Data Preparation Final Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1: Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the dataset FinalRaw.csv as data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"FinalRaw.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "710201\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GoverningStateCode</th>\n",
       "      <th>DistributionChannelName</th>\n",
       "      <th>FirmCode</th>\n",
       "      <th>total_ann_prem</th>\n",
       "      <th>total_fee</th>\n",
       "      <th>CL_Limit</th>\n",
       "      <th>CP_Limit</th>\n",
       "      <th>BI_Limit</th>\n",
       "      <th>PD_Limit</th>\n",
       "      <th>MP_Limit</th>\n",
       "      <th>...</th>\n",
       "      <th>vehxdrv</th>\n",
       "      <th>year</th>\n",
       "      <th>Yearmonth</th>\n",
       "      <th>season</th>\n",
       "      <th>paymentmethodlong</th>\n",
       "      <th>assoc_grp</th>\n",
       "      <th>affin_grp</th>\n",
       "      <th>maxvehvalue</th>\n",
       "      <th>noncancelendmts</th>\n",
       "      <th>firstterm_survival</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Direct</td>\n",
       "      <td>19</td>\n",
       "      <td>519.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>250/500</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Direct Bill</td>\n",
       "      <td>GOOD SAM</td>\n",
       "      <td>AFFRV</td>\n",
       "      <td>86672</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SC</td>\n",
       "      <td>Agency</td>\n",
       "      <td>28</td>\n",
       "      <td>824.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>25/50</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Direct Bill</td>\n",
       "      <td>PRIMARY AGENCY PLAN CODE</td>\n",
       "      <td>AGNCY</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SC</td>\n",
       "      <td>Direct</td>\n",
       "      <td>18</td>\n",
       "      <td>2905.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>100/300</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>33</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Auto Pay - Checking/Savings</td>\n",
       "      <td>ALLY MORTGAGE</td>\n",
       "      <td>ALLY</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Direct</td>\n",
       "      <td>20</td>\n",
       "      <td>363.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100/300</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Direct Bill</td>\n",
       "      <td>LEGIONNAIRES</td>\n",
       "      <td>AFFAUxRLC</td>\n",
       "      <td>45000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Direct</td>\n",
       "      <td>19</td>\n",
       "      <td>587.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>250/500</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Direct Bill</td>\n",
       "      <td>GOOD SAM</td>\n",
       "      <td>AFFRV</td>\n",
       "      <td>91279</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  GoverningStateCode DistributionChannelName  FirmCode  total_ann_prem  \\\n",
       "0                 AZ                  Direct        19           519.0   \n",
       "1                 SC                  Agency        28           824.0   \n",
       "2                 SC                  Direct        18          2905.0   \n",
       "3                 AZ                  Direct        20           363.0   \n",
       "4                 AZ                  Direct        19           587.0   \n",
       "\n",
       "   total_fee  CL_Limit  CP_Limit BI_Limit  PD_Limit  MP_Limit  \\\n",
       "0       21.0     500.0     500.0  250/500     100.0    5000.0   \n",
       "1        0.0     500.0     500.0    25/50      25.0       NaN   \n",
       "2        1.0     500.0     500.0  100/300     100.0       NaN   \n",
       "3       15.0     500.0       NaN  100/300      50.0       NaN   \n",
       "4       15.0     500.0     250.0  250/500     100.0   10000.0   \n",
       "\n",
       "         ...          vehxdrv  year Yearmonth  season  \\\n",
       "0        ...               32  2011         1       4   \n",
       "1        ...               11  2011         1       4   \n",
       "2        ...               33  2011         1       4   \n",
       "3        ...               32  2011         1       4   \n",
       "4        ...               42  2011         1       4   \n",
       "\n",
       "             paymentmethodlong                 assoc_grp  affin_grp  \\\n",
       "0                  Direct Bill                  GOOD SAM      AFFRV   \n",
       "1                  Direct Bill  PRIMARY AGENCY PLAN CODE      AGNCY   \n",
       "2  Auto Pay - Checking/Savings             ALLY MORTGAGE       ALLY   \n",
       "3                  Direct Bill              LEGIONNAIRES  AFFAUxRLC   \n",
       "4                  Direct Bill                  GOOD SAM      AFFRV   \n",
       "\n",
       "   maxvehvalue  noncancelendmts firstterm_survival  \n",
       "0        86672                2                  1  \n",
       "1            0                0                  1  \n",
       "2            0                3                  1  \n",
       "3        45000                4                  1  \n",
       "4        91279                1                  1  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print len(data)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Produce one new variable GRP_AFFIN_GRP using AFFIN_GRP by combining all categories other than \"AFFRV\", \"AGNCY\", and \"BDMKT\" to a new category “Others”."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Frequency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>affin_grp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AFFAUxRLC</th>\n",
       "      <td>10251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AFFRV</th>\n",
       "      <td>87640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGNCY</th>\n",
       "      <td>514571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALLY</th>\n",
       "      <td>3278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BDMKT</th>\n",
       "      <td>59970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FF</th>\n",
       "      <td>15615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMREL</th>\n",
       "      <td>11042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INTER</th>\n",
       "      <td>358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RLC</th>\n",
       "      <td>4905</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Frequency\n",
       "affin_grp           \n",
       "AFFAUxRLC      10251\n",
       "AFFRV          87640\n",
       "AGNCY         514571\n",
       "ALLY            3278\n",
       "BDMKT          59970\n",
       "FF             15615\n",
       "GMREL          11042\n",
       "INTER            358\n",
       "RLC             4905"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('affin_grp')['affin_grp'].agg({'Frequency':'count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grp_affin_grp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AFFRV</th>\n",
       "      <td>87640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGNCY</th>\n",
       "      <td>514571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BDMKT</th>\n",
       "      <td>59970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>48020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Frequency\n",
       "grp_affin_grp           \n",
       "AFFRV              87640\n",
       "AGNCY             514571\n",
       "BDMKT              59970\n",
       "Other              48020"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['grp_affin_grp'] = data['affin_grp'].fillna('Other')\n",
    "clean_affin_grp = {'ALLY' : 'Other' , 'AFFAUxRLC' : 'Other' , 'GMREL' : 'Other' ,\n",
    "                   'FF' : 'Other' , 'RLC' : 'Other' , 'INTER' : 'Other'}\n",
    "data['grp_affin_grp'].replace(clean_affin_grp, inplace = True)\n",
    "data.groupby('grp_affin_grp')['grp_affin_grp'].agg({'Frequency':'count'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Produce a vertical bar char using SGPLOT on this newly created variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEiCAYAAADu2jXpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f3c2dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_obs = float(len(data))\n",
    "grp_affin_grp_pct = {'grp_affin_grp': ['AFFRV','AGNCY','BDMKT','Other'],\n",
    "                     'percentage':[(len(data[data['grp_affin_grp'] == 'AFFRV']))/n_obs*100,\n",
    "                                   (len(data[data['grp_affin_grp'] == 'AGNCY']))/n_obs*100,\n",
    "                                   (len(data[data['grp_affin_grp'] == 'BDMKT']))/n_obs*100,\n",
    "                                   (len(data[data['grp_affin_grp'] == 'Other']))/n_obs*100]}\n",
    "\n",
    "sns.set_context(\"notebook\", font_scale=1.5, rc={\"lines.linewidth\": 2.5})\n",
    "with sns.color_palette(\"Paired\"):\n",
    "    plt = sns.barplot(x ='grp_affin_grp', y ='percentage', data = grp_affin_grp_pct)\n",
    "    plt.set(xlabel = 'grp_affin_grp', ylabel = 'percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Produce a histogram with kernel density line imposed using SGPLOT on the variable total_fee."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaoAAAEtCAYAAABZOiSWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOeecyzRPVM455zLt/wMlHObwDYtuFwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa70ae48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('notebook', font_scale=1.5, rc={'lines.linewidth': 2.5})\n",
    "with sns.color_palette('Paired'):\n",
    "    plt = sns.distplot(data['total_fee'], bins=50, kde=True)\n",
    "    plt.set(xlabel = 'total_fee', ylabel='frequency', title = 'total_fee histogram with kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Step 2: Categorical Variables Recoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fix affin_grp: Reduce Categories to four categories \"AFFRV\", \"AGNCY\", \"BDMKT\", and “OTHERS”. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['AFFRV', 'AGNCY', 'Other', 'BDMKT'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['affin_grp']= data['grp_affin_grp'] \n",
    "data['affin_grp'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove GRP_AFFIN_GRP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = data.drop('grp_affin_grp', axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create Three Missing Value Indicators:\n",
    "* MI_PIP_limit = \"Missing Indicator: PIP_Limit\"\n",
    "* MI_CP_limit = \"Missing Indicator: CP_Limit\"\n",
    "* MI_Others = \"Missing Indicator: All Other Categorical Variables with Missing\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['MI_PIP_limit'] = data['PIP_Limit'].fillna(1)\n",
    "data.loc[data['MI_PIP_limit'] != 1] = 0\n",
    "\n",
    "data['MI_CP_limit'] = data['CP_Limit'].fillna(1)\n",
    "data.loc[data['MI_CP_limit'] != 1] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, True], dtype=object)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "catCols = ['BI_Limit', 'CreditActionCode', 'DistributionChannelName', \n",
    "           'GoverningStateCode', 'HomeownerInd', 'MarketingPlanCode',\n",
    "          'PaymentMethodName', 'PaymentPlanDesc', 'PreferredMailDocumentsCode',\n",
    "          'PriorCarrierTypeCode', 'ProductVersionName', 'StateGroup',\n",
    "          'StateRegion', 'affin_grp', 'assoc_grp', 'paymentmethodlong',\n",
    "          'premcat']\n",
    "\n",
    "data['MI_Others'] = data[catCols].isnull().any(axis=1)\n",
    "bool_map = {True : 1, False: 0}\n",
    "data['MI_Others'].replace(bool_map, inplace = True)\n",
    "data['MI_Others'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Mode Imputation** for Categorical Variables: BIGroupNum , HomeownerInd , PreferredMailDocumentsCode, and PriorCarrierTypeCode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data['BIGroupNum'].fillna(data['BIGroupNum'].mode(), inplace=True)\n",
    "data['HomeownerInd'].fillna(data['HomeownerInd'].mode(), inplace=True)\n",
    "data['PreferredMailDocumentsCode'].fillna(data['PreferredMailDocumentsCode'] \\\n",
    "                                          .mode(), inplace=True)\n",
    "data['PriorCarrierTypeCode'].fillna(data['PriorCarrierTypeCode'].mode()\n",
    "                                    , inplace=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop variables: assoc_grp, MarketingPlanCode, GoverningStateCode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = data.drop('assoc_grp', axis = 1)\n",
    "data = data.drop('GoverningStateCode', axis = 1)\n",
    "data = data.drop('MarketingPlanCode', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['20% Down, 4 Payments', 'Pay in Full', '16.7% Down, 5 Payments',\n",
       "       '33.3% Down, 4 Payments', '20% Down, 5 Payments',\n",
       "       '10% Down, 10 Payments', '12.5% Down, 10 Payments',\n",
       "       '8.3% Down, 11 Payments', '25% Down, 4 Payments',\n",
       "       '16.7% Down, 10 Payments', '25% Down, 3 Payments',\n",
       "       '40% Down, 3 Payments', '50% down, 1 Payment',\n",
       "       '25% Down, 5 Payments', '20% Down, 8 Payments',\n",
       "       '50% Down, 1 Payments', '16% Down, 10 Payments',\n",
       "       '20% Down, 6 Payments', '40% Down, 4 Payments',\n",
       "       '20% down, 11 Payments', '50% Down, 2 Payments',\n",
       "       '33% Down, 4 Payments', '33.4% Down, 4 Payments',\n",
       "       '8.3% down, 11 Payments', '10% Down, 11 Payments',\n",
       "       '8.33% Down, 11 Payments', '20% down, 10 Payments',\n",
       "       '16.7% Down, 11 Payments', '20% Down, 10 Payments',\n",
       "       '5% Down, 23 Payments', '20% Down, 11 Payments',\n",
       "       '9% Down, 11 Payments', '33% Down, 3 Payments',\n",
       "       '34% Down, 4 Payments', '15% Down, 9 Payments',\n",
       "       '9.5% Down, 11 Payments', '25% Down, 10 Payments',\n",
       "       '12.5% Down, 11 Payments', '16% Down, 5 Payments',\n",
       "       '12.5% Down, 8 Payments', '14% Down, 5 Payments',\n",
       "       '7% Down, 10 Payments', '15% Down, 10 Payments',\n",
       "       '50% Down, 1 Payment', '30% Down, 4 Payments', 'Better Budget',\n",
       "       '15% Down, 5 Payments', '17% Down, 5 Payments',\n",
       "       '19% Down, 5 Payments', '18% Down, 5 Payments',\n",
       "       '10% Down, 8 Payments', '20% Down, 3 Payments',\n",
       "       '15.9% Down, 5 Payments', '9.9% Down, 11 Payments',\n",
       "       '33.4% Down, 5 Payments', '10% Down, 9 Payments',\n",
       "       '33.3% Down, 5 Payments', '8.75% Down, 11 Payments',\n",
       "       '17.5% Down, 10 Payments', '17.5% Down, 5 Payments',\n",
       "       '17.5% Down, 11 Payments', '35% Down, 4 Payments',\n",
       "       '8.75% Down, 10 Payments', '22% Down, 5 Payments',\n",
       "       '16.70% Down, 5 Payments', '7.5% Down, 11 Payments',\n",
       "       '14.5% Down, 5 Payments', '6.5% Down, 11 Payments',\n",
       "       '13% Down, 5 Payments', '19D-9P-1MO-P', '33D-4P-2M-P'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['PaymentPlanDesc'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split PaymentPlanDesc into two numerical variables N_Payment, DownPayPct "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#if plan is missing, then dp = 100 and n = 0\n",
    "#if plan is better budget then dp = 0 and n = 1\n",
    "#if plan is pay in full then dp = 100 and n = 0\n",
    "data['PaymentPlanDesc'].fillna('100% Down, 0 Payments')\n",
    "pay_plan_map = {'Pay in Full' : '100% Down, 0 Payments', \n",
    "                'Better Budget': '0% Down, 1 Payments',\n",
    "                0 : '100% Down, 0 Payments'}\n",
    "data['PaymentPlanDesc'].replace(pay_plan_map, inplace = True)\n",
    "data['DownPayPct'] = data['PaymentPlanDesc'].str.split(', ').str[0] \\\n",
    ".str.split(' ').str[0].str.split('%').str[0]\n",
    "data['N_Payment'] = data['PaymentPlanDesc'].str.split(', ').str[1] \\\n",
    ".str.split(' ').str[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "For **ProductVersionName**, we can see that we need to combine low frequency categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pay_plan_map = {'NY Direct no tier' : 'NY RAD','NY non-RAD Agency': 'NY RAD',\n",
    "                'RAD5LowCost' : 'RAD5' , 'NCAA' : 'NC non-RAD', \n",
    "                '4 Tier' : '2 Tier'}\n",
    "data['ProductVersionName'].replace(pay_plan_map, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For **CreditActionCode**, take the first letter of the CreditActionCode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'Q', 'T', 'Z', 'W', 'S', 'V', 'L', 'P', 'I', 'E', 'K', 'M',\n",
       "       'H', 'G', 'D', 'U', 'J', 'F', 'C', 'N', 'A', '?', 'B', 'Y'], dtype=object)"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['FL_CreditActionCode'] = data['CreditActionCode'].str.replace('R','S'). \\\n",
    "str.slice(0,1,1)\n",
    "data['FL_CreditActionCode'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obtain **logit transformation** of the following variables StateGroup, ProductVersionName, and FL_CreditActionCode.\n",
    "* Combine categories with frequency < threshold together\n",
    "* Obtain proportion of response variable for each level of the categorical variable\n",
    "* Obtain proportion of target = \"Yes\" and select smoothing factor\n",
    "* Compute smoothed logeit for each level of the categorical variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the distribution of StateGroup:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StateGroup</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>695596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeAL</th>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeAZ</th>\n",
