Python-Cohort-Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A cohort is a group of users who share something in common, be it their sign-up date, first purchase month, birth date, acquisition channel, etc. Cohort analysis is the method by which these groups are tracked over time, helping you spot trends, understand repeat behaviors (purchases, engagement, amount spent, etc.), and monitor your customer and revenue retention."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OrderId</th>\n",
       "      <th>OrderDate</th>\n",
       "      <th>UserId</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>CommonId</th>\n",
       "      <th>PupId</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>262</td>\n",
       "      <td>2009-01-11</td>\n",
       "      <td>47</td>\n",
       "      <td>50.6700</td>\n",
       "      <td>TRQKD</td>\n",
       "      <td>2</td>\n",
       "      <td>2009-01-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>278</td>\n",
       "      <td>2009-01-20</td>\n",
       "      <td>47</td>\n",
       "      <td>26.6000</td>\n",
       "      <td>4HH2S</td>\n",
       "      <td>3</td>\n",
       "      <td>2009-01-20</td>\n",
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       "      <td>47</td>\n",
       "      <td>38.7100</td>\n",
       "      <td>3TRDC</td>\n",
       "      <td>2</td>\n",
       "      <td>2009-02-04</td>\n",
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       "      <td>2009-02-06</td>\n",
       "      <td>47</td>\n",
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       "      <td>NGAZJ</td>\n",
       "      <td>2</td>\n",
       "      <td>2009-02-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>302</td>\n",
       "      <td>2009-02-06</td>\n",
       "      <td>47</td>\n",
       "      <td>14.2800</td>\n",
       "      <td>FFYHD</td>\n",
       "      <td>2</td>\n",
       "      <td>2009-02-09</td>\n",
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       "      <td>2886</td>\n",
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       "      <td>393616</td>\n",
       "      <td>60.0155</td>\n",
       "      <td>AWNXG</td>\n",
       "      <td>15</td>\n",
       "      <td>2010-03-08</td>\n",
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       "      <td>12</td>\n",
       "      <td>2010-03-10</td>\n",
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       "      <td>2888</td>\n",
       "      <td>3193</td>\n",
       "      <td>2010-03-07</td>\n",
       "      <td>394346</td>\n",
       "      <td>14.2100</td>\n",
       "      <td>2B47R</td>\n",
       "      <td>15</td>\n",
       "      <td>2010-03-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2889</td>\n",
       "      <td>3207</td>\n",
       "      <td>2010-03-08</td>\n",
       "      <td>395039</td>\n",
       "      <td>34.6200</td>\n",
       "      <td>H6E23</td>\n",
       "      <td>4</td>\n",
       "      <td>2010-03-08</td>\n",
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       "      <td>2890</td>\n",
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       "      <td>2010-03-08</td>\n",
       "      <td>396551</td>\n",
       "      <td>127.2800</td>\n",
       "      <td>9SBZ8</td>\n",
       "      <td>3</td>\n",
       "      <td>2010-03-09</td>\n",
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       "<p>2891 rows × 7 columns</p>\n",
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      ],
      "text/plain": [
       "      OrderId  OrderDate  UserId  TotalCharges CommonId  PupId PickupDate\n",
       "0         262 2009-01-11      47       50.6700    TRQKD      2 2009-01-12\n",
       "1         278 2009-01-20      47       26.6000    4HH2S      3 2009-01-20\n",
       "2         294 2009-02-03      47       38.7100    3TRDC      2 2009-02-04\n",
       "3         301 2009-02-06      47       53.3800    NGAZJ      2 2009-02-09\n",
       "4         302 2009-02-06      47       14.2800    FFYHD      2 2009-02-09\n",
       "...       ...        ...     ...           ...      ...    ...        ...\n",
       "2886     3220 2010-03-08  393616       60.0155    AWNXG     15 2010-03-08\n",
       "2887     3192 2010-03-07  394290       45.4300    AKGQT     12 2010-03-10\n",
       "2888     3193 2010-03-07  394346       14.2100    2B47R     15 2010-03-08\n",
       "2889     3207 2010-03-08  395039       34.6200    H6E23      4 2010-03-08\n",
       "2890     3231 2010-03-08  396551      127.2800    9SBZ8      3 2010-03-09\n",
       "\n",
       "[2891 rows x 7 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import custoemr data\n",
    "xls=pd.ExcelFile('relay-foods.xlsx')\n",
    "\n",
    "#read the second sheet of the excel\n",
    "udata=xls.parse(1)\n",
    "udata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a period column based on the OrderDate\n",
    "#### Use monthly cohorts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
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       "      <th>UserId</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>CommonId</th>\n",
       "      <th>PupId</th>\n",
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       "      <td>TRQKD</td>\n",
       "      <td>2</td>\n",
