LinhNguyen_RFM_analysis_project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e5d0a0c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: squarify in c:\\users\\meebo\\anaconda3\\lib\\site-packages (0.4.3)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sb\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as mp\n",
    "!pip install squarify\n",
    "import squarify as sq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "57d8544b",
   "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>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Channel</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Unit Cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>646</td>\n",
       "      <td>CA-2017-126221</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>CC-12430</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>47201</td>\n",
       "      <td>OFF-AP-10002457</td>\n",
       "      <td>209.300</td>\n",
       "      <td>2</td>\n",
       "      <td>76.3945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>907</td>\n",
       "      <td>CA-2017-143259</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>PO-18865</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>10009</td>\n",
       "      <td>FUR-BO-10003441</td>\n",
       "      <td>323.136</td>\n",
       "      <td>4</td>\n",
       "      <td>77.7546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>908</td>\n",
       "      <td>CA-2017-143259</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>PO-18865</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>10009</td>\n",
       "      <td>TEC-PH-10004774</td>\n",
       "      <td>90.930</td>\n",
       "      <td>7</td>\n",
       "      <td>12.6003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>909</td>\n",
       "      <td>CA-2017-143259</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>PO-18865</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>10009</td>\n",
       "      <td>OFF-BI-10003684</td>\n",
       "      <td>52.776</td>\n",
       "      <td>3</td>\n",
       "      <td>10.9950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1297</td>\n",
       "      <td>CA-2017-115427</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>EB-13975</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>94533</td>\n",
       "      <td>OFF-BI-10002103</td>\n",
       "      <td>13.904</td>\n",
       "      <td>2</td>\n",
       "      <td>4.6926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1298</td>\n",
       "      <td>CA-2017-115427</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>EB-13975</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>94533</td>\n",
       "      <td>OFF-BI-10004632</td>\n",
       "      <td>20.720</td>\n",
       "      <td>2</td>\n",
       "      <td>7.1225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5092</td>\n",
       "      <td>CA-2017-156720</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>JM-15580</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>80538</td>\n",
       "      <td>OFF-FA-10003472</td>\n",
       "      <td>3.024</td>\n",
       "      <td>3</td>\n",
       "      <td>1.2096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1878</td>\n",
       "      <td>CA-2017-118885</td>\n",
       "      <td>2017-12-29</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>JG-15160</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>90049</td>\n",
       "      <td>FUR-CH-10002880</td>\n",
       "      <td>393.568</td>\n",
       "      <td>4</td>\n",
       "      <td>109.4611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1879</td>\n",
       "      <td>CA-2017-118885</td>\n",
       "      <td>2017-12-29</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>JG-15160</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>90049</td>\n",
       "      <td>TEC-PH-10002563</td>\n",
       "      <td>302.376</td>\n",
       "      <td>3</td>\n",
       "      <td>93.2326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2875</td>\n",
       "      <td>US-2017-102638</td>\n",
       "      <td>2017-12-29</td>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>First Class</td>\n",
       "      <td>MC-17845</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>10035</td>\n",
       "      <td>OFF-FA-10002988</td>\n",
       "      <td>6.030</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Row ID        Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "0     646  CA-2017-126221 2017-12-30 2018-01-05  Standard Class    CC-12430   \n",
       "1     907  CA-2017-143259 2017-12-30 2018-01-03  Standard Class    PO-18865   \n",
       "2     908  CA-2017-143259 2017-12-30 2018-01-03  Standard Class    PO-18865   \n",
       "3     909  CA-2017-143259 2017-12-30 2018-01-03  Standard Class    PO-18865   \n",
       "4    1297  CA-2017-115427 2017-12-30 2018-01-03  Standard Class    EB-13975   \n",
       "5    1298  CA-2017-115427 2017-12-30 2018-01-03  Standard Class    EB-13975   \n",
       "6    5092  CA-2017-156720 2017-12-30 2018-01-03  Standard Class    JM-15580   \n",
       "7    1878  CA-2017-118885 2017-12-29 2018-01-02  Standard Class    JG-15160   \n",
       "8    1879  CA-2017-118885 2017-12-29 2018-01-02  Standard Class    JG-15160   \n",
       "9    2875  US-2017-102638 2017-12-29 2017-12-31     First Class    MC-17845   \n",
       "\n",
       "       Channel  Postal Code       Product ID    Sales  Quantity  Unit Cost  \n",
       "0  Home Office        47201  OFF-AP-10002457  209.300         2    76.3945  \n",
       "1     Consumer        10009  FUR-BO-10003441  323.136         4    77.7546  \n",
       "2     Consumer        10009  TEC-PH-10004774   90.930         7    12.6003  \n",
       "3     Consumer        10009  OFF-BI-10003684   52.776         3    10.9950  \n",
       "4    Corporate        94533  OFF-BI-10002103   13.904         2     4.6926  \n",
