module2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0deb9658",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pandasql import sqldf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a6ccdfc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pysqldf = lambda q: sqldf(q, globals())\n",
    "%precision %.2f\n",
    "pd.options.display.float_format = '{:,.2f}'.format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a7d31df5",
   "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>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": 30,
     "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": 31,
   "id": "48cdbb77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 31,
     "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": 32,
   "id": "9d51264f",
   "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": 33,
   "id": "1873a838",
   "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": 34,
   "id": "9aebf322",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute recency, frequency, and average purchase amount\n",
    "q = \"\"\"\n",
    "        SELECT customer_id,\n",
    "        MIN(days_since) AS 'recency',\n",
    "        MAX(days_since) AS 'first_purchase',\n",
    "        COUNT(*) AS 'frequency',\n",
    "        AVG(purchase_amount) AS 'amount'\n",
    "        FROM df GROUP BY 1\"\"\"\n",
    "customers_2015 = sqldf(q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9db2db60",
   "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>first_purchase</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>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>3751</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>3783</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>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>3710</td>\n",
       "      <td>2</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id  recency  first_purchase  frequency  amount\n",
       "0           10     3829            3829          1   30.00\n",
       "1           80      343            3751          7   71.43\n",
       "2           90      758            3783         10  115.80\n",
       "3          120     1401            1401          1   20.00\n",
       "4          130     2970            3710          2   50.00"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_2015.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f48ff4ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>recency</th>\n",
       "      <th>first_purchase</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",
       "      <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>1,984.01</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>1,133.41</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>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>988.00</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>2,087.00</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>2,992.00</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>4,016.00</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  first_purchase  frequency    amount\n",
       "count    18,417.00 18,417.00       18,417.00  18,417.00 18,417.00\n",
       "mean    137,573.51  1,253.04        1,984.01       2.78     57.79\n",
       "std      69,504.60  1,081.44        1,133.41       2.94    154.36\n",
       "min          10.00      1.00            1.00       1.00      5.00\n",
       "25%      81,990.00    244.00          988.00       1.00     21.67\n",
       "50%     136,430.00  1,070.00        2,087.00       2.00     30.00\n",
       "75%     195,100.00  2,130.00        2,992.00       3.00     50.00\n",
       "max     264,200.00  4,014.00        4,016.00      45.00  4,500.00"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_2015.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c6393523",
   "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>first_purchase</th>\n",
       "      <th>frequency</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3829</td>\n",
       "      <td>1</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3751</td>\n",
       "      <td>7</td>\n",
       "      <td>71.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3783</td>\n",
       "      <td>10</td>\n",
       "      <td>115.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1401</td>\n",
       "      <td>1</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3710</td>\n",
       "      <td>2</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   first_purchase  frequency  amount\n",
       "0            3829          1   30.00\n",
       "1            3751          7   71.43\n",
       "2            3783         10  115.80\n",
       "3            1401          1   20.00\n",
       "4            3710          2   50.00"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_2015.iloc[:,2:].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e7af2076",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'recency'}>,\n",
       "        <AxesSubplot:title={'center':'first_purchase'}>],\n",
       "       [<AxesSubplot:title={'center':'frequency'}>,\n",
       "        <AxesSubplot:title={'center':'amount'}>]], dtype=object)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "customers_2015.iloc[:,1:].hist(bins=30, figsize=(15, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5901aa97",
   "metadata": {},
   "source": [
    "# Coding a Managerial Segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cbaca542",
   "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>customer_id</th>\n",
       "      <th>recency</th>\n",
       "      <th>first_purchase</th>\n",
       "      <th>frequency</th>\n",
       "      <th>amount</th>\n",
       "      <th>segments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>3829</td>\n",
       "      <td>3829</td>\n",
       "      <td>1</td>\n",
       "      <td>30.00</td>\n",