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       "      <td>2009-01</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2009-01-20</td>\n",
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       "      <td>47</td>\n",
       "      <td>38.71</td>\n",
       "      <td>3TRDC</td>\n",
       "      <td>2</td>\n",
       "      <td>2009-02-04</td>\n",
       "      <td>2009-02</td>\n",
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       "      <td>2009-02-09</td>\n",
       "      <td>2009-02</td>\n",
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       "      <td>4</td>\n",
       "      <td>302</td>\n",
       "      <td>2009-02-06</td>\n",
       "      <td>47</td>\n",
       "      <td>14.28</td>\n",
       "      <td>FFYHD</td>\n",
       "      <td>2</td>\n",
       "      <td>2009-02-09</td>\n",
       "      <td>2009-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OrderId  OrderDate  UserId  TotalCharges CommonId  PupId PickupDate  \\\n",
       "0      262 2009-01-11      47         50.67    TRQKD      2 2009-01-12   \n",
       "1      278 2009-01-20      47         26.60    4HH2S      3 2009-01-20   \n",
       "2      294 2009-02-03      47         38.71    3TRDC      2 2009-02-04   \n",
       "3      301 2009-02-06      47         53.38    NGAZJ      2 2009-02-09   \n",
       "4      302 2009-02-06      47         14.28    FFYHD      2 2009-02-09   \n",
       "\n",
       "  OrderPeriod  \n",
       "0     2009-01  \n",
       "1     2009-01  \n",
       "2     2009-02  \n",
       "3     2009-02  \n",
       "4     2009-02  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract year and month from the OrderDate column. strftim convert datetime to string \n",
    "udata['OrderPeriod'] = udata.OrderDate.apply(lambda g: g.strftime('%Y-%m')) #'%y-%m' will return 09 instead of 2009 as the year\n",
    "udata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderId                  int64\n",
       "OrderDate       datetime64[ns]\n",
       "UserId                   int64\n",
       "TotalCharges           float64\n",
       "CommonId                object\n",
       "PupId                    int64\n",
       "PickupDate      datetime64[ns]\n",
       "OrderPeriod             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the data type of each column\n",
    "udata.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determine the user's cohort group according to their first order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new column called CohortGroup, which is the year and month in which the user's first purchase occurred.\n",
    "# first, set UserId as index\n",
    "udata.set_index('UserId', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
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       "      <td>2009-01-11</td>\n",
       "      <td>50.67</td>\n",
       "      <td>TRQKD</td>\n",
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       "      <td>2009-01-12</td>\n",
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       "      <td>302</td>\n",
       "      <td>2009-02-06</td>\n",
       "      <td>14.28</td>\n",
       "      <td>FFYHD</td>\n",
       "      <td>2</td>\n",
       "      <td>2009-02-09</td>\n",
       "      <td>2009-02</td>\n",
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      ],
      "text/plain": [
       "        OrderId  OrderDate  TotalCharges CommonId  PupId PickupDate  \\\n",
       "UserId                                                                \n",
       "47          262 2009-01-11         50.67    TRQKD      2 2009-01-12   \n",
       "47          278 2009-01-20         26.60    4HH2S      3 2009-01-20   \n",
       "47          294 2009-02-03         38.71    3TRDC      2 2009-02-04   \n",
       "47          301 2009-02-06         53.38    NGAZJ      2 2009-02-09   \n",
       "47          302 2009-02-06         14.28    FFYHD      2 2009-02-09   \n",
       "\n",
       "       OrderPeriod  \n",
       "UserId              \n",
       "47         2009-01  \n",
       "47         2009-01  \n",
       "47         2009-02  \n",
       "47         2009-02  \n",
       "47         2009-02  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "udata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UserId\n",
       "47       2009-01-11\n",
       "95       2009-03-10\n",
       "98       2009-01-29\n",
       "112      2009-01-19\n",
       "141      2009-11-13\n",
       "            ...    \n",
       "393616   2010-03-08\n",
       "394290   2010-03-07\n",
       "394346   2010-03-07\n",
       "395039   2010-03-08\n",
       "396551   2010-03-08\n",
       "Name: OrderDate, Length: 757, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find out the earliest order date of each user id\n",
    "udata.groupby(level=0)['OrderDate'].min() # level=0 means groupby the first index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate cohort group based the the earliest order date for each user id\n",
    "udata['CohortGroup']=udata.groupby(level=0)['OrderDate'].min().apply(lambda g: g.strftime('%Y-%m'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
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       "      <td>3TRDC</td>\n",
       "      <td>2</td>\n",
