AM etc      100.0  5.0  1.0   \n",
       "\n",
       "               Area   Main Category Timeslot  Time Taken  Efficiency  \n",
       "0             Tills        Customer  Morning         0.0         5.0  \n",
       "1  Collections Desk        Customer  Morning         0.0         5.0  \n",
       "2  Collections Desk             NVA  Morning         0.0         0.0  \n",
       "3             Tills             NVA  Morning         0.0         0.0  \n",
       "4  Collections Desk  Task & Process  Morning         5.0         0.0  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
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   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fcc8873",
   "metadata": {},
   "source": [
    "In ``df``<br>There are 5 ``Location``, 9 kind of ``Rating``<br> 15 types of ``Role``<br> ``El Code`` and ``Elements`` have same 27 types - with 19 types have 50 or less occurences<br>\n",
    "9 types of ``BMS`` <br>\n",
    "8 types of ``Area``, 3 types of ``Main Category`` and  4 types of ``Timeslot``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "68d95e13",
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:26</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Wait No Customers</td>\n",
       "      <td>Per Occasion</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1239</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:33</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Call Up Next Customer &amp; Start Transaction</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.116667</td>\n",
       "      <td>-0.054667</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:37</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Determine Customer Requirements</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>-0.011367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:40</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Request &amp; Process Advantage Card (Chip)</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.076200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:48</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Scan &amp; Bag Item(s) - Number of Items</td>\n",
       "      <td>Number of Items</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.2318</td>\n",
       "      <td>0.2318</td>\n",
       "      <td>0.1159</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.098467</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                Location        Date      Day                Time  \\\n",
       "0  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:26   \n",
       "1  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:33   \n",
       "2  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:37   \n",
       "3  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:40   \n",
       "4  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:48   \n",
       "\n",
       "             Area                               Task  \\\n",
       "0  Main Till Bank  Till Transaction - Main Till bank   \n",
       "1  Main Till Bank  Till Transaction - Main Till bank   \n",
       "2  Main Till Bank  Till Transaction - Main Till bank   \n",
       "3  Main Till Bank  Till Transaction - Main Till bank   \n",
       "4  Main Till Bank  Till Transaction - Main Till bank   \n",
       "\n",
       "                                     Element              UOM     Rating  \\\n",
       "0                          Wait No Customers     Per Occasion  Not Rated   \n",
       "1  Call Up Next Customer & Start Transaction                0      100.0   \n",
       "2            Determine Customer Requirements                0      100.0   \n",
       "3    Request & Process Advantage Card (Chip)                0      100.0   \n",
       "4       Scan & Bag Item(s) - Number of Items  Number of Items      100.0   \n",
       "\n",
       "   Frequency  Obs Time     BMS  BMs per UOM Main Category Timeslot  \\\n",
       "0        1.0    0.1239  0.0000       0.0000           NVA  Morning   \n",
       "1        1.0    0.0620  0.0620       0.0620      Customer  Morning   \n",
       "2        1.0    0.0553  0.0553       0.0553      Customer  Morning   \n",
       "3        1.0    0.1262  0.1262       0.1262      Customer  Morning   \n",
       "4        2.0    0.2318  0.2318       0.1159      Customer  Morning   \n",
       "\n",
       "   Time Taken  Efficiency  \n",
       "0    0.000000    0.000000  \n",
       "1    0.116667   -0.054667  \n",
       "2    0.066667   -0.011367  \n",
       "3    0.050000    0.076200  \n",
       "4    0.133333    0.098467  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e60f8dcb",
   "metadata": {},
   "source": [
    "Here, in second dataset, `BMs per UOM` is same thing as `BMS` in **df1**,we need to drop `BMS` and keep `BMs per UOM` as ``BMS``<br> ``Frequency`` in df2 is same as ``QTY`` in df1. Rename it.<br>\n",
    "``Obs Time`` is observation time, result of observation has already been given in ``Rating``. so drop `Obs Time` too<br>\n",
    "``Element`` needs to be ``Elements``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e124d9a0",
   "metadata": {},
   "outputs": [
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       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
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       "      <td>2021-08-17 10:03:26</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:33</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
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       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.116667</td>\n",
       "      <td>-0.054667</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:37</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Determine Customer Requirements</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>-0.011367</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:40</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Request &amp; Process Advantage Card (Chip)</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.076200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>17/08/2021</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:48</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Till Transaction - Main Till bank</td>\n",
       "      <td>Scan &amp; Bag Item(s) - Number of Items</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.1159</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.098467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Location        Date      Day                Time  \\\n",
       "0  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:26   \n",
       "1  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:33   \n",
