HandsON_RFM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'renders'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-132-bd7b0744dc73>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[1;32mimport\u001b[0m \u001b[0mrenders\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilterwarnings\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'ignore'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[1;31m#df = pd.read_excel(\"C:/Users/meharDesktop/Dell/Customer_Segmentation_RFM/Hands_ON/Online_Retail.xlsx\")\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'renders'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "#df = pd.read_excel(\"C:/Users/meharDesktop/Dell/Customer_Segmentation_RFM/Hands_ON/Online_Retail.xlsx\")\n",
    "\n",
    "df = pd.read_excel(r\"C:\\Users\\mehar\\Online_Retail.xlsx\")\n",
    "df.head()\n",
    "df1 = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of unique countries in current dataset\n",
    "df1.Country.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['United Kingdom', 'France', 'Australia', 'Netherlands', 'Germany',\n",
       "       'Norway', 'EIRE', 'Switzerland', 'Spain', 'Poland', 'Portugal',\n",
       "       'Italy', 'Belgium', 'Lithuania', 'Japan', 'Iceland',\n",
       "       'Channel Islands', 'Denmark', 'Cyprus', 'Sweden', 'Austria',\n",
       "       'Israel', 'Finland', 'Bahrain', 'Greece', 'Hong Kong', 'Singapore',\n",
       "       'Lebanon', 'United Arab Emirates', 'Saudi Arabia',\n",
       "       'Czech Republic', 'Canada', 'Unspecified', 'Brazil', 'USA',\n",
       "       'European Community', 'Malta', 'RSA'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#List of unique countries in the dataset\n",
    "df1.Country.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>CustomerID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>3950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Germany</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>France</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Spain</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Italy</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Finland</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Norway</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Channel Islands</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Cyprus</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Japan</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Poland</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>USA</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Canada</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Unspecified</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Israel</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Greece</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>EIRE</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Malta</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Lithuania</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Lebanon</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>RSA</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>European Community</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Country  CustomerID\n",
       "36        United Kingdom        3950\n",
       "14               Germany          95\n",
       "13                France          87\n",
       "31                 Spain          31\n",
       "3                Belgium          25\n",
       "33           Switzerland          21\n",
       "27              Portugal          19\n",
       "19                 Italy          15\n",
       "12               Finland          12\n",
       "1                Austria          11\n",
       "25                Norway          10\n",
       "24           Netherlands           9\n",
       "0              Australia           9\n",
       "6        Channel Islands           9\n",
       "9                Denmark           9\n",
       "7                 Cyprus           8\n",
       "32                Sweden           8\n",
       "20                 Japan           8\n",
       "26                Poland           6\n",
       "34                   USA           4\n",
       "5                 Canada           4\n",
       "37           Unspecified           4\n",
       "18                Israel           4\n",
       "15                Greece           4\n",
       "10                  EIRE           3\n",
       "23                 Malta           2\n",
       "35  United Arab Emirates           2\n",
       "2                Bahrain           2\n",
       "22             Lithuania           1\n",
       "8         Czech Republic           1\n",
       "21               Lebanon           1\n",
       "28                   RSA           1\n",
       "29          Saudi Arabia           1\n",
       "30             Singapore           1\n",
       "17               Iceland           1\n",
       "4                 Brazil           1\n",
       "11    European Community           1\n",
       "16             Hong Kong           0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_country=df1[['Country','CustomerID']].drop_duplicates()\n",
    "\n",
    "customer_country.groupby(['Country'])['CustomerID'].aggregate('count').reset_index().sort_values('CustomerID', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#From the data, we see that 90%of the customers are from UK. so restricting the data to UK\n",