       "      <td>3471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeCA</th>\n",
       "      <td>607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeFL</th>\n",
       "      <td>782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeGA</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeIN</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeLA</th>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeMI</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeNC</th>\n",
       "      <td>4591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeNY</th>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeOH</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeOR</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeSC</th>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeTX</th>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeVA</th>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeWA</th>\n",
       "      <td>775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeWV</th>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SmallHi</th>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SmallLow</th>\n",
       "      <td>3128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SmallMed</th>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Frequency\n",
       "StateGroup           \n",
       "0              695596\n",
       "LargeAL           105\n",
       "LargeAZ          3471\n",
       "LargeCA           607\n",
       "LargeFL           782\n",
       "LargeGA            12\n",
       "LargeIN            14\n",
       "LargeLA            79\n",
       "LargeMI            13\n",
       "LargeNC          4591\n",
       "LargeNY           171\n",
       "LargeOH            23\n",
       "LargeOR            53\n",
       "LargeSC           116\n",
       "LargeTX            58\n",
       "LargeVA           235\n",
       "LargeWA           775\n",
       "LargeWV            66\n",
       "SmallHi           177\n",
       "SmallLow         3128\n",
       "SmallMed          129"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('StateGroup')['StateGroup'].agg({'Frequency':'count'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combiine all categories with less than 25 observations together into a category called 'Other'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "state_group_map = {'LargeGA' : 'Other' , 'LargeIN' : 'Other', 'LargeMI' : 'Other',\n",
    "                'LargeOH' : 'Other'}\n",
    "data['StateGroup'].replace(state_group_map, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check the counts again to see that they are more evenly distributed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StateGroup</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>695596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeAL</th>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeAZ</th>\n",
       "      <td>3471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeCA</th>\n",
       "      <td>607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeFL</th>\n",
       "      <td>782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeLA</th>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeNC</th>\n",
       "      <td>4591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeNY</th>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeOR</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeSC</th>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeTX</th>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeVA</th>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeWA</th>\n",
       "      <td>775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeWV</th>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SmallHi</th>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SmallLow</th>\n",
       "      <td>3128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SmallMed</th>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Frequency\n",
       "StateGroup           \n",
       "0              695596\n",
       "LargeAL           105\n",
       "LargeAZ          3471\n",
       "LargeCA           607\n",
       "LargeFL           782\n",
       "LargeLA            79\n",
       "LargeNC          4591\n",
       "LargeNY           171\n",
       "LargeOR            53\n",
       "LargeSC           116\n",
       "LargeTX            58\n",
       "LargeVA           235\n",
       "LargeWA           775\n",
       "LargeWV            66\n",
       "Other              62\n",
       "SmallHi           177\n",
       "SmallLow         3128\n",
       "SmallMed          129"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('StateGroup')['StateGroup'].agg({'Frequency':'count'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repeat the provess for ProductVersionName."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductVersionName</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>695596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 Tier</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 Tier</th>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agency non-RAD</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA RAD 2.0</th>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA no tier</th>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CASummit</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Imperial</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC RAD5</th>\n",
       "      <td>345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC non-RAD</th>\n",
       "      <td>4244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY RAD 3.5</th>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R5RVOnly</th>\n",
       "      <td>380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD 2.0</th>\n",
       "      <td>661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD 2.5</th>\n",
       "      <td>377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD 3.1</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD 3.5</th>\n",
       "      <td>5513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD Low Cost</th>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD5</th>\n",
       "      <td>2445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Frequency\n",
       "ProductVersionName           \n",
       "0                      695596\n",
       "10 Tier                    10\n",
       "2 Tier                     97\n",
       "Agency non-RAD             29\n",
       "CA RAD 2.0                156\n",
       "CA no tier                 71\n",
       "CASummit                    2\n",
       "Imperial                   14\n",
       "NC RAD5                   345\n",
       "NC non-RAD               4244\n",
       "NY RAD 3.5                171\n",
       "R5RVOnly                  380\n",
       "RAD 2.0                   661\n",
       "RAD 2.5                   377\n",
       "RAD 3.1                    14\n",
       "RAD 3.5                  5513\n",
       "RAD Low Cost               76\n",
       "RAD5                     2445"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('ProductVersionName')['ProductVersionName'].agg({'Frequency':'count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "product_version_map = {'10 Tier' : 'Other', 'CASummit' : 'Other',\n",
    "                       'Imperial' : 'Other', 'Rad2.1' : 'Other'}\n",
    "data['ProductVersionName'].replace(product_version_map, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The FL_CreditActionCode is distributed evenly enough that we do not need to cluster the low frequency categories."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's view the proportion of the response variable, 'firstterm_survival', for each level of 'StateGroup'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>firstterm_survival</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StateGroup</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>695596.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeAL</th>\n",
       "      <td>31.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeAZ</th>\n",
       "      <td>488.0</td>\n",
       "      <td>2983.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeCA</th>\n",
       "      <td>156.0</td>\n",
       "      <td>451.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeFL</th>\n",
       "      <td>123.0</td>\n",
       "      <td>659.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeLA</th>\n",
       "      <td>21.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeNC</th>\n",
       "      <td>708.0</td>\n",
       "      <td>3883.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeNY</th>\n",
       "      <td>29.0</td>\n",
       "      <td>142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeOR</th>\n",
       "      <td>13.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeSC</th>\n",
       "      <td>10.0</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeTX</th>\n",
       "      <td>12.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeVA</th>\n",
       "      <td>59.0</td>\n",
       "      <td>176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeWA</th>\n",
       "      <td>286.0</td>\n",
       "      <td>489.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeWV</th>\n",
       "      <td>7.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>9.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SmallHi</th>\n",
       "      <td>42.0</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SmallLow</th>\n",
       "      <td>600.0</td>\n",
       "      <td>2528.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SmallMed</th>\n",
       "      <td>29.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "firstterm_survival         0       1\n",
       "StateGroup                          \n",
       "0                   695596.0     0.0\n",
       "LargeAL                 31.0    74.0\n",
       "LargeAZ                488.0  2983.0\n",
       "LargeCA                156.0   451.0\n",
       "LargeFL                123.0   659.0\n",
       "LargeLA                 21.0    58.0\n",
       "LargeNC                708.0  3883.0\n",
       "LargeNY                 29.0   142.0\n",
       "LargeOR                 13.0    40.0\n",
       "LargeSC                 10.0   106.0\n",
       "LargeTX                 12.0    46.0\n",
       "LargeVA                 59.0   176.0\n",
       "LargeWA                286.0   489.0\n",
       "LargeWV                  7.0    59.0\n",
       "Other                    9.0    53.0\n",
       "SmallHi                 42.0   135.0\n",
       "SmallLow               600.0  2528.0\n",
       "SmallMed                29.0   100.0"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "StateGroupSmoothingDf = data.groupby(['StateGroup','firstterm_survival'], as_index = False)['StateGroup'] \\\n",
    ".agg({'Frequency':'count'}).pivot('StateGroup','firstterm_survival').fillna(0)['Frequency']\n",
    "StateGroupSmoothingDf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's calculate the smoothed logit for each category of StateGroup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>firstterm_survival</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Churn_Frequency</th>\n",
       "      <th>Churn_Rate</th>\n",
       "      <th>Logit_StateGroup</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StateGroup</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>0</th>\n",
       "      <td>695596.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>695596.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-16.841522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeAL</th>\n",
       "      <td>31.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.704762</td>\n",
       "      <td>0.809036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeAZ</th>\n",
       "      <td>488.0</td>\n",
       "      <td>2983.0</td>\n",
       "      <td>3471.0</td>\n",
       "      <td>2983.0</td>\n",
       "      <td>0.859407</td>\n",
       "      <td>1.806360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeCA</th>\n",
       "      <td>156.0</td>\n",
       "      <td>451.0</td>\n",
       "      <td>607.0</td>\n",
       "      <td>451.0</td>\n",
       "      <td>0.742998</td>\n",
       "      <td>1.049161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeFL</th>\n",
       "      <td>123.0</td>\n",
       "      <td>659.0</td>\n",
       "      <td>782.0</td>\n",
       "      <td>659.0</td>\n",
       "      <td>0.842711</td>\n",
       "      <td>1.662731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeLA</th>\n",
       "      <td>21.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.734177</td>\n",
       "      <td>0.926999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeNC</th>\n",
       "      <td>708.0</td>\n",
       "      <td>3883.0</td>\n",
       "      <td>4591.0</td>\n",
       "      <td>3883.0</td>\n",
       "      <td>0.845785</td>\n",
       "      <td>1.699155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeNY</th>\n",
       "      <td>29.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.830409</td>\n",
       "      <td>1.523167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeOR</th>\n",
       "      <td>13.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.754717</td>\n",
       "      <td>0.983924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeSC</th>\n",
       "      <td>10.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>0.913793</td>\n",
       "      <td>2.181667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeTX</th>\n",
       "      <td>12.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.793103</td>\n",
       "      <td>1.192730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeVA</th>\n",
       "      <td>59.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>0.748936</td>\n",
       "      <td>1.060355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeWA</th>\n",
       "      <td>286.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>775.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>0.630968</td>\n",
       "      <td>0.529588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LargeWV</th>\n",
       "      <td>7.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.893939</td>\n",
       "      <td>1.884641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>9.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.854839</td>\n",
       "      <td>1.576105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SmallHi</th>\n",
       "      <td>42.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0.762712</td>\n",
       "      <td>1.122102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SmallLow</th>\n",
       "      <td>600.0</td>\n",
       "      <td>2528.0</td>\n",
       "      <td>3128.0</td>\n",
       "      <td>2528.0</td>\n",
       "      <td>0.808184</td>\n",
       "      <td>1.434996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SmallMed</th>\n",
       "      <td>29.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.775194</td>\n",
       "      <td>1.172609</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "firstterm_survival         0       1  Frequency  Churn_Frequency  Churn_Rate  \\\n",
       "StateGroup                                                                     \n",
       "0                   695596.0     0.0   695596.0              0.0    0.000000   \n",
       "LargeAL                 31.0    74.0      105.0             74.0    0.704762   \n",
       "LargeAZ                488.0  2983.0     3471.0           2983.0    0.859407   \n",
       "LargeCA                156.0   451.0      607.0            451.0    0.742998   \n",
       "LargeFL                123.0   659.0      782.0            659.0    0.842711   \n",
       "LargeLA                 21.0    58.0       79.0             58.0    0.734177   \n",
       "LargeNC                708.0  3883.0     4591.0           3883.0    0.845785   \n",
       "LargeNY                 29.0   142.0      171.0            142.0    0.830409   \n",
       "LargeOR                 13.0    40.0       53.0             40.0    0.754717   \n",
       "LargeSC                 10.0   106.0      116.0            106.0    0.913793   \n",
       "LargeTX                 12.0    46.0       58.0             46.0    0.793103   \n",
       "LargeVA                 59.0   176.0      235.0            176.0    0.748936   \n",
       "LargeWA                286.0   489.0      775.0            489.0    0.630968   \n",
       "LargeWV                  7.0    59.0       66.0             59.0    0.893939   \n",
       "Other                    9.0    53.0       62.0             53.0    0.854839   \n",
       "SmallHi                 42.0   135.0      177.0            135.0    0.762712   \n",
       "SmallLow               600.0  2528.0     3128.0           2528.0    0.808184   \n",
       "SmallMed                29.0   100.0      129.0            100.0    0.775194   \n",
       "\n",
       "firstterm_survival  Logit_StateGroup  \n",
       "StateGroup                            \n",
       "0                         -16.841522  \n",
       "LargeAL                     0.809036  \n",
       "LargeAZ                     1.806360  \n",
       "LargeCA                     1.049161  \n",
       "LargeFL                     1.662731  \n",
       "LargeLA                     0.926999  \n",
       "LargeNC                     1.699155  \n",
       "LargeNY                     1.523167  \n",
       "LargeOR                     0.983924  \n",
       "LargeSC                     2.181667  \n",
       "LargeTX                     1.192730  \n",
       "LargeVA                     1.060355  \n",
       "LargeWA                     0.529588  \n",
       "LargeWV                     1.884641  \n",
       "Other                       1.576105  \n",