       "      <td>2009-02-04</td>\n",
       "      <td>2009-02</td>\n",
       "      <td>2009-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>301</td>\n",
       "      <td>2009-02-06</td>\n",
       "      <td>53.38</td>\n",
       "      <td>NGAZJ</td>\n",
       "      <td>2</td>\n",
       "      <td>2009-02-09</td>\n",
       "      <td>2009-02</td>\n",
       "      <td>2009-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>302</td>\n",
       "      <td>2009-02-06</td>\n",
       "      <td>14.28</td>\n",
       "      <td>FFYHD</td>\n",
       "      <td>2</td>\n",
       "      <td>2009-02-09</td>\n",
       "      <td>2009-02</td>\n",
       "      <td>2009-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        OrderId  OrderDate  TotalCharges CommonId  PupId PickupDate  \\\n",
       "UserId                                                                \n",
       "47          262 2009-01-11         50.67    TRQKD      2 2009-01-12   \n",
       "47          278 2009-01-20         26.60    4HH2S      3 2009-01-20   \n",
       "47          294 2009-02-03         38.71    3TRDC      2 2009-02-04   \n",
       "47          301 2009-02-06         53.38    NGAZJ      2 2009-02-09   \n",
       "47          302 2009-02-06         14.28    FFYHD      2 2009-02-09   \n",
       "\n",
       "       OrderPeriod CohortGroup  \n",
       "UserId                          \n",
       "47         2009-01     2009-01  \n",
       "47         2009-01     2009-01  \n",
       "47         2009-02     2009-01  \n",
       "47         2009-02     2009-01  \n",
       "47         2009-02     2009-01  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "udata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UserId</th>\n",
       "      <th>OrderId</th>\n",
       "      <th>OrderDate</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>CommonId</th>\n",
       "      <th>PupId</th>\n",
       "      <th>PickupDate</th>\n",
       "      <th>OrderPeriod</th>\n",
       "      <th>CohortGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>262</td>\n",
       "      <td>2009-01-11</td>\n",
       "      <td>50.67</td>\n",
       "      <td>TRQKD</td>\n",
       "      <td>2</td>\n",
       "      <td>2009-01-12</td>\n",
       "      <td>2009-01</td>\n",
       "      <td>2009-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>278</td>\n",
       "      <td>2009-01-20</td>\n",
       "      <td>26.60</td>\n",
       "      <td>4HH2S</td>\n",
       "      <td>3</td>\n",
       "      <td>2009-01-20</td>\n",
       "      <td>2009-01</td>\n",
       "      <td>2009-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>294</td>\n",
       "      <td>2009-02-03</td>\n",
       "      <td>38.71</td>\n",
       "      <td>3TRDC</td>\n",
       "      <td>2</td>\n",
       "      <td>2009-02-04</td>\n",
       "      <td>2009-02</td>\n",
       "      <td>2009-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>47</td>\n",
       "      <td>301</td>\n",
       "      <td>2009-02-06</td>\n",
       "      <td>53.38</td>\n",
       "      <td>NGAZJ</td>\n",
       "      <td>2</td>\n",
       "      <td>2009-02-09</td>\n",
       "      <td>2009-02</td>\n",
       "      <td>2009-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>47</td>\n",
       "      <td>302</td>\n",
       "      <td>2009-02-06</td>\n",
       "      <td>14.28</td>\n",
       "      <td>FFYHD</td>\n",
       "      <td>2</td>\n",
       "      <td>2009-02-09</td>\n",
       "      <td>2009-02</td>\n",
       "      <td>2009-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   UserId  OrderId  OrderDate  TotalCharges CommonId  PupId PickupDate  \\\n",
       "0      47      262 2009-01-11         50.67    TRQKD      2 2009-01-12   \n",
       "1      47      278 2009-01-20         26.60    4HH2S      3 2009-01-20   \n",
       "2      47      294 2009-02-03         38.71    3TRDC      2 2009-02-04   \n",
       "3      47      301 2009-02-06         53.38    NGAZJ      2 2009-02-09   \n",
       "4      47      302 2009-02-06         14.28    FFYHD      2 2009-02-09   \n",
       "\n",
       "  OrderPeriod CohortGroup  \n",
       "0     2009-01     2009-01  \n",
       "1     2009-01     2009-01  \n",
       "2     2009-02     2009-01  \n",
       "3     2009-02     2009-01  \n",
       "4     2009-02     2009-01  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reset index and covert UserId to a column\n",
    "udata.reset_index(inplace=True)\n",
    "udata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rollup data by CohortGroup & OrderPeriod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>TotalOrders</th>\n",
       "      <th>TotalUsers</th>\n",
       "      <th>TotalCharges</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CohortGroup</th>\n",
       "      <th>OrderPeriod</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">2009-01</td>\n",
       "      <td>2009-01</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1850.2550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-02</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1351.0650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-03</td>\n",
       "      <td>26.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1357.3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-04</td>\n",