       "2  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:37   \n",
       "3  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:40   \n",
       "4  Bristol Emerson Green  17/08/2021  Tuesday 2021-08-17 10:03:48   \n",
       "\n",
       "             Area                               Task  \\\n",
       "0  Main Till Bank  Till Transaction - Main Till bank   \n",
       "1  Main Till Bank  Till Transaction - Main Till bank   \n",
       "2  Main Till Bank  Till Transaction - Main Till bank   \n",
       "3  Main Till Bank  Till Transaction - Main Till bank   \n",
       "4  Main Till Bank  Till Transaction - Main Till bank   \n",
       "\n",
       "                                    Elements     Rating  Qty     BMS  \\\n",
       "0                          Wait No Customers  Not Rated  1.0  0.0000   \n",
       "1  Call Up Next Customer & Start Transaction      100.0  1.0  0.0620   \n",
       "2            Determine Customer Requirements      100.0  1.0  0.0553   \n",
       "3    Request & Process Advantage Card (Chip)      100.0  1.0  0.1262   \n",
       "4       Scan & Bag Item(s) - Number of Items      100.0  2.0  0.1159   \n",
       "\n",
       "  Main Category Timeslot  Time Taken  Efficiency  \n",
       "0           NVA  Morning    0.000000    0.000000  \n",
       "1      Customer  Morning    0.116667   -0.054667  \n",
       "2      Customer  Morning    0.066667   -0.011367  \n",
       "3      Customer  Morning    0.050000    0.076200  \n",
       "4      Customer  Morning    0.133333    0.098467  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_2 = df2.drop(['UOM','Obs Time','BMS',], axis=1)\n",
    "df2_2.rename(columns={\"Frequency\":\"Qty\"},inplace=True)\n",
    "df2_2.rename(columns={\"BMs per UOM\":\"BMS\"},inplace=True)\n",
    "df2_2.rename(columns={\"Element\":\"Elements\"},inplace=True)\n",
    "df2_2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76da616b",
   "metadata": {},
   "source": [
    "**Main Till Bank** in ``df2['Area']`` and **Tills** in ``df1['Area']`` is same, therefore making it homogeneous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "c5173486",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['Area'].mask(df1['Area'] == 'Tills', 'Main Till Bank', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "1bd5ffc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['Elements'].mask(df1['Elements'] == 'Wait No Customer', 'Wait No Customers', inplace=True)\n",
    "df1['Elements'].mask(df1['Elements'] == 'Serve Customer at Till', 'Call Up Next Customer & Start Transaction', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e4f8ca4",
   "metadata": {},
   "source": [
    "Howsoever, we would have wanted to keep ``Role`` in df1, we have to drop it, as it is not available in df2<br>\n",
    "Also, dropping ``Date`` We can keep ``Efficiency`` despite it being a calculated field<br>\n",
    "``El Code`` is numeric representation of ``Elements``, so dropping it too<br>\n",
    "**Not Rated** Rating as 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "af98d4a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>Elements</th>\n",
       "      <th>Rating</th>\n",
       "      <th>BMS</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Area</th>\n",
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       "      <th>Time Taken</th>\n",
       "      <th>Efficiency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Call Up Next Customer &amp; Start Transaction</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Serve Customer at Collections/Click&amp;Collect</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:52:00</td>\n",
       "      <td>Deal with Store Visit RM / AM etc</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Task &amp; Process</td>\n",
       "      <td>Morning</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location     Day                Time  \\\n",
       "0  Stamford  Monday 2019-07-10 10:47:00   \n",
       "1  Stamford  Monday 2019-07-10 10:47:00   \n",
       "2  Stamford  Monday 2019-07-10 10:47:00   \n",
       "3  Stamford  Monday 2019-07-10 10:47:00   \n",
       "4  Stamford  Monday 2019-07-10 10:52:00   \n",
       "\n",
       "                                      Elements Rating  BMS  Qty  \\\n",
       "0    Call Up Next Customer & Start Transaction  100.0  5.0  1.0   \n",
       "1  Serve Customer at Collections/Click&Collect  100.0  5.0  1.0   \n",
       "2                               Customer Count      0  0.0  1.0   \n",
       "3                               Customer Count      0  0.0  1.0   \n",
       "4            Deal with Store Visit RM / AM etc  100.0  5.0  1.0   \n",
       "\n",
       "               Area   Main Category Timeslot  Time Taken  Efficiency  \n",
       "0    Main Till Bank        Customer  Morning         0.0         5.0  \n",
       "1  Collections Desk        Customer  Morning         0.0         5.0  \n",
       "2  Collections Desk             NVA  Morning         0.0         0.0  \n",
       "3    Main Till Bank             NVA  Morning         0.0         0.0  \n",
       "4  Collections Desk  Task & Process  Morning         5.0         0.0  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_2 = df1.drop(['Date','Role','El Code'], axis=1)\n",
    "df1_2['Rating'].mask(df1_2['Rating'] == 'Not Rated', 0, inplace=True)\n",
    "df1_2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8c658bc",
   "metadata": {},
   "source": [
    "``Area`` and ``Task`` are very similar, and many of task aspect already pointed out in Elements. So dropping `Task`<br>\n",
    "and, **Not Rated** ``Rating`` as 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "7d7ac099",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>Elements</th>\n",
       "      <th>Rating</th>\n",
       "      <th>BMS</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Area</th>\n",
       "      <th>Main Category</th>\n",
       "      <th>Timeslot</th>\n",
       "      <th>Time Taken</th>\n",
       "      <th>Efficiency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:26</td>\n",
       "      <td>Wait No Customers</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:33</td>\n",
       "      <td>Call Up Next Customer &amp; Start Transaction</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0620</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.116667</td>\n",
       "      <td>-0.054667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:37</td>\n",
       "      <td>Determine Customer Requirements</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>-0.011367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:40</td>\n",
