module1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5b08ec10",
   "metadata": {},
   "source": [
    "# Computing Recency, Frequency, Monetary Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ec16824c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pandasql import sqldf\n",
    "from sklearn.preprocessing import scale\n",
    "from scipy.spatial.distance import pdist\n",
    "from scipy.cluster.hierarchy import dendrogram , linkage, cut_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cde67132",
   "metadata": {},
   "outputs": [],
   "source": [
    "pysqldf = lambda q: sqldf(q, globals())\n",
    "%precision %.2f\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "%matplotlib widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b7a1e71b",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>purchase_amount</th>\n",
       "      <th>date_of_purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4510</th>\n",
       "      <td>8060</td>\n",
       "      <td>30.00</td>\n",
       "      <td>2014-12-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17761</th>\n",
       "      <td>109180</td>\n",
       "      <td>50.00</td>\n",
       "      <td>2009-11-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39110</th>\n",
       "      <td>9830</td>\n",
       "      <td>30.00</td>\n",
       "      <td>2007-06-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37183</th>\n",
       "      <td>56400</td>\n",
       "      <td>60.00</td>\n",
       "      <td>2009-09-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33705</th>\n",
       "      <td>41290</td>\n",
       "      <td>60.00</td>\n",
       "      <td>2007-08-21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id  purchase_amount date_of_purchase\n",
       "4510          8060            30.00       2014-12-24\n",
       "17761       109180            50.00       2009-11-25\n",
       "39110         9830            30.00       2007-06-12\n",
       "37183        56400            60.00       2009-09-30\n",
       "33705        41290            60.00       2007-08-21"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = ['customer_id', 'purchase_amount', 'date_of_purchase']\n",
    "df = pd.read_csv('purchases.txt', header=None, sep='\\t',\n",
    "            names=columns)\n",
    "df.sample(n=5, random_state=57)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0b7e8f7a",
   "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>customer_id</th>\n",
       "      <th>purchase_amount</th>\n",
       "      <th>date_of_purchase</th>\n",
       "      <th>year_of_purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>760</td>\n",
       "      <td>25.00</td>\n",
       "      <td>2009-11-06</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>860</td>\n",
       "      <td>50.00</td>\n",
       "      <td>2012-09-28</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1200</td>\n",
       "      <td>100.00</td>\n",
       "      <td>2005-10-25</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1420</td>\n",
       "      <td>50.00</td>\n",
       "      <td>2009-07-09</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1940</td>\n",
       "      <td>70.00</td>\n",
       "      <td>2013-01-25</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id  purchase_amount date_of_purchase  year_of_purchase\n",
       "0          760            25.00       2009-11-06              2009\n",
       "1          860            50.00       2012-09-28              2012\n",
       "2         1200           100.00       2005-10-25              2005\n",
       "3         1420            50.00       2009-07-09              2009\n",
       "4         1940            70.00       2013-01-25              2013"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# interpret the last column as datetime\n",
    "df['date_of_purchase'] = pd.to_datetime(df['date_of_purchase'], \n",
    "                                        format='%Y-%m-%d')\n",
    "# Extract year of purchase and save it as a column\n",
    "df['year_of_purchase'] = df['date_of_purchase'].dt.year\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1f49f7f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add a day_since column showing the difference between last purchase and a basedate\n",
    "basedate = pd.Timestamp('2016-01-01')\n",
    "df['days_since'] = (basedate - df['date_of_purchase']).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0ec2c924",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 51243 entries, 0 to 51242\n",
      "Data columns (total 5 columns):\n",
      " #   Column            Non-Null Count  Dtype         \n",
      "---  ------            --------------  -----         \n",
      " 0   customer_id       51243 non-null  int64         \n",
      " 1   purchase_amount   51243 non-null  float64       \n",
      " 2   date_of_purchase  51243 non-null  datetime64[ns]\n",
      " 3   year_of_purchase  51243 non-null  int64         \n",
      " 4   days_since        51243 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(1), int64(3)\n",
      "memory usage: 2.0 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7edeb82a",
   "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>customer_id</th>\n",
       "      <th>purchase_amount</th>\n",
       "      <th>date_of_purchase</th>\n",
       "      <th>year_of_purchase</th>\n",
       "      <th>days_since</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>760</td>\n",
       "      <td>25.00</td>\n",
       "      <td>2009-11-06</td>\n",
       "      <td>2009</td>\n",
       "      <td>2247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>860</td>\n",
       "      <td>50.00</td>\n",
       "      <td>2012-09-28</td>\n",
       "      <td>2012</td>\n",
       "      <td>1190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1200</td>\n",
       "      <td>100.00</td>\n",
       "      <td>2005-10-25</td>\n",
       "      <td>2005</td>\n",
       "      <td>3720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1420</td>\n",
       "      <td>50.00</td>\n",
       "      <td>2009-07-09</td>\n",
       "      <td>2009</td>\n",
       "      <td>2367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1940</td>\n",
       "      <td>70.00</td>\n",
       "      <td>2013-01-25</td>\n",
       "      <td>2013</td>\n",