       "      <td>inactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80</td>\n",
       "      <td>343</td>\n",
       "      <td>3751</td>\n",
       "      <td>7</td>\n",
       "      <td>71.43</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90</td>\n",
       "      <td>758</td>\n",
       "      <td>3783</td>\n",
       "      <td>10</td>\n",
       "      <td>115.80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>120</td>\n",
       "      <td>1401</td>\n",
       "      <td>1401</td>\n",
       "      <td>1</td>\n",
       "      <td>20.00</td>\n",
       "      <td>inactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>130</td>\n",
       "      <td>2970</td>\n",
       "      <td>3710</td>\n",
       "      <td>2</td>\n",
       "      <td>50.00</td>\n",
       "      <td>inactive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id  recency  first_purchase  frequency  amount  segments\n",
       "0           10     3829            3829          1   30.00  inactive\n",
       "1           80      343            3751          7   71.43       NaN\n",
       "2           90      758            3783         10  115.80       NaN\n",
       "3          120     1401            1401          1   20.00  inactive\n",
       "4          130     2970            3710          2   50.00  inactive"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_2015.loc[customers_2015['recency'] > 365*3, 'segment'] = 'inactive'\n",
    "customers_2015.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4036f19",
   "metadata": {},
   "source": [
    "## Simple 2-segment solution based on recency alone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "06ad3003",
   "metadata": {},
   "outputs": [],
   "source": [
    "#customers_2015.rename(columns={'segments': 'segment'}, inplace=True)\n",
    "customers_2015['segment'] =  customers_2015['segment'].fillna('NA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3fcab835",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NA          9259\n",
       "inactive    9158\n",
       "Name: segment, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_2015['segment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0bf62c88",
   "metadata": {},
   "outputs": [],
   "source": [
    "two_seg = customers_2015[['recency', 'frequency', 'amount', 'segment']].groupby('segment').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7bfb3a5e",
   "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>segment</th>\n",
       "      <th>NA</th>\n",
       "      <th>inactive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>recency</th>\n",
       "      <td>338.06</td>\n",
       "      <td>2,178.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frequency</th>\n",
       "      <td>3.74</td>\n",
       "      <td>1.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>67.37</td>\n",
       "      <td>48.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "segment       NA  inactive\n",
       "recency   338.06  2,178.11\n",
       "frequency   3.74      1.81\n",
       "amount     67.37     48.11"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "two_seg.head().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d37f13ec",
   "metadata": {},
   "source": [
    "## A more complex 3-segment solution based on recency alone\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b6146110",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "inactive    9158\n",
       "NA          7354\n",
       "cold        1905\n",
       "Name: segment, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_2015.loc[(customers_2015['recency']<= 365*3) & \n",
    "                   (customers_2015['recency'] > 356*2), 'segment'] = \"cold\"\n",
    "customers_2015['segment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0fc36f2f",
   "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>segment</th>\n",
       "      <th>NA</th>\n",
       "      <th>cold</th>\n",
       "      <th>inactive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>recency</th>\n",
       "      <td>203.46</td>\n",
       "      <td>857.63</td>\n",
       "      <td>2,178.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frequency</th>\n",
       "      <td>4.11</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>71.42</td>\n",
       "      <td>51.72</td>\n",
       "      <td>48.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "segment       NA   cold  inactive\n",
       "recency   203.46 857.63  2,178.11\n",
       "frequency   4.11   2.30      1.81\n",
       "amount     71.42  51.72     48.11"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "three_seg = customers_2015[['recency', 'frequency', 'amount', 'segment']].groupby('segment').mean()\n",
    "three_seg.head().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4aed6800",
   "metadata": {},
   "source": [
    "## A more complex 4-segment solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "aa395da3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "inactive    9158\n",
       "active      5398\n",
       "warm        1958\n",
       "cold        1903\n",
       "Name: segment, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_2015.loc[(customers_2015['recency']<= 365*2) & \n",
    "                   (customers_2015['recency'] > 365*1), 'segment'] = \"warm\"\n",
    "customers_2015.loc[customers_2015['recency']<= 365, 'segment'] = \"active\"\n",
    "customers_2015['segment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5bd4db82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "inactive           9158\n",
       "active             5398\n",
       "cold               1903\n",
       "new warm            938\n",
       "warm low value      901\n",
       "warm high value     119\n",
       "Name: segment, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Further segmentation of the warm segment:\n",
    "customers_2015.loc[(customers_2015['segment'] == \"warm\") &\n",
