{
 "cells": [
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   "metadata": {
    "collapsed": true,
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   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "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": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "      <th>PickupDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>3</td>\n",
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       "      <td>2009-02-04</td>\n",
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       "      <th>3</th>\n",
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       "      <td>53.38</td>\n",
       "      <td>NGAZJ</td>\n",
       "      <td>2</td>\n",
       "      <td>2009-02-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>302</td>\n",
       "      <td>2009-02-06</td>\n",
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       "      <td>14.28</td>\n",
       "      <td>FFYHD</td>\n",
       "      <td>2</td>\n",
       "      <td>2009-02-09</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"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('/Users/gjreda/Dropbox/datasets/relay-foods.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1. Create a period column based on the OrderDate\n",
    "Since we're doing monthly cohorts, we'll be looking at the total monthly behavior of our users. Therefore, we don't want granular OrderDate data (right now)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "      <th>PickupDate</th>\n",
       "      <th>OrderPeriod</th>\n",
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       "      <td>2009-01-20</td>\n",
       "      <td>2009-01</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>294</td>\n",
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       "      <th>3</th>\n",
       "      <td>301</td>\n",
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       "      <td>2009-02-09</td>\n",
       "      <td>2009-02</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OrderPeriod'] = df.OrderDate.apply(lambda x: x.strftime('%Y-%m'))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 2. Determine the user's cohort group (based on their first order)\n",
    "Create a new column called `CohortGroup`, which is the year and month in which the user's first purchase occurred."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<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",
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       "  </thead>\n",
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       "      <td>2009-01</td>\n",
       "      <td>2009-01</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\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",
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       "      <td>2009-02</td>\n",
       "      <td>2009-01</td>\n",
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       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>301</td>\n",
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       "      <td>NGAZJ</td>\n",
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       "      <td>2009-02-09</td>\n",
       "      <td>2009-02</td>\n",
       "      <td>2009-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('UserId', inplace=True)\n",
    "\n",
    "df['CohortGroup'] = df.groupby(level=0)['OrderDate'].min().apply(lambda x: x.strftime('%Y-%m'))\n",
    "df.reset_index(inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 3. Rollup data by CohortGroup & OrderPeriod\n",
    "\n",
    "Since we're looking at monthly cohorts, we need to aggregate users, orders, and amount spent by the CohortGroup within the month (OrderPeriod)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<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",
       "      <th rowspan=\"5\" valign=\"top\">2009-01</th>\n",
       "      <th>2009-01</th>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "      <td>1850.255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-02</th>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>1351.065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03</th>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>1357.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-04</th>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>1604.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-05</th>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>1575.625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         TotalOrders  TotalUsers  TotalCharges\n",
       "CohortGroup OrderPeriod                                       \n",
       "2009-01     2009-01               30          22      1850.255\n",
       "            2009-02               25           8      1351.065\n",
       "            2009-03               26          10      1357.360\n",
       "            2009-04               28           9      1604.500\n",
       "            2009-05               26          10      1575.625"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df.groupby(['CohortGroup', 'OrderPeriod'])\n",
    "\n",
    "# count the unique users, orders, and total revenue per Group + Period\n",
    "cohorts = grouped.agg({'UserId': pd.Series.nunique,\n",
    "                       'OrderId': pd.Series.nunique,\n",
    "                       'TotalCharges': np.sum})\n",
    "\n",
    "# make the column names more meaningful\n",
    "cohorts.rename(columns={'UserId': 'TotalUsers',\n",
    "                        'OrderId': 'TotalOrders'}, inplace=True)\n",
    "cohorts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 4. Label the CohortPeriod for each CohortGroup\n",
    "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": 6,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<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",
       "      <th rowspan=\"5\" valign=\"top\">2009-01</th>\n",