       "5    Corporate        94533  OFF-BI-10004632   20.720         2     7.1225  \n",
       "6     Consumer        80538  OFF-FA-10003472    3.024         3     1.2096  \n",
       "7     Consumer        90049  FUR-CH-10002880  393.568         4   109.4611  \n",
       "8     Consumer        90049  TEC-PH-10002563  302.376         3    93.2326  \n",
       "9     Consumer        10035  OFF-FA-10002988    6.030         3     1.0251  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load dataframe of sheet Order\n",
    "orders = pd.read_excel('D:\\STUDY\\STUDY\\PYTHON\\Final_project_RFM\\Dataset.xlsx',sheet_name='Orders')\n",
    "orders.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "795667f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9994 entries, 0 to 9993\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype         \n",
      "---  ------       --------------  -----         \n",
      " 0   Row ID       9994 non-null   int64         \n",
      " 1   Order ID     9994 non-null   object        \n",
      " 2   Order Date   9994 non-null   datetime64[ns]\n",
      " 3   Ship Date    9994 non-null   datetime64[ns]\n",
      " 4   Ship Mode    9994 non-null   object        \n",
      " 5   Customer ID  9994 non-null   object        \n",
      " 6   Channel      9994 non-null   object        \n",
      " 7   Postal Code  9994 non-null   int64         \n",
      " 8   Product ID   9994 non-null   object        \n",
      " 9   Sales        9994 non-null   float64       \n",
      " 10  Quantity     9994 non-null   int64         \n",
      " 11  Unit Cost    9994 non-null   float64       \n",
      "dtypes: datetime64[ns](2), float64(2), int64(3), object(5)\n",
      "memory usage: 937.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check null value for sheet Orders\n",
    "orders.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "133f85a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>ship_date</th>\n",
       "      <th>ship_mode</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>channel</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>product_id</th>\n",
       "      <th>sales</th>\n",
       "      <th>quantity</th>\n",
       "      <th>unit_cost</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [row_id, order_id, order_date, ship_date, ship_mode, customer_id, channel, postal_code, product_id, sales, quantity, unit_cost]\n",
       "Index: []"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove blank spaces between words in column name\n",
    "orders.columns = [col.lower().replace(' ','_') for col in orders.columns]\n",
    "orders.head(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c55f036d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA-2014-100006</td>\n",
       "      <td>2014-09-07</td>\n",
       "      <td>DK-13375</td>\n",
       "      <td>377.970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA-2014-100090</td>\n",
       "      <td>2014-07-08</td>\n",
       "      <td>EB-13705</td>\n",
       "      <td>699.192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA-2014-100293</td>\n",
       "      <td>2014-03-14</td>\n",
       "      <td>NF-18475</td>\n",
       "      <td>91.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA-2014-100328</td>\n",
       "      <td>2014-01-28</td>\n",
       "      <td>JC-15340</td>\n",
       "      <td>3.928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA-2014-100363</td>\n",
       "      <td>2014-04-08</td>\n",
       "      <td>JM-15655</td>\n",
       "      <td>21.376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5004</th>\n",
       "      <td>US-2017-168802</td>\n",
       "      <td>2017-11-03</td>\n",
       "      <td>JO-15145</td>\n",
       "      <td>18.368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5005</th>\n",
       "      <td>US-2017-169320</td>\n",
       "      <td>2017-07-23</td>\n",
       "      <td>LH-16900</td>\n",
       "      <td>171.430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5006</th>\n",
       "      <td>US-2017-169488</td>\n",
       "      <td>2017-09-07</td>\n",
       "      <td>AA-10375</td>\n",
       "      <td>56.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5007</th>\n",
       "      <td>US-2017-169502</td>\n",
       "      <td>2017-08-28</td>\n",
       "      <td>MG-17650</td>\n",
       "      <td>113.410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5008</th>\n",
       "      <td>US-2017-169551</td>\n",
       "      <td>2017-07-07</td>\n",
       "      <td>RL-19615</td>\n",
       "      <td>1344.838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5009 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            order_id order_date customer_id     sales\n",
       "0     CA-2014-100006 2014-09-07    DK-13375   377.970\n",
       "1     CA-2014-100090 2014-07-08    EB-13705   699.192\n",
       "2     CA-2014-100293 2014-03-14    NF-18475    91.056\n",
       "3     CA-2014-100328 2014-01-28    JC-15340     3.928\n",
       "4     CA-2014-100363 2014-04-08    JM-15655    21.376\n",
       "...              ...        ...         ...       ...\n",
       "5004  US-2017-168802 2017-11-03    JO-15145    18.368\n",
       "5005  US-2017-169320 2017-07-23    LH-16900   171.430\n",
       "5006  US-2017-169488 2017-09-07    AA-10375    56.860\n",
       "5007  US-2017-169502 2017-08-28    MG-17650   113.410\n",
       "5008  US-2017-169551 2017-07-07    RL-19615  1344.838\n",
       "\n",
       "[5009 rows x 4 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute Total value of each order (group by order_id, order_date and customer_id then sum the sales column for each group)\n",
    "# DataFrame.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, observed=False, **kwargs)\n",
    "\n",