       "      <td>Request &amp; Process Advantage Card (Chip)</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.076200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2021-08-17 10:03:48</td>\n",
       "      <td>Scan &amp; Bag Item(s) - Number of Items</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.1159</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.098467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Location      Day                Time  \\\n",
       "0  Bristol Emerson Green  Tuesday 2021-08-17 10:03:26   \n",
       "1  Bristol Emerson Green  Tuesday 2021-08-17 10:03:33   \n",
       "2  Bristol Emerson Green  Tuesday 2021-08-17 10:03:37   \n",
       "3  Bristol Emerson Green  Tuesday 2021-08-17 10:03:40   \n",
       "4  Bristol Emerson Green  Tuesday 2021-08-17 10:03:48   \n",
       "\n",
       "                                    Elements Rating     BMS  Qty  \\\n",
       "0                          Wait No Customers      0  0.0000  1.0   \n",
       "1  Call Up Next Customer & Start Transaction  100.0  0.0620  1.0   \n",
       "2            Determine Customer Requirements  100.0  0.0553  1.0   \n",
       "3    Request & Process Advantage Card (Chip)  100.0  0.1262  1.0   \n",
       "4       Scan & Bag Item(s) - Number of Items  100.0  0.1159  2.0   \n",
       "\n",
       "             Area Main Category Timeslot  Time Taken  Efficiency  \n",
       "0  Main Till Bank           NVA  Morning    0.000000    0.000000  \n",
       "1  Main Till Bank      Customer  Morning    0.116667   -0.054667  \n",
       "2  Main Till Bank      Customer  Morning    0.066667   -0.011367  \n",
       "3  Main Till Bank      Customer  Morning    0.050000    0.076200  \n",
       "4  Main Till Bank      Customer  Morning    0.133333    0.098467  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_2 = df2_2.drop(['Date','Task'],axis=1)\n",
    "df2_2['Rating'].mask(df2_2['Rating'] == 'Not Rated', 0, inplace=True)\n",
    "\n",
    "#and, then rearranging order of columns\n",
    "df2_2 = df2_2[['Location', 'Day', 'Time', 'Elements','Rating','BMS','Qty','Area','Main Category','Timeslot','Time Taken','Efficiency']]\n",
    "df2_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "c2546d21",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Elements</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Call Up Next Customer &amp; Start Transaction</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Serve Customer at Collections/Click&amp;Collect</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:47:00</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-07-10 10:52:00</td>\n",
       "      <td>Deal with Store Visit RM / AM etc</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Task &amp; Process</td>\n",
       "      <td>Morning</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location     Day                Time  \\\n",
       "0  Stamford  Monday 2019-07-10 10:47:00   \n",
       "1  Stamford  Monday 2019-07-10 10:47:00   \n",
       "2  Stamford  Monday 2019-07-10 10:47:00   \n",
       "3  Stamford  Monday 2019-07-10 10:47:00   \n",
       "4  Stamford  Monday 2019-07-10 10:52:00   \n",
       "\n",
       "                                      Elements Rating  BMS  Qty  \\\n",
       "0    Call Up Next Customer & Start Transaction  100.0  5.0  1.0   \n",
       "1  Serve Customer at Collections/Click&Collect  100.0  5.0  1.0   \n",
       "2                               Customer Count      0  0.0  1.0   \n",
       "3                               Customer Count      0  0.0  1.0   \n",
       "4            Deal with Store Visit RM / AM etc  100.0  5.0  1.0   \n",
       "\n",
       "               Area   Main Category Timeslot  Time Taken  Efficiency  \n",
       "0    Main Till Bank        Customer  Morning         0.0         5.0  \n",
       "1  Collections Desk        Customer  Morning         0.0         5.0  \n",
       "2  Collections Desk             NVA  Morning         0.0         0.0  \n",
       "3    Main Till Bank             NVA  Morning         0.0         0.0  \n",
       "4  Collections Desk  Task & Process  Morning         5.0         0.0  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = pd.concat([df1_2,df2_2])\n",
    "final_df = final_df.reset_index(drop=True)\n",
    "final_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73dd9050",
   "metadata": {},
   "source": [
    "As we remember, we had 4 type of ``TimeSlot`` in **df1** <br>\n",
    "and 2 types of ``Timeslot`` in df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "499605c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Stripping trailing date as we dont need date while modelling\n",
    "final_df[\"Time\"] = final_df[\"Time\"].dt.strftime(\"%H:%M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "103169b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('19:54', '07:33')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['Time'].max(), final_df['Time'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "9c6c8a4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def timeSlot(x):\n",
    "    hr = pd.to_datetime(x).hour\n",
    "    if hr < 13:\n",
    "        slot = 'Morning'\n",
    "    elif hr < 15:\n",
    "        slot = 'Lunchtime'\n",
    "    elif hr < 18:\n",
    "        slot = 'Afternoon'\n",
    "    else:\n",
    "        slot = 'Evening'\n",
    "    return slot\n",
    "\n",
    "final_df['Timeslot'] = final_df['Time'].apply(lambda x: timeSlot(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "baa818f7",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Location</th>\n",
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       "      <td>Call Up Next Customer &amp; Start Transaction</td>\n",
       "      <td>100.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Customer</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>Monday</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10:47</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10:52</td>\n",
       "      <td>Deal with Store Visit RM / AM etc</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Task &amp; Process</td>\n",
       "      <td>Morning</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location     Day   Time                                     Elements  \\\n",
       "0  Stamford  Monday  10:47    Call Up Next Customer & Start Transaction   \n",
       "1  Stamford  Monday  10:47  Serve Customer at Collections/Click&Collect   \n",
       "2  Stamford  Monday  10:47                               Customer Count   \n",
       "3  Stamford  Monday  10:47                               Customer Count   \n",
       "4  Stamford  Monday  10:52            Deal with Store Visit RM / AM etc   \n",
       "\n",
