1-CVA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn as sk\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "game_csv = pd.read_csv('./data/Training_dataset_Original.csv',na_values=[\"NA\", \"na\", \"missing\"])\n",
    "gameDf = pd.DataFrame(game_csv)\n",
    "\n",
    "dataDict = pd.read_csv('./data/Data_Dictionary.csv',na_values=\"NA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80000, 49)\n",
      "(49, 2)\n"
     ]
    }
   ],
   "source": [
    "print(gameDf.shape)\n",
    "print(dataDict.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dic = dict(zip(dataDict.values[:, 1], dataDict.values[:, 0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'mvar25'"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic['Average tenure of active revolving credit cards (in days)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'application_key', u'mvar1', u'mvar2', u'mvar3', u'mvar4', u'mvar5',\n",
       "       u'mvar6', u'mvar7', u'mvar8', u'mvar9', u'mvar10', u'mvar11', u'mvar12',\n",
       "       u'mvar13', u'mvar14', u'mvar15', u'mvar16', u'mvar17', u'mvar18',\n",
       "       u'mvar19', u'mvar20', u'mvar21', u'mvar22', u'mvar23', u'mvar24',\n",
       "       u'mvar25', u'mvar26', u'mvar27', u'mvar28', u'mvar29', u'mvar30',\n",
       "       u'mvar31', u'mvar32', u'mvar33', u'mvar34', u'mvar35', u'mvar36',\n",
       "       u'mvar37', u'mvar38', u'mvar39', u'mvar40', u'mvar41', u'mvar42',\n",
       "       u'mvar43', u'mvar44', u'mvar45', u'mvar46', u'mvar47', u'default_ind'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#remove ID column\n",
    "del gameDf[dic['Application ID (primary key)']]\n",
    "del dic['Application ID (primary key)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'gameX' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-330-10163001a44f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#one hot encoding C=1 L=0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mgameX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mvar47'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_dummies\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgameX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mvar47'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mleaderX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mvar47'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_dummies\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleaderX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mvar47'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'gameX' is not defined"
     ]
    }
   ],
   "source": [
    "#one hot encoding C=1 L=0\n",
    "gameX['mvar47'] = pd.get_dummies(gameX['mvar47'])\n",
    "\n",
    "leaderX['mvar47'] = pd.get_dummies(leaderX['mvar47'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Seperate target variable y (Default or not)\n",
    "#y= gameDf.pop(dic['Customer spending capacity. 0 = No data available'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'mvar1', u'mvar2', u'mvar3', u'mvar4', u'mvar5', u'mvar6', u'mvar7',\n",
       "       u'mvar8', u'mvar9', u'mvar10', u'mvar11', u'mvar12', u'mvar13',\n",
       "       u'mvar14', u'mvar15', u'mvar16', u'mvar17', u'mvar18', u'mvar19',\n",
       "       u'mvar20', u'mvar21', u'mvar22', u'mvar23', u'mvar24', u'mvar25',\n",
       "       u'mvar26', u'mvar27', u'mvar28', u'mvar29', u'mvar30', u'mvar31',\n",
       "       u'mvar32', u'mvar33', u'mvar34', u'mvar35', u'mvar36', u'mvar37',\n",
       "       u'mvar38', u'mvar39', u'mvar40', u'mvar41', u'mvar42', u'mvar43',\n",
       "       u'mvar44', u'mvar45', u'mvar46', u'mvar47', u'default_ind'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(gameDf.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features = list(dic.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#mvar3\n",
    "df = gameDf.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python2.7/dist-packages/matplotlib/font_manager.py:281: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  'Matplotlib is building the font cache using fc-list. '\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEdCAYAAADjFntmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAAIABJREFUeJzt3X2cVnWd//HXm8GYGCBhzYk7wU1r\nQX4bKVkZ7cKSa2CCu3bjdLO2sFKbsrWaUDu/Ld1fsxt2ty62lu7MimuOuKVC3rQVzmRsZKKhq0xu\npKgjJCGCAoINfX5/nDPHi+GaW2Y4M9e8n4/H9ZjrOte5+VznOnN9zvfmfI8iAjMzM4AheQdgZmb9\nh5OCmZllnBTMzCzjpGBmZhknBTMzyzgpmJlZxknBekTS9ZK+kHcceetoP0j6qKR1fbTdyyXd2MV5\nKyXdK+lFSV85wu326DNJeqekx45guyHppJ4ub13npDDASdoi6SVJeyQ9L+lOSRPzjquQ/6FztxjY\nAYyKiEt7c8Vd/W4j4scR8cbe3Lb1DSeF0nBORIwAxgLPAityjqfPKOHjtnsmAZvCV6paF/ifq4RE\nxH7g28DU1mmSXiPpBkm/kfSkpP/b+qMq6RpJ3y6Yd7mktekP7yxJzZL+TtKOtETyofa2LelCSZsl\n7ZS0RtK4dPq96SwPpaWZDxRZtkzSV9LtPCHp4vQMdGj6fqOkGkn/DewDfl/SuHQ7O9PtXliwvkOq\ndFo/S8HrLZI+K2lTWrr6d0nlBe+/R9JGSbsk/UTSHxa892ZJD6ZVMauAbLn2d41WSNot6ReS5qQT\n3yfpgTYzXirp9nZWcqKkH6Xb/QFwXJv335bGukvSQ5Jmte4L4AJgabr/3yXpdEnr03m3Sbpa0qvS\n+ScX7vuC/f9XRWLq9LstmLfYd/BpSQ+n+2ZVm+/gsjS2rZIWtrde6wMR4ccAfgBbgHelz4cDK4Eb\nCt6/AVgNjAQmA/8LLCqY/3+BjwLvJKlimJC+NwtoAb4KDAP+GNgLvDF9/3rgC+nzP0mXPTWddwVw\nb0EMAZzUwWf4OLAJmACMBn6YLjM0fb8ReAo4BRgKHAP8CPhXkh/l6cBvgDltYyv4LM1t9tkjwERg\nDPDfBZ/lVGA78FagjOQHdUv6uV4FPAn8bRrDe4HfFm6rzef6aLoPW+f/ALA73eYwYCcwpWD+nwPn\ntbOu9QXfxR8BLwI3pu+NB54D5pGc6J2Zvn5tO/vjNOBt6b6cDDQBn0rfm1y47wv2/18VfKZ1Xf1u\nO/kOfgaMS/dHE/Dx9L13k5R4pwEVwE1d3Y4fR/5wSaE03C5pF/ACyQ/ClyA5Ayf5IfpsRLwYEVuA\nrwAfAYiIfcCHSX5sbgSWRERzm3X/fUQciIgfAXcC7y+y/Q8BdRHxYEQcAD4LvF3S5C7G/37gqoho\njojngS8Wmef6iHg0IlqA1wEzgWURsT8iNgL/1vq5uujqiHg6InYCNUBVOv1C4JsRcV9EHIyIlcAB\nkh/Rt5H8uP9zRPw2Ir4N3N/JdrYXzL8KeAw4O91Pq0j2P5JOIflBvqPtCiSdALyFV76Le4HvFszy\nYeCuiLgrIn4XET8ANpAkicNExAMR8dOIaEmPiW+SJP2j7V8iYmv6HXyXJLlDcjz8e0Q8EhF7gctz\niG3QclIoDedGxLEkZ5EXAz+S9DqSKobWs9tWT5KcWQIQET8DHgcE3NJmvc+n/5SFy44rsv1xhduI\niD0kZ6rji8xbzDjg6YLXTxeZp3DaOGBnRLzYJraubq/t+go/1yTg0rRqZVeabCem748DnomIaLNs\nR4rN37qtlcAHJYkkod2SJou2xlH8u2g1CXhfm5hnkrQxHUbSGyTdIenXkl4A/pE21VFHya8Lnu8D\nRqTP2x4Pne1j60VOCiUkPbO9FThI8qOwg6R6Y1LBbCcAz7S+kHQRSTLZCixts8rRkiraLLu1yKa3\nFm4jXeb3CrfTiW0kVUetivWeKvxh3QqMkTSyTWyt29tLUjXW6nVF1le4jcLP9TRQExHHFjyGR0R9\nGuf49Ee8cNmOFJt/K0BE/BR4maTq7oPAf7Szjm0U/y5aPQ38R5uYKyKiWIkL4BrgF8DJETEK+DuS\nkwJI9h10vv/60jYO/37sKHFSKCFJ+7AWkNTLN0XEQZKz/xpJIyVNAi4hqSpC0huAL5BUP3yEpDFy\nepvVXiHpVZLeCbwH+M8im74J+EtJ0yUNIznzvC+tmoCkfvj3Owj9FuCTksZLOhZY1tHnjIingZ8A\n/ySpPG0IXgR8K51lIzBP0pi0xPSpIqu5SNIESWNIfhRXpdOvAz4u6a3p/qyQdHaagNaTtBH8jaSh\nkv4cOL2jWIHj0/k/JOmZdP6vSlopaRRJm8/VQEtEFO3/HxFPklQHtX4XM4FzCma5EThH0llKGu3L\n04bdCcXWR9K+9AKwR9IfAH9dsK3fkCTXD6frWgi8voPP19l32xO3AB+VNFXScODzvbx+64CTQmn4\nrqQ9JP/oNcAFEfFo+t4SkrO/x4F1JD/gdWnvkhuB5RHxUET8kuTH8T/SH3ZIivfPk5zZfoukIfAX\nbTceEWuBvwe+Q3KW93rg/IJZLgdWplUbxdokrgO+DzxM0th6F8mP78EOPnMVSR38VuA24PNpXTok\nZ9wPkTRmfp9XfvAL3ZS+93j6+EL6WTaQtCtcnX72zSSNq0TEy8Cfp6+fJ2mvubWDGAHuA95Acnb+\nEnAWcCJJI+8X0lin0X4podUHSRq/d5L8SN7Q+kaaJBeQfH+/ISk5XEb7/9+fTtf3Ism+b7t/LkyX\nf46kcf8nHcR1OR1/t90WEXcD/wzcQ7L/7+mN9VrX6NDqTrNE2qXxxoho72yzL7c9F/hGREzqdObu\nr3sLMArYBVQCN5P8mF5PUuV2H/A+oB64IyKuLlj2IeCKiLhV0lUkCeI1wC9Jeu/8OJ3vcpIf+v3A\nfOCSiPi3gvWMIOk5dRxwHklj9KlpYjbLlUsKljtJr5Y0L62SGU9yJnxbH26ygqT95A0k1TB3kySG\n40j+J/6GpCTR2iMJSVNJ2k3uTCfdT9JbZkw6738W9rMnOXP/NnAsabWWpJmSdpOcoZ9Hcjb818D9\nTgjWXzgpWH8g4AqSKpmfk/RZ/1wfbu8FYFdEPAP8mKT94+dpz5/bgDenf6en7TCQdLu9tbV3UETc\nGBHPpd06v0LSWF84jMP6iLg97SL6UrrMuoh4DUmj+peAOuCTQK8OPZEXJRc67inyuDvv2KzrnBSs\nqIhoPFpVRxGxLyLeEhEjI+L4iPjLiHihDzdZFRE/TJ+/RNJYSsHrEWl31zt5pW3kfF5pyG69+rgp\nvRp3F0k1UmG3zmLdagFIk9H3gO0RMSkifn7En6gfiIh/jIgRRR5z847Nus5Jwax99UCVpLcDrwYa\nIBnxk6SH1PuB0ek1Irt5pVsnHNqFtpihdNyrxywXTgpm7buLpB3hH4BVEfG7dPpIkt5RvwGGSvoc\nSeN1u9IuqSek3VwnkfQSW9t3oZv1jJOCWTvS9oNbgXeRNCa3+i+Sxun/Jbnadj8dVBelppJ07dxD\nMtbSYyRdP836FXdJNTOzjEsKZmaWcVIwM7NMp0khHUflZ0pu3PGopCvS6SdKuk/SL5XcIKP1Jh3D\n0teb0/cn9+1HMDOz3tKVksIB4E8i4k0kV3C+W9LbgOXA1yLiZJKLjhal8y8iGeb3JOBr6XxmZjYA\ndJoUIrEnfXlM+giSu2213spxJXBu+nxB+pr0/Tlthg42M7N+amjns2R38HoAOAn4OvArkmECWtJZ\nmnnlBifjSbvnRURLOtbL75GM7V/UcccdF5MnT+5J/NaJvXv3UlFR0fmMZv2Aj9e+88ADD+yIiNd2\nNl+XkkI6Lv/0dKz724ApxWZL/xYrFRzW71XSYmAxQGVlJV/+8pe7Eop10549exgxYkTnM5r1Az5e\n+87s2bO7dAe7LiWFVhGxS1Ijyb1qj5U0NC0tTOCVO1c1k9w1qTkds/81JGPAt13XtcC1ADNmzIhZ\ns2Z1JxTrosbGRrxvbaDw8Zq/rvQ+em1aQkDSq0mu7mwiGQfmvelsFwCr0+dr0tek798TvkLOzGxA\n6EpJYSzJnZXKSJLILRFxh6RNwM2SvkAy3HFtOn8tyd27NpOUEM4vtlIzM+t/Ok0KEfEwyfjybac/\nTpH700bEfpI7V5mZ2QDjK5rNzCzjpGBmuauvr2fatGnMmTOHadOmUV9fn3dIg1a3eh+ZmfW2+vp6\nqqurqa2t5eDBg5SVlbFoUTJAQlVVVSdLW29zScHMclVTU0NtbS2zZ89m6NChzJ49m9raWmpqavIO\nbVByUjCzXDU1NTFz5sxDps2cOZOmpqacIhrcnBTMLFdTpkxh3bp1h0xbt24dU6YUGzjB+pqTgpnl\nqrq6mkWLFtHQ0EBLSwsNDQ0sWrSI6urqvEMblNzQbGa5am1MXrJkCU1NTUyZMoWamho3MufEScHM\ncldVVUVVVZXHPuoHXH1kZmYZJwUzM8s4KZiZWcZJwczMMk4KZmaWcVIwM7OMk4KZmWWcFMzMLOOk\nYGZmGScFMzPLOCmYmVnGScHMzDJOCmZmlnFSMDOzjJOCmZllnBTMzCzTaVKQNFFSg6QmSY9K+mQ6\n/XJJz0jamD7mFSzzWUmbJT0m6ay+/ABmZtZ7unLntRbg0oh4UNJI4AFJP0jf+1pEfLlwZklTgfOB\nU4BxwA8lvSEiDvZm4GZm1vs6LSlExLaIeDB9/iLQBIzvYJEFwM0RcSAingA2A6f3RrBmZta3utWm\nIGky8GbgvnTSxZIellQnaXQ6bTzwdMFizXScRMzMrJ/oSvURAJJGAN8BPhURL0i6Bvh/QKR/vwIs\nBFRk8SiyvsXAYoDKykoaGxu7Hbx1bs+ePd63NmD4eM1fl5KCpGNIEsK3IuJWgIh4tuD964A70pfN\nwMSCxScAW9uuMyKuBa4FmDFjRsyaNasH4VtnGhsb8b61gcLHa/660vtIQC3QFBFfLZg+tmC2PwMe\nSZ+vAc6XNEzSicDJwM96L2QzM+srXSkpvAP4CPA/kjam0/4OqJI0naRqaAvwMYCIeFTSLcAmkp5L\nF7nnkZnZwNBpUoiIdRRvJ7irg2VqgJojiMvMzHLgK5rNzCzjpGBmZhknBTMzyzgpmJlZxknBzMwy\nTgpmZpZxUjAzs4yTgpmZZZwUzMws46RgZmYZJwUzM8s4KZiZWcZJwczMMk4KZmaWcVIwM7OMk4KZ\nmWWcFMzMLOOkYGZmGScFMzPLOCmYmVnGScHMzDJOCmZmlnFSMDOzjJOCmZllnBTMzCzjpGBmZplO\nk4KkiZIaJDVJelTSJ9PpYyT9QNIv07+j0+mS9C+SNkt6WNKpff0hzMysd3SlpNACXBoRU4C3ARdJ\nmgp8BlgbEScDa9PXAHOBk9PHYuCaXo/azMz6RKdJISK2RcSD6fMXgSZgPLAAWJnOthI4N32+ALgh\nEj8FjpU0ttcjNzOzXje0OzNLmgy8GbgPqIyIbZAkDknHp7ONB54uWKw5nbatzboWk5QkqKyspLGx\nsfvRW6f27NnjfWsDho/X/HU5KUgaAXwH+FREvCCp3VmLTIvDJkRcC1wLMGPGjJg1a1ZXQ7FuaGxs\nxPvWBgofr/nrUu8jSceQJIRvRcSt6eRnW6uF0r/b0+nNwMSCxScAW3snXDMrRfX19UybNo05c+Yw\nbdo06uvr8w5p0Oq0pKCkSFALNEXEVwveWgNcAHwx/bu6YPrFkm4G3grsbq1mMjNrq76+nurqampr\nazl48CBlZWUsWrQIgKqqqpyjG3y6UlJ4B/AR4E8kbUwf80iSwZmSfgmcmb4GuAt4HNgMXAd8ovfD\nNrNSUVNTQ21tLbNnz2bo0KHMnj2b2tpaampq8g5tUOq0pBAR6yjeTgAwp8j8AVx0hHGZ2SDR1NTE\nzJkzD5k2c+ZMmpqacopocPMVzWaWqylTprBu3bpDpq1bt44pU6bkFNHg5qRgZrmqrq5m0aJFNDQ0\n0NLSQkNDA4sWLaK6ujrv0Aalbl2nYANHfX09NTU1NDU1MWXKFKqrq91oZ/1S63G5ZMmS7Hitqanx\n8ZoTJ4US5N4cNtBUVVVRVVXl6xT6AVcflSD35jCznnJSKEHuzWFmPeWkUILcm8PMespJoQS5N4eZ\n9ZQbmkuQe3OYWU+5pGBmZhmXFEqQu6TaQOPravoPJ4USVNgltbXfd21tLUuWLPE/mvU7PonpX1x9\nVILcJdUGEl9X0784KZQgd0m1gcQnMf2Lq49KUHV1NR/4wAeoqKjgySefZNKkSezdu5errroq79DM\nDjNlyhSuuOIKbr/99qxN4dxzz/VJTE6cFEpcB/fSNusXZs+ezfLly1m+fDlTp05l06ZNLFu2jI9/\n/ON5hzYoufqoBNXU1LBq1SqeeOIJ1q5dyxNPPMGqVatcR2v9UkNDA8uWLaOuro6zzz6buro6li1b\nRkNDQ96hDUpKbpSWrxkzZsSGDRvyDqNklJWVsX//fo455pis99Fvf/tbysvLOXjwYN7hmR3Cx+vR\nIemBiJjR2XwuKZQgNzTbQOLjtX9xm0IJKmxofuqppzjhhBPc0Gz9VutYXa3XKbSO1eXqznw4KZS4\n/lA9aNYRj9XVv7hNoQRNmzaNc88997AufrfffjuPPPJI3uGZtct3Xus7XW1TcEmhBG3atInt27dT\nUVFBRLB3716uvfZaduzYkXdoZtbPOSmUoLKyMlpaWqirq8vGkjnvvPMoKyvLOzQz6+fc+6gEtbS0\nMGzYsEOmDRs2jJaWlpwiMrOBotOkIKlO0nZJjxRMu1zSM5I2po95Be99VtJmSY9JOquvAreOnX76\n6cydO5czzzyTuXPncvrpp+cdkpkNAF2pProeuBq4oc30r0XElwsnSJoKnA+cAowDfijpDRHhK1CO\nojFjxnDnnXdy5ZVXZsMGLF26lDFjxuQdmpn1c52WFCLiXmBnF9e3ALg5Ig5ExBPAZsCnqEfZ8OHD\nGTFiBCtWrODss89mxYoVjBgxguHDh+cdmllR9fX1TJs2jTlz5jBt2jTq6+vzDmnQOpKG5osl/QWw\nAbg0Ip4HxgM/LZinOZ12GEmLgcUAlZWVNDY2HkEoVmjr1q0sW7bskH+sT3ziEyxfvtz72fqdtWvX\nUltby2WXXcaJJ57IE088waWXXsqmTZuYM2dO3uENPhHR6QOYDDxS8LoSKCMpadQAden0rwMfLpiv\nFjivs/WfdtppYb3nlFNOiXvuuSciIhoaGiIi4p577olTTjklx6jMivPxenQAG6ILv/c9KilExLOt\nzyVdB9yRvmwGJhbMOgHY2pNtWM/5fgo2kPgmO/1Lj7qkShpb8PLPgNaeSWuA8yUNk3QicDLwsyML\n0Xpi//79PPPMM0QEzzzzDPv37887JLOiPCBe/9KVLqn1wHrgjZKaJS0CrpT0P5IeBmYDfwsQEY8C\ntwCbgO8BF4V7Hh11S5cuZciQIYwfP/6Qv0uXLs07NLPDtA6I19DQQEtLSzYgXnV1dd6hDUoe+6gE\nSeJ1r3sdN910U3ZF8wc/+EF+/etfe4A865fq6+upqanJxuqqrq72gHi9rKtjHzkplCBJvP3tb+fB\nBx/kwIEDDBs2jFNPPZX169c7KVi/5gHx+o5vsjPIrV+/noULF/Ld736XhQsXsn79+rxDMrMBwEmh\nRJWXl3P33XezYMEC7r77bsrLy/MOyaxdS5Ysoby8nNmzZ1NeXs6SJUvyDmnQ8iipJWrEiBHAKzfZ\nGTFihHsgWb+0ZMkSvvGNb7B8+fJsWJZly5YBsGLFipyjG3xcUihBw4YN46yzzqKiogJJVFRUcNZZ\nZx02cqpZf3DdddexfPlyLrnkEsrLy7nkkktYvnw51113Xd6hDUpOCiXowgsvpL6+nh07dhAR7Nix\ng/r6ei688MK8QzM7zIEDBxg9evQhYx+NHj2aAwcO5B3aoOSkUILOOOMMKioq2LlzJxHBzp07qaio\n4Iwzzsg7NLPDDB06lCVLlrB3714A9u7dy5IlSxg61LXbeXBSKEE1NTWsXr2al19+mYaGBl5++WVW\nr15NTU1N3qGZHWbYsGHs3buXuXPnsnr1aubOncvevXtd3ZkTJ4US1NTURHNz8yHF8ebmZo8lY/3S\n3r17mT9/PnV1dZxzzjnU1dUxf/78rORgR5fLZyVo3LhxLF269LArmseNG5d3aGZFTZ06lV/96lc0\nNTVx0kknMXXqVNasWZN3WIOSk0KJktTha7P+YsyYMVx55ZV86UtfyrqkXnbZZb5TYE6cFErQ1q1b\n+djHPsbcuXOzYS4WLlzIN7/5zbxDMzvM8OHD+d3vfseKFSuyod5HjRrlOwXmxG0KJWjcuHHU19cz\nduxYhgwZwtixY6mvr3f1kfVLW7dupaqqim3bthERbNu2jaqqKrZu9a1Y8uCSQgnat28fu3btYteu\nXQBs2bIFgCFDfA5g/U/hScyTTz7pk5ic+VeiBO3cuROA0aNHH/K3dbpZf7Jv3z52797N/v37kcT+\n/fvZvXs3+/btyzu0QclJoURNnz6dcePGMWTIEMaNG8f06dPzDsmsqJ07dzJq1CjKy8uJCMrLyxk1\napRPYnLipFCiNm7cyHPPPQfAc889x8aNG3OOyKx98+bNO2Ssrnnz5uUd0qDlm+yUoI66n/aH79us\nkCQkcfzxx/Pss89SWVnJ9u3biQgfr73IN9kxysrKkERZWVneoZi1q6ysjIg4pGQbET5uc+LeRyXs\n4MGDh/w1648OHjyIpKxUEBFI8nGbE5cUStjIkSMZMmQII0eOzDsUsw4NHz6ciRMnMmTIECZOnOgL\n13LkkkIJe/HFFw/5a9ZfDRs2jLq6umysrve+970eEC8nTgpmlruWlhYWLlzIU089xQknnEBLS0ve\nIQ1aTgpmlqsJEyawbds2XnjhBSC5Ar+srIwJEybkHNng5DaFEjZ//nxuu+025s+fn3coZu0aPXr0\nYY3KBw8ezK7Et6Or0+sUJNUB7wG2R8S0dNoYYBUwGdgCvD8inlfSQf4qYB6wD/hoRDzYWRC+TqF3\n+ToFG0h8vB4dvXmdwvXAu9tM+wywNiJOBtamrwHmAienj8XANV0N2MzM8tdpUoiIe4G2g5AsAFam\nz1cC5xZMvyESPwWOlTS2t4I1M7O+1dM2hcqI2AaQ/j0+nT4eeLpgvuZ0mpmZDQC93fuoWOVg0UpB\nSYtJqpiorKyksbGxl0OxYryfbSDx8Xr09TQpPCtpbERsS6uHtqfTm4GJBfNNAIrePikirgWuhaSh\nedasWT0MxbrD+9n6q9ahLgqHvPDxevT1tPpoDXBB+vwCYHXB9L9Q4m3A7tZqJjOzjhSOfWT56bSk\nIKkemAUcJ6kZ+DzwReAWSYuAp4D3pbPfRdIddTNJl9S/7IOYzcysj3SaFCKiqp235hSZN4CLjjQo\nMzPLh69oNjOzjJOCmZllnBTMzCzjpGBmZhknBTMzyzgpmJlZxknBzMwyTgpmZpZxUjAzs4yTgpmZ\nZZwUzMws46RgZmYZJwUzM8s4KZiZWcZJwczMMk4KZmaWcVIwM7OMk4KZmWWcFMzMLOOkYGZmGScF\nMzPLOCmYmVnGScHMzDJOCmZmlnFSMDOzzNAjWVjSFuBF4CDQEhEzJI0BVgGTgS3A+yPi+SML08zM\njobeKCnMjojpETEjff0ZYG1EnAysTV+bmdkA0BfVRwuAlenzlcC5fbANMzPrA0eaFAL4vqQHJC1O\np1VGxDaA9O/xR7gNMzM7So6oTQF4R0RslXQ88ANJv+jqgmkSWQxQWVlJY2PjEYZiXeH9bAOJj9ej\nTxHROyuSLgf2ABcCsyJim6SxQGNEvLGjZWfMmBEbNmzolTgMJLX7Xm9932a9xcfr0SHpgYK233b1\nuPpIUoWkka3PgT8FHgHWABeks10ArO7pNszM7Og6kuqjSuC2NMsPBW6KiO9Juh+4RdIi4CngfUce\nppmZHQ09TgoR8TjwpiLTnwPmHElQZmaWD1/RbGZmGScFMzPLOCmYmVnGScHMzDJOCmZmlnFSMDOz\njJOCmZllnBTMzCxzpAPimZl1W0fjHXU0n8dC6ntOCmZ21BX+uHtAvP7F1UdmZpZxUjCzXLVXGnAp\nIR9OCmaWu4ggIpi07I7sueXDScHMzDJOCmZmlnFSMDOzjJOCmZllnBTMzCzjpGBmZhknBTMzy3iY\nCzPrE2+64vvsfum33V5u8mfu7Nb8r3n1MTz0+T/t9nasOCcFM+sTu1/6LVu+eHa3lmlsbGTWrFnd\nWqa7ScQ65uojMzPLuKRQAro6DHHbeT2UgJm15aRQAtr+uHsoYjPrqT6rPpL0bkmPSdos6TN9tR0z\nM+s9fVJSkFQGfB04E2gG7pe0JiI29cX27FARUbS04FKCHU0jp3yG/7OyB+eDK7u7HYDuNWhb+/qq\n+uh0YHNEPA4g6WZgAeCkcAS608Vv0rI7DpvW1V4a7uJnveHFpi+699EA1FdJYTzwdMHrZuCtfbSt\nQcNd/Mysr/VVUijW0nlI3YWkxcBigMrKShobG/solNJxNIvjjY0V3d+OWRvtnWA8ufw9PVpfsRJw\nxTH496MXqS/qmSW9Hbg8Is5KX38WICL+qdj8M2bMiA0bNvR6HNazkoJZXny89h1JD0TEjM7m66ve\nR/cDJ0s6UdKrgPOBNX20LTMz6yV9Un0UES2SLgb+CygD6iLi0b7YlpmZ9Z4+u3gtIu4C7uqr9ZuZ\nWe/z2EdmZpZxUjAzs4yTgpmZZZwUzMws46RgZmaZPrl4rdtBSL8Bnsw7jhJ1HLAj7yDMusjHa9+Z\nFBGv7WymfpEUrO9I2tCVqxjN+gMfr/lz9ZGZmWWcFMzMLOOkUPquzTsAs27w8ZoztymYmVnGJQUz\nM8s4KZQoSe+W9JikzZJ6cGces6NHUp2k7ZIeyTuWwc5JoQRJKgO+DswFpgJVkqbmG5VZh64H3p13\nEOakUKpOBzZHxOMR8TJwM7Ag55jM2hUR9wI7847DnBRK1Xjg6YLXzek0M7MOOSmUJhWZ5m5mZtYp\nJ4XS1AxMLHg9AdiaUyxmNoA4KZSm+4GTJZ0o6VXA+cCanGMyswHASaEERUQLcDHwX0ATcEtEPJpv\nVGbtk1QPrAfeKKlZ0qK8Yxp8bl8uAAAChUlEQVSsfEWzmZllXFIwM7OMk4KZmWWcFMzMLOOkYGZm\nGScFMzPLOCmYmVnGScFKkqTLJX26g/dfK+k+ST+X9M4erP+jkq5On5/b2Si0kv5B0ru6uY0tko7r\nbmxmR2Jo3gGY5WQO8IuIuKAX1nUucAewqb0ZIuJzvbAdsz7nkoKVDEnV6Y2Ffgi8MZ32eknfk/SA\npB9L+gNJ04ErgXmSNkp6taRrJG2Q9KikKwrWmZ2tS5ohqbHNNs8A5gNfStf1+nZiu17SewvWeYWk\nByX9j6Q/SKf/nqTvp6WXb1J8YEOzPuWkYCVB0mkkYzy9Gfhz4C3pW9cCSyLiNODTwL9GxEbgc8Cq\niJgeES8B1RExA/hD4I8l/WFXthsRPyEZV+qydF2/6mLIOyLiVOCaNC6AzwPrIuLN6TpP6OK6zHqN\nq4+sVLwTuC0i9gFIWgOUA2cA/yllJ93D2ln+/ZIWk/xPjCW5Y93DfRjvrenfB0iSGMAftT6PiDsl\nPd+H2zcryknBSknbgbyGALsiYnpHC0k6keRs/S0R8byk60kSCkALr5Soy4ss3lMH0r8HOfT/0IOR\nWa5cfWSl4l7gz9L2gZHAOcA+4AlJ7wNQ4k1Flh0F7AV2S6okubd1qy3Aaenz89rZ9ovAyCP/CNwL\nfCiNdS4wuhfWadYtTgpWEiLiQWAVsBH4DvDj9K0PAYskPQQ8SpF7VUfEQ8DP0/frgP8uePsK4CpJ\nPyY5qy/mZuCytIG4aENzF10B/JGkB4E/BZ46gnWZ9YiHzjYzs4xLCmZmlnFDs1kvkvR14B1tJl8V\nEf+eRzxm3eXqIzMzy7j6yMzMMk4KZmaWcVIwM7OMk4KZmWWcFMzMLPP/ASvLTQTX+bvlAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f08c055bbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gameDf.boxplot(column = 'mvar3',by = 'default_ind')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEdCAYAAADjFntmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f08c0817f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gameDf.boxplot(column = 'mvar4',by = 'default_ind')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEdCAYAAADjFntmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f08c33dcd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gameDf.boxplot(column = 'mvar4',by = 'default_ind')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['mvar3'] = (1+df['mvar3'])*(1+df['mvar4'])*(1+df['mvar5'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEdCAYAAADjFntmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f08c33bc750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gameDf.boxplot(column = 'mvar3',by = 'default_ind')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#mvar6 7 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "check for nan vlaues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    18964.000000\n",
       "mean         0.406613\n",
       "std          0.491214\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          1.000000\n",
       "max          1.000000\n",
       "Name: default_ind, dtype: float64"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = gameDf.copy()\n",
    "df['default_ind'][df['mvar6'].isnull()].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7394.000000\n",
       "mean        0.445496\n",
       "std         0.497054\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         0.000000\n",
       "75%         1.000000\n",
       "max         1.000000\n",
       "Name: default_ind, dtype: float64"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = gameDf.copy()\n",
    "df['default_ind'][df['mvar7'].isnull()].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    18964.000000\n",
       "mean         0.406613\n",
       "std          0.491214\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          1.000000\n",
       "max          1.000000\n",
       "Name: default_ind, dtype: float64"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = gameDf.copy()\n",
    "df['default_ind'][df['mvar8'].isnull()].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['mvar7']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "df['mvar7'][df['mvar7'].isnull()] = -1 \n",
    "df['mvar8'][df['mvar8'].isnull()] = -1\n",
    "\n",
    "df['mvar7'] = df['mvar7'] + df['mvar8'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6135.000000\n",
       "mean        0.054605\n",
       "std         0.227226\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         0.000000\n",
       "75%         0.000000\n",
       "max         1.000000\n",
       "Name: default_ind, dtype: float64"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['default_ind'][df['mvar7']>=60000].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ4AAAEdCAYAAAA8WAF5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f08c06b29d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gameDf.boxplot(column = 'mvar7',by = 'default_ind')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEdCAYAAAAxRnE+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njJmZlYUDjJmZlYUDjJmZlcX/B4FwqDtCHZQfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f08c06bb2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gameDf.boxplot(column = 'mvar8',by = 'default_ind')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8203"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(df['mvar5']!=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "501"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(df['mvar5'].isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mvar1</th>\n",
       "      <th>mvar2</th>\n",
       "      <th>mvar3</th>\n",
       "      <th>mvar4</th>\n",
       "      <th>mvar5</th>\n",
       "      <th>mvar6</th>\n",
       "      <th>mvar7</th>\n",
       "      <th>mvar8</th>\n",
       "      <th>mvar9</th>\n",
       "      <th>mvar10</th>\n",
       "      <th>...</th>\n",
       "      <th>mvar39</th>\n",
       "      <th>mvar40</th>\n",
       "      <th>mvar41</th>\n",
       "      <th>mvar42</th>\n",
       "      <th>mvar43</th>\n",
       "      <th>mvar44</th>\n",
       "      <th>mvar45</th>\n",
       "      <th>mvar46</th>\n",
       "      <th>mvar47</th>\n",
       "      <th>default_ind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1696.0</td>\n",
       "      <td>1.6541</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6015.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>40369.0</td>\n",
       "      <td>18414.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>73.780</td>\n",
       "      <td>82.547</td>\n",
       "      <td>0.08696</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.63899</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1846.0</td>\n",
       "      <td>0.8095</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>102.0</td>\n",
       "      <td>7532.0</td>\n",
       "      <td>3171.0</td>\n",
       "      <td>18234.0</td>\n",
       "      <td>13664.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.129</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.63836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1745.0</td>\n",
       "      <td>0.4001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2536.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2536.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.290</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1739.0</td>\n",
       "      <td>0.2193</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>26440.0</td>\n",
       "      <td>4955.0</td>\n",
       "      <td>20316.0</td>\n",
       "      <td>37013.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.272</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.15385</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.53241</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1787.0</td>\n",
       "      <td>0.0118</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5451.0</td>\n",
       "      <td>5494.0</td>\n",
       "      <td>5494.0</td>\n",
       "      <td>7987.0</td>\n",
       "      <td>4696.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115.019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.92665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1579.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.502</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1818.0</td>\n",
       "      <td>0.4001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1088.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1536.0</td>\n",
       "      <td>1498.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.171</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.87224</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1836.0</td>\n",
       "      <td>0.1358</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>347.0</td>\n",
       "      <td>38964.0</td>\n",
       "      <td>17828.0</td>\n",
       "      <td>70729.0</td>\n",
       "      <td>65843.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.89868</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1839.0</td>\n",
       "      <td>0.1981</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>793.0</td>\n",
       "      <td>6131.0</td>\n",
       "      <td>6045.0</td>\n",
       "      <td>48959.0</td>\n",
       "      <td>31640.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.590</td>\n",
       "      <td>0.08824</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.33834</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1903.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19518.0</td>\n",
       "      <td>9910.0</td>\n",
       "      <td>90618.0</td>\n",
       "      <td>110271.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.80620</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1681.0</td>\n",
       "      <td>1.5888</td>\n",
       "      <td>5.685</td>\n",
       "      <td>3.566</td>\n",
       "      <td>0.000</td>\n",
       "      <td>93.0</td>\n",
       "      <td>3554.0</td>\n",
       "      <td>439.0</td>\n",
       "      <td>2527.0</td>\n",
       "      <td>7086.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.61111</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.81172</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1841.0</td>\n",
       "      <td>0.5388</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1487.0</td>\n",
       "      <td>19449.0</td>\n",
       "      <td>19449.0</td>\n",
       "      <td>6739.0</td>\n",
       "      <td>6612.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.98031</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1794.0</td>\n",
       "      <td>1.9684</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>856.0</td>\n",
       "      <td>8609.0</td>\n",
       "      <td>8609.0</td>\n",
       "      <td>60545.0</td>\n",
       "      <td>24893.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.35724</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1669.0</td>\n",
       "      <td>3.8558</td>\n",
       "      <td>18.465</td>\n",
       "      <td>6.292</td>\n",
       "      <td>1.200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>142.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>107.825</td>\n",
       "      <td>98.664</td>\n",
       "      <td>0.56000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.29169</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1583.0</td>\n",
       "      <td>3.0284</td>\n",
       "      <td>16.325</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51450.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.32353</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1899.0</td>\n",
       "      <td>0.0970</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>13379.0</td>\n",
       "      <td>13379.0</td>\n",
       "      <td>246906.0</td>\n",
       "      <td>56533.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.384</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.47480</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1690.0</td>\n",
       "      <td>8.7747</td>\n",
       "      <td>10.437</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>122.0</td>\n",
       "      <td>623.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>1140.0</td>\n",
       "      <td>2043.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.63636</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.94951</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1599.0</td>\n",
       "      <td>0.8601</td>\n",
       "      <td>1.525</td>\n",
       "      <td>1.525</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>878.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>878.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.175</td>\n",
       "      <td>0.80000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1859.0</td>\n",
       "      <td>0.0155</td>\n",
       "      <td>0.368</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>424.0</td>\n",
       "      <td>86509.0</td>\n",
       "      <td>12451.0</td>\n",
       "      <td>86635.0</td>\n",
       "      <td>142081.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.34730</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1880.0</td>\n",
       "      <td>2.6101</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19252.0</td>\n",
       "      <td>6342.0</td>\n",
       "      <td>59601.0</td>\n",
       "      <td>86906.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.497</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.28708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1852.0</td>\n",