    "df1 = df1.loc[df1['Country'] == 'United Kingdom']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo           0\n",
       "StockCode           0\n",
       "Description      1454\n",
       "Quantity            0\n",
       "InvoiceDate         0\n",
       "UnitPrice           0\n",
       "CustomerID     133600\n",
       "Country             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to check the missing values in the dataset. \n",
    "df1.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo      0\n",
       "StockCode      0\n",
       "Description    0\n",
       "Quantity       0\n",
       "InvoiceDate    0\n",
       "UnitPrice      0\n",
       "CustomerID     0\n",
       "Country        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Removing the missing values from the dataset.\n",
    "df1 = df1[pd.notnull(df1['CustomerID'])]\n",
    "df1.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the maximum and minimum values in the UnitPrice column\n",
    "df1.UnitPrice.min()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38970.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.UnitPrice.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-80995\n",
      "80995\n"
     ]
    }
   ],
   "source": [
    "print(df1.Quantity.min())\n",
    "print(df1.Quantity.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 354345 entries, 0 to 541893\n",
      "Data columns (total 8 columns):\n",
      "InvoiceNo      354345 non-null object\n",
      "StockCode      354345 non-null object\n",
      "Description    354345 non-null object\n",
      "Quantity       354345 non-null int64\n",
      "InvoiceDate    354345 non-null datetime64[ns]\n",
      "UnitPrice      354345 non-null float64\n",
      "CustomerID     354345 non-null float64\n",
      "Country        354345 non-null object\n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 24.3+ MB\n"
     ]
    }
   ],
   "source": [
    "#Quantity cannot be negative, Remove these values from dataset\n",
    "df1 = df1[(df1['Quantity']>0)]\n",
    "df1.shape\n",
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Now we have just 354345 rows and 8 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# # DF TO EXCEL\n",
    "# from pandas import ExcelWriter\n",
    "\n",
    "# writer = ExcelWriter('Online_Retail_CLEAN.xlsx')\n",
    "# df1.to_excel(writer,'Sheet5')\n",
    "# writer.save()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "InvoiceNo :  16649\n",
      "StockCode :  3645\n",
      "Description :  3844\n",
      "Quantity :  294\n",
      "InvoiceDate :  15615\n",
      "UnitPrice :  403\n",
      "CustomerID :  3921\n",
      "Country :  1\n"
     ]
    }
   ],
   "source": [
    "#Unique value for each column\n",
    "def unique_counts(df1):\n",
    "   for i in df1.columns:\n",
    "       count = df1[i].nunique()\n",
    "       print(i, \": \", count)\n",
    "unique_counts(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Adding a column for the total price\n",
    "df1['TotalPrice'] = df1['Quantity'] * df1['UnitPrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2010-12-01 08:26:00')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding the first and last order dates in the data\n",
    "df1['InvoiceDate'].min()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-12-09 12:49:00')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['InvoiceDate'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "import numpy as np\n",
    "\n",
    "NOW = dt.datetime(2011,12,10)\n",
    "#NOW = np.datetime64(NOW)\n",
    "df1['InvoiceDate'] = pd.to_datetime(df1['InvoiceDate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#RFM Customer Segmentation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rfmTable = df1.groupby('CustomerID').agg({'InvoiceDate': lambda x: (NOW - x.max()).days, # Recency\n",
    "                                        'InvoiceNo': lambda x: len(x),      # Frequency\n",
    "                                        'TotalPrice': lambda x: x.sum()}) # Monetary Value\n",
    "\n",
    "# milliseconds = int(round(dt.timestamp() * 1000))\n",
    "# print(milliseconds\n",
    "\n",
    "rfmTable['InvoiceDate'] = rfmTable['InvoiceDate'].values.astype(int)\n",
    "rfmTable.rename(columns={'InvoiceDate': 'recency', \n",
    "                         'InvoiceNo': 'frequency', \n",
    "                         'TotalPrice': 'monetary_value'}, inplace=True)\n",
    "                                        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346.0</th>\n",
       "      <td>325</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12747.0</th>\n",
       "      <td>2</td>\n",
       "      <td>103</td>\n",
       "      <td>4196.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12748.0</th>\n",
       "      <td>0</td>\n",
       "      <td>4596</td>\n",
       "      <td>33719.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12749.0</th>\n",
       "      <td>3</td>\n",
       "      <td>199</td>\n",
       "      <td>4090.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12820.0</th>\n",
       "      <td>3</td>\n",
       "      <td>59</td>\n",
       "      <td>942.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            recency  frequency  monetary_value\n",
       "CustomerID                                    \n",
       "12346.0         325          1        77183.60\n",
       "12747.0           2        103         4196.01\n",
