Ej1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "\n",
    "online = pd.read_csv('Online Retail.csv', sep=';')\n",
    "\n",
    "# convert object to datetime\n",
    "online['InvoiceDate'] = pd.to_datetime(online['InvoiceDate'])\n",
    "\n",
    "# convert object to float\n",
    "online['UnitPrice'] = online['UnitPrice'].apply(lambda x: x.replace(',', '.'))\n",
    "online['UnitPrice'] = online['UnitPrice'].apply(lambda col:pd.to_numeric(col, errors='coerce'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo              object\n",
       "StockCode              object\n",
       "Description            object\n",
       "Quantity                int64\n",
       "InvoiceDate    datetime64[ns]\n",
       "UnitPrice             float64\n",
       "CustomerID            float64\n",
       "Country                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "online.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_month(x): return dt.datetime(x.year, x.month, 1)\n",
    "\n",
    "online['InvoiceMonth'] = online['InvoiceDate'].apply(get_month)\n",
    "\n",
    "grouping = online.groupby('CustomerID')['InvoiceMonth']\n",
    "\n",
    "online['CohortMonth'] = grouping.transform('min')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_date_int(df, column):\n",
    "    year = df[column].dt.year\n",
    "    month = df[column].dt.month\n",
    "    day = df[column].dt.day\n",
    "    return year, month, day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "invoice_year, invoice_month, _ = get_date_int(online, 'InvoiceMonth')\n",
    "cohort_year, cohort_month, _ = get_date_int(online, 'CohortMonth')\n",
    "\n",
    "years_diff = invoice_year - cohort_year\n",
    "\n",
    "months_diff = invoice_month - cohort_month\n",
    "\n",
    "online['CohortIndex'] = years_diff * 12 + months_diff + 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>InvoiceMonth</th>\n",
       "      <th>CohortMonth</th>\n",
       "      <th>CohortIndex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-01-12 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-01-12 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-01-12 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-01-12 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-01-12 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country InvoiceMonth  \\\n",
       "0 2010-01-12 08:26:00       2.55     17850.0  United Kingdom   2010-01-01   \n",
       "1 2010-01-12 08:26:00       3.39     17850.0  United Kingdom   2010-01-01   \n",
       "2 2010-01-12 08:26:00       2.75     17850.0  United Kingdom   2010-01-01   \n",
       "3 2010-01-12 08:26:00       3.39     17850.0  United Kingdom   2010-01-01   \n",
       "4 2010-01-12 08:26:00       3.39     17850.0  United Kingdom   2010-01-01   \n",
       "\n",
       "  CohortMonth  CohortIndex  \n",
       "0  2010-01-01          1.0  \n",
       "1  2010-01-01          1.0  \n",
       "2  2010-01-01          1.0  \n",
       "3  2010-01-01          1.0  \n",
       "4  2010-01-01          1.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "online.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CohortIndex   1.0    2.0    3.0    4.0    5.0    6.0    7.0    8.0    9.0   \\\n",
      "CohortMonth                                                                  \n",
      "2010-01-01    98.0    9.0    4.0    NaN    7.0    7.0    4.0   10.0   10.0   \n",
      "2010-02-01   108.0    2.0    NaN    NaN    3.0    7.0    5.0   12.0    5.0   \n",
      "2010-03-01    49.0    NaN    1.0    1.0    1.0    2.0    5.0    NaN    NaN   \n",
      "2010-05-01    68.0    3.0    3.0    2.0    3.0    1.0    NaN   24.0   32.0   \n",
      "2010-06-01    76.0    2.0    4.0    4.0    1.0    NaN   26.0   29.0   25.0   \n",
      "2010-07-01    53.0    2.0    2.0    1.0    NaN    9.0   24.0   22.0   22.0   \n",
      "2010-08-01    87.0    3.0    2.0    NaN   30.0   37.0   30.0   34.0   33.0   \n",
      "2010-09-01    83.0    1.0    NaN   23.0   29.0   20.0   30.0   23.0   29.0   \n",
      "2010-10-01    38.0    NaN   11.0   15.0   12.0   15.0   18.0    8.0   14.0   \n",
      "2010-12-01   288.0  102.0   79.0   97.0   93.0  105.0  108.0   96.0   93.0   \n",
      "2011-01-01   599.0  142.0  154.0  145.0  169.0  186.0  182.0  147.0  195.0   \n",
      "2011-02-01   427.0   91.0   86.0  101.0  114.0  116.0  107.0  118.0  113.0   \n",
      "2011-03-01   409.0   74.0   86.0   88.0   98.0   84.0   89.0  103.0  113.0   \n",
      "2011-04-01   325.0   68.0   61.0   63.0   59.0   66.0   90.0   80.0   16.0   \n",
      "2011-05-01   271.0   54.0   52.0   48.0   63.0   48.0   72.0   18.0    NaN   \n",
      "2011-06-01   271.0   47.0   46.0   58.0   53.0   74.0   20.0    NaN    NaN   \n",
      "2011-07-01   226.0   44.0   42.0   48.0   60.0    7.0    NaN    NaN    NaN   \n",
      "2011-08-01   166.0   27.0   39.0   43.0    9.0    NaN    NaN    NaN    NaN   \n",
      "2011-09-01   240.0   37.0   51.0    6.0    NaN    NaN    NaN    NaN    NaN   \n",
      "2011-10-01   250.0   50.0   10.0    NaN    NaN    NaN    NaN    NaN    NaN   \n",
      "2011-11-01   218.0   10.0    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
      "2011-12-01    22.0    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
      "\n",
      "CohortIndex   10.0  ...   15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  \\\n",
      "CohortMonth         ...                                                    \n",
      "2010-01-01     5.0  ...   38.0  40.0  36.0  44.0  41.0  37.0  41.0  44.0   \n",
      "2010-02-01     NaN  ...   39.0  38.0  43.0  48.0  34.0  41.0  45.0  53.0   \n",
      "2010-03-01    14.0  ...   23.0  16.0  19.0  19.0  13.0  24.0  23.0   9.0   \n",
      "2010-05-01    20.0  ...   30.0  32.0  31.0  33.0  40.0  20.0   NaN   NaN   \n",
      "2010-06-01    25.0  ...   27.0  33.0  26.0  39.0   5.0   NaN   NaN   NaN   \n",
      "2010-07-01    19.0  ...   22.0  20.0  29.0   6.0   NaN   NaN   NaN   NaN   \n",