       "      <td>3.6201</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>799.0</td>\n",
       "      <td>16515.0</td>\n",
       "      <td>16515.0</td>\n",
       "      <td>110651.0</td>\n",
       "      <td>41311.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.75349</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1655.0</td>\n",
       "      <td>3.0268</td>\n",
       "      <td>14.748</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1863.0</td>\n",
       "      <td>0.0401</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1487.0</td>\n",
       "      <td>4955.0</td>\n",
       "      <td>4955.0</td>\n",
       "      <td>22902.0</td>\n",
       "      <td>21772.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.895</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.40853</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1753.0</td>\n",
       "      <td>0.2251</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1894.0</td>\n",
       "      <td>0.0318</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4655.0</td>\n",
       "      <td>7433.0</td>\n",
       "      <td>7433.0</td>\n",
       "      <td>25667.0</td>\n",
       "      <td>12345.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.99419</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1803.0</td>\n",
       "      <td>0.3993</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>496.0</td>\n",
       "      <td>10505.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>991.0</td>\n",
       "      <td>18163.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.752</td>\n",
       "      <td>87.306</td>\n",
       "      <td>0.20000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.75289</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1932.0</td>\n",
       "      <td>0.3276</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6982.0</td>\n",
       "      <td>21569.0</td>\n",
       "      <td>18829.0</td>\n",
       "      <td>187209.0</td>\n",
       "      <td>113813.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.99710</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1714.0</td>\n",
       "      <td>5.1904</td>\n",
       "      <td>0.770</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1889.0</td>\n",
       "      <td>595.0</td>\n",
       "      <td>12574.0</td>\n",
       "      <td>4825.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.35714</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.85082</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0457</td>\n",
       "      <td>4.514</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.16667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L</td>\n",
       "      <td>0</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>79970</th>\n",
       "      <td>1834.0</td>\n",
       "      <td>1.2164</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2212.0</td>\n",
       "      <td>5244.0</td>\n",
       "      <td>4203.0</td>\n",
       "      <td>16743.0</td>\n",
       "      <td>13831.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.398</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.58040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79971</th>\n",
       "      <td>1729.0</td>\n",
       "      <td>1.3696</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>356.0</td>\n",
       "      <td>4218.0</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>18657.0</td>\n",
       "      <td>8921.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83.663</td>\n",
       "      <td>0.09091</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.62632</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79972</th>\n",
       "      <td>1753.0</td>\n",
       "      <td>5.8283</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>4014.0</td>\n",
       "      <td>617.0</td>\n",
       "      <td>3667.0</td>\n",
       "      <td>9786.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87.094</td>\n",
       "      <td>0.11111</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.59364</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79973</th>\n",
       "      <td>1748.0</td>\n",
       "      <td>1.9635</td>\n",
       "      <td>0.642</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>291.0</td>\n",
       "      <td>3469.0</td>\n",
       "      <td>3469.0</td>\n",
       "      <td>13131.0</td>\n",
       "      <td>14393.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.244</td>\n",
       "      <td>0.44000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.83809</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79974</th>\n",
       "      <td>1771.0</td>\n",
       "      <td>1.7588</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>66.0</td>\n",
       "      <td>12626.0</td>\n",
       "      <td>6433.0</td>\n",
       "      <td>27946.0</td>\n",
       "      <td>24401.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.477</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.46863</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79975</th>\n",
       "      <td>1766.0</td>\n",
       "      <td>0.9540</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1140.0</td>\n",
       "      <td>10207.0</td>\n",
       "      <td>10207.0</td>\n",
       "      <td>55001.0</td>\n",
       "      <td>20636.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.54754</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79976</th>\n",
       "      <td>1822.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.853</td>\n",
       "      <td>1.853</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2581.0</td>\n",
       "      <td>13361.0</td>\n",
       "      <td>5946.0</td>\n",
       "      <td>31018.0</td>\n",
       "      <td>76774.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73.611</td>\n",
       "      <td>44.656</td>\n",
       "      <td>0.03448</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.23177</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79977</th>\n",
       "      <td>1824.0</td>\n",
       "      <td>0.5634</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3964.0</td>\n",
       "      <td>4683.0</td>\n",
       "      <td>4683.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8647.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.67222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79978</th>\n",
       "      <td>1914.0</td>\n",
       "      <td>0.0544</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3469.0</td>\n",
       "      <td>20424.0</td>\n",
       "      <td>20424.0</td>\n",
       "      <td>78774.0</td>\n",
       "      <td>27856.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79979</th>\n",
       "      <td>1600.0</td>\n",
       "      <td>2.6649</td>\n",
       "      <td>18.282</td>\n",
       "      <td>1.078</td>\n",
       "      <td>2.678</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7205.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>956.0</td>\n",
       "      <td>7360.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>132.335</td>\n",
       "      <td>56.867</td>\n",
       "      <td>0.55263</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.11763</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>L</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79980</th>\n",
       "      <td>1822.0</td>\n",
       "      <td>0.5851</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23685.0</td>\n",
       "      <td>23685.0</td>\n",
       "      <td>208174.0</td>\n",
       "      <td>55719.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.81438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79981</th>\n",
       "      <td>1693.0</td>\n",
       "      <td>0.1828</td>\n",
       "      <td>17.767</td>\n",
       "      <td>2.024</td>\n",
       "      <td>0.000</td>\n",
       "      <td>114.0</td>\n",
       "      <td>27514.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>2329.0</td>\n",
       "      <td>48080.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.814</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.17857</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.53340</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79982</th>\n",
       "      <td>1735.0</td>\n",
       "      <td>3.1701</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>117.0</td>\n",
       "      <td>892.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>3766.0</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.026</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.96747</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79983</th>\n",
       "      <td>1671.0</td>\n",
       "      <td>0.1380</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>491.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>491.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.80000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79984</th>\n",
       "      <td>1924.0</td>\n",
       "      <td>0.1221</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2478.0</td>\n",
       "      <td>266683.0</td>\n",
       "      <td>266683.0</td>\n",
       "      <td>405300.0</td>\n",
       "      <td>390558.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.799</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.75322</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79985</th>\n",
       "      <td>1670.0</td>\n",
       "      <td>0.0347</td>\n",
       "      <td>9.836</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>212.0</td>\n",
       "      <td>7848.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>29785.0</td>\n",
       "      <td>16226.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.700</td>\n",
       "      <td>0.13636</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.85771</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79986</th>\n",
       "      <td>1901.0</td>\n",
       "      <td>0.1782</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4955.0</td>\n",
       "      <td>21753.0</td>\n",
       "      <td>12673.0</td>\n",
       "      <td>70293.0</td>\n",
       "      <td>62980.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.301</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.86356</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79987</th>\n",
       "      <td>1835.0</td>\n",
       "      <td>0.5372</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29723.0</td>\n",
       "      <td>3964.0</td>\n",
       "      <td>66770.0</td>\n",
       "      <td>85900.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.41685</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79988</th>\n",
       "      <td>1927.0</td>\n",
       "      <td>0.0410</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>30046.0</td>\n",
       "      <td>24165.0</td>\n",
       "      <td>75019.0</td>\n",
       "      <td>88343.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.444</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.53750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79989</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1451</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79990</th>\n",
       "      <td>1941.0</td>\n",
       "      <td>0.4834</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>10846.0</td>\n",
       "      <td>52806.0</td>\n",
       "      <td>35478.0</td>\n",
       "      <td>136461.0</td>\n",
       "      <td>176240.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.05882</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.84170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79991</th>\n",
       "      <td>1899.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7310.0</td>\n",
       "      <td>14519.0</td>\n",
       "      <td>14519.0</td>\n",
       "      <td>5252.0</td>\n",
       "      <td>4949.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.44615</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79992</th>\n",
       "      <td>1761.0</td>\n",
       "      <td>2.0999</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>47.0</td>\n",
       "      <td>13477.0</td>\n",
       "      <td>1814.0</td>\n",
       "      <td>9320.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.12500</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.73640</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79993</th>\n",
       "      <td>1700.0</td>\n",
       "      <td>0.3831</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>344.0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>991.0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.66667</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.97374</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79994</th>\n",
       "      <td>1910.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4955.0</td>\n",
       "      <td>47306.0</td>\n",
       "      <td>47306.0</td>\n",
       "      <td>88124.0</td>\n",
       "      <td>107148.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.79867</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79995</th>\n",
       "      <td>1736.0</td>\n",
       "      <td>2.1740</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4248.0</td>\n",
       "      <td>1577.0</td>\n",
       "      <td>13379.0</td>\n",
       "      <td>6671.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.378</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.43829</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79996</th>\n",
       "      <td>1724.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.108</td>\n",
       "      <td>0.768</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64041.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10926.0</td>\n",
       "      <td>84839.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.325</td>\n",
       "      <td>0.16667</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.57931</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79997</th>\n",
       "      <td>1605.0</td>\n",
       "      <td>0.2901</td>\n",
       "      <td>11.561</td>\n",
       "      <td>0.937</td>\n",
       "      <td>2.976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3964.0</td>\n",
       "      <td>5709.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>101.850</td>\n",
       "      <td>93.142</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.42069</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79998</th>\n",
       "      <td>1780.0</td>\n",
       "      <td>1.1874</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6356.0</td>\n",
       "      <td>4802.0</td>\n",
       "      <td>3206.0</td>\n",
       "      <td>18180.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.022</td>\n",
       "      <td>0.06061</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.53251</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>L</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79999</th>\n",
       "      <td>1727.0</td>\n",
       "      <td>1.9288</td>\n",
       "      <td>1.441</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25773.0</td>\n",
       "      <td>2869.0</td>\n",
       "      <td>132985.0</td>\n",
       "      <td>71788.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.07143</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.68482</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80000 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        mvar1   mvar2   mvar3  mvar4  mvar5    mvar6     mvar7     mvar8  \\\n",
       "0      1696.0  1.6541   0.000  0.000  0.000      0.0    6015.0     322.0   \n",
       "1      1846.0  0.8095   0.000  0.000  0.000    102.0    7532.0    3171.0   \n",
       "2      1745.0  0.4001   0.000  0.000  0.000      NaN    2536.0       NaN   \n",
       "3      1739.0  0.2193   0.000  0.000  0.000   1982.0   26440.0    4955.0   \n",
       "4      1787.0  0.0118   0.225  0.000  0.000   5451.0    5494.0    5494.0   \n",
       "5      1579.0     NaN   3.502  0.000  0.000      NaN       NaN       NaN   \n",
       "6      1818.0  0.4001   0.000  0.000  0.000      NaN    1088.0       NaN   \n",
       "7         NaN     NaN     NaN    NaN    NaN      NaN       NaN       NaN   \n",
       "8      1836.0  0.1358   0.000  0.000  0.000    347.0   38964.0   17828.0   \n",
       "9      1839.0  0.1981   0.000  0.000  0.000    793.0    6131.0    6045.0   \n",
       "10     1903.0  0.0000   0.000  0.000  0.000     22.0   19518.0    9910.0   \n",
       "11     1681.0  1.5888   5.685  3.566  0.000     93.0    3554.0     439.0   \n",
       "12     1841.0  0.5388   0.000  0.000  0.000   1487.0   19449.0   19449.0   \n",
       "13     1794.0  1.9684   0.000  0.000  0.000    856.0    8609.0    8609.0   \n",
       "14     1669.0  3.8558  18.465  6.292  1.200      NaN     142.0       NaN   \n",
       "15     1583.0  3.0284  16.325  0.000  0.000      NaN       NaN       NaN   \n",
       "16     1899.0  0.0970   0.000  0.000  0.000   1015.0   13379.0   13379.0   \n",
       "17     1690.0  8.7747  10.437  0.000  0.000    122.0     623.0     299.0   \n",
       "18     1599.0  0.8601   1.525  1.525  0.000      NaN     878.0       NaN   \n",
       "19     1859.0  0.0155   0.368  0.000  0.000    424.0   86509.0   12451.0   \n",
       "20     1880.0  2.6101   0.000  0.000  0.000      0.0   19252.0    6342.0   \n",
       "21     1852.0  3.6201   0.000  0.000  0.000    799.0   16515.0   16515.0   \n",
       "22     1655.0  3.0268  14.748  0.000  0.000     48.0      48.0      48.0   \n",
       "23     1863.0  0.0401   0.000  0.000  0.000   1487.0    4955.0    4955.0   \n",
       "24     1753.0  0.2251   0.000  0.000  0.000   1124.0    1124.0    1124.0   \n",
       "25     1894.0  0.0318   0.000  0.000  0.000   4655.0    7433.0    7433.0   \n",
       "26     1803.0  0.3993   0.000  0.000  0.000    496.0   10505.0     496.0   \n",
       "27     1932.0  0.3276   0.000  0.000  0.000   6982.0   21569.0   18829.0   \n",
       "28     1714.0  5.1904   0.770  0.000  0.000      0.0    1889.0     595.0   \n",
       "29        NaN  2.0457   4.514  0.000  0.000      NaN       NaN       NaN   \n",
       "...       ...     ...     ...    ...    ...      ...       ...       ...   \n",
       "79970  1834.0  1.2164   0.000  0.000  0.000   2212.0    5244.0    4203.0   \n",
       "79971  1729.0  1.3696   0.000  0.000  0.000    356.0    4218.0    1982.0   \n",
       "79972  1753.0  5.8283   0.000  0.000  0.000    180.0    4014.0     617.0   \n",
       "79973  1748.0  1.9635   0.642  0.000  0.000    291.0    3469.0    3469.0   \n",
       "79974  1771.0  1.7588   0.000  0.000  0.000     66.0   12626.0    6433.0   \n",
       "79975  1766.0  0.9540   0.000  0.000  0.000   1140.0   10207.0   10207.0   \n",
       "79976  1822.0  0.0000   1.853  1.853  0.000   2581.0   13361.0    5946.0   \n",
       "79977  1824.0  0.5634   0.000  0.000  0.000   3964.0    4683.0    4683.0   \n",
       "79978  1914.0  0.0544   0.000  0.000  0.000   3469.0   20424.0   20424.0   \n",
       "79979  1600.0  2.6649  18.282  1.078  2.678      0.0    7205.0       0.0   \n",
       "79980  1822.0  0.5851   0.000  0.000  0.000      0.0   23685.0   23685.0   \n",
       "79981  1693.0  0.1828  17.767  2.024  0.000    114.0   27514.0     384.0   \n",
       "79982  1735.0  3.1701   0.260  0.000  0.000    117.0     892.0     117.0   \n",
       "79983  1671.0  0.1380   0.000  0.000  0.000      NaN     491.0       NaN   \n",
       "79984  1924.0  0.1221   0.000  0.000  0.000   2478.0  266683.0  266683.0   \n",
       "79985  1670.0  0.0347   9.836  0.000  0.000    212.0    7848.0     491.0   \n",
       "79986  1901.0  0.1782   0.000  0.000  0.000   4955.0   21753.0   12673.0   \n",
       "79987  1835.0  0.5372   0.000  0.000  0.000      0.0   29723.0    3964.0   \n",
       "79988  1927.0  0.0410   0.000  0.000  0.000  22000.0   30046.0   24165.0   \n",
       "79989     NaN  0.1451   0.000  0.000  0.000      NaN       NaN       NaN   \n",
       "79990  1941.0  0.4834   0.000  0.000  0.000  10846.0   52806.0   35478.0   \n",
       "79991  1899.0     NaN   0.000  0.000  0.000   7310.0   14519.0   14519.0   \n",
       "79992  1761.0  2.0999   0.000  0.000  0.000     47.0   13477.0    1814.0   \n",
       "79993  1700.0  0.3831   0.000  0.000  0.000    344.0     344.0     344.0   \n",
       "79994  1910.0  0.0000   0.000  0.000  0.000   4955.0   47306.0   47306.0   \n",
       "79995  1736.0  2.1740   0.000  0.000  0.000     11.0    4248.0    1577.0   \n",
       "79996  1724.0  0.0000   1.108  0.768  0.000      NaN   64041.0       NaN   \n",
       "79997  1605.0  0.2901  11.561  0.937  2.976      NaN    2277.0       NaN   \n",
       "79998  1780.0  1.1874   0.000  0.000  0.000      0.0    6356.0    4802.0   \n",
       "79999  1727.0  1.9288   1.441  0.000  0.000      0.0   25773.0    2869.0   \n",
       "\n",
       "          mvar9    mvar10     ...       mvar39   mvar40  mvar41   mvar42  \\\n",
       "0       40369.0   18414.0     ...          1.0   73.780  82.547  0.08696   \n",
       "1       18234.0   13664.0     ...          0.0   99.129     NaN  0.00000   \n",
       "2           NaN    2536.0     ...          0.0      NaN  29.290  0.00000   \n",
       "3       20316.0   37013.0     ...          0.0   96.272     NaN  0.15385   \n",
       "4        7987.0    4696.0     ...          0.0  115.019     NaN  0.00000   \n",
       "5           NaN       0.0     ...          NaN      NaN     NaN  1.50000   \n",
       "6        1536.0    1498.0     ...          0.0   88.171     NaN  0.00000   \n",
       "7           NaN       NaN     ...          NaN      NaN     NaN      NaN   \n",
       "8       70729.0   65843.0     ...          0.0      NaN     NaN  0.00000   \n",
       "9       48959.0   31640.0     ...          0.0      NaN  45.590  0.08824   \n",
       "10      90618.0  110271.0     ...          0.0      NaN     NaN  0.00000   \n",
       "11       2527.0    7086.0     ...          1.0      NaN     NaN  0.61111   \n",
       "12       6739.0    6612.0     ...          0.0      NaN     NaN  0.00000   \n",
       "13      60545.0   24893.0     ...          0.0      NaN     NaN  0.00000   \n",
       "14         38.0     142.0     ...          0.0  107.825  98.664  0.56000   \n",
       "15      51450.0       0.0     ...          0.0      NaN     NaN  0.32353   \n",
       "16     246906.0   56533.0     ...          0.0   10.384     NaN  0.00000   \n",
       "17       1140.0    2043.0     ...          0.0      NaN     NaN  0.63636   \n",
       "18          NaN     878.0     ...          1.0      NaN  91.175  0.80000   \n",
       "19      86635.0  142081.0     ...          0.0      NaN     NaN  0.00000   \n",
       "20      59601.0   86906.0     ...          0.0      NaN  72.497  0.00000   \n",
       "21     110651.0   41311.0     ...          0.0      NaN     NaN  0.00000   \n",
       "22        198.0      48.0     ...          0.0      NaN     NaN  1.00000   \n",
       "23      22902.0   21772.0     ...          0.0   96.895     NaN  0.00000   \n",
       "24          0.0    1124.0     ...          0.0      NaN     NaN      NaN   \n",
       "25      25667.0   12345.0     ...          0.0      NaN   0.000  0.00000   \n",
       "26        991.0   18163.0     ...          0.0   91.752  87.306  0.20000   \n",
       "27     187209.0  113813.0     ...          0.0      NaN     NaN  0.00000   \n",
       "28      12574.0    4825.0     ...          0.0      NaN     NaN  0.35714   \n",
       "29          NaN       0.0     ...          NaN      NaN     NaN  1.16667   \n",
       "...         ...       ...     ...          ...      ...     ...      ...   \n",
       "79970   16743.0   13831.0     ...          0.0      NaN  97.398  0.00000   \n",
       "79971   18657.0    8921.0     ...          0.0      NaN  83.663  0.09091   \n",
       "79972    3667.0    9786.0     ...          0.0      NaN  87.094  0.11111   \n",
       "79973   13131.0   14393.0     ...          0.0      NaN  92.244  0.44000   \n",
       "79974   27946.0   24401.0     ...          0.0      NaN  40.477  0.00000   \n",
       "79975   55001.0   20636.0     ...          0.0      NaN     NaN  0.05000   \n",
       "79976   31018.0   76774.0     ...          0.0   73.611  44.656  0.03448   \n",
       "79977       NaN    8647.0     ...          0.0      NaN     NaN      NaN   \n",
       "79978   78774.0   27856.0     ...          0.0      NaN     NaN  0.00000   \n",
       "79979     956.0    7360.0     ...          1.0  132.335  56.867  0.55263   \n",
       "79980  208174.0   55719.0     ...          0.0      NaN     NaN  0.00000   \n",
       "79981    2329.0   48080.0     ...          0.0   74.814     NaN  0.17857   \n",
       "79982    3766.0    1265.0     ...          0.0      NaN  99.026  0.00000   \n",
       "79983       NaN     491.0     ...          0.0      NaN     NaN  0.80000   \n",
       "79984  405300.0  390558.0     ...          0.0      NaN  70.799  0.00000   \n",
       "79985   29785.0   16226.0     ...          0.0      NaN  78.700  0.13636   \n",
       "79986   70293.0   62980.0     ...          0.0      NaN  61.301  0.00000   \n",
       "79987   66770.0   85900.0     ...          0.0      NaN     NaN  0.00000   \n",
       "79988   75019.0   88343.0     ...          0.0      NaN  19.444  0.00000   \n",
       "79989       NaN       0.0     ...          NaN      NaN     NaN  1.50000   \n",
       "79990  136461.0  176240.0     ...          0.0      NaN     NaN  0.05882   \n",
       "79991    5252.0    4949.0     ...          0.0      NaN     NaN  0.00000   \n",
       "79992    9320.0    2500.0     ...          0.0      NaN     NaN  0.12500   \n",
       "79993     991.0     344.0     ...          0.0      NaN     NaN  0.66667   \n",
       "79994   88124.0  107148.0     ...          0.0      NaN     NaN  0.00000   \n",
       "79995   13379.0    6671.0     ...          0.0      NaN  78.378  0.00000   \n",
       "79996   10926.0   84839.0     ...          0.0      NaN  38.325  0.16667   \n",
       "79997    3964.0    5709.0     ...          2.0  101.850  93.142  0.50000   \n",
       "79998    3206.0   18180.0     ...          0.0      NaN  77.022  0.06061   \n",
       "79999  132985.0   71788.0     ...          0.0   32.400     NaN  0.07143   \n",
       "\n",
       "       mvar43   mvar44  mvar45  mvar46  mvar47  default_ind  \n",
       "0        10.0  0.63899     NaN     0.0       C            0  \n",
       "1        13.0  0.63836     NaN     NaN       L            1  \n",
       "2         1.0  1.00000     NaN     0.0       C            1  \n",
       "3         3.0  0.53241     0.0     0.0       L            0  \n",
       "4         1.0  0.92665     NaN     NaN       L            0  \n",
       "5         0.0      NaN     NaN     NaN       C            1  \n",
       "6         2.0  0.87224     NaN     0.0       C            1  \n",
       "7         NaN      NaN     NaN     NaN       C            0  \n",
       "8        10.0  0.89868     0.0     0.0       L            0  \n",
       "9        14.0  0.33834     NaN     0.0       L            0  \n",
       "10       17.0  0.80620     0.0     0.0       L            0  \n",
       "11        7.0  0.81172     0.0     0.0       C            0  \n",
       "12        5.0  0.98031     NaN     NaN       C            0  \n",
       "13       13.0  0.35724     NaN     0.0       C            0  \n",
       "14       11.0  0.29169     NaN     1.0       C            0  \n",
       "15        2.0      NaN     0.0     NaN       C            0  \n",
       "16       19.0  0.47480     0.0     0.0       L            0  \n",
       "17        5.0  0.94951     NaN     0.0       L            0  \n",
       "18        0.0  1.00000     NaN     0.0       C            1  \n",
       "19       12.0  0.34730     0.0     0.0       L            0  \n",
       "20       14.0  0.28708     NaN     0.0       L            0  \n",
       "21       13.0  0.75349     NaN     NaN       L            0  \n",
       "22        1.0  1.00000     NaN     NaN       C            1  \n",