       "12748.0           0       4596        33719.73\n",
       "12749.0           3        199         4090.88\n",
       "12820.0           3         59          942.34"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfmTable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "373\n",
      "7847\n"
     ]
    }
   ],
   "source": [
    "print(rfmTable['recency'].max())\n",
    "print(rfmTable['frequency'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>TotalPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61619</th>\n",
       "      <td>541431</td>\n",
       "      <td>23166</td>\n",
       "      <td>MEDIUM CERAMIC TOP STORAGE JAR</td>\n",
       "      <td>74215</td>\n",
       "      <td>2011-01-18 10:01:00</td>\n",
       "      <td>1.04</td>\n",
       "      <td>12346.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>77183.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      InvoiceNo StockCode                     Description  Quantity  \\\n",
       "61619    541431     23166  MEDIUM CERAMIC TOP STORAGE JAR     74215   \n",
       "\n",
       "              InvoiceDate  UnitPrice  CustomerID         Country  TotalPrice  \n",
       "61619 2011-01-18 10:01:00       1.04     12346.0  United Kingdom     77183.6  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the details of the first customer\n",
    "first_customer = df1[df1['CustomerID']== 12346.0]\n",
    "first_customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(NOW - dt.datetime(2011,1,18)).days==326"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>r_quartile</th>\n",
       "      <th>f_quartile</th>\n",
       "      <th>m_quartile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>300.04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>50.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>651.82</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>142.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1575.89</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>373.0</td>\n",
       "      <td>7847.0</td>\n",
       "      <td>259657.30</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      recency  frequency  monetary_value  r_quartile  f_quartile  m_quartile\n",
       "0.25     17.0       17.0          300.04         1.0         2.0         2.0\n",
       "0.50     50.0       41.0          651.82         2.0         3.0         3.0\n",
       "0.75    142.0       99.0         1575.89         3.0         4.0         4.0\n",
       "1.00    373.0     7847.0       259657.30         4.0         4.0         4.0"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Split the metrics into segments using quartiles\n",
    "quantiles = rfmTable.quantile(q=[0.25,0.5,0.75,1])\n",
    "quantiles\n",
    "# quantiles = quantiles.to_dict()\n",
    "# segmented_rfm = rfmTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'frequency': {0.25: 17.0, 0.5: 41.0, 0.75: 99.0},\n",
       " 'monetary_value': {0.25: 300.03999999999996,\n",
       "  0.5: 651.8200000000002,\n",
       "  0.75: 1575.8900000000003},\n",
       " 'recency': {0.25: 17.0, 0.5: 50.0, 0.75: 142.0}}"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantiles = quantiles.to_dict()\n",
    "quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "segmented_rfm = rfmTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Lowest recency and highest frequency and mometary are our best customers\n",
    "def RScore(x,p,d):\n",
    "    if x <= d[p][0.25]:\n",
    "        return 1\n",
    "    elif x <= d[p][0.50]:\n",
    "        return 2\n",
    "    elif x <= d[p][0.75]: \n",
    "        return 3\n",
    "    else:\n",
    "        return 4\n",
    "    \n",
    "def FMScore(x,p,d):\n",
    "    if x <= d[p][0.25]:\n",
    "        return 4\n",
    "    elif x <= d[p][0.50]:\n",
    "        return 3\n",
    "    elif x <= d[p][0.75]: \n",
    "        return 2\n",
    "    else:\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('recency', {'recency': {0.25: 17.0, 0.5: 50.0, 0.75: 142.0}, 'frequency': {0.25: 17.0, 0.5: 41.0, 0.75: 99.0}, 'monetary_value': {0.25: 300.03999999999996, 0.5: 651.8200000000002, 0.75: 1575.8900000000003}})\n"
     ]
    }
   ],
   "source": [
    "args=('recency',quantiles,)\n",
    "print(args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>r_quartile</th>\n",
       "      <th>f_quartile</th>\n",
       "      <th>m_quartile</th>\n",
       "      <th>RFMScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346.0</th>\n",
       "      <td>325</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12747.0</th>\n",
       "      <td>2</td>\n",
       "      <td>103</td>\n",
       "      <td>4196.01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12748.0</th>\n",
       "      <td>0</td>\n",
       "      <td>4596</td>\n",
       "      <td>33719.73</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12749.0</th>\n",
       "      <td>3</td>\n",
       "      <td>199</td>\n",
       "      <td>4090.88</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12820.0</th>\n",
       "      <td>3</td>\n",
       "      <td>59</td>\n",
       "      <td>942.34</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            recency  frequency  monetary_value  r_quartile  f_quartile  \\\n",
       "CustomerID                                                               \n",
       "12346.0         325          1        77183.60           4           4   \n",