       "SmallHi                     1.122102  \n",
       "SmallLow                    1.434996  \n",
       "SmallMed                    1.172609  "
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "StateGroupSmoothingDf['Frequency'] = StateGroupSmoothingDf[0] + StateGroupSmoothingDf[1]\n",
    "StateGroupSmoothingDf['Churn_Frequency'] = StateGroupSmoothingDf[1]\n",
    "StateGroupSmoothingDf['Churn_Rate'] = StateGroupSmoothingDf['Churn_Frequency']/StateGroupSmoothingDf['Frequency']\n",
    "StateGroupLength = len(data['StateGroup'])\n",
    "StateGroupYes = len(data['StateGroup'][data['firstterm_survival'] == 1])\n",
    "StateGroupNo = len(data['StateGroup'][data['firstterm_survival'] == 0])\n",
    "Population_Proportion_Yes = float(StateGroupYes)/float(StateGroupLength)\n",
    "Population_Proportion_No = float(StateGroupNo)/float(StateGroupLength)\n",
    "SmoothingFactor = 2\n",
    "StateGroupSmoothingDf['Logit_StateGroup'] = np.log((StateGroupSmoothingDf[1] + Population_Proportion_Yes*SmoothingFactor)/\n",
    "                                       (StateGroupSmoothingDf[0] + Population_Proportion_No*SmoothingFactor))\n",
    "StateGroupSmoothingDf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repeat logit smoothing for ProductVersionName and FL_CreditActionCode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>firstterm_survival</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Churn_Frequency</th>\n",
       "      <th>Churn_Rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductVersionName</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>0</th>\n",
       "      <td>695596.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>695596.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 Tier</th>\n",
       "      <td>24.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.752577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agency non-RAD</th>\n",
       "      <td>7.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.758621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA RAD 2.0</th>\n",
       "      <td>53.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>0.660256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA no tier</th>\n",
       "      <td>17.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.760563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC RAD5</th>\n",
       "      <td>106.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>0.692754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC non-RAD</th>\n",
       "      <td>600.0</td>\n",
       "      <td>3644.0</td>\n",
       "      <td>4244.0</td>\n",
       "      <td>3644.0</td>\n",
       "      <td>0.858624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY RAD 3.5</th>\n",
       "      <td>29.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.830409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>8.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R5RVOnly</th>\n",
       "      <td>88.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>0.768421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD 2.0</th>\n",
       "      <td>259.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>661.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>0.608169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD 2.5</th>\n",
       "      <td>80.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>0.787798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD 3.1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD 3.5</th>\n",
       "      <td>947.0</td>\n",
       "      <td>4566.0</td>\n",
       "      <td>5513.0</td>\n",
       "      <td>4566.0</td>\n",
       "      <td>0.828224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD Low Cost</th>\n",
       "      <td>20.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.736842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD5</th>\n",
       "      <td>383.0</td>\n",
       "      <td>2062.0</td>\n",
       "      <td>2445.0</td>\n",
       "      <td>2062.0</td>\n",
       "      <td>0.843354</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "firstterm_survival         0       1  Frequency  Churn_Frequency  Churn_Rate\n",
       "ProductVersionName                                                          \n",
       "0                   695596.0     0.0   695596.0              0.0    0.000000\n",
       "2 Tier                  24.0    73.0       97.0             73.0    0.752577\n",
       "Agency non-RAD           7.0    22.0       29.0             22.0    0.758621\n",
       "CA RAD 2.0              53.0   103.0      156.0            103.0    0.660256\n",
       "CA no tier              17.0    54.0       71.0             54.0    0.760563\n",
       "NC RAD5                106.0   239.0      345.0            239.0    0.692754\n",
       "NC non-RAD             600.0  3644.0     4244.0           3644.0    0.858624\n",
       "NY RAD 3.5              29.0   142.0      171.0            142.0    0.830409\n",
       "Other                    8.0    18.0       26.0             18.0    0.692308\n",
       "R5RVOnly                88.0   292.0      380.0            292.0    0.768421\n",
       "RAD 2.0                259.0   402.0      661.0            402.0    0.608169\n",
       "RAD 2.5                 80.0   297.0      377.0            297.0    0.787798\n",
       "RAD 3.1                  2.0    12.0       14.0             12.0    0.857143\n",
       "RAD 3.5                947.0  4566.0     5513.0           4566.0    0.828224\n",
       "RAD Low Cost            20.0    56.0       76.0             56.0    0.736842\n",
       "RAD5                   383.0  2062.0     2445.0           2062.0    0.843354"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ProductSmoothingDf = data.groupby(['ProductVersionName','firstterm_survival'], as_index = False)['ProductVersionName'] \\\n",
    ".agg({'Frequency':'count'}).pivot('ProductVersionName','firstterm_survival').fillna(0)['Frequency']\n",
    "ProductSmoothingDf['Frequency'] = ProductSmoothingDf[0] + ProductSmoothingDf[1]\n",
    "ProductSmoothingDf['Churn_Frequency'] = ProductSmoothingDf[1]\n",
    "ProductSmoothingDf['Churn_Rate'] = ProductSmoothingDf['Churn_Frequency']/ProductSmoothingDf['Frequency']\n",
    "ProductSmoothingDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>firstterm_survival</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Churn_Frequency</th>\n",
       "      <th>Churn_Rate</th>\n",
       "      <th>Logit_ProductVersionName</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductVersionName</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>0</th>\n",
       "      <td>695596.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>695596.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-16.841522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 Tier</th>\n",
       "      <td>24.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.752577</td>\n",
       "      <td>1.034124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agency non-RAD</th>\n",
       "      <td>7.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.758621</td>\n",
       "      <td>0.899107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA RAD 2.0</th>\n",
       "      <td>53.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>0.660256</td>\n",
       "      <td>0.628337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA no tier</th>\n",
       "      <td>17.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.760563</td>\n",
       "      <td>1.046947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC RAD5</th>\n",
       "      <td>106.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>0.692754</td>\n",
       "      <td>0.794786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC non-RAD</th>\n",
       "      <td>600.0</td>\n",
       "      <td>3644.0</td>\n",
       "      <td>4244.0</td>\n",
       "      <td>3644.0</td>\n",
       "      <td>0.858624</td>\n",
       "      <td>1.800645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY RAD 3.5</th>\n",
       "      <td>29.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.830409</td>\n",
       "      <td>1.523167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>8.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.593039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R5RVOnly</th>\n",
       "      <td>88.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>0.768421</td>\n",
       "      <td>1.177435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD 2.0</th>\n",
       "      <td>259.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>661.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>0.608169</td>\n",
       "      <td>0.432145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD 2.5</th>\n",
       "      <td>80.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>0.787798</td>\n",
       "      <td>1.287538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD 3.1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>1.109892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD 3.5</th>\n",
       "      <td>947.0</td>\n",
       "      <td>4566.0</td>\n",
       "      <td>5513.0</td>\n",
       "      <td>4566.0</td>\n",
       "      <td>0.828224</td>\n",
       "      <td>1.571027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD Low Cost</th>\n",
       "      <td>20.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.936447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD5</th>\n",
       "      <td>383.0</td>\n",
       "      <td>2062.0</td>\n",
       "      <td>2445.0</td>\n",
       "      <td>2062.0</td>\n",
       "      <td>0.843354</td>\n",
       "      <td>1.678292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "firstterm_survival         0       1  Frequency  Churn_Frequency  Churn_Rate  \\\n",
       "ProductVersionName                                                             \n",
       "0                   695596.0     0.0   695596.0              0.0    0.000000   \n",
       "2 Tier                  24.0    73.0       97.0             73.0    0.752577   \n",
       "Agency non-RAD           7.0    22.0       29.0             22.0    0.758621   \n",
       "CA RAD 2.0              53.0   103.0      156.0            103.0    0.660256   \n",
       "CA no tier              17.0    54.0       71.0             54.0    0.760563   \n",
       "NC RAD5                106.0   239.0      345.0            239.0    0.692754   \n",
       "NC non-RAD             600.0  3644.0     4244.0           3644.0    0.858624   \n",
       "NY RAD 3.5              29.0   142.0      171.0            142.0    0.830409   \n",
       "Other                    8.0    18.0       26.0             18.0    0.692308   \n",
       "R5RVOnly                88.0   292.0      380.0            292.0    0.768421   \n",
       "RAD 2.0                259.0   402.0      661.0            402.0    0.608169   \n",
       "RAD 2.5                 80.0   297.0      377.0            297.0    0.787798   \n",
       "RAD 3.1                  2.0    12.0       14.0             12.0    0.857143   \n",
       "RAD 3.5                947.0  4566.0     5513.0           4566.0    0.828224   \n",
       "RAD Low Cost            20.0    56.0       76.0             56.0    0.736842   \n",
       "RAD5                   383.0  2062.0     2445.0           2062.0    0.843354   \n",
       "\n",
       "firstterm_survival  Logit_ProductVersionName  \n",
       "ProductVersionName                            \n",
       "0                                 -16.841522  \n",
       "2 Tier                              1.034124  \n",
       "Agency non-RAD                      0.899107  \n",
       "CA RAD 2.0                          0.628337  \n",
       "CA no tier                          1.046947  \n",
       "NC RAD5                             0.794786  \n",
       "NC non-RAD                          1.800645  \n",
       "NY RAD 3.5                          1.523167  \n",
       "Other                               0.593039  \n",
       "R5RVOnly                            1.177435  \n",
       "RAD 2.0                             0.432145  \n",
       "RAD 2.5                             1.287538  \n",
       "RAD 3.1                             1.109892  \n",
       "RAD 3.5                             1.571027  \n",
       "RAD Low Cost                        0.936447  \n",
       "RAD5                                1.678292  "
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ProductLength = len(data['ProductVersionName'])\n",
    "ProductYes = len(data['ProductVersionName'][data['firstterm_survival'] == 1])\n",
    "ProductNo = len(data['ProductVersionName'][data['firstterm_survival'] == 0])\n",
    "Population_Proportion_Yes = float(ProductYes)/float(ProductLength)\n",
    "Population_Proportion_No = float(ProductNo)/float(ProductLength)\n",
    "SmoothingFactor = 2\n",
    "\n",
    "ProductSmoothingDf['Logit_ProductVersionName'] = np.log((ProductSmoothingDf[1] + Population_Proportion_Yes*SmoothingFactor)/\n",
    "                                     (ProductSmoothingDf[0] + Population_Proportion_No*SmoothingFactor))\n",
    "ProductSmoothingDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>firstterm_survival</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Churn_Frequency</th>\n",
       "      <th>Churn_Rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL_CreditActionCode</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>?</th>\n",
       "      <td>119</td>\n",
       "      <td>354</td>\n",
       "      <td>473</td>\n",
       "      <td>354</td>\n",
       "      <td>0.748414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>28</td>\n",
       "      <td>22</td>\n",
       "      <td>0.785714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>23</td>\n",
       "      <td>60</td>\n",
       "      <td>83</td>\n",
       "      <td>60</td>\n",
       "      <td>0.722892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>17</td>\n",
       "      <td>75</td>\n",
       "      <td>92</td>\n",
       "      <td>75</td>\n",
       "      <td>0.815217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>135</td>\n",
       "      <td>112</td>\n",
       "      <td>0.829630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>31</td>\n",
       "      <td>188</td>\n",
       "      <td>219</td>\n",
       "      <td>188</td>\n",
       "      <td>0.858447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>85</td>\n",
       "      <td>335</td>\n",
       "      <td>420</td>\n",
       "      <td>335</td>\n",
       "      <td>0.797619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>68</td>\n",
       "      <td>274</td>\n",
       "      <td>342</td>\n",
       "      <td>274</td>\n",
       "      <td>0.801170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>76</td>\n",
       "      <td>327</td>\n",
       "      <td>403</td>\n",
       "      <td>327</td>\n",
       "      <td>0.811414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>79</td>\n",
       "      <td>369</td>\n",
       "      <td>448</td>\n",
       "      <td>369</td>\n",
       "      <td>0.823661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>91</td>\n",
       "      <td>421</td>\n",
       "      <td>512</td>\n",
       "      <td>421</td>\n",
       "      <td>0.822266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K</th>\n",
       "      <td>102</td>\n",
       "      <td>481</td>\n",
       "      <td>583</td>\n",
       "      <td>481</td>\n",
       "      <td>0.825043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <td>110</td>\n",
       "      <td>569</td>\n",
       "      <td>679</td>\n",
       "      <td>569</td>\n",
       "      <td>0.837997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>136</td>\n",
       "      <td>611</td>\n",
       "      <td>747</td>\n",
       "      <td>611</td>\n",
       "      <td>0.817938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>148</td>\n",
       "      <td>451</td>\n",
       "      <td>599</td>\n",
       "      <td>451</td>\n",
       "      <td>0.752922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P</th>\n",
       "      <td>163</td>\n",
       "      <td>736</td>\n",
       "      <td>899</td>\n",
       "      <td>736</td>\n",
       "      <td>0.818687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>163</td>\n",
       "      <td>865</td>\n",
       "      <td>1028</td>\n",
       "      <td>865</td>\n",
       "      <td>0.841440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>167</td>\n",
       "      <td>897</td>\n",
       "      <td>1064</td>\n",
       "      <td>897</td>\n",
       "      <td>0.843045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>146</td>\n",
       "      <td>447</td>\n",
       "      <td>593</td>\n",
       "      <td>447</td>\n",
       "      <td>0.753794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U</th>\n",
       "      <td>204</td>\n",
       "      <td>1002</td>\n",
       "      <td>1206</td>\n",
       "      <td>1002</td>\n",
       "      <td>0.830846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V</th>\n",
       "      <td>222</td>\n",
       "      <td>999</td>\n",
       "      <td>1221</td>\n",
       "      <td>999</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>194</td>\n",
       "      <td>985</td>\n",
       "      <td>1179</td>\n",
       "      <td>985</td>\n",
       "      <td>0.835454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>24</td>\n",
       "      <td>94</td>\n",
       "      <td>118</td>\n",
       "      <td>94</td>\n",
       "      <td>0.796610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Z</th>\n",
       "      <td>226</td>\n",
       "      <td>1308</td>\n",
       "      <td>1534</td>\n",
       "      <td>1308</td>\n",