    "grouped_orders = orders.groupby(['order_id', 'order_date', 'customer_id'], as_index = False)['sales'].sum()\n",
    "grouped_orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3bf28c99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 296 entries, 0 to 295\n",
      "Data columns (total 2 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   Returned  296 non-null    object\n",
      " 1   Order ID  296 non-null    object\n",
      "dtypes: object(2)\n",
      "memory usage: 4.8+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['returned', 'order_id'], dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load dataframe for Sheet Return, check null value and remove blank spaces between words in column name (same as sheet Order)\n",
    "returns = pd.read_excel('D:\\STUDY\\STUDY\\PYTHON\\Final_project_RFM\\Dataset.xlsx',sheet_name='Return')\n",
    "returns.info()\n",
    "returns.columns = [col.lower().replace(' ','_') for col in returns.columns]\n",
    "returns.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7d0bb89b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA-2014-100006</td>\n",
       "      <td>2014-09-07</td>\n",
       "      <td>DK-13375</td>\n",
       "      <td>377.970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA-2014-100090</td>\n",
       "      <td>2014-07-08</td>\n",
       "      <td>EB-13705</td>\n",
       "      <td>699.192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA-2014-100293</td>\n",
       "      <td>2014-03-14</td>\n",
       "      <td>NF-18475</td>\n",
       "      <td>91.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA-2014-100328</td>\n",
       "      <td>2014-01-28</td>\n",
       "      <td>JC-15340</td>\n",
       "      <td>3.928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA-2014-100363</td>\n",
       "      <td>2014-04-08</td>\n",
       "      <td>JM-15655</td>\n",
       "      <td>21.376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5004</th>\n",
       "      <td>US-2017-168802</td>\n",
       "      <td>2017-11-03</td>\n",
       "      <td>JO-15145</td>\n",
       "      <td>18.368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5005</th>\n",
       "      <td>US-2017-169320</td>\n",
       "      <td>2017-07-23</td>\n",
       "      <td>LH-16900</td>\n",
       "      <td>171.430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5006</th>\n",
       "      <td>US-2017-169488</td>\n",
       "      <td>2017-09-07</td>\n",
       "      <td>AA-10375</td>\n",
       "      <td>56.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5007</th>\n",
       "      <td>US-2017-169502</td>\n",
       "      <td>2017-08-28</td>\n",
       "      <td>MG-17650</td>\n",
       "      <td>113.410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5008</th>\n",
       "      <td>US-2017-169551</td>\n",
       "      <td>2017-07-07</td>\n",
       "      <td>RL-19615</td>\n",
       "      <td>1344.838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4713 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            order_id order_date customer_id     sales\n",
       "0     CA-2014-100006 2014-09-07    DK-13375   377.970\n",
       "1     CA-2014-100090 2014-07-08    EB-13705   699.192\n",
       "2     CA-2014-100293 2014-03-14    NF-18475    91.056\n",
       "3     CA-2014-100328 2014-01-28    JC-15340     3.928\n",
       "4     CA-2014-100363 2014-04-08    JM-15655    21.376\n",
       "...              ...        ...         ...       ...\n",
       "5004  US-2017-168802 2017-11-03    JO-15145    18.368\n",
       "5005  US-2017-169320 2017-07-23    LH-16900   171.430\n",
       "5006  US-2017-169488 2017-09-07    AA-10375    56.860\n",
       "5007  US-2017-169502 2017-08-28    MG-17650   113.410\n",
       "5008  US-2017-169551 2017-07-07    RL-19615  1344.838\n",
       "\n",
       "[4713 rows x 4 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merger grouped_orders with returns in order to include all rows from the left DataFrame (grouped_order), and only matching rows from the right DataFrame (returns)\n",
    "# DataFrame.merge(right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, suffixes=('_x', '_y'), copy=True, indicator=False, validate=None)\n",
    "\n",
    "merge_grorder_return = grouped_orders.merge(returns, how = 'left', on = 'order_id')\n",
    "\n",
    "# Exclude stage: \n",
    "## Create boolean mask to filter data where the column order_id of merger_grorder_return is not match order_id of return.\n",
    "## Use '~' operator to negate the mask above so that only rows where the 'order_id' is not in the return DataFrame are kept\n",
    "\n",
    "valid_orders = merge_grorder_return[~merge_grorder_return['order_id'].isin(returns['order_id'])].drop('returned', axis=1)\n",
    "valid_orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "23d0bb82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>customer_id</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Monetary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AA-10315</td>\n",
       "      <td>185</td>\n",
       "      <td>5</td>\n",
       "      <td>5563.560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AA-10375</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>1056.390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AA-10480</td>\n",
       "      <td>260</td>\n",
       "      <td>4</td>\n",
       "      <td>1790.512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AA-10645</td>\n",
       "      <td>483</td>\n",
       "      <td>5</td>\n",
       "      <td>5073.975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AB-10015</td>\n",
       "      <td>416</td>\n",
       "      <td>3</td>\n",
       "      <td>886.156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>786</th>\n",
       "      <td>XP-21865</td>\n",
       "      <td>83</td>\n",
       "      <td>9</td>\n",