       "12747.0           2        103         4196.01           1           1   \n",
       "12748.0           0       4596        33719.73           1           1   \n",
       "12749.0           3        199         4090.88           1           1   \n",
       "12820.0           3         59          942.34           1           2   \n",
       "\n",
       "            m_quartile RFMScore  \n",
       "CustomerID                       \n",
       "12346.0              1      441  \n",
       "12747.0              1      111  \n",
       "12748.0              1      111  \n",
       "12749.0              1      111  \n",
       "12820.0              2      122  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "segmented_rfm['r_quartile'] = segmented_rfm['recency'].apply(RScore, args=('recency',quantiles,))\n",
    "segmented_rfm['f_quartile'] = segmented_rfm['frequency'].apply(FMScore, args=('frequency',quantiles,))\n",
    "segmented_rfm['m_quartile'] = segmented_rfm['monetary_value'].apply(FMScore, args=('monetary_value',quantiles,))\n",
    "segmented_rfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>r_quartile</th>\n",
       "      <th>f_quartile</th>\n",
       "      <th>m_quartile</th>\n",
       "      <th>RFMScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346.0</th>\n",
       "      <td>325</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12747.0</th>\n",
       "      <td>2</td>\n",
       "      <td>103</td>\n",
       "      <td>4196.01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12748.0</th>\n",
       "      <td>0</td>\n",
       "      <td>4596</td>\n",
       "      <td>33719.73</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12749.0</th>\n",
       "      <td>3</td>\n",
       "      <td>199</td>\n",
       "      <td>4090.88</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12820.0</th>\n",
       "      <td>3</td>\n",
       "      <td>59</td>\n",
       "      <td>942.34</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            recency  frequency  monetary_value  r_quartile  f_quartile  \\\n",
       "CustomerID                                                               \n",
       "12346.0         325          1        77183.60           4           4   \n",
       "12747.0           2        103         4196.01           1           1   \n",
       "12748.0           0       4596        33719.73           1           1   \n",
       "12749.0           3        199         4090.88           1           1   \n",
       "12820.0           3         59          942.34           1           2   \n",
       "\n",
       "            m_quartile RFMScore  \n",
       "CustomerID                       \n",
       "12346.0              1      441  \n",
       "12747.0              1      111  \n",
       "12748.0              1      111  \n",
       "12749.0              1      111  \n",
       "12820.0              2      122  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Add a new column to combine RFM score\n",
    "segmented_rfm['RFMScore'] = segmented_rfm.r_quartile.map(str) \\\n",
    "                            + segmented_rfm.f_quartile.map(str) \\\n",
    "                            + segmented_rfm.m_quartile.map(str)\n",
    "segmented_rfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>r_quartile</th>\n",
       "      <th>f_quartile</th>\n",
       "      <th>m_quartile</th>\n",
       "      <th>RFMScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>17511.0</th>\n",
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       "    <tr>\n",
       "      <th>16684.0</th>\n",
       "      <td>4</td>\n",
       "      <td>277</td>\n",
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       "      <th>14096.0</th>\n",
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       "    <tr>\n",
       "      <th>13694.0</th>\n",
       "      <td>3</td>\n",
       "      <td>568</td>\n",
       "      <td>65039.62</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <th>13089.0</th>\n",
       "      <td>2</td>\n",
       "      <td>1818</td>\n",
       "      <td>58825.83</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
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       "    <tr>\n",
       "      <th>15769.0</th>\n",
       "      <td>7</td>\n",
       "      <td>130</td>\n",
       "      <td>56252.72</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
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       "    <tr>\n",
       "      <th>15061.0</th>\n",
       "      <td>3</td>\n",
       "      <td>403</td>\n",
       "      <td>54534.14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
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       "    <tr>\n",
       "      <th>14298.0</th>\n",
       "      <td>8</td>\n",
       "      <td>1637</td>\n",
       "      <td>51527.30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14088.0</th>\n",
       "      <td>10</td>\n",
       "      <td>589</td>\n",
       "      <td>50491.81</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
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       "    <tr>\n",
       "      <th>17841.0</th>\n",
       "      <td>1</td>\n",
       "      <td>7847</td>\n",
       "      <td>40991.57</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
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       "    <tr>\n",
       "      <th>13798.0</th>\n",
       "      <td>1</td>\n",
       "      <td>349</td>\n",