       "  Rating  BMS  Qty              Area   Main Category Timeslot  Time Taken  \\\n",
       "0  100.0  5.0  1.0    Main Till Bank        Customer  Morning         0.0   \n",
       "1  100.0  5.0  1.0  Collections Desk        Customer  Morning         0.0   \n",
       "2      0  0.0  1.0  Collections Desk             NVA  Morning         0.0   \n",
       "3      0  0.0  1.0    Main Till Bank             NVA  Morning         0.0   \n",
       "4  100.0  5.0  1.0  Collections Desk  Task & Process  Morning         5.0   \n",
       "\n",
       "   Efficiency  \n",
       "0         5.0  \n",
       "1         5.0  \n",
       "2         0.0  \n",
       "3         0.0  \n",
       "4         0.0  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df_copy = final_df.copy()\n",
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "65cff531",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "       Location  Day   Time  Elements Rating     BMS  Qty  Area  Category  \\\n",
       "0             0    0  10:47         0  100.0  5.0000  1.0     0         0   \n",
       "1             0    0  10:47         1  100.0  5.0000  1.0     1         0   \n",
       "2             0    0  10:47         2      0  0.0000  1.0     1         1   \n",
       "3             0    0  10:47         2      0  0.0000  1.0     0         1   \n",
       "4             0    0  10:52         3  100.0  5.0000  1.0     1         2   \n",
       "...         ...  ...    ...       ...    ...     ...  ...   ...       ...   \n",
       "13629        11    5  15:53        33  100.0  0.0638  2.0     9         0   \n",
       "13630        11    5  15:53        30  100.0  0.1523  1.0     9         0   \n",
       "13631        11    5  15:53        31  100.0  0.1188  1.0     9         0   \n",
       "13632        11    5  15:54        32  100.0  0.0781  1.0     9         0   \n",
       "13633        11    5  15:54         5      0  0.0000  1.0     9         1   \n",
       "\n",
       "       Timeslot  Time Taken  Efficiency  \n",
       "0             0    0.000000    5.000000  \n",
       "1             0    0.000000    5.000000  \n",
       "2             0    0.000000    0.000000  \n",
       "3             0    0.000000    0.000000  \n",
       "4             0    5.000000    0.000000  \n",
       "...         ...         ...         ...  \n",
       "13629         2    0.100000    0.027600  \n",
       "13630         2    0.133333    0.018967  \n",
       "13631         2    0.150000   -0.031200  \n",
       "13632         2    0.116667   -0.038567  \n",
       "13633         2    0.083333   -0.083333  \n",
       "\n",
       "[13634 rows x 12 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['Area'] = final_df.Area.factorize()[0]\n",
    "final_df['Location']= final_df.Location.factorize()[0]\n",
    "final_df['Day']= final_df.Day.factorize()[0]\n",
    "final_df['Elements']= final_df.Elements.factorize()[0]\n",
    "final_df.rename(columns={\"Main Category\":\"Category\"},inplace=True)\n",
    "final_df.drop(['Time'],axis=1) #as we dont necessarily need time, perfectly created timeslot\n",
    "final_df['Category']=final_df.Category.factorize()[0]\n",
    "final_df['Timeslot']= final_df.Timeslot.factorize()[0]\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "ba7433b5",
   "metadata": {},
   "outputs": [
    {
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       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13629</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0638</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.027600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13630</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.1523</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.018967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13631</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.1188</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>-0.031200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13632</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0781</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.116667</td>\n",
       "      <td>-0.038567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13633</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>-0.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13634 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Location  Day  Elements Rating     BMS  Qty  Area  Category  Timeslot  \\\n",
       "0             0    0         0  100.0  5.0000  1.0     0         0         0   \n",
       "1             0    0         1  100.0  5.0000  1.0     1         0         0   \n",
       "2             0    0         2      0  0.0000  1.0     1         1         0   \n",
       "3             0    0         2      0  0.0000  1.0     0         1         0   \n",
       "4             0    0         3  100.0  5.0000  1.0     1         2         0   \n",
       "...         ...  ...       ...    ...     ...  ...   ...       ...       ...   \n",
       "13629        11    5        33  100.0  0.0638  2.0     9         0         2   \n",
       "13630        11    5        30  100.0  0.1523  1.0     9         0         2   \n",
       "13631        11    5        31  100.0  0.1188  1.0     9         0         2   \n",
       "13632        11    5        32  100.0  0.0781  1.0     9         0         2   \n",
       "13633        11    5         5      0  0.0000  1.0     9         1         2   \n",
       "\n",
       "       Time Taken  Efficiency  \n",
       "0        0.000000    5.000000  \n",
       "1        0.000000    5.000000  \n",
       "2        0.000000    0.000000  \n",
       "3        0.000000    0.000000  \n",
       "4        5.000000    0.000000  \n",
       "...           ...         ...  \n",
       "13629    0.100000    0.027600  \n",
       "13630    0.133333    0.018967  \n",
       "13631    0.150000   -0.031200  \n",
       "13632    0.116667   -0.038567  \n",
       "13633    0.083333   -0.083333  \n",
       "\n",
       "[13634 rows x 11 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.drop(['Time'],axis=1,inplace=True) #as we dont necessarily need time, perfectly created timeslot\n",
    "final_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d576a7ba",
   "metadata": {},
   "source": [
    "## Now, lets start to model Customer Satisfaction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c78b30d",
   "metadata": {},
   "source": [
    "``Rating`` can be a good target class for our model.<br>- \n",
    "- Also, as ``Rating`` is being done by Rethink staff, and not by Customers themselves, we perhaps should label anything less **100.0** as **not satisfactory** <br>\n",