      "2010-08-01    36.0  ...   41.0  49.0  22.0   NaN   NaN   NaN   NaN   NaN   \n",
      "2010-09-01    26.0  ...   32.0  10.0   NaN   NaN   NaN   NaN   NaN   NaN   \n",
      "2010-10-01    11.0  ...    6.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
      "2010-12-01   104.0  ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
      "2011-01-01   201.0  ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
      "2011-02-01   136.0  ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
      "2011-03-01    27.0  ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
      "2011-04-01     NaN  ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
      "2011-05-01     NaN  ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
      "2011-06-01     NaN  ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
      "2011-07-01     NaN  ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
      "2011-08-01     NaN  ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
      "2011-09-01     NaN  ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
      "2011-10-01     NaN  ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
      "2011-11-01     NaN  ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
      "2011-12-01     NaN  ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
      "\n",
      "CohortIndex  23.0  24.0  \n",
      "CohortMonth              \n",
      "2010-01-01   40.0  23.0  \n",
      "2010-02-01   19.0   NaN  \n",
      "2010-03-01    NaN   NaN  \n",
      "2010-05-01    NaN   NaN  \n",
      "2010-06-01    NaN   NaN  \n",
      "2010-07-01    NaN   NaN  \n",
      "2010-08-01    NaN   NaN  \n",
      "2010-09-01    NaN   NaN  \n",
      "2010-10-01    NaN   NaN  \n",
      "2010-12-01    NaN   NaN  \n",
      "2011-01-01    NaN   NaN  \n",
      "2011-02-01    NaN   NaN  \n",
      "2011-03-01    NaN   NaN  \n",
      "2011-04-01    NaN   NaN  \n",
      "2011-05-01    NaN   NaN  \n",
      "2011-06-01    NaN   NaN  \n",
      "2011-07-01    NaN   NaN  \n",
      "2011-08-01    NaN   NaN  \n",
      "2011-09-01    NaN   NaN  \n",
      "2011-10-01    NaN   NaN  \n",
      "2011-11-01    NaN   NaN  \n",
      "2011-12-01    NaN   NaN  \n",
      "\n",
      "[22 rows x 24 columns]\n"
     ]
    }
   ],
   "source": [
    "# Count monthly active customers from each cohort\n",
    "grouping = online.groupby(['CohortMonth', 'CohortIndex'])\n",
    "\n",
    "cohort_data = grouping['CustomerID'].apply(pd.Series.nunique)\n",
    "cohort_data = cohort_data.reset_index()\n",
    "\n",
    "cohort_counts = cohort_data.pivot(index='CohortMonth', columns='CohortIndex', values='CustomerID')\n",
    "\n",
    "print(cohort_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "cohort_sizes = cohort_counts.iloc[:,0]\n",
    "retention = cohort_counts.divide(cohort_sizes, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>CohortIndex</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
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       "      <th>8.0</th>\n",
       "      <th>9.0</th>\n",
       "      <th>10.0</th>\n",
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       "      <th>15.0</th>\n",
       "      <th>16.0</th>\n",
       "      <th>17.0</th>\n",
       "      <th>18.0</th>\n",
       "      <th>19.0</th>\n",
       "      <th>20.0</th>\n",
       "      <th>21.0</th>\n",
       "      <th>22.0</th>\n",
       "      <th>23.0</th>\n",
       "      <th>24.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CohortMonth</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.091837</td>\n",
       "      <td>0.040816</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.040816</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.051020</td>\n",
       "      <td>...</td>\n",
       "      <td>0.387755</td>\n",
       "      <td>0.408163</td>\n",
       "      <td>0.367347</td>\n",
       "      <td>0.448980</td>\n",
       "      <td>0.418367</td>\n",
       "      <td>0.377551</td>\n",
       "      <td>0.418367</td>\n",
       "      <td>0.448980</td>\n",
       "      <td>0.408163</td>\n",
       "      <td>0.234694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.018519</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.064815</td>\n",
       "      <td>0.046296</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.046296</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.361111</td>\n",
       "      <td>0.351852</td>\n",
       "      <td>0.398148</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.314815</td>\n",
       "      <td>0.379630</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.490741</td>\n",
       "      <td>0.175926</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.040816</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>...</td>\n",
       "      <td>0.469388</td>\n",
       "      <td>0.326531</td>\n",
       "      <td>0.387755</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2010-05-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.044118</td>\n",
       "      <td>0.044118</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>0.044118</td>\n",
       "      <td>0.014706</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.294118</td>\n",
       "      <td>...</td>\n",
       "      <td>0.441176</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.455882</td>\n",
       "      <td>0.485294</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.294118</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.013158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.342105</td>\n",
       "      <td>0.381579</td>\n",
       "      <td>0.328947</td>\n",
       "      <td>0.328947</td>\n",
       "      <td>...</td>\n",
       "      <td>0.355263</td>\n",
       "      <td>0.434211</td>\n",
       "      <td>0.342105</td>\n",
       "      <td>0.513158</td>\n",
       "      <td>0.065789</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>2010-07-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.037736</td>\n",
       "      <td>0.037736</td>\n",
