UrbanClap_Customer Analytics_EDA_RepeatRates_Vikrant.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from datetime import datetime,timedelta\n",
    "import calendar\n",
    "\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transaction_ID</th>\n",
       "      <th>Profile ID</th>\n",
       "      <th>Date_of_Booking</th>\n",
       "      <th>Date_of_Service_Requested</th>\n",
       "      <th>Source</th>\n",
       "      <th>Slot of Booking (Hour of the Day)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BBCHH</td>\n",
       "      <td>1</td>\n",
       "      <td>5/20/2018</td>\n",
       "      <td>5/21/2018</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHWFD</td>\n",
       "      <td>1</td>\n",
       "      <td>9/23/2018</td>\n",
       "      <td>9/23/2018</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DYDMF</td>\n",
       "      <td>2</td>\n",
       "      <td>11/10/2018</td>\n",
       "      <td>11/13/2018</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EZYSA</td>\n",
       "      <td>3</td>\n",
       "      <td>4/12/2018</td>\n",
       "      <td>4/16/2018</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HWAKX</td>\n",
       "      <td>3</td>\n",
       "      <td>8/5/2018</td>\n",
       "      <td>8/6/2018</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Transaction_ID  Profile ID Date_of_Booking Date_of_Service_Requested  \\\n",
       "0          BBCHH           1       5/20/2018                 5/21/2018   \n",
       "1          CHWFD           1       9/23/2018                 9/23/2018   \n",
       "2          DYDMF           2      11/10/2018                11/13/2018   \n",
       "3          EZYSA           3       4/12/2018                 4/16/2018   \n",
       "4          HWAKX           3        8/5/2018                  8/6/2018   \n",
       "\n",
       "   Source  Slot of Booking (Hour of the Day)  \n",
       "0       4                                 14  \n",
       "1       3                                  8  \n",
       "2       3                                 11  \n",
       "3       2                                  8  \n",
       "4       2                                 11  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"Urbanclap.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30940 entries, 0 to 30939\n",
      "Data columns (total 6 columns):\n",
      "Transaction_ID                       30940 non-null object\n",
      "Profile ID                           30940 non-null int64\n",
      "Date_of_Booking                      30940 non-null object\n",
      "Date_of_Service_Requested            30940 non-null object\n",
      "Source                               30940 non-null int64\n",
      "Slot of Booking (Hour of the Day)    30940 non-null int64\n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns=['T_ID','P_ID','DOB','DOSR','Source','Slot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['DOB']=pd.to_datetime(df['DOB'])\n",
    "df['DOSR']=pd.to_datetime(df['DOSR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>T_ID</th>\n",
       "      <th>P_ID</th>\n",
       "      <th>DOB</th>\n",
       "      <th>DOSR</th>\n",
       "      <th>Source</th>\n",
       "      <th>Slot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30940</td>\n",
       "      <td>30940.000000</td>\n",
       "      <td>30940</td>\n",
       "      <td>30940</td>\n",
       "      <td>30940.000000</td>\n",
       "      <td>30940.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>30940</td>\n",
       "      <td>NaN</td>\n",
       "      <td>365</td>\n",
       "      <td>370</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>OTYAO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-12-01 00:00:00</td>\n",
       "      <td>2018-08-21 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>188</td>\n",
       "      <td>109</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-12-01 00:00:00</td>\n",
       "      <td>2017-12-01 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-11-30 00:00:00</td>\n",
       "      <td>2018-12-05 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8329.657854</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.673142</td>\n",
       "      <td>13.060440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4807.274974</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.025224</td>\n",
       "      <td>3.135142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4142.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8375.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12432.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16711.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         T_ID          P_ID                  DOB                 DOSR  \\\n",
       "count   30940  30940.000000                30940                30940   \n",
       "unique  30940           NaN                  365                  370   \n",
       "top     OTYAO           NaN  2017-12-01 00:00:00  2018-08-21 00:00:00   \n",
       "freq        1           NaN                  188                  109   \n",