       "23       12.0  0.40853     NaN     0.0       L            0  \n",
       "24        0.0  1.00000     NaN     NaN       C            1  \n",
       "25        3.0  0.99419     0.0     0.0       L            0  \n",
       "26        5.0  0.75289     0.0     0.0       L            0  \n",
       "27       11.0  0.99710     0.0     0.0       C            0  \n",
       "28        6.0  0.85082     0.0     0.0       L            0  \n",
       "29        0.0      NaN     NaN     NaN       L            0  \n",
       "...       ...      ...     ...     ...     ...          ...  \n",
       "79970     8.0  0.58040     NaN     0.0       C            0  \n",
       "79971     6.0  0.62632     0.0     0.0       C            0  \n",
       "79972     9.0  0.59364     NaN     0.0       L            0  \n",
       "79973    14.0  0.83809     0.0     0.0       L            0  \n",
       "79974    10.0  0.46863     NaN     0.0       C            0  \n",
       "79975    14.0  0.54754     NaN     NaN       C            0  \n",
       "79976    20.0  0.23177     NaN     0.0       C            0  \n",
       "79977     NaN  0.67222     NaN     NaN       C            0  \n",
       "79978     8.0  1.00000     0.0     0.0       L            0  \n",
       "79979    14.0  0.11763     NaN     0.0       L            1  \n",
       "79980    17.0  0.81438     0.0     NaN       L            0  \n",
       "79981     5.0  0.53340     NaN     0.0       C            0  \n",
       "79982     2.0  0.96747     NaN     0.0       C            1  \n",
       "79983     2.0  1.00000     NaN     NaN       C            0  \n",
       "79984    13.0  0.75322     0.0     0.0       L            0  \n",
       "79985    10.0  0.85771     NaN     0.0       L            0  \n",
       "79986    10.0  0.86356     0.0     0.0       L            0  \n",
       "79987     9.0  0.41685     0.0     0.0       L            0  \n",
       "79988     7.0  0.53750     0.0     0.0       L            0  \n",
       "79989     0.0      NaN     NaN     NaN       L            0  \n",
       "79990     8.0  0.84170     0.0     0.0       L            0  \n",
       "79991     3.0  0.44615     NaN     NaN       L            0  \n",
       "79992     5.0  0.73640     0.0     0.0       C            1  \n",
       "79993     2.0  0.97374     0.0     0.0       C            1  \n",
       "79994     4.0  0.79867     NaN     NaN       L            0  \n",
       "79995     4.0  0.43829     NaN     0.0       C            0  \n",
       "79996    14.0  0.57931     0.0     0.0       C            0  \n",
       "79997     4.0  0.42069     NaN     0.0       C            1  \n",
       "79998     9.0  0.53251     NaN     0.0       L            0  \n",
       "79999    12.0  0.68482     0.0     0.0       C            1  \n",
       "\n",
       "[80000 rows x 48 columns]"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#mvar 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    18510.000000\n",
       "mean         0.409562\n",
       "std          0.491766\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          1.000000\n",
       "max          1.000000\n",
       "Name: default_ind, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf['default_ind'][gameDf['mvar16'].isnull()].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    61490.000000\n",
       "mean         0.197040\n",
       "std          0.397766\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          0.000000\n",
       "max          1.000000\n",
       "Name: default_ind, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf['default_ind'][gameDf['mvar16'].isnull()==False].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2.0\n",
       "1        0.0\n",
       "2        NaN\n",
       "3        0.0\n",
       "4        0.0\n",
       "5        NaN\n",
       "6        0.0\n",
       "7        NaN\n",
       "8        0.0\n",
       "9        0.0\n",
       "10       0.0\n",
       "11       0.0\n",
       "12       0.0\n",
       "13       0.0\n",
       "14       NaN\n",
       "15       0.0\n",
       "16       0.0\n",
       "17       0.0\n",
       "18       NaN\n",
       "19       0.0\n",
       "20       4.0\n",
       "21       0.0\n",
       "22       0.0\n",
       "23       0.0\n",
       "24       0.0\n",
       "25       0.0\n",
       "26       0.0\n",
       "27       0.0\n",
       "28       2.0\n",
       "29       NaN\n",
       "        ... \n",
       "79970    0.0\n",
       "79971    0.0\n",
       "79972    0.0\n",
       "79973    0.0\n",
       "79974    0.0\n",
       "79975    0.0\n",
       "79976    0.0\n",
       "79977    0.0\n",
       "79978    0.0\n",
       "79979    2.0\n",
       "79980    4.0\n",
       "79981    0.0\n",
       "79982    0.0\n",
       "79983    NaN\n",
       "79984    0.0\n",
       "79985    0.0\n",
       "79986    0.0\n",
       "79987    2.0\n",
       "79988    0.0\n",
       "79989    NaN\n",
       "79990    0.0\n",
       "79991    0.0\n",
       "79992    0.0\n",
       "79993    0.0\n",
       "79994    0.0\n",
       "79995    0.0\n",
       "79996    0.0\n",
       "79997    NaN\n",
       "79998    2.0\n",
       "79999    2.0\n",
       "Length: 80000, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf['mvar16'].add(gameDf['mvar17'], fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gameDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar16</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>51396.0</td>\n",
       "      <td>0.148241</td>\n",
       "      <td>0.355342</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>7350.0</td>\n",
       "      <td>0.393469</td>\n",
       "      <td>0.488553</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>1867.0</td>\n",
       "      <td>0.541510</td>\n",
       "      <td>0.498407</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>558.0</td>\n",
       "      <td>0.645161</td>\n",
       "      <td>0.478894</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>196.0</td>\n",
       "      <td>0.724490</td>\n",
       "      <td>0.447915</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>72.0</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>0.457719</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>34.0</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.386953</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.467099</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std  min   25%  50%   75%  max\n",
       "mvar16                                                        \n",
       "0.0     51396.0  0.148241  0.355342  0.0  0.00  0.0  0.00  1.0\n",
       "1.0      7350.0  0.393469  0.488553  0.0  0.00  0.0  1.00  1.0\n",
       "2.0      1867.0  0.541510  0.498407  0.0  0.00  1.0  1.00  1.0\n",
       "3.0       558.0  0.645161  0.478894  0.0  0.00  1.0  1.00  1.0\n",
       "4.0       196.0  0.724490  0.447915  0.0  0.00  1.0  1.00  1.0\n",
       "5.0        72.0  0.708333  0.457719  0.0  0.00  1.0  1.00  1.0\n",
       "6.0        34.0  0.823529  0.386953  0.0  1.00  1.0  1.00  1.0\n",
       "7.0        11.0  0.727273  0.467099  0.0  0.50  1.0  1.00  1.0\n",
       "8.0         2.0  1.000000  0.000000  1.0  1.00  1.0  1.00  1.0\n",
       "9.0         1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
       "10.0        1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
       "11.0        2.0  0.500000  0.707107  0.0  0.25  0.5  0.75  1.0"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf.groupby('mvar16')['default_ind'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#mvar 17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar17</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>52719.0</td>\n",
       "      <td>0.149377</td>\n",
       "      <td>0.356463</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>8042.0</td>\n",
       "      <td>0.385228</td>\n",
       "      <td>0.486679</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>2150.0</td>\n",
       "      <td>0.506512</td>\n",
       "      <td>0.500074</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>725.0</td>\n",
       "      <td>0.597241</td>\n",
       "      <td>0.490792</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>286.0</td>\n",
       "      <td>0.685315</td>\n",
       "      <td>0.465204</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>124.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.434769</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>42.0</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.397437</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0.771429</td>\n",
       "      <td>0.426043</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.377964</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std  min   25%  50%  75%  max\n",
       "mvar17                                                       \n",
       "0.0     52719.0  0.149377  0.356463  0.0  0.00  0.0  0.0  1.0\n",
       "1.0      8042.0  0.385228  0.486679  0.0  0.00  0.0  1.0  1.0\n",
       "2.0      2150.0  0.506512  0.500074  0.0  0.00  1.0  1.0  1.0\n",
       "3.0       725.0  0.597241  0.490792  0.0  0.00  1.0  1.0  1.0\n",
       "4.0       286.0  0.685315  0.465204  0.0  0.00  1.0  1.0  1.0\n",
       "5.0       124.0  0.750000  0.434769  0.0  0.75  1.0  1.0  1.0\n",
       "6.0        42.0  0.809524  0.397437  0.0  1.00  1.0  1.0  1.0\n",
       "7.0        35.0  0.771429  0.426043  0.0  1.00  1.0  1.0  1.0\n",
       "8.0        12.0  0.916667  0.288675  0.0  1.00  1.0  1.0  1.0\n",
       "9.0         7.0  0.857143  0.377964  0.0  1.00  1.0  1.0  1.0\n",
       "10.0        5.0  1.000000  0.000000  1.0  1.00  1.0  1.0  1.0\n",
       "11.0        3.0  0.666667  0.577350  0.0  0.50  1.0  1.0  1.0\n",
       "13.0        2.0  1.000000  0.000000  1.0  1.00  1.0  1.0  1.0\n",
       "16.0        1.0  0.000000       NaN  0.0  0.00  0.0  0.0  0.0\n",
       "21.0        1.0  1.000000       NaN  1.0  1.00  1.0  1.0  1.0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf.groupby('mvar17')['default_ind'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    15846.000000\n",
       "mean         0.430708\n",
       "std          0.495191\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          1.000000\n",
       "max          1.000000\n",
       "Name: default_ind, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf['default_ind'][gameDf['mvar17'].isnull()].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    64154.000000\n",
       "mean         0.200642\n",
       "std          0.400484\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          0.000000\n",
       "max          1.000000\n",
       "Name: default_ind, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf['default_ind'][gameDf['mvar17'].isnull()==False].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#mvar 18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar18</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>53325.0</td>\n",
       "      <td>0.150905</td>\n",
       "      <td>0.357959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>8390.0</td>\n",
       "      <td>0.378188</td>\n",
       "      <td>0.484964</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>2231.0</td>\n",
       "      <td>0.494845</td>\n",
       "      <td>0.500086</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>751.0</td>\n",
       "      <td>0.607190</td>\n",
       "      <td>0.488701</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>296.0</td>\n",
       "      <td>0.672297</td>\n",
       "      <td>0.470171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>118.0</td>\n",
       "      <td>0.745763</td>\n",
       "      <td>0.437288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.404061</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>37.0</td>\n",
       "      <td>0.783784</td>\n",
       "      <td>0.417342</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.377964</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std  min   25%  50%  75%  max\n",
       "mvar18                                                       \n",
       "0.0     53325.0  0.150905  0.357959  0.0  0.00  0.0  0.0  1.0\n",
       "1.0      8390.0  0.378188  0.484964  0.0  0.00  0.0  1.0  1.0\n",
       "2.0      2231.0  0.494845  0.500086  0.0  0.00  0.0  1.0  1.0\n",
       "3.0       751.0  0.607190  0.488701  0.0  0.00  1.0  1.0  1.0\n",
       "4.0       296.0  0.672297  0.470171  0.0  0.00  1.0  1.0  1.0\n",
       "5.0       118.0  0.745763  0.437288  0.0  0.25  1.0  1.0  1.0\n",
       "6.0        50.0  0.800000  0.404061  0.0  1.00  1.0  1.0  1.0\n",
       "7.0        37.0  0.783784  0.417342  0.0  1.00  1.0  1.0  1.0\n",
       "8.0        12.0  0.916667  0.288675  0.0  1.00  1.0  1.0  1.0\n",
       "9.0         7.0  0.857143  0.377964  0.0  1.00  1.0  1.0  1.0\n",
       "10.0        5.0  1.000000  0.000000  1.0  1.00  1.0  1.0  1.0\n",
       "11.0        2.0  1.000000  0.000000  1.0  1.00  1.0  1.0  1.0\n",
       "12.0        1.0  0.000000       NaN  0.0  0.00  0.0  0.0  0.0\n",
       "13.0        2.0  1.000000  0.000000  1.0  1.00  1.0  1.0  1.0\n",
       "16.0        1.0  0.000000       NaN  0.0  0.00  0.0  0.0  0.0\n",
       "21.0        1.0  1.000000       NaN  1.0  1.00  1.0  1.0  1.0"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf.groupby('mvar18')['default_ind'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    14771.000000\n",
       "mean         0.442353\n",
       "std          0.496683\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          1.000000\n",
       "max          1.000000\n",
       "Name: default_ind, dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf['default_ind'][gameDf['mvar18'].isnull()].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    65229.000000\n",
       "mean         0.201797\n",
       "std          0.401344\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          0.000000\n",
       "max          1.000000\n",
       "Name: default_ind, dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf['default_ind'][gameDf['mvar18'].isnull()==False].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#mvar16+17+18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = gameDf.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mvar161718 = (gameDf['mvar16'].add(gameDf['mvar17'], fill_value=0)).add(gameDf['mvar18'], fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['mvar16'] = mvar161718"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar16</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>53325.0</td>\n",
       "      <td>0.150905</td>\n",
       "      <td>0.357959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>455.0</td>\n",
       "      <td>0.257143</td>\n",
       "      <td>0.437540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>1245.0</td>\n",
       "      <td>0.359839</td>\n",
       "      <td>0.480146</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>6717.0</td>\n",
       "      <td>0.389311</td>\n",
       "      <td>0.487630</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>169.0</td>\n",
       "      <td>0.437870</td>\n",
       "      <td>0.497599</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>477.0</td>\n",
       "      <td>0.423480</td>\n",
       "      <td>0.494629</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>1583.0</td>\n",
       "      <td>0.527479</td>\n",
       "      <td>0.499402</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>81.0</td>\n",
       "      <td>0.592593</td>\n",
       "      <td>0.494413</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>214.0</td>\n",
       "      <td>0.579439</td>\n",
       "      <td>0.494807</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>453.0</td>\n",
       "      <td>0.626932</td>\n",
       "      <td>0.484155</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>58.0</td>\n",
       "      <td>0.655172</td>\n",
       "      <td>0.479463</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>91.0</td>\n",
       "      <td>0.703297</td>\n",
       "      <td>0.459335</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>143.0</td>\n",
       "      <td>0.678322</td>\n",
       "      <td>0.468762</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>14.0</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.497245</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.343776</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>55.0</td>\n",
       "      <td>0.690909</td>\n",
       "      <td>0.466378</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>14.0</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.497245</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>13.0</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.277350</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.383482</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.0</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.467099</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.0</th>\n",
       "      <td>15.0</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.351866</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.0</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.462910</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23.0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24.0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25.0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29.0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std  min   25%  50%  75%  max\n",
       "mvar16                                                       \n",
       "0.0     53325.0  0.150905  0.357959  0.0  0.00  0.0  0.0  1.0\n",
       "1.0       455.0  0.257143  0.437540  0.0  0.00  0.0  1.0  1.0\n",
       "2.0      1245.0  0.359839  0.480146  0.0  0.00  0.0  1.0  1.0\n",
       "3.0      6717.0  0.389311  0.487630  0.0  0.00  0.0  1.0  1.0\n",
       "4.0       169.0  0.437870  0.497599  0.0  0.00  0.0  1.0  1.0\n",
       "5.0       477.0  0.423480  0.494629  0.0  0.00  0.0  1.0  1.0\n",
       "6.0      1583.0  0.527479  0.499402  0.0  0.00  1.0  1.0  1.0\n",
       "7.0        81.0  0.592593  0.494413  0.0  0.00  1.0  1.0  1.0\n",
       "8.0       214.0  0.579439  0.494807  0.0  0.00  1.0  1.0  1.0\n",
       "9.0       453.0  0.626932  0.484155  0.0  0.00  1.0  1.0  1.0\n",
       "10.0       58.0  0.655172  0.479463  0.0  0.00  1.0  1.0  1.0\n",
       "11.0       91.0  0.703297  0.459335  0.0  0.00  1.0  1.0  1.0\n",
       "12.0      143.0  0.678322  0.468762  0.0  0.00  1.0  1.0  1.0\n",
       "13.0       14.0  0.642857  0.497245  0.0  0.00  1.0  1.0  1.0\n",
       "14.0       45.0  0.866667  0.343776  0.0  1.00  1.0  1.0  1.0\n",
       "15.0       55.0  0.690909  0.466378  0.0  0.00  1.0  1.0  1.0\n",
       "16.0       14.0  0.642857  0.497245  0.0  0.00  1.0  1.0  1.0\n",
       "17.0       13.0  0.923077  0.277350  0.0  1.00  1.0  1.0  1.0\n",
       "18.0       18.0  0.833333  0.383482  0.0  1.00  1.0  1.0  1.0\n",
       "19.0       11.0  0.727273  0.467099  0.0  0.50  1.0  1.0  1.0\n",
       "20.0       15.0  0.866667  0.351866  0.0  1.00  1.0  1.0  1.0\n",
       "21.0        8.0  0.750000  0.462910  0.0  0.75  1.0  1.0  1.0\n",
       "22.0        5.0  1.000000  0.000000  1.0  1.00  1.0  1.0  1.0\n",
       "23.0        3.0  0.666667  0.577350  0.0  0.50  1.0  1.0  1.0\n",
       "24.0        6.0  0.833333  0.408248  0.0  1.00  1.0  1.0  1.0\n",
       "25.0        2.0  1.000000  0.000000  1.0  1.00  1.0  1.0  1.0\n",
       "27.0        1.0  1.000000       NaN  1.0  1.00  1.0  1.0  1.0\n",
       "29.0        2.0  1.000000  0.000000  1.0  1.00  1.0  1.0  1.0\n",
       "32.0        1.0  1.000000       NaN  1.0  1.00  1.0  1.0  1.0\n",
       "33.0        1.0  1.000000       NaN  1.0  1.00  1.0  1.0  1.0\n",
       "34.0        1.0  0.000000       NaN  0.0  0.00  0.0  0.0  0.0\n",
       "36.0        1.0  0.000000       NaN  0.0  0.00  0.0  0.0  0.0\n",
       "37.0        1.0  1.000000       NaN  1.0  1.00  1.0  1.0  1.0\n",
       "46.0        1.0  1.000000       NaN  1.0  1.00  1.0  1.0  1.0"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('mvar16')['default_ind'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar16</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>51396.0</td>\n",
       "      <td>0.148241</td>\n",
       "      <td>0.355342</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>7350.0</td>\n",
       "      <td>0.393469</td>\n",
       "      <td>0.488553</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>1867.0</td>\n",
       "      <td>0.541510</td>\n",
       "      <td>0.498407</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>558.0</td>\n",
       "      <td>0.645161</td>\n",
       "      <td>0.478894</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>196.0</td>\n",
       "      <td>0.724490</td>\n",
       "      <td>0.447915</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>72.0</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>0.457719</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>34.0</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.386953</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.467099</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std  min   25%  50%   75%  max\n",
       "mvar16                                                        \n",
       "0.0     51396.0  0.148241  0.355342  0.0  0.00  0.0  0.00  1.0\n",
       "1.0      7350.0  0.393469  0.488553  0.0  0.00  0.0  1.00  1.0\n",
       "2.0      1867.0  0.541510  0.498407  0.0  0.00  1.0  1.00  1.0\n",
       "3.0       558.0  0.645161  0.478894  0.0  0.00  1.0  1.00  1.0\n",
       "4.0       196.0  0.724490  0.447915  0.0  0.00  1.0  1.00  1.0\n",
       "5.0        72.0  0.708333  0.457719  0.0  0.00  1.0  1.00  1.0\n",
       "6.0        34.0  0.823529  0.386953  0.0  1.00  1.0  1.00  1.0\n",
       "7.0        11.0  0.727273  0.467099  0.0  0.50  1.0  1.00  1.0\n",
       "8.0         2.0  1.000000  0.000000  1.0  1.00  1.0  1.00  1.0\n",
       "9.0         1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
       "10.0        1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
       "11.0        2.0  0.500000  0.707107  0.0  0.25  0.5  0.75  1.0"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf.groupby('mvar16')['default_ind'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    14771.000000\n",
       "mean         0.442353\n",
       "std          0.496683\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          1.000000\n",
       "max          1.000000\n",
       "Name: default_ind, dtype: float64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['default_ind'][df['mvar16'].isnull()].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    53325.000000\n",
       "mean         0.150905\n",
       "std          0.357959\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          0.000000\n",
       "max          1.000000\n",
       "Name: default_ind, dtype: float64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['default_ind'][df['mvar16']==0].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    11904.000000\n",
       "mean         0.429772\n",
       "std          0.495064\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          1.000000\n",
       "max          1.000000\n",
       "Name: default_ind, dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['default_ind'][df['mvar16']>0].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "df = gameDf.copy()\n",
    "df['mvar16'][df['mvar16'].isnull()] = -1\n",
    "df['mvar17'][df['mvar17'].isnull()] = -1\n",
    "df['mvar18'][df['mvar18'].isnull()] = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar16</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1.0</th>\n",
       "      <td>18510.0</td>\n",
       "      <td>0.409562</td>\n",
       "      <td>0.491766</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>51396.0</td>\n",
       "      <td>0.148241</td>\n",
       "      <td>0.355342</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>7350.0</td>\n",
       "      <td>0.393469</td>\n",
       "      <td>0.488553</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>1867.0</td>\n",
       "      <td>0.541510</td>\n",
       "      <td>0.498407</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>558.0</td>\n",
       "      <td>0.645161</td>\n",
       "      <td>0.478894</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>196.0</td>\n",
       "      <td>0.724490</td>\n",
       "      <td>0.447915</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>72.0</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>0.457719</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>34.0</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.386953</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.467099</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std  min   25%  50%   75%  max\n",
       "mvar16                                                        \n",
       "-1.0    18510.0  0.409562  0.491766  0.0  0.00  0.0  1.00  1.0\n",
       " 0.0    51396.0  0.148241  0.355342  0.0  0.00  0.0  0.00  1.0\n",
       " 1.0     7350.0  0.393469  0.488553  0.0  0.00  0.0  1.00  1.0\n",
       " 2.0     1867.0  0.541510  0.498407  0.0  0.00  1.0  1.00  1.0\n",
       " 3.0      558.0  0.645161  0.478894  0.0  0.00  1.0  1.00  1.0\n",
       " 4.0      196.0  0.724490  0.447915  0.0  0.00  1.0  1.00  1.0\n",
       " 5.0       72.0  0.708333  0.457719  0.0  0.00  1.0  1.00  1.0\n",
       " 6.0       34.0  0.823529  0.386953  0.0  1.00  1.0  1.00  1.0\n",
       " 7.0       11.0  0.727273  0.467099  0.0  0.50  1.0  1.00  1.0\n",
       " 8.0        2.0  1.000000  0.000000  1.0  1.00  1.0  1.00  1.0\n",
       " 9.0        1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
       " 10.0       1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
       " 11.0       2.0  0.500000  0.707107  0.0  0.25  0.5  0.75  1.0"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('mvar16')['default_ind'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar17</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1.0</th>\n",
       "      <td>15846.0</td>\n",
       "      <td>0.430708</td>\n",
       "      <td>0.495191</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>52719.0</td>\n",
       "      <td>0.149377</td>\n",
       "      <td>0.356463</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>8042.0</td>\n",
       "      <td>0.385228</td>\n",
       "      <td>0.486679</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>2150.0</td>\n",
       "      <td>0.506512</td>\n",
       "      <td>0.500074</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>725.0</td>\n",
       "      <td>0.597241</td>\n",
       "      <td>0.490792</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>286.0</td>\n",
       "      <td>0.685315</td>\n",
       "      <td>0.465204</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>124.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.434769</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>42.0</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.397437</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0.771429</td>\n",
       "      <td>0.426043</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.377964</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std  min   25%  50%  75%  max\n",
       "mvar17                                                       \n",
       "-1.0    15846.0  0.430708  0.495191  0.0  0.00  0.0  1.0  1.0\n",
       " 0.0    52719.0  0.149377  0.356463  0.0  0.00  0.0  0.0  1.0\n",
       " 1.0     8042.0  0.385228  0.486679  0.0  0.00  0.0  1.0  1.0\n",
       " 2.0     2150.0  0.506512  0.500074  0.0  0.00  1.0  1.0  1.0\n",
       " 3.0      725.0  0.597241  0.490792  0.0  0.00  1.0  1.0  1.0\n",
       " 4.0      286.0  0.685315  0.465204  0.0  0.00  1.0  1.0  1.0\n",
       " 5.0      124.0  0.750000  0.434769  0.0  0.75  1.0  1.0  1.0\n",
       " 6.0       42.0  0.809524  0.397437  0.0  1.00  1.0  1.0  1.0\n",