    "                  (customers_2015['first_purchase'] <= 365*2), 'segment'] = \"new warm\"\n",
    "\n",
    "customers_2015.loc[(customers_2015['segment'] == \"warm\") &\n",
    "                  (customers_2015['amount'] < 100), 'segment'] = \"warm low value\"\n",
    "\n",
    "customers_2015.loc[(customers_2015['segment'] == \"warm\") &\n",
    "                  (customers_2015['amount'] >= 100), 'segment'] = \"warm high value\"\n",
    "customers_2015['segment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "02ece3e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "inactive             9158\n",
       "active low value     3313\n",
       "cold                 1903\n",
       "new active           1512\n",
       "new warm              938\n",
       "warm low value        901\n",
       "active high value     573\n",
       "warm high value       119\n",
       "Name: segment, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Further segmentation of the active segment:\n",
    "customers_2015.loc[(customers_2015['segment'] == \"active\") &\n",
    "                  (customers_2015['first_purchase'] <= 365), 'segment'] = \"new active\"\n",
    "\n",
    "customers_2015.loc[(customers_2015['segment'] == \"active\") &\n",
    "                  (customers_2015['amount'] < 100), 'segment'] = \"active low value\"\n",
    "\n",
    "customers_2015.loc[(customers_2015['segment'] == \"active\") &\n",
    "                  (customers_2015['amount'] >= 100), 'segment'] = \"active high value\"\n",
    "customers_2015['segment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "72bddd50",
   "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>recency</th>\n",
       "      <th>first_purchase</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>1,253.04</td>\n",
       "      <td>1,984.01</td>\n",
       "      <td>2.78</td>\n",
       "      <td>57.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1,081.44</td>\n",
       "      <td>1,133.41</td>\n",
       "      <td>2.94</td>\n",
       "      <td>154.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00</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>244.00</td>\n",
       "      <td>988.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>21.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1,070.00</td>\n",
       "      <td>2,087.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2,130.00</td>\n",
       "      <td>2,992.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4,014.00</td>\n",
       "      <td>4,016.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>4,500.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        recency  first_purchase  frequency    amount\n",
       "count 18,417.00       18,417.00  18,417.00 18,417.00\n",
       "mean   1,253.04        1,984.01       2.78     57.79\n",
       "std    1,081.44        1,133.41       2.94    154.36\n",
       "min        1.00            1.00       1.00      5.00\n",
       "25%      244.00          988.00       1.00     21.67\n",
       "50%    1,070.00        2,087.00       2.00     30.00\n",
       "75%    2,130.00        2,992.00       3.00     50.00\n",
       "max    4,014.00        4,016.00      45.00  4,500.00"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_2015.iloc[:,1:].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "eb3b0571",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18417 entries, 0 to 18416\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count  Dtype   \n",
      "---  ------          --------------  -----   \n",
      " 0   customer_id     18417 non-null  int64   \n",
      " 1   recency         18417 non-null  int64   \n",
      " 2   first_purchase  18417 non-null  int64   \n",
      " 3   frequency       18417 non-null  int64   \n",
      " 4   amount          18417 non-null  float64 \n",
      " 5   segment         18417 non-null  category\n",
      " 6   segment_sort    18417 non-null  category\n",
      "dtypes: category(2), float64(1), int64(4)\n",
      "memory usage: 756.2 KB\n"
     ]
    }
   ],
   "source": [
    "# Re-order factor in a way that makes sense\n",
    "customers_2015['segment'] = customers_2015['segment'].astype('category')\n",
    "customers_2015.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "aac04efa",
   "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>customer_id</th>\n",
       "      <th>recency</th>\n",
       "      <th>first_purchase</th>\n",
       "      <th>frequency</th>\n",
       "      <th>amount</th>\n",
       "      <th>segment</th>\n",
       "      <th>segment_sort</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>3829</td>\n",
       "      <td>3829</td>\n",
       "      <td>1</td>\n",
       "      <td>30.00</td>\n",
       "      <td>inactive</td>\n",
       "      <td>inactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80</td>\n",
       "      <td>343</td>\n",
       "      <td>3751</td>\n",
       "      <td>7</td>\n",
       "      <td>71.43</td>\n",
       "      <td>active low value</td>\n",
       "      <td>active low value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90</td>\n",
       "      <td>758</td>\n",
       "      <td>3783</td>\n",
       "      <td>10</td>\n",
       "      <td>115.80</td>\n",
       "      <td>cold</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>120</td>\n",
       "      <td>1401</td>\n",
       "      <td>1401</td>\n",
       "      <td>1</td>\n",
       "      <td>20.00</td>\n",
       "      <td>inactive</td>\n",
       "      <td>inactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>130</td>\n",
       "      <td>2970</td>\n",
       "      <td>3710</td>\n",
       "      <td>2</td>\n",
       "      <td>50.00</td>\n",
       "      <td>inactive</td>\n",
       "      <td>inactive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id  recency  first_purchase  frequency  amount           segment  \\\n",
       "0           10     3829            3829          1   30.00          inactive   \n",