       "      <td>1071</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id  purchase_amount date_of_purchase  year_of_purchase  days_since\n",
       "0          760            25.00       2009-11-06              2009        2247\n",
       "1          860            50.00       2012-09-28              2012        1190\n",
       "2         1200           100.00       2005-10-25              2005        3720\n",
       "3         1420            50.00       2009-07-09              2009        2367\n",
       "4         1940            70.00       2013-01-25              2013        1071"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34f2aec8",
   "metadata": {},
   "source": [
    "Next we are going to compute the customers recency, frequency and average purchase amount. This part is a big tricky specially when it's done with pandas. \n",
    "\n",
    "The trick here is that the customer ID will only appear once for every customer. So even though we have 51,000 purchases we'll only have as many unique customer IDs as there are in the database.\n",
    "\n",
    "Now, for each customer, we need to compute the minimum number of days between all of his or her purchases and january 1st, 2016. Of course, if we take the minimum number of days, then we are going to have the day of the last purchase, which is the very definition of **recency**.\n",
    "\n",
    "Then for each customer we need to compute the frequency, which is basically how many purchases that customer has made.\n",
    "\n",
    " The asterisk here basically means anything in the data that is related to that customer we could have put any known variable here, it wouldn't change and then for the amount we going to compute the average of purchase amount for that specific customer ID and name that aggregate computation as amount. Now, the trick is that we'd like to make sure that each row only appears one for each customer. So we going to compute that from the data and group by one, meaning that everything here is going to be computed and grouped per customer ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f53784bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute recency, frequency, and average purchase amount\n",
    "q = \"\"\"\n",
    "        SELECT customer_id,\n",
    "        MIN(days_since) AS 'recency',\n",
    "        COUNT(*) AS 'frequency',\n",
    "        AVG(purchase_amount) AS 'amount'\n",
    "        FROM df GROUP BY 1\"\"\"\n",
    "customers = sqldf(q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2b886797",
   "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>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>3829</td>\n",
       "      <td>1</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80</td>\n",
       "      <td>343</td>\n",
       "      <td>7</td>\n",
       "      <td>71.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90</td>\n",
       "      <td>758</td>\n",
       "      <td>10</td>\n",
       "      <td>115.80</td>\n",
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       "      <th>3</th>\n",
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       "      <td>20.00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>2970</td>\n",
       "      <td>2</td>\n",
       "      <td>50.00</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>18412</th>\n",
       "      <td>263820</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18413</th>\n",
       "      <td>263870</td>\n",
       "      <td>135</td>\n",
       "      <td>1</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18414</th>\n",
       "      <td>263880</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18415</th>\n",
       "      <td>263890</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>54.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18416</th>\n",
       "      <td>264200</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>500.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18417 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id  recency  frequency  amount\n",
       "0               10     3829          1   30.00\n",
       "1               80      343          7   71.43\n",
       "2               90      758         10  115.80\n",
       "3              120     1401          1   20.00\n",
       "4              130     2970          2   50.00\n",
       "...            ...      ...        ...     ...\n",
       "18412       263820        1          1   10.00\n",
       "18413       263870      135          1   50.00\n",
       "18414       263880       34          1   20.00\n",
       "18415       263890        5          1   54.00\n",
       "18416       264200        1          1  500.00\n",
       "\n",
       "[18417 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ac4ad309",
   "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>customer_id</th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18,417.00</td>\n",
       "      <td>18,417.00</td>\n",
       "      <td>18,417.00</td>\n",
       "      <td>18,417.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>137,573.51</td>\n",
       "      <td>1,253.04</td>\n",
       "      <td>2.78</td>\n",
       "      <td>57.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>69,504.60</td>\n",
       "      <td>1,081.44</td>\n",
       "      <td>2.94</td>\n",
       "      <td>154.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>81,990.00</td>\n",
       "      <td>244.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>21.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>136,430.00</td>\n",
       "      <td>1,070.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>195,100.00</td>\n",
       "      <td>2,130.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>264,200.00</td>\n",
       "      <td>4,014.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>4,500.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id   recency  frequency    amount\n",
       "count    18,417.00 18,417.00  18,417.00 18,417.00\n",
       "mean    137,573.51  1,253.04       2.78     57.79\n",
       "std      69,504.60  1,081.44       2.94    154.36\n",
       "min          10.00      1.00       1.00      5.00\n",
       "25%      81,990.00    244.00       1.00     21.67\n",
       "50%     136,430.00  1,070.00       2.00     30.00\n",