       "first     NaN           NaN  2017-12-01 00:00:00  2017-12-01 00:00:00   \n",
       "last      NaN           NaN  2018-11-30 00:00:00  2018-12-05 00:00:00   \n",
       "mean      NaN   8329.657854                  NaN                  NaN   \n",
       "std       NaN   4807.274974                  NaN                  NaN   \n",
       "min       NaN      1.000000                  NaN                  NaN   \n",
       "25%       NaN   4142.750000                  NaN                  NaN   \n",
       "50%       NaN   8375.000000                  NaN                  NaN   \n",
       "75%       NaN  12432.000000                  NaN                  NaN   \n",
       "max       NaN  16711.000000                  NaN                  NaN   \n",
       "\n",
       "              Source          Slot  \n",
       "count   30940.000000  30940.000000  \n",
       "unique           NaN           NaN  \n",
       "top              NaN           NaN  \n",
       "freq             NaN           NaN  \n",
       "first            NaN           NaN  \n",
       "last             NaN           NaN  \n",
       "mean        2.673142     13.060440  \n",
       "std         1.025224      3.135142  \n",
       "min         1.000000      6.000000  \n",
       "25%         2.000000     12.000000  \n",
       "50%         3.000000     14.000000  \n",
       "75%         3.000000     14.000000  \n",
       "max         4.000000     19.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='DOB',inplace=True)\n",
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique Customers Count: 16711\n"
     ]
    }
   ],
   "source": [
    "print('Unique Customers Count: {}'.format(df['P_ID'].nunique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>DOB</th>\n",
       "      <th>T_ID</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>mmm-yy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>2761</td>\n",
       "      <td>Dec</td>\n",
       "      <td>2017</td>\n",
       "      <td>Dec-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-31</td>\n",
       "      <td>2675</td>\n",
       "      <td>Jan</td>\n",
       "      <td>2018</td>\n",
       "      <td>Jan-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-02-28</td>\n",
       "      <td>2403</td>\n",
       "      <td>Feb</td>\n",
       "      <td>2018</td>\n",
       "      <td>Feb-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>2600</td>\n",
       "      <td>Mar</td>\n",
       "      <td>2018</td>\n",
       "      <td>Mar-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-04-30</td>\n",
       "      <td>2483</td>\n",
       "      <td>Apr</td>\n",
       "      <td>2018</td>\n",
       "      <td>Apr-2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DOB  T_ID month  year    mmm-yy\n",
       "0 2017-12-31  2761   Dec  2017  Dec-2017\n",
       "1 2018-01-31  2675   Jan  2018  Jan-2018\n",
       "2 2018-02-28  2403   Feb  2018  Feb-2018\n",
       "3 2018-03-31  2600   Mar  2018  Mar-2018\n",
       "4 2018-04-30  2483   Apr  2018  Apr-2018"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bookings=pd.DataFrame(df.groupby('DOB')[\"T_ID\"].count())\n",
    "\n",
    "bookings_df=bookings.resample(rule='M').sum()\n",
    "\n",
    "bookings_df.reset_index(inplace=True)\n",
    "\n",
    "bookings_df['month']=bookings_df['DOB'].apply(lambda x:x.month)\n",
    "bookings_df['month']=bookings_df['month'].apply(lambda x: calendar.month_abbr[x])\n",
    "\n",
    "bookings_df['year']=bookings_df['DOB'].apply(lambda x:x.year)\n",
    "\n",
    "bookings_df['mmm-yy']=bookings_df['month'].astype(str)+\"-\"+bookings_df['year'].astype(str)\n",
    "\n",
    "\n",
    "bookings_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=bookings_df.plot(x=\"mmm-yy\",y='T_ID',kind=\"bar\",figsize=(13,5),legend=False,fontsize=10,rot=0)\n",
    "g.set_xlabel('Month',size=12)\n",
    "g.set_title('Bookings by Month',size=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Slot</th>\n",
       "      <th>Bookings</th>\n",
       "      <th>Bookings percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>1193</td>\n",
       "      <td>3.855850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>1232</td>\n",
       "      <td>3.981900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>1318</td>\n",
       "      <td>4.259858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>1358</td>\n",
       "      <td>4.389140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>1289</td>\n",
       "      <td>4.166128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Slot  Bookings  Bookings percentage\n",
       "0     6      1193             3.855850\n",
       "1     7      1232             3.981900\n",
       "2     8      1318             4.259858\n",
       "3     9      1358             4.389140\n",
       "4    10      1289             4.166128"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slot=pd.DataFrame(df.groupby('Slot')['T_ID'].count())\n",
    "slot['Bookings percentage']=slot['T_ID']*100/(slot['T_ID'].sum())\n",
    "slot.reset_index(inplace=True)\n",
    "slot.rename(columns= {\"T_ID\":\"Bookings\"},inplace=True)\n",