    "We will use **1** for **satisfied**, and **0** for **not-satisfied**. This way it would be binary-classification problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "e078ba15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Day</th>\n",
       "      <th>Elements</th>\n",
       "      <th>Rating</th>\n",
       "      <th>BMS</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Area</th>\n",
       "      <th>Category</th>\n",
       "      <th>Timeslot</th>\n",
       "      <th>Time Taken</th>\n",
       "      <th>Efficiency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>5.000000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <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>13629</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0638</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.027600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13630</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1523</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.018967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13631</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1188</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>-0.031200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13632</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0781</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.116667</td>\n",
       "      <td>-0.038567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13633</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>-0.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13634 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Location  Day  Elements  Rating     BMS  Qty  Area  Category  Timeslot  \\\n",
       "0             0    0         0       1  5.0000  1.0     0         0         0   \n",
       "1             0    0         1       1  5.0000  1.0     1         0         0   \n",
       "2             0    0         2       0  0.0000  1.0     1         1         0   \n",
       "3             0    0         2       0  0.0000  1.0     0         1         0   \n",
       "4             0    0         3       1  5.0000  1.0     1         2         0   \n",
       "...         ...  ...       ...     ...     ...  ...   ...       ...       ...   \n",
       "13629        11    5        33       1  0.0638  2.0     9         0         2   \n",
       "13630        11    5        30       1  0.1523  1.0     9         0         2   \n",
       "13631        11    5        31       1  0.1188  1.0     9         0         2   \n",
       "13632        11    5        32       1  0.0781  1.0     9         0         2   \n",
       "13633        11    5         5       0  0.0000  1.0     9         1         2   \n",
       "\n",
       "       Time Taken  Efficiency  \n",
       "0        0.000000    5.000000  \n",
       "1        0.000000    5.000000  \n",
       "2        0.000000    0.000000  \n",
       "3        0.000000    0.000000  \n",
       "4        5.000000    0.000000  \n",
       "...           ...         ...  \n",
       "13629    0.100000    0.027600  \n",
       "13630    0.133333    0.018967  \n",
       "13631    0.150000   -0.031200  \n",
       "13632    0.116667   -0.038567  \n",
       "13633    0.083333   -0.083333  \n",
       "\n",
       "[13634 rows x 11 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['Rating'] = np.where(final_df['Rating']>=100, 1, 0)\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "38210db8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    10088\n",
       "0     3546\n",
       "Name: Rating, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['Rating'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bba235c",
   "metadata": {},
   "source": [
    " As 10088*100 / (10088+3546) = 73.99, we expect our model to exceed this "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b438d1b7",
   "metadata": {},
   "source": [
    "### Now, lets separate our features and class label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "accb19eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 5., 1., 0., 0., 0., 0., 5.],\n",
       "       [0., 0., 1., 5., 1., 1., 0., 0., 0., 5.],\n",
       "       [0., 0., 2., 0., 1., 1., 1., 0., 0., 0.],\n",
       "       [0., 0., 2., 0., 1., 0., 1., 0., 0., 0.],\n",
       "       [0., 0., 3., 5., 1., 1., 2., 0., 5., 0.]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.asarray(final_df[['Location', 'Day', 'Elements', 'BMS', 'Qty', 'Area', 'Category','Timeslot','Time Taken','Efficiency']])\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "f5692b75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 0, 1])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.asarray(final_df['Rating'])\n",
    "y [0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "270ff175",
   "metadata": {},
   "source": [
    "### We will be using Logistic Regression to get this model\n",
    "##### first we need to import key libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "7cf02a17",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix, classification_report,accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db54f4d1",
   "metadata": {},
   "source": [
    "##### Next, we are normalizing our dataset, the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "57266028",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.15364641, -1.23012367, -0.97446543,  2.16538754, -0.24776668,\n",
       "        -0.52963602, -0.58782546, -0.97295621, -0.18825699,  1.15205752],\n",
       "       [-2.15364641, -1.23012367, -0.92701643,  2.16538754, -0.24776668,\n",
       "        -0.23452558, -0.58782546, -0.97295621, -0.18825699,  1.15205752],\n",
       "       [-2.15364641, -1.23012367, -0.87956743, -0.47433573, -0.24776668,\n",
       "        -0.23452558,  1.21154865, -0.97295621, -0.18825699, -0.04095732],\n",
       "       [-2.15364641, -1.23012367, -0.87956743, -0.47433573, -0.24776668,\n",
       "        -0.52963602,  1.21154865, -0.97295621, -0.18825699, -0.04095732],\n",
       "       [-2.15364641, -1.23012367, -0.83211843,  2.16538754, -0.24776668,\n",
       "        -0.23452558,  3.01092276, -0.97295621,  1.08216226, -0.04095732]])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = preprocessing.StandardScaler().fit(X).transform(X)\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54f96801",
   "metadata": {},
   "source": [
    "##### Next, creating train-test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "c8086c6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (10907, 10) (10907,)\n",
      "Test set: (2727, 10) (2727,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\n",
    "print ('Train set:', X_train.shape,  y_train.shape)\n",