       "75%     195,100.00  2,130.00       3.00     50.00\n",
       "max     264,200.00  4,014.00      45.00  4,500.00"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b3d792b9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>purchase_amount</th>\n",
       "      <th>year_of_purchase</th>\n",
       "      <th>days_since</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>51,243.00</td>\n",
       "      <td>51,243.00</td>\n",
       "      <td>51,243.00</td>\n",
       "      <td>51,243.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>108,934.55</td>\n",
       "      <td>62.34</td>\n",
       "      <td>2,010.87</td>\n",
       "      <td>1,631.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>67,650.61</td>\n",
       "      <td>156.61</td>\n",
       "      <td>2.88</td>\n",
       "      <td>1,061.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2,005.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>57,720.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>2,009.00</td>\n",
       "      <td>733.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>102,440.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>2,011.00</td>\n",
       "      <td>1,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>160,525.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>2,013.00</td>\n",
       "      <td>2,540.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>264,200.00</td>\n",
       "      <td>4,500.00</td>\n",
       "      <td>2,015.00</td>\n",
       "      <td>4,016.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id  purchase_amount  year_of_purchase  days_since\n",
       "count    51,243.00        51,243.00         51,243.00   51,243.00\n",
       "mean    108,934.55            62.34          2,010.87    1,631.94\n",
       "std      67,650.61           156.61              2.88    1,061.08\n",
       "min          10.00             5.00          2,005.00        1.00\n",
       "25%      57,720.00            25.00          2,009.00      733.00\n",
       "50%     102,440.00            30.00          2,011.00    1,500.00\n",
       "75%     160,525.00            60.00          2,013.00    2,540.00\n",
       "max     264,200.00         4,500.00          2,015.00    4,016.00"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ebba6de0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b5664610875448678ff829780976c74e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.00, 4214.65)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.hist(column='recency', bins=70);\n",
    "plt.xlim(0,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "27b4f2f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "41b20a4ad73148c088c451954a6925f9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.00, 30.00)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.hist(column='frequency', bins=40);\n",
    "plt.xlim(0,30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "72da1e9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "607d75729f564bf18cd5780d18443b0b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.00, 5008.50)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.hist(column='amount', bins=421);\n",
    "plt.xlim(0,400)\n",
    "plt.ylim(0,)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08ccd641",
   "metadata": {},
   "source": [
    "# Preparing and Transforming Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "75adb81f",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = customers.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e94ea9c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
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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>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>3829</td>\n",
       "      <td>1</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80</td>\n",
       "      <td>343</td>\n",
       "      <td>7</td>\n",
       "      <td>71.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90</td>\n",
       "      <td>758</td>\n",
       "      <td>10</td>\n",
       "      <td>115.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>120</td>\n",
       "      <td>1401</td>\n",
       "      <td>1</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>130</td>\n",
       "      <td>2970</td>\n",
       "      <td>2</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id  recency  frequency  amount\n",
       "0           10     3829          1   30.00\n",
       "1           80      343          7   71.43\n",
       "2           90      758         10  115.80\n",
       "3          120     1401          1   20.00\n",
       "4          130     2970          2   50.00"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e8b11270",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data.set_index('customer_id', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "91affb25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3829</td>\n",
       "      <td>1</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>343</td>\n",
       "      <td>7</td>\n",
       "      <td>71.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>758</td>\n",
       "      <td>10</td>\n",
       "      <td>115.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>1401</td>\n",
       "      <td>1</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2970</td>\n",
       "      <td>2</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             recency  frequency  amount\n",
       "customer_id                            \n",
       "10              3829          1   30.00\n",
       "80               343          7   71.43\n",
       "90               758         10  115.80\n",
       "120             1401          1   20.00\n",
       "130             2970          2   50.00"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bc8ea3a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0e467a8d5599432dba43f52341078799",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# As we have seen in the previous plots, the histogram of average purchase amounts\n",
    "# is extremely skewed to the left. Which makes it unuseful for segmentation purpose\n",
    "# So let's take the log of it\n",
    "new_data['amount'] = np.log10(new_data['amount'])\n",