       "      <td>0.852673</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "firstterm_survival     0     1  Frequency  Churn_Frequency  Churn_Rate\n",
       "FL_CreditActionCode                                                   \n",
       "?                    119   354        473              354    0.748414\n",
       "A                      6    22         28               22    0.785714\n",
       "B                     23    60         83               60    0.722892\n",
       "C                     17    75         92               75    0.815217\n",
       "D                     23   112        135              112    0.829630\n",
       "E                     31   188        219              188    0.858447\n",
       "F                     85   335        420              335    0.797619\n",
       "G                     68   274        342              274    0.801170\n",
       "H                     76   327        403              327    0.811414\n",
       "I                     79   369        448              369    0.823661\n",
       "J                     91   421        512              421    0.822266\n",
       "K                    102   481        583              481    0.825043\n",
       "L                    110   569        679              569    0.837997\n",
       "M                    136   611        747              611    0.817938\n",
       "N                    148   451        599              451    0.752922\n",
       "P                    163   736        899              736    0.818687\n",
       "Q                    163   865       1028              865    0.841440\n",
       "S                    167   897       1064              897    0.843045\n",
       "T                    146   447        593              447    0.753794\n",
       "U                    204  1002       1206             1002    0.830846\n",
       "V                    222   999       1221              999    0.818182\n",
       "W                    194   985       1179              985    0.835454\n",
       "Y                     24    94        118               94    0.796610\n",
       "Z                    226  1308       1534             1308    0.852673"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CreditActionSmoothingDf = data.groupby(['FL_CreditActionCode','firstterm_survival'], as_index = False)['FL_CreditActionCode'] \\\n",
    ".agg({'Frequency':'count'}).pivot('FL_CreditActionCode','firstterm_survival').fillna(0)['Frequency']\n",
    "CreditActionSmoothingDf['Frequency'] = CreditActionSmoothingDf[0] + CreditActionSmoothingDf[1]\n",
    "CreditActionSmoothingDf['Churn_Frequency'] = CreditActionSmoothingDf[1]\n",
    "CreditActionSmoothingDf['Churn_Rate'] = CreditActionSmoothingDf['Churn_Frequency']/CreditActionSmoothingDf['Frequency']\n",
    "CreditActionSmoothingDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>firstterm_survival</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Churn_Frequency</th>\n",
       "      <th>Churn_Rate</th>\n",
       "      <th>Logit_FL_CreditActionCode</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL_CreditActionCode</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>?</th>\n",
       "      <td>119</td>\n",
       "      <td>354</td>\n",
       "      <td>473</td>\n",
       "      <td>354</td>\n",
       "      <td>0.748414</td>\n",
       "      <td>1.073881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>28</td>\n",
       "      <td>22</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>1.017360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>23</td>\n",
       "      <td>60</td>\n",
       "      <td>83</td>\n",
       "      <td>60</td>\n",
       "      <td>0.722892</td>\n",
       "      <td>0.877382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>17</td>\n",
       "      <td>75</td>\n",
       "      <td>92</td>\n",
       "      <td>75</td>\n",
       "      <td>0.815217</td>\n",
       "      <td>1.375276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>135</td>\n",
       "      <td>112</td>\n",
       "      <td>0.829630</td>\n",
       "      <td>1.501275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>31</td>\n",
       "      <td>188</td>\n",
       "      <td>219</td>\n",
       "      <td>188</td>\n",
       "      <td>0.858447</td>\n",
       "      <td>1.741137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>85</td>\n",
       "      <td>335</td>\n",
       "      <td>420</td>\n",
       "      <td>335</td>\n",
       "      <td>0.797619</td>\n",
       "      <td>1.348711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>68</td>\n",
       "      <td>274</td>\n",
       "      <td>342</td>\n",
       "      <td>274</td>\n",
       "      <td>0.801170</td>\n",
       "      <td>1.365238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>76</td>\n",
       "      <td>327</td>\n",
       "      <td>403</td>\n",
       "      <td>327</td>\n",
       "      <td>0.811414</td>\n",
       "      <td>1.433787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>79</td>\n",
       "      <td>369</td>\n",
       "      <td>448</td>\n",
       "      <td>369</td>\n",
       "      <td>0.823661</td>\n",
       "      <td>1.516856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>91</td>\n",
       "      <td>421</td>\n",
       "      <td>512</td>\n",
       "      <td>421</td>\n",
       "      <td>0.822266</td>\n",
       "      <td>1.510476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K</th>\n",
       "      <td>102</td>\n",
       "      <td>481</td>\n",
       "      <td>583</td>\n",
       "      <td>481</td>\n",
       "      <td>0.825043</td>\n",
       "      <td>1.531871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <td>110</td>\n",
       "      <td>569</td>\n",
       "      <td>679</td>\n",
       "      <td>569</td>\n",
       "      <td>0.837997</td>\n",
       "      <td>1.625742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>136</td>\n",
       "      <td>611</td>\n",
       "      <td>747</td>\n",
       "      <td>611</td>\n",
       "      <td>0.817938</td>\n",
       "      <td>1.488143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>148</td>\n",
       "      <td>451</td>\n",
       "      <td>599</td>\n",
       "      <td>451</td>\n",
       "      <td>0.752922</td>\n",
       "      <td>1.101132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P</th>\n",
       "      <td>163</td>\n",
       "      <td>736</td>\n",
       "      <td>899</td>\n",
       "      <td>736</td>\n",
       "      <td>0.818687</td>\n",
       "      <td>1.495535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>163</td>\n",
       "      <td>865</td>\n",
       "      <td>1028</td>\n",
       "      <td>865</td>\n",
       "      <td>0.841440</td>\n",
       "      <td>1.657028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>167</td>\n",
       "      <td>897</td>\n",
       "      <td>1064</td>\n",
       "      <td>897</td>\n",
       "      <td>0.843045</td>\n",
       "      <td>1.669394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>146</td>\n",
       "      <td>447</td>\n",
       "      <td>593</td>\n",
       "      <td>447</td>\n",
       "      <td>0.753794</td>\n",
       "      <td>1.105650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U</th>\n",
       "      <td>204</td>\n",
       "      <td>1002</td>\n",
       "      <td>1206</td>\n",
       "      <td>1002</td>\n",
       "      <td>0.830846</td>\n",
       "      <td>1.582075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V</th>\n",
       "      <td>222</td>\n",
       "      <td>999</td>\n",
       "      <td>1221</td>\n",
       "      <td>999</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.495293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>194</td>\n",
       "      <td>985</td>\n",
       "      <td>1179</td>\n",
       "      <td>985</td>\n",
       "      <td>0.835454</td>\n",
       "      <td>1.614733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>24</td>\n",
       "      <td>94</td>\n",
       "      <td>118</td>\n",
       "      <td>94</td>\n",
       "      <td>0.796610</td>\n",
       "      <td>1.286856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Z</th>\n",
       "      <td>226</td>\n",
       "      <td>1308</td>\n",
       "      <td>1534</td>\n",
       "      <td>1308</td>\n",
       "      <td>0.852673</td>\n",
       "      <td>1.747083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "firstterm_survival     0     1  Frequency  Churn_Frequency  Churn_Rate  \\\n",
       "FL_CreditActionCode                                                      \n",
       "?                    119   354        473              354    0.748414   \n",
       "A                      6    22         28               22    0.785714   \n",
       "B                     23    60         83               60    0.722892   \n",
       "C                     17    75         92               75    0.815217   \n",
       "D                     23   112        135              112    0.829630   \n",
       "E                     31   188        219              188    0.858447   \n",
       "F                     85   335        420              335    0.797619   \n",
       "G                     68   274        342              274    0.801170   \n",
       "H                     76   327        403              327    0.811414   \n",
       "I                     79   369        448              369    0.823661   \n",
       "J                     91   421        512              421    0.822266   \n",
       "K                    102   481        583              481    0.825043   \n",
       "L                    110   569        679              569    0.837997   \n",
       "M                    136   611        747              611    0.817938   \n",
       "N                    148   451        599              451    0.752922   \n",
       "P                    163   736        899              736    0.818687   \n",
       "Q                    163   865       1028              865    0.841440   \n",
       "S                    167   897       1064              897    0.843045   \n",
       "T                    146   447        593              447    0.753794   \n",
       "U                    204  1002       1206             1002    0.830846   \n",
       "V                    222   999       1221              999    0.818182   \n",
       "W                    194   985       1179              985    0.835454   \n",
       "Y                     24    94        118               94    0.796610   \n",
       "Z                    226  1308       1534             1308    0.852673   \n",
       "\n",
       "firstterm_survival   Logit_FL_CreditActionCode  \n",
       "FL_CreditActionCode                             \n",
       "?                                     1.073881  \n",
       "A                                     1.017360  \n",
       "B                                     0.877382  \n",
       "C                                     1.375276  \n",
       "D                                     1.501275  \n",
       "E                                     1.741137  \n",
       "F                                     1.348711  \n",
       "G                                     1.365238  \n",
       "H                                     1.433787  \n",
       "I                                     1.516856  \n",
       "J                                     1.510476  \n",
       "K                                     1.531871  \n",
       "L                                     1.625742  \n",
       "M                                     1.488143  \n",
       "N                                     1.101132  \n",
       "P                                     1.495535  \n",
       "Q                                     1.657028  \n",
       "S                                     1.669394  \n",
       "T                                     1.105650  \n",
       "U                                     1.582075  \n",
       "V                                     1.495293  \n",
       "W                                     1.614733  \n",
       "Y                                     1.286856  \n",
       "Z                                     1.747083  "
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CreditActionLength = len(data['FL_CreditActionCode'])\n",
    "CreditActionYes = len(data['FL_CreditActionCode'][data['firstterm_survival'] == 1])\n",
    "CreditActionNo = len(data['FL_CreditActionCode'][data['firstterm_survival'] == 0])\n",
    "Population_Proportion_Yes = float(CreditActionYes)/float(CreditActionLength)\n",
    "Population_Proportion_No = float(CreditActionNo)/float(CreditActionLength)\n",
    "SmoothingFactor = 2\n",
    "CreditActionSmoothingDf['Logit_FL_CreditActionCode'] = np.log((CreditActionSmoothingDf[1] + Population_Proportion_Yes*SmoothingFactor)/\n",
    "                                     (CreditActionSmoothingDf[0] + Population_Proportion_No*SmoothingFactor))\n",
    "CreditActionSmoothingDf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge dataframes to assign smoothed logit to original dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = data.join(StateGroupSmoothingDf['Logit_StateGroup'], \n",
    "                 on='StateGroup', how='inner')\n",
    "data = data.join(ProductSmoothingDf['Logit_ProductVersionName'], \n",
    "                 on='ProductVersionName', how='inner')\n",
    "data = data.join(CreditActionSmoothingDf['Logit_FL_CreditActionCode'], \n",
    "                 on='FL_CreditActionCode', how='inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a Cardinality Table for all categorical variables at this stage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AGNCY    8088\n",
      "BDMKT    2901\n",
      "AFFRV    2183\n",
      "Other    1433\n",
      "Name: affin_grp, dtype: int64\n",
      "0.0    12630\n",
      "1.0     1389\n",
      "Name: AIRB_disc, dtype: int64\n",
      "1.0    9991\n",
      "0.0    4614\n",
      "Name: BIGroupNum, dtype: int64\n",
      "Agency      11182\n",
      "Direct       2797\n",
      "Internet      626\n",
      "Name: DistributionChannelName, dtype: int64\n",
      "1    11982\n",
      "0     2623\n",
      "Name: firstterm_survival, dtype: int64\n",
      "Z    1534\n",
      "V    1221\n",
      "U    1206\n",
      "W    1179\n",
      "S    1064\n",
      "Q    1028\n",
      "P     899\n",
      "M     747\n",
      "L     679\n",
      "N     599\n",
      "T     593\n",
      "K     583\n",
      "J     512\n",
      "?     473\n",
      "I     448\n",
      "F     420\n",
      "H     403\n",
      "G     342\n",
      "E     219\n",
      "D     135\n",
      "Y     118\n",
      "C      92\n",
      "B      83\n",
      "A      28\n",
      "Name: FL_CreditActionCode, dtype: int64\n",
      "0    13309\n",
      "1     1296\n",
      "Name: HomeOwner_disc, dtype: int64\n",
      "N    7511\n",
      "Y    7094\n",
      "Name: HomeownerInd, dtype: int64\n",
      "0    10540\n",
      "1     4065\n",
      "Name: lonly, dtype: int64\n",
      "0    12519\n",
      "1     2086\n",
      "Name: max22, dtype: int64\n",
      "0    12448\n",
      "1     2157\n",
      "Name: max30, dtype: int64\n",
      "1.0    14605\n",
      "Name: MI_CP_limit, dtype: int64\n",
      "1.0    14605\n",
      "Name: MI_PIP_limit, dtype: int64\n",
      "0    13804\n",
      "1      801\n",
      "Name: move, dtype: int64\n",
      "0.0    12509\n",
      "1.0     1510\n",
      "Name: Multicar_disc, dtype: int64\n",
      "Direct Bill                    11163\n",
      "Auto Pay - Checking/Savings     2590\n",
      "Auto Pay - Credit Card           626\n",
      "Premium Finance                  226\n",
      "Name: paymentmethodlong, dtype: int64\n",
      "D     11163\n",
      "AS     2590\n",
      "AC      626\n",
      "PF      226\n",
      "Name: PaymentMethodName, dtype: int64\n",
      "0.0    12295\n",
      "1.0     1724\n",
      "Name: PIF_disc, dtype: int64\n",
      "1    11502\n",
      "0     3103\n",
      "Name: pointed, dtype: int64\n",
      "USPS     11620\n",
      "EMAIL     2985\n",
      "Name: PreferredMailDocumentsCode, dtype: int64\n",
      "e2 0-250       4893\n",
      "e 250-500      4698\n",
      "d 500-1000     2776\n",
      "c 1000-1500    1199\n",
      "b 1500-2000     538\n",
      "a 2000+         501\n",
      "Name: premcat, dtype: int64\n",
      "S    9053\n",
      "X    4004\n",
      "N    1548\n",
      "Name: PriorCarrierTypeCode, dtype: int64\n",
      "RAD 3.5           5513\n",
      "NC non-RAD        4244\n",
      "RAD5              2445\n",
      "RAD 2.0            661\n",
      "R5RVOnly           380\n",
      "RAD 2.5            377\n",
      "NC RAD5            345\n",
      "NY RAD 3.5         171\n",
      "CA RAD 2.0         156\n",
      "2 Tier              97\n",
      "RAD Low Cost        76\n",
      "CA no tier          71\n",
      "Agency non-RAD      29\n",
      "Other               26\n",
      "RAD 3.1             14\n",
      "Name: ProductVersionName, dtype: int64\n",
      "1    4010\n",
      "4    3820\n",
      "3    3716\n",
      "2    3059\n",
      "Name: season, dtype: int64\n",
      "LargeNC     4591\n",
      "LargeAZ     3471\n",
      "SmallLow    3128\n",
      "LargeFL      782\n",
      "LargeWA      775\n",
      "LargeCA      607\n",
      "LargeVA      235\n",
      "SmallHi      177\n",
      "LargeNY      171\n",
      "SmallMed     129\n",
      "LargeSC      116\n",
      "LargeAL      105\n",
      "LargeLA       79\n",
      "LargeWV       66\n",
      "Other         62\n",
      "LargeTX       58\n",
      "LargeOR       53\n",
      "Name: StateGroup, dtype: int64\n",
      "Mid-Atlantic    5050\n",
      "Western         5001\n",
      "Northern        3445\n",
      "Southern        1109\n",
