01-07-2016-rrfd-churn-analysis-cohort-v2.ipynb

{
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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
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <td>262</td>\n",
       "      <td>2009-01-11</td>\n",
       "      <td>47</td>\n",
       "      <td>50.67</td>\n",
       "      <td>TRQKD</td>\n",
       "      <td>2</td>\n",
       "      <td>2009-01-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>278</td>\n",
       "      <td>2009-01-20</td>\n",
       "      <td>47</td>\n",
       "      <td>26.60</td>\n",
       "      <td>4HH2S</td>\n",
       "      <td>3</td>\n",
       "      <td>2009-01-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>294</td>\n",
       "      <td>2009-02-03</td>\n",
       "      <td>47</td>\n",
       "      <td>38.71</td>\n",
       "      <td>3TRDC</td>\n",
       "      <td>2</td>\n",
       "      <td>2009-02-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>301</td>\n",
       "      <td>2009-02-06</td>\n",
       "      <td>47</td>\n",
       "      <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",
       "      <td>47</td>\n",
       "      <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/robertdefilippi/Google Drive/Programming/data-science-ipython-notebooks/Data/cohort_xlsx.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47</td>\n",
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       "      <td>2009-01-11</td>\n",
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       "      <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",
       "      <th>1</th>\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",
       "      <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",
       "      <td>2009-02-04</td>\n",
       "      <td>2009-02</td>\n",
       "      <td>2009-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "      <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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OrderPeriod'] = df.OrderDate.apply(lambda x: x.strftime('%Y-%m'))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# This shifts the index from OrderId to UserId\n",
    "df.set_index('UserId', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47</td>\n",
       "      <td>262</td>\n",
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       "      <td>TRQKD</td>\n",
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       "      <td>2009-01</td>\n",
       "      <td>2009-01</td>\n",
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       "      <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",
       "      <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",
       "      <td>2009-02-04</td>\n",
       "      <td>2009-02</td>\n",
       "      <td>2009-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "      <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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# New column\n",
    "# groupby() --> Group series using mapper \n",
    "df['CohortGroup'] = df.groupby(level=0)['OrderDate'].min().apply(lambda x: x.strftime('%Y-%m'))\n",
    "\n",
    "# Set the new index to \"0\" and do not make a new object.\n",
    "df.reset_index(inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Find this group first\n",
    "# Only values of ChortGroup and OrderPeriod\n",
    "grouped = df.groupby(['CohortGroup', 'OrderPeriod'])\n",
    "\n",
    "# grouped.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 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})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>1850.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-02</th>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>1351.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03</th>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>1357.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-04</th>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>1604.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-05</th>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>1575.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         TotalOrders  TotalUsers  TotalCharges\n",
       "CohortGroup OrderPeriod                                       \n",
       "2009-01     2009-01               30          22       1850.26\n",
       "            2009-02               25           8       1351.07\n",
       "            2009-03               26          10       1357.36\n",
       "            2009-04               28           9       1604.50\n",
       "            2009-05               26          10       1575.63"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make the column names more meaningful\n",
    "cohorts.rename(columns={'UserId': 'TotalUsers',\n",
    "                        'OrderId': 'TotalOrders'}, inplace=True)\n",
    "cohorts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to see how each cohort has behaved in the months follow their first pruchase. So we need to index each cohort to their first purchase month.\n",
    "\n",
    "To so this we need to create a function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>1850.26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-02</th>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>1351.07</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03</th>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>1357.36</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-04</th>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>1604.50</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-05</th>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>1575.63</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       1850.26             1\n",
       "            2009-02               25           8       1351.07             2\n",
       "            2009-03               26          10       1357.36             3\n",
       "            2009-04               28           9       1604.50             4\n",
       "            2009-05               26          10       1575.63             5"
      ]
     },
     "execution_count": 9,
     "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\n",
    "    first purchase.\n",
    "    \n",
    "    Example:\n",
    "    ------\n",
    "    Say you want to get the 3rd month for every user:\n",
    "        \n",
    "        df.sort(['UserId', 'OrderTime'], inplace = True])\n",
    "        \n",
    "        df = df.groupby('UserId').apply(cohort_period)\n",
    "        \n",
    "        df[df.CohortPeriod == 3]\n",
    "            \n",
    "    \"\"\"\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": {},
   "source": [
    "Ok, lets take some time to test if the DataFrames are correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = df[(df.CohortGroup == '2009-01') & (df.OrderPeriod == '2009-01')]\n",
    "# Remember ix is for rows\n",
    "y = cohorts.ix[('2009-01', '2009-01')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unbound method repeat() must be called with Series instance as first argument (got int instance instead)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-9e00282c847a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'TotalCharges'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'TotalCharges'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/robertdefilippi/miniconda2/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   2297\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2298\u001b[0m             \u001b[0;31m# set column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2299\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2301\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_setitem_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/robertdefilippi/miniconda2/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   2364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2365\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ensure_valid_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2366\u001b[0;31m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2367\u001b[0m         \u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2368\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/robertdefilippi/miniconda2/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_sanitize_column\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   2540\u001b[0m             \u001b[0;31m# upcast the scalar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2541\u001b[0m             \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_infer_dtype_from_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2542\u001b[0;31m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2543\u001b[0m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_possibly_cast_to_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2544\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/robertdefilippi/miniconda2/lib/python2.7/site-packages/numpy/core/fromnumeric.pyc\u001b[0m in \u001b[0;36mrepeat\u001b[0;34m(a, repeats, axis)\u001b[0m\n\u001b[1;32m    395\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    396\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'repeat'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrepeats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 397\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mrepeat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    398\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unbound method repeat() must be called with Series instance as first argument (got int instance instead)"
     ]
    }
   ],
   "source": [
    "x['TotalCharges'] = type(x['TotalCharges'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "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": 23,
     "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": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts['TotalUsers'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "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",
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       "      <th>2010-03</th>\n",
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       "    <tr>\n",
       "      <th>CohortPeriod</th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>39</td>\n",
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       "      <td>65</td>\n",
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       "      <td>100</td>\n",
       "      <td>24</td>\n",
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       "      <th>2</th>\n",
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       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
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       "      <td>15</td>\n",
       "      <td>17</td>\n",
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       "      <td>17</td>\n",
       "      <td>50</td>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>10</td>\n",
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       "      <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",
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       "      <td>18</td>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "      <td>8</td>\n",
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       "      <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",
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      "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts['TotalUsers'].unstack(0).head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <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": 27,
     "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": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FRkYSGhoKgIuLC3369OHYsWMA9OjRAzc3NxRFoXPnzqSkpHD+/HlatmxJq1atABg7dixH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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bb4c710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style='white')\n",
    "\n",
    "user_retention[['2009-06', '2009-07', '2009-08']].plot(figsize=(10,5))\n",
    "sns.plt.title('Cohorts: User Retention')\n",
    "sns.plt.xticks(np.arange(1, 12.1, 1))\n",
    "sns.plt.xlim(1, 12)\n",
    "sns.plt.ylabel('% of Cohort Purchasing');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/robertdefilippi/miniconda2/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "image/png": 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o2q7Ckpdpu56lvE7b9azkZdquZyWv03Y9K3mZtutllpdpu4T4r7xM2/Us5WXa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\nzDMGzXGnlStX4uHhwfjx4x/r8hlxf1LQVjI5OTn4+vri7+9f7C9xQ1Cr1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10987cf10>"
      ]
     },
     "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": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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
}