       "      <td>28.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1604.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-05</td>\n",
       "      <td>26.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1575.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-12</td>\n",
       "      <td>2010-02</td>\n",
       "      <td>28.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1785.7853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">2009-11</td>\n",
       "      <td>2010-02</td>\n",
       "      <td>64.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3486.0959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010-03</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>961.3681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-12</td>\n",
       "      <td>2010-03</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>534.0929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010-03</td>\n",
       "      <td>2010-03</td>\n",
       "      <td>26.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1099.5471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>119 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         TotalOrders  TotalUsers  TotalCharges\n",
       "CohortGroup OrderPeriod                                       \n",
       "2009-01     2009-01             30.0        22.0     1850.2550\n",
       "            2009-02             25.0         8.0     1351.0650\n",
       "            2009-03             26.0        10.0     1357.3600\n",
       "            2009-04             28.0         9.0     1604.5000\n",
       "            2009-05             26.0        10.0     1575.6250\n",
       "...                              ...         ...           ...\n",
       "2009-12     2010-02             28.0        18.0     1785.7853\n",
       "2009-11     2010-02             64.0        29.0     3486.0959\n",
       "            2010-03             17.0        13.0      961.3681\n",
       "2009-12     2010-03              8.0         7.0      534.0929\n",
       "2010-03     2010-03             26.0        24.0     1099.5471\n",
       "\n",
       "[119 rows x 3 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group customers by CohortGroup and OrderPeriod, and then calcualte the total users, total orders and total charges for each group\n",
    "cohorts = udata.groupby(['CohortGroup','OrderPeriod'],sort=0).apply( lambda g: pd.Series({\\\n",
    "    'TotalOrders': g.OrderId.nunique(),\\\n",
    "    'TotalUsers': g.UserId.nunique(),\\\n",
    "    'TotalCharges': g.TotalCharges.sum(),\\\n",
    "    }))\n",
    "cohorts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label the CohortPeriod for each CohortGroup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We want to look at how each cohort has behaved in the months following their first purchase, so we'll need to index each cohort to their first purchase month. For example, CohortPeriod = 1 will be the cohort's first month, CohortPeriod = 2 is their second, and so on.\n",
    "\n",
    "#### This allows us to compare cohorts across various stages of their lifetime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define a function to calculate CohortPeriod\n",
    "def cohort_period(udata):\n",
    "    ##Creates a `CohortPeriod` column, which is the Nth period based on the user's first purchase.\n",
    "    udata['CohortPeriod'] = np.arange(len(udata)) + 1\n",
    "    return udata\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>TotalOrders</th>\n",
       "      <th>TotalUsers</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>CohortPeriod</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CohortGroup</th>\n",
       "      <th>OrderPeriod</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">2009-01</td>\n",
       "      <td>2009-01</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1850.255</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-02</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1351.065</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-03</td>\n",
       "      <td>26.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1357.360</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-04</td>\n",
       "      <td>28.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1604.500</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-05</td>\n",
       "      <td>26.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1575.625</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         TotalOrders  TotalUsers  TotalCharges  CohortPeriod\n",
       "CohortGroup OrderPeriod                                                     \n",
       "2009-01     2009-01             30.0        22.0      1850.255             1\n",
       "            2009-02             25.0         8.0      1351.065             2\n",
       "            2009-03             26.0        10.0      1357.360             3\n",
       "            2009-04             28.0         9.0      1604.500             4\n",
       "            2009-05             26.0        10.0      1575.625             5"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate cohort period using the cohort_period function\n",