       "      <td>0.018868</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.169811</td>\n",
       "      <td>0.452830</td>\n",
       "      <td>0.415094</td>\n",
       "      <td>0.415094</td>\n",
       "      <td>0.358491</td>\n",
       "      <td>...</td>\n",
       "      <td>0.415094</td>\n",
       "      <td>0.377358</td>\n",
       "      <td>0.547170</td>\n",
       "      <td>0.113208</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>2010-08-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.022989</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.344828</td>\n",
       "      <td>0.425287</td>\n",
       "      <td>0.344828</td>\n",
       "      <td>0.390805</td>\n",
       "      <td>0.379310</td>\n",
       "      <td>0.413793</td>\n",
       "      <td>...</td>\n",
       "      <td>0.471264</td>\n",
       "      <td>0.563218</td>\n",
       "      <td>0.252874</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>2010-09-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.277108</td>\n",
       "      <td>0.349398</td>\n",
       "      <td>0.240964</td>\n",
       "      <td>0.361446</td>\n",
       "      <td>0.277108</td>\n",
       "      <td>0.349398</td>\n",
       "      <td>0.313253</td>\n",
       "      <td>...</td>\n",
       "      <td>0.385542</td>\n",
       "      <td>0.120482</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>2010-10-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.289474</td>\n",
       "      <td>0.394737</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.394737</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.289474</td>\n",
       "      <td>...</td>\n",
       "      <td>0.157895</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",
       "    <tr>\n",
       "      <th>2010-12-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.354167</td>\n",
       "      <td>0.274306</td>\n",
       "      <td>0.336806</td>\n",
       "      <td>0.322917</td>\n",
       "      <td>0.364583</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.322917</td>\n",
       "      <td>0.361111</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.237062</td>\n",
       "      <td>0.257095</td>\n",
       "      <td>0.242070</td>\n",
       "      <td>0.282137</td>\n",
       "      <td>0.310518</td>\n",
       "      <td>0.303840</td>\n",
       "      <td>0.245409</td>\n",
       "      <td>0.325543</td>\n",
       "      <td>0.335559</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-02-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.213115</td>\n",
       "      <td>0.201405</td>\n",
       "      <td>0.236534</td>\n",
       "      <td>0.266979</td>\n",
       "      <td>0.271663</td>\n",
       "      <td>0.250585</td>\n",
       "      <td>0.276347</td>\n",
       "      <td>0.264637</td>\n",
       "      <td>0.318501</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.180929</td>\n",
       "      <td>0.210269</td>\n",
       "      <td>0.215159</td>\n",
       "      <td>0.239609</td>\n",
       "      <td>0.205379</td>\n",
       "      <td>0.217604</td>\n",
       "      <td>0.251834</td>\n",
       "      <td>0.276284</td>\n",
       "      <td>0.066015</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.209231</td>\n",
       "      <td>0.187692</td>\n",
       "      <td>0.193846</td>\n",
       "      <td>0.181538</td>\n",
       "      <td>0.203077</td>\n",
       "      <td>0.276923</td>\n",
       "      <td>0.246154</td>\n",
       "      <td>0.049231</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.199262</td>\n",
       "      <td>0.191882</td>\n",
       "      <td>0.177122</td>\n",
       "      <td>0.232472</td>\n",
       "      <td>0.177122</td>\n",
       "      <td>0.265683</td>\n",
       "      <td>0.066421</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.173432</td>\n",
       "      <td>0.169742</td>\n",
       "      <td>0.214022</td>\n",
       "      <td>0.195572</td>\n",
       "      <td>0.273063</td>\n",
       "      <td>0.073801</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.194690</td>\n",
       "      <td>0.185841</td>\n",
       "      <td>0.212389</td>\n",
       "      <td>0.265487</td>\n",
       "      <td>0.030973</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.162651</td>\n",
       "      <td>0.234940</td>\n",
       "      <td>0.259036</td>\n",
       "      <td>0.054217</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.154167</td>\n",
       "      <td>0.212500</td>\n",
       "      <td>0.025000</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>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.040000</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>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.045872</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>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-01</th>\n",
       "      <td>1.0</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",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "CohortIndex  1.0       2.0       3.0       4.0       5.0       6.0       7.0   \\\n",
       "CohortMonth                                                                     \n",
       "2010-01-01    1.0  0.091837  0.040816       NaN  0.071429  0.071429  0.040816   \n",
       "2010-02-01    1.0  0.018519       NaN       NaN  0.027778  0.064815  0.046296   \n",
       "2010-03-01    1.0       NaN  0.020408  0.020408  0.020408  0.040816  0.102041   \n",
       "2010-05-01    1.0  0.044118  0.044118  0.029412  0.044118  0.014706       NaN   \n",
       "2010-06-01    1.0  0.026316  0.052632  0.052632  0.013158       NaN  0.342105   \n",
       "2010-07-01    1.0  0.037736  0.037736  0.018868       NaN  0.169811  0.452830   \n",
       "2010-08-01    1.0  0.034483  0.022989       NaN  0.344828  0.425287  0.344828   \n",
       "2010-09-01    1.0  0.012048       NaN  0.277108  0.349398  0.240964  0.361446   \n",
       "2010-10-01    1.0       NaN  0.289474  0.394737  0.315789  0.394737  0.473684   \n",