       " 7.0       35.0  0.771429  0.426043  0.0  1.00  1.0  1.0  1.0\n",
       " 8.0       12.0  0.916667  0.288675  0.0  1.00  1.0  1.0  1.0\n",
       " 9.0        7.0  0.857143  0.377964  0.0  1.00  1.0  1.0  1.0\n",
       " 10.0       5.0  1.000000  0.000000  1.0  1.00  1.0  1.0  1.0\n",
       " 11.0       3.0  0.666667  0.577350  0.0  0.50  1.0  1.0  1.0\n",
       " 13.0       2.0  1.000000  0.000000  1.0  1.00  1.0  1.0  1.0\n",
       " 16.0       1.0  0.000000       NaN  0.0  0.00  0.0  0.0  0.0\n",
       " 21.0       1.0  1.000000       NaN  1.0  1.00  1.0  1.0  1.0"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('mvar17')['default_ind'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar18</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1.0</th>\n",
       "      <td>14771.0</td>\n",
       "      <td>0.442353</td>\n",
       "      <td>0.496683</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>53325.0</td>\n",
       "      <td>0.150905</td>\n",
       "      <td>0.357959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>8390.0</td>\n",
       "      <td>0.378188</td>\n",
       "      <td>0.484964</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>2231.0</td>\n",
       "      <td>0.494845</td>\n",
       "      <td>0.500086</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>751.0</td>\n",
       "      <td>0.607190</td>\n",
       "      <td>0.488701</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>296.0</td>\n",
       "      <td>0.672297</td>\n",
       "      <td>0.470171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>118.0</td>\n",
       "      <td>0.745763</td>\n",
       "      <td>0.437288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.404061</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>37.0</td>\n",
       "      <td>0.783784</td>\n",
       "      <td>0.417342</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.377964</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std  min   25%  50%  75%  max\n",
       "mvar18                                                       \n",
       "-1.0    14771.0  0.442353  0.496683  0.0  0.00  0.0  1.0  1.0\n",
       " 0.0    53325.0  0.150905  0.357959  0.0  0.00  0.0  0.0  1.0\n",
       " 1.0     8390.0  0.378188  0.484964  0.0  0.00  0.0  1.0  1.0\n",
       " 2.0     2231.0  0.494845  0.500086  0.0  0.00  0.0  1.0  1.0\n",
       " 3.0      751.0  0.607190  0.488701  0.0  0.00  1.0  1.0  1.0\n",
       " 4.0      296.0  0.672297  0.470171  0.0  0.00  1.0  1.0  1.0\n",
       " 5.0      118.0  0.745763  0.437288  0.0  0.25  1.0  1.0  1.0\n",
       " 6.0       50.0  0.800000  0.404061  0.0  1.00  1.0  1.0  1.0\n",
       " 7.0       37.0  0.783784  0.417342  0.0  1.00  1.0  1.0  1.0\n",
       " 8.0       12.0  0.916667  0.288675  0.0  1.00  1.0  1.0  1.0\n",
       " 9.0        7.0  0.857143  0.377964  0.0  1.00  1.0  1.0  1.0\n",
       " 10.0       5.0  1.000000  0.000000  1.0  1.00  1.0  1.0  1.0\n",
       " 11.0       2.0  1.000000  0.000000  1.0  1.00  1.0  1.0  1.0\n",
       " 12.0       1.0  0.000000       NaN  0.0  0.00  0.0  0.0  0.0\n",
       " 13.0       2.0  1.000000  0.000000  1.0  1.00  1.0  1.0  1.0\n",
       " 16.0       1.0  0.000000       NaN  0.0  0.00  0.0  0.0  0.0\n",
       " 21.0       1.0  1.000000       NaN  1.0  1.00  1.0  1.0  1.0"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('mvar18')['default_ind'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['mvar16'] = df['mvar16']+df['mvar17']+df['mvar18']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df['mvar16'][df['mvar16']>1] = df['mvar16'][df['mvar16']>1] //5\n",
    "df['mvar16'] = df['mvar16'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar16</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-3</th>\n",
       "      <td>14771.0</td>\n",
       "      <td>0.442353</td>\n",
       "      <td>0.496683</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <td>1004.0</td>\n",
       "      <td>0.253984</td>\n",
       "      <td>0.435506</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1</th>\n",
       "      <td>2482.0</td>\n",
       "      <td>0.266317</td>\n",
       "      <td>0.442121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>57853.0</td>\n",
       "      <td>0.176292</td>\n",
       "      <td>0.381071</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3380.0</td>\n",
       "      <td>0.496746</td>\n",
       "      <td>0.500063</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>351.0</td>\n",
       "      <td>0.703704</td>\n",
       "      <td>0.457275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>111.0</td>\n",
       "      <td>0.738739</td>\n",
       "      <td>0.441314</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37.0</td>\n",
       "      <td>0.837838</td>\n",
       "      <td>0.373684</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std  min   25%  50%   75%  max\n",
       "mvar16                                                        \n",
       "-3      14771.0  0.442353  0.496683  0.0  0.00  0.0  1.00  1.0\n",
       "-2       1004.0  0.253984  0.435506  0.0  0.00  0.0  1.00  1.0\n",
       "-1       2482.0  0.266317  0.442121  0.0  0.00  0.0  1.00  1.0\n",
       " 0      57853.0  0.176292  0.381071  0.0  0.00  0.0  0.00  1.0\n",
       " 1       3380.0  0.496746  0.500063  0.0  0.00  0.0  1.00  1.0\n",
       " 2        351.0  0.703704  0.457275  0.0  0.00  1.0  1.00  1.0\n",
       " 3        111.0  0.738739  0.441314  0.0  0.00  1.0  1.00  1.0\n",
       " 4         37.0  0.837838  0.373684  0.0  1.00  1.0  1.00  1.0\n",
       " 5          5.0  1.000000  0.000000  1.0  1.00  1.0  1.00  1.0\n",
       " 6          3.0  0.666667  0.577350  0.0  0.50  1.0  1.00  1.0\n",
       " 7          2.0  0.500000  0.707107  0.0  0.25  0.5  0.75  1.0\n",
       " 9          1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('mvar16')['default_ind'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.52647814910025703"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(df['default_ind'][df['mvar16']>0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#mvar 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar6</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>10707.0</td>\n",
       "      <td>0.434669</td>\n",
       "      <td>0.495737</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>197.0</td>\n",
       "      <td>0.411168</td>\n",
       "      <td>0.493299</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>169.0</td>\n",
       "      <td>0.449704</td>\n",
       "      <td>0.498942</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>139.0</td>\n",
       "      <td>0.438849</td>\n",
       "      <td>0.498041</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>159.0</td>\n",
       "      <td>0.402516</td>\n",
       "      <td>0.491954</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>134.0</td>\n",
       "      <td>0.425373</td>\n",
       "      <td>0.496255</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>132.0</td>\n",
       "      <td>0.371212</td>\n",
       "      <td>0.484970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>143.0</td>\n",
       "      <td>0.377622</td>\n",
       "      <td>0.486496</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>135.0</td>\n",
       "      <td>0.362963</td>\n",
       "      <td>0.482645</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>111.0</td>\n",
       "      <td>0.396396</td>\n",
       "      <td>0.491367</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>120.0</td>\n",
       "      <td>0.383333</td>\n",
       "      <td>0.488237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>144.0</td>\n",
       "      <td>0.368056</td>\n",
       "      <td>0.483960</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>118.0</td>\n",
       "      <td>0.355932</td>\n",
       "      <td>0.480837</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>130.0</td>\n",
       "      <td>0.423077</td>\n",
       "      <td>0.495959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>119.0</td>\n",
       "      <td>0.268908</td>\n",
       "      <td>0.445267</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>124.0</td>\n",
       "      <td>0.370968</td>\n",
       "      <td>0.485024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>129.0</td>\n",
       "      <td>0.372093</td>\n",
       "      <td>0.485247</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>137.0</td>\n",
       "      <td>0.386861</td>\n",
       "      <td>0.488819</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.0</th>\n",
       "      <td>121.0</td>\n",
       "      <td>0.413223</td>\n",
       "      <td>0.494460</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.0</th>\n",
       "      <td>147.0</td>\n",
       "      <td>0.306122</td>\n",
       "      <td>0.462457</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.0</th>\n",
       "      <td>115.0</td>\n",
       "      <td>0.365217</td>\n",
       "      <td>0.483598</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.0</th>\n",
       "      <td>126.0</td>\n",
       "      <td>0.317460</td>\n",
       "      <td>0.467346</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.0</th>\n",
       "      <td>129.0</td>\n",
       "      <td>0.341085</td>\n",
       "      <td>0.475922</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23.0</th>\n",
       "      <td>122.0</td>\n",
       "      <td>0.344262</td>\n",
       "      <td>0.477086</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24.0</th>\n",
       "      <td>124.0</td>\n",
       "      <td>0.427419</td>\n",
       "      <td>0.496711</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25.0</th>\n",
       "      <td>129.0</td>\n",
       "      <td>0.286822</td>\n",
       "      <td>0.454041</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26.0</th>\n",
       "      <td>116.0</td>\n",
       "      <td>0.362069</td>\n",
       "      <td>0.482684</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27.0</th>\n",
       "      <td>117.0</td>\n",
       "      <td>0.324786</td>\n",
       "      <td>0.470309</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28.0</th>\n",
       "      <td>127.0</td>\n",
       "      <td>0.370079</td>\n",
       "      <td>0.484738</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29.0</th>\n",
       "      <td>103.0</td>\n",
       "      <td>0.320388</td>\n",
       "      <td>0.468908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44186.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44197.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44391.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44586.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45157.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45160.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45187.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45788.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45799.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46086.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46698.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46867.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46889.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48480.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49257.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49467.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49534.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49550.0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51935.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54403.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62053.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63789.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64572.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71446.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73334.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85393.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87875.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94302.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123875.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136595.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7117 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            count      mean       std  min  25%  50%  75%  max\n",
       "mvar6                                                         \n",
       "0.0       10707.0  0.434669  0.495737  0.0  0.0  0.0  1.0  1.0\n",
       "1.0         197.0  0.411168  0.493299  0.0  0.0  0.0  1.0  1.0\n",
       "2.0         169.0  0.449704  0.498942  0.0  0.0  0.0  1.0  1.0\n",
       "3.0         139.0  0.438849  0.498041  0.0  0.0  0.0  1.0  1.0\n",
       "4.0         159.0  0.402516  0.491954  0.0  0.0  0.0  1.0  1.0\n",
       "5.0         134.0  0.425373  0.496255  0.0  0.0  0.0  1.0  1.0\n",
       "6.0         132.0  0.371212  0.484970  0.0  0.0  0.0  1.0  1.0\n",
       "7.0         143.0  0.377622  0.486496  0.0  0.0  0.0  1.0  1.0\n",
       "8.0         135.0  0.362963  0.482645  0.0  0.0  0.0  1.0  1.0\n",
       "9.0         111.0  0.396396  0.491367  0.0  0.0  0.0  1.0  1.0\n",
       "10.0        120.0  0.383333  0.488237  0.0  0.0  0.0  1.0  1.0\n",
       "11.0        144.0  0.368056  0.483960  0.0  0.0  0.0  1.0  1.0\n",
       "12.0        118.0  0.355932  0.480837  0.0  0.0  0.0  1.0  1.0\n",
       "13.0        130.0  0.423077  0.495959  0.0  0.0  0.0  1.0  1.0\n",
       "14.0        119.0  0.268908  0.445267  0.0  0.0  0.0  1.0  1.0\n",
       "15.0        124.0  0.370968  0.485024  0.0  0.0  0.0  1.0  1.0\n",
       "16.0        129.0  0.372093  0.485247  0.0  0.0  0.0  1.0  1.0\n",
       "17.0        137.0  0.386861  0.488819  0.0  0.0  0.0  1.0  1.0\n",
       "18.0        121.0  0.413223  0.494460  0.0  0.0  0.0  1.0  1.0\n",
       "19.0        147.0  0.306122  0.462457  0.0  0.0  0.0  1.0  1.0\n",
       "20.0        115.0  0.365217  0.483598  0.0  0.0  0.0  1.0  1.0\n",
       "21.0        126.0  0.317460  0.467346  0.0  0.0  0.0  1.0  1.0\n",
       "22.0        129.0  0.341085  0.475922  0.0  0.0  0.0  1.0  1.0\n",
       "23.0        122.0  0.344262  0.477086  0.0  0.0  0.0  1.0  1.0\n",
       "24.0        124.0  0.427419  0.496711  0.0  0.0  0.0  1.0  1.0\n",
       "25.0        129.0  0.286822  0.454041  0.0  0.0  0.0  1.0  1.0\n",
       "26.0        116.0  0.362069  0.482684  0.0  0.0  0.0  1.0  1.0\n",
       "27.0        117.0  0.324786  0.470309  0.0  0.0  0.0  1.0  1.0\n",
       "28.0        127.0  0.370079  0.484738  0.0  0.0  0.0  1.0  1.0\n",
       "29.0        103.0  0.320388  0.468908  0.0  0.0  0.0  1.0  1.0\n",
       "...           ...       ...       ...  ...  ...  ...  ...  ...\n",
       "44186.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "44197.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "44391.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "44586.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "45157.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "45160.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "45187.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "45788.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "45799.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "46086.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "46698.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "46867.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "46889.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "48480.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "49257.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "49467.0       1.0  1.000000       NaN  1.0  1.0  1.0  1.0  1.0\n",
       "49534.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "49550.0       2.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
       "51935.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "54403.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "62053.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "63789.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "64572.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "71446.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "73334.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "85393.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "87875.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "94302.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "123875.0      1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "136595.0      1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       "\n",
       "[7117 rows x 8 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf.groupby('mvar6')['default_ind'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    18964.000000\n",
       "mean         0.406613\n",
       "std          0.491214\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          1.000000\n",
       "max          1.000000\n",
       "Name: default_ind, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf['default_ind'][gameDf['mvar6'].isnull()].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    61036.000000\n",
       "mean         0.196376\n",
       "std          0.397259\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          0.000000\n",
       "max          1.000000\n",
       "Name: default_ind, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf['default_ind'][gameDf['mvar6'].isnull()==False].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#mvar 19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar19</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>48041.0</td>\n",
       "      <td>0.209404</td>\n",
       "      <td>0.406888</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>14590.0</td>\n",
       "      <td>0.253119</td>\n",
       "      <td>0.434813</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>7327.0</td>\n",
       "      <td>0.294800</td>\n",
       "      <td>0.455984</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>4074.0</td>\n",
       "      <td>0.328670</td>\n",
       "      <td>0.469787</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>2318.0</td>\n",
       "      <td>0.370578</td>\n",
       "      <td>0.483064</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>1333.0</td>\n",
       "      <td>0.422356</td>\n",
       "      <td>0.494120</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>843.0</td>\n",
       "      <td>0.404508</td>\n",
       "      <td>0.491088</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>530.0</td>\n",
       "      <td>0.447170</td>\n",
       "      <td>0.497671</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>316.0</td>\n",
       "      <td>0.455696</td>\n",
       "      <td>0.498823</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>227.0</td>\n",
       "      <td>0.436123</td>\n",
       "      <td>0.496999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>112.0</td>\n",
       "      <td>0.446429</td>\n",
       "      <td>0.499356</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>90.0</td>\n",
       "      <td>0.477778</td>\n",
       "      <td>0.502304</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>76.0</td>\n",
       "      <td>0.539474</td>\n",
       "      <td>0.501751</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>37.0</td>\n",
       "      <td>0.540541</td>\n",
       "      <td>0.505228</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.501631</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.487950</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>14.0</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.468807</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.0</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.517549</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24.0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28.0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std  min   25%  50%   75%  max\n",
       "mvar19                                                        \n",
       "0.0     48041.0  0.209404  0.406888  0.0  0.00  0.0  0.00  1.0\n",
       "1.0     14590.0  0.253119  0.434813  0.0  0.00  0.0  1.00  1.0\n",
       "2.0      7327.0  0.294800  0.455984  0.0  0.00  0.0  1.00  1.0\n",
       "3.0      4074.0  0.328670  0.469787  0.0  0.00  0.0  1.00  1.0\n",
       "4.0      2318.0  0.370578  0.483064  0.0  0.00  0.0  1.00  1.0\n",
       "5.0      1333.0  0.422356  0.494120  0.0  0.00  0.0  1.00  1.0\n",
       "6.0       843.0  0.404508  0.491088  0.0  0.00  0.0  1.00  1.0\n",
       "7.0       530.0  0.447170  0.497671  0.0  0.00  0.0  1.00  1.0\n",
       "8.0       316.0  0.455696  0.498823  0.0  0.00  0.0  1.00  1.0\n",
       "9.0       227.0  0.436123  0.496999  0.0  0.00  0.0  1.00  1.0\n",
       "10.0      112.0  0.446429  0.499356  0.0  0.00  0.0  1.00  1.0\n",
       "11.0       90.0  0.477778  0.502304  0.0  0.00  0.0  1.00  1.0\n",
       "12.0       76.0  0.539474  0.501751  0.0  0.00  1.0  1.00  1.0\n",
       "13.0       37.0  0.540541  0.505228  0.0  0.00  1.0  1.00  1.0\n",
       "14.0       18.0  0.611111  0.501631  0.0  0.00  1.0  1.00  1.0\n",
       "15.0       16.0  0.625000  0.500000  0.0  0.00  1.0  1.00  1.0\n",
       "16.0        7.0  0.285714  0.487950  0.0  0.00  0.0  0.50  1.0\n",
       "17.0       14.0  0.714286  0.468807  0.0  0.25  1.0  1.00  1.0\n",
       "18.0        4.0  0.250000  0.500000  0.0  0.00  0.0  0.25  1.0\n",
       "19.0        4.0  0.500000  0.577350  0.0  0.00  0.5  1.00  1.0\n",
       "20.0        8.0  0.375000  0.517549  0.0  0.00  0.0  1.00  1.0\n",
       "21.0        4.0  0.750000  0.500000  0.0  0.75  1.0  1.00  1.0\n",
       "22.0        2.0  0.500000  0.707107  0.0  0.25  0.5  0.75  1.0\n",
       "24.0        2.0  1.000000  0.000000  1.0  1.00  1.0  1.00  1.0\n",
       "25.0        1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
       "28.0        2.0  0.500000  0.707107  0.0  0.25  0.5  0.75  1.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf.groupby('mvar19')['default_ind'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4.00\n",
       "mean     0.25\n",
       "std      0.50\n",
       "min      0.00\n",
       "25%      0.00\n",
       "50%      0.00\n",
       "75%      0.25\n",
       "max      1.00\n",
       "Name: default_ind, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf['default_ind'][gameDf['mvar19'].isnull()].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    79996.000000\n",
       "mean         0.246212\n",
       "std          0.430806\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          0.000000\n",
       "max          1.000000\n",
       "Name: default_ind, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf['default_ind'][gameDf['mvar19'].isnull()==False].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#mvar20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar20</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>44996.0</td>\n",
       "      <td>0.212641</td>\n",
       "      <td>0.409180</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>15713.0</td>\n",
       "      <td>0.241902</td>\n",
       "      <td>0.428249</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>7982.0</td>\n",
       "      <td>0.283388</td>\n",
       "      <td>0.450671</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>4311.0</td>\n",
       "      <td>0.317328</td>\n",
       "      <td>0.465490</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>2541.0</td>\n",
       "      <td>0.357733</td>\n",
       "      <td>0.479427</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>1471.0</td>\n",
       "      <td>0.401088</td>\n",
       "      <td>0.490285</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>904.0</td>\n",
       "      <td>0.399336</td>\n",
       "      <td>0.490033</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>556.0</td>\n",
       "      <td>0.446043</td>\n",
       "      <td>0.497528</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>356.0</td>\n",
       "      <td>0.457865</td>\n",
       "      <td>0.498923</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>234.0</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.497969</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>134.0</td>\n",
       "      <td>0.432836</td>\n",
       "      <td>0.497328</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>91.0</td>\n",
       "      <td>0.472527</td>\n",
       "      <td>0.502011</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>74.0</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.503229</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.502519</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>21.0</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.507093</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.501631</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.516398</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>13.0</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.480384</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.547723</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.0</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.517549</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24.0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std  min   25%  50%   75%  max\n",
       "mvar20                                                        \n",
       "0.0     44996.0  0.212641  0.409180  0.0  0.00  0.0  0.00  1.0\n",
       "1.0     15713.0  0.241902  0.428249  0.0  0.00  0.0  0.00  1.0\n",
       "2.0      7982.0  0.283388  0.450671  0.0  0.00  0.0  1.00  1.0\n",
       "3.0      4311.0  0.317328  0.465490  0.0  0.00  0.0  1.00  1.0\n",
       "4.0      2541.0  0.357733  0.479427  0.0  0.00  0.0  1.00  1.0\n",
       "5.0      1471.0  0.401088  0.490285  0.0  0.00  0.0  1.00  1.0\n",
       "6.0       904.0  0.399336  0.490033  0.0  0.00  0.0  1.00  1.0\n",
       "7.0       556.0  0.446043  0.497528  0.0  0.00  0.0  1.00  1.0\n",
       "8.0       356.0  0.457865  0.498923  0.0  0.00  0.0  1.00  1.0\n",
       "9.0       234.0  0.444444  0.497969  0.0  0.00  0.0  1.00  1.0\n",
       "10.0      134.0  0.432836  0.497328  0.0  0.00  0.0  1.00  1.0\n",
       "11.0       91.0  0.472527  0.502011  0.0  0.00  0.0  1.00  1.0\n",
       "12.0       74.0  0.513514  0.503229  0.0  0.00  1.0  1.00  1.0\n",
       "13.0       45.0  0.555556  0.502519  0.0  0.00  1.0  1.00  1.0\n",
       "14.0       21.0  0.571429  0.507093  0.0  0.00  1.0  1.00  1.0\n",
       "15.0       18.0  0.611111  0.501631  0.0  0.00  1.0  1.00  1.0\n",
       "16.0       10.0  0.400000  0.516398  0.0  0.00  0.0  1.00  1.0\n",
       "17.0       13.0  0.692308  0.480384  0.0  0.00  1.0  1.00  1.0\n",
       "18.0        4.0  0.250000  0.500000  0.0  0.00  0.0  0.25  1.0\n",
       "19.0        5.0  0.600000  0.547723  0.0  0.00  1.0  1.00  1.0\n",
       "20.0        8.0  0.375000  0.517549  0.0  0.00  0.0  1.00  1.0\n",
       "21.0        4.0  0.750000  0.500000  0.0  0.75  1.0  1.00  1.0\n",
       "22.0        1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
       "23.0        1.0  0.000000       NaN  0.0  0.00  0.0  0.00  0.0\n",
       "24.0        2.0  1.000000  0.000000  1.0  1.00  1.0  1.00  1.0\n",
       "25.0        1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
       "28.0        1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
       "29.0        1.0  0.000000       NaN  0.0  0.00  0.0  0.00  0.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf.groupby('mvar20')['default_ind'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    502.000000\n",
       "mean       0.215139\n",
       "std        0.411329\n",
       "min        0.000000\n",
       "25%        0.000000\n",
       "50%        0.000000\n",
       "75%        0.000000\n",
       "max        1.000000\n",
       "Name: default_ind, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf['default_ind'][gameDf['mvar20'].isnull()].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    79498.000000\n",