    "slot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g1=slot.plot(x=\"Slot\", y= 'Bookings',kind='bar',grid=False,fontsize=10, figsize=(13,6));\n",
    "g1.set_title('Bookings by Time Slot',size=15)\n",
    "g1.set_ylabel('Bookings Count')\n",
    "\n",
    "\n",
    "g2=slot.plot(y='Bookings percentage', kind='line',fontsize=10,secondary_y=True,color='green',ax=g1,\n",
    "             grid=False)\n",
    "g2.set_ylabel('Bookings Percentage Share')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Source_1</th>\n",
       "      <th>Source_2</th>\n",
       "      <th>Source_3</th>\n",
       "      <th>Source_4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOB</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-01</th>\n",
       "      <td>33</td>\n",
       "      <td>54</td>\n",
       "      <td>59</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-02</th>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-03</th>\n",
       "      <td>16</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-04</th>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-05</th>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Source_1  Source_2  Source_3  Source_4\n",
       "DOB                                               \n",
       "2017-12-01        33        54        59        42\n",
       "2017-12-02        19        21        30        18\n",
       "2017-12-03        16        21        27        23\n",
       "2017-12-04        13        23        17        18\n",
       "2017-12-05        18        21        26        24"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source=pd.pivot_table(df,values='T_ID',index='DOB',columns='Source',aggfunc=len)\n",
    "source.columns=['Source_1','Source_2','Source_3',\"Source_4\"]\n",
    "source.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>DOB</th>\n",
       "      <th>Source_1</th>\n",
       "      <th>Source_2</th>\n",
       "      <th>Source_3</th>\n",
       "      <th>Source_4</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>mmm-yy</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>464</td>\n",
       "      <td>673</td>\n",
       "      <td>912</td>\n",
       "      <td>712</td>\n",
       "      <td>Dec</td>\n",
       "      <td>2017</td>\n",
       "      <td>Dec-2017</td>\n",
       "      <td>2761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-31</td>\n",
       "      <td>434</td>\n",
       "      <td>642</td>\n",
       "      <td>920</td>\n",
       "      <td>679</td>\n",
       "      <td>Jan</td>\n",
       "      <td>2018</td>\n",
       "      <td>Jan-2018</td>\n",
       "      <td>2675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-02-28</td>\n",
       "      <td>402</td>\n",
       "      <td>577</td>\n",
       "      <td>795</td>\n",
       "      <td>629</td>\n",
       "      <td>Feb</td>\n",
       "      <td>2018</td>\n",
       "      <td>Feb-2018</td>\n",
       "      <td>2403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>425</td>\n",
       "      <td>620</td>\n",
       "      <td>909</td>\n",
       "      <td>646</td>\n",
       "      <td>Mar</td>\n",
       "      <td>2018</td>\n",
       "      <td>Mar-2018</td>\n",
       "      <td>2600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-04-30</td>\n",
       "      <td>409</td>\n",
       "      <td>611</td>\n",
       "      <td>844</td>\n",
       "      <td>619</td>\n",
       "      <td>Apr</td>\n",
       "      <td>2018</td>\n",
       "      <td>Apr-2018</td>\n",
       "      <td>2483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-05-31</td>\n",
       "      <td>392</td>\n",
       "      <td>625</td>\n",
       "      <td>844</td>\n",
       "      <td>649</td>\n",
       "      <td>May</td>\n",
       "      <td>2018</td>\n",
       "      <td>May-2018</td>\n",
       "      <td>2510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-06-30</td>\n",
       "      <td>419</td>\n",
       "      <td>641</td>\n",
       "      <td>860</td>\n",
       "      <td>651</td>\n",
       "      <td>Jun</td>\n",
       "      <td>2018</td>\n",
       "      <td>Jun-2018</td>\n",
       "      <td>2571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>443</td>\n",
       "      <td>668</td>\n",
       "      <td>866</td>\n",
       "      <td>639</td>\n",
       "      <td>Jul</td>\n",
       "      <td>2018</td>\n",
       "      <td>Jul-2018</td>\n",
       "      <td>2616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>437</td>\n",
       "      <td>615</td>\n",
       "      <td>887</td>\n",
       "      <td>617</td>\n",
       "      <td>Aug</td>\n",
       "      <td>2018</td>\n",
       "      <td>Aug-2018</td>\n",
       "      <td>2556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-09-30</td>\n",
       "      <td>439</td>\n",
       "      <td>588</td>\n",
       "      <td>846</td>\n",
       "      <td>635</td>\n",
       "      <td>Sep</td>\n",
       "      <td>2018</td>\n",
       "      <td>Sep-2018</td>\n",
       "      <td>2508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018-10-31</td>\n",
       "      <td>436</td>\n",
       "      <td>678</td>\n",