       "2010-12-01    1.0  0.354167  0.274306  0.336806  0.322917  0.364583  0.375000   \n",
       "2011-01-01    1.0  0.237062  0.257095  0.242070  0.282137  0.310518  0.303840   \n",
       "2011-02-01    1.0  0.213115  0.201405  0.236534  0.266979  0.271663  0.250585   \n",
       "2011-03-01    1.0  0.180929  0.210269  0.215159  0.239609  0.205379  0.217604   \n",
       "2011-04-01    1.0  0.209231  0.187692  0.193846  0.181538  0.203077  0.276923   \n",
       "2011-05-01    1.0  0.199262  0.191882  0.177122  0.232472  0.177122  0.265683   \n",
       "2011-06-01    1.0  0.173432  0.169742  0.214022  0.195572  0.273063  0.073801   \n",
       "2011-07-01    1.0  0.194690  0.185841  0.212389  0.265487  0.030973       NaN   \n",
       "2011-08-01    1.0  0.162651  0.234940  0.259036  0.054217       NaN       NaN   \n",
       "2011-09-01    1.0  0.154167  0.212500  0.025000       NaN       NaN       NaN   \n",
       "2011-10-01    1.0  0.200000  0.040000       NaN       NaN       NaN       NaN   \n",
       "2011-11-01    1.0  0.045872       NaN       NaN       NaN       NaN       NaN   \n",
       "2011-12-01    1.0       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "CohortIndex      8.0       9.0       10.0    ...         15.0      16.0  \\\n",
       "CohortMonth                                  ...                          \n",
       "2010-01-01   0.102041  0.102041  0.051020    ...     0.387755  0.408163   \n",
       "2010-02-01   0.111111  0.046296       NaN    ...     0.361111  0.351852   \n",
       "2010-03-01        NaN       NaN  0.285714    ...     0.469388  0.326531   \n",
       "2010-05-01   0.352941  0.470588  0.294118    ...     0.441176  0.470588   \n",
       "2010-06-01   0.381579  0.328947  0.328947    ...     0.355263  0.434211   \n",
       "2010-07-01   0.415094  0.415094  0.358491    ...     0.415094  0.377358   \n",
       "2010-08-01   0.390805  0.379310  0.413793    ...     0.471264  0.563218   \n",
       "2010-09-01   0.277108  0.349398  0.313253    ...     0.385542  0.120482   \n",
       "2010-10-01   0.210526  0.368421  0.289474    ...     0.157895       NaN   \n",
       "2010-12-01   0.333333  0.322917  0.361111    ...          NaN       NaN   \n",
       "2011-01-01   0.245409  0.325543  0.335559    ...          NaN       NaN   \n",
       "2011-02-01   0.276347  0.264637  0.318501    ...          NaN       NaN   \n",
       "2011-03-01   0.251834  0.276284  0.066015    ...          NaN       NaN   \n",
       "2011-04-01   0.246154  0.049231       NaN    ...          NaN       NaN   \n",
       "2011-05-01   0.066421       NaN       NaN    ...          NaN       NaN   \n",
       "2011-06-01        NaN       NaN       NaN    ...          NaN       NaN   \n",
       "2011-07-01        NaN       NaN       NaN    ...          NaN       NaN   \n",
       "2011-08-01        NaN       NaN       NaN    ...          NaN       NaN   \n",
       "2011-09-01        NaN       NaN       NaN    ...          NaN       NaN   \n",
       "2011-10-01        NaN       NaN       NaN    ...          NaN       NaN   \n",
       "2011-11-01        NaN       NaN       NaN    ...          NaN       NaN   \n",
       "2011-12-01        NaN       NaN       NaN    ...          NaN       NaN   \n",
       "\n",
       "CohortIndex      17.0      18.0      19.0      20.0      21.0      22.0  \\\n",
       "CohortMonth                                                               \n",
       "2010-01-01   0.367347  0.448980  0.418367  0.377551  0.418367  0.448980   \n",
       "2010-02-01   0.398148  0.444444  0.314815  0.379630  0.416667  0.490741   \n",
       "2010-03-01   0.387755  0.387755  0.265306  0.489796  0.469388  0.183673   \n",
       "2010-05-01   0.455882  0.485294  0.588235  0.294118       NaN       NaN   \n",
       "2010-06-01   0.342105  0.513158  0.065789       NaN       NaN       NaN   \n",
       "2010-07-01   0.547170  0.113208       NaN       NaN       NaN       NaN   \n",
       "2010-08-01   0.252874       NaN       NaN       NaN       NaN       NaN   \n",
       "2010-09-01        NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2010-10-01        NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2010-12-01        NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2011-01-01        NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2011-02-01        NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2011-03-01        NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2011-04-01        NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2011-05-01        NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2011-06-01        NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2011-07-01        NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2011-08-01        NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2011-09-01        NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2011-10-01        NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2011-11-01        NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2011-12-01        NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "CohortIndex      23.0      24.0  \n",
       "CohortMonth                      \n",
       "2010-01-01   0.408163  0.234694  \n",
       "2010-02-01   0.175926       NaN  \n",
       "2010-03-01        NaN       NaN  \n",
       "2010-05-01        NaN       NaN  \n",
       "2010-06-01        NaN       NaN  \n",
       "2010-07-01        NaN       NaN  \n",
       "2010-08-01        NaN       NaN  \n",
       "2010-09-01        NaN       NaN  \n",
       "2010-10-01        NaN       NaN  \n",
       "2010-12-01        NaN       NaN  \n",
       "2011-01-01        NaN       NaN  \n",
       "2011-02-01        NaN       NaN  \n",
       "2011-03-01        NaN       NaN  \n",
       "2011-04-01        NaN       NaN  \n",
       "2011-05-01        NaN       NaN  \n",
       "2011-06-01        NaN       NaN  \n",
       "2011-07-01        NaN       NaN  \n",
       "2011-08-01        NaN       NaN  \n",