       "mean         0.246409\n",
       "std          0.430922\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          0.000000\n",
       "max          1.000000\n",
       "Name: default_ind, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf['default_ind'][gameDf['mvar20'].isnull()==False].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Cannot convert non-finite values (NA or inf) to integer",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-45-c4050b5f647d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgameDf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mvar26'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'int64'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mvar26'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mvar26'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mvar26'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mvar26'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'int64'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m//\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'mvar26'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'default_ind'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/pandas/util/_decorators.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    116\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    119\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_deprecate_kwarg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors, **kwargs)\u001b[0m\n\u001b[1;32m   4002\u001b[0m         \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4003\u001b[0m         new_data = self._data.astype(dtype=dtype, copy=copy, errors=errors,\n\u001b[0;32m-> 4004\u001b[0;31m                                      **kwargs)\n\u001b[0m\u001b[1;32m   4005\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4006\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, **kwargs)\u001b[0m\n\u001b[1;32m   3460\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3461\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3462\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'astype'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3463\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3464\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)\u001b[0m\n\u001b[1;32m   3327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3328\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mgr'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3329\u001b[0;31m             \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3330\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3331\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors, values, **kwargs)\u001b[0m\n\u001b[1;32m    542\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'raise'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    543\u001b[0m         return self._astype(dtype, copy=copy, errors=errors, values=values,\n\u001b[0;32m--> 544\u001b[0;31m                             **kwargs)\n\u001b[0m\u001b[1;32m    545\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    546\u001b[0m     def _astype(self, dtype, copy=False, errors='raise', values=None,\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36m_astype\u001b[0;34m(self, dtype, copy, errors, values, klass, mgr, **kwargs)\u001b[0m\n\u001b[1;32m    623\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    624\u001b[0m                 \u001b[0;31m# _astype_nansafe works fine with 1-d only\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 625\u001b[0;31m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    626\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    627\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/pandas/core/dtypes/cast.pyc\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy)\u001b[0m\n\u001b[1;32m    685\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    686\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 687\u001b[0;31m             raise ValueError('Cannot convert non-finite values (NA or inf) to '\n\u001b[0m\u001b[1;32m    688\u001b[0m                              'integer')\n\u001b[1;32m    689\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Cannot convert non-finite values (NA or inf) to integer"
     ]
    }
   ],
   "source": [
    "#mvar26\n",
    "df = gameDf.copy()\n",
    "\n",
    "df['mvar26'][df['mvar26'].isnull()==False]  = ( (df['mvar26'][df['mvar26'].isnull()==False]).astype('int64'))//12\n",
    "print(df.groupby('mvar26')['default_ind'].describe())\n",
    "print(\"////////////\")\n",
    "\n",
    "print(\"////////////\")\n",
    "print(df['default_ind'][df['mvar26'].isnull()==False].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    10610.000000\n",
      "mean         0.402639\n",
      "std          0.490452\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          1.000000\n",
      "max          1.000000\n",
      "Name: default_ind, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df['default_ind'][df['mvar26'].isnull()].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    69390.000000\n",
      "mean         0.222294\n",
      "std          0.415791\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max          1.000000\n",
      "Name: default_ind, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df['default_ind'][df['mvar26'].isnull()==False].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "df = gameDf.copy()\n",
    "# df['mvar26'][df['mvar26'].isnull()] = np.mean(df['mvar26'])\n",
    "df['mvar26'][df['mvar26'].isnull()] = -365"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    80000.000000\n",
       "mean      2873.883937\n",
       "std       3009.429675\n",
       "min       -365.000000\n",
       "25%        578.000000\n",
       "50%       2190.000000\n",
       "75%       4258.000000\n",
       "max      24425.000000\n",
       "Name: mvar26, dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mvar26'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['mvar26'] = df['mvar26'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['mvar26'] = df['mvar26']//365"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3224666666666667"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(0.3857+0.3559+0.3155+0.3119+0.2956+0.2702)/6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15643482736221456"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(0.25*80000-(0.4026*10610+0.3224*(5721+5795+5103+5171+4536+3038)))/(80000-10610-(5721+5795+5103+5171+4536+3038))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar26</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1</th>\n",
       "      <td>10610.0</td>\n",
       "      <td>0.402639</td>\n",
       "      <td>0.490452</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5721.0</td>\n",
       "      <td>0.385772</td>\n",
       "      <td>0.486820</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5795.0</td>\n",
       "      <td>0.355997</td>\n",
       "      <td>0.478855</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5103.0</td>\n",
       "      <td>0.315501</td>\n",
       "      <td>0.464761</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5171.0</td>\n",
       "      <td>0.311932</td>\n",
       "      <td>0.463327</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4536.0</td>\n",
       "      <td>0.295635</td>\n",
       "      <td>0.456378</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3038.0</td>\n",
       "      <td>0.270244</td>\n",
       "      <td>0.444159</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3028.0</td>\n",
       "      <td>0.239762</td>\n",
       "      <td>0.427009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4235.0</td>\n",
       "      <td>0.212987</td>\n",
       "      <td>0.409467</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4237.0</td>\n",
       "      <td>0.193061</td>\n",
       "      <td>0.394747</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3616.0</td>\n",
       "      <td>0.175332</td>\n",
       "      <td>0.380303</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3051.0</td>\n",
       "      <td>0.155359</td>\n",
       "      <td>0.362306</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>0.135607</td>\n",
       "      <td>0.342438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2051.0</td>\n",
       "      <td>0.120429</td>\n",
       "      <td>0.325542</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1994.0</td>\n",
       "      <td>0.126379</td>\n",
       "      <td>0.332360</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1899.0</td>\n",
       "      <td>0.109531</td>\n",
       "      <td>0.312387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1741.0</td>\n",
       "      <td>0.107410</td>\n",
       "      <td>0.309722</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1360.0</td>\n",
       "      <td>0.115441</td>\n",
       "      <td>0.319671</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1076.0</td>\n",
       "      <td>0.100372</td>\n",
       "      <td>0.300635</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>991.0</td>\n",
       "      <td>0.089808</td>\n",
       "      <td>0.286051</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>996.0</td>\n",
       "      <td>0.085341</td>\n",
       "      <td>0.279529</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>932.0</td>\n",
       "      <td>0.085837</td>\n",
       "      <td>0.280273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>826.0</td>\n",
       "      <td>0.083535</td>\n",
       "      <td>0.276857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>673.0</td>\n",
       "      <td>0.081724</td>\n",
       "      <td>0.274147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>530.0</td>\n",
       "      <td>0.084906</td>\n",
       "      <td>0.279005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>516.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.276654</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>509.0</td>\n",
       "      <td>0.060904</td>\n",
       "      <td>0.239389</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>438.0</td>\n",
       "      <td>0.059361</td>\n",
       "      <td>0.236569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>392.0</td>\n",
       "      <td>0.063776</td>\n",
       "      <td>0.244665</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>393.0</td>\n",
       "      <td>0.071247</td>\n",
       "      <td>0.257565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>304.0</td>\n",
       "      <td>0.055921</td>\n",
       "      <td>0.230148</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>237.0</td>\n",
       "      <td>0.097046</td>\n",
       "      <td>0.296647</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>199.0</td>\n",
       "      <td>0.075377</td>\n",
       "      <td>0.264664</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>161.0</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.204567</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>148.0</td>\n",
       "      <td>0.074324</td>\n",
       "      <td>0.263189</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>119.0</td>\n",
       "      <td>0.084034</td>\n",
       "      <td>0.278611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>97.0</td>\n",
       "      <td>0.051546</td>\n",
       "      <td>0.222258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>101.0</td>\n",
       "      <td>0.059406</td>\n",
       "      <td>0.237562</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>114.0</td>\n",
       "      <td>0.043860</td>\n",
       "      <td>0.205687</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>83.0</td>\n",
       "      <td>0.084337</td>\n",
       "      <td>0.279582</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>75.0</td>\n",
       "      <td>0.093333</td>\n",
       "      <td>0.292858</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>59.0</td>\n",
       "      <td>0.101695</td>\n",
       "      <td>0.304841</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>54.0</td>\n",
       "      <td>0.018519</td>\n",
       "      <td>0.136083</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42.0</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.297102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43.0</td>\n",
       "      <td>0.116279</td>\n",
       "      <td>0.324353</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0.103448</td>\n",
       "      <td>0.309934</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>30.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.305129</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>33.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>17.0</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.242536</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>17.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.316228</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>15.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.404520</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std  min  25%  50%  75%  max\n",
       "mvar26                                                      \n",
       "-1      10610.0  0.402639  0.490452  0.0  0.0  0.0  1.0  1.0\n",
       " 0       5721.0  0.385772  0.486820  0.0  0.0  0.0  1.0  1.0\n",
       " 1       5795.0  0.355997  0.478855  0.0  0.0  0.0  1.0  1.0\n",
       " 2       5103.0  0.315501  0.464761  0.0  0.0  0.0  1.0  1.0\n",
       " 3       5171.0  0.311932  0.463327  0.0  0.0  0.0  1.0  1.0\n",
       " 4       4536.0  0.295635  0.456378  0.0  0.0  0.0  1.0  1.0\n",
       " 5       3038.0  0.270244  0.444159  0.0  0.0  0.0  1.0  1.0\n",
       " 6       3028.0  0.239762  0.427009  0.0  0.0  0.0  0.0  1.0\n",
       " 7       4235.0  0.212987  0.409467  0.0  0.0  0.0  0.0  1.0\n",
       " 8       4237.0  0.193061  0.394747  0.0  0.0  0.0  0.0  1.0\n",
       " 9       3616.0  0.175332  0.380303  0.0  0.0  0.0  0.0  1.0\n",
       " 10      3051.0  0.155359  0.362306  0.0  0.0  0.0  0.0  1.0\n",
       " 11      2522.0  0.135607  0.342438  0.0  0.0  0.0  0.0  1.0\n",
       " 12      2051.0  0.120429  0.325542  0.0  0.0  0.0  0.0  1.0\n",
       " 13      1994.0  0.126379  0.332360  0.0  0.0  0.0  0.0  1.0\n",
       " 14      1899.0  0.109531  0.312387  0.0  0.0  0.0  0.0  1.0\n",
       " 15      1741.0  0.107410  0.309722  0.0  0.0  0.0  0.0  1.0\n",
       " 16      1360.0  0.115441  0.319671  0.0  0.0  0.0  0.0  1.0\n",
       " 17      1076.0  0.100372  0.300635  0.0  0.0  0.0  0.0  1.0\n",
       " 18       991.0  0.089808  0.286051  0.0  0.0  0.0  0.0  1.0\n",
       " 19       996.0  0.085341  0.279529  0.0  0.0  0.0  0.0  1.0\n",
       " 20       932.0  0.085837  0.280273  0.0  0.0  0.0  0.0  1.0\n",
       " 21       826.0  0.083535  0.276857  0.0  0.0  0.0  0.0  1.0\n",
       " 22       673.0  0.081724  0.274147  0.0  0.0  0.0  0.0  1.0\n",
       " 23       530.0  0.084906  0.279005  0.0  0.0  0.0  0.0  1.0\n",
       " 24       516.0  0.083333  0.276654  0.0  0.0  0.0  0.0  1.0\n",
       " 25       509.0  0.060904  0.239389  0.0  0.0  0.0  0.0  1.0\n",
       " 26       438.0  0.059361  0.236569  0.0  0.0  0.0  0.0  1.0\n",
       " 27       392.0  0.063776  0.244665  0.0  0.0  0.0  0.0  1.0\n",
       " 28       393.0  0.071247  0.257565  0.0  0.0  0.0  0.0  1.0\n",
       " 29       304.0  0.055921  0.230148  0.0  0.0  0.0  0.0  1.0\n",
       " 30       237.0  0.097046  0.296647  0.0  0.0  0.0  0.0  1.0\n",
       " 31       199.0  0.075377  0.264664  0.0  0.0  0.0  0.0  1.0\n",
       " 32       161.0  0.043478  0.204567  0.0  0.0  0.0  0.0  1.0\n",
       " 33       148.0  0.074324  0.263189  0.0  0.0  0.0  0.0  1.0\n",
       " 34       119.0  0.084034  0.278611  0.0  0.0  0.0  0.0  1.0\n",
       " 35        97.0  0.051546  0.222258  0.0  0.0  0.0  0.0  1.0\n",
       " 36       101.0  0.059406  0.237562  0.0  0.0  0.0  0.0  1.0\n",
       " 37       114.0  0.043860  0.205687  0.0  0.0  0.0  0.0  1.0\n",
       " 38        83.0  0.084337  0.279582  0.0  0.0  0.0  0.0  1.0\n",
       " 39        75.0  0.093333  0.292858  0.0  0.0  0.0  0.0  1.0\n",
       " 40        59.0  0.101695  0.304841  0.0  0.0  0.0  0.0  1.0\n",
       " 41        54.0  0.018519  0.136083  0.0  0.0  0.0  0.0  1.0\n",
       " 42        42.0  0.095238  0.297102  0.0  0.0  0.0  0.0  1.0\n",
       " 43        43.0  0.116279  0.324353  0.0  0.0  0.0  0.0  1.0\n",
       " 44        29.0  0.103448  0.309934  0.0  0.0  0.0  0.0  1.0\n",
       " 45        30.0  0.100000  0.305129  0.0  0.0  0.0  0.0  1.0\n",
       " 46        33.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
       " 47        17.0  0.058824  0.242536  0.0  0.0  0.0  0.0  1.0\n",
       " 48        17.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
       " 49        10.0  0.100000  0.316228  0.0  0.0  0.0  0.0  1.0\n",
       " 50        15.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
       " 51        11.0  0.181818  0.404520  0.0  0.0  0.0  0.0  1.0\n",
       " 52         6.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
       " 53         3.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
       " 54         2.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
       " 55         6.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
       " 58         2.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
       " 60         2.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
       " 66         1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('mvar26')['default_ind'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    66791.000000\n",
       "mean      3096.580153\n",
       "std       2695.058155\n",
       "min          0.000000\n",
       "25%       1034.000000\n",
       "50%       2555.000000\n",
       "75%       4319.000000\n",
       "max      22052.000000\n",
       "Name: mvar27, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#MVAR27\n",
    "df['mvar27'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    13209.000000\n",
      "mean         0.381331\n",
      "std          0.485732\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          1.000000\n",
      "max          1.000000\n",
      "Name: default_ind, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df['default_ind'][df['mvar27'].isnull()].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    80000.000000\n",
      "mean         0.246213\n",
      "std          0.430806\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max          1.000000\n",
      "Name: default_ind, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df['default_ind'][df['mvar26'].isnull()==False].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df['mvar27'][df['mvar27'].isnull()] = -365\n",
    "df['mvar27'] = df['mvar27'] //365\n",
    "df['mvar27'] = df['mvar27'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar27</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1</th>\n",
       "      <td>13209.0</td>\n",
       "      <td>0.381331</td>\n",
       "      <td>0.485732</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6230.0</td>\n",
       "      <td>0.377368</td>\n",
       "      <td>0.484767</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6054.0</td>\n",
       "      <td>0.340106</td>\n",
       "      <td>0.473784</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5163.0</td>\n",
       "      <td>0.306992</td>\n",
       "      <td>0.461291</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5285.0</td>\n",
       "      <td>0.298392</td>\n",
       "      <td>0.457596</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4604.0</td>\n",
       "      <td>0.283015</td>\n",
       "      <td>0.450512</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3017.0</td>\n",
       "      <td>0.269804</td>\n",
       "      <td>0.443932</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2894.0</td>\n",
       "      <td>0.227021</td>\n",
       "      <td>0.418979</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4142.0</td>\n",
       "      <td>0.195799</td>\n",
       "      <td>0.396863</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4183.0</td>\n",
       "      <td>0.180014</td>\n",
       "      <td>0.384245</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3405.0</td>\n",
       "      <td>0.166520</td>\n",
       "      <td>0.372601</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2963.0</td>\n",
       "      <td>0.147486</td>\n",
       "      <td>0.354649</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2353.0</td>\n",
       "      <td>0.124947</td>\n",
       "      <td>0.330729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1917.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.314352</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1879.0</td>\n",
       "      <td>0.119745</td>\n",
       "      <td>0.324749</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1712.0</td>\n",
       "      <td>0.101051</td>\n",
       "      <td>0.301485</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1561.0</td>\n",
       "      <td>0.096092</td>\n",
       "      <td>0.294812</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1255.0</td>\n",
       "      <td>0.103586</td>\n",
       "      <td>0.304844</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>912.0</td>\n",
       "      <td>0.073465</td>\n",
       "      <td>0.261041</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>850.0</td>\n",
       "      <td>0.074118</td>\n",
       "      <td>0.262116</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>844.0</td>\n",
       "      <td>0.077014</td>\n",
       "      <td>0.266772</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>768.0</td>\n",
       "      <td>0.066406</td>\n",
       "      <td>0.249153</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>695.0</td>\n",
       "      <td>0.064748</td>\n",
       "      <td>0.246258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>570.0</td>\n",
       "      <td>0.070175</td>\n",
       "      <td>0.255667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>461.0</td>\n",
       "      <td>0.069414</td>\n",
       "      <td>0.254433</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>420.0</td>\n",
       "      <td>0.073810</td>\n",
       "      <td>0.261772</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>437.0</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.204165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>337.0</td>\n",
       "      <td>0.056380</td>\n",
       "      <td>0.230997</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>338.0</td>\n",
       "      <td>0.056213</td>\n",
       "      <td>0.230674</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>334.0</td>\n",
       "      <td>0.071856</td>\n",
       "      <td>0.258637</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>243.0</td>\n",
       "      <td>0.053498</td>\n",
       "      <td>0.225489</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>152.0</td>\n",
       "      <td>0.085526</td>\n",
       "      <td>0.280588</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>112.0</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.258697</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>100.0</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.219043</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>81.0</td>\n",
       "      <td>0.061728</td>\n",
       "      <td>0.242161</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>48.0</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.244623</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>49.0</td>\n",
       "      <td>0.061224</td>\n",
       "      <td>0.242226</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>58.0</td>\n",
       "      <td>0.051724</td>\n",
       "      <td>0.223404</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>57.0</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.225282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>56.0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.333712</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.252262</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0.156250</td>\n",
       "      <td>0.368902</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.169031</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>21.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.358569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>21.0</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.300793</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.403113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.337832</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>17.0</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.242536</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>13.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std  min  25%  50%  75%  max\n",
       "mvar27                                                      \n",
       "-1      13209.0  0.381331  0.485732  0.0  0.0  0.0  1.0  1.0\n",
       " 0       6230.0  0.377368  0.484767  0.0  0.0  0.0  1.0  1.0\n",
       " 1       6054.0  0.340106  0.473784  0.0  0.0  0.0  1.0  1.0\n",
       " 2       5163.0  0.306992  0.461291  0.0  0.0  0.0  1.0  1.0\n",
       " 3       5285.0  0.298392  0.457596  0.0  0.0  0.0  1.0  1.0\n",
       " 4       4604.0  0.283015  0.450512  0.0  0.0  0.0  1.0  1.0\n",
       " 5       3017.0  0.269804  0.443932  0.0  0.0  0.0  1.0  1.0\n",
       " 6       2894.0  0.227021  0.418979  0.0  0.0  0.0  0.0  1.0\n",
       " 7       4142.0  0.195799  0.396863  0.0  0.0  0.0  0.0  1.0\n",
       " 8       4183.0  0.180014  0.384245  0.0  0.0  0.0  0.0  1.0\n",
       " 9       3405.0  0.166520  0.372601  0.0  0.0  0.0  0.0  1.0\n",
       " 10      2963.0  0.147486  0.354649  0.0  0.0  0.0  0.0  1.0\n",
       " 11      2353.0  0.124947  0.330729  0.0  0.0  0.0  0.0  1.0\n",
       " 12      1917.0  0.111111  0.314352  0.0  0.0  0.0  0.0  1.0\n",
       " 13      1879.0  0.119745  0.324749  0.0  0.0  0.0  0.0  1.0\n",
       " 14      1712.0  0.101051  0.301485  0.0  0.0  0.0  0.0  1.0\n",
       " 15      1561.0  0.096092  0.294812  0.0  0.0  0.0  0.0  1.0\n",
       " 16      1255.0  0.103586  0.304844  0.0  0.0  0.0  0.0  1.0\n",
       " 17       912.0  0.073465  0.261041  0.0  0.0  0.0  0.0  1.0\n",
       " 18       850.0  0.074118  0.262116  0.0  0.0  0.0  0.0  1.0\n",
       " 19       844.0  0.077014  0.266772  0.0  0.0  0.0  0.0  1.0\n",
       " 20       768.0  0.066406  0.249153  0.0  0.0  0.0  0.0  1.0\n",
       " 21       695.0  0.064748  0.246258  0.0  0.0  0.0  0.0  1.0\n",
       " 22       570.0  0.070175  0.255667  0.0  0.0  0.0  0.0  1.0\n",
       " 23       461.0  0.069414  0.254433  0.0  0.0  0.0  0.0  1.0\n",
       " 24       420.0  0.073810  0.261772  0.0  0.0  0.0  0.0  1.0\n",
       " 25       437.0  0.043478  0.204165  0.0  0.0  0.0  0.0  1.0\n",
       " 26       337.0  0.056380  0.230997  0.0  0.0  0.0  0.0  1.0\n",
       " 27       338.0  0.056213  0.230674  0.0  0.0  0.0  0.0  1.0\n",
       " 28       334.0  0.071856  0.258637  0.0  0.0  0.0  0.0  1.0\n",
       " 29       243.0  0.053498  0.225489  0.0  0.0  0.0  0.0  1.0\n",
       " 30       152.0  0.085526  0.280588  0.0  0.0  0.0  0.0  1.0\n",
       " 31       112.0  0.071429  0.258697  0.0  0.0  0.0  0.0  1.0\n",
       " 32       100.0  0.050000  0.219043  0.0  0.0  0.0  0.0  1.0\n",
       " 33        81.0  0.061728  0.242161  0.0  0.0  0.0  0.0  1.0\n",
       " 34        48.0  0.062500  0.244623  0.0  0.0  0.0  0.0  1.0\n",
       " 35        49.0  0.061224  0.242226  0.0  0.0  0.0  0.0  1.0\n",
       " 36        58.0  0.051724  0.223404  0.0  0.0  0.0  0.0  1.0\n",
       " 37        57.0  0.052632  0.225282  0.0  0.0  0.0  0.0  1.0\n",
       " 38        56.0  0.125000  0.333712  0.0  0.0  0.0  0.0  1.0\n",
       " 39        45.0  0.066667  0.252262  0.0  0.0  0.0  0.0  1.0\n",