       "      <td>923</td>\n",
       "      <td>643</td>\n",
       "      <td>Oct</td>\n",
       "      <td>2018</td>\n",
       "      <td>Oct-2018</td>\n",
       "      <td>2680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018-11-30</td>\n",
       "      <td>429</td>\n",
       "      <td>644</td>\n",
       "      <td>896</td>\n",
       "      <td>608</td>\n",
       "      <td>Nov</td>\n",
       "      <td>2018</td>\n",
       "      <td>Nov-2018</td>\n",
       "      <td>2577</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          DOB  Source_1  Source_2  Source_3  Source_4 month  year    mmm-yy  \\\n",
       "0  2017-12-31       464       673       912       712   Dec  2017  Dec-2017   \n",
       "1  2018-01-31       434       642       920       679   Jan  2018  Jan-2018   \n",
       "2  2018-02-28       402       577       795       629   Feb  2018  Feb-2018   \n",
       "3  2018-03-31       425       620       909       646   Mar  2018  Mar-2018   \n",
       "4  2018-04-30       409       611       844       619   Apr  2018  Apr-2018   \n",
       "5  2018-05-31       392       625       844       649   May  2018  May-2018   \n",
       "6  2018-06-30       419       641       860       651   Jun  2018  Jun-2018   \n",
       "7  2018-07-31       443       668       866       639   Jul  2018  Jul-2018   \n",
       "8  2018-08-31       437       615       887       617   Aug  2018  Aug-2018   \n",
       "9  2018-09-30       439       588       846       635   Sep  2018  Sep-2018   \n",
       "10 2018-10-31       436       678       923       643   Oct  2018  Oct-2018   \n",
       "11 2018-11-30       429       644       896       608   Nov  2018  Nov-2018   \n",
       "\n",
       "    Total  \n",
       "0    2761  \n",
       "1    2675  \n",
       "2    2403  \n",
       "3    2600  \n",
       "4    2483  \n",
       "5    2510  \n",
       "6    2571  \n",
       "7    2616  \n",
       "8    2556  \n",
       "9    2508  \n",
       "10   2680  \n",
       "11   2577  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source=source.resample(rule='M').sum()\n",
    "source.reset_index(inplace=True)\n",
    "source['month']=source['DOB'].apply(lambda x:x.month)\n",
    "source['month']=source['month'].apply(lambda x: calendar.month_abbr[x])\n",
    "\n",
    "source['year']=source['DOB'].apply(lambda x:x.year)\n",
    "\n",
    "source['mmm-yy']=source['month'].astype(str)+\"-\"+source['year'].astype(str)\n",
    "\n",
    "source['Total']=source[\"Source_1\"]+source[\"Source_2\"]+source[\"Source_3\"]+source[\"Source_4\"]\n",
    "\n",
    "source"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bookings By Source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "ax=source.plot(x='mmm-yy', y=['Source_1',\"Source_2\",\"Source_3\",\"Source_4\"],kind='bar',figsize=(14,5),rot=0,fontsize=8)\n",
    "\n",
    "ax.set_xlabel('Month',fontsize=12)\n",
    "ax.set_ylabel('Bookings Count',fontsize=12)\n",
    "ax.legend(loc='upper center',ncol=4,fontsize=8);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# New Customer Acquisiton by Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>P_ID</th>\n",
       "      <th>DOB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2018-05-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2018-11-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2018-04-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2018-03-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2018-01-06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   P_ID        DOB\n",
       "0     1 2018-05-20\n",
       "1     2 2018-11-10\n",
       "2     3 2018-04-12\n",
       "3     4 2018-03-02\n",
       "4     5 2018-01-06"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=pd.DataFrame(df.groupby('P_ID')['DOB'].min())\n",
    "df1.reset_index(inplace=True)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>P_ID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOB</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-01</th>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-02</th>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-03</th>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-04</th>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-05</th>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            P_ID\n",
       "DOB             \n",
       "2017-12-01   187\n",
       "2017-12-02    86\n",
       "2017-12-03    84\n",
       "2017-12-04    68\n",
       "2017-12-05    85"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=pd.DataFrame(df1.groupby('DOB')['P_ID'].count())\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>DOB</th>\n",
       "      <th>P_ID</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>mmm-yy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>2424</td>\n",
       "      <td>Dec</td>\n",
       "      <td>2017</td>\n",
       "      <td>Dec-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-31</td>\n",
       "      <td>1892</td>\n",
       "      <td>Jan</td>\n",
       "      <td>2018</td>\n",
       "      <td>Jan-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-02-28</td>\n",