      "Name: StateRegion, dtype: int64\n",
      "6     9948\n",
      "12    4657\n",
      "Name: TermInMonthsNum, dtype: int64\n",
      "10    5629\n",
      "0     4360\n",
      "6     2587\n",
      "3     2029\n",
      "Name: uwtiergroup, dtype: int64\n",
      "2012    3764\n",
      "2014    3743\n",
      "2015    3325\n",
      "2013    2528\n",
      "2011    1245\n",
      "Name: year, dtype: int64\n",
      "2     1398\n",
      "3     1382\n",
      "1     1369\n",
      "5     1363\n",
      "10    1309\n",
      "4     1265\n",
      "9     1216\n",
      "11    1191\n",
      "8     1103\n",
      "12    1053\n",
      "7     1010\n",
      "6      946\n",
      "Name: Yearmonth, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "catCols = ['affin_grp', 'AIRB_disc', 'BIGroupNum', 'DistributionChannelName',\n",
    "           'firstterm_survival', 'FL_CreditActionCode', 'HomeOwner_disc',\n",
    "           'HomeownerInd', 'lonly', 'max22', 'max30', 'MI_CP_limit', 'MI_PIP_limit', \n",
    "           'move', 'Multicar_disc', 'paymentmethodlong', 'PaymentMethodName',\n",
    "           'PIF_disc', 'pointed', 'PreferredMailDocumentsCode', 'premcat', \n",
    "           'PriorCarrierTypeCode', 'ProductVersionName', 'season', 'StateGroup', \n",
    "           'StateRegion', 'TermInMonthsNum', 'uwtiergroup', 'year', 'Yearmonth']\n",
    "for col in catCols:\n",
    "    print data[col].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Step 3: Numerical Variables Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create missing value indicator for variable MP_LIMIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data['MI_MP_Limit'] = data['MP_Limit'].fillna(1)\n",
    "data.loc[data['MI_MP_Limit'] != 1] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use Median imputation on the following variables: CL_Limit, PD_Limit, MP_LIMIT, CREDITSCORENUM, INSURANCEEXPERIENCEDAYSNUM, PRECREDITTIERNUM, PRIORSWITCHESCOUNT, RATEMANUALNUM, and UWTIERNUM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data['CL_Limit'].fillna(data['CL_Limit'].median(), inplace=True)\n",
    "data['PD_Limit'].fillna(data['PD_Limit'].median(), inplace=True)\n",
    "data['MP_Limit'].fillna(data['MP_Limit'].median(), inplace=True)\n",
    "data['CreditScoreNum'].fillna(data['CreditScoreNum'].median(), inplace=True)\n",
    "data['InsuranceExperienceDaysNum'].fillna(data['InsuranceExperienceDaysNum'].median(), inplace=True)\n",
    "data['PreCreditTierNum'].fillna(data['PreCreditTierNum'].median(), inplace=True)\n",
    "data['PriorSwitchesCount'].fillna(data['PriorSwitchesCount'].median(), inplace=True)\n",
    "data['RateManualNum'].fillna(data['RateManualNum'].median(), inplace=True)\n",
    "data['uwtiergroup'].fillna(data['uwtiergroup'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop N_PAYMENT due to high correlation with PAYINSTALLMENTSNUM "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = data.drop('N_Payment', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Restrict the variable PNIAGE in the interval [16, 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = data[data['PNIAge'].between(16,100, inclusive = True)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the variable VEHXDRV to two variables N_Vehicles and N_Drivers.  The first digit to represent the number of vehicle and the second digit to represent the number of drivers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data['N_Vehicles'] = data['vehxdrv'].astype(str).str.slice(0,1,1)\n",
    "data['N_Drivers'] = data['vehxdrv'].astype(str).str.slice(1,2,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform rank transformation on the following variables RATEMANUALNUM N_MOD_BI_LIMIT FIRMCODE AGEGE75_LT30_LT21_POINTED PD_LIMIT MP_LIMIT MAXVEHVALUE PIP_LIMIT NEXTPREMCH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data['Rank_RateManualNum'] = data['RateManualNum'].rank(axis = 0, method='average', na_option='bottom', ascending=True)\n",
    "data['Rank_BI_Limit'] = data['BI_Limit'].rank(axis = 0, method='average', na_option='bottom', ascending=True)\n",
    "data['Rank_FirmCode'] = data['FirmCode'].rank(axis = 0, method='average', na_option='bottom', ascending=True)\n",
    "data['Rank_agege75_lt30_lt21_pointed'] = data['agege75_lt30_lt21_pointed'].rank(axis = 0, method='average', na_option='bottom', ascending=True)\n",
    "data['Rank_PD_Limit'] = data['PD_Limit'].rank(axis = 0, method='average', na_option='bottom', ascending=True)\n",
    "data['Rank_MP_Limit'] = data['MP_Limit'].rank(axis = 0, method='average', na_option='bottom', ascending=True)\n",
    "data['Rank_maxvehvalue'] = data['maxvehvalue'].rank(axis = 0, method='average', na_option='bottom', ascending=True)\n",
    "data['Rank_PIP_Limit'] = data['PIP_Limit'].rank(axis = 0, method='average', na_option='bottom', ascending=True)\n",
    "data['Rank_nextpremch'] = data['nextpremch'].rank(axis = 0, method='average', na_option='bottom', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform log transformation on the following variables: UWTIERNUM NONCANCELENDMTS INCURRED_LOSS. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data['Log_uwtiernum'] = np.log(data['uwtiergroup'])\n",
    "data['Log_noncancelendmts'] = np.log(data['noncancelendmts'])\n",
    "data['Log_incurred_loss'] = np.log(data['incurred_loss'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform rank transformation of the following three variables: DAYSLAPSENUM LOGIT_PRODUCTVERSIONNAME LASTENDMT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['Rank_DaysLapseNum'] = data['DaysLapseNum'].rank(axis = 0, method='average', na_option='bottom', ascending=True)\n",
    "data['Rank_Logit_ProductVersionName'] = data['Logit_ProductVersionName'].rank(axis = 0, method='average', na_option='bottom', ascending=True)\n",
    "data['Rank_lastendmt'] = data['lastendmt'].rank(axis = 0, method='average', na_option='bottom', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform square transformation of the following variables: LOGIT_FL_CREDITACTIONCODE and LOGIT_STATEGROUP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['Square_Logit_FL_CreditActionCode']= data['Logit_FL_CreditActionCode']**2\n",
    "data['Square_Logit_StateGroup']= data['Logit_StateGroup']**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform power transformation of the following variables: \n",
    "* TOTAL_FEE: Power 0.5 \n",
    "* TOTAL_ANN_PREM: Power 0.3125 \n",
    "* CL_LIMIT: Power 0.25 \n",
    "* NPCHCAT: Power 0.125 \n",
    "* INSURANCEEXPERIENCEDAYSNUM: Power 0.375 \n",
    "* CP_LIMIT: Power 0.4325"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['Power_total_fee']= data['total_fee']**(.5)\n",
    "data['Power_total_ann_prem']= data['total_ann_prem']**(.3125)\n",
    "data['Power_CL_Limit']= data['CL_Limit']**(.25)\n",
    "data['Power_npchcat']= data['npchcat']**(.125)\n",
    "data['Power_InsuranceExperienceDaysNum']= data['InsuranceExperienceDaysNum']**(.375)\n",
    "data['Power_CP_Limit']= data['CP_Limit']**(.4325)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DistributionChannelName</th>\n",
       "      <th>FirmCode</th>\n",
       "      <th>total_ann_prem</th>\n",
       "      <th>total_fee</th>\n",
       "      <th>CL_Limit</th>\n",
       "      <th>CP_Limit</th>\n",
       "      <th>BI_Limit</th>\n",
       "      <th>PD_Limit</th>\n",
       "      <th>MP_Limit</th>\n",
       "      <th>PIP_Limit</th>\n",
       "      <th>...</th>\n",
       "      <th>Rank_Logit_ProductVersionName</th>\n",
       "      <th>Rank_lastendmt</th>\n",
       "      <th>Square_Logit_FL_CreditActionCode</th>\n",
       "      <th>Square_Logit_StateGroup</th>\n",
       "      <th>Power_total_fee</th>\n",
       "      <th>Power_total_ann_prem</th>\n",
       "      <th>Power_CL_Limit</th>\n",
       "      <th>Power_npchcat</th>\n",
       "      <th>Power_InsuranceExperienceDaysNum</th>\n",
       "      <th>Power_CP_Limit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Direct</td>\n",
       "      <td>20</td>\n",
       "      <td>363.000000</td>\n",
       "      <td>15.00</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100/300</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>380.5</td>\n",
       "      <td>1053.5</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>3.262935</td>\n",
       "      <td>3.872983</td>\n",
       "      <td>6.309150</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83042</th>\n",
       "      <td>Agency</td>\n",
       "      <td>19</td>\n",
       "      <td>610.000000</td>\n",
       "      <td>19.50</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50/100</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>380.5</td>\n",
       "      <td>217.5</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>2.059213</td>\n",
       "      <td>4.415880</td>\n",
       "      <td>7.420199</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>1.333521</td>\n",
       "      <td>16.829350</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103189</th>\n",
       "      <td>Agency</td>\n",
       "      <td>19</td>\n",
       "      <td>1002.000000</td>\n",
       "      <td>41.00</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30/60</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>380.5</td>\n",
       "      <td>957.0</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>2.059213</td>\n",
       "      <td>6.403124</td>\n",
       "      <td>8.665052</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>1.454215</td>\n",
       "      <td>21.386438</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164650</th>\n",
       "      <td>Agency</td>\n",
       "      <td>19</td>\n",
       "      <td>883.000000</td>\n",
       "      <td>29.00</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100/300</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>380.5</td>\n",
       "      <td>217.5</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>2.059213</td>\n",
       "      <td>5.385165</td>\n",
       "      <td>8.329381</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.844205</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256768</th>\n",
       "      <td>Agency</td>\n",
       "      <td>19</td>\n",
       "      <td>716.000000</td>\n",
       "      <td>21.50</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100/300</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>380.5</td>\n",
       "      <td>217.5</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>2.059213</td>\n",
       "      <td>4.636809</td>\n",
       "      <td>7.801180</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.867664</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391990</th>\n",
       "      <td>Internet</td>\n",
       "      <td>18</td>\n",
       "      <td>512.000000</td>\n",
       "      <td>31.00</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100/300</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>380.5</td>\n",
       "      <td>1343.0</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>2.059213</td>\n",
       "      <td>5.567764</td>\n",
       "      <td>7.025009</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.023666</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460760</th>\n",
       "      <td>Internet</td>\n",
       "      <td>18</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>31.00</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>380.5</td>\n",
       "      <td>571.5</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>2.059213</td>\n",
       "      <td>5.567764</td>\n",
       "      <td>5.999458</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>1.333521</td>\n",
       "      <td>23.296521</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526070</th>\n",
       "      <td>Direct</td>\n",
       "      <td>18</td>\n",
       "      <td>760.000000</td>\n",
       "      <td>26.00</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100/300</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>380.5</td>\n",
       "      <td>1053.5</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>2.059213</td>\n",
       "      <td>5.099020</td>\n",
       "      <td>7.947934</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>1.668280</td>\n",
       "      <td>20.534574</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541345</th>\n",
       "      <td>Direct</td>\n",
       "      <td>18</td>\n",
       "      <td>1635.000000</td>\n",
       "      <td>25.00</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500/500</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>380.5</td>\n",
       "      <td>217.5</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>2.059213</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.097743</td>\n",
       "      <td>5.623413</td>\n",
       "      <td>1.333521</td>\n",
       "      <td>16.713322</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557770</th>\n",
       "      <td>Internet</td>\n",
       "      <td>18</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>95.62</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100/300</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>380.5</td>\n",
       "      <td>806.0</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>2.059213</td>\n",
       "      <td>9.778548</td>\n",
       "      <td>5.784842</td>\n",
       "      <td>5.623413</td>\n",
       "      <td>1.333521</td>\n",
       "      <td>26.741773</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608335</th>\n",
       "      <td>Internet</td>\n",
       "      <td>18</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>16.00</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100/300</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>380.5</td>\n",
       "      <td>806.0</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>2.059213</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.102178</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.796237</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625878</th>\n",
       "      <td>Internet</td>\n",
       "      <td>18</td>\n",
       "      <td>1688.000000</td>\n",
       "      <td>26.00</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500/500</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>380.5</td>\n",
       "      <td>571.5</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>2.059213</td>\n",
       "      <td>5.099020</td>\n",
       "      <td>10.198913</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>1.454215</td>\n",
       "      <td>30.734048</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629459</th>\n",
       "      <td>Direct</td>\n",
       "      <td>19</td>\n",
       "      <td>414.000000</td>\n",
       "      <td>20.50</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50/100</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>380.5</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>2.059213</td>\n",
       "      <td>4.527693</td>\n",
       "      <td>6.573741</td>\n",
       "      <td>5.623413</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.946700</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652067</th>\n",
       "      <td>Direct</td>\n",
       "      <td>19</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>15.00</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100/300</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>380.5</td>\n",
       "      <td>571.5</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>2.059213</td>\n",
       "      <td>3.872983</td>\n",
       "      <td>5.277437</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.330373</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62736</th>\n",
       "      <td>Direct</td>\n",
       "      <td>19</td>\n",
       "      <td>258.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25/50</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>380.5</td>\n",
       "      <td>1053.5</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>1.375013</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.670628</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>1.333521</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649632</th>\n",
       "      <td>Direct</td>\n",
       "      <td>19</td>\n",
       "      <td>1004.000000</td>\n",
       "      <td>5.00</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25/50</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>380.5</td>\n",
       "      <td>571.5</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>1.375013</td>\n",
       "      <td>2.236068</td>\n",
       "      <td>8.670453</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.663958</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578250</th>\n",
       "      <td>Direct</td>\n",
       "      <td>19</td>\n",
       "      <td>630.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100/300</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>380.5</td>\n",
       "      <td>571.5</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>0.280464</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.495385</td>\n",
       "      <td>5.623413</td>\n",
       "      <td>1.454215</td>\n",
       "      <td>30.494982</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333334</th>\n",
       "      <td>Agency</td>\n",
       "      <td>18</td>\n",
       "      <td>832.000000</td>\n",
       "      <td>35.50</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50/100</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>662.5</td>\n",
       "      <td>806.0</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>3.262935</td>\n",
       "      <td>5.958188</td>\n",
       "      <td>8.175956</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.286398</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342123</th>\n",
       "      <td>Agency</td>\n",