       " 40        32.0  0.156250  0.368902  0.0  0.0  0.0  0.0  1.0\n",
       " 41        35.0  0.028571  0.169031  0.0  0.0  0.0  0.0  1.0\n",
       " 42        21.0  0.142857  0.358569  0.0  0.0  0.0  0.0  1.0\n",
       " 43        21.0  0.095238  0.300793  0.0  0.0  0.0  0.0  1.0\n",
       " 44        16.0  0.187500  0.403113  0.0  0.0  0.0  0.0  1.0\n",
       " 45        24.0  0.125000  0.337832  0.0  0.0  0.0  0.0  1.0\n",
       " 46        17.0  0.058824  0.242536  0.0  0.0  0.0  0.0  1.0\n",
       " 47         6.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
       " 48        13.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
       " 49         3.0  0.666667  0.577350  0.0  0.5  1.0  1.0  1.0\n",
       " 50         8.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
       " 52         3.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
       " 53         2.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
       " 54         2.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
       " 55         2.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
       " 58         1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       " 60         1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('mvar27')['default_ind'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#mvar26+27\n",
    "df = gameDf.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df['mvar27'][df['mvar27'].isnull()] = -365"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['mvar26'] = (df['mvar26']+df['mvar27'])/2/365"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['mvar26'] = df['mvar26'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32436095164459905"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(6660*0.3774+6000*0.3543+5138*0.3102+5231*0.3045+4730*0.289+3272*0.2676)/(6660+6000+5138+5231+4739+3273)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar26</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1</th>\n",
       "      <td>10610.0</td>\n",
       "      <td>0.402639</td>\n",
       "      <td>0.490452</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6660.0</td>\n",
       "      <td>0.377477</td>\n",
       "      <td>0.484792</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6000.0</td>\n",
       "      <td>0.354333</td>\n",
       "      <td>0.478351</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5138.0</td>\n",
       "      <td>0.310237</td>\n",
       "      <td>0.462636</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5231.0</td>\n",
       "      <td>0.304531</td>\n",
       "      <td>0.460252</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4730.0</td>\n",
       "      <td>0.289006</td>\n",
       "      <td>0.453349</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3273.0</td>\n",
       "      <td>0.267644</td>\n",
       "      <td>0.442799</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3110.0</td>\n",
       "      <td>0.234405</td>\n",
       "      <td>0.423695</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4149.0</td>\n",
       "      <td>0.199807</td>\n",
       "      <td>0.399904</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4193.0</td>\n",
       "      <td>0.186740</td>\n",
       "      <td>0.389749</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3483.0</td>\n",
       "      <td>0.169681</td>\n",
       "      <td>0.375406</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3032.0</td>\n",
       "      <td>0.148087</td>\n",
       "      <td>0.355245</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2460.0</td>\n",
       "      <td>0.141463</td>\n",
       "      <td>0.348570</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021.0</td>\n",
       "      <td>0.120732</td>\n",
       "      <td>0.325897</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1970.0</td>\n",
       "      <td>0.116751</td>\n",
       "      <td>0.321205</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1849.0</td>\n",
       "      <td>0.111952</td>\n",
       "      <td>0.315393</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1680.0</td>\n",
       "      <td>0.101786</td>\n",
       "      <td>0.302456</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1312.0</td>\n",
       "      <td>0.099848</td>\n",
       "      <td>0.299911</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1017.0</td>\n",
       "      <td>0.079646</td>\n",
       "      <td>0.270878</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>928.0</td>\n",
       "      <td>0.079741</td>\n",
       "      <td>0.271038</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>935.0</td>\n",
       "      <td>0.079144</td>\n",
       "      <td>0.270108</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>850.0</td>\n",
       "      <td>0.078824</td>\n",
       "      <td>0.269622</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>744.0</td>\n",
       "      <td>0.068548</td>\n",
       "      <td>0.252855</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>647.0</td>\n",
       "      <td>0.064915</td>\n",
       "      <td>0.246566</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>509.0</td>\n",
       "      <td>0.076621</td>\n",
       "      <td>0.266251</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>479.0</td>\n",
       "      <td>0.064718</td>\n",
       "      <td>0.246285</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>472.0</td>\n",
       "      <td>0.044492</td>\n",
       "      <td>0.206403</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>381.0</td>\n",
       "      <td>0.065617</td>\n",
       "      <td>0.247937</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>362.0</td>\n",
       "      <td>0.060773</td>\n",
       "      <td>0.239245</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>358.0</td>\n",
       "      <td>0.067039</td>\n",
       "      <td>0.250440</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>273.0</td>\n",
       "      <td>0.051282</td>\n",
       "      <td>0.220978</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>187.0</td>\n",
       "      <td>0.080214</td>\n",
       "      <td>0.272353</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>142.0</td>\n",
       "      <td>0.056338</td>\n",
       "      <td>0.231390</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>125.0</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.196748</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>102.0</td>\n",
       "      <td>0.039216</td>\n",
       "      <td>0.195066</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>62.0</td>\n",
       "      <td>0.064516</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>63.0</td>\n",
       "      <td>0.063492</td>\n",
       "      <td>0.245805</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>69.0</td>\n",
       "      <td>0.057971</td>\n",
       "      <td>0.235401</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>63.0</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.214669</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>64.0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>53.0</td>\n",
       "      <td>0.056604</td>\n",
       "      <td>0.233295</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>33.0</td>\n",
       "      <td>0.151515</td>\n",
       "      <td>0.364110</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>31.0</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.179605</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.351250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.294245</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.366348</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>25.0</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.331662</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>9.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>14.0</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.267261</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>9.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std  min   25%  50%   75%  max\n",
       "mvar26                                                        \n",
       "-1      10610.0  0.402639  0.490452  0.0  0.00  0.0  1.00  1.0\n",
       " 0       6660.0  0.377477  0.484792  0.0  0.00  0.0  1.00  1.0\n",
       " 1       6000.0  0.354333  0.478351  0.0  0.00  0.0  1.00  1.0\n",
       " 2       5138.0  0.310237  0.462636  0.0  0.00  0.0  1.00  1.0\n",
       " 3       5231.0  0.304531  0.460252  0.0  0.00  0.0  1.00  1.0\n",
       " 4       4730.0  0.289006  0.453349  0.0  0.00  0.0  1.00  1.0\n",
       " 5       3273.0  0.267644  0.442799  0.0  0.00  0.0  1.00  1.0\n",
       " 6       3110.0  0.234405  0.423695  0.0  0.00  0.0  0.00  1.0\n",
       " 7       4149.0  0.199807  0.399904  0.0  0.00  0.0  0.00  1.0\n",
       " 8       4193.0  0.186740  0.389749  0.0  0.00  0.0  0.00  1.0\n",
       " 9       3483.0  0.169681  0.375406  0.0  0.00  0.0  0.00  1.0\n",
       " 10      3032.0  0.148087  0.355245  0.0  0.00  0.0  0.00  1.0\n",
       " 11      2460.0  0.141463  0.348570  0.0  0.00  0.0  0.00  1.0\n",
       " 12      2021.0  0.120732  0.325897  0.0  0.00  0.0  0.00  1.0\n",
       " 13      1970.0  0.116751  0.321205  0.0  0.00  0.0  0.00  1.0\n",
       " 14      1849.0  0.111952  0.315393  0.0  0.00  0.0  0.00  1.0\n",
       " 15      1680.0  0.101786  0.302456  0.0  0.00  0.0  0.00  1.0\n",
       " 16      1312.0  0.099848  0.299911  0.0  0.00  0.0  0.00  1.0\n",
       " 17      1017.0  0.079646  0.270878  0.0  0.00  0.0  0.00  1.0\n",
       " 18       928.0  0.079741  0.271038  0.0  0.00  0.0  0.00  1.0\n",
       " 19       935.0  0.079144  0.270108  0.0  0.00  0.0  0.00  1.0\n",
       " 20       850.0  0.078824  0.269622  0.0  0.00  0.0  0.00  1.0\n",
       " 21       744.0  0.068548  0.252855  0.0  0.00  0.0  0.00  1.0\n",
       " 22       647.0  0.064915  0.246566  0.0  0.00  0.0  0.00  1.0\n",
       " 23       509.0  0.076621  0.266251  0.0  0.00  0.0  0.00  1.0\n",
       " 24       479.0  0.064718  0.246285  0.0  0.00  0.0  0.00  1.0\n",
       " 25       472.0  0.044492  0.206403  0.0  0.00  0.0  0.00  1.0\n",
       " 26       381.0  0.065617  0.247937  0.0  0.00  0.0  0.00  1.0\n",
       " 27       362.0  0.060773  0.239245  0.0  0.00  0.0  0.00  1.0\n",
       " 28       358.0  0.067039  0.250440  0.0  0.00  0.0  0.00  1.0\n",
       " 29       273.0  0.051282  0.220978  0.0  0.00  0.0  0.00  1.0\n",
       " 30       187.0  0.080214  0.272353  0.0  0.00  0.0  0.00  1.0\n",
       " 31       142.0  0.056338  0.231390  0.0  0.00  0.0  0.00  1.0\n",
       " 32       125.0  0.040000  0.196748  0.0  0.00  0.0  0.00  1.0\n",
       " 33       102.0  0.039216  0.195066  0.0  0.00  0.0  0.00  1.0\n",
       " 34        62.0  0.064516  0.247676  0.0  0.00  0.0  0.00  1.0\n",
       " 35        63.0  0.063492  0.245805  0.0  0.00  0.0  0.00  1.0\n",
       " 36        69.0  0.057971  0.235401  0.0  0.00  0.0  0.00  1.0\n",
       " 37        63.0  0.047619  0.214669  0.0  0.00  0.0  0.00  1.0\n",
       " 38        64.0  0.125000  0.333333  0.0  0.00  0.0  0.00  1.0\n",
       " 39        53.0  0.056604  0.233295  0.0  0.00  0.0  0.00  1.0\n",
       " 40        33.0  0.151515  0.364110  0.0  0.00  0.0  0.00  1.0\n",
       " 41        31.0  0.032258  0.179605  0.0  0.00  0.0  0.00  1.0\n",
       " 42        22.0  0.136364  0.351250  0.0  0.00  0.0  0.00  1.0\n",
       " 43        22.0  0.090909  0.294245  0.0  0.00  0.0  0.00  1.0\n",
       " 44        20.0  0.150000  0.366348  0.0  0.00  0.0  0.00  1.0\n",
       " 45        25.0  0.120000  0.331662  0.0  0.00  0.0  0.00  1.0\n",
       " 46        16.0  0.000000  0.000000  0.0  0.00  0.0  0.00  0.0\n",
       " 47         9.0  0.000000  0.000000  0.0  0.00  0.0  0.00  0.0\n",
       " 48        14.0  0.071429  0.267261  0.0  0.00  0.0  0.00  1.0\n",
       " 49         2.0  0.500000  0.707107  0.0  0.25  0.5  0.75  1.0\n",
       " 50         9.0  0.111111  0.333333  0.0  0.00  0.0  0.00  1.0\n",
       " 52         3.0  0.000000  0.000000  0.0  0.00  0.0  0.00  0.0\n",
       " 53         2.0  0.000000  0.000000  0.0  0.00  0.0  0.00  0.0\n",
       " 54         2.0  0.000000  0.000000  0.0  0.00  0.0  0.00  0.0\n",
       " 55         2.0  0.000000  0.000000  0.0  0.00  0.0  0.00  0.0\n",
       " 58         1.0  0.000000       NaN  0.0  0.00  0.0  0.00  0.0\n",
       " 60         1.0  0.000000       NaN  0.0  0.00  0.0  0.00  0.0"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('mvar26')['default_ind'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#mvar28\n",
    "df['mvar28'][df['mvar28'].isnull()] = -365\n",
    "df['mvar28'] = df['mvar28'] //365\n",
    "df['mvar28'] = df['mvar28'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar28</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1</th>\n",
       "      <td>502.0</td>\n",
       "      <td>0.215139</td>\n",
       "      <td>0.411329</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42750.0</td>\n",
       "      <td>0.360468</td>\n",
       "      <td>0.480142</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5804.0</td>\n",
       "      <td>0.161096</td>\n",
       "      <td>0.367651</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2936.0</td>\n",
       "      <td>0.133856</td>\n",
       "      <td>0.340555</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2104.0</td>\n",
       "      <td>0.127852</td>\n",
       "      <td>0.334004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1738.0</td>\n",
       "      <td>0.116801</td>\n",
       "      <td>0.321275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1285.0</td>\n",
       "      <td>0.121401</td>\n",
       "      <td>0.326720</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>675.0</td>\n",
       "      <td>0.099259</td>\n",
       "      <td>0.299232</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.377964</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>22189.0</td>\n",
       "      <td>0.097120</td>\n",
       "      <td>0.296128</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std  min  25%  50%  75%  max\n",
       "mvar28                                                      \n",
       "-1        502.0  0.215139  0.411329  0.0  0.0  0.0  0.0  1.0\n",
       " 0      42750.0  0.360468  0.480142  0.0  0.0  0.0  1.0  1.0\n",
       " 1       5804.0  0.161096  0.367651  0.0  0.0  0.0  0.0  1.0\n",
       " 2       2936.0  0.133856  0.340555  0.0  0.0  0.0  0.0  1.0\n",
       " 3       2104.0  0.127852  0.334004  0.0  0.0  0.0  0.0  1.0\n",
       " 4       1738.0  0.116801  0.321275  0.0  0.0  0.0  0.0  1.0\n",
       " 5       1285.0  0.121401  0.326720  0.0  0.0  0.0  0.0  1.0\n",
       " 6        675.0  0.099259  0.299232  0.0  0.0  0.0  0.0  1.0\n",
       " 7          7.0  0.142857  0.377964  0.0  0.0  0.0  0.0  1.0\n",
       " 8          7.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
       " 9          3.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
       " 83     22189.0  0.097120  0.296128  0.0  0.0  0.0  0.0  1.0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('mvar28')['default_ind'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12318456375838926"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(80000*0.25-42750*0.3605)/(80000-42750)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#mvar30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "df = gameDf.copy()\n",
    "df['mvar30'][df['mvar30'].isnull()] = -365\n",
    "df['mvar30'] = df['mvar30'] //365\n",
    "df['mvar30'] = df['mvar30'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar30</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1</th>\n",
       "      <td>36417.0</td>\n",
       "      <td>0.271000</td>\n",
       "      <td>0.444482</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3570.0</td>\n",
       "      <td>0.306443</td>\n",
       "      <td>0.461080</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3514.0</td>\n",
       "      <td>0.317302</td>\n",
       "      <td>0.465492</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3219.0</td>\n",
       "      <td>0.296676</td>\n",
       "      <td>0.456863</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2821.0</td>\n",
       "      <td>0.266218</td>\n",
       "      <td>0.442058</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2391.0</td>\n",
       "      <td>0.261397</td>\n",
       "      <td>0.439488</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1910.0</td>\n",
       "      <td>0.237696</td>\n",
       "      <td>0.425784</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2104.0</td>\n",
       "      <td>0.226236</td>\n",
       "      <td>0.418493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3110.0</td>\n",
       "      <td>0.222830</td>\n",
       "      <td>0.416212</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3561.0</td>\n",
       "      <td>0.207807</td>\n",
       "      <td>0.405795</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3618.0</td>\n",
       "      <td>0.188226</td>\n",
       "      <td>0.390946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4442.0</td>\n",
       "      <td>0.181000</td>\n",
       "      <td>0.385061</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3605.0</td>\n",
       "      <td>0.154508</td>\n",
       "      <td>0.361485</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2613.0</td>\n",
       "      <td>0.141217</td>\n",
       "      <td>0.348312</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1579.0</td>\n",
       "      <td>0.160861</td>\n",
       "      <td>0.367519</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>794.0</td>\n",
       "      <td>0.153652</td>\n",
       "      <td>0.360843</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>328.0</td>\n",
       "      <td>0.222561</td>\n",
       "      <td>0.416601</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>120.0</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.407819</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>75.0</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.369075</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>94.0</td>\n",
       "      <td>0.117021</td>\n",
       "      <td>0.323169</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>42.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.377195</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>31.0</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>0.373878</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21.0</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.300793</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.447214</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std  min  25%  50%  75%  max\n",
       "mvar30                                                      \n",
       "-1      36417.0  0.271000  0.444482  0.0  0.0  0.0  1.0  1.0\n",
       " 0       3570.0  0.306443  0.461080  0.0  0.0  0.0  1.0  1.0\n",
       " 1       3514.0  0.317302  0.465492  0.0  0.0  0.0  1.0  1.0\n",
       " 2       3219.0  0.296676  0.456863  0.0  0.0  0.0  1.0  1.0\n",
       " 3       2821.0  0.266218  0.442058  0.0  0.0  0.0  1.0  1.0\n",
       " 4       2391.0  0.261397  0.439488  0.0  0.0  0.0  1.0  1.0\n",
       " 5       1910.0  0.237696  0.425784  0.0  0.0  0.0  0.0  1.0\n",
       " 6       2104.0  0.226236  0.418493  0.0  0.0  0.0  0.0  1.0\n",
       " 7       3110.0  0.222830  0.416212  0.0  0.0  0.0  0.0  1.0\n",
       " 8       3561.0  0.207807  0.405795  0.0  0.0  0.0  0.0  1.0\n",
       " 9       3618.0  0.188226  0.390946  0.0  0.0  0.0  0.0  1.0\n",
       " 10      4442.0  0.181000  0.385061  0.0  0.0  0.0  0.0  1.0\n",
       " 11      3605.0  0.154508  0.361485  0.0  0.0  0.0  0.0  1.0\n",
       " 12      2613.0  0.141217  0.348312  0.0  0.0  0.0  0.0  1.0\n",
       " 13      1579.0  0.160861  0.367519  0.0  0.0  0.0  0.0  1.0\n",
       " 14       794.0  0.153652  0.360843  0.0  0.0  0.0  0.0  1.0\n",
       " 15       328.0  0.222561  0.416601  0.0  0.0  0.0  0.0  1.0\n",
       " 16       120.0  0.208333  0.407819  0.0  0.0  0.0  0.0  1.0\n",
       " 17        75.0  0.160000  0.369075  0.0  0.0  0.0  0.0  1.0\n",
       " 18        94.0  0.117021  0.323169  0.0  0.0  0.0  0.0  1.0\n",
       " 19        42.0  0.166667  0.377195  0.0  0.0  0.0  0.0  1.0\n",
       " 20        31.0  0.161290  0.373878  0.0  0.0  0.0  0.0  1.0\n",
       " 21        21.0  0.095238  0.300793  0.0  0.0  0.0  0.0  1.0\n",
       " 22         8.0  0.125000  0.353553  0.0  0.0  0.0  0.0  1.0\n",
       " 23         5.0  0.200000  0.447214  0.0  0.0  0.0  0.0  1.0\n",
       " 24         3.0  0.333333  0.577350  0.0  0.0  0.0  0.5  1.0\n",
       " 25         2.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
       " 26         1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       " 27         1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
       " 35         1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('mvar30')['default_ind'].describe() \n",
    "# print(df.groupby('mvar30')['default_ind'].describe() )\n",
    "# print(\"///////\")\n",
    "# print(df['default_ind'][df['mvar30'].isnull()==False].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#mvar31"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "df = gameDf.copy()\n",
    "df['mvar31'][df['mvar31'].isnull()] = -365\n",
    "df['mvar31'] = df['mvar31'] //365\n",
    "df['mvar31'] = df['mvar31'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3327715612648221"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(20000-56218*0.214985)/(80000-56218)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar31</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1</th>\n",
       "      <td>56218.0</td>\n",
       "      <td>0.214985</td>\n",
       "      <td>0.410815</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>502.0</td>\n",
       "      <td>0.330677</td>\n",
       "      <td>0.470926</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>745.0</td>\n",
       "      <td>0.472483</td>\n",
       "      <td>0.499578</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>830.0</td>\n",
       "      <td>0.445783</td>\n",
       "      <td>0.497352</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1126.0</td>\n",
       "      <td>0.388099</td>\n",
       "      <td>0.487534</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1398.0</td>\n",
       "      <td>0.390558</td>\n",
       "      <td>0.488050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1763.0</td>\n",
       "      <td>0.385139</td>\n",
       "      <td>0.486766</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1658.0</td>\n",
       "      <td>0.387817</td>\n",
       "      <td>0.487399</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1364.0</td>\n",
       "      <td>0.332845</td>\n",
       "      <td>0.471404</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1396.0</td>\n",
       "      <td>0.331662</td>\n",
       "      <td>0.470979</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1503.0</td>\n",
       "      <td>0.307385</td>\n",
       "      <td>0.461564</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2011.0</td>\n",
       "      <td>0.291895</td>\n",
       "      <td>0.454747</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1836.0</td>\n",
       "      <td>0.277233</td>\n",
       "      <td>0.447755</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1670.0</td>\n",
       "      <td>0.280838</td>\n",
       "      <td>0.449543</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1236.0</td>\n",
       "      <td>0.251618</td>\n",
       "      <td>0.434119</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>919.0</td>\n",
       "      <td>0.250272</td>\n",
       "      <td>0.433406</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>738.0</td>\n",
       "      <td>0.252033</td>\n",
       "      <td>0.434474</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>582.0</td>\n",
       "      <td>0.214777</td>\n",
       "      <td>0.411021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>462.0</td>\n",
       "      <td>0.225108</td>\n",
       "      <td>0.418106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>417.0</td>\n",
       "      <td>0.235012</td>\n",
       "      <td>0.424516</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>411.0</td>\n",
       "      <td>0.282238</td>\n",
       "      <td>0.450637</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>311.0</td>\n",
       "      <td>0.270096</td>\n",
       "      <td>0.444725</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>225.0</td>\n",
       "      <td>0.226667</td>\n",
       "      <td>0.419609</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>139.0</td>\n",
       "      <td>0.215827</td>\n",
       "      <td>0.412883</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>121.0</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.387298</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>98.0</td>\n",
       "      <td>0.265306</td>\n",
       "      <td>0.443766</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>70.0</td>\n",
       "      <td>0.257143</td>\n",
       "      <td>0.440215</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>52.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.437237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>56.0</td>\n",
       "      <td>0.321429</td>\n",
       "      <td>0.471251</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0.379310</td>\n",
       "      <td>0.493804</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0.259259</td>\n",
       "      <td>0.446576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.470679</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.404520</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>13.0</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.518875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.404520</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.447214</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std  min   25%  50%   75%  max\n",
       "mvar31                                                        \n",
       "-1      56218.0  0.214985  0.410815  0.0  0.00  0.0  0.00  1.0\n",
       " 0        502.0  0.330677  0.470926  0.0  0.00  0.0  1.00  1.0\n",
       " 1        745.0  0.472483  0.499578  0.0  0.00  0.0  1.00  1.0\n",
       " 2        830.0  0.445783  0.497352  0.0  0.00  0.0  1.00  1.0\n",
       " 3       1126.0  0.388099  0.487534  0.0  0.00  0.0  1.00  1.0\n",
       " 4       1398.0  0.390558  0.488050  0.0  0.00  0.0  1.00  1.0\n",
       " 5       1763.0  0.385139  0.486766  0.0  0.00  0.0  1.00  1.0\n",
       " 6       1658.0  0.387817  0.487399  0.0  0.00  0.0  1.00  1.0\n",
       " 7       1364.0  0.332845  0.471404  0.0  0.00  0.0  1.00  1.0\n",
       " 8       1396.0  0.331662  0.470979  0.0  0.00  0.0  1.00  1.0\n",
       " 9       1503.0  0.307385  0.461564  0.0  0.00  0.0  1.00  1.0\n",
       " 10      2011.0  0.291895  0.454747  0.0  0.00  0.0  1.00  1.0\n",
       " 11      1836.0  0.277233  0.447755  0.0  0.00  0.0  1.00  1.0\n",
       " 12      1670.0  0.280838  0.449543  0.0  0.00  0.0  1.00  1.0\n",
       " 13      1236.0  0.251618  0.434119  0.0  0.00  0.0  1.00  1.0\n",
       " 14       919.0  0.250272  0.433406  0.0  0.00  0.0  0.50  1.0\n",
       " 15       738.0  0.252033  0.434474  0.0  0.00  0.0  1.00  1.0\n",
       " 16       582.0  0.214777  0.411021  0.0  0.00  0.0  0.00  1.0\n",
       " 17       462.0  0.225108  0.418106  0.0  0.00  0.0  0.00  1.0\n",
       " 18       417.0  0.235012  0.424516  0.0  0.00  0.0  0.00  1.0\n",
       " 19       411.0  0.282238  0.450637  0.0  0.00  0.0  1.00  1.0\n",
       " 20       311.0  0.270096  0.444725  0.0  0.00  0.0  1.00  1.0\n",
       " 21       225.0  0.226667  0.419609  0.0  0.00  0.0  0.00  1.0\n",
       " 22       139.0  0.215827  0.412883  0.0  0.00  0.0  0.00  1.0\n",
       " 23       121.0  0.181818  0.387298  0.0  0.00  0.0  0.00  1.0\n",