    "print ('Test set:', X_test.shape,  y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "071bc6fd",
   "metadata": {},
   "source": [
    "##### Now creating model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "67bf5a8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.01, class_weight='balanced', max_iter=10000,\n",
       "                   multi_class='ovr')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_model = LogisticRegression(C=0.01, solver='lbfgs',max_iter=10000,\n",
    "                                    multi_class='ovr', class_weight='balanced').fit(X_train,y_train)\n",
    "#C is inverse of regularization, smaller values means, stronger regularization\n",
    "#multi-class is set to 'ovr' to make sure it remains a binary fit problem\n",
    "#solver 'lbfgs' gave better result than solver 'liblinear'\n",
    "#class-weight is set to balanced so that weight can be adjusted as per class frequency. Essential as our has more +ve\n",
    "logistic_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c575dad",
   "metadata": {},
   "source": [
    "##### Then, predict using test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "15ffb1f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, ..., 0, 1, 1])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = logistic_model.predict(X_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b326abde",
   "metadata": {},
   "source": [
    "##### Now, getting our prediction reports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "856635df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix: \n",
      " [[ 719    5]\n",
      " [  54 1949]]\n",
      "=============================================\n",
      "Classification Report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.99      0.96       724\n",
      "           1       1.00      0.97      0.99      2003\n",
      "\n",
      "    accuracy                           0.98      2727\n",
      "   macro avg       0.96      0.98      0.97      2727\n",
      "weighted avg       0.98      0.98      0.98      2727\n",
      "\n",
      "=============================================\n",
      "Accuracy Score: \n",
      " 0.9783645031169783\n"
     ]
    }
   ],
   "source": [
    "print(\"Confusion Matrix: \\n\",confusion_matrix(y_test, predictions))\n",
    "print(\"=============================================\")\n",
    "print(\"Classification Report: \\n\",classification_report(y_test,predictions))\n",
    "print(\"=============================================\")\n",
    "print(\"Accuracy Score: \\n\", accuracy_score(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af61acbb",
   "metadata": {},
   "source": [
    "# Proposal for other possible measures to evaluate clients with respect to efficiency, roles, and activities"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f50a784f",
   "metadata": {},
   "source": [
    "1. First of all, ``Rating`` perhaps should be taken from customers at stores, at the end of their visits, if possible <br>\n",
    "2. Activity should be classified distinctly and thoughtfully into quite a few than here. e.g.<br>\n",
    "Here, we have **Wait No Customer**, & **Wait No Customers** - these two means same thing.<br>\n",
    "Similarly, **Not Working**, **Facing Up - away from till area**, **Personal Needs**, **Talk Not work** again means same activity <br>\n",
    "3. ``Role`` of staff who performed certain activity must be included. Second dataset lacaked that.<br>\n",
    "4. ``BMS`` i.e, Benchmark minutes are too tight e.g,<br>\n",
    "``Determine Customer Requirements`` has BMS of 0.05 minutes - it is less than a second. <br>\n",
    "If ``Determine Customer Requirements`` includes listening to customer and then determine - well, it will take longer.<br>\n",
    "It will defnitely improve efficiency"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a07c3ec",
   "metadata": {},
   "source": [
    "## Now, lets model our clustering of clients"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11383775",
   "metadata": {},
   "source": [
    "Lets first copy from the copied model of ``final_df_copy``<br>\n",
    "``final_df_copy`` has been processed till ``Timeslot`` has been divided properly into 4 slots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "83c90781",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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>Location</th>\n",
       "      <th>Day</th>\n",
       "      <th>Elements</th>\n",
       "      <th>Rating</th>\n",
       "      <th>BMS</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Area</th>\n",
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       "      <th>Time Taken</th>\n",
       "      <th>Efficiency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Call Up Next Customer &amp; Start Transaction</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Serve Customer at Collections/Click&amp;Collect</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Deal with Store Visit RM / AM etc</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Task &amp; Process</td>\n",
       "      <td>Morning</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location     Day                                     Elements Rating  BMS  \\\n",
       "0  Stamford  Monday    Call Up Next Customer & Start Transaction  100.0  5.0   \n",
       "1  Stamford  Monday  Serve Customer at Collections/Click&Collect  100.0  5.0   \n",
       "2  Stamford  Monday                               Customer Count      0  0.0   \n",
       "3  Stamford  Monday                               Customer Count      0  0.0   \n",
       "4  Stamford  Monday            Deal with Store Visit RM / AM etc  100.0  5.0   \n",
       "\n",
       "   Qty              Area   Main Category Timeslot  Time Taken  Efficiency  \n",
       "0  1.0    Main Till Bank        Customer  Morning         0.0         5.0  \n",
       "1  1.0  Collections Desk        Customer  Morning         0.0         5.0  \n",
       "2  1.0  Collections Desk             NVA  Morning         0.0         0.0  \n",
       "3  1.0    Main Till Bank             NVA  Morning         0.0         0.0  \n",
       "4  1.0  Collections Desk  Task & Process  Morning         5.0         0.0  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = final_df_copy.copy()\n",
    "final_df.drop(['Time'],axis=1,inplace=True) #as we dont necessarily need time, perfectly created timeslot\n",
    "final_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f59ddb3b",
   "metadata": {},
   "source": [
    "As most of our columns are **categorical**<br>\n",
    "So, famous KMeans clustering and my favourtite DBSCAN (for being shaping cluster within cluster, and kicking out outliers)<br>\n",