       "      <td>2110.726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>YC-21895</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5438.650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>YS-21880</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>6720.444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>789</th>\n",
       "      <td>ZC-21910</td>\n",
       "      <td>55</td>\n",
       "      <td>12</td>\n",
       "      <td>7892.998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>ZD-21925</td>\n",
       "      <td>203</td>\n",
       "      <td>4</td>\n",
       "      <td>1249.184</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>791 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    customer_id  Recency  Frequency  Monetary\n",
       "0      AA-10315      185          5  5563.560\n",
       "1      AA-10375       20          9  1056.390\n",
       "2      AA-10480      260          4  1790.512\n",
       "3      AA-10645      483          5  5073.975\n",
       "4      AB-10015      416          3   886.156\n",
       "..          ...      ...        ...       ...\n",
       "786    XP-21865       83          9  2110.726\n",
       "787    YC-21895        5          4  5438.650\n",
       "788    YS-21880       10          8  6720.444\n",
       "789    ZC-21910       55         12  7892.998\n",
       "790    ZD-21925      203          4  1249.184\n",
       "\n",
       "[791 rows x 4 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate R value, F value, M value for valid_orders\n",
    "## Reference point for calculating recency values 2017-12-31 -> creates a datetime object called today that represents 2017-12-31\n",
    "today = dt.datetime.strptime('2017-12-31', '%Y-%m-%d')\n",
    "\n",
    "## Group the valid_orders by customer_id and calculates the maximum order date, the number of orders (frequency), and the total sales (monetary value) for each customer\n",
    "RFM = valid_orders.groupby('customer_id').agg({'order_date': 'max', 'order_id': 'count', 'sales': 'sum'}).reset_index()\n",
    "\n",
    "## Rename columns of RFM dataframe (optional)\n",
    "RFM.columns = ['customer_id', 'max_date', 'Frequency', 'Monetary']\n",
    "\n",
    "## Calculates the Recency value for each customer (minus the maximum order date from the reference date today) and then convert object to days\n",
    "RFM['Recency'] = (today - RFM['max_date']).dt.days\n",
    "\n",
    "## Drop the max_date since we won't need it anymore (optional)\n",
    "RFM = RFM.drop('max_date', axis=1)\n",
    "\n",
    "## Rearrange columns (optional)\n",
    "RFM = RFM[['customer_id', 'Recency', 'Frequency', 'Monetary']]\n",
    "RFM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bb8a657f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign range for RFM then calculate R score, F score, and M score using quintiles method. R_variable range from 5 to 1 (in descending order) and R_variable will contain the integers 5, 4, 3, 2, and 1\n",
    "# Note: set q parameter = 5 since we want to divide data into 5 quantiles\n",
    "# Using qcut() function to calculate the recency score, frequency score, monetary score for each row. \n",
    "# pandas.qcut(x, q, labels=None, retbins=False, precision=3, duplicates='raise')\n",
    "\n",
    "R_variable = range(5, 0, -1)\n",
    "F_variable = range(1, 6)\n",
    "M_variable = range(1, 6)\n",
    "\n",
    "RFM['R_scores'] = pd.qcut(RFM['Recency'], q = 5, labels = R_variable)\n",
    "RFM['F_scores'] = pd.qcut(RFM['Frequency'], q = 5, labels = F_variable)\n",
    "RFM['M_scores'] = pd.qcut(RFM['Monetary'], q = 5, labels = M_variable)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c97e1ef0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>customer_id</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Monetary</th>\n",
       "      <th>R_scores</th>\n",
       "      <th>F_scores</th>\n",
       "      <th>M_scores</th>\n",
       "      <th>RFM_scores</th>\n",
       "      <th>RFM_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AA-10315</td>\n",
       "      <td>185</td>\n",
       "      <td>5</td>\n",
       "      <td>5563.560</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>225</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AA-10375</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>1056.390</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>552</td>\n",
       "      <td>552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AA-10480</td>\n",
       "      <td>260</td>\n",
       "      <td>4</td>\n",
       "      <td>1790.512</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AA-10645</td>\n",
       "      <td>483</td>\n",
       "      <td>5</td>\n",
       "      <td>5073.975</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>125</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AB-10015</td>\n",
       "      <td>416</td>\n",
       "      <td>3</td>\n",
       "      <td>886.156</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AB-10060</td>\n",
       "      <td>55</td>\n",
       "      <td>8</td>\n",
       "      <td>7755.620</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>445</td>\n",
       "      <td>445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AB-10105</td>\n",
       "      <td>42</td>\n",
       "      <td>9</td>\n",
       "      <td>13881.263</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>455</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AB-10150</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "      <td>966.710</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>422</td>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AB-10165</td>\n",
       "      <td>26</td>\n",
       "      <td>8</td>\n",
       "      <td>1113.838</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>542</td>\n",
       "      <td>542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AB-10255</td>\n",