       "      <td>37153.85</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
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       "    <tr>\n",
       "      <th>16013.0</th>\n",
       "      <td>3</td>\n",
       "      <td>139</td>\n",
       "      <td>37130.60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
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       "    <tr>\n",
       "      <th>16422.0</th>\n",
       "      <td>17</td>\n",
       "      <td>369</td>\n",
       "      <td>34684.40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
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       "    <tr>\n",
       "      <th>12748.0</th>\n",
       "      <td>0</td>\n",
       "      <td>4596</td>\n",
       "      <td>33719.73</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
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       "    <tr>\n",
       "      <th>15838.0</th>\n",
       "      <td>11</td>\n",
       "      <td>167</td>\n",
       "      <td>33643.08</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
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       "    <tr>\n",
       "      <th>17389.0</th>\n",
       "      <td>0</td>\n",
       "      <td>213</td>\n",
       "      <td>31833.68</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13098.0</th>\n",
       "      <td>1</td>\n",
       "      <td>572</td>\n",
       "      <td>28882.44</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13081.0</th>\n",
       "      <td>11</td>\n",
       "      <td>1028</td>\n",
       "      <td>28337.38</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13408.0</th>\n",
       "      <td>1</td>\n",
       "      <td>478</td>\n",
       "      <td>28117.04</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13777.0</th>\n",
       "      <td>0</td>\n",
       "      <td>197</td>\n",
       "      <td>25977.16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16210.0</th>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>21086.30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17675.0</th>\n",
       "      <td>1</td>\n",
       "      <td>705</td>\n",
       "      <td>20374.28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17381.0</th>\n",
       "      <td>8</td>\n",
       "      <td>109</td>\n",
       "      <td>20275.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15039.0</th>\n",
       "      <td>9</td>\n",
       "      <td>1502</td>\n",
       "      <td>19914.44</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15159.0</th>\n",
       "      <td>1</td>\n",
       "      <td>710</td>\n",
       "      <td>18641.01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12901.0</th>\n",
       "      <td>8</td>\n",
       "      <td>116</td>\n",
       "      <td>17654.54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14031.0</th>\n",
       "      <td>7</td>\n",
       "      <td>250</td>\n",
       "      <td>17286.86</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13969.0</th>\n",
       "      <td>7</td>\n",
       "      <td>626</td>\n",
       "      <td>9053.94</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13488.0</th>\n",
       "      <td>17</td>\n",
       "      <td>422</td>\n",
       "      <td>8968.31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13090.0</th>\n",
       "      <td>8</td>\n",
       "      <td>155</td>\n",
       "      <td>8910.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16033.0</th>\n",
       "      <td>5</td>\n",
       "      <td>1137</td>\n",
       "      <td>8816.40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17017.0</th>\n",
       "      <td>2</td>\n",
       "      <td>252</td>\n",
       "      <td>8694.26</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17243.0</th>\n",
       "      <td>1</td>\n",
       "      <td>400</td>\n",
       "      <td>8676.85</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14527.0</th>\n",
       "      <td>2</td>\n",
       "      <td>972</td>\n",
       "      <td>8508.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18139.0</th>\n",
       "      <td>17</td>\n",
       "      <td>159</td>\n",
       "      <td>8438.34</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15358.0</th>\n",
       "      <td>4</td>\n",
       "      <td>551</td>\n",
       "      <td>8347.20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16558.0</th>\n",
       "      <td>0</td>\n",
       "      <td>460</td>\n",
       "      <td>8338.49</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14056.0</th>\n",
       "      <td>1</td>\n",
       "      <td>1106</td>\n",
       "      <td>8214.65</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16672.0</th>\n",
       "      <td>2</td>\n",
       "      <td>114</td>\n",
       "      <td>8134.94</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13854.0</th>\n",
       "      <td>8</td>\n",
       "      <td>110</td>\n",
       "      <td>8109.52</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18109.0</th>\n",
       "      <td>1</td>\n",
       "      <td>444</td>\n",
       "      <td>8052.97</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14796.0</th>\n",
       "      <td>1</td>\n",
       "      <td>1141</td>\n",
       "      <td>8022.49</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15290.0</th>\n",
       "      <td>4</td>\n",
       "      <td>204</td>\n",