    "Therfore, we will be using ``KModes`` for this modelling "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5208a27e",
   "metadata": {},
   "source": [
    "For that, first of all, we need to see what are the **non-categorical** column we have, and change it back to **categorical**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "08b02fa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BMS</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Time Taken</th>\n",
       "      <th>Efficiency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13634.000000</td>\n",
       "      <td>13634.000000</td>\n",
       "      <td>13634.000000</td>\n",
       "      <td>13634.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.898457</td>\n",
       "      <td>1.289277</td>\n",
       "      <td>0.740925</td>\n",
       "      <td>0.171655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.894207</td>\n",
       "      <td>1.167580</td>\n",
       "      <td>3.935853</td>\n",
       "      <td>4.191216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-266.000000</td>\n",
       "      <td>-113.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.029413</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>-0.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.089800</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.116667</td>\n",
       "      <td>-0.006933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.247825</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.058092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>57.999100</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>113.283333</td>\n",
       "      <td>266.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                BMS           Qty    Time Taken    Efficiency\n",
       "count  13634.000000  13634.000000  13634.000000  13634.000000\n",
       "mean       0.898457      1.289277      0.740925      0.171655\n",
       "std        1.894207      1.167580      3.935853      4.191216\n",
       "min        0.000000      0.000000   -266.000000   -113.283333\n",
       "25%        0.029413      1.000000      0.050000     -0.133333\n",
       "50%        0.089800      1.000000      0.116667     -0.006933\n",
       "75%        0.247825      1.000000      0.333333      0.058092\n",
       "max       57.999100     60.000000    113.283333    266.000000"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "44b6b53c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for columns in ['BMS', 'Qty', 'Time Taken', 'Efficiency']:\n",
    "    final_df[columns] = final_df[columns].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "899d2b4c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>13634</td>\n",
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       "      <th>unique</th>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>106</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3671.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>395.0</td>\n",
       "      <td>7476.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Bristol Emerson Green</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Call Up Next Customer &amp; Start Transaction</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>2891</td>\n",
       "      <td>4252</td>\n",
       "      <td>2610</td>\n",
       "      <td>10084.0</td>\n",
       "      <td>3243.0</td>\n",
       "      <td>12088.0</td>\n",
       "      <td>9603</td>\n",
       "      <td>9786</td>\n",
       "      <td>6130</td>\n",
       "      <td>2751.0</td>\n",
       "      <td>1936.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Location      Day  \\\n",
       "count                   13634    13634   \n",
       "unique                     12        7   \n",
       "top     Bristol Emerson Green  Tuesday   \n",
       "freq                     2891     4252   \n",
       "\n",
       "                                         Elements   Rating      BMS      Qty  \\\n",
       "count                                       13634  13634.0  13634.0  13634.0   \n",
       "unique                                        106     12.0   3671.0     20.0   \n",
       "top     Call Up Next Customer & Start Transaction    100.0      0.0      1.0   \n",
       "freq                                         2610  10084.0   3243.0  12088.0   \n",
       "\n",
       "                  Area Main Category Timeslot  Time Taken  Efficiency  \n",
       "count            13634         13634    13634     13634.0     13634.0  \n",
       "unique              10             3        4       395.0      7476.0  \n",
       "top     Main Till Bank      Customer  Morning         0.0         0.0  \n",
       "freq              9603          9786     6130      2751.0      1936.0  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db76fe72",
   "metadata": {},
   "source": [
    "Now, we need to use ``LabelEncoder`` to encode our categorical, just like previous modelling where we factorized to parse into model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "9dc1dac9",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df_copy = final_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "bd36df16",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoding = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "71cf460c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>9</td>\n",
       "      <td>3659</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>261</td>\n",
       "      <td>4567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location  Day  Elements  Rating   BMS  Qty  Area  Main Category  Timeslot  \\\n",
       "0        11    1        20       9  3659    1     4              0         3   \n",
       "1        11    1        78       9  3659    1     1              0         3   \n",
       "2        11    1        30       0     0    1     1              1         3   \n",
       "3        11    1        30       0     0    1     4              1         3   \n",
       "4        11    1        32       9  3659    1     1              2         3   \n",
       "\n",
       "   Time Taken  Efficiency  \n",
       "0           2        7464  \n",
       "1           2        7464  \n",
       "2           2        4567  \n",
       "3           2        4567  \n",
       "4         261        4567  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = final_df.apply(encoding.fit_transform)\n",
    "final_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9717891",
   "metadata": {},
   "source": [
    "##### we are importing KModes to cluster our categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "2c09bbb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from kmodes.kmodes import KModes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "671908d3",