       "      <td>167</td>\n",
       "      <td>8</td>\n",
       "      <td>897.482</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customer_id  Recency  Frequency   Monetary R_scores F_scores M_scores  \\\n",
       "0    AA-10315      185          5   5563.560        2        2        5   \n",
       "1    AA-10375       20          9   1056.390        5        5        2   \n",
       "2    AA-10480      260          4   1790.512        2        1        3   \n",
       "3    AA-10645      483          5   5073.975        1        2        5   \n",
       "4    AB-10015      416          3    886.156        1        1        2   \n",
       "5    AB-10060       55          8   7755.620        4        4        5   \n",
       "6    AB-10105       42          9  13881.263        4        5        5   \n",
       "7    AB-10150       42          5    966.710        4        2        2   \n",
       "8    AB-10165       26          8   1113.838        5        4        2   \n",
       "9    AB-10255      167          8    897.482        2        4        2   \n",
       "\n",
       "   RFM_scores  RFM_Score  \n",
       "0         225        225  \n",
       "1         552        552  \n",
       "2         213        213  \n",
       "3         125        125  \n",
       "4         112        112  \n",
       "5         445        445  \n",
       "6         455        455  \n",
       "7         422        422  \n",
       "8         542        542  \n",
       "9         242        242  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combines the three individual RFM scores (recency, frequency, and monetary) into a single integer score\n",
    "\n",
    "RFM['RFM_Score'] = RFM['R_scores'].astype(str) + RFM['F_scores'].astype(str) + RFM['M_scores'].astype(str)\n",
    "RFM['RFM_Score'] = RFM['RFM_Score'].astype(int)\n",
    "RFM.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c418b717",
   "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>Segment</th>\n",
       "      <th>RFM_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Champions</td>\n",
       "      <td>555, 554, 544, 545, 454, 455, 445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Loyal</td>\n",
       "      <td>543, 444, 435, 355, 354, 345, 344, 335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Potential Loyalist</td>\n",
       "      <td>553, 551, 552, 541, 542, 533, 532, 531, 452, 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New Customers</td>\n",
       "      <td>512, 511, 422, 421, 412, 411, 311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Promising</td>\n",
       "      <td>525, 524, 523, 522, 521, 515, 514, 513, 425,42...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Need Attention</td>\n",
       "      <td>535, 534, 443, 434, 343, 334, 325, 324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>About To Sleep</td>\n",
       "      <td>331, 321, 312, 221, 213, 231, 241, 251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>At Risk</td>\n",
       "      <td>255, 254, 245, 244, 253, 252, 243, 242, 235, 2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Cannot Lose Them</td>\n",
       "      <td>155, 154, 144, 214,215,115, 114, 113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Hibernating customers</td>\n",
       "      <td>332, 322, 233, 232, 223, 222, 132, 123, 122, 2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Lost customers</td>\n",
       "      <td>111, 112, 121, 131,141,151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Segment                                          RFM_Score\n",
       "0               Champions                  555, 554, 544, 545, 454, 455, 445\n",
       "1                   Loyal             543, 444, 435, 355, 354, 345, 344, 335\n",
       "2      Potential Loyalist  553, 551, 552, 541, 542, 533, 532, 531, 452, 4...\n",
       "3           New Customers                  512, 511, 422, 421, 412, 411, 311\n",
       "4               Promising  525, 524, 523, 522, 521, 515, 514, 513, 425,42...\n",
       "5          Need Attention             535, 534, 443, 434, 343, 334, 325, 324\n",
       "6          About To Sleep             331, 321, 312, 221, 213, 231, 241, 251\n",
       "7                 At Risk  255, 254, 245, 244, 253, 252, 243, 242, 235, 2...\n",
       "8        Cannot Lose Them               155, 154, 144, 214,215,115, 114, 113\n",
       "9   Hibernating customers  332, 322, 233, 232, 223, 222, 132, 123, 122, 2...\n",
       "10         Lost customers                         111, 112, 121, 131,141,151"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load dataframe of customer segmentation to each RFM_scores (same as order and return sheet)\n",
    "\n",
    "segment = pd.read_excel('D:\\STUDY\\STUDY\\PYTHON\\Final_project_RFM\\Dataset.xlsx', sheet_name='Segmentation')\n",
    "segment.columns = [col.replace(' ', '_') for col in segment.columns]\n",
    "segment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "eb46baf7",
   "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>Segment</th>\n",
       "      <th>RFM_Score</th>\n",
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       "  </thead>\n",
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       "      <td>Champions</td>\n",
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       "      <td>Champions</td>\n",
       "      <td>554</td>\n",
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       "      <th>2</th>\n",
       "      <td>Champions</td>\n",
       "      <td>544</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Champions</td>\n",
       "      <td>545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Champions</td>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
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       "      <th>5</th>\n",