       " 24        98.0  0.265306  0.443766  0.0  0.00  0.0  1.00  1.0\n",
       " 25        70.0  0.257143  0.440215  0.0  0.00  0.0  0.75  1.0\n",
       " 26        52.0  0.250000  0.437237  0.0  0.00  0.0  0.25  1.0\n",
       " 27        56.0  0.321429  0.471251  0.0  0.00  0.0  1.00  1.0\n",
       " 28        29.0  0.379310  0.493804  0.0  0.00  0.0  1.00  1.0\n",
       " 29        27.0  0.259259  0.446576  0.0  0.00  0.0  0.50  1.0\n",
       " 30        26.0  0.307692  0.470679  0.0  0.00  0.0  1.00  1.0\n",
       " 31        11.0  0.181818  0.404520  0.0  0.00  0.0  0.00  1.0\n",
       " 32        13.0  0.461538  0.518875  0.0  0.00  0.0  1.00  1.0\n",
       " 33        12.0  0.083333  0.288675  0.0  0.00  0.0  0.00  1.0\n",
       " 34        11.0  0.181818  0.404520  0.0  0.00  0.0  0.00  1.0\n",
       " 35         5.0  0.200000  0.447214  0.0  0.00  0.0  0.00  1.0\n",
       " 36         1.0  0.000000       NaN  0.0  0.00  0.0  0.00  0.0\n",
       " 37         1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
       " 38         2.0  1.000000  0.000000  1.0  1.00  1.0  1.00  1.0\n",
       " 39         1.0  0.000000       NaN  0.0  0.00  0.0  0.00  0.0\n",
       " 40         2.0  0.500000  0.707107  0.0  0.25  0.5  0.75  1.0\n",
       " 41         1.0  0.000000       NaN  0.0  0.00  0.0  0.00  0.0\n",
       " 43         1.0  0.000000       NaN  0.0  0.00  0.0  0.00  0.0"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('mvar31')['default_ind'].describe() \n",
    "# print(df.groupby('mvar30')['default_ind'].describe() )\n",
    "# print(\"///////\")\n",
    "# print(df['default_ind'][df['mvar30'].isnull()==False].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = gameDf.copy()\n",
    "df2['mvar30'] = df2[[\"mvar30\", \"mvar31\"]].max(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26200"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(df2['mvar30'].isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36417"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(gameDf[\"mvar30\"].isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df2['mvar30'][df2['mvar30'].isnull()] = -365\n",
    "df2['mvar30'] = df2['mvar30'] //365\n",
    "df2['mvar30'] = df2['mvar30'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar30</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1</th>\n",
       "      <td>26200.0</td>\n",
       "      <td>0.235534</td>\n",
       "      <td>0.424340</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2783.0</td>\n",
       "      <td>0.278836</td>\n",
       "      <td>0.448507</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2936.0</td>\n",
       "      <td>0.328678</td>\n",
       "      <td>0.469813</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2829.0</td>\n",
       "      <td>0.310004</td>\n",
       "      <td>0.462576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2692.0</td>\n",
       "      <td>0.294205</td>\n",
       "      <td>0.455769</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2730.0</td>\n",
       "      <td>0.309524</td>\n",
       "      <td>0.462382</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2710.0</td>\n",
       "      <td>0.318081</td>\n",
       "      <td>0.465817</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2847.0</td>\n",
       "      <td>0.302775</td>\n",
       "      <td>0.459539</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3480.0</td>\n",
       "      <td>0.254885</td>\n",
       "      <td>0.435859</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3882.0</td>\n",
       "      <td>0.246007</td>\n",
       "      <td>0.430738</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4098.0</td>\n",
       "      <td>0.220351</td>\n",
       "      <td>0.414534</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5416.0</td>\n",
       "      <td>0.215473</td>\n",
       "      <td>0.411188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4725.0</td>\n",
       "      <td>0.199153</td>\n",
       "      <td>0.399406</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3899.0</td>\n",
       "      <td>0.195691</td>\n",
       "      <td>0.396783</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2633.0</td>\n",
       "      <td>0.201671</td>\n",
       "      <td>0.401324</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1633.0</td>\n",
       "      <td>0.206981</td>\n",
       "      <td>0.405266</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1041.0</td>\n",
       "      <td>0.243036</td>\n",
       "      <td>0.429123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>689.0</td>\n",
       "      <td>0.216255</td>\n",
       "      <td>0.411990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>530.0</td>\n",
       "      <td>0.216981</td>\n",
       "      <td>0.412579</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>509.0</td>\n",
       "      <td>0.214145</td>\n",
       "      <td>0.410632</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>451.0</td>\n",
       "      <td>0.270510</td>\n",
       "      <td>0.444717</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>342.0</td>\n",
       "      <td>0.260234</td>\n",
       "      <td>0.439405</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>245.0</td>\n",
       "      <td>0.216327</td>\n",
       "      <td>0.412582</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>147.0</td>\n",
       "      <td>0.210884</td>\n",
       "      <td>0.409331</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>126.0</td>\n",
       "      <td>0.182540</td>\n",
       "      <td>0.387831</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>101.0</td>\n",
       "      <td>0.267327</td>\n",
       "      <td>0.444772</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>72.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.436051</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>53.0</td>\n",
       "      <td>0.245283</td>\n",
       "      <td>0.434372</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>57.0</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.468961</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0.379310</td>\n",
       "      <td>0.493804</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0.259259</td>\n",
       "      <td>0.446576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.470679</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.404520</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>13.0</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.518875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.404520</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std  min   25%  50%   75%  max\n",
       "mvar30                                                        \n",
       "-1      26200.0  0.235534  0.424340  0.0  0.00  0.0  0.00  1.0\n",
       " 0       2783.0  0.278836  0.448507  0.0  0.00  0.0  1.00  1.0\n",
       " 1       2936.0  0.328678  0.469813  0.0  0.00  0.0  1.00  1.0\n",
       " 2       2829.0  0.310004  0.462576  0.0  0.00  0.0  1.00  1.0\n",
       " 3       2692.0  0.294205  0.455769  0.0  0.00  0.0  1.00  1.0\n",
       " 4       2730.0  0.309524  0.462382  0.0  0.00  0.0  1.00  1.0\n",
       " 5       2710.0  0.318081  0.465817  0.0  0.00  0.0  1.00  1.0\n",
       " 6       2847.0  0.302775  0.459539  0.0  0.00  0.0  1.00  1.0\n",
       " 7       3480.0  0.254885  0.435859  0.0  0.00  0.0  1.00  1.0\n",
       " 8       3882.0  0.246007  0.430738  0.0  0.00  0.0  0.00  1.0\n",
       " 9       4098.0  0.220351  0.414534  0.0  0.00  0.0  0.00  1.0\n",
       " 10      5416.0  0.215473  0.411188  0.0  0.00  0.0  0.00  1.0\n",
       " 11      4725.0  0.199153  0.399406  0.0  0.00  0.0  0.00  1.0\n",
       " 12      3899.0  0.195691  0.396783  0.0  0.00  0.0  0.00  1.0\n",
       " 13      2633.0  0.201671  0.401324  0.0  0.00  0.0  0.00  1.0\n",
       " 14      1633.0  0.206981  0.405266  0.0  0.00  0.0  0.00  1.0\n",
       " 15      1041.0  0.243036  0.429123  0.0  0.00  0.0  0.00  1.0\n",
       " 16       689.0  0.216255  0.411990  0.0  0.00  0.0  0.00  1.0\n",
       " 17       530.0  0.216981  0.412579  0.0  0.00  0.0  0.00  1.0\n",
       " 18       509.0  0.214145  0.410632  0.0  0.00  0.0  0.00  1.0\n",
       " 19       451.0  0.270510  0.444717  0.0  0.00  0.0  1.00  1.0\n",
       " 20       342.0  0.260234  0.439405  0.0  0.00  0.0  1.00  1.0\n",
       " 21       245.0  0.216327  0.412582  0.0  0.00  0.0  0.00  1.0\n",
       " 22       147.0  0.210884  0.409331  0.0  0.00  0.0  0.00  1.0\n",
       " 23       126.0  0.182540  0.387831  0.0  0.00  0.0  0.00  1.0\n",
       " 24       101.0  0.267327  0.444772  0.0  0.00  0.0  1.00  1.0\n",
       " 25        72.0  0.250000  0.436051  0.0  0.00  0.0  0.25  1.0\n",
       " 26        53.0  0.245283  0.434372  0.0  0.00  0.0  0.00  1.0\n",
       " 27        57.0  0.315789  0.468961  0.0  0.00  0.0  1.00  1.0\n",
       " 28        29.0  0.379310  0.493804  0.0  0.00  0.0  1.00  1.0\n",
       " 29        27.0  0.259259  0.446576  0.0  0.00  0.0  0.50  1.0\n",
       " 30        26.0  0.307692  0.470679  0.0  0.00  0.0  1.00  1.0\n",
       " 31        11.0  0.181818  0.404520  0.0  0.00  0.0  0.00  1.0\n",
       " 32        13.0  0.461538  0.518875  0.0  0.00  0.0  1.00  1.0\n",
       " 33        12.0  0.083333  0.288675  0.0  0.00  0.0  0.00  1.0\n",
       " 34        11.0  0.181818  0.404520  0.0  0.00  0.0  0.00  1.0\n",
       " 35         6.0  0.166667  0.408248  0.0  0.00  0.0  0.00  1.0\n",
       " 36         1.0  0.000000       NaN  0.0  0.00  0.0  0.00  0.0\n",
       " 37         1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
       " 38         2.0  1.000000  0.000000  1.0  1.00  1.0  1.00  1.0\n",
       " 39         1.0  0.000000       NaN  0.0  0.00  0.0  0.00  0.0\n",
       " 40         2.0  0.500000  0.707107  0.0  0.25  0.5  0.75  1.0\n",
       " 41         1.0  0.000000       NaN  0.0  0.00  0.0  0.00  0.0\n",
       " 43         1.0  0.000000       NaN  0.0  0.00  0.0  0.00  0.0"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby('mvar30')['default_ind'].describe() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#mvar33"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "df2 = gameDf.copy()\n",
    "df2['mvar33'][df2['mvar33'].isnull()] = -5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2['mvar33'] = df2['mvar33'] //5\n",
    "df2['mvar33'] = df2['mvar33'].round(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16017985106084023"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(df2['default_ind'][df2['mvar33']>1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar33</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1.0</th>\n",
       "      <td>1751.0</td>\n",
       "      <td>0.311822</td>\n",
       "      <td>0.463370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>43402.0</td>\n",
       "      <td>0.295954</td>\n",
       "      <td>0.456476</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>13496.0</td>\n",
       "      <td>0.213841</td>\n",
       "      <td>0.410031</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>9441.0</td>\n",
       "      <td>0.177841</td>\n",
       "      <td>0.382399</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>5930.0</td>\n",
       "      <td>0.165599</td>\n",
       "      <td>0.371751</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>3075.0</td>\n",
       "      <td>0.144065</td>\n",
       "      <td>0.351213</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>1738.0</td>\n",
       "      <td>0.114499</td>\n",
       "      <td>0.318509</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>780.0</td>\n",
       "      <td>0.112821</td>\n",
       "      <td>0.316576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>230.0</td>\n",
       "      <td>0.078261</td>\n",
       "      <td>0.269167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>99.0</td>\n",
       "      <td>0.060606</td>\n",
       "      <td>0.239821</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>33.0</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.291937</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>14.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std  min  25%  50%  75%  max\n",
       "mvar33                                                      \n",
       "-1.0     1751.0  0.311822  0.463370  0.0  0.0  0.0  1.0  1.0\n",
       " 0.0    43402.0  0.295954  0.456476  0.0  0.0  0.0  1.0  1.0\n",
       " 1.0    13496.0  0.213841  0.410031  0.0  0.0  0.0  0.0  1.0\n",
       " 2.0     9441.0  0.177841  0.382399  0.0  0.0  0.0  0.0  1.0\n",
       " 3.0     5930.0  0.165599  0.371751  0.0  0.0  0.0  0.0  1.0\n",
       " 4.0     3075.0  0.144065  0.351213  0.0  0.0  0.0  0.0  1.0\n",
       " 5.0     1738.0  0.114499  0.318509  0.0  0.0  0.0  0.0  1.0\n",
       " 6.0      780.0  0.112821  0.316576  0.0  0.0  0.0  0.0  1.0\n",
       " 7.0      230.0  0.078261  0.269167  0.0  0.0  0.0  0.0  1.0\n",
       " 8.0       99.0  0.060606  0.239821  0.0  0.0  0.0  0.0  1.0\n",
       " 9.0       33.0  0.090909  0.291937  0.0  0.0  0.0  0.0  1.0\n",
       " 10.0      14.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
       " 11.0       6.0  0.166667  0.408248  0.0  0.0  0.0  0.0  1.0\n",
       " 12.0       3.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
       " 14.0       1.0  1.000000       NaN  1.0  1.0  1.0  1.0  1.0\n",
       " 15.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby('mvar33')['default_ind'].describe() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          count      mean       std  min   25%  50%   75%  max\n",
      "mvar34                                                        \n",
      "-1.0      502.0  0.215139  0.411329  0.0  0.00  0.0  0.00  1.0\n",
      " 0.0    67048.0  0.218485  0.413221  0.0  0.00  0.0  0.00  1.0\n",
      " 1.0     8989.0  0.358994  0.479732  0.0  0.00  0.0  1.00  1.0\n",
      " 2.0     2213.0  0.455942  0.498168  0.0  0.00  0.0  1.00  1.0\n",
      " 3.0      768.0  0.529948  0.499428  0.0  0.00  1.0  1.00  1.0\n",
      " 4.0      248.0  0.600806  0.490723  0.0  0.00  1.0  1.00  1.0\n",
      " 5.0       90.0  0.600000  0.492642  0.0  0.00  1.0  1.00  1.0\n",
      " 6.0       49.0  0.775510  0.421570  0.0  1.00  1.0  1.00  1.0\n",
      " 7.0       33.0  0.515152  0.507519  0.0  0.00  1.0  1.00  1.0\n",
      " 8.0       23.0  0.652174  0.486985  0.0  0.00  1.0  1.00  1.0\n",
      " 9.0       13.0  0.769231  0.438529  0.0  1.00  1.0  1.00  1.0\n",
      " 10.0       7.0  0.714286  0.487950  0.0  0.50  1.0  1.00  1.0\n",
      " 11.0       3.0  0.666667  0.577350  0.0  0.50  1.0  1.00  1.0\n",
      " 12.0       2.0  1.000000  0.000000  1.0  1.00  1.0  1.00  1.0\n",
      " 13.0       4.0  0.000000  0.000000  0.0  0.00  0.0  0.00  0.0\n",
      " 14.0       2.0  0.500000  0.707107  0.0  0.25  0.5  0.75  1.0\n",
      " 15.0       4.0  0.500000  0.577350  0.0  0.00  0.5  1.00  1.0\n",
      " 17.0       1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
      " 21.0       1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
      "/////\n",
      "       mvar1  mvar2  mvar3  mvar4  mvar5  mvar6  mvar7  mvar8  mvar9  mvar10  \\\n",
      "count    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0     0.0   \n",
      "mean     NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN     NaN   \n",
      "std      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN     NaN   \n",
      "min      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN     NaN   \n",
      "25%      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN     NaN   \n",
      "50%      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN     NaN   \n",
      "75%      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN     NaN   \n",
      "max      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN     NaN   \n",
      "\n",
      "          ...       mvar38  mvar39  mvar40  mvar41  mvar42  mvar43  mvar44  \\\n",
      "count     ...          0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
      "mean      ...          NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "std       ...          NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "min       ...          NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "25%       ...          NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "50%       ...          NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "75%       ...          NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "max       ...          NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "\n",
      "       mvar45  mvar46  default_ind  \n",
      "count     0.0     0.0          0.0  \n",
      "mean      NaN     NaN          NaN  \n",
      "std       NaN     NaN          NaN  \n",
      "min       NaN     NaN          NaN  \n",
      "25%       NaN     NaN          NaN  \n",
      "50%       NaN     NaN          NaN  \n",
      "75%       NaN     NaN          NaN  \n",
      "max       NaN     NaN          NaN  \n",
      "\n",
      "[8 rows x 47 columns]\n",
      "/////\n",
      "              mvar1         mvar2         mvar3         mvar4        mvar5  \\\n",
      "count  76361.000000  74447.000000  79499.000000  79499.000000  79499.00000   \n",
      "mean    1745.971085      1.105629      5.280955      0.474516      1.12472   \n",
      "std       94.537617      1.645299     10.815463      1.796261      5.99545   \n",
      "min     1493.000000      0.000000      0.000000      0.000000      0.00000   \n",
      "25%     1679.000000      0.140100      0.000000      0.000000      0.00000   \n",
      "50%     1742.000000      0.538900      0.300000      0.000000      0.00000   \n",
      "75%     1810.000000      1.445050      6.447000      0.000000      0.00000   \n",
      "max     1950.000000     44.630100    286.858000    114.696000    226.52700   \n",
      "\n",
      "               mvar6         mvar7         mvar8         mvar9        mvar10  \\\n",
      "count   61036.000000  7.260600e+04   61036.00000  6.859200e+04  7.949800e+04   \n",
      "mean     1606.702372  1.759581e+04    6773.68666  3.409744e+04  3.049237e+04   \n",
      "std      3703.654127  4.708164e+04   10127.74526  5.337692e+04  6.717785e+04   \n",
      "min         0.000000  0.000000e+00       0.00000  0.000000e+00  0.000000e+00   \n",
      "25%        36.000000  1.756000e+03     490.00000  3.498000e+03  1.134000e+03   \n",
      "50%       297.000000  6.967000e+03    2478.00000  1.417100e+04  9.492500e+03   \n",
      "75%      1343.250000  1.805575e+04    9684.25000  4.385375e+04  3.402550e+04   \n",
      "max    136595.000000  5.549600e+06  291810.00000  3.711880e+06  5.633320e+06   \n",
      "\n",
      "           ...             mvar38        mvar39        mvar40        mvar41  \\\n",
      "count      ...       79498.000000  73960.000000  17443.000000  25422.000000   \n",
      "mean       ...           4.286422      0.090819     98.497452     72.096758   \n",
      "std        ...           3.964464      0.419301     29.762399     24.442156   \n",
      "min        ...           0.000000      0.000000      0.014000      0.000000   \n",
      "25%        ...           2.000000      0.000000     90.689000     58.153000   \n",
      "50%        ...           3.000000      0.000000    100.000000     78.883500   \n",
      "75%        ...           6.000000      0.000000    111.698000     91.639750   \n",
      "max        ...          95.000000     21.000000    430.300000    170.000000   \n",
      "\n",
      "             mvar42        mvar43        mvar44        mvar45        mvar46  \\\n",
      "count  78099.000000  79182.000000  72255.000000  35145.000000  57211.000000   \n",
      "mean       0.335142      6.552777      0.705285      0.070736      0.096135   \n",
      "std        0.375656      6.311087      0.245879      0.287365      0.346109   \n",
      "min        0.000000      0.000000      0.060660      0.000000      0.000000   \n",
      "25%        0.000000      2.000000      0.515220      0.000000      0.000000   \n",
      "50%        0.200000      5.000000      0.737030      0.000000      0.000000   \n",
      "75%        0.571430     10.000000      0.943475      0.000000      0.000000   \n",
      "max        2.000000     91.000000      1.000000      5.000000     19.000000   \n",
      "\n",
      "        default_ind  \n",
      "count  80000.000000  \n",
      "mean       0.246213  \n",
      "std        0.430806  \n",
      "min        0.000000  \n",
      "25%        0.000000  \n",
      "50%        0.000000  \n",
      "75%        0.000000  \n",
      "max        1.000000  \n",
      "\n",
      "[8 rows x 47 columns]\n",
      "/////\n"
     ]
    }
   ],
   "source": [
    "#mvar34\n",
    "gameDf['mvar34'][gameDf['mvar34'].isnull()] = -1\n",
    "print(gameDf.groupby('mvar34')['default_ind'].describe())\n",
    "print(\"/////\")\n",
    "print(gameDf[gameDf['mvar34'].isnull()].describe())\n",
    "print(\"/////\")\n",
    "print(gameDf[gameDf['mvar34'].isnull()==False].describe())\n",
    "print(\"/////\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5641025641025641"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(gameDf['default_ind'][gameDf['mvar34']>2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          count      mean       std  min   25%  50%   75%  max\n",
      "mvar35                                                        \n",
      "-1.0    33014.0  0.262343  0.439915  0.0  0.00  0.0  1.00  1.0\n",
      " 0.0    40222.0  0.187982  0.390702  0.0  0.00  0.0  0.00  1.0\n",
      " 1.0     5076.0  0.476753  0.499509  0.0  0.00  0.0  1.00  1.0\n",
      " 2.0     1135.0  0.607930  0.488427  0.0  0.00  1.0  1.00  1.0\n",
      " 3.0      359.0  0.635097  0.482075  0.0  0.00  1.0  1.00  1.0\n",
      " 4.0      126.0  0.690476  0.464143  0.0  0.00  1.0  1.00  1.0\n",
      " 5.0       41.0  0.731707  0.448575  0.0  0.00  1.0  1.00  1.0\n",
      " 6.0       19.0  0.684211  0.477567  0.0  0.00  1.0  1.00  1.0\n",
      " 7.0        4.0  1.000000  0.000000  1.0  1.00  1.0  1.00  1.0\n",
      " 8.0        2.0  0.500000  0.707107  0.0  0.25  0.5  0.75  1.0\n",
      " 9.0        1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
      " 10.0       1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
      "/////\n",
      "       mvar1  mvar2  mvar3  mvar4  mvar5  mvar6  mvar7  mvar8  mvar9  mvar10  \\\n",
      "count    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0     0.0   \n",
      "mean     NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN     NaN   \n",
      "std      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN     NaN   \n",
      "min      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN     NaN   \n",
      "25%      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN     NaN   \n",
      "50%      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN     NaN   \n",
      "75%      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN     NaN   \n",
      "max      NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN     NaN   \n",
      "\n",
      "          ...       mvar38  mvar39  mvar40  mvar41  mvar42  mvar43  mvar44  \\\n",
      "count     ...          0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
      "mean      ...          NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "std       ...          NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "min       ...          NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "25%       ...          NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "50%       ...          NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "75%       ...          NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "max       ...          NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
      "\n",
      "       mvar45  mvar46  default_ind  \n",
      "count     0.0     0.0          0.0  \n",
      "mean      NaN     NaN          NaN  \n",
      "std       NaN     NaN          NaN  \n",
      "min       NaN     NaN          NaN  \n",
      "25%       NaN     NaN          NaN  \n",
      "50%       NaN     NaN          NaN  \n",
      "75%       NaN     NaN          NaN  \n",
      "max       NaN     NaN          NaN  \n",
      "\n",
      "[8 rows x 47 columns]\n",
      "/////\n",
      "              mvar1         mvar2         mvar3         mvar4        mvar5  \\\n",
      "count  76361.000000  74447.000000  79499.000000  79499.000000  79499.00000   \n",
      "mean    1745.971085      1.105629      5.280955      0.474516      1.12472   \n",
      "std       94.537617      1.645299     10.815463      1.796261      5.99545   \n",
      "min     1493.000000      0.000000      0.000000      0.000000      0.00000   \n",
      "25%     1679.000000      0.140100      0.000000      0.000000      0.00000   \n",
      "50%     1742.000000      0.538900      0.300000      0.000000      0.00000   \n",
      "75%     1810.000000      1.445050      6.447000      0.000000      0.00000   \n",
      "max     1950.000000     44.630100    286.858000    114.696000    226.52700   \n",
      "\n",
      "               mvar6         mvar7         mvar8         mvar9        mvar10  \\\n",
      "count   61036.000000  7.260600e+04   61036.00000  6.859200e+04  7.949800e+04   \n",
      "mean     1606.702372  1.759581e+04    6773.68666  3.409744e+04  3.049237e+04   \n",
      "std      3703.654127  4.708164e+04   10127.74526  5.337692e+04  6.717785e+04   \n",
      "min         0.000000  0.000000e+00       0.00000  0.000000e+00  0.000000e+00   \n",
      "25%        36.000000  1.756000e+03     490.00000  3.498000e+03  1.134000e+03   \n",
      "50%       297.000000  6.967000e+03    2478.00000  1.417100e+04  9.492500e+03   \n",
      "75%      1343.250000  1.805575e+04    9684.25000  4.385375e+04  3.402550e+04   \n",
      "max    136595.000000  5.549600e+06  291810.00000  3.711880e+06  5.633320e+06   \n",
      "\n",
      "           ...             mvar38        mvar39        mvar40        mvar41  \\\n",
      "count      ...       79498.000000  73960.000000  17443.000000  25422.000000   \n",
      "mean       ...           4.286422      0.090819     98.497452     72.096758   \n",
      "std        ...           3.964464      0.419301     29.762399     24.442156   \n",
      "min        ...           0.000000      0.000000      0.014000      0.000000   \n",
      "25%        ...           2.000000      0.000000     90.689000     58.153000   \n",
      "50%        ...           3.000000      0.000000    100.000000     78.883500   \n",
      "75%        ...           6.000000      0.000000    111.698000     91.639750   \n",
      "max        ...          95.000000     21.000000    430.300000    170.000000   \n",
      "\n",
      "             mvar42        mvar43        mvar44        mvar45        mvar46  \\\n",
      "count  78099.000000  79182.000000  72255.000000  35145.000000  57211.000000   \n",
      "mean       0.335142      6.552777      0.705285      0.070736      0.096135   \n",
      "std        0.375656      6.311087      0.245879      0.287365      0.346109   \n",
      "min        0.000000      0.000000      0.060660      0.000000      0.000000   \n",
      "25%        0.000000      2.000000      0.515220      0.000000      0.000000   \n",
      "50%        0.200000      5.000000      0.737030      0.000000      0.000000   \n",
      "75%        0.571430     10.000000      0.943475      0.000000      0.000000   \n",
      "max        2.000000     91.000000      1.000000      5.000000     19.000000   \n",
      "\n",
      "        default_ind  \n",
      "count  80000.000000  \n",
      "mean       0.246213  \n",
      "std        0.430806  \n",
      "min        0.000000  \n",
      "25%        0.000000  \n",
      "50%        0.000000  \n",
      "75%        0.000000  \n",
      "max        1.000000  \n",
      "\n",
      "[8 rows x 47 columns]\n",
      "/////\n"
     ]
    }
   ],
   "source": [
    "#mvar35\n",