       "      <td>1549</td>\n",
       "      <td>Feb</td>\n",
       "      <td>2018</td>\n",
       "      <td>Feb-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>1490</td>\n",
       "      <td>Mar</td>\n",
       "      <td>2018</td>\n",
       "      <td>Mar-2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-04-30</td>\n",
       "      <td>1346</td>\n",
       "      <td>Apr</td>\n",
       "      <td>2018</td>\n",
       "      <td>Apr-2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DOB  P_ID month  year    mmm-yy\n",
       "0 2017-12-31  2424   Dec  2017  Dec-2017\n",
       "1 2018-01-31  1892   Jan  2018  Jan-2018\n",
       "2 2018-02-28  1549   Feb  2018  Feb-2018\n",
       "3 2018-03-31  1490   Mar  2018  Mar-2018\n",
       "4 2018-04-30  1346   Apr  2018  Apr-2018"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_customers_df=df2.resample(rule='M').sum()\n",
    "new_customers_df.reset_index(inplace=True)\n",
    "\n",
    "#adding month column \n",
    "new_customers_df['month']=new_customers_df['DOB'].apply(lambda x: x.month)\n",
    "\n",
    "\n",
    "new_customers_df['month']=new_customers_df['month'].apply(lambda x: calendar.month_abbr[x])\n",
    "\n",
    "\n",
    "#adding year\n",
    "new_customers_df['year']=new_customers_df['DOB'].apply(lambda x: x.year)\n",
    "#https://stackoverflow.com/questions/37625334/python-pandas-convert-month-int-to-month-name\n",
    "\n",
    "new_customers_df['mmm-yy']=new_customers_df['month'].astype(str)+'-'+new_customers_df['year'].astype(str)\n",
    "\n",
    "new_customers_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=new_customers_df.plot(x=\"mmm-yy\",y='P_ID',kind=\"bar\",figsize=(14,5),legend=False,fontsize=12,rot=0,\n",
    "                        yticks=np.arange(0,3000,step=500),grid=True)\n",
    "g.set_xlabel('Month',size=12)\n",
    "g.set_title('New Customers Acquired by Month',size=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# REPEAT RATES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>T_ID</th>\n",
       "      <th>P_ID</th>\n",
       "      <th>DOB</th>\n",
       "      <th>DOSR</th>\n",
       "      <th>Source</th>\n",
       "      <th>Slot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BBCHH</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-05-20</td>\n",
       "      <td>2018-05-21</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DYDMF</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-11-10</td>\n",
       "      <td>2018-11-13</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EZYSA</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-04-12</td>\n",
       "      <td>2018-04-16</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>YRKFO</td>\n",
       "      <td>4</td>\n",
       "      <td>2018-03-02</td>\n",
       "      <td>2018-03-05</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>JSFWY</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-01-06</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    T_ID  P_ID        DOB       DOSR  Source  Slot\n",
       "0  BBCHH     1 2018-05-20 2018-05-21       4    14\n",
       "2  DYDMF     2 2018-11-10 2018-11-13       3    11\n",
       "3  EZYSA     3 2018-04-12 2018-04-16       2     8\n",
       "5  YRKFO     4 2018-03-02 2018-03-05       1    13\n",
       "6  JSFWY     5 2018-01-06 2018-01-07       3    14"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fetching 1st order details of customers \n",
    "first_order_df=df.loc[df.groupby(\"P_ID\")[\"DOB\"].idxmin()]\n",
    "first_order_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>T_ID</th>\n",
       "      <th>P_ID</th>\n",
       "      <th>DOB</th>\n",
       "      <th>DOSR</th>\n",
       "      <th>Source</th>\n",
       "      <th>Slot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHWFD</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-09-23</td>\n",
       "      <td>2018-09-23</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HWAKX</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-08-05</td>\n",
       "      <td>2018-08-06</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>LRPOS</td>\n",
       "      <td>8</td>\n",
       "      <td>2018-11-30</td>\n",
       "      <td>2018-11-30</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PRFVG</td>\n",
       "      <td>14</td>\n",
       "      <td>2018-06-07</td>\n",
       "      <td>2018-06-07</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>UVFIY</td>\n",
       "      <td>18</td>\n",
       "      <td>2018-11-06</td>\n",
       "      <td>2018-11-07</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     T_ID  P_ID        DOB       DOSR  Source  Slot\n",
       "1   CHWFD     1 2018-09-23 2018-09-23       3     8\n",
       "4   HWAKX     3 2018-08-05 2018-08-06       2    11\n",
       "10  LRPOS     8 2018-11-30 2018-11-30       3    14\n",
       "17  PRFVG    14 2018-06-07 2018-06-07       4     6\n",
       "26  UVFIY    18 2018-11-06 2018-11-07       3    13"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#second order details of customers\n",
    "nonfirst_order_df= df.drop(index=first_order_df.index)\n",