       "      <td>7943.22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17811.0</th>\n",
       "      <td>4</td>\n",
       "      <td>851</td>\n",
       "      <td>7837.73</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15194.0</th>\n",
       "      <td>3</td>\n",
       "      <td>310</td>\n",
       "      <td>7616.53</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18172.0</th>\n",
       "      <td>14</td>\n",
       "      <td>189</td>\n",
       "      <td>7561.68</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13263.0</th>\n",
       "      <td>1</td>\n",
       "      <td>1677</td>\n",
       "      <td>7454.07</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13018.0</th>\n",
       "      <td>16</td>\n",
       "      <td>458</td>\n",
       "      <td>7277.70</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
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       "    <tr>\n",
       "      <th>18229.0</th>\n",
       "      <td>11</td>\n",
       "      <td>164</td>\n",
       "      <td>7276.90</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16923.0</th>\n",
       "      <td>12</td>\n",
       "      <td>672</td>\n",
       "      <td>7247.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16873.0</th>\n",
       "      <td>4</td>\n",
       "      <td>279</td>\n",
       "      <td>7123.62</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13985.0</th>\n",
       "      <td>4</td>\n",
       "      <td>351</td>\n",
       "      <td>7060.13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14961.0</th>\n",
       "      <td>10</td>\n",
       "      <td>312</td>\n",
       "      <td>7044.68</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15023.0</th>\n",
       "      <td>3</td>\n",
       "      <td>562</td>\n",
       "      <td>6802.76</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18055.0</th>\n",
       "      <td>7</td>\n",
       "      <td>360</td>\n",
       "      <td>6784.19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15601.0</th>\n",
       "      <td>11</td>\n",
       "      <td>410</td>\n",
       "      <td>6776.56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13576.0</th>\n",
       "      <td>10</td>\n",
       "      <td>126</td>\n",
       "      <td>6717.34</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            recency  frequency  monetary_value  r_quartile  f_quartile  \\\n",
       "CustomerID                                                               \n",
       "18102.0           0        431       259657.30           1           1   \n",
       "17450.0           8        337       194550.79           1           1   \n",
       "17511.0           2        963        91062.38           1           1   \n",
       "16684.0           4        277        66653.56           1           1   \n",
       "14096.0           4       5111        65164.79           1           1   \n",
       "13694.0           3        568        65039.62           1           1   \n",
       "15311.0           0       2379        60767.90           1           1   \n",
       "13089.0           2       1818        58825.83           1           1   \n",
       "15769.0           7        130        56252.72           1           1   \n",
       "15061.0           3        403        54534.14           1           1   \n",
       "14298.0           8       1637        51527.30           1           1   \n",
       "14088.0          10        589        50491.81           1           1   \n",
       "17841.0           1       7847        40991.57           1           1   \n",
       "13798.0           1        349        37153.85           1           1   \n",
       "16013.0           3        139        37130.60           1           1   \n",
       "16422.0          17        369        34684.40           1           1   \n",
       "12748.0           0       4596        33719.73           1           1   \n",
       "15838.0          11        167        33643.08           1           1   \n",
       "17389.0           0        213        31833.68           1           1   \n",
       "13098.0           1        572        28882.44           1           1   \n",
       "13081.0          11       1028        28337.38           1           1   \n",
       "13408.0           1        478        28117.04           1           1   \n",
       "13777.0           0        197        25977.16           1           1   \n",
       "16210.0           1        123        21086.30           1           1   \n",
       "17675.0           1        705        20374.28           1           1   \n",
       "17381.0           8        109        20275.61           1           1   \n",
       "15039.0           9       1502        19914.44           1           1   \n",
       "15159.0           1        710        18641.01           1           1   \n",
       "12901.0           8        116        17654.54           1           1   \n",
       "14031.0           7        250        17286.86           1           1   \n",
       "...             ...        ...             ...         ...         ...   \n",
       "13969.0           7        626         9053.94           1           1   \n",
       "13488.0          17        422         8968.31           1           1   \n",
       "13090.0           8        155         8910.82           1           1   \n",
       "16033.0           5       1137         8816.40           1           1   \n",