       "      <td>18</td>\n",
       "      <td>1603.000000</td>\n",
       "      <td>14.00</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50/100</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>662.5</td>\n",
       "      <td>806.0</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>3.262935</td>\n",
       "      <td>3.741657</td>\n",
       "      <td>10.035563</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>1.729363</td>\n",
       "      <td>16.093672</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353790</th>\n",
       "      <td>Internet</td>\n",
       "      <td>18</td>\n",
       "      <td>282.000000</td>\n",
       "      <td>20.50</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100/300</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>662.5</td>\n",
       "      <td>806.0</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>3.262935</td>\n",
       "      <td>4.527693</td>\n",
       "      <td>5.830460</td>\n",
       "      <td>5.623413</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.844811</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404794</th>\n",
       "      <td>Direct</td>\n",
       "      <td>18</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>10.00</td>\n",
       "      <td>250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100/300</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>662.5</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>3.262935</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>5.636054</td>\n",
       "      <td>3.976354</td>\n",
       "      <td>1.333521</td>\n",
       "      <td>32.138699</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444850</th>\n",
       "      <td>Internet</td>\n",
       "      <td>18</td>\n",
       "      <td>1479.000000</td>\n",
       "      <td>34.00</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100/300</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>662.5</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>3.262935</td>\n",
       "      <td>5.830952</td>\n",
       "      <td>9.786222</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>1.529819</td>\n",
       "      <td>23.013441</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480726</th>\n",
       "      <td>Internet</td>\n",
       "      <td>18</td>\n",
       "      <td>1062.000000</td>\n",
       "      <td>21.50</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100/300</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>662.5</td>\n",
       "      <td>217.5</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>3.262935</td>\n",
       "      <td>4.636809</td>\n",
       "      <td>8.823968</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>1.333521</td>\n",
       "      <td>22.908313</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535088</th>\n",
       "      <td>Direct</td>\n",
       "      <td>18</td>\n",
       "      <td>914.000000</td>\n",
       "      <td>10.50</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50/100</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>662.5</td>\n",
       "      <td>217.5</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>3.262935</td>\n",
       "      <td>3.240370</td>\n",
       "      <td>8.419682</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.020901</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554667</th>\n",
       "      <td>Direct</td>\n",
       "      <td>18</td>\n",
       "      <td>795.000000</td>\n",
       "      <td>10.50</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250/500</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>662.5</td>\n",
       "      <td>217.5</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>3.262935</td>\n",
       "      <td>3.240370</td>\n",
       "      <td>8.060551</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>1.333521</td>\n",
       "      <td>20.350554</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560302</th>\n",
       "      <td>Direct</td>\n",
       "      <td>18</td>\n",
       "      <td>382.000000</td>\n",
       "      <td>11.00</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100/300</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>662.5</td>\n",
       "      <td>217.5</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>3.262935</td>\n",
       "      <td>3.316625</td>\n",
       "      <td>6.410543</td>\n",
       "      <td>5.623413</td>\n",
       "      <td>1.333521</td>\n",
       "      <td>36.476331</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578651</th>\n",
       "      <td>Agency</td>\n",
       "      <td>18</td>\n",
       "      <td>3047.000000</td>\n",
       "      <td>24.00</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100/300</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>662.5</td>\n",
       "      <td>1053.5</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>3.262935</td>\n",
       "      <td>4.898979</td>\n",
       "      <td>12.266203</td>\n",
       "      <td>5.623413</td>\n",
       "      <td>1.333521</td>\n",
       "      <td>24.202307</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584882</th>\n",
       "      <td>Direct</td>\n",
       "      <td>18</td>\n",
       "      <td>779.000000</td>\n",
       "      <td>10.50</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250/500</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>662.5</td>\n",
       "      <td>217.5</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>3.262935</td>\n",
       "      <td>3.240370</td>\n",
       "      <td>8.009501</td>\n",
       "      <td>5.623413</td>\n",
       "      <td>1.333521</td>\n",
       "      <td>31.953119</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602648</th>\n",
       "      <td>Agency</td>\n",
       "      <td>18</td>\n",
       "      <td>1485.000000</td>\n",
       "      <td>13.50</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15/30</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>662.5</td>\n",
       "      <td>806.0</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>3.262935</td>\n",
       "      <td>3.674235</td>\n",
       "      <td>9.798611</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>1.454215</td>\n",
       "      <td>24.560827</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603827</th>\n",
       "      <td>Direct</td>\n",
       "      <td>18</td>\n",
       "      <td>537.000000</td>\n",
       "      <td>10.50</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100/300</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>662.5</td>\n",
       "      <td>571.5</td>\n",
       "      <td>2.74574</td>\n",
       "      <td>3.262935</td>\n",
       "      <td>3.240370</td>\n",
       "      <td>7.130450</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>1.333521</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664970</th>\n",
       "      <td>Agency</td>\n",
       "      <td>23</td>\n",
       "      <td>495.000000</td>\n",
       "      <td>37.44</td>\n",
       "      <td>250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15/30</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>158.0</td>\n",
       "      <td>217.5</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>1.100738</td>\n",
       "      <td>6.118823</td>\n",
       "      <td>6.951269</td>\n",
       "      <td>3.976354</td>\n",
       "      <td>1.333521</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679198</th>\n",
       "      <td>Direct</td>\n",
       "      <td>18</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15/30</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>218.0</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>1.100738</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.623413</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.453509</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68715</th>\n",
       "      <td>Agency</td>\n",
       "      <td>28</td>\n",
       "      <td>412.000000</td>\n",
       "      <td>52.00</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15/30</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>167.0</td>\n",
       "      <td>571.5</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>1.100738</td>\n",
       "      <td>7.211103</td>\n",
       "      <td>6.563801</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>1.529819</td>\n",
       "      <td>11.850756</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325271</th>\n",
       "      <td>Agency</td>\n",
       "      <td>28</td>\n",
       "      <td>461.000000</td>\n",
       "      <td>50.80</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15/30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>167.0</td>\n",
       "      <td>1513.0</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>1.100738</td>\n",
       "      <td>7.127412</td>\n",
       "      <td>6.798397</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>1.333521</td>\n",
       "      <td>9.933718</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346530</th>\n",
       "      <td>Agency</td>\n",
       "      <td>28</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>25.90</td>\n",
       "      <td>250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15/30</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>167.0</td>\n",
       "      <td>217.5</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>1.100738</td>\n",
       "      <td>5.089204</td>\n",
       "      <td>5.725000</td>\n",
       "      <td>3.976354</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350063</th>\n",
       "      <td>Agency</td>\n",
       "      <td>28</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>20.90</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15/30</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>167.0</td>\n",
       "      <td>217.5</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>1.100738</td>\n",
       "      <td>4.571652</td>\n",
       "      <td>5.245040</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350071</th>\n",
       "      <td>Agency</td>\n",
       "      <td>28</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>53.80</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15/30</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>167.0</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>1.100738</td>\n",
       "      <td>7.334848</td>\n",
       "      <td>6.498364</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>1.333521</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448648</th>\n",
       "      <td>Agency</td>\n",
       "      <td>28</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>25.90</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15/30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>167.0</td>\n",
       "      <td>217.5</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>1.100738</td>\n",
       "      <td>5.089204</td>\n",
       "      <td>5.433217</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542878</th>\n",
       "      <td>Agency</td>\n",
       "      <td>28</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>25.88</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15/30</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>167.0</td>\n",
       "      <td>217.5</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>1.100738</td>\n",
       "      <td>5.087239</td>\n",
       "      <td>4.845643</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>1.585833</td>\n",
       "      <td>14.278841</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546155</th>\n",
       "      <td>Agency</td>\n",
       "      <td>28</td>\n",
       "      <td>1021.000000</td>\n",
       "      <td>83.75</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15/30</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>167.0</td>\n",
       "      <td>217.5</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>1.100738</td>\n",
       "      <td>9.151503</td>\n",
       "      <td>8.716067</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>1.630689</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547156</th>\n",
       "      <td>Agency</td>\n",
       "      <td>28</td>\n",
       "      <td>278.000000</td>\n",
       "      <td>34.88</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15/30</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>167.0</td>\n",
       "      <td>571.5</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>1.100738</td>\n",
       "      <td>5.905929</td>\n",
       "      <td>5.804489</td>\n",
       "      <td>5.623413</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.138182</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555383</th>\n",
       "      <td>Agency</td>\n",
       "      <td>28</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>20.88</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15/30</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>167.0</td>\n",
       "      <td>217.5</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>1.100738</td>\n",
       "      <td>4.569464</td>\n",
       "      <td>5.084782</td>\n",
       "      <td>5.623413</td>\n",
       "      <td>1.585833</td>\n",
       "      <td>9.240499</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561070</th>\n",
       "      <td>Agency</td>\n",
       "      <td>28</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>20.88</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15/30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>167.0</td>\n",
       "      <td>217.5</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>1.100738</td>\n",
       "      <td>4.569464</td>\n",
       "      <td>4.405225</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>1.630689</td>\n",
       "      <td>15.606246</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586501</th>\n",
       "      <td>Agency</td>\n",
       "      <td>28</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>53.76</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15/30</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>167.0</td>\n",
       "      <td>1300.5</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>1.100738</td>\n",
       "      <td>7.332121</td>\n",
       "      <td>6.477935</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>1.585833</td>\n",
       "      <td>23.887802</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640703</th>\n",
       "      <td>Agency</td>\n",
       "      <td>28</td>\n",
       "      <td>575.000000</td>\n",
       "      <td>29.64</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15/30</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>167.0</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>1.100738</td>\n",
       "      <td>5.444263</td>\n",
       "      <td>7.284441</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>1.585833</td>\n",
       "      <td>17.409715</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347952</th>\n",
       "      <td>Direct</td>\n",
       "      <td>18</td>\n",
       "      <td>817.000000</td>\n",
       "      <td>3.00</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100/300</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>215.5</td>\n",
       "      <td>1191.0</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>1.259113</td>\n",
       "      <td>1.732051</td>\n",
       "      <td>8.129604</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>1.333521</td>\n",
       "      <td>22.798283</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462744</th>\n",
       "      <td>Direct</td>\n",
       "      <td>18</td>\n",
       "      <td>656.000000</td>\n",
       "      <td>3.00</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25/50</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>215.5</td>\n",
       "      <td>806.0</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>1.259113</td>\n",
       "      <td>1.732051</td>\n",
       "      <td>7.590711</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.369701</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605210</th>\n",
       "      <td>Direct</td>\n",
       "      <td>18</td>\n",
       "      <td>419.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100/300</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>215.5</td>\n",
       "      <td>217.5</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>1.259113</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.598449</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.519059</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623823</th>\n",
       "      <td>Direct</td>\n",
       "      <td>18</td>\n",
       "      <td>814.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250/500</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>215.5</td>\n",
       "      <td>806.0</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>1.259113</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.120264</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.054857</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126365</th>\n",
       "      <td>Agency</td>\n",
       "      <td>23</td>\n",
       "      <td>178.350006</td>\n",
       "      <td>0.00</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30/60</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1201.0</td>\n",
       "      <td>806.0</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>2.887126</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.052692</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.191171</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132974</th>\n",
       "      <td>Agency</td>\n",
       "      <td>23</td>\n",
       "      <td>671.690002</td>\n",
       "      <td>18.00</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30/60</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1201.0</td>\n",