    "gameDf['mvar35'][gameDf['mvar35'].isnull()] = -1\n",
    "print(gameDf.groupby('mvar35')['default_ind'].describe())\n",
    "print(\"/////\")\n",
    "print(gameDf[gameDf['mvar35'].isnull()].describe())\n",
    "print(\"/////\")\n",
    "print(gameDf[gameDf['mvar35'].isnull()==False].describe())\n",
    "print(\"/////\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.51374926079243055"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(df2['default_ind'][df2['mvar35']>0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          count      mean       std  min  25%  50%  75%  max\n",
      "mvar35                                                      \n",
      "-2.0      502.0  0.215139  0.411329  0.0  0.0  0.0  0.0  1.0\n",
      "-1.0    27802.0  0.247428  0.431525  0.0  0.0  0.0  0.0  1.0\n",
      " 0.0    39663.0  0.190581  0.392764  0.0  0.0  0.0  0.0  1.0\n",
      " 1.0     6558.0  0.349497  0.476847  0.0  0.0  0.0  1.0  1.0\n",
      " 2.0     3032.0  0.457784  0.498297  0.0  0.0  0.0  1.0  1.0\n",
      " 3.0     1170.0  0.541026  0.498527  0.0  0.0  1.0  1.0  1.0\n",
      " 4.0      612.0  0.609477  0.488267  0.0  0.0  1.0  1.0  1.0\n",
      " 5.0      282.0  0.687943  0.464157  0.0  0.0  1.0  1.0  1.0\n",
      " 6.0      158.0  0.689873  0.464016  0.0  0.0  1.0  1.0  1.0\n",
      " 7.0       91.0  0.747253  0.436995  0.0  0.5  1.0  1.0  1.0\n",
      " 8.0       43.0  0.744186  0.441481  0.0  0.5  1.0  1.0  1.0\n",
      " 9.0       34.0  0.852941  0.359491  0.0  1.0  1.0  1.0  1.0\n",
      " 10.0      17.0  0.411765  0.507300  0.0  0.0  0.0  1.0  1.0\n",
      " 11.0       5.0  0.800000  0.447214  0.0  1.0  1.0  1.0  1.0\n",
      " 12.0       9.0  0.666667  0.500000  0.0  0.0  1.0  1.0  1.0\n",
      " 13.0       4.0  0.500000  0.577350  0.0  0.0  0.5  1.0  1.0\n",
      " 14.0       6.0  0.833333  0.408248  0.0  1.0  1.0  1.0  1.0\n",
      " 15.0       7.0  0.714286  0.487950  0.0  0.5  1.0  1.0  1.0\n",
      " 17.0       3.0  0.666667  0.577350  0.0  0.5  1.0  1.0  1.0\n",
      " 20.0       1.0  1.000000       NaN  1.0  1.0  1.0  1.0  1.0\n",
      " 21.0       1.0  1.000000       NaN  1.0  1.0  1.0  1.0  1.0\n"
     ]
    }
   ],
   "source": [
    "gameDf['mvar35'] = gameDf['mvar35'] + gameDf['mvar34']\n",
    "print(gameDf.groupby('mvar35')['default_ind'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.46747420367877973"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(gameDf['default_ind'][gameDf['mvar36']>=20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mvar36\n",
       "1.0     0.163769\n",
       "2.0     0.170170\n",
       "3.0     0.190171\n",
       "4.0     0.211005\n",
       "5.0     0.226932\n",
       "6.0     0.247148\n",
       "7.0     0.271086\n",
       "8.0     0.296529\n",
       "9.0     0.309190\n",
       "10.0    0.323132\n",
       "Name: default_ind, dtype: float64"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf.groupby('mvar36')['default_ind'].mean().iloc[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         count      mean       std  min   25%  50%   75%  max\n",
      "mvar36                                                       \n",
      "-1.0    2898.0  0.176674  0.381458  0.0  0.00  0.0  0.00  1.0\n",
      " 1.0    6558.0  0.163769  0.370094  0.0  0.00  0.0  0.00  1.0\n",
      " 2.0    8897.0  0.170170  0.375803  0.0  0.00  0.0  0.00  1.0\n",
      " 3.0    9360.0  0.190171  0.392457  0.0  0.00  0.0  0.00  1.0\n",
      " 4.0    9232.0  0.211005  0.408044  0.0  0.00  0.0  0.00  1.0\n",
      " 5.0    7998.0  0.226932  0.418874  0.0  0.00  0.0  0.00  1.0\n",
      " 6.0    6749.0  0.247148  0.431385  0.0  0.00  0.0  0.00  1.0\n",
      " 7.0    5596.0  0.271086  0.444560  0.0  0.00  0.0  1.00  1.0\n",
      " 8.0    4465.0  0.296529  0.456778  0.0  0.00  0.0  1.00  1.0\n",
      " 9.0    3493.0  0.309190  0.462226  0.0  0.00  0.0  1.00  1.0\n",
      " 10.0   2810.0  0.323132  0.467756  0.0  0.00  0.0  1.00  1.0\n",
      " 11.0   2195.0  0.330296  0.470427  0.0  0.00  0.0  1.00  1.0\n",
      " 12.0   1727.0  0.338159  0.473220  0.0  0.00  0.0  1.00  1.0\n",
      " 13.0   1403.0  0.352103  0.477796  0.0  0.00  0.0  1.00  1.0\n",
      " 14.0   1177.0  0.370433  0.483126  0.0  0.00  0.0  1.00  1.0\n",
      " 15.0    929.0  0.365985  0.481965  0.0  0.00  0.0  1.00  1.0\n",
      " 16.0    743.0  0.384926  0.486906  0.0  0.00  0.0  1.00  1.0\n",
      " 17.0    597.0  0.395310  0.489327  0.0  0.00  0.0  1.00  1.0\n",
      " 18.0    512.0  0.449219  0.497901  0.0  0.00  0.0  1.00  1.0\n",
      " 19.0    432.0  0.425926  0.495056  0.0  0.00  0.0  1.00  1.0\n",
      " 20.0    385.0  0.441558  0.497219  0.0  0.00  0.0  1.00  1.0\n",
      " 21.0    287.0  0.477352  0.500359  0.0  0.00  0.0  1.00  1.0\n",
      " 22.0    235.0  0.425532  0.495479  0.0  0.00  0.0  1.00  1.0\n",
      " 23.0    202.0  0.480198  0.500849  0.0  0.00  0.0  1.00  1.0\n",
      " 24.0    178.0  0.477528  0.500904  0.0  0.00  0.0  1.00  1.0\n",
      " 25.0    158.0  0.436709  0.497555  0.0  0.00  0.0  1.00  1.0\n",
      " 26.0    107.0  0.411215  0.494370  0.0  0.00  0.0  1.00  1.0\n",
      " 27.0    102.0  0.470588  0.501599  0.0  0.00  0.0  1.00  1.0\n",
      " 28.0     93.0  0.376344  0.487094  0.0  0.00  0.0  1.00  1.0\n",
      " 29.0     70.0  0.457143  0.501757  0.0  0.00  0.0  1.00  1.0\n",
      "...        ...       ...       ...  ...   ...  ...   ...  ...\n",
      " 41.0     11.0  0.818182  0.404520  0.0  1.00  1.0  1.00  1.0\n",
      " 42.0      5.0  0.600000  0.547723  0.0  0.00  1.0  1.00  1.0\n",
      " 43.0      8.0  0.500000  0.534522  0.0  0.00  0.5  1.00  1.0\n",
      " 44.0      8.0  0.500000  0.534522  0.0  0.00  0.5  1.00  1.0\n",
      " 45.0      2.0  0.500000  0.707107  0.0  0.25  0.5  0.75  1.0\n",
      " 46.0      4.0  0.750000  0.500000  0.0  0.75  1.0  1.00  1.0\n",
      " 47.0     10.0  0.500000  0.527046  0.0  0.00  0.5  1.00  1.0\n",
      " 48.0      9.0  0.666667  0.500000  0.0  0.00  1.0  1.00  1.0\n",
      " 49.0      4.0  0.750000  0.500000  0.0  0.75  1.0  1.00  1.0\n",
      " 50.0      3.0  0.666667  0.577350  0.0  0.50  1.0  1.00  1.0\n",
      " 52.0      4.0  0.750000  0.500000  0.0  0.75  1.0  1.00  1.0\n",
      " 53.0      5.0  0.800000  0.447214  0.0  1.00  1.0  1.00  1.0\n",
      " 54.0      2.0  0.500000  0.707107  0.0  0.25  0.5  0.75  1.0\n",
      " 55.0      5.0  0.800000  0.447214  0.0  1.00  1.0  1.00  1.0\n",
      " 56.0      1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
      " 57.0      3.0  1.000000  0.000000  1.0  1.00  1.0  1.00  1.0\n",
      " 59.0      1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
      " 60.0      1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
      " 61.0      1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
      " 62.0      2.0  0.500000  0.707107  0.0  0.25  0.5  0.75  1.0\n",
      " 63.0      1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
      " 67.0      1.0  0.000000       NaN  0.0  0.00  0.0  0.00  0.0\n",
      " 68.0      3.0  1.000000  0.000000  1.0  1.00  1.0  1.00  1.0\n",
      " 69.0      1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
      " 70.0      1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
      " 78.0      1.0  0.000000       NaN  0.0  0.00  0.0  0.00  0.0\n",
      " 83.0      1.0  0.000000       NaN  0.0  0.00  0.0  0.00  0.0\n",
      " 96.0      1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
      " 97.0      1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
      " 100.0     1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
      "\n",
      "[71 rows x 8 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#mvar36\n",
    "gameDf['mvar36'][gameDf['mvar36'].isnull()] = -1\n",
    "print(gameDf.groupby('mvar36')['default_ind'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.090388360636957266"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(gameDf['default_ind'][gameDf['mvar37']>=10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          count      mean       std  min  25%  50%  75%  max\n",
      "mvar37                                                      \n",
      "-1.0     7706.0  0.458344  0.498294  0.0  0.0  0.0  1.0  1.0\n",
      " 0.0    16277.0  0.381028  0.485654  0.0  0.0  0.0  1.0  1.0\n",
      " 1.0     9855.0  0.309995  0.462515  0.0  0.0  0.0  1.0  1.0\n",
      " 2.0     7281.0  0.239528  0.426824  0.0  0.0  0.0  0.0  1.0\n",
      " 3.0     6393.0  0.186923  0.389880  0.0  0.0  0.0  0.0  1.0\n",
      " 4.0     5674.0  0.159852  0.366501  0.0  0.0  0.0  0.0  1.0\n",
      " 5.0     4998.0  0.144858  0.351993  0.0  0.0  0.0  0.0  1.0\n",
      " 6.0     4245.0  0.131449  0.337930  0.0  0.0  0.0  0.0  1.0\n",
      " 7.0     3609.0  0.118592  0.323353  0.0  0.0  0.0  0.0  1.0\n",
      " 8.0     2885.0  0.111612  0.314943  0.0  0.0  0.0  0.0  1.0\n",
      " 9.0     2348.0  0.102641  0.303553  0.0  0.0  0.0  0.0  1.0\n",
      " 10.0    1842.0  0.105320  0.307049  0.0  0.0  0.0  0.0  1.0\n",
      " 11.0    1463.0  0.110048  0.313056  0.0  0.0  0.0  0.0  1.0\n",
      " 12.0    1155.0  0.099567  0.299552  0.0  0.0  0.0  0.0  1.0\n",
      " 13.0     910.0  0.098901  0.298693  0.0  0.0  0.0  0.0  1.0\n",
      " 14.0     725.0  0.080000  0.271480  0.0  0.0  0.0  0.0  1.0\n",
      " 15.0     539.0  0.096475  0.295516  0.0  0.0  0.0  0.0  1.0\n",
      " 16.0     418.0  0.047847  0.213698  0.0  0.0  0.0  0.0  1.0\n",
      " 17.0     345.0  0.081159  0.273476  0.0  0.0  0.0  0.0  1.0\n",
      " 18.0     262.0  0.064885  0.246795  0.0  0.0  0.0  0.0  1.0\n",
      " 19.0     233.0  0.055794  0.230018  0.0  0.0  0.0  0.0  1.0\n",
      " 20.0     149.0  0.046980  0.212309  0.0  0.0  0.0  0.0  1.0\n",
      " 21.0     117.0  0.068376  0.253476  0.0  0.0  0.0  0.0  1.0\n",
      " 22.0      96.0  0.062500  0.243332  0.0  0.0  0.0  0.0  1.0\n",
      " 23.0      85.0  0.023529  0.152477  0.0  0.0  0.0  0.0  1.0\n",
      " 24.0      73.0  0.054795  0.229154  0.0  0.0  0.0  0.0  1.0\n",
      " 25.0      57.0  0.017544  0.132453  0.0  0.0  0.0  0.0  1.0\n",
      " 26.0      39.0  0.025641  0.160128  0.0  0.0  0.0  0.0  1.0\n",
      " 27.0      34.0  0.058824  0.238833  0.0  0.0  0.0  0.0  1.0\n",
      " 28.0      25.0  0.040000  0.200000  0.0  0.0  0.0  0.0  1.0\n",
      " 29.0      26.0  0.153846  0.367946  0.0  0.0  0.0  0.0  1.0\n",
      " 30.0      22.0  0.045455  0.213201  0.0  0.0  0.0  0.0  1.0\n",
      " 31.0      18.0  0.055556  0.235702  0.0  0.0  0.0  0.0  1.0\n",
      " 32.0      15.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
      " 33.0      10.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
      " 34.0       8.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
      " 35.0       7.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
      " 36.0       7.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
      " 37.0       7.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
      " 38.0       2.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
      " 39.0       3.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
      " 40.0       7.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
      " 41.0       5.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
      " 42.0       4.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
      " 43.0       3.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
      " 44.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
      " 45.0       5.0  0.200000  0.447214  0.0  0.0  0.0  0.0  1.0\n",
      " 46.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
      " 47.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
      " 48.0       1.0  1.000000       NaN  1.0  1.0  1.0  1.0  1.0\n",
      " 50.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
      " 51.0       2.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
      " 52.0       3.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
      " 53.0       1.0  1.000000       NaN  1.0  1.0  1.0  1.0  1.0\n",
      " 58.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
      " 60.0       1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#mvar37\n",
    "gameDf['mvar37'][gameDf['mvar37'].isnull()] = -1\n",
    "print(gameDf.groupby('mvar37')['default_ind'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.39066971951917573"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(gameDf['default_ind'][gameDf['mvar38']>=10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          count      mean       std  min   25%  50%   75%  max\n",
      "mvar38                                                        \n",
      "-1.0      502.0  0.215139  0.411329  0.0  0.00  0.0  0.00  1.0\n",
      " 0.0     7014.0  0.145566  0.352696  0.0  0.00  0.0  0.00  1.0\n",
      " 1.0    11440.0  0.176224  0.381027  0.0  0.00  0.0  0.00  1.0\n",
      " 2.0    12032.0  0.200382  0.400303  0.0  0.00  0.0  0.00  1.0\n",
      " 3.0    10953.0  0.232083  0.422181  0.0  0.00  0.0  0.00  1.0\n",
      " 4.0     9228.0  0.252492  0.434466  0.0  0.00  0.0  1.00  1.0\n",
      " 5.0     7080.0  0.275282  0.446688  0.0  0.00  0.0  1.00  1.0\n",
      " 6.0     5398.0  0.297332  0.457126  0.0  0.00  0.0  1.00  1.0\n",
      " 7.0     4085.0  0.298409  0.457616  0.0  0.00  0.0  1.00  1.0\n",
      " 8.0     3042.0  0.328731  0.469829  0.0  0.00  0.0  1.00  1.0\n",
      " 9.0     2238.0  0.342270  0.474575  0.0  0.00  0.0  1.00  1.0\n",
      " 10.0    1671.0  0.351287  0.477515  0.0  0.00  0.0  1.00  1.0\n",
      " 11.0    1266.0  0.369668  0.482906  0.0  0.00  0.0  1.00  1.0\n",
      " 12.0     966.0  0.381988  0.486125  0.0  0.00  0.0  1.00  1.0\n",
      " 13.0     715.0  0.393007  0.488760  0.0  0.00  0.0  1.00  1.0\n",
      " 14.0     542.0  0.420664  0.494122  0.0  0.00  0.0  1.00  1.0\n",
      " 15.0     408.0  0.428922  0.495530  0.0  0.00  0.0  1.00  1.0\n",
      " 16.0     299.0  0.434783  0.496560  0.0  0.00  0.0  1.00  1.0\n",
      " 17.0     233.0  0.416309  0.494007  0.0  0.00  0.0  1.00  1.0\n",
      " 18.0     171.0  0.391813  0.489589  0.0  0.00  0.0  1.00  1.0\n",
      " 19.0     155.0  0.496774  0.501610  0.0  0.00  0.0  1.00  1.0\n",
      " 20.0     108.0  0.472222  0.501555  0.0  0.00  0.0  1.00  1.0\n",
      " 21.0      91.0  0.461538  0.501280  0.0  0.00  0.0  1.00  1.0\n",
      " 22.0      58.0  0.431034  0.499546  0.0  0.00  0.0  1.00  1.0\n",
      " 23.0      54.0  0.351852  0.482032  0.0  0.00  0.0  1.00  1.0\n",
      " 24.0      35.0  0.257143  0.443440  0.0  0.00  0.0  0.50  1.0\n",
      " 25.0      25.0  0.440000  0.506623  0.0  0.00  0.0  1.00  1.0\n",
      " 26.0      27.0  0.444444  0.506370  0.0  0.00  0.0  1.00  1.0\n",
      " 27.0      22.0  0.363636  0.492366  0.0  0.00  0.0  1.00  1.0\n",
      " 28.0      19.0  0.315789  0.477567  0.0  0.00  0.0  1.00  1.0\n",
      "...         ...       ...       ...  ...   ...  ...   ...  ...\n",
      " 31.0      15.0  0.600000  0.507093  0.0  0.00  1.0  1.00  1.0\n",
      " 32.0       6.0  0.500000  0.547723  0.0  0.00  0.5  1.00  1.0\n",
      " 33.0       8.0  0.750000  0.462910  0.0  0.75  1.0  1.00  1.0\n",
      " 34.0       9.0  0.555556  0.527046  0.0  0.00  1.0  1.00  1.0\n",
      " 35.0       7.0  1.000000  0.000000  1.0  1.00  1.0  1.00  1.0\n",
      " 36.0       6.0  0.500000  0.547723  0.0  0.00  0.5  1.00  1.0\n",
      " 37.0       4.0  0.500000  0.577350  0.0  0.00  0.5  1.00  1.0\n",
      " 38.0       3.0  0.666667  0.577350  0.0  0.50  1.0  1.00  1.0\n",
      " 39.0       5.0  0.400000  0.547723  0.0  0.00  0.0  1.00  1.0\n",
      " 40.0       8.0  0.625000  0.517549  0.0  0.00  1.0  1.00  1.0\n",
      " 41.0       1.0  0.000000       NaN  0.0  0.00  0.0  0.00  0.0\n",
      " 42.0       1.0  0.000000       NaN  0.0  0.00  0.0  0.00  0.0\n",
      " 43.0       4.0  0.250000  0.500000  0.0  0.00  0.0  0.25  1.0\n",
      " 45.0       3.0  1.000000  0.000000  1.0  1.00  1.0  1.00  1.0\n",
      " 46.0       2.0  1.000000  0.000000  1.0  1.00  1.0  1.00  1.0\n",
      " 47.0       1.0  0.000000       NaN  0.0  0.00  0.0  0.00  0.0\n",
      " 48.0       1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
      " 49.0       2.0  0.500000  0.707107  0.0  0.25  0.5  0.75  1.0\n",
      " 50.0       2.0  0.500000  0.707107  0.0  0.25  0.5  0.75  1.0\n",
      " 51.0       1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
      " 52.0       1.0  0.000000       NaN  0.0  0.00  0.0  0.00  0.0\n",
      " 53.0       3.0  0.666667  0.577350  0.0  0.50  1.0  1.00  1.0\n",
      " 55.0       2.0  0.500000  0.707107  0.0  0.25  0.5  0.75  1.0\n",
      " 59.0       1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
      " 66.0       1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
      " 71.0       1.0  0.000000       NaN  0.0  0.00  0.0  0.00  0.0\n",
      " 72.0       1.0  0.000000       NaN  0.0  0.00  0.0  0.00  0.0\n",
      " 75.0       1.0  0.000000       NaN  0.0  0.00  0.0  0.00  0.0\n",
      " 78.0       1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0\n",
      " 95.0       1.0  0.000000       NaN  0.0  0.00  0.0  0.00  0.0\n",
      "\n",
      "[62 rows x 8 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#mvar38\n",
    "gameDf['mvar38'][gameDf['mvar38'].isnull()] = -1\n",
    "print(gameDf.groupby('mvar38')['default_ind'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.49249951295538669"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(gameDf['default_ind'][gameDf['mvar39']>0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          count      mean       std  min  25%  50%   75%  max\n",
      "mvar39                                                       \n",
      "-1.0     6040.0  0.439073  0.496315  0.0  0.0  0.0  1.00  1.0\n",
      " 0.0    68827.0  0.210920  0.407965  0.0  0.0  0.0  0.00  1.0\n",
      " 1.0     4190.0  0.467064  0.498974  0.0  0.0  0.0  1.00  1.0\n",
      " 2.0      670.0  0.579104  0.494072  0.0  0.0  1.0  1.00  1.0\n",
      " 3.0      149.0  0.677852  0.468875  0.0  0.0  1.0  1.00  1.0\n",
      " 4.0       48.0  0.687500  0.468417  0.0  0.0  1.0  1.00  1.0\n",
      " 5.0       25.0  0.720000  0.458258  0.0  0.0  1.0  1.00  1.0\n",
      " 6.0       19.0  0.736842  0.452414  0.0  0.5  1.0  1.00  1.0\n",
      " 7.0       10.0  0.300000  0.483046  0.0  0.0  0.0  0.75  1.0\n",
      " 8.0        7.0  0.571429  0.534522  0.0  0.0  1.0  1.00  1.0\n",
      " 9.0        3.0  1.000000  0.000000  1.0  1.0  1.0  1.00  1.0\n",
      " 10.0       4.0  1.000000  0.000000  1.0  1.0  1.0  1.00  1.0\n",
      " 11.0       1.0  1.000000       NaN  1.0  1.0  1.0  1.00  1.0\n",
      " 13.0       3.0  0.000000  0.000000  0.0  0.0  0.0  0.00  0.0\n",
      " 15.0       3.0  0.333333  0.577350  0.0  0.0  0.0  0.50  1.0\n",
      " 21.0       1.0  1.000000       NaN  1.0  1.0  1.0  1.00  1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#mvar39\n",
    "gameDf['mvar39'][gameDf['mvar39'].isnull()] = -1\n",
    "print(gameDf.groupby('mvar39')['default_ind'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#mvar40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          count      mean       std  min  25%  50%   75%  max\n",
      "mvar40                                                       \n",
      "-1      62557.0  0.219895  0.414179  0.0  0.0  0.0  0.00  1.0\n",
      " 0        435.0  0.154023  0.361386  0.0  0.0  0.0  0.00  1.0\n",
      " 1       2410.0  0.164315  0.370638  0.0  0.0  0.0  0.00  1.0\n",
      " 2      12632.0  0.370646  0.482997  0.0  0.0  0.0  1.00  1.0\n",
      " 3       1790.0  0.405587  0.491142  0.0  0.0  0.0  1.00  1.0\n",
      " 4        133.0  0.413534  0.494329  0.0  0.0  0.0  1.00  1.0\n",
      " 5         32.0  0.343750  0.482559  0.0  0.0  0.0  1.00  1.0\n",
      " 6          4.0  0.250000  0.500000  0.0  0.0  0.0  0.25  1.0\n",
      " 7          6.0  0.500000  0.547723  0.0  0.0  0.5  1.00  1.0\n",
      " 9          1.0  0.000000       NaN  0.0  0.0  0.0  0.00  0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "df2 = gameDf.copy()\n",
    "binn = 50\n",
    "df2['mvar40'][df2['mvar40'].isnull()] = -binn\n",
    "df2['mvar40'] = df2['mvar40'] /binn\n",
    "df2['mvar40'] = df2['mvar40'].round(0)\n",
    "df2['mvar40'] = df2['mvar40'].astype('int64')\n",
    "print(df2.groupby('mvar40')['default_ind'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.37525688450472666"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(df2['default_ind'][df2['mvar40']>=2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# mvar41"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          count      mean       std  min  25%  50%  75%  max\n",
      "mvar41                                                      \n",
      "-1      54578.0  0.252629  0.434524  0.0  0.0  0.0  1.0  1.0\n",
      " 0        678.0  0.160767  0.367587  0.0  0.0  0.0  0.0  1.0\n",
      " 1       2215.0  0.151242  0.358365  0.0  0.0  0.0  0.0  1.0\n",
      " 2       4523.0  0.171346  0.376853  0.0  0.0  0.0  0.0  1.0\n",
      " 3       9346.0  0.223197  0.416412  0.0  0.0  0.0  0.0  1.0\n",
      " 4       8637.0  0.299988  0.458279  0.0  0.0  0.0  1.0  1.0\n",
      " 5         20.0  0.550000  0.510418  0.0  0.0  1.0  1.0  1.0\n",
      " 6          2.0  1.000000  0.000000  1.0  1.0  1.0  1.0  1.0\n",
      " 7          1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "df2 = gameDf.copy()\n",
    "binn = 25\n",
    "df2['mvar41'][df2['mvar41'].isnull()] = -binn\n",
    "df2['mvar41'] = df2['mvar41'] /binn\n",
    "df2['mvar41'] = df2['mvar41'].round(0)\n",
    "df2['mvar41'] = df2['mvar41'].astype('int64')\n",
    "print(df2.groupby('mvar41')['default_ind'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3482082488167681"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(df2['default_ind'][df2['mvar45']>0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          count      mean       std  min  25%  50%   75%  max\n",
      "mvar45                                                       \n",
      "-1.0    44855.0  0.328079  0.469519  0.0  0.0  0.0  1.00  1.0\n",
      " 0.0    32905.0  0.126759  0.332708  0.0  0.0  0.0  0.00  1.0\n",
      " 1.0     2031.0  0.360906  0.480382  0.0  0.0  0.0  1.00  1.0\n",
      " 2.0      182.0  0.384615  0.487846  0.0  0.0  0.0  1.00  1.0\n",
      " 3.0       20.0  0.250000  0.444262  0.0  0.0  0.0  0.25  1.0\n",
      " 4.0        4.0  0.250000  0.500000  0.0  0.0  0.0  0.25  1.0\n",
      " 5.0        3.0  0.333333  0.577350  0.0  0.0  0.0  0.50  1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "df2 = gameDf.copy()\n",
    "# binn = 25\n",
    "df2['mvar45'][df2['mvar45'].isnull()] = -1\n",
    "# df2['mvar41'] = df2['mvar41'] /binn\n",
    "# df2['mvar41'] = df2['mvar41'].round(0)\n",
    "# df2['mvar41'] = df2['mvar41'].astype('int64')\n",
    "print(df2.groupby('mvar45')['default_ind'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.49792960662525881"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(df2['default_ind'][df2['mvar46']>0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          count      mean       std  min  25%  50%  75%  max\n",
      "mvar46                                                      \n",
      "-1.0    22789.0  0.296810  0.456862  0.0  0.0  0.0  1.0  1.0\n",
      " 0.0    52381.0  0.200989  0.400744  0.0  0.0  0.0  0.0  1.0\n",
      " 1.0     4259.0  0.488143  0.499918  0.0  0.0  0.0  1.0  1.0\n",
      " 2.0      506.0  0.569170  0.495682  0.0  0.0  1.0  1.0  1.0\n",
      " 3.0       54.0  0.555556  0.501570  0.0  0.0  1.0  1.0  1.0\n",
      " 4.0        6.0  0.833333  0.408248  0.0  1.0  1.0  1.0  1.0\n",
      " 5.0        3.0  0.666667  0.577350  0.0  0.5  1.0  1.0  1.0\n",
      " 9.0        1.0  0.000000       NaN  0.0  0.0  0.0  0.0  0.0\n",
      " 19.0       1.0  1.000000       NaN  1.0  1.0  1.0  1.0  1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "df2 = gameDf.copy()\n",
    "# binn = 25\n",
    "df2['mvar46'][df2['mvar46'].isnull()] = -1\n",
    "# df2['mvar41'] = df2['mvar41'] /binn\n",
    "# df2['mvar41'] = df2['mvar41'].round(0)\n",
    "# df2['mvar41'] = df2['mvar41'].astype('int64')\n",
    "print(df2.groupby('mvar46')['default_ind'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "xx = (df['mvar40'][df['mvar31'].isnull()==False]).isnull()==False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'Series' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-311-9d8b4ae23cd1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mvar5'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mvar40'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mvar31'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'Series' object is not callable"
     ]
    }
   ],
   "source": [
    "df['mvar5'] (df['mvar40'][df['mvar31'].isnull()==False]).isnull()==False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexingError",
     "evalue": "Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexingError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-322-f3d1fa8602aa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mvar5'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mvar40'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mvar31'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mFalse\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    660\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    661\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 662\u001b[0;31m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    663\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    664\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36mcheck_bool_indexer\u001b[0;34m(ax, key)\u001b[0m\n\u001b[1;32m   2021\u001b[0m         \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2022\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2023\u001b[0;31m             raise IndexingError('Unalignable boolean Series provided as '\n\u001b[0m\u001b[1;32m   2024\u001b[0m                                 \u001b[0;34m'indexer (index of the boolean Series and of '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2025\u001b[0m                                 'the indexed object do not match')\n",
      "\u001b[0;31mIndexingError\u001b[0m: Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match"
     ]
    }
   ],
   "source": [
    "df['mvar5'][ (df['mvar40'][df['mvar31'].isnull()==False]).isnull()==False ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexingError",
     "evalue": "Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexingError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-301-be97b5f18496>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#edcuation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mvar5'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mvar40'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mvar31'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mFalse\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    660\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    661\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 662\u001b[0;31m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    663\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    664\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36mcheck_bool_indexer\u001b[0;34m(ax, key)\u001b[0m\n\u001b[1;32m   2021\u001b[0m         \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2022\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2023\u001b[0;31m             raise IndexingError('Unalignable boolean Series provided as '\n\u001b[0m\u001b[1;32m   2024\u001b[0m                                 \u001b[0;34m'indexer (index of the boolean Series and of '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2025\u001b[0m                                 'the indexed object do not match')\n",
      "\u001b[0;31mIndexingError\u001b[0m: Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match"
     ]
    }
   ],
   "source": [
    "#edcuation\n",
    "np.sum(df['mvar5'][ (df['mvar40'][df['mvar31'].isnull()==False]).isnull()==False ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}