    "new_data.hist(column='amount');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1601cdb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.38</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>-0.84</td>\n",
       "      <td>1.44</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>-0.46</td>\n",
       "      <td>2.46</td>\n",
       "      <td>1.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>0.14</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>1.59</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             recency  frequency  amount\n",
       "customer_id                            \n",
       "10              2.38      -0.61   -0.24\n",
       "80             -0.84       1.44    0.89\n",
       "90             -0.46       2.46    1.52\n",
       "120             0.14      -0.61   -0.76\n",
       "130             1.59      -0.27    0.43"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scaling using the sklearn scaling function\n",
    "new_data = pd.DataFrame(scale(new_data), index=new_data.index, columns=new_data.columns)\n",
    "new_data.head()\n",
    "\n",
    "# Now the data is ready for being segmented and analyzed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49983d34",
   "metadata": {},
   "source": [
    "# Running a Hierarchical Segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1c7146d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(339185889, 3)\n"
     ]
    }
   ],
   "source": [
    "# Compute distance metrics on standardized data\n",
    "# This will likely generate an error on most machines\n",
    "# R: d = dist(new_data) \n",
    "# since it generate a matrix with:\n",
    "print(f'({new_data.shape[0]**2}, {new_data.shape[1]})')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "88a1ede2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# so Let's take a 10% sample\n",
    "sample = np.arange(0, 18417, 10)\n",
    "sample[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0230d38f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.38</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>2.27</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-1.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>2.26</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>-1.14</td>\n",
       "      <td>2.46</td>\n",
       "      <td>-0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1040</th>\n",
       "      <td>-1.11</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             recency  frequency  amount\n",
       "customer_id                            \n",
       "10              2.38      -0.61   -0.24\n",
       "260             2.27      -0.61   -1.14\n",
       "510             2.26      -0.61   -0.24\n",
       "850            -1.14       2.46   -0.30\n",
       "1040           -1.11       0.41    0.22"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data_sample = new_data.iloc[sample]\n",
    "new_data_sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7986b988",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>3829</td>\n",
       "      <td>1</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>260</td>\n",
       "      <td>3710</td>\n",
       "      <td>1</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>510</td>\n",
       "      <td>3698</td>\n",
       "      <td>1</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>850</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>28.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1040</td>\n",
       "      <td>56</td>\n",
       "      <td>4</td>\n",
       "      <td>42.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    customer_id  recency  frequency  amount\n",
       "0            10     3829          1   30.00\n",
       "10          260     3710          1   15.00\n",
       "20          510     3698          1   30.00\n",
       "30          850       24         10   28.50\n",
       "40         1040       56          4   42.50"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_sample = customers.iloc[sample].copy()\n",
    "customers_sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d2ca803c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1695561,)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the distance metrics on standardized data\n",
    "d = pdist(new_data_sample)\n",
    "d.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "034e1208",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "51312955fccf48faabf3003a919e368b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perform hierarchical clustering on distance metrics\n",
    "c = linkage(d, method='ward')\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.title(\"Customer Dendograms\")\n",
    "\n",
    "# Plot dendrogram\n",
    "dend = dendrogram(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1cb81291",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1841, 4)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e689183c",
   "metadata": {},
   "outputs": [],
   "source": [
    "members = cut_tree(c, n_clusters=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "35887228",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [2],\n",
       "       [3]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "members[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "eed2d01b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1842, 1)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "members.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2f711dcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "groups, counts = np.unique(members, return_counts=True)\n",
    "segments = dict(zip(groups, counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "419ab60b",
   "metadata": {},
   "outputs": [],
   "source": [
    "customers_sample['group'] = members.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e8ad6eee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1842 entries, 0 to 18410\n",
      "Data columns (total 5 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   customer_id  1842 non-null   int64  \n",
      " 1   recency      1842 non-null   int64  \n",
      " 2   frequency    1842 non-null   int64  \n",
      " 3   amount       1842 non-null   float64\n",
      " 4   group        1842 non-null   int64  \n",
      "dtypes: float64(1), int64(4)\n",
      "memory usage: 86.3 KB\n"
     ]
    }
   ],