       "      <th>2009-01</th>\n",
       "      <td>30</td>\n",
       "      <td>22</td>\n",
       "      <td>1850.255</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-02</th>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>1351.065</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03</th>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>1357.360</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-04</th>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>1604.500</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-05</th>\n",
       "      <td>26</td>\n",
       "      <td>10</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          22      1850.255             1\n",
       "            2009-02               25           8      1351.065             2\n",
       "            2009-03               26          10      1357.360             3\n",
       "            2009-04               28           9      1604.500             4\n",
       "            2009-05               26          10      1575.625             5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def cohort_period(df):\n",
    "    \"\"\"\n",
    "    Creates a `CohortPeriod` column, which is the Nth period based on the user's first purchase.\n",
    "    \n",
    "    Example\n",
    "    -------\n",
    "    Say you want to get the 3rd month for every user:\n",
    "        df.sort(['UserId', 'OrderTime', inplace=True)\n",
    "        df = df.groupby('UserId').apply(cohort_period)\n",
    "        df[df.CohortPeriod == 3]\n",
    "    \"\"\"\n",
    "    df['CohortPeriod'] = np.arange(len(df)) + 1\n",
    "    return df\n",
    "\n",
    "cohorts = cohorts.groupby(level=0).apply(cohort_period)\n",
    "cohorts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 5. Make sure we did all that right\n",
    "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": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "x = df[(df.CohortGroup == '2009-01') & (df.OrderPeriod == '2009-01')]\n",
    "y = cohorts.ix[('2009-01', '2009-01')]\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 = df[(df.CohortGroup == '2009-01') & (df.OrderPeriod == '2009-09')]\n",
    "y = cohorts.ix[('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 = df[(df.CohortGroup == '2009-05') & (df.OrderPeriod == '2009-09')]\n",
    "y = cohorts.ix[('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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## User Retention by Cohort Group\n",
    "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](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html) containing each CohortGroup and its size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CohortGroup\n",
       "2009-01    22\n",
       "2009-02    15\n",
       "2009-03    13\n",
       "2009-04    39\n",
       "2009-05    50\n",
       "Name: TotalUsers, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reindex the DataFrame\n",
    "cohorts.reset_index(inplace=True)\n",
    "cohorts.set_index(['CohortGroup', 'CohortPeriod'], inplace=True)\n",
    "\n",
    "# create a Series holding the total size of each CohortGroup\n",
    "cohort_group_size = cohorts['TotalUsers'].groupby(level=0).first()\n",
    "cohort_group_size.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "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`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.unstack.html) 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": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CohortGroup  CohortPeriod\n",
       "2009-01      1               22\n",
       "             2                8\n",
       "             3               10\n",
       "             4                9\n",
       "             5               10\n",
       "Name: TotalUsers, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts['TotalUsers'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "And here's what they look like when we `unstack` the CohortGroup level from the index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>39</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>50</td>\n",
       "      <td>31</td>\n",
       "      <td>37</td>\n",
       "      <td>54</td>\n",
       "      <td>130</td>\n",
       "      <td>65</td>\n",
       "      <td>95</td>\n",
       "      <td>100</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>32</td>\n",
       "      <td>17</td>\n",
       "      <td>50</td>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</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       15       13       39       50       32       50   \n",
       "2                   8        3        4       13       13       15       23   \n",
       "3                  10        5        5       10       12        9       13   \n",
       "4                   9        1        4       13        5        6       10   \n",
       "5                  10        4        1        6        4        7       11   \n",
       "\n",
       "CohortGroup   2009-08  2009-09  2009-10  2009-11  2009-12  2010-01  2010-02  \\\n",
       "CohortPeriod                                                                  \n",
       "1                  31       37       54      130       65       95      100   \n",
       "2                  11       15       17       32       17       50       19   \n",
       "3                   9       14       12       26       18       26      NaN   \n",
       "4                   7        8       13       29        7      NaN      NaN   \n",
       "5                   6       13       13       13      NaN      NaN      NaN   \n",
       "\n",
       "CohortGroup   2010-03  \n",
       "CohortPeriod           \n",
       "1                  24  \n",
       "2                 NaN  \n",
       "3                 NaN  \n",
       "4                 NaN  \n",