    "second_order_df=nonfirst_order_df.loc[nonfirst_order_df.groupby('P_ID')['DOB'].idxmin()]\n",
    "second_order_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>T_ID_first</th>\n",
       "      <th>P_ID</th>\n",
       "      <th>DOB_first</th>\n",
       "      <th>DOSR_first</th>\n",
       "      <th>Source_first</th>\n",
       "      <th>Slot_first</th>\n",
       "      <th>T_ID_second</th>\n",
       "      <th>DOB_second</th>\n",
       "      <th>DOSR_second</th>\n",
       "      <th>Source_second</th>\n",
       "      <th>Slot_second</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BBCHH</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-05-20</td>\n",
       "      <td>2018-05-21</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>CHWFD</td>\n",
       "      <td>2018-09-23</td>\n",
       "      <td>2018-09-23</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DYDMF</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-11-10</td>\n",
       "      <td>2018-11-13</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EZYSA</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-04-12</td>\n",
       "      <td>2018-04-16</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>HWAKX</td>\n",
       "      <td>2018-08-05</td>\n",
       "      <td>2018-08-06</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YRKFO</td>\n",
       "      <td>4</td>\n",
       "      <td>2018-03-02</td>\n",
       "      <td>2018-03-05</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JSFWY</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-01-06</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  T_ID_first  P_ID  DOB_first DOSR_first  Source_first  Slot_first  \\\n",
       "0      BBCHH     1 2018-05-20 2018-05-21             4          14   \n",
       "1      DYDMF     2 2018-11-10 2018-11-13             3          11   \n",
       "2      EZYSA     3 2018-04-12 2018-04-16             2           8   \n",
       "3      YRKFO     4 2018-03-02 2018-03-05             1          13   \n",
       "4      JSFWY     5 2018-01-06 2018-01-07             3          14   \n",
       "\n",
       "  T_ID_second DOB_second DOSR_second  Source_second  Slot_second  \n",
       "0       CHWFD 2018-09-23  2018-09-23            3.0          8.0  \n",
       "1         NaN        NaT         NaT            NaN          NaN  \n",
       "2       HWAKX 2018-08-05  2018-08-06            2.0         11.0  \n",
       "3         NaN        NaT         NaT            NaN          NaN  \n",
       "4         NaN        NaT         NaT            NaN          NaN  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#first & second orders details merged\n",
    "first_second_order_df=pd.merge(first_order_df,second_order_df,how='left',on='P_ID', suffixes=('_first','_second'))\n",
    "first_second_order_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_second_order_df['difference']=first_second_order_df['DOB_second']-first_second_order_df['DOB_first']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>T_ID_first</th>\n",
       "      <th>P_ID</th>\n",
       "      <th>DOB_first</th>\n",
       "      <th>DOSR_first</th>\n",
       "      <th>Source_first</th>\n",
       "      <th>Slot_first</th>\n",
       "      <th>T_ID_second</th>\n",
       "      <th>DOB_second</th>\n",
       "      <th>DOSR_second</th>\n",
       "      <th>Source_second</th>\n",
       "      <th>Slot_second</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BBCHH</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-05-20</td>\n",
       "      <td>2018-05-21</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>CHWFD</td>\n",
       "      <td>2018-09-23</td>\n",
       "      <td>2018-09-23</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>126 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DYDMF</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-11-10</td>\n",
       "      <td>2018-11-13</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EZYSA</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-04-12</td>\n",
       "      <td>2018-04-16</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>HWAKX</td>\n",
       "      <td>2018-08-05</td>\n",
       "      <td>2018-08-06</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>115 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YRKFO</td>\n",
       "      <td>4</td>\n",
       "      <td>2018-03-02</td>\n",
       "      <td>2018-03-05</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JSFWY</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-01-06</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  T_ID_first  P_ID  DOB_first DOSR_first  Source_first  Slot_first  \\\n",
       "0      BBCHH     1 2018-05-20 2018-05-21             4          14   \n",
       "1      DYDMF     2 2018-11-10 2018-11-13             3          11   \n",
       "2      EZYSA     3 2018-04-12 2018-04-16             2           8   \n",
       "3      YRKFO     4 2018-03-02 2018-03-05             1          13   \n",
       "4      JSFWY     5 2018-01-06 2018-01-07             3          14   \n",
       "\n",