    "cohorts = cohorts.groupby(level=0).apply(cohort_period)\n",
    "cohorts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make sure we did all that right¶"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's test data points from the original DataFrame with their corresponding values in the new cohorts DataFrame to make sure all our data transformations worked as expected. As long as none of these raise an exception, we're good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = udata[(udata.CohortGroup == '2009-01') & (udata.OrderPeriod == '2009-01')]\n",
    "y = cohorts.loc[('2009-01', '2009-01')] # loc is index based\n",
    "\n",
    "assert(x['UserId'].nunique() == y['TotalUsers'])\n",
    "assert(x['TotalCharges'].sum().round(2) == y['TotalCharges'].round(2))\n",
    "assert(x['OrderId'].nunique() == y['TotalOrders'])\n",
    "\n",
    "x = udata[(udata.CohortGroup == '2009-01') & (udata.OrderPeriod == '2009-09')]\n",
    "y = cohorts.loc[('2009-01', '2009-09')]\n",
    "\n",
    "assert(x['UserId'].nunique() == y['TotalUsers'])\n",
    "assert(x['TotalCharges'].sum().round(2) == y['TotalCharges'].round(2))\n",
    "assert(x['OrderId'].nunique() == y['TotalOrders'])\n",
    "\n",
    "x = udata[(udata.CohortGroup == '2009-05') & (udata.OrderPeriod == '2009-09')]\n",
    "y = cohorts.loc[('2009-05', '2009-09')]\n",
    "\n",
    "assert(x['UserId'].nunique() == y['TotalUsers'])\n",
    "assert(x['TotalCharges'].sum().round(2) == y['TotalCharges'].round(2))\n",
    "assert(x['OrderId'].nunique() == y['TotalOrders'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User Retention by Cohort Group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to look at the percentage change of each CohortGroup over time -- not the absolute change.\n",
    "\n",
    "To do this, we'll first need to create a pandas Series containing each CohortGroup and its size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# re-set index\n",
    "cohorts.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# set CohortGroup and CohortPeriod as indexes\n",
    "cohorts.set_index(['CohortGroup','CohortPeriod'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>OrderPeriod</th>\n",
       "      <th>TotalOrders</th>\n",
       "      <th>TotalUsers</th>\n",
       "      <th>TotalCharges</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CohortGroup</th>\n",
       "      <th>CohortPeriod</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">2009-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2009-01</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1850.2550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2009-02</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1351.0650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2009-03</td>\n",
       "      <td>26.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1357.3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2009-04</td>\n",
       "      <td>28.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1604.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2009-05</td>\n",
       "      <td>26.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1575.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-12</td>\n",
       "      <td>3</td>\n",
       "      <td>2010-02</td>\n",
       "      <td>28.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1785.7853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">2009-11</td>\n",
       "      <td>4</td>\n",
       "      <td>2010-02</td>\n",
       "      <td>64.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3486.0959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2010-03</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>961.3681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-12</td>\n",
       "      <td>4</td>\n",
       "      <td>2010-03</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>534.0929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010-03</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-03</td>\n",
       "      <td>26.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1099.5471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>119 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         OrderPeriod  TotalOrders  TotalUsers  TotalCharges\n",
       "CohortGroup CohortPeriod                                                   \n",
       "2009-01     1                2009-01         30.0        22.0     1850.2550\n",
       "            2                2009-02         25.0         8.0     1351.0650\n",
       "            3                2009-03         26.0        10.0     1357.3600\n",
       "            4                2009-04         28.0         9.0     1604.5000\n",
       "            5                2009-05         26.0        10.0     1575.6250\n",
       "...                              ...          ...         ...           ...\n",
       "2009-12     3                2010-02         28.0        18.0     1785.7853\n",
       "2009-11     4                2010-02         64.0        29.0     3486.0959\n",
       "            5                2010-03         17.0        13.0      961.3681\n",
       "2009-12     4                2010-03          8.0         7.0      534.0929\n",