       "      <td>Champions</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Champions</td>\n",
       "      <td>445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Loyal</td>\n",
       "      <td>543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Loyal</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Loyal</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Segment  RFM_Score\n",
       "0  Champions        555\n",
       "1  Champions        554\n",
       "2  Champions        544\n",
       "3  Champions        545\n",
       "4  Champions        454\n",
       "5  Champions        455\n",
       "6  Champions        445\n",
       "7      Loyal        543\n",
       "8      Loyal        444\n",
       "9      Loyal        435"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert rfm_score to list\n",
    "## Splits the rfm_score column by commas, creating a nested list of strings in each row\n",
    "segment['RFM_Score'] = segment['RFM_Score'].str.split(',')\n",
    "\n",
    "## Explodes the rfm_score column, creating a new row for each element in the nested list. The resulting DataFrame has more rows and each row corresponds to a unique RFM score for a given customer segment\n",
    "segment = segment.explode('RFM_Score').reset_index(drop=True)\n",
    "\n",
    "## Use astype(int) convert rfm_score column from strings to integers\n",
    "## Note: this step is important since we want to sort the customers by their RFM score, and integer sorting is more efficient and meaningful than string sorting\n",
    "segment['RFM_Score'] = segment['RFM_Score'].astype(int)\n",
    "\n",
    "# Sorting. Puts the segments in order from \"best\" to \"worst\" based on their average RFM score.\n",
    "segment.sort_values(by='RFM_Score')\n",
    "segment.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "51314db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Monetary</th>\n",
       "      <th>R_scores</th>\n",
       "      <th>F_scores</th>\n",
       "      <th>M_scores</th>\n",
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       "      <th>Segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AA-10315</td>\n",
       "      <td>185</td>\n",
       "      <td>5</td>\n",
       "      <td>5563.560</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>225</td>\n",
       "      <td>225</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Potential Loyalist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AA-10480</td>\n",
       "      <td>260</td>\n",
       "      <td>4</td>\n",
       "      <td>1790.512</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>About To Sleep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AA-10645</td>\n",
       "      <td>483</td>\n",
       "      <td>5</td>\n",
       "      <td>5073.975</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>125</td>\n",
       "      <td>125</td>\n",
       "      <td>At Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AB-10015</td>\n",
       "      <td>416</td>\n",
       "      <td>3</td>\n",
       "      <td>886.156</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>Lost customers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>786</th>\n",
       "      <td>XP-21865</td>\n",
       "      <td>83</td>\n",
       "      <td>9</td>\n",
       "      <td>2110.726</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>353</td>\n",
       "      <td>353</td>\n",
       "      <td>Potential Loyalist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>YC-21895</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5438.650</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>515</td>\n",
       "      <td>515</td>\n",
       "      <td>Promising</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>YS-21880</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>6720.444</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>545</td>\n",
       "      <td>545</td>\n",
       "      <td>Champions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>789</th>\n",
       "      <td>ZC-21910</td>\n",
       "      <td>55</td>\n",
       "      <td>12</td>\n",
       "      <td>7892.998</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>455</td>\n",
       "      <td>455</td>\n",
       "      <td>Champions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>ZD-21925</td>\n",
       "      <td>203</td>\n",
       "      <td>4</td>\n",
       "      <td>1249.184</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>212</td>\n",
       "      <td>212</td>\n",
       "      <td>Hibernating customers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>791 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    customer_id  Recency  Frequency  Monetary R_scores F_scores M_scores  \\\n",
       "0      AA-10315      185          5  5563.560        2        2        5   \n",
       "1      AA-10375       20          9  1056.390        5        5        2   \n",
       "2      AA-10480      260          4  1790.512        2        1        3   \n",
       "3      AA-10645      483          5  5073.975        1        2        5   \n",
       "4      AB-10015      416          3   886.156        1        1        2   \n",
       "..          ...      ...        ...       ...      ...      ...      ...   \n",
       "786    XP-21865       83          9  2110.726        3        5        3   \n",
       "787    YC-21895        5          4  5438.650        5        1        5   \n",
       "788    YS-21880       10          8  6720.444        5        4        5   \n",
       "789    ZC-21910       55         12  7892.998        4        5        5   \n",
       "790    ZD-21925      203          4  1249.184        2        1        2   \n",
       "\n",
       "     RFM_scores  RFM_Score                Segment  \n",
       "0           225        225                At Risk  \n",