       "2011-09-01        NaN       NaN  \n",
       "2011-10-01        NaN       NaN  \n",
       "2011-11-01        NaN       NaN  \n",
       "2011-12-01        NaN       NaN  \n",
       "\n",
       "[22 rows x 24 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>CohortIndex</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>6.0</th>\n",
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       "      <th>8.0</th>\n",
       "      <th>9.0</th>\n",
       "      <th>10.0</th>\n",
       "      <th>...</th>\n",
       "      <th>15.0</th>\n",
       "      <th>16.0</th>\n",
       "      <th>17.0</th>\n",
       "      <th>18.0</th>\n",
       "      <th>19.0</th>\n",
       "      <th>20.0</th>\n",
       "      <th>21.0</th>\n",
       "      <th>22.0</th>\n",
       "      <th>23.0</th>\n",
       "      <th>24.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CohortMonth</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",
       "      <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>2010-01-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>10.2</td>\n",
       "      <td>10.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>...</td>\n",
       "      <td>38.8</td>\n",
       "      <td>40.8</td>\n",
       "      <td>36.7</td>\n",
       "      <td>44.9</td>\n",
       "      <td>41.8</td>\n",
       "      <td>37.8</td>\n",
       "      <td>41.8</td>\n",
       "      <td>44.9</td>\n",
       "      <td>40.8</td>\n",
       "      <td>23.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.5</td>\n",
       "      <td>4.6</td>\n",
       "      <td>11.1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>36.1</td>\n",
       "      <td>35.2</td>\n",
       "      <td>39.8</td>\n",
       "      <td>44.4</td>\n",
       "      <td>31.5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>41.7</td>\n",
       "      <td>49.1</td>\n",
       "      <td>17.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>10.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.6</td>\n",
       "      <td>...</td>\n",
       "      <td>46.9</td>\n",
       "      <td>32.7</td>\n",
       "      <td>38.8</td>\n",
       "      <td>38.8</td>\n",
       "      <td>26.5</td>\n",
       "      <td>49.0</td>\n",
       "      <td>46.9</td>\n",
       "      <td>18.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.3</td>\n",
       "      <td>47.1</td>\n",
       "      <td>29.4</td>\n",
       "      <td>...</td>\n",
       "      <td>44.1</td>\n",
       "      <td>47.1</td>\n",
       "      <td>45.6</td>\n",
       "      <td>48.5</td>\n",
       "      <td>58.8</td>\n",
       "      <td>29.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.2</td>\n",
       "      <td>38.2</td>\n",
       "      <td>32.9</td>\n",
       "      <td>32.9</td>\n",
       "      <td>...</td>\n",
       "      <td>35.5</td>\n",
       "      <td>43.4</td>\n",
       "      <td>34.2</td>\n",
       "      <td>51.3</td>\n",
       "      <td>6.6</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>2010-07-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>45.3</td>\n",
       "      <td>41.5</td>\n",
       "      <td>41.5</td>\n",
       "      <td>35.8</td>\n",
       "      <td>...</td>\n",
       "      <td>41.5</td>\n",
       "      <td>37.7</td>\n",
       "      <td>54.7</td>\n",
       "      <td>11.3</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>2010-08-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.5</td>\n",
       "      <td>42.5</td>\n",
       "      <td>34.5</td>\n",
       "      <td>39.1</td>\n",
       "      <td>37.9</td>\n",
       "      <td>41.4</td>\n",
       "      <td>...</td>\n",
       "      <td>47.1</td>\n",
       "      <td>56.3</td>\n",
       "      <td>25.3</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>2010-09-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.7</td>\n",
       "      <td>34.9</td>\n",
       "      <td>24.1</td>\n",
       "      <td>36.1</td>\n",
       "      <td>27.7</td>\n",
       "      <td>34.9</td>\n",
       "      <td>31.3</td>\n",
       "      <td>...</td>\n",
       "      <td>38.6</td>\n",
       "      <td>12.0</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>2010-10-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.9</td>\n",
       "      <td>39.5</td>\n",
       "      <td>31.6</td>\n",
       "      <td>39.5</td>\n",
       "      <td>47.4</td>\n",
       "      <td>21.1</td>\n",
       "      <td>36.8</td>\n",
       "      <td>28.9</td>\n",
       "      <td>...</td>\n",
       "      <td>15.8</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",
       "    <tr>\n",
       "      <th>2010-12-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>35.4</td>\n",
       "      <td>27.4</td>\n",
       "      <td>33.7</td>\n",
       "      <td>32.3</td>\n",
       "      <td>36.5</td>\n",
       "      <td>37.5</td>\n",
       "      <td>33.3</td>\n",
       "      <td>32.3</td>\n",
       "      <td>36.1</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>23.7</td>\n",
       "      <td>25.7</td>\n",
       "      <td>24.2</td>\n",
       "      <td>28.2</td>\n",
       "      <td>31.1</td>\n",
       "      <td>30.4</td>\n",
       "      <td>24.5</td>\n",
       "      <td>32.6</td>\n",
       "      <td>33.6</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-02-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>21.3</td>\n",
       "      <td>20.1</td>\n",
       "      <td>23.7</td>\n",
       "      <td>26.7</td>\n",
       "      <td>27.2</td>\n",
       "      <td>25.1</td>\n",
       "      <td>27.6</td>\n",
       "      <td>26.5</td>\n",
       "      <td>31.9</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>21.8</td>\n",
       "      <td>25.2</td>\n",
       "      <td>27.6</td>\n",
       "      <td>6.6</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>20.9</td>\n",
       "      <td>18.8</td>\n",
       "      <td>19.4</td>\n",
       "      <td>18.2</td>\n",
       "      <td>20.3</td>\n",