       "2010-03     1                2010-03         26.0        24.0     1099.5471\n",
       "\n",
       "[119 rows x 4 columns]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CohortGroup\n",
       "2009-01    22.0\n",
       "2009-02    15.0\n",
       "2009-03    13.0\n",
       "2009-04    39.0\n",
       "2009-05    50.0\n",
       "Name: TotalUsers, dtype: float64"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a Series holding the total size of each CohortGroup\n",
    "cohort_group_size=cohorts['TotalUsers'].groupby(level=0).first() #first() compute first of group values and return Series or DataFrame\n",
    "cohort_group_size.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we'll need to divide the TotalUsers values in cohorts by cohort_group_size. Since DataFrame operations are performed based on the indices of the objects, we'll use unstack on our cohorts DataFrame to create a matrix where each column represents a CohortGroup and each row is the CohortPeriod corresponding to that group.\n",
    "\n",
    "To illustrate what unstack does, recall the first five TotalUsers values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CohortGroup  CohortPeriod\n",
       "2009-01      1               22.0\n",
       "             2                8.0\n",
       "             3               10.0\n",
       "             4                9.0\n",
       "             5               10.0\n",
       "Name: TotalUsers, dtype: float64"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts['TotalUsers'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here's what they look like when we unstack the CohortGroup level from the index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>CohortGroup</th>\n",
       "      <th>2009-01</th>\n",
       "      <th>2009-02</th>\n",
       "      <th>2009-03</th>\n",
       "      <th>2009-04</th>\n",
       "      <th>2009-05</th>\n",
       "      <th>2009-06</th>\n",
       "      <th>2009-07</th>\n",
       "      <th>2009-08</th>\n",
       "      <th>2009-09</th>\n",
       "      <th>2009-10</th>\n",
       "      <th>2009-11</th>\n",
       "      <th>2009-12</th>\n",
       "      <th>2010-01</th>\n",
       "      <th>2010-02</th>\n",
       "      <th>2010-03</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CohortPeriod</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>1</td>\n",
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       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "CohortGroup   2009-01  2009-02  2009-03  2009-04  2009-05  2009-06  2009-07  \\\n",
       "CohortPeriod                                                                  \n",
       "1                22.0     15.0     13.0     39.0     50.0     32.0     50.0   \n",
       "2                 8.0      3.0      4.0     13.0      4.0     15.0     13.0   \n",
       "3                10.0      5.0      5.0     13.0     13.0      7.0     11.0   \n",
       "4                 9.0      4.0      4.0      6.0     12.0      3.0     10.0   \n",
       "5                10.0      4.0      2.0      4.0      6.0     10.0     11.0   \n",
       "\n",
       "CohortGroup   2009-08  2009-09  2009-10  2009-11  2009-12  2010-01  2010-02  \\\n",
       "CohortPeriod                                                                  \n",
       "1                31.0     37.0     54.0    130.0     65.0     95.0    100.0   \n",
       "2                11.0     15.0     13.0     32.0     17.0     26.0     19.0   \n",
       "3                 9.0     14.0     17.0     26.0     18.0     50.0      NaN   \n",
       "4                 7.0     13.0     12.0     29.0      7.0      NaN      NaN   \n",
       "5                 6.0      8.0     13.0     13.0      NaN      NaN      NaN   \n",
       "\n",
       "CohortGroup   2010-03  \n",
       "CohortPeriod           \n",
       "1                24.0  \n",
       "2                 NaN  \n",
       "3                 NaN  \n",
       "4                 NaN  \n",
       "5                 NaN  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts['TotalUsers'].unstack(0).head() # unstack(0) menas split CohortGroup to columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can utilize broadcasting to divide each column by the corresponding cohort_group_size.\n",
    "\n",
    "The resulting DataFrame, user_retention, contains the percentage of users from the cohort purchasing within the given period. For instance, 38.4% of users in the 2009-03 purchased again in month 3 (which would be May 2009)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>CohortGroup</th>\n",
       "      <th>2009-01</th>\n",
       "      <th>2009-02</th>\n",
       "      <th>2009-03</th>\n",
       "      <th>2009-04</th>\n",
       "      <th>2009-05</th>\n",
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       "      <th>2009-08</th>\n",
       "      <th>2009-09</th>\n",
       "      <th>2009-10</th>\n",
       "      <th>2009-11</th>\n",
       "      <th>2009-12</th>\n",
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       "      <th>2010-02</th>\n",
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       "    <tr>\n",
       "      <th>CohortPeriod</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.46875</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.354839</td>\n",