       "1           552        552     Potential Loyalist  \n",
       "2           213        213         About To Sleep  \n",
       "3           125        125                At Risk  \n",
       "4           112        112         Lost customers  \n",
       "..          ...        ...                    ...  \n",
       "786         353        353     Potential Loyalist  \n",
       "787         515        515              Promising  \n",
       "788         545        545              Champions  \n",
       "789         455        455              Champions  \n",
       "790         212        212  Hibernating customers  \n",
       "\n",
       "[791 rows x 10 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the RFM and segment DataFrames based on their RFM_Score column\n",
    "Merge_RFM_segment = RFM.merge(segment, how='left', on='RFM_Score')\n",
    "Merge_RFM_segment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a17ee259",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\meebo\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\meebo\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\meebo\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram distribution of each variable\n",
    "\n",
    "column_names = ['Recency', 'Frequency', 'Monetary']\n",
    "\n",
    "for col in column_names:\n",
    "    fig, ax = mp.subplots(figsize=(15,3))\n",
    "    sb.distplot(Merge_RFM_segment[col])\n",
    "    ax.set_title('Distribution of %s' % col)\n",
    "    mp.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b3e76d76",
   "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>Segment</th>\n",
       "      <th>customer_count</th>\n",
       "      <th>total_monetary</th>\n",
       "      <th>mean_recency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Potential Loyalist</td>\n",
       "      <td>113</td>\n",
       "      <td>190944.9303</td>\n",
       "      <td>45.185841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>At Risk</td>\n",
       "      <td>96</td>\n",
       "      <td>385996.8861</td>\n",
       "      <td>259.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hibernating customers</td>\n",
       "      <td>90</td>\n",
       "      <td>109960.2510</td>\n",
       "      <td>190.044444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lost customers</td>\n",
       "      <td>83</td>\n",
       "      <td>54264.2255</td>\n",
       "      <td>557.963855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Need Attention</td>\n",
       "      <td>72</td>\n",
       "      <td>259412.3922</td>\n",
       "      <td>50.138889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Champions</td>\n",
       "      <td>71</td>\n",
       "      <td>382096.5868</td>\n",
       "      <td>28.521127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Promising</td>\n",
       "      <td>63</td>\n",
       "      <td>224803.8955</td>\n",
       "      <td>35.253968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Loyal</td>\n",
       "      <td>62</td>\n",
       "      <td>270680.2154</td>\n",
       "      <td>60.725806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>New Customers</td>\n",
       "      <td>61</td>\n",
       "      <td>40997.1410</td>\n",
       "      <td>41.475410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>About To Sleep</td>\n",
       "      <td>44</td>\n",
       "      <td>47310.1172</td>\n",
       "      <td>135.204545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cannot Lose Them</td>\n",
       "      <td>36</td>\n",
       "      <td>150229.9414</td>\n",
       "      <td>496.527778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Segment  customer_count  total_monetary  mean_recency\n",
       "9      Potential Loyalist             113     190944.9303     45.185841\n",
       "1                 At Risk              96     385996.8861    259.041667\n",
       "4   Hibernating customers              90     109960.2510    190.044444\n",
       "5          Lost customers              83      54264.2255    557.963855\n",
       "7          Need Attention              72     259412.3922     50.138889\n",
       "3               Champions              71     382096.5868     28.521127\n",
       "10              Promising              63     224803.8955     35.253968\n",
       "6                   Loyal              62     270680.2154     60.725806\n",
       "8           New Customers              61      40997.1410     41.475410\n",
       "0          About To Sleep              44      47310.1172    135.204545\n",
       "2        Cannot Lose Them              36     150229.9414    496.527778"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prepare data for treemap\n",
    "group = Merge_RFM_segment.groupby('Segment').agg({'customer_id': 'count', 'Monetary': 'sum','Recency': 'mean'}).reset_index()\n",
    "group.columns = ['Segment', 'customer_count', 'total_monetary', 'mean_recency']\n",
    "group.sort_values(by='customer_count', ascending=False, inplace=True)\n",
    "group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "abad8cf8",
   "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>Segment</th>\n",
       "      <th>customer_count</th>\n",
       "      <th>total_monetary</th>\n",
       "      <th>mean_recency</th>\n",
       "      <th>count_share</th>\n",
       "      <th>monetary_share</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Potential Loyalist</td>\n",
       "      <td>113</td>\n",
       "      <td>190944.9303</td>\n",
       "      <td>45.185841</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.090209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>At Risk</td>\n",
       "      <td>96</td>\n",
       "      <td>385996.8861</td>\n",
       "      <td>259.041667</td>\n",
       "      <td>0.121365</td>\n",
       "      <td>0.182358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hibernating customers</td>\n",