       "5                 NaN  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts['TotalUsers'].unstack(0).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now, we can utilize [`broadcasting`](http://pandas.pydata.org/pandas-docs/stable/basics.html#matching-broadcasting-behavior) 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": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.46875</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.354839</td>\n",
       "      <td>0.405405</td>\n",
       "      <td>0.314815</td>\n",
       "      <td>0.246154</td>\n",
       "      <td>0.261538</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.19</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.256410</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.28125</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.290323</td>\n",
       "      <td>0.378378</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.276923</td>\n",
       "      <td>0.273684</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.18750</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.225806</td>\n",
       "      <td>0.216216</td>\n",
       "      <td>0.240741</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",
       "      <th>5</th>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.21875</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.193548</td>\n",
       "      <td>0.351351</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",
       "      <th>6</th>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.179487</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.15625</td>\n",
       "      <td>0.20</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",
       "      <th>7</th>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.102564</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.09375</td>\n",
       "      <td>0.22</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",
       "      <th>8</th>\n",
       "      <td>0.318182</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.09375</td>\n",
       "      <td>0.14</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",
       "      <th>9</th>\n",
       "      <td>0.318182</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.051282</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.31250</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",
       "      <th>10</th>\n",
       "      <td>0.318182</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.102564</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     0.26  0.46875   \n",
       "3             0.454545  0.333333  0.384615  0.256410     0.24  0.28125   \n",
       "4             0.409091  0.066667  0.307692  0.333333     0.10  0.18750   \n",
       "5             0.454545  0.266667  0.076923  0.153846     0.08  0.21875   \n",
       "6             0.363636  0.266667  0.153846  0.179487     0.12  0.15625   \n",
       "7             0.363636  0.266667  0.153846  0.102564     0.06  0.09375   \n",
       "8             0.318182  0.333333  0.230769  0.153846     0.10  0.09375   \n",
       "9             0.318182  0.333333  0.153846  0.051282     0.10  0.31250   \n",
       "10            0.318182  0.266667  0.076923  0.102564     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                0.46  0.354839  0.405405  0.314815  0.246154  0.261538   \n",
       "3                0.26  0.290323  0.378378  0.222222  0.200000  0.276923   \n",
       "4                0.20  0.225806  0.216216  0.240741  0.223077  0.107692   \n",
       "5                0.22  0.193548  0.351351  0.240741  0.100000       NaN   \n",
       "6                0.20  0.258065  0.243243  0.129630       NaN       NaN   \n",
       "7                0.22  0.129032  0.216216       NaN       NaN       NaN   \n",
       "8                0.14  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  \n",
       "2             0.526316     0.19      NaN  \n",
       "3             0.273684      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": 11,
     "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": {
    "deletable": true,
    "editable": true
   },
   "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": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hvsGDB+epv6DuxsWLF9O4cWO8vLxISkoCoHbt2oSGhhYa16MMGTIEgPDwcPbu3fsYb6bk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\nsyXNWcWnSQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1129d0d90>"
      ]
     },
     "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.ylabel('% of Cohort Purchasing');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aBrdr/1R5nhUZfusl8N/htwpSbobfepZyM/zWs5Lb4beeldwOv/Ws5Gb4rZdV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akpf/75p+HfDg4OAFy5coV8+fJleGBra2vKli1rOJaTkxO3bt0iIiLCsBKCl5cXe/bsASA8PBx3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\nUhKyAACk9ABahIAFcejDWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112ab4110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating heatmaps in matplotlib is more difficult than it should be.\n",
    "# Thankfully, Seaborn makes them easy for us.\n",
    "# http://stanford.edu/~mwaskom/software/seaborn/\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": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Unsurprisingly, we can see from the above chart that fewer users tend to purchase as time goes on.\n",
    "\n",
    "However, we can also see that the 2009-01 cohort is the strongest, which enables us to ask targeted questions about this cohort compared to others -- what other attributes (besides first purchase month) do these users share which might be causing them to stick around? How were the majority of these users acquired? Was there a specific marketing campaign that brought them in? Did they take advantage of a promotion at sign-up? The answers to these questions would inform future marketing and product efforts."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}