       "      <td>27.7</td>\n",
       "      <td>24.6</td>\n",
       "      <td>4.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>19.9</td>\n",
       "      <td>19.2</td>\n",
       "      <td>17.7</td>\n",
       "      <td>23.2</td>\n",
       "      <td>17.7</td>\n",
       "      <td>26.6</td>\n",
       "      <td>6.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.0</td>\n",
       "      <td>21.4</td>\n",
       "      <td>19.6</td>\n",
       "      <td>27.3</td>\n",
       "      <td>7.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>18.6</td>\n",
       "      <td>21.2</td>\n",
       "      <td>26.5</td>\n",
       "      <td>3.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>16.3</td>\n",
       "      <td>23.5</td>\n",
       "      <td>25.9</td>\n",
       "      <td>5.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>15.4</td>\n",
       "      <td>21.2</td>\n",
       "      <td>2.5</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>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</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>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>4.6</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>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-01</th>\n",
       "      <td>100.0</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",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "CohortIndex   1.0   2.0   3.0   4.0   5.0   6.0   7.0   8.0   9.0   10.0  \\\n",
       "CohortMonth                                                                \n",
       "2010-01-01   100.0   9.2   4.1   NaN   7.1   7.1   4.1  10.2  10.2   5.1   \n",
       "2010-02-01   100.0   1.9   NaN   NaN   2.8   6.5   4.6  11.1   4.6   NaN   \n",
       "2010-03-01   100.0   NaN   2.0   2.0   2.0   4.1  10.2   NaN   NaN  28.6   \n",
       "2010-05-01   100.0   4.4   4.4   2.9   4.4   1.5   NaN  35.3  47.1  29.4   \n",
       "2010-06-01   100.0   2.6   5.3   5.3   1.3   NaN  34.2  38.2  32.9  32.9   \n",
       "2010-07-01   100.0   3.8   3.8   1.9   NaN  17.0  45.3  41.5  41.5  35.8   \n",
       "2010-08-01   100.0   3.4   2.3   NaN  34.5  42.5  34.5  39.1  37.9  41.4   \n",
       "2010-09-01   100.0   1.2   NaN  27.7  34.9  24.1  36.1  27.7  34.9  31.3   \n",
       "2010-10-01   100.0   NaN  28.9  39.5  31.6  39.5  47.4  21.1  36.8  28.9   \n",
       "2010-12-01   100.0  35.4  27.4  33.7  32.3  36.5  37.5  33.3  32.3  36.1   \n",
       "2011-01-01   100.0  23.7  25.7  24.2  28.2  31.1  30.4  24.5  32.6  33.6   \n",
       "2011-02-01   100.0  21.3  20.1  23.7  26.7  27.2  25.1  27.6  26.5  31.9   \n",
       "2011-03-01   100.0  18.1  21.0  21.5  24.0  20.5  21.8  25.2  27.6   6.6   \n",
       "2011-04-01   100.0  20.9  18.8  19.4  18.2  20.3  27.7  24.6   4.9   NaN   \n",
       "2011-05-01   100.0  19.9  19.2  17.7  23.2  17.7  26.6   6.6   NaN   NaN   \n",
       "2011-06-01   100.0  17.3  17.0  21.4  19.6  27.3   7.4   NaN   NaN   NaN   \n",
       "2011-07-01   100.0  19.5  18.6  21.2  26.5   3.1   NaN   NaN   NaN   NaN   \n",
       "2011-08-01   100.0  16.3  23.5  25.9   5.4   NaN   NaN   NaN   NaN   NaN   \n",
       "2011-09-01   100.0  15.4  21.2   2.5   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2011-10-01   100.0  20.0   4.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2011-11-01   100.0   4.6   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2011-12-01   100.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "\n",
       "CohortIndex  ...   15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  \n",
       "CohortMonth  ...                                                               \n",
       "2010-01-01   ...   38.8  40.8  36.7  44.9  41.8  37.8  41.8  44.9  40.8  23.5  \n",
       "2010-02-01   ...   36.1  35.2  39.8  44.4  31.5  38.0  41.7  49.1  17.6   NaN  \n",
       "2010-03-01   ...   46.9  32.7  38.8  38.8  26.5  49.0  46.9  18.4   NaN   NaN  \n",
       "2010-05-01   ...   44.1  47.1  45.6  48.5  58.8  29.4   NaN   NaN   NaN   NaN  \n",
       "2010-06-01   ...   35.5  43.4  34.2  51.3   6.6   NaN   NaN   NaN   NaN   NaN  \n",
       "2010-07-01   ...   41.5  37.7  54.7  11.3   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2010-08-01   ...   47.1  56.3  25.3   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2010-09-01   ...   38.6  12.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2010-10-01   ...   15.8   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2010-12-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2011-01-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2011-02-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2011-03-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2011-04-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2011-05-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2011-06-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2011-07-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2011-08-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2011-09-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2011-10-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2011-11-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2011-12-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[22 rows x 24 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retention.round(3) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>CohortIndex</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>6.0</th>\n",
       "      <th>7.0</th>\n",
       "      <th>8.0</th>\n",
       "      <th>9.0</th>\n",
       "      <th>10.0</th>\n",
       "      <th>...</th>\n",
       "      <th>15.0</th>\n",
       "      <th>16.0</th>\n",
       "      <th>17.0</th>\n",
       "      <th>18.0</th>\n",
       "      <th>19.0</th>\n",
       "      <th>20.0</th>\n",
       "      <th>21.0</th>\n",
       "      <th>22.0</th>\n",
       "      <th>23.0</th>\n",
       "      <th>24.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CohortMonth</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",