       "      <td>0.405405</td>\n",
       "      <td>0.240741</td>\n",
       "      <td>0.246154</td>\n",
       "      <td>0.261538</td>\n",
       "      <td>0.273684</td>\n",
       "      <td>0.19</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.21875</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.290323</td>\n",
       "      <td>0.378378</td>\n",
       "      <td>0.314815</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.276923</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.09375</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.225806</td>\n",
       "      <td>0.351351</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.223077</td>\n",
       "      <td>0.107692</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.102564</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.31250</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.193548</td>\n",
       "      <td>0.216216</td>\n",
       "      <td>0.240741</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.102564</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.28125</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.258065</td>\n",
       "      <td>0.243243</td>\n",
       "      <td>0.129630</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.179487</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.09375</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.129032</td>\n",
       "      <td>0.216216</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.318182</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.18750</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.129032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.318182</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.256410</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.15625</td>\n",
       "      <td>0.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.318182</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.051282</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.09375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "CohortGroup    2009-01   2009-02   2009-03   2009-04  2009-05  2009-06  \\\n",
       "CohortPeriod                                                             \n",
       "1             1.000000  1.000000  1.000000  1.000000     1.00  1.00000   \n",
       "2             0.363636  0.200000  0.307692  0.333333     0.08  0.46875   \n",
       "3             0.454545  0.333333  0.384615  0.333333     0.26  0.21875   \n",
       "4             0.409091  0.266667  0.307692  0.153846     0.24  0.09375   \n",
       "5             0.454545  0.266667  0.153846  0.102564     0.12  0.31250   \n",
       "6             0.363636  0.266667  0.153846  0.102564     0.10  0.28125   \n",
       "7             0.363636  0.333333  0.230769  0.179487     0.06  0.09375   \n",
       "8             0.318182  0.333333  0.153846  0.153846     0.10  0.18750   \n",
       "9             0.318182  0.266667  0.230769  0.256410     0.10  0.15625   \n",
       "10            0.318182  0.200000  0.153846  0.051282     0.08  0.09375   \n",
       "\n",
       "CohortGroup   2009-07   2009-08   2009-09   2009-10   2009-11   2009-12  \\\n",
       "CohortPeriod                                                              \n",
       "1                1.00  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "2                0.26  0.354839  0.405405  0.240741  0.246154  0.261538   \n",
       "3                0.22  0.290323  0.378378  0.314815  0.200000  0.276923   \n",
       "4                0.20  0.225806  0.351351  0.222222  0.223077  0.107692   \n",
       "5                0.22  0.193548  0.216216  0.240741  0.100000       NaN   \n",
       "6                0.14  0.258065  0.243243  0.129630       NaN       NaN   \n",
       "7                0.20  0.129032  0.216216       NaN       NaN       NaN   \n",
       "8                0.46  0.129032       NaN       NaN       NaN       NaN   \n",
       "9                0.14       NaN       NaN       NaN       NaN       NaN   \n",
       "10                NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "CohortGroup    2010-01  2010-02  2010-03  \n",
       "CohortPeriod                              \n",
       "1             1.000000     1.00      1.0  \n",
       "2             0.273684     0.19      NaN  \n",
       "3             0.526316      NaN      NaN  \n",
       "4                  NaN      NaN      NaN  \n",
       "5                  NaN      NaN      NaN  \n",
       "6                  NaN      NaN      NaN  \n",
       "7                  NaN      NaN      NaN  \n",
       "8                  NaN      NaN      NaN  \n",
       "9                  NaN      NaN      NaN  \n",
       "10                 NaN      NaN      NaN  "
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_retention = cohorts['TotalUsers'].unstack(0).divide(cohort_group_size, axis=1)\n",
    "user_retention.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can plot the cohorts over time in an effort to spot behavioral differences or similarities. Two common cohort charts are line graphs and heatmaps, both of which are shown below.\n",
    "\n",