       "  T_ID_second DOB_second DOSR_second  Source_second  Slot_second difference  \n",
       "0       CHWFD 2018-09-23  2018-09-23            3.0          8.0   126 days  \n",
       "1         NaN        NaT         NaT            NaN          NaN        NaT  \n",
       "2       HWAKX 2018-08-05  2018-08-06            2.0         11.0   115 days  \n",
       "3         NaN        NaT         NaT            NaN          NaN        NaT  \n",
       "4         NaN        NaT         NaT            NaN          NaN        NaT  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_second_order_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>T_ID_first</th>\n",
       "      <th>P_ID</th>\n",
       "      <th>DOB_first</th>\n",
       "      <th>DOSR_first</th>\n",
       "      <th>Source_first</th>\n",
       "      <th>Slot_first</th>\n",
       "      <th>T_ID_second</th>\n",
       "      <th>DOB_second</th>\n",
       "      <th>DOSR_second</th>\n",
       "      <th>Source_second</th>\n",
       "      <th>Slot_second</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BBCHH</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-05-20</td>\n",
       "      <td>2018-05-21</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>CHWFD</td>\n",
       "      <td>2018-09-23</td>\n",
       "      <td>2018-09-23</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>126.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DYDMF</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-11-10</td>\n",
       "      <td>2018-11-13</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  T_ID_first  P_ID  DOB_first DOSR_first  Source_first  Slot_first  \\\n",
       "0      BBCHH     1 2018-05-20 2018-05-21             4          14   \n",
       "1      DYDMF     2 2018-11-10 2018-11-13             3          11   \n",
       "\n",
       "  T_ID_second DOB_second DOSR_second  Source_second  Slot_second  difference  \n",
       "0       CHWFD 2018-09-23  2018-09-23            3.0          8.0       126.0  \n",
       "1         NaN        NaT         NaT            NaN          NaN         NaN  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#changing data type of differnce to int\n",
    "first_second_order_df['difference']=first_second_order_df['difference'].astype('timedelta64[D]')\n",
    "first_second_order_df.head(2)\n",
    "\n",
    "#https://stackoverflow.com/questions/33605514/how-to-extract-days-as-integers-from-a-timedelta64ns-object-in-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>T_ID_first</th>\n",
       "      <th>P_ID</th>\n",
       "      <th>DOB_first</th>\n",
       "      <th>DOSR_first</th>\n",
       "      <th>Source_first</th>\n",
       "      <th>Slot_first</th>\n",
       "      <th>T_ID_second</th>\n",
       "      <th>DOB_second</th>\n",
       "      <th>DOSR_second</th>\n",
       "      <th>Source_second</th>\n",
       "      <th>Slot_second</th>\n",
       "      <th>difference</th>\n",
       "      <th>difference_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BBCHH</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-05-20</td>\n",
       "      <td>2018-05-21</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>CHWFD</td>\n",
       "      <td>2018-09-23</td>\n",
       "      <td>2018-09-23</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>[90.0, 180.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DYDMF</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-11-10</td>\n",
       "      <td>2018-11-13</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>not repeated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EZYSA</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-04-12</td>\n",
       "      <td>2018-04-16</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>HWAKX</td>\n",
       "      <td>2018-08-05</td>\n",
       "      <td>2018-08-06</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>[90.0, 180.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YRKFO</td>\n",
       "      <td>4</td>\n",
       "      <td>2018-03-02</td>\n",
       "      <td>2018-03-05</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>not repeated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JSFWY</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-01-06</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>not repeated</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  T_ID_first  P_ID  DOB_first DOSR_first  Source_first  Slot_first  \\\n",
       "0      BBCHH     1 2018-05-20 2018-05-21             4          14   \n",
       "1      DYDMF     2 2018-11-10 2018-11-13             3          11   \n",
       "2      EZYSA     3 2018-04-12 2018-04-16             2           8   \n",
       "3      YRKFO     4 2018-03-02 2018-03-05             1          13   \n",
       "4      JSFWY     5 2018-01-06 2018-01-07             3          14   \n",
       "\n",
       "  T_ID_second DOB_second DOSR_second  Source_second  Slot_second  difference  \\\n",
       "0       CHWFD 2018-09-23  2018-09-23            3.0          8.0       126.0   \n",
       "1         NaN        NaT         NaT            NaN          NaN         NaN   \n",