       "1           80      343            3751          7   71.43  active low value   \n",
       "2           90      758            3783         10  115.80              cold   \n",
       "3          120     1401            1401          1   20.00          inactive   \n",
       "4          130     2970            3710          2   50.00          inactive   \n",
       "\n",
       "       segment_sort  \n",
       "0          inactive  \n",
       "1  active low value  \n",
       "2              cold  \n",
       "3          inactive  \n",
       "4          inactive  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "customers_2015['segment_sort'] = pd.Categorical( customers_2015['segment'],\n",
    "        categories=['inactive', 'cold', 'warm high value','warm low value', \n",
    "           'new warm', 'active high value', 'active low value',\n",
    "           'new active'], ordered=True)\n",
    "customers_2015.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b347d4d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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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>first_purchase</th>\n",
       "      <th>frequency</th>\n",
       "      <th>amount</th>\n",
       "      <th>segment</th>\n",
       "      <th>segment_sort</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>3829</td>\n",
       "      <td>3829</td>\n",
       "      <td>1</td>\n",
       "      <td>30.00</td>\n",
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       "    <tr>\n",
       "      <th>7894</th>\n",
       "      <td>119690</td>\n",
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       "      <td>2443</td>\n",
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       "      <td>inactive</td>\n",
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       "      <th>7895</th>\n",
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       "      <td>2249</td>\n",
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       "      <td>26.67</td>\n",
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       "      <td>inactive</td>\n",
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       "    <tr>\n",
       "      <th>7896</th>\n",
       "      <td>119720</td>\n",
       "      <td>2156</td>\n",
       "      <td>2443</td>\n",
       "      <td>2</td>\n",
       "      <td>40.00</td>\n",
       "      <td>inactive</td>\n",
       "      <td>inactive</td>\n",
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       "    <tr>\n",
       "      <th>7897</th>\n",
       "      <td>119730</td>\n",
       "      <td>2443</td>\n",
       "      <td>2443</td>\n",
       "      <td>1</td>\n",
       "      <td>50.00</td>\n",
       "      <td>inactive</td>\n",
       "      <td>inactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17404</th>\n",
       "      <td>252380</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>10.00</td>\n",
       "      <td>new active</td>\n",
       "      <td>new active</td>\n",
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       "    <tr>\n",
       "      <th>17403</th>\n",
       "      <td>252370</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
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       "      <td>45.00</td>\n",
       "      <td>new active</td>\n",
       "      <td>new active</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17402</th>\n",
       "      <td>252360</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>20.00</td>\n",
       "      <td>new active</td>\n",
       "      <td>new active</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17411</th>\n",
       "      <td>252450</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>45.00</td>\n",
       "      <td>new active</td>\n",
       "      <td>new active</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18416</th>\n",
       "      <td>264200</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>500.00</td>\n",
       "      <td>new active</td>\n",
       "      <td>new active</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18417 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id  recency  first_purchase  frequency  amount     segment  \\\n",
       "0               10     3829            3829          1   30.00    inactive   \n",
       "7894        119690     1500            2443          4   30.00    inactive   \n",
       "7895        119710     2249            2443          3   26.67    inactive   \n",
       "7896        119720     2156            2443          2   40.00    inactive   \n",
       "7897        119730     2443            2443          1   50.00    inactive   \n",
       "...            ...      ...             ...        ...     ...         ...   \n",
       "17404       252380       73              73          1   10.00  new active   \n",
       "17403       252370       73              73          1   45.00  new active   \n",
       "17402       252360       73              73          1   20.00  new active   \n",
       "17411       252450       72              72          1   45.00  new active   \n",
       "18416       264200        1               1          1  500.00  new active   \n",
       "\n",
       "      segment_sort  \n",
       "0         inactive  \n",
       "7894      inactive  \n",
       "7895      inactive  \n",
       "7896      inactive  \n",
       "7897      inactive  \n",
       "...            ...  \n",
       "17404   new active  \n",
       "17403   new active  \n",
       "17402   new active  \n",
       "17411   new active  \n",
       "18416   new active  \n",
       "\n",
       "[18417 rows x 7 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_2015.sort_values('segment_sort')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79ed5aad",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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