    "Notice that the first period of each cohort is 100% -- this is because our cohorts are based on each user's first purchase, meaning everyone in the cohort purchased in month 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "pd.set_option('max_columns', 50)\n",
    "mpl.rcParams['lines.linewidth'] = 2\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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>CohortGroup</th>\n",
       "      <th>2009-06</th>\n",
       "      <th>2009-07</th>\n",
       "      <th>2009-08</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CohortPeriod</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.46875</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.354839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.21875</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.290323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.09375</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.225806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.31250</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.193548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.28125</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.258065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.09375</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.129032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.18750</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.129032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.15625</td>\n",
       "      <td>0.14</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.09375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "CohortGroup   2009-06  2009-07   2009-08\n",
       "CohortPeriod                            \n",
       "1             1.00000     1.00  1.000000\n",
       "2             0.46875     0.26  0.354839\n",
       "3             0.21875     0.22  0.290323\n",
       "4             0.09375     0.20  0.225806\n",
       "5             0.31250     0.22  0.193548\n",
       "6             0.28125     0.14  0.258065\n",
       "7             0.09375     0.20  0.129032\n",
       "8             0.18750     0.46  0.129032\n",
       "9             0.15625     0.14       NaN\n",
       "10            0.09375      NaN       NaN\n",
       "11                NaN      NaN       NaN\n",
       "12                NaN      NaN       NaN\n",
       "13                NaN      NaN       NaN\n",
       "14                NaN      NaN       NaN\n",
       "15                NaN      NaN       NaN"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_retention[['2009-06','2009-07','2009-08']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'% of Cohort Purchasing')"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HP/whw4cPZ8SIEbz22msB+taetpmcmZEaNxYA1/UrFm7KDXJAIiIi0pzCwsL49a9/TVZW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\nqjMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1094fa908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_retention[['2009-06','2009-07','2009-08']].plot(figsize=(10,5))\n",
    "plt.title('Cohorts: User Retention')\n",
    "plt.xticks(np.arange(1,12.1,1))\n",
    "plt.xlim(1,12)\n",
    "plt.xlabel('Cohort Period')\n",
    "plt.ylabel('% of Cohort Purchasing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PPNwqFcPKwTbTOqrm0j7mdu6YU54PPipKnaBmTG88ktSEFPyDO9NkWFsSn8Sj1miY1mgk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\nIiKu9QOfp6ZlQf3JXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110c35b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating heatmaps in matplotlib is more difficult than it should be.\n",
    "# But Seaborn makes them easy for us.\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(style='white')\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.title('Cohorts: User Retention')\n",
    "sns.heatmap(user_retention.T, mask=user_retention.T.isnull(), annot=True, fmt='.0%');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AhBBCCCGEyDtSrAohhBBCCLMlxaoQQgghhDBbMmfVzCjFyuR3hKx0Vk9v8yIV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+8uWNsix50AQL4aBwPGgm1NyMsFo1KgVCEZGoE6F6lhc0hLhtu/v1QmgHdrVePQyiAMVCoe5eSQ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\nSzMAeHt7U6JECQBKlixJdna2S1Yy/7B3717q1q1LXFwcjz32GBUrVnRZl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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110b14198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How about we change the color? cmap is used to identify color palettes and center is used to identify the center of the heat\n",
    "sns.set(style='white')\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.title('Cohorts: User Retention')\n",
    "sns.heatmap(user_retention.T, mask=user_retention.T.isnull(), annot=True, fmt='.0%',cmap=\"Reds\",center=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Insights:\n",
    "\n",
    "### 1) we can see from the above chart that fewer users tend to purchase as time goes on.\n",
    "\n",
    "### 2) The 2009-01 cohort is the strongest. Then the next question is: what other attributes (besides first purchase month) do these users share which might be causing them to stick around? Is that something about the markeitng channels, campaigns or others?"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}