       "      <td>1499.0</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>2.887126</td>\n",
       "      <td>4.242641</td>\n",
       "      <td>7.646986</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.508454</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244701</th>\n",
       "      <td>Agency</td>\n",
       "      <td>23</td>\n",
       "      <td>415.420013</td>\n",
       "      <td>18.00</td>\n",
       "      <td>250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30/60</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1201.0</td>\n",
       "      <td>217.5</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>2.887126</td>\n",
       "      <td>4.242641</td>\n",
       "      <td>6.580779</td>\n",
       "      <td>3.976354</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.907090</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270186</th>\n",
       "      <td>Agency</td>\n",
       "      <td>23</td>\n",
       "      <td>172.979996</td>\n",
       "      <td>0.00</td>\n",
       "      <td>250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30/60</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1201.0</td>\n",
       "      <td>217.5</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>2.887126</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.004650</td>\n",
       "      <td>3.976354</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.883735</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332685</th>\n",
       "      <td>Agency</td>\n",
       "      <td>23</td>\n",
       "      <td>260.109985</td>\n",
       "      <td>0.00</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30/60</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1201.0</td>\n",
       "      <td>571.5</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>2.887126</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.685080</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.124521</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350764</th>\n",
       "      <td>Agency</td>\n",
       "      <td>23</td>\n",
       "      <td>891.520020</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30/60</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1201.0</td>\n",
       "      <td>217.5</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>2.887126</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.354414</td>\n",
       "      <td>7.071068</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.899456</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485794</th>\n",
       "      <td>Agency</td>\n",
       "      <td>23</td>\n",
       "      <td>374.739990</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30/60</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1201.0</td>\n",
       "      <td>217.5</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>2.887126</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.372218</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.498566</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490032</th>\n",
       "      <td>Agency</td>\n",
       "      <td>23</td>\n",
       "      <td>248.520004</td>\n",
       "      <td>0.00</td>\n",
       "      <td>250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30/60</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1201.0</td>\n",
       "      <td>571.5</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>2.887126</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.604675</td>\n",
       "      <td>3.976354</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.243899</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586302</th>\n",
       "      <td>Agency</td>\n",
       "      <td>23</td>\n",
       "      <td>150.720001</td>\n",
       "      <td>18.00</td>\n",
       "      <td>250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30/60</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1201.0</td>\n",
       "      <td>1230.5</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>2.887126</td>\n",
       "      <td>4.242641</td>\n",
       "      <td>4.793783</td>\n",
       "      <td>3.976354</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.278841</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605623</th>\n",
       "      <td>Agency</td>\n",
       "      <td>23</td>\n",
       "      <td>193.929993</td>\n",
       "      <td>18.00</td>\n",
       "      <td>500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30/60</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1201.0</td>\n",
       "      <td>1191.0</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>2.887126</td>\n",
       "      <td>4.242641</td>\n",
       "      <td>5.186675</td>\n",
       "      <td>4.728708</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707281</th>\n",
       "      <td>Agency</td>\n",
       "      <td>23</td>\n",
       "      <td>641.390015</td>\n",
       "      <td>0.00</td>\n",
       "      <td>250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30/60</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1201.0</td>\n",
       "      <td>217.5</td>\n",
       "      <td>1.15322</td>\n",
       "      <td>2.887126</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.537472</td>\n",
       "      <td>3.976354</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1552 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       DistributionChannelName  FirmCode  total_ann_prem  total_fee  CL_Limit  \\\n",
       "3                       Direct        20      363.000000      15.00     500.0   \n",
       "83042                   Agency        19      610.000000      19.50     500.0   \n",
       "103189                  Agency        19     1002.000000      41.00     500.0   \n",
       "164650                  Agency        19      883.000000      29.00     500.0   \n",
       "256768                  Agency        19      716.000000      21.50     500.0   \n",
       "391990                Internet        18      512.000000      31.00     500.0   \n",
       "460760                Internet        18      309.000000      31.00     500.0   \n",
       "526070                  Direct        18      760.000000      26.00     500.0   \n",
       "541345                  Direct        18     1635.000000      25.00    1000.0   \n",
       "557770                Internet        18      275.000000      95.62    1000.0   \n",
       "608335                Internet        18      184.000000      16.00     500.0   \n",
       "625878                Internet        18     1688.000000      26.00     500.0   \n",
       "629459                  Direct        19      414.000000      20.50    1000.0   \n",
       "652067                  Direct        19      205.000000      15.00     500.0   \n",
       "62736                   Direct        19      258.000000       0.00     500.0   \n",
       "649632                  Direct        19     1004.000000       5.00     500.0   \n",
       "578250                  Direct        19      630.000000       0.00    1000.0   \n",
       "333334                  Agency        18      832.000000      35.50     500.0   \n",
       "342123                  Agency        18     1603.000000      14.00     500.0   \n",
       "353790                Internet        18      282.000000      20.50    1000.0   \n",
       "404794                  Direct        18      253.000000      10.00     250.0   \n",
       "444850                Internet        18     1479.000000      34.00     500.0   \n",
       "480726                Internet        18     1062.000000      21.50     500.0   \n",
       "535088                  Direct        18      914.000000      10.50     500.0   \n",
       "554667                  Direct        18      795.000000      10.50     500.0   \n",
       "560302                  Direct        18      382.000000      11.00    1000.0   \n",
       "578651                  Agency        18     3047.000000      24.00    1000.0   \n",
       "584882                  Direct        18      779.000000      10.50    1000.0   \n",
       "602648                  Agency        18     1485.000000      13.50     500.0   \n",
       "603827                  Direct        18      537.000000      10.50     500.0   \n",
       "...                        ...       ...             ...        ...       ...   \n",
       "664970                  Agency        23      495.000000      37.44     250.0   \n",
       "679198                  Direct        18        0.000000       0.00    1000.0   \n",
       "68715                   Agency        28      412.000000      52.00     500.0   \n",
       "325271                  Agency        28      461.000000      50.80     500.0   \n",
       "346530                  Agency        28      266.000000      25.90     250.0   \n",
       "350063                  Agency        28      201.000000      20.90     500.0   \n",
       "350071                  Agency        28      399.000000      53.80     500.0   \n",
       "448648                  Agency        28      225.000000      25.90     500.0   \n",
       "542878                  Agency        28      156.000000      25.88     500.0   \n",
       "546155                  Agency        28     1021.000000      83.75     500.0   \n",
       "547156                  Agency        28      278.000000      34.88    1000.0   \n",
       "555383                  Agency        28      182.000000      20.88    1000.0   \n",
       "561070                  Agency        28      115.000000      20.88     500.0   \n",
       "586501                  Agency        28      395.000000      53.76     500.0   \n",
       "640703                  Agency        28      575.000000      29.64     500.0   \n",
       "347952                  Direct        18      817.000000       3.00     500.0   \n",
       "462744                  Direct        18      656.000000       3.00     500.0   \n",
       "605210                  Direct        18      419.000000       0.00     500.0   \n",
       "623823                  Direct        18      814.000000       0.00     500.0   \n",
       "126365                  Agency        23      178.350006       0.00     500.0   \n",
       "132974                  Agency        23      671.690002      18.00     500.0   \n",
       "244701                  Agency        23      415.420013      18.00     250.0   \n",
       "270186                  Agency        23      172.979996       0.00     250.0   \n",
       "332685                  Agency        23      260.109985       0.00     500.0   \n",
       "350764                  Agency        23      891.520020       0.00    2500.0   \n",
       "485794                  Agency        23      374.739990       0.00     100.0   \n",
       "490032                  Agency        23      248.520004       0.00     250.0   \n",
       "586302                  Agency        23      150.720001      18.00     250.0   \n",
       "605623                  Agency        23      193.929993      18.00     500.0   \n",
       "707281                  Agency        23      641.390015       0.00     250.0   \n",
       "\n",
       "        CP_Limit BI_Limit  PD_Limit  MP_Limit  PIP_Limit      ...        \\\n",
       "3            NaN  100/300      50.0       0.0        NaN      ...         \n",
       "83042        NaN   50/100      50.0       0.0        NaN      ...         \n",
       "103189       NaN    30/60      25.0       0.0        NaN      ...         \n",
       "164650       NaN  100/300     100.0       0.0        NaN      ...         \n",
       "256768       NaN  100/300     100.0       0.0        NaN      ...         \n",
       "391990       NaN  100/300     100.0       0.0        NaN      ...         \n",
       "460760       NaN      300      50.0       0.0        NaN      ...         \n",
       "526070       NaN  100/300     100.0       0.0        NaN      ...         \n",
       "541345       NaN  500/500     500.0       0.0        NaN      ...         \n",
       "557770       NaN  100/300     100.0       0.0        NaN      ...         \n",
       "608335       NaN  100/300     100.0       0.0        NaN      ...         \n",
       "625878       NaN  500/500     500.0       0.0        NaN      ...         \n",
       "629459       NaN   50/100      50.0       0.0        NaN      ...         \n",
       "652067       NaN  100/300     100.0       0.0        NaN      ...         \n",
       "62736        NaN    25/50      25.0       0.0        NaN      ...         \n",
       "649632       NaN    25/50      25.0       0.0        NaN      ...         \n",
       "578250       NaN  100/300      50.0       0.0        NaN      ...         \n",
       "333334       NaN   50/100      50.0       0.0        NaN      ...         \n",
       "342123       NaN   50/100      50.0       0.0        NaN      ...         \n",
       "353790       NaN  100/300     100.0       0.0        NaN      ...         \n",
       "404794       NaN  100/300     100.0       0.0        NaN      ...         \n",
       "444850       NaN  100/300      50.0       0.0        NaN      ...         \n",
       "480726       NaN  100/300     100.0       0.0        NaN      ...         \n",
       "535088       NaN   50/100      50.0       0.0        NaN      ...         \n",
       "554667       NaN  250/500     100.0       0.0        NaN      ...         \n",
       "560302       NaN  100/300     100.0       0.0        NaN      ...         \n",
       "578651       NaN  100/300     100.0       0.0        NaN      ...         \n",
       "584882       NaN  250/500     100.0       0.0        NaN      ...         \n",
       "602648       NaN    15/30      15.0       0.0        NaN      ...         \n",
       "603827       NaN  100/300     100.0       0.0        NaN      ...         \n",
       "...          ...      ...       ...       ...        ...      ...         \n",
       "664970       NaN    15/30      10.0       0.0        NaN      ...         \n",
       "679198       NaN    15/30      10.0       0.0        NaN      ...         \n",
       "68715        NaN    15/30      10.0       0.0        NaN      ...         \n",
       "325271       NaN    15/30       5.0       0.0        NaN      ...         \n",
       "346530       NaN    15/30      10.0       0.0        NaN      ...         \n",
       "350063       NaN    15/30      10.0       0.0        NaN      ...         \n",
       "350071       NaN    15/30      10.0       0.0        NaN      ...         \n",
       "448648       NaN    15/30       5.0       0.0        NaN      ...         \n",
       "542878       NaN    15/30      10.0       0.0        NaN      ...         \n",
       "546155       NaN    15/30      10.0       0.0        NaN      ...         \n",
       "547156       NaN    15/30      10.0       0.0        NaN      ...         \n",
       "555383       NaN    15/30      10.0       0.0        NaN      ...         \n",
       "561070       NaN    15/30       5.0       0.0        NaN      ...         \n",
       "586501       NaN    15/30      10.0       0.0        NaN      ...         \n",
       "640703       NaN    15/30      10.0       0.0        NaN      ...         \n",
       "347952       NaN  100/300     100.0       0.0        NaN      ...         \n",
       "462744       NaN    25/50      25.0       0.0        NaN      ...         \n",
       "605210       NaN  100/300     100.0       0.0        NaN      ...         \n",
       "623823       NaN  250/500     100.0       0.0        NaN      ...         \n",
       "126365       NaN    30/60      25.0       0.0        NaN      ...         \n",
       "132974       NaN    30/60      25.0       0.0        NaN      ...         \n",
       "244701       NaN    30/60      25.0       0.0        NaN      ...         \n",
       "270186       NaN    30/60      50.0       0.0        NaN      ...         \n",
       "332685       NaN    30/60      25.0       0.0        NaN      ...         \n",
       "350764       NaN    30/60      25.0       0.0        NaN      ...         \n",
       "485794       NaN    30/60      50.0       0.0        NaN      ...         \n",
       "490032       NaN    30/60      50.0       0.0        NaN      ...         \n",
       "586302       NaN    30/60      25.0       0.0        NaN      ...         \n",
       "605623       NaN    30/60      25.0       0.0        NaN      ...         \n",
       "707281       NaN    30/60      25.0       0.0        NaN      ...         \n",
       "\n",
       "        Rank_Logit_ProductVersionName Rank_lastendmt  \\\n",
       "3                               380.5         1053.5   \n",
       "83042                           380.5          217.5   \n",
       "103189                          380.5          957.0   \n",
       "164650                          380.5          217.5   \n",
       "256768                          380.5          217.5   \n",
       "391990                          380.5         1343.0   \n",
       "460760                          380.5          571.5   \n",
       "526070                          380.5         1053.5   \n",
       "541345                          380.5          217.5   \n",
       "557770                          380.5          806.0   \n",
       "608335                          380.5          806.0   \n",
       "625878                          380.5          571.5   \n",
       "629459                          380.5         1133.0   \n",
       "652067                          380.5          571.5   \n",
       "62736                           380.5         1053.5   \n",
       "649632                          380.5          571.5   \n",
       "578250                          380.5          571.5   \n",
       "333334                          662.5          806.0   \n",
       "342123                          662.5          806.0   \n",