       "      <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>2010-01-01</th>\n",
       "      <td>12.2</td>\n",
       "      <td>7.1</td>\n",
       "      <td>13.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>8.8</td>\n",
       "      <td>13.8</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.9</td>\n",
       "      <td>...</td>\n",
       "      <td>14.7</td>\n",
       "      <td>13.6</td>\n",
       "      <td>13.7</td>\n",
       "      <td>10.3</td>\n",
       "      <td>14.9</td>\n",
       "      <td>11.9</td>\n",
       "      <td>15.3</td>\n",
       "      <td>12.5</td>\n",
       "      <td>10.3</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-01</th>\n",
       "      <td>10.6</td>\n",
       "      <td>378.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.3</td>\n",
       "      <td>27.9</td>\n",
       "      <td>11.1</td>\n",
       "      <td>10.9</td>\n",
       "      <td>30.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>24.1</td>\n",
       "      <td>18.9</td>\n",
       "      <td>25.8</td>\n",
       "      <td>19.3</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>22.1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-01</th>\n",
       "      <td>8.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>7.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.5</td>\n",
       "      <td>...</td>\n",
       "      <td>13.4</td>\n",
       "      <td>29.1</td>\n",
       "      <td>27.6</td>\n",
       "      <td>25.8</td>\n",
       "      <td>22.1</td>\n",
       "      <td>25.1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-01</th>\n",
       "      <td>6.0</td>\n",
       "      <td>17.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.6</td>\n",
       "      <td>12.9</td>\n",
       "      <td>10.3</td>\n",
       "      <td>...</td>\n",
       "      <td>10.3</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.4</td>\n",
       "      <td>6.6</td>\n",
       "      <td>8.3</td>\n",
       "      <td>10.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-01</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>16.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.6</td>\n",
       "      <td>8.6</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>...</td>\n",
       "      <td>10.3</td>\n",
       "      <td>11.1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>13.3</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>2010-07-01</th>\n",
       "      <td>18.1</td>\n",
       "      <td>6.6</td>\n",
       "      <td>11.9</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.2</td>\n",
       "      <td>8.8</td>\n",
       "      <td>13.2</td>\n",
       "      <td>24.2</td>\n",
       "      <td>31.2</td>\n",
       "      <td>...</td>\n",
       "      <td>53.7</td>\n",
       "      <td>20.5</td>\n",
       "      <td>19.4</td>\n",
       "      <td>13.6</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>2010-08-01</th>\n",
       "      <td>10.2</td>\n",
       "      <td>10.5</td>\n",
       "      <td>54.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.4</td>\n",
       "      <td>12.4</td>\n",
       "      <td>10.7</td>\n",
       "      <td>9.9</td>\n",
       "      <td>8.3</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>12.4</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>2010-09-01</th>\n",
       "      <td>10.3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.7</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.9</td>\n",
       "      <td>13.6</td>\n",
       "      <td>7.7</td>\n",
       "      <td>14.4</td>\n",
       "      <td>10.5</td>\n",
       "      <td>...</td>\n",
       "      <td>14.5</td>\n",
       "      <td>15.4</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>2010-10-01</th>\n",
       "      <td>8.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.2</td>\n",
       "      <td>13.9</td>\n",
       "      <td>12.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11.9</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>16.6</td>\n",
       "      <td>...</td>\n",
       "      <td>37.9</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",
       "    <tr>\n",
       "      <th>2010-12-01</th>\n",
       "      <td>13.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>17.8</td>\n",
       "      <td>19.6</td>\n",
       "      <td>22.2</td>\n",
       "      <td>20.2</td>\n",
       "      <td>16.2</td>\n",
       "      <td>18.1</td>\n",
       "      <td>11.5</td>\n",
       "      <td>19.8</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>9.7</td>\n",
       "      <td>12.3</td>\n",
       "      <td>14.5</td>\n",
       "      <td>13.3</td>\n",
       "      <td>15.2</td>\n",
       "      <td>12.5</td>\n",
       "      <td>13.2</td>\n",
       "      <td>13.3</td>\n",
       "      <td>10.3</td>\n",
       "      <td>9.7</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-02-01</th>\n",
       "      <td>10.6</td>\n",
       "      <td>10.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>10.2</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.7</td>\n",
       "      <td>11.7</td>\n",
       "      <td>10.5</td>\n",
       "      <td>6.9</td>\n",
       "      <td>8.3</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-01</th>\n",
       "      <td>8.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>10.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>8.9</td>\n",
       "      <td>9.1</td>\n",
       "      <td>10.9</td>\n",
       "      <td>11.7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-01</th>\n",
       "      <td>9.6</td>\n",
       "      <td>10.2</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>9.7</td>\n",
       "      <td>8.2</td>\n",
       "      <td>11.8</td>\n",
       "      <td>22.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-01</th>\n",
       "      <td>10.1</td>\n",
       "      <td>8.8</td>\n",
       "      <td>14.2</td>\n",