   "source": [
    "customers_sample.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d53f7be8",
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters = customers_sample[['recency', 'frequency', 'amount', 'group']].groupby('group').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a70b5c6e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2,563.24</td>\n",
       "      <td>1.28</td>\n",
       "      <td>37.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2,684.29</td>\n",
       "      <td>1.31</td>\n",
       "      <td>16.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>193.65</td>\n",
       "      <td>10.62</td>\n",
       "      <td>42.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162.10</td>\n",
       "      <td>2.40</td>\n",
       "      <td>41.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2,567.04</td>\n",
       "      <td>1.86</td>\n",
       "      <td>214.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>448.58</td>\n",
       "      <td>6.58</td>\n",
       "      <td>261.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1,143.69</td>\n",
       "      <td>4.47</td>\n",
       "      <td>29.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>922.83</td>\n",
       "      <td>1.28</td>\n",
       "      <td>22.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>799.34</td>\n",
       "      <td>1.40</td>\n",
       "      <td>87.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       recency  frequency  amount\n",
       "group                            \n",
       "0     2,563.24       1.28   37.78\n",
       "1     2,684.29       1.31   16.26\n",
       "2       193.65      10.62   42.02\n",
       "3       162.10       2.40   41.11\n",
       "4     2,567.04       1.86  214.86\n",
       "5       448.58       6.58  261.22\n",
       "6     1,143.69       4.47   29.85\n",
       "7       922.83       1.28   22.43\n",
       "8       799.34       1.40   87.43"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaf2163d",
   "metadata": {},
   "source": [
    "Q1: What is the size of the largest segment?\n",
    "\n",
    "Answer Q1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f2cf01dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "317"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(segments.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93567668",
   "metadata": {},
   "source": [
    "Q2: What is the average purchase amount of the segment which contains, on average, the customers who have made their last purchase the most recently?\n",
    "\n",
    "Answer Q2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b6198c91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41.10640522875818"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters.iloc[clusters['recency'].idxmin()]['amount']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2ed9bb05",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of ['customer_id'] are in the columns\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-48-67364f9da957>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcustomers_sample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'customer_id'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mcustomers_sample\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mset_index\u001b[0;34m(self, keys, drop, append, inplace, verify_integrity)\u001b[0m\n\u001b[1;32m   4725\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4726\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmissing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4727\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"None of {missing} are in the columns\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4729\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"None of ['customer_id'] are in the columns\""
     ]
    }
   ],
   "source": [
    "customers_sample.set_index('customer_id', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a9428857",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_sample.loc[260]['group'] == customers_sample.loc[5920]['group']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c773ca9d",
   "metadata": {},
   "source": [
    "Looking at the average profile of segment 1, would you say that members of this segment are typically...(you'll have to choose the right proposition)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "55910804",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "</style>\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>amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2,563.24</td>\n",
       "      <td>1.28</td>\n",
       "      <td>37.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2,684.29</td>\n",
       "      <td>1.31</td>\n",
       "      <td>16.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>193.65</td>\n",
       "      <td>10.62</td>\n",
       "      <td>42.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162.10</td>\n",
       "      <td>2.40</td>\n",
       "      <td>41.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2,567.04</td>\n",
       "      <td>1.86</td>\n",
       "      <td>214.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>448.58</td>\n",
       "      <td>6.58</td>\n",
       "      <td>261.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1,143.69</td>\n",
       "      <td>4.47</td>\n",
       "      <td>29.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>922.83</td>\n",
       "      <td>1.28</td>\n",
       "      <td>22.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>799.34</td>\n",
       "      <td>1.40</td>\n",
       "      <td>87.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       recency  frequency  amount\n",
       "group                            \n",
       "0     2,563.24       1.28   37.78\n",
       "1     2,684.29       1.31   16.26\n",
       "2       193.65      10.62   42.02\n",
       "3       162.10       2.40   41.11\n",
       "4     2,567.04       1.86  214.86\n",
       "5       448.58       6.58  261.22\n",
       "6     1,143.69       4.47   29.85\n",
       "7       922.83       1.28   22.43\n",
       "8       799.34       1.40   87.43"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61f2402d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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