       "353790                          662.5          806.0   \n",
       "404794                          662.5         1133.0   \n",
       "444850                          662.5         1133.0   \n",
       "480726                          662.5          217.5   \n",
       "535088                          662.5          217.5   \n",
       "554667                          662.5          217.5   \n",
       "560302                          662.5          217.5   \n",
       "578651                          662.5         1053.5   \n",
       "584882                          662.5          217.5   \n",
       "602648                          662.5          806.0   \n",
       "603827                          662.5          571.5   \n",
       "...                               ...            ...   \n",
       "664970                          158.0          217.5   \n",
       "679198                          218.0         1133.0   \n",
       "68715                           167.0          571.5   \n",
       "325271                          167.0         1513.0   \n",
       "346530                          167.0          217.5   \n",
       "350063                          167.0          217.5   \n",
       "350071                          167.0         1133.0   \n",
       "448648                          167.0          217.5   \n",
       "542878                          167.0          217.5   \n",
       "546155                          167.0          217.5   \n",
       "547156                          167.0          571.5   \n",
       "555383                          167.0          217.5   \n",
       "561070                          167.0          217.5   \n",
       "586501                          167.0         1300.5   \n",
       "640703                          167.0         1133.0   \n",
       "347952                          215.5         1191.0   \n",
       "462744                          215.5          806.0   \n",
       "605210                          215.5          217.5   \n",
       "623823                          215.5          806.0   \n",
       "126365                         1201.0          806.0   \n",
       "132974                         1201.0         1499.0   \n",
       "244701                         1201.0          217.5   \n",
       "270186                         1201.0          217.5   \n",
       "332685                         1201.0          571.5   \n",
       "350764                         1201.0          217.5   \n",
       "485794                         1201.0          217.5   \n",
       "490032                         1201.0          571.5   \n",
       "586302                         1201.0         1230.5   \n",
       "605623                         1201.0         1191.0   \n",
       "707281                         1201.0          217.5   \n",
       "\n",
       "        Square_Logit_FL_CreditActionCode  Square_Logit_StateGroup  \\\n",
       "3                                2.74574                 3.262935   \n",
       "83042                            2.74574                 2.059213   \n",
       "103189                           2.74574                 2.059213   \n",
       "164650                           2.74574                 2.059213   \n",
       "256768                           2.74574                 2.059213   \n",
       "391990                           2.74574                 2.059213   \n",
       "460760                           2.74574                 2.059213   \n",
       "526070                           2.74574                 2.059213   \n",
       "541345                           2.74574                 2.059213   \n",
       "557770                           2.74574                 2.059213   \n",
       "608335                           2.74574                 2.059213   \n",
       "625878                           2.74574                 2.059213   \n",
       "629459                           2.74574                 2.059213   \n",
       "652067                           2.74574                 2.059213   \n",
       "62736                            2.74574                 1.375013   \n",
       "649632                           2.74574                 1.375013   \n",
       "578250                           2.74574                 0.280464   \n",
       "333334                           2.74574                 3.262935   \n",
       "342123                           2.74574                 3.262935   \n",
       "353790                           2.74574                 3.262935   \n",
       "404794                           2.74574                 3.262935   \n",
       "444850                           2.74574                 3.262935   \n",
       "480726                           2.74574                 3.262935   \n",
       "535088                           2.74574                 3.262935   \n",
       "554667                           2.74574                 3.262935   \n",
       "560302                           2.74574                 3.262935   \n",
       "578651                           2.74574                 3.262935   \n",
       "584882                           2.74574                 3.262935   \n",
       "602648                           2.74574                 3.262935   \n",
       "603827                           2.74574                 3.262935   \n",
       "...                                  ...                      ...   \n",
       "664970                           1.15322                 1.100738   \n",
       "679198                           1.15322                 1.100738   \n",
       "68715                            1.15322                 1.100738   \n",
       "325271                           1.15322                 1.100738   \n",
       "346530                           1.15322                 1.100738   \n",
       "350063                           1.15322                 1.100738   \n",
       "350071                           1.15322                 1.100738   \n",
       "448648                           1.15322                 1.100738   \n",
       "542878                           1.15322                 1.100738   \n",
       "546155                           1.15322                 1.100738   \n",
       "547156                           1.15322                 1.100738   \n",
       "555383                           1.15322                 1.100738   \n",
       "561070                           1.15322                 1.100738   \n",
       "586501                           1.15322                 1.100738   \n",
       "640703                           1.15322                 1.100738   \n",
       "347952                           1.15322                 1.259113   \n",
       "462744                           1.15322                 1.259113   \n",
       "605210                           1.15322                 1.259113   \n",
       "623823                           1.15322                 1.259113   \n",
       "126365                           1.15322                 2.887126   \n",
       "132974                           1.15322                 2.887126   \n",
       "244701                           1.15322                 2.887126   \n",
       "270186                           1.15322                 2.887126   \n",
       "332685                           1.15322                 2.887126   \n",
       "350764                           1.15322                 2.887126   \n",
       "485794                           1.15322                 2.887126   \n",
       "490032                           1.15322                 2.887126   \n",
       "586302                           1.15322                 2.887126   \n",
       "605623                           1.15322                 2.887126   \n",
       "707281                           1.15322                 2.887126   \n",
       "\n",
       "        Power_total_fee  Power_total_ann_prem  Power_CL_Limit  Power_npchcat  \\\n",
       "3              3.872983              6.309150        4.728708       0.000000   \n",
       "83042          4.415880              7.420199        4.728708       1.333521   \n",
       "103189         6.403124              8.665052        4.728708       1.454215   \n",
       "164650         5.385165              8.329381        4.728708       0.000000   \n",
       "256768         4.636809              7.801180        4.728708       0.000000   \n",
       "391990         5.567764              7.025009        4.728708            NaN   \n",
       "460760         5.567764              5.999458        4.728708       1.333521   \n",
       "526070         5.099020              7.947934        4.728708       1.668280   \n",
       "541345         5.000000             10.097743        5.623413       1.333521   \n",
       "557770         9.778548              5.784842        5.623413       1.333521   \n",
       "608335         4.000000              5.102178        4.728708            NaN   \n",
       "625878         5.099020             10.198913        4.728708       1.454215   \n",
       "629459         4.527693              6.573741        5.623413       0.000000   \n",
       "652067         3.872983              5.277437        4.728708       0.000000   \n",
       "62736          0.000000              5.670628        4.728708       1.333521   \n",
       "649632         2.236068              8.670453        4.728708       0.000000   \n",
       "578250         0.000000              7.495385        5.623413       1.454215   \n",
       "333334         5.958188              8.175956        4.728708            NaN   \n",
       "342123         3.741657             10.035563        4.728708       1.729363   \n",
       "353790         4.527693              5.830460        5.623413       0.000000   \n",
       "404794         3.162278              5.636054        3.976354       1.333521   \n",
       "444850         5.830952              9.786222        4.728708       1.529819   \n",
       "480726         4.636809              8.823968        4.728708       1.333521   \n",
       "535088         3.240370              8.419682        4.728708       0.000000   \n",
       "554667         3.240370              8.060551        4.728708       1.333521   \n",
       "560302         3.316625              6.410543        5.623413       1.333521   \n",
       "578651         4.898979             12.266203        5.623413       1.333521   \n",
       "584882         3.240370              8.009501        5.623413       1.333521   \n",
       "602648         3.674235              9.798611        4.728708       1.454215   \n",
       "603827         3.240370              7.130450        4.728708       1.333521   \n",
       "...                 ...                   ...             ...            ...   \n",
       "664970         6.118823              6.951269        3.976354       1.333521   \n",
       "679198         0.000000              0.000000        5.623413       0.000000   \n",
       "68715          7.211103              6.563801        4.728708       1.529819   \n",
       "325271         7.127412              6.798397        4.728708       1.333521   \n",
       "346530         5.089204              5.725000        3.976354       0.000000   \n",
       "350063         4.571652              5.245040        4.728708       0.000000   \n",
       "350071         7.334848              6.498364        4.728708       1.333521   \n",
       "448648         5.089204              5.433217        4.728708       0.000000   \n",
       "542878         5.087239              4.845643        4.728708       1.585833   \n",
       "546155         9.151503              8.716067        4.728708       1.630689   \n",
       "547156         5.905929              5.804489        5.623413       0.000000   \n",
       "555383         4.569464              5.084782        5.623413       1.585833   \n",
       "561070         4.569464              4.405225        4.728708       1.630689   \n",
       "586501         7.332121              6.477935        4.728708       1.585833   \n",
       "640703         5.444263              7.284441        4.728708       1.585833   \n",
       "347952         1.732051              8.129604        4.728708       1.333521   \n",
       "462744         1.732051              7.590711        4.728708       0.000000   \n",
       "605210         0.000000              6.598449        4.728708       0.000000   \n",
       "623823         0.000000              8.120264        4.728708       0.000000   \n",
       "126365         0.000000              5.052692        4.728708       0.000000   \n",
       "132974         4.242641              7.646986        4.728708            NaN   \n",
       "244701         4.242641              6.580779        3.976354       0.000000   \n",
       "270186         0.000000              5.004650        3.976354       0.000000   \n",
       "332685         0.000000              5.685080        4.728708       0.000000   \n",
       "350764         0.000000              8.354414        7.071068       0.000000   \n",
       "485794         0.000000              6.372218        3.162278            NaN   \n",
       "490032         0.000000              5.604675        3.976354       0.000000   \n",
       "586302         4.242641              4.793783        3.976354       0.000000   \n",
       "605623         4.242641              5.186675        4.728708       0.000000   \n",
       "707281         0.000000              7.537472        3.976354       0.000000   \n",
       "\n",
       "       Power_InsuranceExperienceDaysNum Power_CP_Limit  \n",
       "3                              0.000000            NaN  \n",
       "83042                         16.829350            NaN  \n",
       "103189                        21.386438            NaN  \n",
       "164650                        23.844205            NaN  \n",
       "256768                        22.867664            NaN  \n",
       "391990                        22.023666            NaN  \n",
       "460760                        23.296521            NaN  \n",
       "526070                        20.534574            NaN  \n",
       "541345                        16.713322            NaN  \n",
       "557770                        26.741773            NaN  \n",
       "608335                        22.796237            NaN  \n",
       "625878                        30.734048            NaN  \n",
       "629459                        13.946700            NaN  \n",
       "652067                        26.330373            NaN  \n",
       "62736                          0.000000            NaN  \n",
       "649632                        18.663958            NaN  \n",
       "578250                        30.494982            NaN  \n",
       "333334                        18.286398            NaN  \n",
       "342123                        16.093672            NaN  \n",
       "353790                        20.844811            NaN  \n",
       "404794                        32.138699            NaN  \n",
       "444850                        23.013441            NaN  \n",
       "480726                        22.908313            NaN  \n",
       "535088                        19.020901            NaN  \n",
       "554667                        20.350554            NaN  \n",
       "560302                        36.476331            NaN  \n",
       "578651                        24.202307            NaN  \n",
       "584882                        31.953119            NaN  \n",
       "602648                        24.560827            NaN  \n",
       "603827                         0.000000            NaN  \n",
       "...                                 ...            ...  \n",
       "664970                         0.000000            NaN  \n",
       "679198                        23.453509            NaN  \n",
       "68715                         11.850756            NaN  \n",
       "325271                         9.933718            NaN  \n",
       "346530                         0.000000            NaN  \n",
       "350063                         0.000000            NaN  \n",
       "350071                         0.000000            NaN  \n",
       "448648                         0.000000            NaN  \n",
       "542878                        14.278841            NaN  \n",
       "546155                         0.000000            NaN  \n",
       "547156                         9.138182            NaN  \n",
       "555383                         9.240499            NaN  \n",
       "561070                        15.606246            NaN  \n",
       "586501                        23.887802            NaN  \n",
       "640703                        17.409715            NaN  \n",
       "347952                        22.798283            NaN  \n",
       "462744                        19.369701            NaN  \n",
       "605210                        19.519059            NaN  \n",
       "623823                        25.054857            NaN  \n",
       "126365                        19.191171            NaN  \n",
       "132974                        19.508454            NaN  \n",
       "244701                        16.907090            NaN  \n",
       "270186                        33.883735            NaN  \n",
       "332685                        15.124521            NaN  \n",
       "350764                        10.899456            NaN  \n",
       "485794                        13.498566            NaN  \n",
       "490032                         5.243899            NaN  \n",
       "586302                        14.278841            NaN  \n",
       "605623                         0.000000            NaN  \n",
       "707281                         0.000000            NaN  \n",
       "\n",
       "[1552 rows x 95 columns]"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}