       "      <td>10.4</td>\n",
       "      <td>9.6</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-01</th>\n",
       "      <td>8.6</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.9</td>\n",
       "      <td>10.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9.9</td>\n",
       "      <td>10.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01</th>\n",
       "      <td>10.4</td>\n",
       "      <td>10.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-01</th>\n",
       "      <td>11.4</td>\n",
       "      <td>10.7</td>\n",
       "      <td>11.1</td>\n",
       "      <td>9.1</td>\n",
       "      <td>10.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-01</th>\n",
       "      <td>12.7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>12.3</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>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-01</th>\n",
       "      <td>8.6</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1.3</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>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-01</th>\n",
       "      <td>9.7</td>\n",
       "      <td>-2.3</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>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-01</th>\n",
       "      <td>9.6</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",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "CohortIndex  1.0    2.0   3.0   4.0   5.0   6.0   7.0   8.0   9.0   10.0  \\\n",
       "CohortMonth                                                                \n",
       "2010-01-01   12.2    7.1  13.2   NaN   6.0  10.7   8.8  13.8   6.2   6.9   \n",
       "2010-02-01   10.6  378.0   NaN   NaN  38.3  27.9  11.1  10.9  30.4   NaN   \n",
       "2010-03-01    8.5    NaN   5.9   1.8   0.0  -1.7   7.8   NaN   NaN   7.5   \n",
       "2010-05-01    6.0   17.1   8.0  12.2   5.1   1.8   NaN  11.6  12.9  10.3   \n",
       "2010-06-01    6.9    3.8  16.1   4.0   4.6   NaN   6.6   8.6   7.9   8.2   \n",
       "2010-07-01   18.1    6.6  11.9  38.0   NaN  14.2   8.8  13.2  24.2  31.2   \n",
       "2010-08-01   10.2   10.5  54.7   NaN   8.4  12.4  10.7   9.9   8.3  11.0   \n",
       "2010-09-01   10.3    6.8   NaN  12.7   9.4   9.9  13.6   7.7  14.4  10.5   \n",
       "2010-10-01    8.2    NaN   4.2  13.9  12.3   9.1  11.9  17.0   8.4  16.6   \n",
       "2010-12-01   13.0   18.4  17.8  19.6  22.2  20.2  16.2  18.1  11.5  19.8   \n",
       "2011-01-01    9.7   12.3  14.5  13.3  15.2  12.5  13.2  13.3  10.3   9.7   \n",
       "2011-02-01   10.6   10.7   9.6  10.2  14.0  14.7  11.7  10.5   6.9   8.3   \n",
       "2011-03-01    8.9    8.2  10.9   7.8   8.9   9.1  10.9  11.7   9.0  10.1   \n",
       "2011-04-01    9.6   10.2  10.9   8.4   8.4   9.7   8.2  11.8  22.3   NaN   \n",
       "2011-05-01   10.1    8.8  14.2  10.4   9.6  12.0   9.0  13.8   NaN   NaN   \n",
       "2011-06-01    8.6    9.6   9.9  10.5   9.5   9.9  10.2   NaN   NaN   NaN   \n",
       "2011-07-01   10.4   10.7   8.5   7.5   6.0   5.9   NaN   NaN   NaN   NaN   \n",
       "2011-08-01   11.4   10.7  11.1   9.1  10.6   NaN   NaN   NaN   NaN   NaN   \n",
       "2011-09-01   12.7    7.0   7.5  12.3   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2011-10-01    8.6    9.5   1.3   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2011-11-01    9.7   -2.3   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2011-12-01    9.6    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "\n",
       "CohortIndex  ...   15.0  16.0  17.0  18.0  19.0  20.0  21.0  22.0  23.0  24.0  \n",
       "CohortMonth  ...                                                               \n",
       "2010-01-01   ...   14.7  13.6  13.7  10.3  14.9  11.9  15.3  12.5  10.3   8.9  \n",
       "2010-02-01   ...   24.1  18.9  25.8  19.3  13.0  16.9  22.1  20.0  15.1   NaN  \n",
       "2010-03-01   ...   13.4  29.1  27.6  25.8  22.1  25.1  18.0  23.0   NaN   NaN  \n",
       "2010-05-01   ...   10.3   9.2   9.4   6.6   8.3  10.5   NaN   NaN   NaN   NaN  \n",
       "2010-06-01   ...   10.3  11.1  10.0   9.5  13.3   NaN   NaN   NaN   NaN   NaN  \n",
       "2010-07-01   ...   53.7  20.5  19.4  13.6   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2010-08-01   ...   11.4  10.9  12.4   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2010-09-01   ...   14.5  15.4   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2010-10-01   ...   37.9   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2010-12-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2011-01-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2011-02-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2011-03-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2011-04-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2011-05-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2011-06-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2011-07-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2011-08-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2011-09-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2011-10-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2011-11-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2011-12-01   ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[22 rows x 24 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouping = online.groupby(['CohortMonth', 'CohortIndex'])\n",
    "\n",
    "cohort_data = grouping['Quantity'].mean()\n",
    "cohort_data = cohort_data.reset_index()\n",
    "\n",
    "average_quantity = cohort_data.pivot(index='CohortMonth', columns='CohortIndex', values='Quantity')\n",
    "average_quantity.round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x1728 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(22, 24))\n",
    "plt.title('Retention rates')\n",
    "\n",
    "sns.heatmap(data = retention, annot = True, fmt = '.0%', vmin = 0.0, vmax = 0.5, cmap = 'BuGn')\n",
    "plt.show()"
   ]
  }
 ],
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