   "metadata": {},
   "source": [
    "``KModes`` have two popular initialization methods :<br>\n",
    "1. Huang - It proposed two simple initialization methods for k-modes, in which the first method selects the first k objects from the dataset as initial cluster centers, and the second method assigns the most frequent categories equally to the k initial cluster centers.\n",
    "2. CAO - It has a new dissimilarity measure to take into account of the distribution of attribute values on the whole universe. Convergence and time complexity is proven to be better with CAO when working on large datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b46ae6d",
   "metadata": {},
   "source": [
    "#### We will be using CAO initialization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f4c19e4",
   "metadata": {},
   "source": [
    "### First, we need to select K by comparing Cost against each K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "1085b69c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cost = []\n",
    "for num_clusters in list(range(1,5)):\n",
    "    kmode = KModes(n_clusters=num_clusters, init = \"Cao\", max_iter=1000, n_init = 50)\n",
    "    kmode.fit_predict(final_df)\n",
    "    cost.append(kmode.cost_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "cb671678",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2eed1c6ea90>]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.array([i for i in range(1,5,1)])\n",
    "plt.plot(y,cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eeba1fb",
   "metadata": {},
   "source": [
    "#### Choosing K=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "5bd7e930",
   "metadata": {},
   "outputs": [],
   "source": [
    "cao_model = KModes(n_clusters=3, init = \"Cao\", max_iter=1000, n_init = 50)\n",
    "cao_fit_cluster = cao_model.fit_predict(final_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "9e9627e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, ..., 2, 0, 1], dtype=uint16)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cao_fit_cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "a8919e03",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = final_df_copy.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "a9641368",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_df = pd.DataFrame(cao_fit_cluster)\n",
    "cluster_df.columns = ['predicted_cluster']\n",
    "combined_df = pd.concat([final_df, cluster_df], axis = 1).reset_index()\n",
    "combined_df = combined_df.drop(['index', 'level_0'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "63cdcc85",
   "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>Location</th>\n",
       "      <th>Day</th>\n",
       "      <th>Elements</th>\n",
       "      <th>Rating</th>\n",
       "      <th>BMS</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Area</th>\n",
       "      <th>Main Category</th>\n",
       "      <th>Timeslot</th>\n",
       "      <th>Time Taken</th>\n",
       "      <th>Efficiency</th>\n",
       "      <th>predicted_cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Call Up Next Customer &amp; Start Transaction</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Serve Customer at Collections/Click&amp;Collect</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Customer Count</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Main Till Bank</td>\n",
       "      <td>NVA</td>\n",
       "      <td>Morning</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stamford</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Deal with Store Visit RM / AM etc</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Collections Desk</td>\n",
       "      <td>Task &amp; Process</td>\n",
       "      <td>Morning</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Location     Day                                     Elements Rating  BMS  \\\n",
       "0  Stamford  Monday    Call Up Next Customer & Start Transaction  100.0  5.0   \n",
       "1  Stamford  Monday  Serve Customer at Collections/Click&Collect  100.0  5.0   \n",
       "2  Stamford  Monday                               Customer Count      0  0.0   \n",
       "3  Stamford  Monday                               Customer Count      0  0.0   \n",
       "4  Stamford  Monday            Deal with Store Visit RM / AM etc  100.0  5.0   \n",
       "\n",
       "   Qty              Area   Main Category Timeslot Time Taken Efficiency  \\\n",
       "0  1.0    Main Till Bank        Customer  Morning        0.0        5.0   \n",
       "1  1.0  Collections Desk        Customer  Morning        0.0        5.0   \n",
       "2  1.0  Collections Desk             NVA  Morning        0.0        0.0   \n",
       "3  1.0    Main Till Bank             NVA  Morning        0.0        0.0   \n",
       "4  1.0  Collections Desk  Task & Process  Morning        5.0        0.0   \n",
       "\n",
       "   predicted_cluster  \n",
       "0                  0  \n",
       "1                  0  \n",
       "2                  1  \n",
       "3                  1  \n",
       "4                  2  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "999e03f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7441\n",
       "1    3311\n",
       "2    2882\n",
       "Name: predicted_cluster, dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df['predicted_cluster'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "e919d79e",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_0 = combined_df[combined_df['predicted_cluster'] == 0]\n",
    "cluster_1 = combined_df[combined_df['predicted_cluster'] == 1]\n",
    "cluster_2 = combined_df[combined_df['predicted_cluster'] == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "667eee4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({'font.size': 18})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "1c5f4b61",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2520x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize = (35,15))\n",
    "sns.countplot(x=combined_df['Location'],order=combined_df['Location'].value_counts().index, hue=combined_df['predicted_cluster'])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "42bee8b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize = (20,5))\n",
    "sns.countplot(x=combined_df['Main Category'],order=combined_df['Main Category'].value_counts().index, hue=combined_df['predicted_cluster'])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3897ef79",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88ddf771",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}