       "2       HWAKX 2018-08-05  2018-08-06            2.0         11.0       115.0   \n",
       "3         NaN        NaT         NaT            NaN          NaN         NaN   \n",
       "4         NaN        NaT         NaT            NaN          NaN         NaN   \n",
       "\n",
       "  difference_cat  \n",
       "0  [90.0, 180.0)  \n",
       "1   not repeated  \n",
       "2  [90.0, 180.0)  \n",
       "3   not repeated  \n",
       "4   not repeated  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "difference_bins=[0,30,60,90,180,370]\n",
    "cats=pd.cut(first_second_order_df['difference'],difference_bins,right=False)\n",
    "first_second_order_df['difference_cat']=cats\n",
    "first_second_order_df['difference_cat'].replace(np.nan,\"not repeated\",inplace=True)\n",
    "first_second_order_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "not repeated      12020\n",
       "[90.0, 180.0)      1250\n",
       "[0.0, 30.0)        1230\n",
       "[30.0, 60.0)        800\n",
       "[180.0, 370.0)      771\n",
       "[60.0, 90.0)        640\n",
       "Name: difference_cat, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_second_order_df['difference_cat'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#changing to string for string operations\n",
    "first_second_order_df['difference_cat']=first_second_order_df['difference_cat'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 30 Days Repeat Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Within 30 Days Repeat Rate is :8.0 percent\n"
     ]
    }
   ],
   "source": [
    "#fetching customers who are atleast 30 days old, so that repeat rate calculation is not influenced by customers<30 days old\n",
    "\n",
    "atleast_30days_old_df =first_second_order_df[(df['DOB'].max()-first_second_order_df[\"DOB_first\"]).astype('timedelta64[D]')>=30]\n",
    "\n",
    "\n",
    "print(\"Within 30 Days Repeat Rate is :{} percent\".\n",
    "      format(round((atleast_30days_old_df['difference_cat']==\"[0.0, 30.0)\").sum()*100/atleast_30days_old_df['P_ID'].nunique()),3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 60 Days Repeat Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Within 60 Days Repeat Rate is :14.0 percent\n"
     ]
    }
   ],
   "source": [
    "atleast_60days_old_df =first_second_order_df[(df['DOB'].max()-first_second_order_df[\"DOB_first\"]).astype('timedelta64[D]')>=60]\n",
    "\n",
    "print(\"Within 60 Days Repeat Rate is :{} percent\".\n",
    "      format(round((atleast_60days_old_df['difference']<=60).sum()*100/atleast_60days_old_df['P_ID'].nunique()),3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 90 Days Repeat Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Within 90 Days Repeat Rate is :19.0 percent\n"
     ]
    }
   ],
   "source": [
    "atleast_90days_old_df =first_second_order_df[(df['DOB'].max()-first_second_order_df[\"DOB_first\"]).astype('timedelta64[D]')>=90]\n",
    "\n",
    "print(\"Within 90 Days Repeat Rate is :{} percent\".\n",
    "      format(round((atleast_90days_old_df['difference']<90.0).sum()*100/atleast_90days_old_df['P_ID'].nunique()),3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 6 Months Repeat Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Within 6 months Repeat Rate is :32.0 percent\n"
     ]
    }
   ],
   "source": [
    "atleast_180days_old_df =first_second_order_df[(df['DOB'].max()-first_second_order_df[\"DOB_first\"]).astype('timedelta64[D]')>=180]\n",
    "\n",
    "print(\"Within 6 months Repeat Rate is :{} percent\".\n",
    "      format(round((atleast_180days_old_df['difference']<180.0).sum()*100/atleast_180days_old_df['P_ID'].nunique()),3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Non Repeating Customers Share**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent of customers who are atleast 30 days old and have not repeated once: 92.0\n",
      "Percent of customers who are atleast 60 days old and have not repeated once: 86.0\n",
      "Percent of customers who are atleast 90 days old and have not repeated once: 81.0\n",
      "Percent of customers who are atleast 6 months old and have not repeated once: 68.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Percent of customers who are atleast 30 days old and have not repeated once: {}\".format(round(100-(atleast_30days_old_df['difference_cat']==\"[0.0, 30.0)\").sum()*100/atleast_30days_old_df['P_ID'].nunique()),2))\n",
    "print(\"Percent of customers who are atleast 60 days old and have not repeated once: {}\".format(round(100-(atleast_60days_old_df['difference']<=60).sum()*100/atleast_60days_old_df['P_ID'].nunique()),2))\n",
    "print(\"Percent of customers who are atleast 90 days old and have not repeated once: {}\".format(round(100-(atleast_90days_old_df['difference']<=90).sum()*100/atleast_90days_old_df['P_ID'].nunique()),2))\n",
    "print(\"Percent of customers who are atleast 6 months old and have not repeated once: {}\".format(round(100-(atleast_180days_old_df['difference']<=180).sum()*100/atleast_180days_old_df['P_ID'].nunique()),2))\n"
   ]
  }
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