       "      <td>90</td>\n",
       "      <td>109960.2510</td>\n",
       "      <td>190.044444</td>\n",
       "      <td>0.113780</td>\n",
       "      <td>0.051949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lost customers</td>\n",
       "      <td>83</td>\n",
       "      <td>54264.2255</td>\n",
       "      <td>557.963855</td>\n",
       "      <td>0.104930</td>\n",
       "      <td>0.025636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Need Attention</td>\n",
       "      <td>72</td>\n",
       "      <td>259412.3922</td>\n",
       "      <td>50.138889</td>\n",
       "      <td>0.091024</td>\n",
       "      <td>0.122555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Champions</td>\n",
       "      <td>71</td>\n",
       "      <td>382096.5868</td>\n",
       "      <td>28.521127</td>\n",
       "      <td>0.089760</td>\n",
       "      <td>0.180516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Promising</td>\n",
       "      <td>63</td>\n",
       "      <td>224803.8955</td>\n",
       "      <td>35.253968</td>\n",
       "      <td>0.079646</td>\n",
       "      <td>0.106205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Loyal</td>\n",
       "      <td>62</td>\n",
       "      <td>270680.2154</td>\n",
       "      <td>60.725806</td>\n",
       "      <td>0.078382</td>\n",
       "      <td>0.127879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>New Customers</td>\n",
       "      <td>61</td>\n",
       "      <td>40997.1410</td>\n",
       "      <td>41.475410</td>\n",
       "      <td>0.077118</td>\n",
       "      <td>0.019368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>About To Sleep</td>\n",
       "      <td>44</td>\n",
       "      <td>47310.1172</td>\n",
       "      <td>135.204545</td>\n",
       "      <td>0.055626</td>\n",
       "      <td>0.022351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cannot Lose Them</td>\n",
       "      <td>36</td>\n",
       "      <td>150229.9414</td>\n",
       "      <td>496.527778</td>\n",
       "      <td>0.045512</td>\n",
       "      <td>0.070974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Segment  customer_count  total_monetary  mean_recency  \\\n",
       "9      Potential Loyalist             113     190944.9303     45.185841   \n",
       "1                 At Risk              96     385996.8861    259.041667   \n",
       "4   Hibernating customers              90     109960.2510    190.044444   \n",
       "5          Lost customers              83      54264.2255    557.963855   \n",
       "7          Need Attention              72     259412.3922     50.138889   \n",
       "3               Champions              71     382096.5868     28.521127   \n",
       "10              Promising              63     224803.8955     35.253968   \n",
       "6                   Loyal              62     270680.2154     60.725806   \n",
       "8           New Customers              61      40997.1410     41.475410   \n",
       "0          About To Sleep              44      47310.1172    135.204545   \n",
       "2        Cannot Lose Them              36     150229.9414    496.527778   \n",
       "\n",
       "    count_share  monetary_share  \n",
       "9      0.142857        0.090209  \n",
       "1      0.121365        0.182358  \n",
       "4      0.113780        0.051949  \n",
       "5      0.104930        0.025636  \n",
       "7      0.091024        0.122555  \n",
       "3      0.089760        0.180516  \n",
       "10     0.079646        0.106205  \n",
       "6      0.078382        0.127879  \n",
       "8      0.077118        0.019368  \n",
       "0      0.055626        0.022351  \n",
       "2      0.045512        0.070974  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group['count_share'] = group['customer_count'] / group['customer_count'].sum()\n",
    "group['monetary_share'] = group['total_monetary'] / group['total_monetary'].sum()\n",
    "group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "60887d42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Treemap. Note: we can use RGB instead of Hex for colors but it will involve setting up a lot of variable for each color\n",
    "\n",
    "colors = ['#FF6103','#98F5FF','#458B00','#7FFF00','#FFB90F','#9932CC','#FF1493','#FF69B4','#7B68EE','#7A67EE','#FFFF00']\n",
    "\n",
    "fig, ax = mp.subplots(1, figsize = (12,8))\n",
    "\n",
    "sq.plot(sizes = group['customer_count'], \n",
    "              label = group['Segment'],\n",
    "              value = [f'{x*100:.2f}%' for x in group['count_share']],\n",
    "              alpha = .8,\n",
    "              color = colors,\n",
    "              bar_kwargs = dict(linewidth=1, edgecolor=\"white\")\n",
    "             )\n",
    "mp.title('RFM Segments of Customer Count', fontsize=12)\n",
    "mp.axis('off')\n",
    "mp.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "78744891",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Note: we can use RGB for colors but it will involve setting up a lot of variable for each color\n",
    "colors = ['#0000FF','#8A2BE2','#FF4040','#FF6103','#66CD00','#458B00','#808000','#DC143C','#FFB90F','#BF3EFF','#00BFFF']\n",
    "\n",
    "fig, ax = mp.subplots(1, figsize = (12,8))\n",
    "\n",
    "sq.plot(sizes = group['monetary_share'], \n",
    "              label = group['Segment'],\n",
    "              value = [f'{x*100:.2f}%' for x in group['monetary_share']],\n",
    "              alpha = 1,\n",
    "              color = colors,\n",
    "              bar_kwargs = dict(linewidth=1.5, edgecolor=\"white\")\n",
    "             )\n",
    "mp.title('RFM Segments of Total Sales', fontsize=16)\n",
    "mp.axis('off')\n",
    "mp.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a21cda6",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}