       "17017.0           2        252         8694.26           1           1   \n",
       "17243.0           1        400         8676.85           1           1   \n",
       "14527.0           2        972         8508.82           1           1   \n",
       "18139.0          17        159         8438.34           1           1   \n",
       "15358.0           4        551         8347.20           1           1   \n",
       "16558.0           0        460         8338.49           1           1   \n",
       "14056.0           1       1106         8214.65           1           1   \n",
       "16672.0           2        114         8134.94           1           1   \n",
       "13854.0           8        110         8109.52           1           1   \n",
       "18109.0           1        444         8052.97           1           1   \n",
       "14796.0           1       1141         8022.49           1           1   \n",
       "15290.0           4        204         7943.22           1           1   \n",
       "17811.0           4        851         7837.73           1           1   \n",
       "15194.0           3        310         7616.53           1           1   \n",
       "18172.0          14        189         7561.68           1           1   \n",
       "13263.0           1       1677         7454.07           1           1   \n",
       "13018.0          16        458         7277.70           1           1   \n",
       "18229.0          11        164         7276.90           1           1   \n",
       "16923.0          12        672         7247.61           1           1   \n",
       "16873.0           4        279         7123.62           1           1   \n",
       "13985.0           4        351         7060.13           1           1   \n",
       "14961.0          10        312         7044.68           1           1   \n",
       "15023.0           3        562         6802.76           1           1   \n",
       "18055.0           7        360         6784.19           1           1   \n",
       "15601.0          11        410         6776.56           1           1   \n",
       "13576.0          10        126         6717.34           1           1   \n",
       "\n",
       "            m_quartile RFMScore  \n",
       "CustomerID                       \n",
       "18102.0              1      111  \n",
       "17450.0              1      111  \n",
       "17511.0              1      111  \n",
       "16684.0              1      111  \n",
       "14096.0              1      111  \n",
       "13694.0              1      111  \n",
       "15311.0              1      111  \n",
       "13089.0              1      111  \n",
       "15769.0              1      111  \n",
       "15061.0              1      111  \n",
       "14298.0              1      111  \n",
       "14088.0              1      111  \n",
       "17841.0              1      111  \n",
       "13798.0              1      111  \n",
       "16013.0              1      111  \n",
       "16422.0              1      111  \n",
       "12748.0              1      111  \n",
       "15838.0              1      111  \n",
       "17389.0              1      111  \n",
       "13098.0              1      111  \n",
       "13081.0              1      111  \n",
       "13408.0              1      111  \n",
       "13777.0              1      111  \n",
       "16210.0              1      111  \n",
       "17675.0              1      111  \n",
       "17381.0              1      111  \n",
       "15039.0              1      111  \n",
       "15159.0              1      111  \n",
       "12901.0              1      111  \n",
       "14031.0              1      111  \n",
       "...                ...      ...  \n",
       "13969.0              1      111  \n",
       "13488.0              1      111  \n",
       "13090.0              1      111  \n",
       "16033.0              1      111  \n",
       "17017.0              1      111  \n",
       "17243.0              1      111  \n",
       "14527.0              1      111  \n",
       "18139.0              1      111  \n",
       "15358.0              1      111  \n",
       "16558.0              1      111  \n",
       "14056.0              1      111  \n",
       "16672.0              1      111  \n",
       "13854.0              1      111  \n",
       "18109.0              1      111  \n",
       "14796.0              1      111  \n",
       "15290.0              1      111  \n",
       "17811.0              1      111  \n",
       "15194.0              1      111  \n",
       "18172.0              1      111  \n",
       "13263.0              1      111  \n",
       "13018.0              1      111  \n",
       "18229.0              1      111  \n",
       "16923.0              1      111  \n",
       "16873.0              1      111  \n",
       "13985.0              1      111  \n",
       "14961.0              1      111  \n",
       "15023.0              1      111  \n",
       "18055.0              1      111  \n",
       "15601.0              1      111  \n",
       "13576.0              1      111  \n",
       "\n",
       "[100 rows x 7 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "segmented_rfm[segmented_rfm['RFMScore']=='111'].sort_values('monetary_value', ascending=False).head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "segmented_rfm."
   ]
  }
 ],
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